diff --git hbase-assembly/pom.xml hbase-assembly/pom.xml
deleted file mode 100644
index b3edd91..0000000
--- hbase-assembly/pom.xml
+++ /dev/null
@@ -1,343 +0,0 @@
-
-
- 4.0.0
-
-
- hbase
- org.apache.hbase
- 0.95-SNAPSHOT
- ..
-
-
- hbase-assembly
- HBase - Assembly
- Assembly all HBase modules into deployable packages
- pom
-
-
-
- hbase-${project.version}
-
-
-
-
-
-
-
- org.eclipse.m2e
- lifecycle-mapping
- 1.0.0
-
-
-
-
-
- org.apache.maven.plugins
- maven-antrun-plugin
- [1.6,)
-
- run
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- maven-assembly-plugin
-
- false
- false
- gnu
- false
-
- src/assembly/all.xml
-
-
-
-
- prepare-package
- prepare-package
-
- single
-
-
-
-
-
-
- maven-antrun-plugin
-
-
-
- arc-setup
- initialize
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- run
-
-
-
-
-
- package
- package
-
-
-
-
-
-
-
- if [ `ls ${project.build.directory}/nativelib |
- wc -l` -ne 0 ]; then
- cp -PR
- ${project.build.directory}/nativelib/lib*
- ${project.build.directory}/${project.build.finalName}/${project.build.finalName}/lib/native/${build.platform}
- fi
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- run
-
-
-
-
-
-
-
-
-
- org.apache.hbase
- hbase-common
-
-
- hbase-server
- org.apache.hbase
- compile
-
-
-
-
-
-
-
-
-
- rpm
-
-
-
- maven-antrun-plugin
- 1.6
-
-
- build-rpm
- package
-
-
-
-
-
-
-
-
-
- run
-
-
-
-
-
-
-
-
- deb
-
-
-
- maven-antrun-plugin
- 1.6
-
-
- build-deb
- package
-
-
-
-
-
-
-
-
-
-
- run
-
-
-
-
-
- org.vafer
- jdeb
- 0.8
-
-
-
-
-
-
-
-
-
- hadoop-snappy
-
- false
-
- snappy
-
-
-
-
- org.apache.hadoop
- hadoop-snappy
- ${hadoop-snappy.version}
-
-
-
-
-
-
- maven-antrun-plugin
-
-
- process-resources
-
-
-
-
-
-
-
-
-
- run
-
-
-
-
-
- org.apache.maven.plugins
- maven-dependency-plugin
-
-
- get-hadoop-snappy-native
- generate-resources
-
- copy
-
-
-
-
- org.apache.hadoop
- hadoop-snappy
- ${hadoop-snappy.version}
- ${build.platform}
- tar
- false
- ${project.build.directory}/nativelib
- hadoop-snappy-nativelibs.tar
-
-
-
-
-
-
-
-
-
-
-
diff --git hbase-assembly/src/assembly/all.xml hbase-assembly/src/assembly/all.xml
deleted file mode 100644
index 1883fb2..0000000
--- hbase-assembly/src/assembly/all.xml
+++ /dev/null
@@ -1,176 +0,0 @@
-
-
-
-
-
- all
-
- dir
-
- false
-
-
-
-
- ${parent.basedir}
- /
-
- *.txt
- pom.xml
-
-
-
-
-
- ${parent.basedir}/conf
- conf
- 0644
- 0755
-
-
-
-
- ${parent.basedir}/bin
- bin
- 0755
- 0755
-
-
-
-
- ${parent.basedir}/hbase-server/src/main/ruby
- lib/ruby
- 0644
- 0755
-
-
-
-
- ${parent.basedir}/hbase-server/target/hbase-webapps
- hbase-webapps
- 0644
- 0755
-
-
-
-
- src/packages
- sbin
- 755
-
- update-hbase-env.sh
-
-
-
-
-
- ${parent.basedir}/hbase-server/target/
- hbase-jars/
-
- ${server.test.jar}
-
- 0644
-
-
-
-
-
-
- /lib
- false
- runtime
-
- org.apache.hbase:hbase-*
-
- 0644
- 0644
-
-
-
-
-
-
-
- true
-
-
-
-
- org.apache.hbase:hbase-*
-
-
- org.apache.hbase:hbase-assembly
-
-
-
-
-
-
- target/
- test/
- .classpath
- .project
- .settings/
-
-
-
-
-
-
- hbase-jars/
- false
-
-
-
-
- true
-
- org.apache.hbase:hbase-assembly
-
-
-
-
-
-
-
- hbase-server-${project.version}.jar
- hbase-common-${project.version>.jar
- target/
- test/
- .classpath
- .project
- .settings/
-
-
-
-
-
-
-
diff --git hbase-assembly/src/packages/build.xml hbase-assembly/src/packages/build.xml
deleted file mode 100644
index e17295f..0000000
--- hbase-assembly/src/packages/build.xml
+++ /dev/null
@@ -1,149 +0,0 @@
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git hbase-assembly/src/packages/conf-pseudo/hbase-site.xml hbase-assembly/src/packages/conf-pseudo/hbase-site.xml
deleted file mode 100644
index b4e8de6..0000000
--- hbase-assembly/src/packages/conf-pseudo/hbase-site.xml
+++ /dev/null
@@ -1,68 +0,0 @@
-
-
-
-
-
- hbase.rootdir
- hdfs://localhost:9000/hbase
-
-
- hbase.cluster.distributed
- true
- The mode the cluster will be in. Possible values are
- false: standalone and pseudo-distributed setups with managed Zookeeper
- true: fully-distributed with unmanaged Zookeeper Quorum (see hbase-env.sh)
-
-
-
- hbase.zookeeper.property.clientPort
- 2181
- Property from ZooKeeper's config zoo.cfg.
- The port at which the clients will connect.
-
-
-
- dfs.replication
- 1
- The replication count for HLog and HFile storage. Should not be greater than HDFS datanode count.
-
-
-
- hbase.zookeeper.quorum
- localhost
- Comma separated list of servers in the ZooKeeper Quorum.
- For example, "host1.mydomain.com,host2.mydomain.com,host3.mydomain.com".
- By default this is set to localhost for local and pseudo-distributed modes
- of operation. For a fully-distributed setup, this should be set to a full
- list of ZooKeeper quorum servers. If HBASE_MANAGES_ZK is set in hbase-env.sh
- this is the list of servers which we will start/stop ZooKeeper on.
-
-
-
- hbase.zookeeper.property.dataDir
- /var/lib/zookeeper/data
- Property from ZooKeeper's config zoo.cfg.
- The directory where the snapshot is stored.
-
-
-
diff --git hbase-assembly/src/packages/deb/conf-pseudo.control/conffile hbase-assembly/src/packages/deb/conf-pseudo.control/conffile
deleted file mode 100644
index 5e838d7..0000000
--- hbase-assembly/src/packages/deb/conf-pseudo.control/conffile
+++ /dev/null
@@ -1,16 +0,0 @@
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements. See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership. The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License. You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-/etc/hbase/hbase-site.xml
diff --git hbase-assembly/src/packages/deb/conf-pseudo.control/control hbase-assembly/src/packages/deb/conf-pseudo.control/control
deleted file mode 100644
index f31c218..0000000
--- hbase-assembly/src/packages/deb/conf-pseudo.control/control
+++ /dev/null
@@ -1,24 +0,0 @@
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements. See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership. The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License. You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-Package: hbase-conf-pseudo
-Version: @version@
-Section: misc
-Priority: optional
-Architecture: all
-Depends: openjdk-6-jre-headless, hadoop, zookeeper, hbase
-Maintainer: Apache Software Foundation
-Description: HBase pseudo cluster configuration for single node cluster testing.
-Distribution: development
diff --git hbase-assembly/src/packages/deb/conf-pseudo.control/postinst hbase-assembly/src/packages/deb/conf-pseudo.control/postinst
deleted file mode 100644
index 7c736fb..0000000
--- hbase-assembly/src/packages/deb/conf-pseudo.control/postinst
+++ /dev/null
@@ -1,26 +0,0 @@
-#!/bin/sh
-
-# Licensed to the Apache Software Foundation (ASF) under one or more
-# contributor license agreements. See the NOTICE file distributed with
-# this work for additional information regarding copyright ownership.
-# The ASF licenses this file to You under the Apache License, Version 2.0
-# (the "License"); you may not use this file except in compliance with
-# the License. You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-/etc/init.d/hadoop-namenode start 2>/dev/null >/dev/null
-/etc/init.d/hadoop-datanode start 2>/dev/null >/dev/null
-su - hdfs -c "hadoop fs -mkdir /hbase" 2>/dev/null >/dev/null
-su - hdfs -c "hadoop fs -chown hbase /hbase" 2>/dev/null >/dev/null
-/etc/init.d/hbase-master start 2>/dev/null >/dev/null
-/etc/init.d/hbase-regionserver start 2>/dev/null >/dev/null
-ln -sf ../init.d/hbase-master /etc/rc2.d/S94hbase-master
-ln -sf ../init.d/hbase-regionserver /etc/rc6.d/S94hbase-regionserver
-
diff --git hbase-assembly/src/packages/deb/conf-pseudo.control/prerm hbase-assembly/src/packages/deb/conf-pseudo.control/prerm
deleted file mode 100644
index fc48dfd..0000000
--- hbase-assembly/src/packages/deb/conf-pseudo.control/prerm
+++ /dev/null
@@ -1,21 +0,0 @@
-#!/bin/sh
-
-# Licensed to the Apache Software Foundation (ASF) under one or more
-# contributor license agreements. See the NOTICE file distributed with
-# this work for additional information regarding copyright ownership.
-# The ASF licenses this file to You under the Apache License, Version 2.0
-# (the "License"); you may not use this file except in compliance with
-# the License. You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-/etc/init.d/hbase-regionserver stop 2>/dev/null >/dev/null
-/etc/init.d/hbase-master stop 2>/dev/null >/dev/null
-rm -f /etc/rc2.d/S94hbase-master
-rm -f /etc/rc2.d/S94hbase-regionserver
diff --git hbase-assembly/src/packages/deb/hbase.control/conffile hbase-assembly/src/packages/deb/hbase.control/conffile
deleted file mode 100644
index 445d095..0000000
--- hbase-assembly/src/packages/deb/hbase.control/conffile
+++ /dev/null
@@ -1,5 +0,0 @@
-/etc/hbase/hadoop-metrics.properties
-/etc/hbase/hbase-env.sh
-/etc/hbase/hbase-site.xml
-/etc/hbase/log4j.properties
-/etc/hbase/regionservers
diff --git hbase-assembly/src/packages/deb/hbase.control/control hbase-assembly/src/packages/deb/hbase.control/control
deleted file mode 100644
index badf17e..0000000
--- hbase-assembly/src/packages/deb/hbase.control/control
+++ /dev/null
@@ -1,24 +0,0 @@
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements. See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership. The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License. You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-Package: hbase
-Version: @version@
-Section: misc
-Priority: optional
-Architecture: all
-Depends: openjdk-6-jre-headless, hadoop, zookeeper
-Maintainer: Apache Software Foundation
-Description: HBase is the Hadoop database. Use it when you need random, realtime read/write access to your Big Data. This project's goal is the hosting of very large tables -- billions of rows X millions of columns -- atop clusters of commodity hardware.
-Distribution: development
diff --git hbase-assembly/src/packages/deb/hbase.control/postinst hbase-assembly/src/packages/deb/hbase.control/postinst
deleted file mode 100644
index a77fe4f..0000000
--- hbase-assembly/src/packages/deb/hbase.control/postinst
+++ /dev/null
@@ -1,24 +0,0 @@
-#!/bin/sh
-
-# Licensed to the Apache Software Foundation (ASF) under one or more
-# contributor license agreements. See the NOTICE file distributed with
-# this work for additional information regarding copyright ownership.
-# The ASF licenses this file to You under the Apache License, Version 2.0
-# (the "License"); you may not use this file except in compliance with
-# the License. You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-bash /usr/share/hbase/sbin/update-hbase-env.sh \
- --prefix=/usr \
- --bin-dir=/usr/bin \
- --conf-dir=/etc/hbase \
- --log-dir=/var/log/hbase \
- --pid-dir=/var/run/hbase
-
diff --git hbase-assembly/src/packages/deb/hbase.control/postrm hbase-assembly/src/packages/deb/hbase.control/postrm
deleted file mode 100644
index 25f73a4..0000000
--- hbase-assembly/src/packages/deb/hbase.control/postrm
+++ /dev/null
@@ -1,20 +0,0 @@
-#!/bin/sh
-
-# Licensed to the Apache Software Foundation (ASF) under one or more
-# contributor license agreements. See the NOTICE file distributed with
-# this work for additional information regarding copyright ownership.
-# The ASF licenses this file to You under the Apache License, Version 2.0
-# (the "License"); you may not use this file except in compliance with
-# the License. You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-/usr/sbin/userdel hbase 2> /dev/null >/dev/null
-exit 0
-
diff --git hbase-assembly/src/packages/deb/hbase.control/preinst hbase-assembly/src/packages/deb/hbase.control/preinst
deleted file mode 100644
index 497bbab..0000000
--- hbase-assembly/src/packages/deb/hbase.control/preinst
+++ /dev/null
@@ -1,21 +0,0 @@
-#!/bin/sh
-
-# Licensed to the Apache Software Foundation (ASF) under one or more
-# contributor license agreements. See the NOTICE file distributed with
-# this work for additional information regarding copyright ownership.
-# The ASF licenses this file to You under the Apache License, Version 2.0
-# (the "License"); you may not use this file except in compliance with
-# the License. You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-getent group hadoop 2>/dev/null >/dev/null || /usr/sbin/groupadd -r hadoop
-
-/usr/sbin/useradd --comment "HBase" --shell /bin/bash -M -r --groups hadoop --home /usr/share/hbase hbase 2> /dev/null || :
-
diff --git hbase-assembly/src/packages/deb/hbase.control/prerm hbase-assembly/src/packages/deb/hbase.control/prerm
deleted file mode 100644
index 55486e6..0000000
--- hbase-assembly/src/packages/deb/hbase.control/prerm
+++ /dev/null
@@ -1,27 +0,0 @@
-#!/bin/sh
-
-# Licensed to the Apache Software Foundation (ASF) under one or more
-# contributor license agreements. See the NOTICE file distributed with
-# this work for additional information regarding copyright ownership.
-# The ASF licenses this file to You under the Apache License, Version 2.0
-# (the "License"); you may not use this file except in compliance with
-# the License. You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-/etc/init.d/hbase-master stop 2>/dev/null >/dev/null
-/etc/init.d/hbase-regionserver stop 2>/dev/null >/dev/null
-bash /usr/share/hbase/sbin/update-hbase-env.sh \
- --prefix=/usr \
- --bin-dir=/usr/bin \
- --conf-dir=/etc/hbase \
- --log-dir=/var/log/hbase \
- --pid-dir=/var/run/hbase \
- --uninstal
-
diff --git hbase-assembly/src/packages/deb/init.d/hbase-master hbase-assembly/src/packages/deb/init.d/hbase-master
deleted file mode 100644
index 55eea9b..0000000
--- hbase-assembly/src/packages/deb/init.d/hbase-master
+++ /dev/null
@@ -1,142 +0,0 @@
-#! /bin/sh
-
-# Licensed to the Apache Software Foundation (ASF) under one or more
-# contributor license agreements. See the NOTICE file distributed with
-# this work for additional information regarding copyright ownership.
-# The ASF licenses this file to You under the Apache License, Version 2.0
-# (the "License"); you may not use this file except in compliance with
-# the License. You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-### BEGIN INIT INFO
-# Provides: hbase-master
-# Required-Start: $remote_fs $syslog
-# Required-Stop: $remote_fs $syslog
-# Default-Start: 2 3 4 5
-# Default-Stop:
-# Short-Description: Apache HBase Master
-### END INIT INFO
-
-set -e
-
-# /etc/init.d/hbase-master: start and stop the Apache HBase Master daemon
-
-test -x /usr/bin/hbase || exit 0
-( /usr/bin/hbase 2>&1 | grep -q hbase ) 2>/dev/null || exit 0
-
-umask 022
-
-if test -f /etc/default/hbase-env.sh; then
- . /etc/default/hbase-env.sh
-fi
-
-. /lib/lsb/init-functions
-
-# Are we running from init?
-run_by_init() {
- ([ "$previous" ] && [ "$runlevel" ]) || [ "$runlevel" = S ]
-}
-
-check_for_no_start() {
- # forget it if we're trying to start, and /etc/hbase/hbase-master_not_to_be_run exists
- if [ -e /etc/hbase/hbase-master_not_to_be_run ]; then
- if [ "$1" = log_end_msg ]; then
- log_end_msg 0
- fi
- if ! run_by_init; then
- log_action_msg "Apache HBase Master server not in use (/etc/hbase/hbase-master_not_to_be_run)"
- fi
- exit 0
- fi
-}
-
-check_privsep_dir() {
- # Create the PrivSep empty dir if necessary
- if [ ! -d ${HBASE_PID_DIR} ]; then
- mkdir -p ${HBASE_PID_DIR}
- chown root:hadoop ${HBASE_PID_DIR}
- chmod 0775 ${HBASE_PID_DIR}
- fi
-}
-
-export PATH="${PATH:+$PATH:}/usr/sbin:/usr/bin"
-
-case "$1" in
- start)
- check_privsep_dir
- check_for_no_start
- log_daemon_msg "Starting Apache HBase Master server" "hbase-master"
- if start-stop-daemon --start --quiet --oknodo --pidfile ${HBASE_PID_DIR}/hbase-hbase-master.pid -c hbase -x ${HBASE_HOME}/bin/hbase-daemon.sh -- --config ${HBASE_CONF_DIR} start master; then
- log_end_msg 0
- else
- log_end_msg 1
- fi
- ;;
- stop)
- log_daemon_msg "Stopping Apache HBase Master server" "hbase-master"
- if start-stop-daemon --stop --quiet --oknodo --pidfile ${HBASE_PID_DIR}/hbase-hbase-master.pid; then
- log_end_msg 0
- else
- log_end_msg 1
- fi
- ;;
-
- restart)
- check_privsep_dir
- log_daemon_msg "Restarting Apache HBase Master server" "hbase-master"
- start-stop-daemon --stop --quiet --oknodo --retry 30 --pidfile ${HBASE_PID_DIR}/hbase-hbase-master.pid
- check_for_no_start log_end_msg
- if start-stop-daemon --start --quiet --oknodo --pidfile ${HBASE_PID_DIR}/hbase-hbase-master.pid -c hbase -x ${HBASE_HOME}/bin/hbase-daemon.sh -- --config ${HBASE_CONF_DIR} start master; then
- log_end_msg 0
- else
- log_end_msg 1
- fi
- ;;
-
- try-restart)
- check_privsep_dir
- log_daemon_msg "Restarting Apache HBase Master server" "hbase-master"
- set +e
- start-stop-daemon --stop --quiet --retry 30 --pidfile ${HBASE_PID_DIR}/hbase-hbase-master.pid
- RET="$?"
- set -e
- case $RET in
- 0)
- # old daemon stopped
- check_for_no_start log_end_msg
- if start-stop-daemon --start --quiet --oknodo --pidfile ${HBASE_PID_DIR}/hbase-hbase-master.pid -c hbase -x ${HBASE_HOME}/bin/hbase-daemon.sh -- --config ${HBASE_CONF_DIR} start master; then
- log_end_msg 0
- else
- log_end_msg 1
- fi
- ;;
- 1)
- # daemon not running
- log_progress_msg "(not running)"
- log_end_msg 0
- ;;
- *)
- # failed to stop
- log_progress_msg "(failed to stop)"
- log_end_msg 1
- ;;
- esac
- ;;
-
- status)
- status_of_proc -p ${HBASE_PID_DIR}/hbase-hbase-master.pid ${JAVA_HOME}/bin/java hbase-master && exit 0 || exit $?
- ;;
-
- *)
- log_action_msg "Usage: /etc/init.d/hbase-master {start|stop|restart|try-restart|status}"
- exit 1
-esac
-
-exit 0
diff --git hbase-assembly/src/packages/deb/init.d/hbase-regionserver hbase-assembly/src/packages/deb/init.d/hbase-regionserver
deleted file mode 100644
index 0aaa507..0000000
--- hbase-assembly/src/packages/deb/init.d/hbase-regionserver
+++ /dev/null
@@ -1,142 +0,0 @@
-#! /bin/sh
-
-# Licensed to the Apache Software Foundation (ASF) under one or more
-# contributor license agreements. See the NOTICE file distributed with
-# this work for additional information regarding copyright ownership.
-# The ASF licenses this file to You under the Apache License, Version 2.0
-# (the "License"); you may not use this file except in compliance with
-# the License. You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-### BEGIN INIT INFO
-# Provides: hbase-regionserver
-# Required-Start: $remote_fs $syslog
-# Required-Stop: $remote_fs $syslog
-# Default-Start: 2 3 4 5
-# Default-Stop:
-# Short-Description: Apache HBase Region Server
-### END INIT INFO
-
-set -e
-
-# /etc/init.d/hbase-regionserver: start and stop the Apache HBase Region Server daemon
-
-test -x /usr/bin/hbase || exit 0
-( /usr/bin/hbase 2>&1 | grep -q hbase ) 2>/dev/null || exit 0
-
-umask 022
-
-if test -f /etc/default/hbase-env.sh; then
- . /etc/default/hbase-env.sh
-fi
-
-. /lib/lsb/init-functions
-
-# Are we running from init?
-run_by_init() {
- ([ "$previous" ] && [ "$runlevel" ]) || [ "$runlevel" = S ]
-}
-
-check_for_no_start() {
- # forget it if we're trying to start, and /etc/hbase/hbase-regionserver_not_to_be_run exists
- if [ -e /etc/hbase/hbase-regionserver_not_to_be_run ]; then
- if [ "$1" = log_end_msg ]; then
- log_end_msg 0
- fi
- if ! run_by_init; then
- log_action_msg "Apache HBase Region Server server not in use (/etc/hbase/hbase-regionserver_not_to_be_run)"
- fi
- exit 0
- fi
-}
-
-check_privsep_dir() {
- # Create the PrivSep empty dir if necessary
- if [ ! -d ${HBASE_PID_DIR} ]; then
- mkdir -p ${HBASE_PID_DIR}
- chown root:hadoop ${HBASE_PID_DIR}
- chmod 0775 ${HBASE_PID_DIR}
- fi
-}
-
-export PATH="${PATH:+$PATH:}/usr/sbin:/usr/bin"
-
-case "$1" in
- start)
- check_privsep_dir
- check_for_no_start
- log_daemon_msg "Starting Apache HBase Region Server server" "hbase-regionserver"
- if start-stop-daemon --start --quiet --oknodo --pidfile ${HBASE_PID_DIR}/hbase-hbase-regionserver.pid -c hbase -x ${HBASE_HOME}/bin/hbase-daemon.sh -- --config ${HBASE_CONF_DIR} start regionserver; then
- log_end_msg 0
- else
- log_end_msg 1
- fi
- ;;
- stop)
- log_daemon_msg "Stopping Apache HBase Region Server server" "hbase-regionserver"
- if start-stop-daemon --stop --quiet --oknodo --pidfile ${HBASE_PID_DIR}/hbase-hbase-regionserver.pid; then
- log_end_msg 0
- else
- log_end_msg 1
- fi
- ;;
-
- restart)
- check_privsep_dir
- log_daemon_msg "Restarting Apache HBase Region Server server" "hbase-regionserver"
- start-stop-daemon --stop --quiet --oknodo --retry 30 --pidfile ${HBASE_PID_DIR}/hbase-hbase-regionserver.pid
- check_for_no_start log_end_msg
- if start-stop-daemon --start --quiet --oknodo --pidfile ${HBASE_PID_DIR}/hbase-hbase-regionserver.pid -c hbase -x ${HBASE_HOME}/bin/hbase-daemon.sh -- --config ${HBASE_CONF_DIR} start regionserver; then
- log_end_msg 0
- else
- log_end_msg 1
- fi
- ;;
-
- try-restart)
- check_privsep_dir
- log_daemon_msg "Restarting Apache HBase Region Server server" "hbase-regionserver"
- set +e
- start-stop-daemon --stop --quiet --retry 30 --pidfile ${HBASE_PID_DIR}/hbase-hbase-regionserver.pid
- RET="$?"
- set -e
- case $RET in
- 0)
- # old daemon stopped
- check_for_no_start log_end_msg
- if start-stop-daemon --start --quiet --oknodo --pidfile ${HBASE_PID_DIR}/hbase-hbase-regionserver.pid -c hbase -x ${HBASE_HOME}/bin/hbase-daemon.sh -- --config ${HBASE_CONF_DIR} start regionserver; then
- log_end_msg 0
- else
- log_end_msg 1
- fi
- ;;
- 1)
- # daemon not running
- log_progress_msg "(not running)"
- log_end_msg 0
- ;;
- *)
- # failed to stop
- log_progress_msg "(failed to stop)"
- log_end_msg 1
- ;;
- esac
- ;;
-
- status)
- status_of_proc -p ${HBASE_PID_DIR}/hbase-hbase-regionserver.pid ${JAVA_HOME}/bin/java hbase-regionserver && exit 0 || exit $?
- ;;
-
- *)
- log_action_msg "Usage: /etc/init.d/hbase-regionserver {start|stop|restart|try-restart|status}"
- exit 1
-esac
-
-exit 0
diff --git hbase-assembly/src/packages/rpm/init.d/hbase-master hbase-assembly/src/packages/rpm/init.d/hbase-master
deleted file mode 100644
index 67752da..0000000
--- hbase-assembly/src/packages/rpm/init.d/hbase-master
+++ /dev/null
@@ -1,84 +0,0 @@
-#!/bin/bash
-
-# Licensed to the Apache Software Foundation (ASF) under one or more
-# contributor license agreements. See the NOTICE file distributed with
-# this work for additional information regarding copyright ownership.
-# The ASF licenses this file to You under the Apache License, Version 2.0
-# (the "License"); you may not use this file except in compliance with
-# the License. You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-#
-# Starts a HBase master
-#
-# chkconfig: 2345 95 10
-# description: HBase master
-
-source /etc/rc.d/init.d/functions
-source /etc/default/hbase-env.sh
-
-RETVAL=0
-PIDFILE="${HBASE_PID_DIR}/hbase-hbase-master.pid"
-desc="HBase master daemon"
-
-start() {
- echo -n $"Starting $desc (hbase-master): "
- daemon --user hbase ${HBASE_HOME}/bin/hbase-daemon.sh --config "${HBASE_CONF_DIR}" start master
- RETVAL=$?
- echo
- [ $RETVAL -eq 0 ] && touch /var/lock/subsys/hbase-master
- return $RETVAL
-}
-
-stop() {
- echo -n $"Stopping $desc (hbase-master): "
- daemon --user hbase ${HBASE_HOME}/bin/hbase-daemon.sh --config "${HBASE_CONF_DIR}" stop master
- RETVAL=$?
- sleep 5
- echo
- [ $RETVAL -eq 0 ] && rm -f /var/lock/subsys/hbase-master $PIDFILE
-}
-
-restart() {
- stop
- start
-}
-
-checkstatus(){
- status -p $PIDFILE ${JAVA_HOME}/bin/java
- RETVAL=$?
-}
-
-condrestart(){
- [ -e /var/lock/subsys/hbase-master ] && restart || :
-}
-
-case "$1" in
- start)
- start
- ;;
- stop)
- stop
- ;;
- status)
- checkstatus
- ;;
- restart)
- restart
- ;;
- condrestart)
- condrestart
- ;;
- *)
- echo $"Usage: $0 {start|stop|status|restart|condrestart}"
- exit 1
-esac
-
-exit $RETVAL
diff --git hbase-assembly/src/packages/rpm/init.d/hbase-regionserver hbase-assembly/src/packages/rpm/init.d/hbase-regionserver
deleted file mode 100644
index 186ea21..0000000
--- hbase-assembly/src/packages/rpm/init.d/hbase-regionserver
+++ /dev/null
@@ -1,84 +0,0 @@
-#!/bin/bash
-
-# Licensed to the Apache Software Foundation (ASF) under one or more
-# contributor license agreements. See the NOTICE file distributed with
-# this work for additional information regarding copyright ownership.
-# The ASF licenses this file to You under the Apache License, Version 2.0
-# (the "License"); you may not use this file except in compliance with
-# the License. You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-#
-# Starts a HBase region server
-#
-# chkconfig: 2345 96 10
-# description: HBase region server
-
-source /etc/rc.d/init.d/functions
-source /etc/default/hbase-env.sh
-
-RETVAL=0
-PIDFILE="${HBASE_PID_DIR}/hbase-hbase-regionserver.pid"
-desc="HBase regionserver daemon"
-
-start() {
- echo -n $"Starting $desc (hbase-regionserver): "
- daemon --user hbase ${HBASE_HOME}/bin/hbase-daemon.sh --config "${HBASE_CONF_DIR}" start regionserver
- RETVAL=$?
- echo
- [ $RETVAL -eq 0 ] && touch /var/lock/subsys/hbase-regionserver
- return $RETVAL
-}
-
-stop() {
- echo -n $"Stopping $desc (hbase-regionserver): "
- daemon --user hbase ${HBASE_HOME}/bin/hbase-daemon.sh --config "${HBASE_CONF_DIR}" stop regionserver
- RETVAL=$?
- sleep 5
- echo
- [ $RETVAL -eq 0 ] && rm -f /var/lock/subsys/hbase-regionserver $PIDFILE
-}
-
-restart() {
- stop
- start
-}
-
-checkstatus(){
- status -p $PIDFILE ${JAVA_HOME}/bin/java
- RETVAL=$?
-}
-
-condrestart(){
- [ -e /var/lock/subsys/hbase-regionserver ] && restart || :
-}
-
-case "$1" in
- start)
- start
- ;;
- stop)
- stop
- ;;
- status)
- checkstatus
- ;;
- restart)
- restart
- ;;
- condrestart)
- condrestart
- ;;
- *)
- echo $"Usage: $0 {start|stop|status|restart|condrestart}"
- exit 1
-esac
-
-exit $RETVAL
diff --git hbase-assembly/src/packages/rpm/spec/conf-pseudo.spec hbase-assembly/src/packages/rpm/spec/conf-pseudo.spec
deleted file mode 100644
index b8350d2..0000000
--- hbase-assembly/src/packages/rpm/spec/conf-pseudo.spec
+++ /dev/null
@@ -1,103 +0,0 @@
-# Licensed to the Apache Software Foundation (ASF) under one or more
-# contributor license agreements. See the NOTICE file distributed with
-# this work for additional information regarding copyright ownership.
-# The ASF licenses this file to You under the Apache License, Version 2.0
-# (the "License"); you may not use this file except in compliance with
-# the License. You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-#
-# RPM Spec file for HBase version @version@
-#
-
-%define name hbase-conf-pseudo
-%define version @version@
-%define release @package.release@
-
-# Installation Locations
-%define _source @package.name@
-%define _final_name @final.name@
-%define _prefix @package.prefix@
-%define _bin_dir %{_prefix}/bin
-%define _conf_dir @package.conf.dir@
-%define _include_dir %{_prefix}/include
-%define _lib_dir %{_prefix}/lib
-%define _lib64_dir %{_prefix}/lib64
-%define _libexec_dir %{_prefix}/libexec
-%define _log_dir @package.log.dir@
-%define _man_dir %{_prefix}/man
-%define _pid_dir @package.pid.dir@
-%define _sbin_dir %{_prefix}/sbin
-%define _share_dir %{_prefix}/share/hbase
-%define _src_dir %{_prefix}/src
-%define _var_dir %{_prefix}/var/lib
-
-# Build time settings
-%define _build_dir @package.build.dir@
-%define _final_name @final.name@
-%define debug_package %{nil}
-
-Summary: Default HBase configuration templates
-License: Apache License, Version 2.0
-URL: http://hbase.apache.org/
-Vendor: Apache Software Foundation
-Group: Development/Libraries
-Name: %{name}
-Version: %{version}
-Release: %{release}
-Source0: %{_source}
-Prefix: %{_prefix}
-Prefix: %{_conf_dir}
-Prefix: %{_log_dir}
-Prefix: %{_pid_dir}
-Buildroot: %{_build_dir}
-Requires: hbase == %{version}, sh-utils, textutils, /usr/sbin/useradd, /usr/sbin/usermod, /sbin/chkconfig, /sbin/service, jdk >= 1.6
-AutoReqProv: no
-Provides: hbase-conf-pseudo
-
-%description
-Installation of this RPM will setup your machine to run in pseudo-distributed mode where each HBase daemon runs in a separate Java process.
-
-%prep
-%setup -n %{_final_name}
-
-%build
-if [ -d ${RPM_BUILD_DIR}%{_conf_dir} ]; then
- rm -rf ${RPM_BUILD_DIR}%{_conf_dir}
-fi
-
-mkdir -p ${RPM_BUILD_DIR}%{_conf_dir}
-mkdir -p ${RPM_BUILD_DIR}%{_share_dir}/src/packages/conf-pseudo
-cp -f ${RPM_BUILD_DIR}/%{_final_name}/src/packages/conf-pseudo/hbase-site.xml ${RPM_BUILD_DIR}%{_share_dir}/src/packages/conf-pseudo/hbase-site.xml
-rm -rf ${RPM_BUILD_DIR}/%{_final_name}
-
-%preun
-/sbin/chkconfig --del hbase-master
-/sbin/chkconfig --del hbase-regionserver
-/etc/init.d/hbase-master stop 2>/dev/null >/dev/null
-/etc/init.d/hbase-regionserver stop 2>/dev/null >/dev/null
-exit 0
-
-%post
-cp -f ${RPM_INSTALL_PREFIX0}/share/hbase/src/packages/conf-pseudo/*.xml ${RPM_INSTALL_PREFIX1} 2>/dev/null >/dev/null
-/etc/init.d/hadoop-namenode start 2>/dev/null >/dev/null
-/etc/init.d/hadoop-datanode start 2>/dev/null >/dev/null
-su - hdfs -c "hadoop fs -mkdir /hbase" 2>/dev/null >/dev/null
-su - hdfs -c "hadoop fs -chown hbase /hbase" 2>/dev/null >/dev/null
-/etc/init.d/hbase-master start 2>/dev/null >/dev/null
-/etc/init.d/hbase-regionserver start 2>/dev/null >/dev/null
-/sbin/chkconfig hbase-master --add
-/sbin/chkconfig hbase-regionserver --add
-/sbin/chkconfig hbase-master on
-/sbin/chkconfig hbase-regionserver on
-
-%files
-%defattr(-,root,root)
-%config %{_share_dir}/src/packages/conf-pseudo/hbase-site.xml
diff --git hbase-assembly/src/packages/rpm/spec/hbase.spec hbase-assembly/src/packages/rpm/spec/hbase.spec
deleted file mode 100644
index 1af874d..0000000
--- hbase-assembly/src/packages/rpm/spec/hbase.spec
+++ /dev/null
@@ -1,135 +0,0 @@
-# Licensed to the Apache Software Foundation (ASF) under one or more
-# contributor license agreements. See the NOTICE file distributed with
-# this work for additional information regarding copyright ownership.
-# The ASF licenses this file to You under the Apache License, Version 2.0
-# (the "License"); you may not use this file except in compliance with
-# the License. You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-#
-# RPM Spec file for HBase version @version@
-#
-
-%define name hbase
-%define version @version@
-%define release @package.release@
-
-# Installation Locations
-%define _source @package.name@
-%define _final_name @final.name@
-%define _prefix @package.prefix@
-%define _bin_dir %{_prefix}/bin
-%define _conf_dir @package.conf.dir@
-%define _include_dir %{_prefix}/include
-%define _lib_dir %{_prefix}/lib
-%define _lib64_dir %{_prefix}/lib64
-%define _libexec_dir %{_prefix}/libexec
-%define _log_dir @package.log.dir@
-%define _man_dir %{_prefix}/man
-%define _pid_dir @package.pid.dir@
-%define _sbin_dir %{_prefix}/sbin
-%define _share_dir %{_prefix}/share/hbase
-%define _src_dir %{_prefix}/src
-%define _var_dir %{_prefix}/var/lib
-
-# Build time settings
-%define _build_dir @package.build.dir@
-%define _final_name @final.name@
-%define debug_package %{nil}
-
-Summary: Default HBase configuration templates
-License: Apache License, Version 2.0
-URL: http://hbase.apache.org/
-Vendor: Apache Software Foundation
-Group: Development/Libraries
-Name: %{name}
-Version: %{version}
-Release: %{release}
-Source0: %{_source}
-Prefix: %{_prefix}
-Prefix: %{_conf_dir}
-Prefix: %{_log_dir}
-Prefix: %{_pid_dir}
-Buildroot: %{_build_dir}
-Requires: sh-utils, textutils, /usr/sbin/useradd, /usr/sbin/usermod, /sbin/chkconfig, /sbin/service, jdk >= 1.6, hadoop
-AutoReqProv: no
-Provides: hbase
-
-%description
-Installation of this RPM will setup your machine to run in pseudo-distributed mode where each HBase daemon runs in a separate Java process.
-
-%prep
-%setup -n %{_final_name}
-
-%build
-if [ -d ${RPM_BUILD_DIR}%{_prefix} ]; then
- rm -rf ${RPM_BUILD_DIR}%{_prefix}
-fi
-
-if [ -d ${RPM_BUILD_DIR}%{_log_dir} ]; then
- rm -rf ${RPM_BUILD_DIR}%{_log_dir}
-fi
-
-if [ -d ${RPM_BUILD_DIR}%{_conf_dir} ]; then
- rm -rf ${RPM_BUILD_DIR}%{_conf_dir}
-fi
-
-if [ -d ${RPM_BUILD_DIR}%{_pid_dir} ]; then
- rm -rf ${RPM_BUILD_DIR}%{_pid_dir}
-fi
-
-mkdir -p ${RPM_BUILD_DIR}%{_conf_dir}
-mkdir -p ${RPM_BUILD_DIR}%{_log_dir}
-mkdir -p ${RPM_BUILD_DIR}%{_conf_dir}
-mkdir -p ${RPM_BUILD_DIR}%{_pid_dir}
-mkdir -p ${RPM_BUILD_DIR}%{_share_dir}
-mkdir -p ${RPM_BUILD_DIR}%{_share_dir}/sbin
-mkdir -p ${RPM_BUILD_DIR}/etc/rc.d/init.d
-
-cp ${RPM_BUILD_DIR}/%{_final_name}/src/packages/update-hbase-env.sh ${RPM_BUILD_DIR}%{_share_dir}/sbin/update-hbase-env.sh
-cp ${RPM_BUILD_DIR}/%{_final_name}/src/packages/rpm/init.d/hbase-master ${RPM_BUILD_DIR}%{_share_dir}/sbin/hbase-master
-cp ${RPM_BUILD_DIR}/%{_final_name}/src/packages/rpm/init.d/hbase-regionserver ${RPM_BUILD_DIR}%{_share_dir}/sbin/hbase-regionserver
-chmod 0755 ${RPM_BUILD_DIR}%{_share_dir}/sbin/*
-rm -f ${RPM_BUILD_DIR}/%{_final_name}/*.txt
-rm -f ${RPM_BUILD_DIR}/%{_final_name}/pom.xml
-mv -f ${RPM_BUILD_DIR}/%{_final_name}/conf/* ${RPM_BUILD_DIR}%{_conf_dir}
-rmdir ${RPM_BUILD_DIR}/%{_final_name}/conf
-rm -rf ${RPM_BUILD_DIR}/%{_final_name}/src
-mv -f ${RPM_BUILD_DIR}/%{_final_name}/* ${RPM_BUILD_DIR}%{_share_dir}
-
-%install
-cp -Rp ${RPM_BUILD_DIR} ${RPM_BUILD_ROOT}
-
-%preun
-${RPM_INSTALL_PREFIX0}/share/hbase/sbin/update-hbase-env.sh \
- --prefix=${RPM_INSTALL_PREFIX0} \
- --bin-dir=${RPM_INSTALL_PREFIX0}/bin \
- --conf-dir=${RPM_INSTALL_PREFIX1} \
- --log-dir=${RPM_INSTALL_PREFIX2} \
- --pid-dir=${RPM_INSTALL_PREFIX3} \
- --uninstall
-
-%pre
-getent group hadoop 2>/dev/null >/dev/null || /usr/sbin/groupadd -r hadoop
-
-/usr/sbin/useradd --comment "HBase" --shell /bin/bash -M -r --groups hadoop --home %{_share_dir} hbase 2> /dev/null || :
-
-%post
-${RPM_INSTALL_PREFIX0}/share/hbase/sbin/update-hbase-env.sh \
- --prefix=${RPM_INSTALL_PREFIX0} \
- --bin-dir=${RPM_INSTALL_PREFIX0}/bin \
- --conf-dir=${RPM_INSTALL_PREFIX1} \
- --log-dir=${RPM_INSTALL_PREFIX2} \
- --pid-dir=${RPM_INSTALL_PREFIX3}
-
-%files
-%defattr(-,root,root)
-%{_prefix}
-%config %{_conf_dir}
diff --git hbase-assembly/src/packages/update-hbase-env.sh hbase-assembly/src/packages/update-hbase-env.sh
deleted file mode 100644
index 45276b2..0000000
--- hbase-assembly/src/packages/update-hbase-env.sh
+++ /dev/null
@@ -1,193 +0,0 @@
-#!/bin/sh
-
-# Licensed to the Apache Software Foundation (ASF) under one or more
-# contributor license agreements. See the NOTICE file distributed with
-# this work for additional information regarding copyright ownership.
-# The ASF licenses this file to You under the Apache License, Version 2.0
-# (the "License"); you may not use this file except in compliance with
-# the License. You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# This script configures hbase-env.sh and symlinkis directories for
-# relocating RPM locations.
-
-usage() {
- echo "
-usage: $0
- Required parameters:
- --prefix=PREFIX path to install into
-
- Optional parameters:
- --arch=i386 OS Architecture
- --bin-dir=PREFIX/bin Executable directory
- --conf-dir=/etc/hbase Configuration directory
- --log-dir=/var/log/hbase Log directory
- --pid-dir=/var/run PID file location
- "
- exit 1
-}
-
-OPTS=$(getopt \
- -n $0 \
- -o '' \
- -l 'arch:' \
- -l 'prefix:' \
- -l 'bin-dir:' \
- -l 'conf-dir:' \
- -l 'lib-dir:' \
- -l 'log-dir:' \
- -l 'pid-dir:' \
- -l 'uninstall' \
- -- "$@")
-
-if [ $? != 0 ] ; then
- usage
-fi
-
-eval set -- "${OPTS}"
-while true ; do
- case "$1" in
- --arch)
- ARCH=$2 ; shift 2
- ;;
- --prefix)
- PREFIX=$2 ; shift 2
- ;;
- --bin-dir)
- BIN_DIR=$2 ; shift 2
- ;;
- --log-dir)
- LOG_DIR=$2 ; shift 2
- ;;
- --lib-dir)
- LIB_DIR=$2 ; shift 2
- ;;
- --conf-dir)
- CONF_DIR=$2 ; shift 2
- ;;
- --pid-dir)
- PID_DIR=$2 ; shift 2
- ;;
- --uninstall)
- UNINSTALL=1; shift
- ;;
- --)
- shift ; break
- ;;
- *)
- echo "Unknown option: $1"
- usage
- exit 1
- ;;
- esac
-done
-
-for var in PREFIX; do
- if [ -z "$(eval "echo \$$var")" ]; then
- echo Missing param: $var
- usage
- fi
-done
-
-ARCH=${ARCH:-i386}
-BIN_DIR=${BIN_DIR:-$PREFIX/share/hbase/bin}
-CONF_DIR=${CONF_DIR:-$PREFIX/conf}
-LIB_DIR=${LIB_DIR:-$PREFIX/lib}
-LOG_DIR=${LOG_DIR:-$PREFIX/var/log}
-PID_DIR=${PID_DIR:-$PREFIX/var/run}
-UNINSTALL=${UNINSTALL:-0}
-
-if [ "${ARCH}" != "i386" ]; then
- LIB_DIR=${LIB_DIR}64
-fi
-
-[ -f /etc/default/hadoop-env.sh ] && . /etc/default/hadoop-env.sh
-[ -f /etc/default/zookeeper-env.sh ] && . /etc/default/zookeeper-env.sh
-
-if [ "${UNINSTALL}" -eq "1" ]; then
- # Remove symlinks
- if [ "${BIN_DIR}" != "${PREFIX}/share/hbase/bin" ]; then
- for var in `ls ${PREFIX}/share/hbase/bin`; do
- rm -f ${BIN_DIR}/${var}
- done
- fi
- if [ -f /etc/default/hbase-env.sh ]; then
- rm -f /etc/default/hbase-env.sh
- fi
- if [ "${CONF_DIR}" != "${PREFIX}/share/hbase/conf" ]; then
- rm -f ${PREFIX}/share/hbase/conf
- fi
-
- rm -f ${PREFIX}/share/hbase/sbin/hbase-master
- rm -f ${PREFIX}/share/hbase/sbin/hbase-regionserver
- rm -f /etc/init.d/hbase-master
- rm -f /etc/init.d/hbase-regionserver
-
-else
- # Create symlinks
- if [ "${BIN_DIR}" != "${PREFIX}/share/hbase/bin" ]; then
- for var in `ls ${PREFIX}/share/hbase/bin`; do
- ln -sf ${PREFIX}/share/hbase/bin/${var} ${BIN_DIR}/${var}
- done
- fi
- if [ "${CONF_DIR}" != "${PREFIX}/share/hbase/conf" ]; then
- ln -sf ${CONF_DIR} ${PREFIX}/share/hbase/conf
- fi
-
- chmod 755 ${PREFIX}/share/hbase/sbin/*
-
- ln -sf ${PREFIX}/share/hbase/sbin/hbase-master /etc/init.d/hbase-master
- ln -sf ${PREFIX}/share/hbase/sbin/hbase-regionserver /etc/init.d/hbase-regionserver
-
- ln -sf ${CONF_DIR}/hbase-env.sh /etc/default/hbase-env.sh
- ln -sf ${CONF_DIR}/hbase-env.sh /etc/profile.d/hbase-env.sh
-
- if [ -n "${HADOOP_HOME}" -a -d "${HADOOP_HOME}" ]; then
- HADOOP_JARS=`ls ${HADOOP_HOME}/*.jar | tr '\n' ':'`
- fi
-
- if [ -n "${ZOOKEEPER_HOME}" -a -d "${ZOOKEEPER_HOME}/share/zookeeper" ]; then
- ZOOKEEPER_JARS=`ls ${ZOOKEEPER_HOME}/share/zookeeper/*.jar | tr '\n' ':'`
- fi
-
- mkdir -p ${PID_DIR}
- mkdir -p ${LOG_DIR}
- chown hbase ${PID_DIR}
- chown hbase ${LOG_DIR}
-
- TFILE="/tmp/$(basename $0).$$.tmp"
- grep -v "^export HBASE_HOME" ${CONF_DIR}/hbase-env.sh | \
- grep -v "^export HBASE_CONF_DIR" | \
- grep -v "^export HBASE_CLASSPATH" | \
- grep -v "^export HBASE_MANAGES_ZK" | \
- grep -v "^export HBASE_IDENT_STRING" | \
- grep -v "^export HBASE_PID_DIR" | \
- grep -v "^export HBASE_LOG_DIR" | \
- grep -v "^export JAVA_HOME" > ${TFILE}
- if [ -z "${JAVA_HOME}" ]; then
- if [ -e /etc/lsb-release ]; then
- JAVA_HOME=`update-alternatives --config java | grep java | cut -f2 -d':' | cut -f2 -d' ' | sed -e 's/\/bin\/java//'`
- else
- JAVA_HOME=/usr/java/default
- fi
- fi
- if [ "${JAVA_HOME}xxx" != "xxx" ]; then
- echo "export JAVA_HOME=${JAVA_HOME}" >> ${TFILE}
- fi
- echo "export HBASE_IDENT_STRING=\`whoami\`" >> ${TFILE}
- echo "export HBASE_HOME=${PREFIX}/share/hbase" >> ${TFILE}
- echo "export HBASE_CONF_DIR=${CONF_DIR}" >> ${TFILE}
- echo "export HBASE_CLASSPATH=${CONF_DIR}:${HADOOP_CONF_DIR}:${HADOOP_JARS}:${ZOOKEEPER_JARS}" >> ${TFILE}
- echo "export HBASE_MANAGES_ZK=false" >> ${TFILE}
- echo "export HBASE_PID_DIR=${PID_DIR}" >> ${TFILE}
- echo "export HBASE_LOG_DIR=${LOG_DIR}" >> ${TFILE}
- cp ${TFILE} ${CONF_DIR}/hbase-env.sh
- rm -f ${TFILE}
-fi
diff --git hbase-common/pom.xml hbase-common/pom.xml
index 9882856..e300c7d 100644
--- hbase-common/pom.xml
+++ hbase-common/pom.xml
@@ -31,27 +31,37 @@
Common functionality for HBase
-
-
-
- maven-surefire-plugin
-
-
-
- secondPartTestsExecution
- test
-
- test
-
-
- true
-
-
-
-
-
-
+
+
+ maven-surefire-plugin
+
+
+
+ secondPartTestsExecution
+ test
+
+ test
+
+
+ true
+
+
+
+
+
+ org.apache.maven.plugins
+ maven-site-plugin
+
+
+ true
+ true
+
+
+
@@ -207,4 +217,4 @@
-
\ No newline at end of file
+
diff --git hbase-server/pom.xml hbase-server/pom.xml
index f14c6c7..7a691c4 100644
--- hbase-server/pom.xml
+++ hbase-server/pom.xml
@@ -32,6 +32,10 @@
HBase - ServerMain functionality for HBase
+
+ 1.5.3
+
+
@@ -62,8 +66,21 @@
+ org.apache.maven.plugins
+ maven-site-plugin
+
+
+ true
+ true
+
+
+ org.apache.avroavro-maven-plugin
+ ${avro.version}generate-avro-sources
@@ -359,14 +376,26 @@
log4jlog4j
-
- org.apache.avro
- avro
-
-
- org.apache.avro
- avro-ipc
-
+
+ org.apache.avro
+ avro
+ ${avro.version}
+
+
+ com.thoughtworks.paranamer
+ paranamer
+
+
+ com.thoughtworks.paranamer
+ paranamer-ant
+
+
+
+
+ org.apache.avro
+ avro-ipc
+ ${avro.version}
+ org.apache.zookeeperzookeeper
diff --git hbase-site/pom.xml hbase-site/pom.xml
deleted file mode 100644
index a012bd2..0000000
--- hbase-site/pom.xml
+++ /dev/null
@@ -1,156 +0,0 @@
-
-
-
- 4.0.0
-
-
- hbase
- org.apache.hbase
- 0.95-SNAPSHOT
-
-
- hbase-site
- pom
-
- HBase - Site
- HBase's website - overall site, reference guide, and javadocs come together here
-
-
-
-
- org.apache.hbase
- hbase-server
-
-
-
-
-
-
-
- maven-resources-plugin
-
-
- copy-javadocs
- pre-site
-
- copy-resources
-
-
- target/site/apidocs
-
-
- ../target/apidocs
-
- **/**
-
-
-
-
-
-
-
-
-
- org.codehaus.mojo
- xml-maven-plugin
-
-
-
- transform
-
- pre-site
-
-
-
-
-
-
- ../hbase-server/src/main/resources/
-
- hbase-default.xml
-
- ${basedir}/src/xslt/configuration_to_docbook_section.xsl
- ${basedir}/target/site/
-
-
-
-
-
- com.agilejava.docbkx
- docbkx-maven-plugin
-
-
- multipage
- pre-site
-
- true
- true
- true
- true
- 100
- true
- true
- target/site/
- ${basedir}/src/resources/css/freebsd_docbook.css
- ${basedir}/src/docbkx/customization.xsl
- ${basedir}/src/resources/images/
- 2
- yes
-
-
- generate-html
-
-
-
- onepage
- pre-site
-
- true
- true
- 100
- true
- true
- target/site/
- css/freebsd_docbook.css
- images/
- 2
- yes
-
-
- generate-html
-
-
-
-
-
-
- maven-site-plugin
-
- UTF-8
- UTF-8
- src/site/site.vm
-
-
-
-
-
diff --git hbase-site/src/docbkx/book.xml hbase-site/src/docbkx/book.xml
deleted file mode 100644
index 66c3f12..0000000
--- hbase-site/src/docbkx/book.xml
+++ /dev/null
@@ -1,3580 +0,0 @@
-
-
-
-
-
-
- Apache HBase Reference Guide
-
-
-
-
-
-
-
-
-
- 2012Apache Software Foundation
-
- This is the official reference guide of
- Apache HBase,
- a distributed, versioned, column-oriented database built on top of
- Apache Hadoop and
- Apache ZooKeeper.
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- Data Model
- In short, applications store data into an HBase table.
- Tables are made of rows and columns.
- All columns in HBase belong to a particular column family.
- Table cells -- the intersection of row and column
- coordinates -- are versioned.
- A cell’s content is an uninterpreted array of bytes.
-
- Table row keys are also byte arrays so almost anything can
- serve as a row key from strings to binary representations of longs or
- even serialized data structures. Rows in HBase tables
- are sorted by row key. The sort is byte-ordered. All table accesses are
- via the table row key -- its primary key.
-
-
- Conceptual View
-
- The following example is a slightly modified form of the one on page
- 2 of the BigTable paper.
- There is a table called webtable that contains two column families named
- contents and anchor.
- In this example, anchor contains two
- columns (anchor:cssnsi.com, anchor:my.look.ca)
- and contents contains one column (contents:html).
-
- Column Names
-
- By convention, a column name is made of its column family prefix and a
- qualifier. For example, the
- column
- contents:html is of the column family contents
- The colon character (:) delimits the column family from the
- column family qualifier.
-
-
-
-
-
- Physical View
-
- Although at a conceptual level tables may be viewed as a sparse set of rows.
- Physically they are stored on a per-column family basis. New columns
- (i.e., columnfamily:column) can be added to any
- column family without pre-announcing them.
-
- It is important to note in the diagram above that the empty cells shown in the
- conceptual view are not stored since they need not be in a column-oriented
- storage format. Thus a request for the value of the contents:html
- column at time stamp t8 would return no value. Similarly, a
- request for an anchor:my.look.ca value at time stamp
- t9 would return no value. However, if no timestamp is
- supplied, the most recent value for a particular column would be returned
- and would also be the first one found since timestamps are stored in
- descending order. Thus a request for the values of all columns in the row
- com.cnn.www if no timestamp is specified would be:
- the value of contents:html from time stamp
- t6, the value of anchor:cnnsi.com
- from time stamp t9, the value of
- anchor:my.look.ca from time stamp t8.
-
- For more information about the internals of how HBase stores data, see .
-
-
-
-
- Table
-
- Tables are declared up front at schema definition time.
-
-
-
-
- Row
- Row keys are uninterrpreted bytes. Rows are
- lexicographically sorted with the lowest order appearing first
- in a table. The empty byte array is used to denote both the
- start and end of a tables' namespace.
-
-
-
- Column FamilyColumn Family
-
- Columns in HBase are grouped into column families.
- All column members of a column family have the same prefix. For example, the
- columns courses:history and
- courses:math are both members of the
- courses column family.
- The colon character (:) delimits the column family from the
- column family qualifierColumn Family Qualifier.
- The column family prefix must be composed of
- printable characters. The qualifying tail, the
- column family qualifier, can be made of any
- arbitrary bytes. Column families must be declared up front
- at schema definition time whereas columns do not need to be
- defined at schema time but can be conjured on the fly while
- the table is up an running.
- Physically, all column family members are stored together on the
- filesystem. Because tunings and
- storage specifications are done at the column family level, it is
- advised that all column family members have the same general access
- pattern and size characteristics.
-
-
-
-
- CellsCells
- A {row, column, version} tuple exactly
- specifies a cell in HBase.
- Cell content is uninterrpreted bytes
-
-
- Data Model Operations
- The four primary data model operations are Get, Put, Scan, and Delete. Operations are applied via
- HTable instances.
-
-
- Get
- Get returns
- attributes for a specified row. Gets are executed via
-
- HTable.get.
-
-
-
- Put
- Put either
- adds new rows to a table (if the key is new) or can update existing rows (if the key already exists). Puts are executed via
-
- HTable.put (writeBuffer) or
- HTable.batch (non-writeBuffer).
-
-
-
- Scans
- Scan allow
- iteration over multiple rows for specified attributes.
-
- The following is an example of a
- on an HTable table instance. Assume that a table is populated with rows with keys "row1", "row2", "row3",
- and then another set of rows with the keys "abc1", "abc2", and "abc3". The following example shows how startRow and stopRow
- can be applied to a Scan instance to return the rows beginning with "row".
-
-HTable htable = ... // instantiate HTable
-
-Scan scan = new Scan();
-scan.addColumn(Bytes.toBytes("cf"),Bytes.toBytes("attr"));
-scan.setStartRow( Bytes.toBytes("row")); // start key is inclusive
-scan.setStopRow( Bytes.toBytes("row" + (char)0)); // stop key is exclusive
-ResultScanner rs = htable.getScanner(scan);
-try {
- for (Result r = rs.next(); r != null; r = rs.next()) {
- // process result...
-} finally {
- rs.close(); // always close the ResultScanner!
-}
-
-
-
-
- Delete
- Delete removes
- a row from a table. Deletes are executed via
-
- HTable.delete.
-
- HBase does not modify data in place, and so deletes are handled by creating new markers called tombstones.
- These tombstones, along with the dead values, are cleaned up on major compactions.
-
- See for more information on deleting versions of columns, and see
- for more information on compactions.
-
-
-
-
-
-
-
-
- VersionsVersions
-
- A {row, column, version} tuple exactly
- specifies a cell in HBase. Its possible to have an
- unbounded number of cells where the row and column are the same but the
- cell address differs only in its version dimension.
-
- While rows and column keys are expressed as bytes, the version is
- specified using a long integer. Typically this long contains time
- instances such as those returned by
- java.util.Date.getTime() or
- System.currentTimeMillis(), that is: the difference,
- measured in milliseconds, between the current time and midnight, January
- 1, 1970 UTC.
-
- The HBase version dimension is stored in decreasing order, so that
- when reading from a store file, the most recent values are found
- first.
-
- There is a lot of confusion over the semantics of
- cell versions, in HBase. In particular, a couple
- questions that often come up are:
-
- If multiple writes to a cell have the same version, are all
- versions maintained or just the last?
- Currently, only the last written is fetchable.
-
-
-
-
- Is it OK to write cells in a non-increasing version
- order?
- Yes
-
-
-
-
- Below we describe how the version dimension in HBase currently
- works
- See HBASE-2406
- for discussion of HBase versions. Bending time
- in HBase makes for a good read on the version, or time,
- dimension in HBase. It has more detail on versioning than is
- provided here. As of this writing, the limiitation
- Overwriting values at existing timestamps
- mentioned in the article no longer holds in HBase. This section is
- basically a synopsis of this article by Bruno Dumon.
- .
-
-
- Versions and HBase Operations
-
- In this section we look at the behavior of the version dimension
- for each of the core HBase operations.
-
-
- Get/Scan
-
- Gets are implemented on top of Scans. The below discussion of
- Get applies equally to Scans.
-
- By default, i.e. if you specify no explicit version, when
- doing a get, the cell whose version has the
- largest value is returned (which may or may not be the latest one
- written, see later). The default behavior can be modified in the
- following ways:
-
-
-
- to return more than one version, see Get.setMaxVersions()
-
-
-
- to return versions other than the latest, see Get.setTimeRange()
-
- To retrieve the latest version that is less than or equal
- to a given value, thus giving the 'latest' state of the record
- at a certain point in time, just use a range from 0 to the
- desired version and set the max versions to 1.
-
-
-
-
-
- Default Get Example
- The following Get will only retrieve the current version of the row
-
-Get get = new Get(Bytes.toBytes("row1"));
-Result r = htable.get(get);
-byte[] b = r.getValue(Bytes.toBytes("cf"), Bytes.toBytes("attr")); // returns current version of value
-
-
-
-
- Versioned Get Example
- The following Get will return the last 3 versions of the row.
-
-Get get = new Get(Bytes.toBytes("row1"));
-get.setMaxVersions(3); // will return last 3 versions of row
-Result r = htable.get(get);
-byte[] b = r.getValue(Bytes.toBytes("cf"), Bytes.toBytes("attr")); // returns current version of value
-List<KeyValue> kv = r.getColumn(Bytes.toBytes("cf"), Bytes.toBytes("attr")); // returns all versions of this column
-
-
-
-
-
- Put
-
- Doing a put always creates a new version of a
- cell, at a certain timestamp. By default the
- system uses the server's currentTimeMillis, but
- you can specify the version (= the long integer) yourself, on a
- per-column level. This means you could assign a time in the past or
- the future, or use the long value for non-time purposes.
-
- To overwrite an existing value, do a put at exactly the same
- row, column, and version as that of the cell you would
- overshadow.
-
- Implicit Version Example
- The following Put will be implicitly versioned by HBase with the current time.
-
-Put put = new Put(Bytes.toBytes(row));
-put.add(Bytes.toBytes("cf"), Bytes.toBytes("attr1"), Bytes.toBytes( data));
-htable.put(put);
-
-
-
-
- Explicit Version Example
- The following Put has the version timestamp explicitly set.
-
-Put put = new Put( Bytes.toBytes(row));
-long explicitTimeInMs = 555; // just an example
-put.add(Bytes.toBytes("cf"), Bytes.toBytes("attr1"), explicitTimeInMs, Bytes.toBytes(data));
-htable.put(put);
-
- Caution: the version timestamp is internally by HBase for things like time-to-live calculations.
- It's usually best to avoid setting this timestamp yourself. Prefer using a separate
- timestamp attribute of the row, or have the timestamp a part of the rowkey, or both.
-
-
-
-
-
-
- Delete
-
- There are three different types of internal delete markers
- See Lars Hofhansl's blog for discussion of his attempt
- adding another, Scanning in HBase: Prefix Delete Marker:
-
- Delete: for a specific version of a column.
-
- Delete column: for all versions of a column.
-
- Delete family: for all columns of a particular ColumnFamily
-
-
- When deleting an entire row, HBase will internally create a tombstone for each ColumnFamily (i.e., not each individual column).
-
- Deletes work by creating tombstone
- markers. For example, let's suppose we want to delete a row. For
- this you can specify a version, or else by default the
- currentTimeMillis is used. What this means is
- delete all cells where the version is less than or equal to
- this version. HBase never modifies data in place, so for
- example a delete will not immediately delete (or mark as deleted)
- the entries in the storage file that correspond to the delete
- condition. Rather, a so-called tombstone is
- written, which will mask the deleted values
- When HBase does a major compaction, the tombstones are
- processed to actually remove the dead values, together with the
- tombstones themselves.
- . If the version you specified when deleting a row is
- larger than the version of any value in the row, then you can
- consider the complete row to be deleted.
- Also see for more information on the internal KeyValue format.
-
-
-
-
-
- Current Limitations
-
- There are still some bugs (or at least 'undecided behavior')
- with the version dimension that will be addressed by later HBase
- releases.
-
-
- Deletes mask Puts
-
- Deletes mask puts, even puts that happened after the delete
- was entered
- HBASE-2256
- . Remember that a delete writes a tombstone, which only
- disappears after then next major compaction has run. Suppose you do
- a delete of everything <= T. After this you do a new put with a
- timestamp <= T. This put, even if it happened after the delete,
- will be masked by the delete tombstone. Performing the put will not
- fail, but when you do a get you will notice the put did have no
- effect. It will start working again after the major compaction has
- run. These issues should not be a problem if you use
- always-increasing versions for new puts to a row. But they can occur
- even if you do not care about time: just do delete and put
- immediately after each other, and there is some chance they happen
- within the same millisecond.
-
-
-
- Major compactions change query results
-
- ...create three cell versions at t1, t2 and t3, with a
- maximum-versions setting of 2. So when getting all versions, only
- the values at t2 and t3 will be returned. But if you delete the
- version at t2 or t3, the one at t1 will appear again. Obviously,
- once a major compaction has run, such behavior will not be the case
- anymore...
- See Garbage Collection in Bending
- time in HBase
-
-
-
-
-
- Sort Order
- All data model operations HBase return data in sorted order. First by row,
- then by ColumnFamily, followed by column qualifier, and finally timestamp (sorted
- in reverse, so newest records are returned first).
-
-
-
- Column Metadata
- There is no store of column metadata outside of the internal KeyValue instances for a ColumnFamily.
- Thus, while HBase can support not only a wide number of columns per row, but a heterogenous set of columns
- between rows as well, it is your responsibility to keep track of the column names.
-
- The only way to get a complete set of columns that exist for a ColumnFamily is to process all the rows.
- For more information about how HBase stores data internally, see .
-
-
- Joins
- Whether HBase supports joins is a common question on the dist-list, and there is a simple answer: it doesn't,
- at not least in the way that RDBMS' support them (e.g., with equi-joins or outer-joins in SQL). As has been illustrated
- in this chapter, the read data model operations in HBase are Get and Scan.
-
- However, that doesn't mean that equivalent join functionality can't be supported in your application, but
- you have to do it yourself. The two primary strategies are either denormalizing the data upon writing to HBase,
- or to have lookup tables and do the join between HBase tables in your application or MapReduce code (and as RDBMS'
- demonstrate, there are several strategies for this depending on the size of the tables, e.g., nested loops vs.
- hash-joins). So which is the best approach? It depends on what you are trying to do, and as such there isn't a single
- answer that works for every use case.
-
-
-
-
-
- HBase and Schema Design
- A good general introduction on the strength and weaknesses modelling on
- the various non-rdbms datastores is Ian Varleys' Master thesis,
- No Relation: The Mixed Blessings of Non-Relational Databases.
- Recommended. Also, read for how HBase stores data internally.
-
-
-
- Schema Creation
-
- HBase schemas can be created or updated with
- or by using HBaseAdmin in the Java API.
-
- Tables must be disabled when making ColumnFamily modifications, for example..
-
-Configuration config = HBaseConfiguration.create();
-HBaseAdmin admin = new HBaseAdmin(conf);
-String table = "myTable";
-
-admin.disableTable(table);
-
-HColumnDescriptor cf1 = ...;
-admin.addColumn(table, cf1); // adding new ColumnFamily
-HColumnDescriptor cf2 = ...;
-admin.modifyColumn(table, cf2); // modifying existing ColumnFamily
-
-admin.enableTable(table);
-
- See for more information about configuring client connections.
- Note: online schema changes are supported in the 0.92.x codebase, but the 0.90.x codebase requires the table
- to be disabled.
-
- Schema Updates
- When changes are made to either Tables or ColumnFamilies (e.g., region size, block size), these changes
- take effect the next time there is a major compaction and the StoreFiles get re-written.
-
- See for more information on StoreFiles.
-
-
-
-
-
- On the number of column families
-
-
- HBase currently does not do well with anything above two or three column families so keep the number
- of column families in your schema low. Currently, flushing and compactions are done on a per Region basis so
- if one column family is carrying the bulk of the data bringing on flushes, the adjacent families
- will also be flushed though the amount of data they carry is small. When many column families the
- flushing and compaction interaction can make for a bunch of needless i/o loading (To be addressed by
- changing flushing and compaction to work on a per column family basis). For more information
- on compactions, see .
-
- Try to make do with one column family if you can in your schemas. Only introduce a
- second and third column family in the case where data access is usually column scoped;
- i.e. you query one column family or the other but usually not both at the one time.
-
- Cardinality of ColumnFamilies
- Where multiple ColumnFamilies exist in a single table, be aware of the cardinality (i.e., number of rows).
- If ColumnFamilyA has 1 million rows and ColumnFamilyB has 1 billion rows, ColumnFamilyA's data will likely be spread
- across many, many regions (and RegionServers). This makes mass scans for ColumnFamilyA less efficient.
-
-
-
- Rowkey Design
-
-
- Monotonically Increasing Row Keys/Timeseries Data
-
-
- In the HBase chapter of Tom White's book Hadoop: The Definitive Guide (O'Reilly) there is a an optimization note on watching out for a phenomenon where an import process walks in lock-step with all clients in concert pounding one of the table's regions (and thus, a single node), then moving onto the next region, etc. With monotonically increasing row-keys (i.e., using a timestamp), this will happen. See this comic by IKai Lan on why monotonically increasing row keys are problematic in BigTable-like datastores:
- monotonically increasing values are bad. The pile-up on a single region brought on
- by monotonically increasing keys can be mitigated by randomizing the input records to not be in sorted order, but in general its best to avoid using a timestamp or a sequence (e.g. 1, 2, 3) as the row-key.
-
-
-
- If you do need to upload time series data into HBase, you should
- study OpenTSDB as a
- successful example. It has a page describing the schema it uses in
- HBase. The key format in OpenTSDB is effectively [metric_type][event_timestamp], which would appear at first glance to contradict the previous advice about not using a timestamp as the key. However, the difference is that the timestamp is not in the lead position of the key, and the design assumption is that there are dozens or hundreds (or more) of different metric types. Thus, even with a continual stream of input data with a mix of metric types, the Puts are distributed across various points of regions in the table.
-
-
-
- Try to minimize row and column sizes
- Or why are my StoreFile indices large?
- In HBase, values are always freighted with their coordinates; as a
- cell value passes through the system, it'll be accompanied by its
- row, column name, and timestamp - always. If your rows and column names
- are large, especially compared to the size of the cell value, then
- you may run up against some interesting scenarios. One such is
- the case described by Marc Limotte at the tail of
- HBASE-3551
- (recommended!).
- Therein, the indices that are kept on HBase storefiles ()
- to facilitate random access may end up occupyng large chunks of the HBase
- allotted RAM because the cell value coordinates are large.
- Mark in the above cited comment suggests upping the block size so
- entries in the store file index happen at a larger interval or
- modify the table schema so it makes for smaller rows and column
- names.
- Compression will also make for larger indices. See
- the thread a question storefileIndexSize
- up on the user mailing list.
-
- Most of the time small inefficiencies don't matter all that much. Unfortunately,
- this is a case where they do. Whatever patterns are selected for ColumnFamilies, attributes, and rowkeys they could be repeated
- several billion times in your data.
- See for more information on HBase stores data internally to see why this is important.
- Column Families
- Try to keep the ColumnFamily names as small as possible, preferably one character (e.g. "d" for data/default).
-
- See for more information on HBase stores data internally to see why this is important.
-
- Attributes
- Although verbose attribute names (e.g., "myVeryImportantAttribute") are easier to read, prefer shorter attribute names (e.g., "via")
- to store in HBase.
-
- See for more information on HBase stores data internally to see why this is important.
-
- Rowkey Length
- Keep them as short as is reasonable such that they can still be useful for required data access (e.g., Get vs. Scan).
- A short key that is useless for data access is not better than a longer key with better get/scan properties. Expect tradeoffs
- when designing rowkeys.
-
-
- Byte Patterns
- A long is 8 bytes. You can store an unsigned number up to 18,446,744,073,709,551,615 in those eight bytes.
- If you stored this number as a String -- presuming a byte per character -- you need nearly 3x the bytes.
-
- Not convinced? Below is some sample code that you can run on your own.
-
-// long
-//
-long l = 1234567890L;
-byte[] lb = Bytes.toBytes(l);
-System.out.println("long bytes length: " + lb.length); // returns 8
-
-String s = "" + l;
-byte[] sb = Bytes.toBytes(s);
-System.out.println("long as string length: " + sb.length); // returns 10
-
-// hash
-//
-MessageDigest md = MessageDigest.getInstance("MD5");
-byte[] digest = md.digest(Bytes.toBytes(s));
-System.out.println("md5 digest bytes length: " + digest.length); // returns 16
-
-String sDigest = new String(digest);
-byte[] sbDigest = Bytes.toBytes(sDigest);
-System.out.println("md5 digest as string length: " + sbDigest.length); // returns 26
-
-
-
-
-
- Reverse Timestamps
- A common problem in database processing is quickly finding the most recent version of a value. A technique using reverse timestamps
- as a part of the key can help greatly with a special case of this problem. Also found in the HBase chapter of Tom White's book Hadoop: The Definitive Guide (O'Reilly),
- the technique involves appending (Long.MAX_VALUE - timestamp) to the end of any key, e.g., [key][reverse_timestamp].
-
- The most recent value for [key] in a table can be found by performing a Scan for [key] and obtaining the first record. Since HBase keys
- are in sorted order, this key sorts before any older row-keys for [key] and thus is first.
-
- This technique would be used instead of using HBase Versioning where the intent is to hold onto all versions
- "forever" (or a very long time) and at the same time quickly obtain access to any other version by using the same Scan technique.
-
-
-
- Rowkeys and ColumnFamilies
- Rowkeys are scoped to ColumnFamilies. Thus, the same rowkey could exist in each ColumnFamily that exists in a table without collision.
-
-
- Immutability of Rowkeys
- Rowkeys cannot be changed. The only way they can be "changed" in a table is if the row is deleted and then re-inserted.
- This is a fairly common question on the HBase dist-list so it pays to get the rowkeys right the first time (and/or before you've
- inserted a lot of data).
-
-
-
-
-
- Number of Versions
-
- Maximum Number of Versions
- The maximum number of row versions to store is configured per column
- family via HColumnDescriptor.
- The default for max versions is 3.
- This is an important parameter because as described in
- section HBase does not overwrite row values, but rather
- stores different values per row by time (and qualifier). Excess versions are removed during major
- compactions. The number of max versions may need to be increased or decreased depending on application needs.
-
- It is not recommended setting the number of max versions to an exceedingly high level (e.g., hundreds or more) unless those old values are
- very dear to you because this will greatly increase StoreFile size.
-
-
-
-
- Minimum Number of Versions
-
- Like maximum number of row versions, the minimum number of row versions to keep is configured per column
- family via HColumnDescriptor.
- The default for min versions is 0, which means the feature is disabled.
- The minimum number of row versions parameter is used together with the time-to-live parameter and can be combined with the
- number of row versions parameter to allow configurations such as
- "keep the last T minutes worth of data, at most N versions, but keep at least M versions around"
- (where M is the value for minimum number of row versions, M<N).
- This parameter should only be set when time-to-live is enabled for a column family and must be less than the
- number of row versions.
-
-
-
-
-
- Supported Datatypes
-
- HBase supports a "bytes-in/bytes-out" interface via Put and
- Result, so anything that can be
- converted to an array of bytes can be stored as a value. Input could be strings, numbers, complex objects, or even images as long as they can rendered as bytes.
-
- There are practical limits to the size of values (e.g., storing 10-50MB objects in HBase would probably be too much to ask);
- search the mailling list for conversations on this topic. All rows in HBase conform to the datamodel, and
- that includes versioning. Take that into consideration when making your design, as well as block size for the ColumnFamily.
-
-
- Counters
-
- One supported datatype that deserves special mention are "counters" (i.e., the ability to do atomic increments of numbers). See
- Increment in HTable.
-
- Synchronization on counters are done on the RegionServer, not in the client.
-
-
-
- Joins
- If you have multiple tables, don't forget to factor in the potential for into the schema design.
-
-
-
- Time To Live (TTL)
- ColumnFamilies can set a TTL length in seconds, and HBase will automatically delete rows once the expiration time is reached.
- This applies to all versions of a row - even the current one. The TTL time encoded in the HBase for the row is specified in UTC.
-
- See HColumnDescriptor for more information.
-
-
-
-
- Keeping Deleted Cells
-
- ColumnFamilies can optionally keep deleted cells. That means deleted cells can still be retrieved with
- Get or
- Scan operations,
- as long these operations have a time range specified that ends before the timestamp of any delete that would affect the cells.
- This allows for point in time queries even in the presence of deletes.
-
-
- Deleted cells are still subject to TTL and there will never be more than "maximum number of versions" deleted cells.
- A new "raw" scan options returns all deleted rows and the delete markers.
-
- See HColumnDescriptor for more information.
-
-
-
-
- Secondary Indexes and Alternate Query Paths
-
- This section could also be titled "what if my table rowkey looks like this but I also want to query my table like that."
- A common example on the dist-list is where a row-key is of the format "user-timestamp" but there are are reporting requirements on activity across users for certain
- time ranges. Thus, selecting by user is easy because it is in the lead position of the key, but time is not.
-
- There is no single answer on the best way to handle this because it depends on...
-
- Number of users
- Data size and data arrival rate
- Flexibility of reporting requirements (e.g., completely ad-hoc date selection vs. pre-configured ranges)
- Desired execution speed of query (e.g., 90 seconds may be reasonable to some for an ad-hoc report, whereas it may be too long for others)
-
- ... and solutions are also influenced by the size of the cluster and how much processing power you have to throw at the solution.
- Common techniques are in sub-sections below. This is a comprehensive, but not exhaustive, list of approaches.
-
- It should not be a surprise that secondary indexes require additional cluster space and processing.
- This is precisely what happens in an RDBMS because the act of creating an alternate index requires both space and processing cycles to update. RBDMS products
- are more advanced in this regard to handle alternative index management out of the box. However, HBase scales better at larger data volumes, so this is a feature trade-off.
-
- Pay attention to when implementing any of these approaches.
- Additionally, see the David Butler response in this dist-list thread HBase, mail # user - Stargate+hbase
-
-
-
- Filter Query
-
- Depending on the case, it may be appropriate to use . In this case, no secondary index is created.
- However, don't try a full-scan on a large table like this from an application (i.e., single-threaded client).
-
-
-
-
- Periodic-Update Secondary Index
-
- A secondary index could be created in an other table which is periodically updated via a MapReduce job. The job could be executed intra-day, but depending on
- load-strategy it could still potentially be out of sync with the main data table.
- See for more information.
-
-
-
- Dual-Write Secondary Index
-
- Another strategy is to build the secondary index while publishing data to the cluster (e.g., write to data table, write to index table).
- If this is approach is taken after a data table already exists, then bootstrapping will be needed for the secondary index with a MapReduce job (see ).
-
-
-
- Summary Tables
-
- Where time-ranges are very wide (e.g., year-long report) and where the data is voluminous, summary tables are a common approach.
- These would be generated with MapReduce jobs into another table.
- See for more information.
-
-
-
- Coprocessor Secondary Index
-
- Coprocessors act like RDBMS triggers. These were added in 0.92. For more information, see
-
-
-
- Schema Design Smackdown
- This section will describe common schema design questions that appear on the dist-list. These are
- general guidelines and not laws - each application must consider it's own needs.
-
- Rows vs. Versions
- A common question is whether one should prefer rows or HBase's built-in-versioning. The context is typically where there are
- "a lot" of versions of a row to be retained (e.g., where it is significantly above the HBase default of 3 max versions). The
- rows-approach would require storing a timstamp in some portion of the rowkey so that they would not overwite with each successive update.
-
- Preference: Rows (generally speaking).
-
-
- Rows vs. Columns
- Another common question is whether one should prefer rows or columns. The context is typically in extreme cases of wide
- tables, such as having 1 row with 1 million attributes, or 1 million rows with 1 columns apiece.
-
- Preference: Rows (generally speaking). To be clear, this guideline is in the context is in extremely wide cases, not in the
- standard use-case where one needs to store a few dozen or hundred columns.
-
-
-
- Operational and Performance Configuration Options
- See the Performance section for more information operational and performance
- schema design options, such as Bloom Filters, Table-configured regionsizes, compression, and blocksizes.
-
-
-
- Constraints
- HBase currently supports 'constraints' in traditional (SQL) database parlance. The advised usage for Constraints is in enforcing business rules for attributes in the table (eg. make sure values are in the range 1-10).
- Constraints could also be used to enforce referential integrity, but this is strongly discouraged as it will dramatically decrease the write throughput of the tables where integrity checking is enabled.
- Extensive documentation on using Constraints can be found at: Constraint since version 0.94.
-
-
-
-
-
-
- HBase and MapReduce
- See
- HBase and MapReduce up in javadocs.
- Start there. Below is some additional help.
- For more information about MapReduce (i.e., the framework in general), see the
- Hadoop MapReduce Tutorial.
-
- Map-Task Spitting
-
- The Default HBase MapReduce Splitter
- When TableInputFormat
- is used to source an HBase table in a MapReduce job,
- its splitter will make a map task for each region of the table.
- Thus, if there are 100 regions in the table, there will be
- 100 map-tasks for the job - regardless of how many column families are selected in the Scan.
-
-
- Custom Splitters
- For those interested in implementing custom splitters, see the method getSplits in
- TableInputFormatBase.
- That is where the logic for map-task assignment resides.
-
-
-
-
- HBase MapReduce Examples
-
- HBase MapReduce Read Example
- The following is an example of using HBase as a MapReduce source in read-only manner. Specifically,
- there is a Mapper instance but no Reducer, and nothing is being emitted from the Mapper. There job would be defined
- as follows...
-
-Configuration config = HBaseConfiguration.create();
-Job job = new Job(config, "ExampleRead");
-job.setJarByClass(MyReadJob.class); // class that contains mapper
-
-Scan scan = new Scan();
-scan.setCaching(500); // 1 is the default in Scan, which will be bad for MapReduce jobs
-scan.setCacheBlocks(false); // don't set to true for MR jobs
-// set other scan attrs
-...
-
-TableMapReduceUtil.initTableMapperJob(
- tableName, // input HBase table name
- scan, // Scan instance to control CF and attribute selection
- MyMapper.class, // mapper
- null, // mapper output key
- null, // mapper output value
- job);
-job.setOutputFormatClass(NullOutputFormat.class); // because we aren't emitting anything from mapper
-
-boolean b = job.waitForCompletion(true);
-if (!b) {
- throw new IOException("error with job!");
-}
-
- ...and the mapper instance would extend TableMapper...
-
-public static class MyMapper extends TableMapper<Text, Text> {
-
- public void map(ImmutableBytesWritable row, Result value, Context context) throws InterruptedException, IOException {
- // process data for the row from the Result instance.
- }
-}
-
-
-
-
- HBase MapReduce Read/Write Example
- The following is an example of using HBase both as a source and as a sink with MapReduce.
- This example will simply copy data from one table to another.
-
-Configuration config = HBaseConfiguration.create();
-Job job = new Job(config,"ExampleReadWrite");
-job.setJarByClass(MyReadWriteJob.class); // class that contains mapper
-
-Scan scan = new Scan();
-scan.setCaching(500); // 1 is the default in Scan, which will be bad for MapReduce jobs
-scan.setCacheBlocks(false); // don't set to true for MR jobs
-// set other scan attrs
-
-TableMapReduceUtil.initTableMapperJob(
- sourceTable, // input table
- scan, // Scan instance to control CF and attribute selection
- MyMapper.class, // mapper class
- null, // mapper output key
- null, // mapper output value
- job);
-TableMapReduceUtil.initTableReducerJob(
- targetTable, // output table
- null, // reducer class
- job);
-job.setNumReduceTasks(0);
-
-boolean b = job.waitForCompletion(true);
-if (!b) {
- throw new IOException("error with job!");
-}
-
- An explanation is required of what TableMapReduceUtil is doing, especially with the reducer.
- TableOutputFormat is being used
- as the outputFormat class, and several parameters are being set on the config (e.g., TableOutputFormat.OUTPUT_TABLE), as
- well as setting the reducer output key to ImmutableBytesWritable and reducer value to Writable.
- These could be set by the programmer on the job and conf, but TableMapReduceUtil tries to make things easier.
- The following is the example mapper, which will create a Put and matching the input Result
- and emit it. Note: this is what the CopyTable utility does.
-
-
-public static class MyMapper extends TableMapper<ImmutableBytesWritable, Put> {
-
- public void map(ImmutableBytesWritable row, Result value, Context context) throws IOException, InterruptedException {
- // this example is just copying the data from the source table...
- context.write(row, resultToPut(row,value));
- }
-
- private static Put resultToPut(ImmutableBytesWritable key, Result result) throws IOException {
- Put put = new Put(key.get());
- for (KeyValue kv : result.raw()) {
- put.add(kv);
- }
- return put;
- }
-}
-
- There isn't actually a reducer step, so TableOutputFormat takes care of sending the Put
- to the target table.
-
- This is just an example, developers could choose not to use TableOutputFormat and connect to the
- target table themselves.
-
-
-
-
- HBase MapReduce Read/Write Example With Multi-Table Output
- TODO: example for MultiTableOutputFormat.
-
-
-
- HBase MapReduce Summary to HBase Example
- The following example uses HBase as a MapReduce source and sink with a summarization step. This example will
- count the number of distinct instances of a value in a table and write those summarized counts in another table.
-
-Configuration config = HBaseConfiguration.create();
-Job job = new Job(config,"ExampleSummary");
-job.setJarByClass(MySummaryJob.class); // class that contains mapper and reducer
-
-Scan scan = new Scan();
-scan.setCaching(500); // 1 is the default in Scan, which will be bad for MapReduce jobs
-scan.setCacheBlocks(false); // don't set to true for MR jobs
-// set other scan attrs
-
-TableMapReduceUtil.initTableMapperJob(
- sourceTable, // input table
- scan, // Scan instance to control CF and attribute selection
- MyMapper.class, // mapper class
- Text.class, // mapper output key
- IntWritable.class, // mapper output value
- job);
-TableMapReduceUtil.initTableReducerJob(
- targetTable, // output table
- MyTableReducer.class, // reducer class
- job);
-job.setNumReduceTasks(1); // at least one, adjust as required
-
-boolean b = job.waitForCompletion(true);
-if (!b) {
- throw new IOException("error with job!");
-}
-
- In this example mapper a column with a String-value is chosen as the value to summarize upon.
- This value is used as the key to emit from the mapper, and an IntWritable represents an instance counter.
-
-public static class MyMapper extends TableMapper<Text, IntWritable> {
-
- private final IntWritable ONE = new IntWritable(1);
- private Text text = new Text();
-
- public void map(ImmutableBytesWritable row, Result value, Context context) throws IOException, InterruptedException {
- String val = new String(value.getValue(Bytes.toBytes("cf"), Bytes.toBytes("attr1")));
- text.set(val); // we can only emit Writables...
-
- context.write(text, ONE);
- }
-}
-
- In the reducer, the "ones" are counted (just like any other MR example that does this), and then emits a Put.
-
-public static class MyTableReducer extends TableReducer<Text, IntWritable, ImmutableBytesWritable> {
-
- public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
- int i = 0;
- for (IntWritable val : values) {
- i += val.get();
- }
- Put put = new Put(Bytes.toBytes(key.toString()));
- put.add(Bytes.toBytes("cf"), Bytes.toBytes("count"), Bytes.toBytes(i));
-
- context.write(null, put);
- }
-}
-
-
-
-
- HBase MapReduce Summary to File Example
- This very similar to the summary example above, with exception that this is using HBase as a MapReduce source
- but HDFS as the sink. The differences are in the job setup and in the reducer. The mapper remains the same.
-
-
-Configuration config = HBaseConfiguration.create();
-Job job = new Job(config,"ExampleSummaryToFile");
-job.setJarByClass(MySummaryFileJob.class); // class that contains mapper and reducer
-
-Scan scan = new Scan();
-scan.setCaching(500); // 1 is the default in Scan, which will be bad for MapReduce jobs
-scan.setCacheBlocks(false); // don't set to true for MR jobs
-// set other scan attrs
-
-TableMapReduceUtil.initTableMapperJob(
- sourceTable, // input table
- scan, // Scan instance to control CF and attribute selection
- MyMapper.class, // mapper class
- Text.class, // mapper output key
- IntWritable.class, // mapper output value
- job);
-job.setReducerClass(MyReducer.class); // reducer class
-job.setNumReduceTasks(1); // at least one, adjust as required
-FileOutputFormat.setOutputPath(job, new Path("/tmp/mr/mySummaryFile")); // adjust directories as required
-
-boolean b = job.waitForCompletion(true);
-if (!b) {
- throw new IOException("error with job!");
-}
-
- As stated above, the previous Mapper can run unchanged with this example.
- As for the Reducer, it is a "generic" Reducer instead of extending TableMapper and emitting Puts.
-
- public static class MyReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
-
- public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
- int i = 0;
- for (IntWritable val : values) {
- i += val.get();
- }
- context.write(key, new IntWritable(i));
- }
-}
-
-
-
- HBase MapReduce Summary to HBase Without Reducer
- It is also possible to perform summaries without a reducer - if you use HBase as the reducer.
-
- An HBase target table would need to exist for the job summary. The HTable method incrementColumnValue
- would be used to atomically increment values. From a performance perspective, it might make sense to keep a Map
- of values with their values to be incremeneted for each map-task, and make one update per key at during the
- cleanup method of the mapper. However, your milage may vary depending on the number of rows to be processed and
- unique keys.
-
- In the end, the summary results are in HBase.
-
-
-
- HBase MapReduce Summary to RDBMS
- Sometimes it is more appropriate to generate summaries to an RDBMS. For these cases, it is possible
- to generate summaries directly to an RDBMS via a custom reducer. The setup method
- can connect to an RDBMS (the connection information can be passed via custom parameters in the context) and the
- cleanup method can close the connection.
-
- It is critical to understand that number of reducers for the job affects the summarization implementation, and
- you'll have to design this into your reducer. Specifically, whether it is designed to run as a singleton (one reducer)
- or multiple reducers. Neither is right or wrong, it depends on your use-case. Recognize that the more reducers that
- are assigned to the job, the more simultaneous connections to the RDBMS will be created - this will scale, but only to a point.
-
-
- public static class MyRdbmsReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
-
- private Connection c = null;
-
- public void setup(Context context) {
- // create DB connection...
- }
-
- public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
- // do summarization
- // in this example the keys are Text, but this is just an example
- }
-
- public void cleanup(Context context) {
- // close db connection
- }
-
-}
-
- In the end, the summary results are written to your RDBMS table/s.
-
-
-
-
-
- Accessing Other HBase Tables in a MapReduce Job
- Although the framework currently allows one HBase table as input to a
- MapReduce job, other HBase tables can
- be accessed as lookup tables, etc., in a
- MapReduce job via creating an HTable instance in the setup method of the Mapper.
- public class MyMapper extends TableMapper<Text, LongWritable> {
- private HTable myOtherTable;
-
- public void setup(Context context) {
- myOtherTable = new HTable("myOtherTable");
- }
-
- public void map(ImmutableBytesWritable row, Result value, Context context) throws IOException, InterruptedException {
- // process Result...
- // use 'myOtherTable' for lookups
- }
-
-
-
-
-
- Speculative Execution
- It is generally advisable to turn off speculative execution for
- MapReduce jobs that use HBase as a source. This can either be done on a
- per-Job basis through properties, on on the entire cluster. Especially
- for longer running jobs, speculative execution will create duplicate
- map-tasks which will double-write your data to HBase; this is probably
- not what you want.
-
- See for more information.
-
-
-
-
-
-
-
- Architecture
-
- Overview
-
- NoSQL?
- HBase is a type of "NoSQL" database. "NoSQL" is a general term meaning that the database isn't an RDBMS which
- supports SQL as it's primary access language, but there are many types of NoSQL databases: BerkeleyDB is an
- example of a local NoSQL database, whereas HBase is very much a distributed database. Technically speaking,
- HBase is really more a "Data Store" than "Data Base" because it lacks many of the features you find in an RDBMS,
- such as typed columns, secondary indexes, triggers, and advanced query languages, etc.
-
- However, HBase has many features which supports both linear and modular scaling. HBase clusters expand
- by adding RegionServers that are hosted on commodity class servers. If a cluster expands from 10 to 20
- RegionServers, for example, it doubles both in terms of storage and as well as processing capacity.
- RDBMS can scale well, but only up to a point - specifically, the size of a single database server - and for the best
- performance requires specialized hardware and storage devices. HBase features of note are:
-
- Strongly consistent reads/writes: HBase is not an "eventually consistent" DataStore. This
- makes it very suitable for tasks such as high-speed counter aggregation.
- Automatic sharding: HBase tables are distributed on the cluster via regions, and regions are
- automatically split and re-distributed as your data grows.
- Automatic RegionServer failover
- Hadoop/HDFS Integration: HBase supports HDFS out of the box as it's distributed file system.
- MapReduce: HBase supports massively parallelized processing via MapReduce for using HBase as both
- source and sink.
- Java Client API: HBase supports an easy to use Java API for programmatic access.
- Thrift/REST API: HBase also supports Thrift and REST for non-Java front-ends.
- Block Cache and Bloom Filters: HBase supports a Block Cache and Bloom Filters for high volume query optimization.
- Operational Management: HBase provides build-in web-pages for operational insight as well as JMX metrics.
-
-
-
-
-
- When Should I Use HBase?
- HBase isn't suitable for every problem.
- First, make sure you have enough data. If you have hundreds of millions or billions of rows, then
- HBase is a good candidate. If you only have a few thousand/million rows, then using a traditional RDBMS
- might be a better choice due to the fact that all of your data might wind up on a single node (or two) and
- the rest of the cluster may be sitting idle.
-
- Second, make sure you can live without all the extra features that an RDBMS provides (e.g., typed columns,
- secondary indexes, transactions, advanced query languages, etc.) An application built against an RDBMS cannot be
- "ported" to HBase by simply changing a JDBC driver, for example. Consider moving from an RDBMS to HBase as a
- complete redesign as opposed to a port.
-
- Third, make sure you have enough hardware. Even HDFS doesn't do well with anything less than
- 5 DataNodes (due to things such as HDFS block replication which has a default of 3), plus a NameNode.
-
- HBase can run quite well stand-alone on a laptop - but this should be considered a development
- configuration only.
-
-
-
- What Is The Difference Between HBase and Hadoop/HDFS?
- HDFS is a distributed file system that is well suited for the storage of large files.
- It's documentation states that it is not, however, a general purpose file system, and does not provide fast individual record lookups in files.
- HBase, on the other hand, is built on top of HDFS and provides fast record lookups (and updates) for large tables.
- This can sometimes be a point of conceptual confusion. HBase internally puts your data in indexed "StoreFiles" that exist
- on HDFS for high-speed lookups. See the and the rest of this chapter for more information on how HBase achieves its goals.
-
-
-
-
-
- Catalog Tables
- The catalog tables -ROOT- and .META. exist as HBase tables. They are are filtered out
- of the HBase shell's list command, but they are in fact tables just like any other.
-
-
- ROOT
- -ROOT- keeps track of where the .META. table is. The -ROOT- table structure is as follows:
-
- Key:
-
- .META. region key (.META.,,1)
-
-
- Values:
-
- info:regioninfo (serialized HRegionInfo
- instance of .META.)
- info:server (server:port of the RegionServer holding .META.)
- info:serverstartcode (start-time of the RegionServer process holding .META.)
-
-
-
-
- META
- The .META. table keeps a list of all regions in the system. The .META. table structure is as follows:
-
- Key:
-
- Region key of the format ([table],[region start key],[region id])
-
-
- Values:
-
- info:regioninfo (serialized
- HRegionInfo instance for this region)
-
- info:server (server:port of the RegionServer containing this region)
- info:serverstartcode (start-time of the RegionServer process containing this region)
-
-
- When a table is in the process of splitting two other columns will be created, info:splitA and info:splitB
- which represent the two daughter regions. The values for these columns are also serialized HRegionInfo instances.
- After the region has been split eventually this row will be deleted.
-
- Notes on HRegionInfo: the empty key is used to denote table start and table end. A region with an empty start key
- is the first region in a table. If region has both an empty start and an empty end key, its the only region in the table
-
- In the (hopefully unlikely) event that programmatic processing of catalog metadata is required, see the
- Writables utility.
-
-
-
- Startup Sequencing
- The META location is set in ROOT first. Then META is updated with server and startcode values.
-
- For information on region-RegionServer assignment, see .
-
-
-
-
-
- Client
- The HBase client
- HTable
- is responsible for finding RegionServers that are serving the
- particular row range of interest. It does this by querying
- the .META. and -ROOT- catalog tables
- (TODO: Explain). After locating the required
- region(s), the client directly contacts
- the RegionServer serving that region (i.e., it does not go
- through the master) and issues the read or write request.
- This information is cached in the client so that subsequent requests
- need not go through the lookup process. Should a region be reassigned
- either by the master load balancer or because a RegionServer has died,
- the client will requery the catalog tables to determine the new
- location of the user region.
-
- See for more information about the impact of the Master on HBase Client
- communication.
-
- Administrative functions are handled through HBaseAdmin
-
- Connections
- For connection configuration information, see .
-
- HTable
-instances are not thread-safe. When creating HTable instances, it is advisable to use the same HBaseConfiguration
-instance. This will ensure sharing of ZooKeeper and socket instances to the RegionServers
-which is usually what you want. For example, this is preferred:
- HBaseConfiguration conf = HBaseConfiguration.create();
-HTable table1 = new HTable(conf, "myTable");
-HTable table2 = new HTable(conf, "myTable");
- as opposed to this:
- HBaseConfiguration conf1 = HBaseConfiguration.create();
-HTable table1 = new HTable(conf1, "myTable");
-HBaseConfiguration conf2 = HBaseConfiguration.create();
-HTable table2 = new HTable(conf2, "myTable");
- For more information about how connections are handled in the HBase client,
- see HConnectionManager.
-
- Connection Pooling
- For applications which require high-end multithreaded access (e.g., web-servers or application servers that may serve many application threads
- in a single JVM), see HTablePool.
-
-
-
- WriteBuffer and Batch Methods
- If is turned off on
- HTable,
- Puts are sent to RegionServers when the writebuffer
- is filled. The writebuffer is 2MB by default. Before an HTable instance is
- discarded, either close() or
- flushCommits() should be invoked so Puts
- will not be lost.
-
- Note: htable.delete(Delete); does not go in the writebuffer! This only applies to Puts.
-
- For additional information on write durability, review the ACID semantics page.
-
- For fine-grained control of batching of
- Puts or Deletes,
- see the batch methods on HTable.
-
-
- External Clients
- Information on non-Java clients and custom protocols is covered in
-
-
- RowLocks
- RowLocks are still
- in the client API however they are discouraged because if not managed properly these can
- lock up the RegionServers.
-
- There is an oustanding ticket HBASE-2332 to
- remove this feature from the client.
-
-
-
-
- Client Request Filters
- Get and Scan instances can be
- optionally configured with filters which are applied on the RegionServer.
-
- Filters can be confusing because there are many different types, and it is best to approach them by understanding the groups
- of Filter functionality.
-
- Structural
- Structural Filters contain other Filters.
- FilterList
- FilterList
- represents a list of Filters with a relationship of FilterList.Operator.MUST_PASS_ALL or
- FilterList.Operator.MUST_PASS_ONE between the Filters. The following example shows an 'or' between two
- Filters (checking for either 'my value' or 'my other value' on the same attribute).
-
-FilterList list = new FilterList(FilterList.Operator.MUST_PASS_ONE);
-SingleColumnValueFilter filter1 = new SingleColumnValueFilter(
- cf,
- column,
- CompareOp.EQUAL,
- Bytes.toBytes("my value")
- );
-list.add(filter1);
-SingleColumnValueFilter filter2 = new SingleColumnValueFilter(
- cf,
- column,
- CompareOp.EQUAL,
- Bytes.toBytes("my other value")
- );
-list.add(filter2);
-scan.setFilter(list);
-
-
-
-
- Column Value
- SingleColumnValueFilter
- SingleColumnValueFilter
- can be used to test column values for equivalence (CompareOp.EQUAL
- ), inequality (CompareOp.NOT_EQUAL), or ranges
- (e.g., CompareOp.GREATER). The folowing is example of testing equivalence a column to a String value "my value"...
-
-SingleColumnValueFilter filter = new SingleColumnValueFilter(
- cf,
- column,
- CompareOp.EQUAL,
- Bytes.toBytes("my value")
- );
-scan.setFilter(filter);
-
-
-
-
- Column Value Comparators
- There are several Comparator classes in the Filter package that deserve special mention.
- These Comparators are used in concert with other Filters, such as .
-
- RegexStringComparator
- RegexStringComparator
- supports regular expressions for value comparisons.
-
-RegexStringComparator comp = new RegexStringComparator("my."); // any value that starts with 'my'
-SingleColumnValueFilter filter = new SingleColumnValueFilter(
- cf,
- column,
- CompareOp.EQUAL,
- comp
- );
-scan.setFilter(filter);
-
- See the Oracle JavaDoc for supported RegEx patterns in Java.
-
-
- SubstringComparator
- SubstringComparator
- can be used to determine if a given substring exists in a value. The comparison is case-insensitive.
-
-
-SubstringComparator comp = new SubstringComparator("y val"); // looking for 'my value'
-SingleColumnValueFilter filter = new SingleColumnValueFilter(
- cf,
- column,
- CompareOp.EQUAL,
- comp
- );
-scan.setFilter(filter);
-
-
- BinaryPrefixComparator
- See BinaryPrefixComparator.
-
- BinaryComparator
- See BinaryComparator.
-
-
- KeyValue Metadata
- As HBase stores data internally as KeyValue pairs, KeyValue Metadata Filters evaluate the existence of keys (i.e., ColumnFamily:Column qualifiers)
- for a row, as opposed to values the previous section.
-
- FamilyFilter
- FamilyFilter can be used
- to filter on the ColumnFamily. It is generally a better idea to select ColumnFamilies in the Scan than to do it with a Filter.
-
- QualifierFilter
- QualifierFilter can be used
- to filter based on Column (aka Qualifier) name.
-
-
- ColumnPrefixFilter
- ColumnPrefixFilter can be used
- to filter based on the lead portion of Column (aka Qualifier) names.
-
- A ColumnPrefixFilter seeks ahead to the first column matching the prefix in each row and for each involved column family. It can be used to efficiently
- get a subset of the columns in very wide rows.
-
- Note: The same column qualifier can be used in different column families. This filter returns all matching columns.
-
- Example: Find all columns in a row and family that start with "abc"
-
-HTableInterface t = ...;
-byte[] row = ...;
-byte[] family = ...;
-byte[] prefix = Bytes.toBytes("abc");
-Scan scan = new Scan(row, row); // (optional) limit to one row
-scan.addFamily(family); // (optional) limit to one family
-Filter f = new ColumnPrefixFilter(prefix);
-scan.setFilter(f);
-scan.setBatch(10); // set this if there could be many columns returned
-ResultScanner rs = t.getScanner(scan);
-for (Result r = rs.next(); r != null; r = rs.next()) {
- for (KeyValue kv : r.raw()) {
- // each kv represents a column
- }
-}
-rs.close();
-
-
-
- MultipleColumnPrefixFilter
- MultipleColumnPrefixFilter behaves like ColumnPrefixFilter
- but allows specifying multiple prefixes.
-
- Like ColumnPrefixFilter, MultipleColumnPrefixFilter efficiently seeks ahead to the first column matching the lowest prefix and also seeks past ranges of columns between prefixes.
- It can be used to efficiently get discontinuous sets of columns from very wide rows.
-
- Example: Find all columns in a row and family that start with "abc" or "xyz"
-
-HTableInterface t = ...;
-byte[] row = ...;
-byte[] family = ...;
-byte[][] prefixes = new byte[][] {Bytes.toBytes("abc"), Bytes.toBytes("xyz")};
-Scan scan = new Scan(row, row); // (optional) limit to one row
-scan.addFamily(family); // (optional) limit to one family
-Filter f = new MultipleColumnPrefixFilter(prefixes);
-scan.setFilter(f);
-scan.setBatch(10); // set this if there could be many columns returned
-ResultScanner rs = t.getScanner(scan);
-for (Result r = rs.next(); r != null; r = rs.next()) {
- for (KeyValue kv : r.raw()) {
- // each kv represents a column
- }
-}
-rs.close();
-
-
-
- ColumnRangeFilter
- A ColumnRangeFilter allows efficient intra row scanning.
-
- A ColumnRangeFilter can seek ahead to the first matching column for each involved column family. It can be used to efficiently
- get a 'slice' of the columns of a very wide row.
- i.e. you have a million columns in a row but you only want to look at columns bbbb-bbdd.
-
- Note: The same column qualifier can be used in different column families. This filter returns all matching columns.
-
- Example: Find all columns in a row and family between "bbbb" (inclusive) and "bbdd" (inclusive)
-
-HTableInterface t = ...;
-byte[] row = ...;
-byte[] family = ...;
-byte[] startColumn = Bytes.toBytes("bbbb");
-byte[] endColumn = Bytes.toBytes("bbdd");
-Scan scan = new Scan(row, row); // (optional) limit to one row
-scan.addFamily(family); // (optional) limit to one family
-Filter f = new ColumnRangeFilter(startColumn, true, endColumn, true);
-scan.setFilter(f);
-scan.setBatch(10); // set this if there could be many columns returned
-ResultScanner rs = t.getScanner(scan);
-for (Result r = rs.next(); r != null; r = rs.next()) {
- for (KeyValue kv : r.raw()) {
- // each kv represents a column
- }
-}
-rs.close();
-
-
- Note: Introduced in HBase 0.92
-
-
- RowKey
- RowFilter
- It is generally a better idea to use the startRow/stopRow methods on Scan for row selection, however
- RowFilter can also be used.
-
-
- Utility
- FirstKeyOnlyFilter
- This is primarily used for rowcount jobs.
- See FirstKeyOnlyFilter.
-
-
-
-
- Master
- HMaster is the implementation of the Master Server. The Master server
- is responsible for monitoring all RegionServer instances in the cluster, and is
- the interface for all metadata changes. In a distributed cluster, the Master typically runs on the .
-
- Startup Behavior
- If run in a multi-Master environment, all Masters compete to run the cluster. If the active
- Master loses it's lease in ZooKeeper (or the Master shuts down), then then the remaining Masters jostle to
- take over the Master role.
-
-
- Runtime Impact
- A common dist-list question is what happens to an HBase cluster when the Master goes down. Because the
- HBase client talks directly to the RegionServers, the cluster can still function in a "steady
- state." Additionally, per ROOT and META exist as HBase tables (i.e., are
- not resident in the Master). However, the Master controls critical functions such as RegionServer failover and
- completing region splits. So while the cluster can still run for a time without the Master,
- the Master should be restarted as soon as possible.
-
-
- Interface
- The methods exposed by HMasterInterface are primarily metadata-oriented methods:
-
- Table (createTable, modifyTable, removeTable, enable, disable)
-
- ColumnFamily (addColumn, modifyColumn, removeColumn)
-
- Region (move, assign, unassign)
-
-
- For example, when the HBaseAdmin method disableTable is invoked, it is serviced by the Master server.
-
-
- Processes
- The Master runs several background threads:
-
- LoadBalancer
- Periodically, and when there are not any regions in transition,
- a load balancer will run and move regions around to balance cluster load.
- See for configuring this property.
- See for more information on region assignment.
-
-
- CatalogJanitor
- Periodically checks and cleans up the .META. table. See for more information on META.
-
-
-
-
- RegionServer
- HRegionServer is the RegionServer implementation. It is responsible for serving and managing regions.
- In a distributed cluster, a RegionServer runs on a .
-
- Interface
- The methods exposed by HRegionRegionInterface contain both data-oriented and region-maintenance methods:
-
- Data (get, put, delete, next, etc.)
-
- Region (splitRegion, compactRegion, etc.)
-
-
- For example, when the HBaseAdmin method majorCompact is invoked on a table, the client is actually iterating through
- all regions for the specified table and requesting a major compaction directly to each region.
-
-
- Processes
- The RegionServer runs a variety of background threads:
- CompactSplitThread
- Checks for splits and handle minor compactions.
-
- MajorCompactionChecker
- Checks for major compactions.
-
- MemStoreFlusher
- Periodically flushes in-memory writes in the MemStore to StoreFiles.
-
- LogRoller
- Periodically checks the RegionServer's HLog.
-
-
-
- Coprocessors
- Coprocessors were added in 0.92. There is a thorough Blog Overview of CoProcessors
- posted. Documentation will eventually move to this reference guide, but the blog is the most current information available at this time.
-
-
-
-
- Block Cache
-
- Design
- The Block Cache is an LRU cache that contains three levels of block priority to allow for scan-resistance and in-memory ColumnFamilies:
-
-
- Single access priority: The first time a block is loaded from HDFS it normally has this priority and it will be part of the first group to be considered
- during evictions. The advantage is that scanned blocks are more likely to get evicted than blocks that are getting more usage.
-
- Mutli access priority: If a block in the previous priority group is accessed again, it upgrades to this priority. It is thus part of the second group
- considered during evictions.
-
- In-memory access priority: If the block's family was configured to be "in-memory", it will be part of this priority disregarding the number of times it
- was accessed. Catalog tables are configured like this. This group is the last one considered during evictions.
-
-
-
- For more information, see the LruBlockCache source
-
-
-
- Usage
- Block caching is enabled by default for all the user tables which means that any read operation will load the LRU cache. This might be good for a large number of use cases,
- but further tunings are usually required in order to achieve better performance. An important concept is the
- working set size, or WSS, which is: "the amount of memory needed to compute the answer to a problem".
- For a website, this would be the data that's needed to answer the queries over a short amount of time.
-
- The way to calculate how much memory is available in HBase for caching is:
-
-
- number of region servers * heap size * hfile.block.cache.size * 0.85
-
- The default value for the block cache is 0.25 which represents 25% of the available heap. The last value (85%) is the default acceptable loading factor in the LRU cache after
- which eviction is started. The reason it is included in this equation is that it would be unrealistic to say that it is possible to use 100% of the available memory since this would
- make the process blocking from the point where it loads new blocks. Here are some examples:
-
-
- One region server with the default heap size (1GB) and the default block cache size will have 217MB of block cache available.
-
- 20 region servers with the heap size set to 8GB and a default block cache size will have 34GB of block cache.
-
- 100 region servers with the heap size set to 24GB and a block cache size of 0.5 will have about 1TB of block cache.
-
-
- Your data isn't the only resident of the block cache, here are others that you may have to take into account:
-
-
- Catalog tables: The -ROOT- and .META. tables are forced into the block cache and have the in-memory priority which means that they are harder to evict. The former never uses
- more than a few hundreds of bytes while the latter can occupy a few MBs (depending on the number of regions).
-
- HFiles indexes: HFile is the file format that HBase uses to store data in HDFS and it contains a multi-layered index in order seek to the data without having to read the whole file.
- The size of those indexes is a factor of the block size (64KB by default), the size of your keys and the amount of data you are storing. For big data sets it's not unusual to see numbers around
- 1GB per region server, although not all of it will be in cache because the LRU will evict indexes that aren't used.
-
- Keys: Taking into account only the values that are being stored is missing half the picture since every value is stored along with its keys
- (row key, family, qualifier, and timestamp). See .
-
- Bloom filters: Just like the HFile indexes, those data structures (when enabled) are stored in the LRU.
-
-
- Currently the recommended way to measure HFile indexes and bloom filters sizes is to look at the region server web UI and checkout the relevant metrics. For keys,
- sampling can be done by using the HFile command line tool and look for the average key size metric.
-
- It's generally bad to use block caching when the WSS doesn't fit in memory. This is the case when you have for example 40GB available across all your region servers' block caches
- but you need to process 1TB of data. One of the reasons is that the churn generated by the evictions will trigger more garbage collections unnecessarily. Here are two use cases:
-
-
- Fully random reading pattern: This is a case where you almost never access the same row twice within a short amount of time such that the chance of hitting a cached block is close
- to 0. Setting block caching on such a table is a waste of memory and CPU cycles, more so that it will generate more garbage to pick up by the JVM. For more information on monitoring GC,
- see .
-
- Mapping a table: In a typical MapReduce job that takes a table in input, every row will be read only once so there's no need to put them into the block cache. The Scan object has
- the option of turning this off via the setCaching method (set it to false). You can still keep block caching turned on on this table if you need fast random read access. An example would be
- counting the number of rows in a table that serves live traffic, caching every block of that table would create massive churn and would surely evict data that's currently in use.
-
-
-
-
-
-
- Write Ahead Log (WAL)
-
-
- Purpose
-
- Each RegionServer adds updates (Puts, Deletes) to its write-ahead log (WAL)
- first, and then to the for the affected .
- This ensures that HBase has durable writes. Without WAL, there is the possibility of data loss in the case of a RegionServer failure
- before each MemStore is flushed and new StoreFiles are written. HLog
- is the HBase WAL implementation, and there is one HLog instance per RegionServer.
- The WAL is in HDFS in /hbase/.logs/ with subdirectories per region.
-
- For more general information about the concept of write ahead logs, see the Wikipedia
- Write-Ahead Log article.
-
-
-
- WAL Flushing
- TODO (describe).
-
-
-
-
- WAL Splitting
-
- How edits are recovered from a crashed RegionServer
- When a RegionServer crashes, it will lose its ephemeral lease in
- ZooKeeper...TODO
-
-
- hbase.hlog.split.skip.errors
-
- When set to true, the default, any error
- encountered splitting will be logged, the problematic WAL will be
- moved into the .corrupt directory under the hbase
- rootdir, and processing will continue. If set to
- false, the exception will be propagated and the
- split logged as failed.
- See HBASE-2958
- When hbase.hlog.split.skip.errors is set to false, we fail the
- split but thats it. We need to do more than just fail split
- if this flag is set.
-
-
-
-
- How EOFExceptions are treated when splitting a crashed
- RegionServers' WALs
-
- If we get an EOF while splitting logs, we proceed with the split
- even when hbase.hlog.split.skip.errors ==
- false. An EOF while reading the last log in the
- set of files to split is near-guaranteed since the RegionServer likely
- crashed mid-write of a record. But we'll continue even if we got an
- EOF reading other than the last file in the set.
- For background, see HBASE-2643
- Figure how to deal with eof splitting logs
-
-
-
-
-
-
-
-
- Regions
- Regions are the basic element of availability and
- distribution for tables, and are comprised of a Store per Column Family. The heirarchy of objects
- is as follows:
-
-Table (HBase table)
- Region (Regions for the table)
- Store (Store per ColumnFamily for each Region for the table)
- MemStore (MemStore for each Store for each Region for the table)
- StoreFile (StoreFiles for each Store for each Region for the table)
- Block (Blocks within a StoreFile within a Store for each Region for the table)
-
- For a description of what HBase files look like when written to HDFS, see .
-
-
-
- Region Size
-
- Determining the "right" region size can be tricky, and there are a few factors
- to consider:
-
-
-
- HBase scales by having regions across many servers. Thus if
- you have 2 regions for 16GB data, on a 20 node machine your data
- will be concentrated on just a few machines - nearly the entire
- cluster will be idle. This really cant be stressed enough, since a
- common problem is loading 200MB data into HBase then wondering why
- your awesome 10 node cluster isn't doing anything.
-
-
-
- On the other hand, high region count has been known to make things slow.
- This is getting better with each release of HBase, but it is probably better to have
- 700 regions than 3000 for the same amount of data.
-
-
-
- There is not much memory footprint difference between 1 region
- and 10 in terms of indexes, etc, held by the RegionServer.
-
-
-
- When starting off, its probably best to stick to the default region-size, perhaps going
- smaller for hot tables (or manually split hot regions to spread the load over
- the cluster), or go with larger region sizes if your cell sizes tend to be
- largish (100k and up).
- See for more information on configuration.
-
-
-
-
- Region-RegionServer Assignment
- This section describes how Regions are assigned to RegionServers.
-
-
-
- Startup
- When HBase starts regions are assigned as follows (short version):
-
- The Master invokes the AssignmentManager upon startup.
-
- The AssignmentManager looks at the existing region assignments in META.
-
- If the region assignment is still valid (i.e., if the RegionServer is still online)
- then the assignment is kept.
-
- If the assignment is invalid, then the LoadBalancerFactory is invoked to assign the
- region. The DefaultLoadBalancer will randomly assign the region to a RegionServer.
-
- META is updated with the RegionServer assignment (if needed) and the RegionServer start codes
- (start time of the RegionServer process) upon region opening by the RegionServer.
-
-
-
-
-
-
- Failover
- When a RegionServer fails (short version):
-
- The regions immediately become unavailable because the RegionServer is down.
-
- The Master will detect that the RegionServer has failed.
-
- The region assignments will be considered invalid and will be re-assigned just
- like the startup sequence.
-
-
-
-
-
-
- Region Load Balancing
-
- Regions can be periodically moved by the .
-
-
-
-
-
-
- Region-RegionServer Locality
- Over time, Region-RegionServer locality is achieved via HDFS block replication.
- The HDFS client does the following by default when choosing locations to write replicas:
-
- First replica is written to local node
-
- Second replica is written to another node in same rack
-
- Third replica is written to a node in another rack (if sufficient nodes)
-
-
- Thus, HBase eventually achieves locality for a region after a flush or a compaction.
- In a RegionServer failover situation a RegionServer may be assigned regions with non-local
- StoreFiles (because none of the replicas are local), however as new data is written
- in the region, or the table is compacted and StoreFiles are re-written, they will become "local"
- to the RegionServer.
-
- For more information, see HDFS Design on Replica Placement
- and also Lars George's blog on HBase and HDFS locality.
-
-
-
-
- Region Splits
-
- Splits run unaided on the RegionServer; i.e. the Master does not
- participate. The RegionServer splits a region, offlines the split
- region and then adds the daughter regions to META, opens daughters on
- the parent's hosting RegionServer and then reports the split to the
- Master. See for how to manually manage
- splits (and for why you might do this)
-
- Custom Split Policies
- The default split policy can be overwritten using a custom RegionSplitPolicy (HBase 0.94+).
- Typically a custom split policy should extend HBase's default split policy: ConstantSizeRegionSplitPolicy.
-
- The policy can set globally through the HBaseConfiguration used or on a per table basis:
-
-HTableDescriptor myHtd = ...;
-myHtd.setValue(HTableDescriptor.SPLIT_POLICY, MyCustomSplitPolicy.class.getName());
-
-
-
-
-
-
- Store
- A Store hosts a MemStore and 0 or more StoreFiles (HFiles). A Store corresponds to a column family for a table for a given region.
-
-
- MemStore
- The MemStore holds in-memory modifications to the Store. Modifications are KeyValues.
- When asked to flush, current memstore is moved to snapshot and is cleared.
- HBase continues to serve edits out of new memstore and backing snapshot until flusher reports in that the
- flush succeeded. At this point the snapshot is let go.
-
-
- StoreFile (HFile)
- StoreFiles are where your data lives.
-
- HFile Format
- The hfile file format is based on
- the SSTable file described in the BigTable [2006] paper and on
- Hadoop's tfile
- (The unit test suite and the compression harness were taken directly from tfile).
- Schubert Zhang's blog post on HFile: A Block-Indexed File Format to Store Sorted Key-Value Pairs makes for a thorough introduction to HBase's hfile. Matteo Bertozzi has also put up a
- helpful description, HBase I/O: HFile.
-
- For more information, see the HFile source code.
- Also see for information about the HFile v2 format that was included in 0.92.
-
-
-
- HFile Tool
-
- To view a textualized version of hfile content, you can do use
- the org.apache.hadoop.hbase.io.hfile.HFile
- tool. Type the following to see usage:$ ${HBASE_HOME}/bin/hbase org.apache.hadoop.hbase.io.hfile.HFile For
- example, to view the content of the file
- hdfs://10.81.47.41:8020/hbase/TEST/1418428042/DSMP/4759508618286845475,
- type the following:$ ${HBASE_HOME}/bin/hbase org.apache.hadoop.hbase.io.hfile.HFile -v -f hdfs://10.81.47.41:8020/hbase/TEST/1418428042/DSMP/4759508618286845475 If
- you leave off the option -v to see just a summary on the hfile. See
- usage for other things to do with the HFile
- tool.
-
-
- StoreFile Directory Structure on HDFS
- For more information of what StoreFiles look like on HDFS with respect to the directory structure, see .
-
-
-
-
-
- Blocks
- StoreFiles are composed of blocks. The blocksize is configured on a per-ColumnFamily basis.
-
- Compression happens at the block level within StoreFiles. For more information on compression, see .
-
- For more information on blocks, see the HFileBlock source code.
-
-
-
- KeyValue
- The KeyValue class is the heart of data storage in HBase. KeyValue wraps a byte array and takes offsets and lengths into passed array
- at where to start interpreting the content as KeyValue.
-
- The KeyValue format inside a byte array is:
-
- keylength
- valuelength
- key
- value
-
-
- The Key is further decomposed as:
-
- rowlength
- row (i.e., the rowkey)
- columnfamilylength
- columnfamily
- columnqualifier
- timestamp
- keytype (e.g., Put, Delete, DeleteColumn, DeleteFamily)
-
-
- KeyValue instances are not split across blocks.
- For example, if there is an 8 MB KeyValue, even if the block-size is 64kb this KeyValue will be read
- in as a coherent block. For more information, see the KeyValue source code.
-
- Example
- To emphasize the points above, examine what happens with two Puts for two different columns for the same row:
-
- Put #1: rowkey=row1, cf:attr1=value1
- Put #2: rowkey=row1, cf:attr2=value2
-
- Even though these are for the same row, a KeyValue is created for each column:
- Key portion for Put #1:
-
- rowlength ------------> 4
- row -----------------> row1
- columnfamilylength ---> 2
- columnfamily --------> cf
- columnqualifier ------> attr1
- timestamp -----------> server time of Put
- keytype -------------> Put
-
-
- Key portion for Put #2:
-
- rowlength ------------> 4
- row -----------------> row1
- columnfamilylength ---> 2
- columnfamily --------> cf
- columnqualifier ------> attr2
- timestamp -----------> server time of Put
- keytype -------------> Put
-
-
-
-
- It is critical to understand that the rowkey, ColumnFamily, and column (aka columnqualifier) are embedded within
- the KeyValue instance. The longer these identifiers are, the bigger the KeyValue is.
-
-
- Compaction
- There are two types of compactions: minor and major. Minor compactions will usually pick up a couple of the smaller adjacent
- StoreFiles and rewrite them as one. Minors do not drop deletes or expired cells, only major compactions do this. Sometimes a minor compaction
- will pick up all the StoreFiles in the Store and in this case it actually promotes itself to being a major compaction.
-
- After a major compaction runs there will be a single StoreFile per Store, and this will help performance usually. Caution: major compactions rewrite all of the Stores data and on a loaded system, this may not be tenable;
- major compactions will usually have to be done manually on large systems. See .
-
- Compactions will not perform region merges. See for more information on region merging.
-
-
- Compaction File Selection
- To understand the core algorithm for StoreFile selection, there is some ASCII-art in the Store source code that
- will serve as useful reference. It has been copied below:
-
-/* normal skew:
- *
- * older ----> newer
- * _
- * | | _
- * | | | | _
- * --|-|- |-|- |-|---_-------_------- minCompactSize
- * | | | | | | | | _ | |
- * | | | | | | | | | | | |
- * | | | | | | | | | | | |
- */
-
- Important knobs:
-
- hbase.store.compaction.ratio Ratio used in compaction
- file selection algorithm (default 1.2f).
- hbase.hstore.compaction.min (.90 hbase.hstore.compactionThreshold) (files) Minimum number
- of StoreFiles per Store to be selected for a compaction to occur (default 2).
- hbase.hstore.compaction.max (files) Maximum number of StoreFiles to compact per minor compaction (default 10).
- hbase.hstore.compaction.min.size (bytes)
- Any StoreFile smaller than this setting with automatically be a candidate for compaction. Defaults to
- hbase.hregion.memstore.flush.size (128 mb).
- hbase.hstore.compaction.max.size (.92) (bytes)
- Any StoreFile larger than this setting with automatically be excluded from compaction (default Long.MAX_VALUE).
-
-
- The minor compaction StoreFile selection logic is size based, and selects a file for compaction when the file
- <= sum(smaller_files) * hbase.hstore.compaction.ratio.
-
-
-
- Minor Compaction File Selection - Example #1 (Basic Example)
- This example mirrors an example from the unit test TestCompactSelection.
-
- hbase.store.compaction.ratio = 1.0f
- hbase.hstore.compaction.min = 3 (files) >
- hbase.hstore.compaction.max = 5 (files) >
- hbase.hstore.compaction.min.size = 10 (bytes) >
- hbase.hstore.compaction.max.size = 1000 (bytes) >
-
- The following StoreFiles exist: 100, 50, 23, 12, and 12 bytes apiece (oldest to newest).
- With the above parameters, the files that would be selected for minor compaction are 23, 12, and 12.
-
- Why?
-
- 100 --> No, because sum(50, 23, 12, 12) * 1.0 = 97.
- 50 --> No, because sum(23, 12, 12) * 1.0 = 47.
- 23 --> Yes, because sum(12, 12) * 1.0 = 24.
- 12 --> Yes, because the previous file has been included, and because this
- does not exceed the the max-file limit of 5
- 12 --> Yes, because the previous file had been included, and because this
- does not exceed the the max-file limit of 5.
-
-
-
-
- Minor Compaction File Selection - Example #2 (Not Enough Files To Compact)
- This example mirrors an example from the unit test TestCompactSelection.
-
- hbase.store.compaction.ratio = 1.0f
- hbase.hstore.compaction.min = 3 (files) >
- hbase.hstore.compaction.max = 5 (files) >
- hbase.hstore.compaction.min.size = 10 (bytes) >
- hbase.hstore.compaction.max.size = 1000 (bytes) >
-
-
- The following StoreFiles exist: 100, 25, 12, and 12 bytes apiece (oldest to newest).
- With the above parameters, the files that would be selected for minor compaction are 23, 12, and 12.
-
- Why?
-
- 100 --> No, because sum(25, 12, 12) * 1.0 = 47
- 25 --> No, because sum(12, 12) * 1.0 = 24
- 12 --> No. Candidate because sum(12) * 1.0 = 12, there are only 2 files to compact and that is less than the threshold of 3
- 12 --> No. Candidate because the previous StoreFile was, but there are not enough files to compact
-
-
-
-
- Minor Compaction File Selection - Example #3 (Limiting Files To Compact)
- This example mirrors an example from the unit test TestCompactSelection.
-
- hbase.store.compaction.ratio = 1.0f
- hbase.hstore.compaction.min = 3 (files) >
- hbase.hstore.compaction.max = 5 (files) >
- hbase.hstore.compaction.min.size = 10 (bytes) >
- hbase.hstore.compaction.max.size = 1000 (bytes) >
-
- The following StoreFiles exist: 7, 6, 5, 4, 3, 2, and 1 bytes apiece (oldest to newest).
- With the above parameters, the files that would be selected for minor compaction are 7, 6, 5, 4, 3.
-
- Why?
-
- 7 --> Yes, because sum(6, 5, 4, 3, 2, 1) * 1.0 = 21. Also, 7 is less than the min-size
- 6 --> Yes, because sum(5, 4, 3, 2, 1) * 1.0 = 15. Also, 6 is less than the min-size.
- 5 --> Yes, because sum(4, 3, 2, 1) * 1.0 = 10. Also, 5 is less than the min-size.
- 4 --> Yes, because sum(3, 2, 1) * 1.0 = 6. Also, 4 is less than the min-size.
- 3 --> Yes, because sum(2, 1) * 1.0 = 3. Also, 3 is less than the min-size.
- 2 --> No. Candidate because previous file was selected and 2 is less than the min-size, but the max-number of files to compact has been reached.
- 1 --> No. Candidate because previous file was selected and 1 is less than the min-size, but max-number of files to compact has been reached.
-
-
-
-
- Impact of Key Configuration Options
- hbase.store.compaction.ratio. A large ratio (e.g., 10) will produce a single giant file. Conversely, a value of .25 will
- produce behavior similar to the BigTable compaction algorithm - resulting in 4 StoreFiles.
-
- hbase.hstore.compaction.min.size. Because
- this limit represents the "automatic include" limit for all StoreFiles smaller than this value, this value may need to
- be adjusted downwards in write-heavy environments where many 1 or 2 mb StoreFiles are being flushed, because every file
- will be targeted for compaction and the resulting files may still be under the min-size and require further compaction, etc.
-
-
-
-
-
-
-
- Bloom Filters
- Bloom filters were developed over in HBase-1200
- Add bloomfilters.
- For description of the development process -- why static blooms
- rather than dynamic -- and for an overview of the unique properties
- that pertain to blooms in HBase, as well as possible future
- directions, see the Development Process section
- of the document BloomFilters
- in HBase attached to HBase-1200.
-
- The bloom filters described here are actually version two of
- blooms in HBase. In versions up to 0.19.x, HBase had a dynamic bloom
- option based on work done by the European Commission One-Lab
- Project 034819. The core of the HBase bloom work was later
- pulled up into Hadoop to implement org.apache.hadoop.io.BloomMapFile.
- Version 1 of HBase blooms never worked that well. Version 2 is a
- rewrite from scratch though again it starts with the one-lab
- work.
-
- See also and .
-
-
-
- Bloom StoreFile footprint
-
- Bloom filters add an entry to the StoreFile
- general FileInfo data structure and then two
- extra entries to the StoreFile metadata
- section.
-
-
- BloomFilter in the StoreFile
- FileInfo data structure
-
- FileInfo has a
- BLOOM_FILTER_TYPE entry which is set to
- NONE, ROW or
- ROWCOL.
-
-
-
- BloomFilter entries in StoreFile
- metadata
-
- BLOOM_FILTER_META holds Bloom Size, Hash
- Function used, etc. Its small in size and is cached on
- StoreFile.Reader load
- BLOOM_FILTER_DATA is the actual bloomfilter
- data. Obtained on-demand. Stored in the LRU cache, if it is enabled
- (Its enabled by default).
-
-
-
-
-
-
- Bulk Loading
- Overview
-
- HBase includes several methods of loading data into tables.
- The most straightforward method is to either use the TableOutputFormat
- class from a MapReduce job, or use the normal client APIs; however,
- these are not always the most efficient methods.
-
-
- The bulk load feature uses a MapReduce job to output table data in HBase's internal
- data format, and then directly loads the generated StoreFiles into a running
- cluster. Using bulk load will use less CPU and network resources than
- simply using the HBase API.
-
-
- Bulk Load Architecture
-
- The HBase bulk load process consists of two main steps.
-
- Preparing data via a MapReduce job
-
- The first step of a bulk load is to generate HBase data files (StoreFiles) from
- a MapReduce job using HFileOutputFormat. This output format writes
- out data in HBase's internal storage format so that they can be
- later loaded very efficiently into the cluster.
-
-
- In order to function efficiently, HFileOutputFormat must be
- configured such that each output HFile fits within a single region.
- In order to do this, jobs whose output will be bulk loaded into HBase
- use Hadoop's TotalOrderPartitioner class to partition the map output
- into disjoint ranges of the key space, corresponding to the key
- ranges of the regions in the table.
-
-
- HFileOutputFormat includes a convenience function,
- configureIncrementalLoad(), which automatically sets up
- a TotalOrderPartitioner based on the current region boundaries of a
- table.
-
-
- Completing the data load
-
- After the data has been prepared using
- HFileOutputFormat, it is loaded into the cluster using
- completebulkload. This command line tool iterates
- through the prepared data files, and for each one determines the
- region the file belongs to. It then contacts the appropriate Region
- Server which adopts the HFile, moving it into its storage directory
- and making the data available to clients.
-
-
- If the region boundaries have changed during the course of bulk load
- preparation, or between the preparation and completion steps, the
- completebulkloads utility will automatically split the
- data files into pieces corresponding to the new boundaries. This
- process is not optimally efficient, so users should take care to
- minimize the delay between preparing a bulk load and importing it
- into the cluster, especially if other clients are simultaneously
- loading data through other means.
-
-
-
- Importing the prepared data using the completebulkload tool
-
- After a data import has been prepared, either by using the
- importtsv tool with the
- "importtsv.bulk.output" option or by some other MapReduce
- job using the HFileOutputFormat, the
- completebulkload tool is used to import the data into the
- running cluster.
-
-
- The completebulkload tool simply takes the output path
- where importtsv or your MapReduce job put its results, and
- the table name to import into. For example:
-
- $ hadoop jar hbase-VERSION.jar completebulkload [-c /path/to/hbase/config/hbase-site.xml] /user/todd/myoutput mytable
-
- The -c config-file option can be used to specify a file
- containing the appropriate hbase parameters (e.g., hbase-site.xml) if
- not supplied already on the CLASSPATH (In addition, the CLASSPATH must
- contain the directory that has the zookeeper configuration file if
- zookeeper is NOT managed by HBase).
-
-
- Note: If the target table does not already exist in HBase, this
- tool will create the table automatically.
-
- This tool will run quickly, after which point the new data will be visible in
- the cluster.
-
-
- See Also
- For more information about the referenced utilities, see and .
-
-
- Advanced Usage
-
- Although the importtsv tool is useful in many cases, advanced users may
- want to generate data programatically, or import data from other formats. To get
- started doing so, dig into ImportTsv.java and check the JavaDoc for
- HFileOutputFormat.
-
-
- The import step of the bulk load can also be done programatically. See the
- LoadIncrementalHFiles class for more information.
-
-
-
-
- HDFS
- As HBase runs on HDFS (and each StoreFile is written as a file on HDFS),
- it is important to have an understanding of the HDFS Architecture
- especially in terms of how it stores files, handles failovers, and replicates blocks.
-
- See the Hadoop documentation on HDFS Architecture
- for more information.
-
- NameNode
- The NameNode is responsible for maintaining the filesystem metadata. See the above HDFS Architecture link
- for more information.
-
-
- DataNode
- The DataNodes are responsible for storing HDFS blocks. See the above HDFS Architecture link
- for more information.
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- FAQ
-
- General
-
- When should I use HBase?
-
- See the in the Architecture chapter.
-
-
-
-
- Are there other HBase FAQs?
-
-
- See the FAQ that is up on the wiki, HBase Wiki FAQ.
-
-
-
-
- Does HBase support SQL?
-
-
- Not really. SQL-ish support for HBase via Hive is in development, however Hive is based on MapReduce which is not generally suitable for low-latency requests.
- See the section for examples on the HBase client.
-
-
-
-
- How can I find examples of NoSQL/HBase?
-
- See the link to the BigTable paper in in the appendix, as
- well as the other papers.
-
-
-
-
- What is the history of HBase?
-
- See .
-
-
-
-
- Architecture
-
- How does HBase handle Region-RegionServer assignment and locality?
-
-
- See .
-
-
-
-
- Configuration
-
- How can I get started with my first cluster?
-
-
- See .
-
-
-
-
- Where can I learn about the rest of the configuration options?
-
-
- See .
-
-
-
-
- Schema Design / Data Access
-
- How should I design my schema in HBase?
-
-
- See and
-
-
-
-
-
- How can I store (fill in the blank) in HBase?
-
-
-
- See .
-
-
-
-
-
- How can I handle secondary indexes in HBase?
-
-
-
- See
-
-
-
-
- Can I change a table's rowkeys?
-
-
- This is a very common quesiton. You can't. See .
-
-
-
-
- What APIs does HBase support?
-
-
- See , and .
-
-
-
-
- MapReduce
-
- How can I use MapReduce with HBase?
-
-
- See
-
-
-
-
- Performance and Troubleshooting
-
-
- How can I improve HBase cluster performance?
-
-
-
- See .
-
-
-
-
-
- How can I troubleshoot my HBase cluster?
-
-
-
- See .
-
-
-
-
- Amazon EC2
-
-
- I am running HBase on Amazon EC2 and...
-
-
-
- EC2 issues are a special case. See Troubleshooting and Performance sections.
-
-
-
-
- Operations
-
-
- How do I manage my HBase cluster?
-
-
-
- See
-
-
-
-
-
- How do I back up my HBase cluster?
-
-
-
- See
-
-
-
-
- HBase in Action
-
- Where can I find interesting videos and presentations on HBase?
-
-
- See
-
-
-
-
-
-
-
-
- hbck In Depth
- HBaseFsck (hbck) is a tool for checking for region consistency and table integrity problems
-and repairing a corrupted HBase. It works in two basic modes -- a read-only inconsistency
-identifying mode and a multi-phase read-write repair mode.
-
-
- Running hbck to identify inconsistencies
-To check to see if your HBase cluster has corruptions, run hbck against your HBase cluster:
-
-$ ./bin/hbase hbck
-
-
-At the end of the commands output it prints OK or tells you the number of INCONSISTENCIES
-present. You may also want to run run hbck a few times because some inconsistencies can be
-transient (e.g. cluster is starting up or a region is splitting). Operationally you may want to run
-hbck regularly and setup alert (e.g. via nagios) if it repeatedly reports inconsistencies .
-A run of hbck will report a list of inconsistencies along with a brief description of the regions and
-tables affected. The using the -details option will report more details including a representative
-listing of all the splits present in all the tables.
-
-
-$ ./bin/hbase hbck -details
-
-
- Inconsistencies
-
- If after several runs, inconsistencies continue to be reported, you may have encountered a
-corruption. These should be rare, but in the event they occur newer versions of HBase include
-the hbck tool enabled with automatic repair options.
-
-
- There are two invariants that when violated create inconsistencies in HBase:
-
-
- HBase’s region consistency invariant is satisfied if every region is assigned and
-deployed on exactly one region server, and all places where this state kept is in
-accordance.
-
- HBase’s table integrity invariant is satisfied if for each table, every possible row key
-resolves to exactly one region.
-
-
-
-Repairs generally work in three phases -- a read-only information gathering phase that identifies
-inconsistencies, a table integrity repair phase that restores the table integrity invariant, and then
-finally a region consistency repair phase that restores the region consistency invariant.
-Starting from version 0.90.0, hbck could detect region consistency problems report on a subset
-of possible table integrity problems. It also included the ability to automatically fix the most
-common inconsistency, region assignment and deployment consistency problems. This repair
-could be done by using the -fix command line option. These problems close regions if they are
-open on the wrong server or on multiple region servers and also assigns regions to region
-servers if they are not open.
-
-
-Starting from HBase versions 0.90.7, 0.92.2 and 0.94.0, several new command line options are
-introduced to aid repairing a corrupted HBase. This hbck sometimes goes by the nickname
-“uberhbck”. Each particular version of uber hbck is compatible with the HBase’s of the same
-major version (0.90.7 uberhbck can repair a 0.90.4). However, versions <=0.90.6 and versions
-<=0.92.1 may require restarting the master or failing over to a backup master.
-
-
- Localized repairs
-
- When repairing a corrupted HBase, it is best to repair the lowest risk inconsistencies first.
-These are generally region consistency repairs -- localized single region repairs, that only modify
-in-memory data, ephemeral zookeeper data, or patch holes in the META table.
-Region consistency requires that the HBase instance has the state of the region’s data in HDFS
-(.regioninfo files), the region’s row in the .META. table., and region’s deployment/assignments on
-region servers and the master in accordance. Options for repairing region consistency include:
-
- -fixAssignments (equivalent to the 0.90 -fix option) repairs unassigned, incorrectly
-assigned or multiply assigned regions.
-
- -fixMeta which removes meta rows when corresponding regions are not present in
-HDFS and adds new meta rows if they regions are present in HDFS while not in META.
-
-
- To fix deployment and assignment problems you can run this command:
-
-
-$ ./bin/hbase hbck -fixAssignments
-
-To fix deployment and assignment problems as well as repairing incorrect meta rows you can
-run this command:.
-
-$ ./bin/hbase hbck -fixAssignments -fixMeta
-
-There are a few classes of table integrity problems that are low risk repairs. The first two are
-degenerate (startkey == endkey) regions and backwards regions (startkey > endkey). These are
-automatically handled by sidelining the data to a temporary directory (/hbck/xxxx).
-The third low-risk class is hdfs region holes. This can be repaired by using the:
-
- -fixHdfsHoles option for fabricating new empty regions on the file system.
-If holes are detected you can use -fixHdfsHoles and should include -fixMeta and -fixAssignments to make the new region consistent.
-
-
-
-$ ./bin/hbase hbck -fixAssignments -fixMeta -fixHdfsHoles
-
-Since this is a common operation, we’ve added a the -repairHoles flag that is equivalent to the
-previous command:
-
-$ ./bin/hbase hbck -repairHoles
-
-If inconsistencies still remain after these steps, you most likely have table integrity problems
-related to orphaned or overlapping regions.
-
- Region Overlap Repairs
-Table integrity problems can require repairs that deal with overlaps. This is a riskier operation
-because it requires modifications to the file system, requires some decision making, and may
-require some manual steps. For these repairs it is best to analyze the output of a hbck -details
-run so that you isolate repairs attempts only upon problems the checks identify. Because this is
-riskier, there are safeguard that should be used to limit the scope of the repairs.
-WARNING: This is a relatively new and have only been tested on online but idle HBase instances
-(no reads/writes). Use at your own risk in an active production environment!
-The options for repairing table integrity violations include:
-
- -fixHdfsOrphans option for “adopting” a region directory that is missing a region
-metadata file (the .regioninfo file).
-
- -fixHdfsOverlaps ability for fixing overlapping regions
-
-
-When repairing overlapping regions, a region’s data can be modified on the file system in two
-ways: 1) by merging regions into a larger region or 2) by sidelining regions by moving data to
-“sideline” directory where data could be restored later. Merging a large number of regions is
-technically correct but could result in an extremely large region that requires series of costly
-compactions and splitting operations. In these cases, it is probably better to sideline the regions
-that overlap with the most other regions (likely the largest ranges) so that merges can happen on
-a more reasonable scale. Since these sidelined regions are already laid out in HBase’s native
-directory and HFile format, they can be restored by using HBase’s bulk load mechanism.
-The default safeguard thresholds are conservative. These options let you override the default
-thresholds and to enable the large region sidelining feature.
-
- -maxMerge <n> maximum number of overlapping regions to merge
-
- -sidelineBigOverlaps if more than maxMerge regions are overlapping, sideline attempt
-to sideline the regions overlapping with the most other regions.
-
- -maxOverlapsToSideline <n> if sidelining large overlapping regions, sideline at most n
-regions.
-
-
-
-Since often times you would just want to get the tables repaired, you can use this option to turn
-on all repair options:
-
- -repair includes all the region consistency options and only the hole repairing table
-integrity options.
-
-
-Finally, there are safeguards to limit repairs to only specific tables. For example the following
-command would only attempt to repair table TableFoo and TableBar.
-
-$ ./bin/hbase/ hbck -repair TableFoo TableBar
-
- Special cases: Meta is not properly assigned
-There are a few special cases that hbck can handle as well.
-Sometimes the meta table’s only region is inconsistently assigned or deployed. In this case
-there is a special -fixMetaOnly option that can try to fix meta assignments.
-
-$ ./bin/hbase hbck -fixMetaOnly -fixAssignments
-
-
- Special cases: HBase version file is missing
-HBase’s data on the file system requires a version file in order to start. If this flie is missing, you
-can use the -fixVersionFile option to fabricating a new HBase version file. This assumes that
-the version of hbck you are running is the appropriate version for the HBase cluster.
-
- Special case: Root and META are corrupt.
-The most drastic corruption scenario is the case where the ROOT or META is corrupted and
-HBase will not start. In this case you can use the OfflineMetaRepair tool create new ROOT
-and META regions and tables.
-This tool assumes that HBase is offline. It then marches through the existing HBase home
-directory, loads as much information from region metadata files (.regioninfo files) as possible
-from the file system. If the region metadata has proper table integrity, it sidelines the original root
-and meta table directories, and builds new ones with pointers to the region directories and their
-data.
-
-$ ./bin/hbase org.apache.hadoop.hbase.util.OfflineMetaRepair
-
-NOTE: This tool is not as clever as uberhbck but can be used to bootstrap repairs that uberhbck
-can complete.
-If the tool succeeds you should be able to start hbase and run online repairs if necessary.
-
-
-
-
-
-
- Compression In HBaseCompression
-
-
- CompressionTest Tool
-
- HBase includes a tool to test compression is set up properly.
- To run it, type /bin/hbase org.apache.hadoop.hbase.util.CompressionTest.
- This will emit usage on how to run the tool.
-
-
-
-
-
-
- hbase.regionserver.codecs
-
-
-
- To have a RegionServer test a set of codecs and fail-to-start if any
- code is missing or misinstalled, add the configuration
-
- hbase.regionserver.codecs
-
- to your hbase-site.xml with a value of
- codecs to test on startup. For example if the
-
- hbase.regionserver.codecs
- value is lzo,gz and if lzo is not present
- or improperly installed, the misconfigured RegionServer will fail
- to start.
-
-
- Administrators might make use of this facility to guard against
- the case where a new server is added to cluster but the cluster
- requires install of a particular coded.
-
-
-
-
-
- LZO
-
- Unfortunately, HBase cannot ship with LZO because of
- the licensing issues; HBase is Apache-licensed, LZO is GPL.
- Therefore LZO install is to be done post-HBase install.
- See the Using LZO Compression
- wiki page for how to make LZO work with HBase.
-
- A common problem users run into when using LZO is that while initial
- setup of the cluster runs smooth, a month goes by and some sysadmin goes to
- add a machine to the cluster only they'll have forgotten to do the LZO
- fixup on the new machine. In versions since HBase 0.90.0, we should
- fail in a way that makes it plain what the problem is, but maybe not.
- See
- for a feature to help protect against failed LZO install.
-
-
-
-
- GZIP
-
-
- GZIP will generally compress better than LZO though slower.
- For some setups, better compression may be preferred.
- Java will use java's GZIP unless the native Hadoop libs are
- available on the CLASSPATH; in this case it will use native
- compressors instead (If the native libs are NOT present,
- you will see lots of Got brand-new compressor
- reports in your logs; see ).
-
-
-
-
- SNAPPY
-
-
- If snappy is installed, HBase can make use of it (courtesy of
- hadoop-snappy
- See Alejandro's note up on the list on difference between Snappy in Hadoop
- and Snappy in HBase).
-
-
-
-
- Build and install snappy on all nodes
- of your cluster.
-
-
-
-
- Use CompressionTest to verify snappy support is enabled and the libs can be loaded ON ALL NODES of your cluster:
- $ hbase org.apache.hadoop.hbase.util.CompressionTest hdfs://host/path/to/hbase snappy
-
-
-
-
- Create a column family with snappy compression and verify it in the hbase shell:
- $ hbase> create 't1', { NAME => 'cf1', COMPRESSION => 'SNAPPY' }
-hbase> describe 't1'
- In the output of the "describe" command, you need to ensure it lists "COMPRESSION => 'SNAPPY'"
-
-
-
-
-
-
-
-
- Changing Compression Schemes
- A frequent question on the dist-list is how to change compression schemes for ColumnFamilies. This is actually quite simple,
- and can be done via an alter command. Because the compression scheme is encoded at the block-level in StoreFiles, the table does
- not need to be re-created and the data does not copied somewhere else. Just make sure
- the old codec is still available until you are sure that all of the old StoreFiles have been compacted.
-
-
-
-
-
- YCSB: The Yahoo! Cloud Serving Benchmark and HBase
- TODO: Describe how YCSB is poor for putting up a decent cluster load.
- TODO: Describe setup of YCSB for HBase
- Ted Dunning redid YCSB so its mavenized and added facility for verifying workloads. See Ted Dunning's YCSB.
-
-
-
-
- HFile format version 2
-
- Motivation
- Note: this feature was introduced in HBase 0.92
- We found it necessary to revise the HFile format after encountering high memory usage and slow startup times caused by large Bloom filters and block indexes in the region server. Bloom filters can get as large as 100 MB per HFile, which adds up to 2 GB when aggregated over 20 regions. Block indexes can grow as large as 6 GB in aggregate size over the same set of regions. A region is not considered opened until all of its block index data is loaded. Large Bloom filters produce a different performance problem: the first get request that requires a Bloom filter lookup will incur the latency of loading the entire Bloom filter bit array.
- To speed up region server startup we break Bloom filters and block indexes into multiple blocks and write those blocks out as they fill up, which also reduces the HFile writer’s memory footprint. In the Bloom filter case, “filling up a block” means accumulating enough keys to efficiently utilize a fixed-size bit array, and in the block index case we accumulate an “index block” of the desired size. Bloom filter blocks and index blocks (we call these “inline blocks”) become interspersed with data blocks, and as a side effect we can no longer rely on the difference between block offsets to determine data block length, as it was done in version 1.
- HFile is a low-level file format by design, and it should not deal with application-specific details such as Bloom filters, which are handled at StoreFile level. Therefore, we call Bloom filter blocks in an HFile "inline" blocks. We also supply HFile with an interface to write those inline blocks.
- Another format modification aimed at reducing the region server startup time is to use a contiguous “load-on-open” section that has to be loaded in memory at the time an HFile is being opened. Currently, as an HFile opens, there are separate seek operations to read the trailer, data/meta indexes, and file info. To read the Bloom filter, there are two more seek operations for its “data” and “meta” portions. In version 2, we seek once to read the trailer and seek again to read everything else we need to open the file from a contiguous block.
- HFile format version 1 overview As we will be discussing the changes we are making to the HFile format, it is useful to give a short overview of the previous (HFile version 1) format. An HFile in the existing format is structured as follows:
-
-
-
-
-
- HFile Version 1
-
-
- HFile Version 1
-
-
-
- Image courtesy of Lars George, hbase-architecture-101-storage.html.
-
- Block index format in version 1
- The block index in version 1 is very straightforward. For each entry, it contains:
-
-
- Offset (long)
-
-
- Uncompressed size (int)
-
-
- Key (a serialized byte array written using Bytes.writeByteArray)
-
-
- Key length as a variable-length integer (VInt)
-
-
-
-
- Key bytes
-
-
-
-
-
- The number of entries in the block index is stored in the fixed file trailer, and has to be passed in to the method that reads the block index. One of the limitations of the block index in version 1 is that it does not provide the compressed size of a block, which turns out to be necessary for decompression. Therefore, the HFile reader has to infer this compressed size from the offset difference between blocks. We fix this limitation in version 2, where we store on-disk block size instead of uncompressed size, and get uncompressed size from the block header.
- HBase file format with inline blocks (version 2)
-
- Overview
- The version of HBase introducing the above features reads both version 1 and 2 HFiles, but only writes version 2 HFiles. A version 2 HFile is structured as follows:
-
-
-
-
-
- HFile Version 2
-
-
- HFile Version 2
-
-
-
-
-
-
- Unified version 2 block format
- In the version 2 every block in the data section contains the following fields:
-
-
- 8 bytes: Block type, a sequence of bytes equivalent to version 1's "magic records". Supported block types are:
-
-
- DATA – data blocks
-
-
-
-
- LEAF_INDEX – leaf-level index blocks in a multi-level-block-index
-
-
-
-
- BLOOM_CHUNK – Bloom filter chunks
-
-
-
-
- META – meta blocks (not used for Bloom filters in version 2 anymore)
-
-
-
-
- INTERMEDIATE_INDEX – intermediate-level index blocks in a multi-level blockindex
-
-
-
-
- ROOT_INDEX – root>level index blocks in a multi>level block index
-
-
-
-
- FILE_INFO – the “file info” block, a small key>value map of metadata
-
-
-
-
- BLOOM_META – a Bloom filter metadata block in the load>on>open section
-
-
-
-
- TRAILER – a fixed>size file trailer. As opposed to the above, this is not an
- HFile v2 block but a fixed>size (for each HFile version) data structure
-
-
-
-
- INDEX_V1 – this block type is only used for legacy HFile v1 block
-
-
-
-
-
- Compressed size of the block's data, not including the header (int).
-
-
-Can be used for skipping the current data block when scanning HFile data.
-
-
-
- Uncompressed size of the block's data, not including the header (int)
-
- This is equal to the compressed size if the compression algorithm is NON
-
-
-
- File offset of the previous block of the same type (long)
-
- Can be used for seeking to the previous data/index block
-
-
-
- Compressed data (or uncompressed data if the compression algorithm is NONE).
-
-
- The above format of blocks is used in the following HFile sections:
-
-
- Scanned block section. The section is named so because it contains all data blocks that need to be read when an HFile is scanned sequentially. Also contains leaf block index and Bloom chunk blocks.
-
-
- Non-scanned block section. This section still contains unified-format v2 blocks but it does not have to be read when doing a sequential scan. This section contains “meta” blocks and intermediate-level index blocks.
-
-
-
- We are supporting “meta” blocks in version 2 the same way they were supported in version 1, even though we do not store Bloom filter data in these blocks anymore.
-
- Block index in version 2
- There are three types of block indexes in HFile version 2, stored in two different formats (root and non-root):
-
-
- Data index — version 2 multi-level block index, consisting of:
-
-
-
- Version 2 root index, stored in the data block index section of the file
-
-
-
-
-Optionally, version 2 intermediate levels, stored in the non%root format in the data index section of the file. Intermediate levels can only be present if leaf level blocks are present
-
-
-
-
-Optionally, version 2 leaf levels, stored in the non%root format inline with data blocks
-
-
-
-
-
- Meta index — version 2 root index format only, stored in the meta index section of the file
-
-
- Bloom index — version 2 root index format only, stored in the “load-on-open” section as part of Bloom filter metadata.
-
-
-
- Root block index format in version 2
- This format applies to:
-
-
- Root level of the version 2 data index
-
-
- Entire meta and Bloom indexes in version 2, which are always single-level.
-
-
- A version 2 root index block is a sequence of entries of the following format, similar to entries of a version 1 block index, but storing on-disk size instead of uncompressed size.
-
-
- Offset (long)
-
-This offset may point to a data block or to a deeper>level index block.
-
-
-
- On-disk size (int)
-
-
- Key (a serialized byte array stored using Bytes.writeByteArray)
-
-
- Key (VInt)
-
-
-
- Key bytes
-
-
-
-
-
- A single-level version 2 block index consists of just a single root index block. To read a root index block of version 2, one needs to know the number of entries. For the data index and the meta index the number of entries is stored in the trailer, and for the Bloom index it is stored in the compound Bloom filter metadata.
-
- For a multi-level block index we also store the following fields in the root index block in the load-on-open section of the HFile, in addition to the data structure described above:
-
-
- Middle leaf index block offset
-
-
- Middle leaf block on-disk size (meaning the leaf index block containing the reference to the “middle” data block of the file)
-
-
- The index of the mid-key (defined below) in the middle leaf-level block.
-
-
-
- These additional fields are used to efficiently retrieve the mid-key of the HFile used in HFile splits, which we define as the first key of the block with a zero-based index of (n – 1) / 2, if the total number of blocks in the HFile is n. This definition is consistent with how the mid-key was determined in HFile version 1, and is reasonable in general, because blocks are likely to be the same size on average, but we don’t have any estimates on individual key/value pair sizes.
-
- When writing a version 2 HFile, the total number of data blocks pointed to by every leaf-level index block is kept track of. When we finish writing and the total number of leaf-level blocks is determined, it is clear which leaf-level block contains the mid-key, and the fields listed above are computed. When reading the HFile and the mid-key is requested, we retrieve the middle leaf index block (potentially from the block cache) and get the mid-key value from the appropriate position inside that leaf block.
-
- Non-root block index format in version 2
- This format applies to intermediate-level and leaf index blocks of a version 2 multi-level data block index. Every non-root index block is structured as follows.
-
-
- numEntries: the number of entries (int).
-
-
- entryOffsets: the “secondary index” of offsets of entries in the block, to facilitate a quick binary search on the key (numEntries + 1 int values). The last value is the total length of all entries in this index block. For example, in a non-root index block with entry sizes 60, 80, 50 the “secondary index” will contain the following int array: {0, 60, 140, 190}.
-
-
- Entries. Each entry contains:
-
-
-
-Offset of the block referenced by this entry in the file (long)
-
-
-
-
-On>disk size of the referenced block (int)
-
-
-
-
-Key. The length can be calculated from entryOffsets.
-
-
-
-
-
-
- Bloom filters in version 2
- In contrast with version 1, in a version 2 HFile Bloom filter metadata is stored in the load-on-open section of the HFile for quick startup.
-
-
- A compound Bloom filter.
-
-
-
- Bloom filter version = 3 (int). There used to be a DynamicByteBloomFilter class that had the Bloom filter version number 2
-
-
-
-
-The total byte size of all compound Bloom filter chunks (long)
-
-
-
-
- Number of hash functions (int
-
-
-
-
-Type of hash functions (int)
-
-
-
-
-The total key count inserted into the Bloom filter (long)
-
-
-
-
-The maximum total number of keys in the Bloom filter (long)
-
-
-
-
-The number of chunks (int)
-
-
-
-
-Comparator class used for Bloom filter keys, a UTF>8 encoded string stored using Bytes.writeByteArray
-
-
-
-
- Bloom block index in the version 2 root block index format
-
-
-
-
- File Info format in versions 1 and 2
- The file info block is a serialized HbaseMapWritable (essentially a map from byte arrays to byte arrays) with the following keys, among others. StoreFile-level logic adds more keys to this.
-
-
-
- hfile.LASTKEY
-
-
- The last key of the file (byte array)
-
-
-
-
- hfile.AVG_KEY_LEN
-
-
- The average key length in the file (int)
-
-
-
-
- hfile.AVG_VALUE_LEN
-
-
- The average value length in the file (int)
-
-
-
- File info format did not change in version 2. However, we moved the file info to the final section of the file, which can be loaded as one block at the time the HFile is being opened. Also, we do not store comparator in the version 2 file info anymore. Instead, we store it in the fixed file trailer. This is because we need to know the comparator at the time of parsing the load-on-open section of the HFile.
- Fixed file trailer format differences between versions 1 and 2
- The following table shows common and different fields between fixed file trailers in versions 1 and 2. Note that the size of the trailer is different depending on the version, so it is “fixed” only within one version. However, the version is always stored as the last four-byte integer in the file.
-
-
-
-
-
-
-
-
- Version 1
-
-
- Version 2
-
-
-
-
- File info offset (long)
-
-
-
-
- Data index offset (long)
-
-
- loadOnOpenOffset (long)
- The offset of the section that we need toload when opening the file.
-
-
-
-
- Number of data index entries (int)
-
-
-
-
- metaIndexOffset (long)
- This field is not being used by the version 1 reader, so we removed it from version 2.
-
-
- uncompressedDataIndexSize (long)
- The total uncompressed size of the whole data block index, including root-level, intermediate-level, and leaf-level blocks.
-
-
-
-
- Number of meta index entries (int)
-
-
-
-
- Total uncompressed bytes (long)
-
-
-
-
- numEntries (int)
-
-
- numEntries (long)
-
-
-
-
- Compression codec: 0 = LZO, 1 = GZ, 2 = NONE (int)
-
-
-
-
-
-
-
- The number of levels in the data block index (int)
-
-
-
-
-
-
-
- firstDataBlockOffset (long)
- The offset of the first first data block. Used when scanning.
-
-
-
-
-
-
-
- lastDataBlockEnd (long)
- The offset of the first byte after the last key/value data block. We don't need to go beyond this offset when scanning.
-
-
-
-
- Version: 1 (int)
-
-
- Version: 2 (int)
-
-
-
-
-
-
- Other Information About HBase
- HBase Videos
- Introduction to HBase
-
- Introduction to HBase by Todd Lipcon (Chicago Data Summit 2011).
-
- Introduction to HBase by Todd Lipcon (2010).
-
-
-
- Building Real Time Services at Facebook with HBase by Jonathan Gray (Hadoop World 2011).
-
- HBase and Hadoop, Mixing Real-Time and Batch Processing at StumbleUpon by JD Cryans (Hadoop World 2010).
-
-
- HBase Presentations (Slides)
- Advanced HBase Schema Design by Lars George (Hadoop World 2011).
-
- Introduction to HBase by Todd Lipcon (Chicago Data Summit 2011).
-
- Getting The Most From Your HBase Install by Ryan Rawson, Jonathan Gray (Hadoop World 2009).
-
-
- HBase Papers
- BigTable by Google (2006).
-
- HBase and HDFS Locality by Lars George (2010).
-
- No Relation: The Mixed Blessings of Non-Relational Databases by Ian Varley (2009).
-
-
- HBase Sites
- Cloudera's HBase Blog has a lot of links to useful HBase information.
-
- CAP Confusion is a relevant entry for background information on
- distributed storage systems.
-
-
-
- HBase Wiki has a page with a number of presentations.
-
-
- HBase Books
- HBase: The Definitive Guide by Lars George.
-
-
- Hadoop Books
- Hadoop: The Definitive Guide by Tom White.
-
-
-
-
-
- HBase History
-
- 2006: BigTable paper published by Google.
-
- 2006 (end of year): HBase development starts.
-
- 2008: HBase becomes Hadoop sub-project.
-
- 2010: HBase becomes Apache top-level project.
-
-
-
-
- HBase and the Apache Software Foundation
- HBase is a project in the Apache Software Foundation and as such there are responsibilities to the ASF to ensure
- a healthy project.
- ASF Development Process
- See the Apache Development Process page
- for all sorts of information on how the ASF is structured (e.g., PMC, committers, contributors), to tips on contributing
- and getting involved, and how open-source works at ASF.
-
-
- ASF Board Reporting
- Once a quarter, each project in the ASF portfolio submits a report to the ASF board. This is done by the HBase project
- lead and the committers. See ASF board reporting for more information.
-
-
-
-
-
- Index
-
-
diff --git hbase-site/src/docbkx/case_studies.xml hbase-site/src/docbkx/case_studies.xml
deleted file mode 100644
index a10f53c..0000000
--- hbase-site/src/docbkx/case_studies.xml
+++ /dev/null
@@ -1,324 +0,0 @@
-
-
-
- Case Studies
-
- Overview
- This chapter will describe a variety of performance and troubleshooting case studies that can
- provide a useful blueprint on diagnosing cluster issues.
- For more information on Performance and Troubleshooting, see and .
-
-
-
-
- Schema Design
-
-
- List Data
- The following is an exchange from the user dist-list regarding a fairly common question:
- how to handle per-user list data in HBase.
-
- *** QUESTION ***
-
- We're looking at how to store a large amount of (per-user) list data in
-HBase, and we were trying to figure out what kind of access pattern made
-the most sense. One option is store the majority of the data in a key, so
-we could have something like:
-
-
-
-<FixedWidthUserName><FixedWidthValueId1>:"" (no value)
-<FixedWidthUserName><FixedWidthValueId2>:"" (no value)
-<FixedWidthUserName><FixedWidthValueId3>:"" (no value)
-
-
-The other option we had was to do this entirely using:
-
-<FixedWidthUserName><FixedWidthPageNum0>:<FixedWidthLength><FixedIdNextPageNum><ValueId1><ValueId2><ValueId3>...
-<FixedWidthUserName><FixedWidthPageNum1>:<FixedWidthLength><FixedIdNextPageNum><ValueId1><ValueId2><ValueId3>...
-
-
-where each row would contain multiple values.
-So in one case reading the first thirty values would be:
-
-
-scan { STARTROW => 'FixedWidthUsername' LIMIT => 30}
-
-And in the second case it would be
-
-get 'FixedWidthUserName\x00\x00\x00\x00'
-
-
-The general usage pattern would be to read only the first 30 values of
-these lists, with infrequent access reading deeper into the lists. Some
-users would have <= 30 total values in these lists, and some users would
-have millions (i.e. power-law distribution)
-
-
- The single-value format seems like it would take up more space on HBase,
-but would offer some improved retrieval / pagination flexibility. Would
-there be any significant performance advantages to be able to paginate via
-gets vs paginating with scans?
-
-
- My initial understanding was that doing a scan should be faster if our
-paging size is unknown (and caching is set appropriately), but that gets
-should be faster if we'll always need the same page size. I've ended up
-hearing different people tell me opposite things about performance. I
-assume the page sizes would be relatively consistent, so for most use cases
-we could guarantee that we only wanted one page of data in the
-fixed-page-length case. I would also assume that we would have infrequent
-updates, but may have inserts into the middle of these lists (meaning we'd
-need to update all subsequent rows).
-
-
-Thanks for help / suggestions / follow-up questions.
-
- *** ANSWER ***
-
-If I understand you correctly, you're ultimately trying to store
-triples in the form "user, valueid, value", right? E.g., something
-like:
-
-
-"user123, firstname, Paul",
-"user234, lastname, Smith"
-
-
-(But the usernames are fixed width, and the valueids are fixed width).
-
-
-And, your access pattern is along the lines of: "for user X, list the
-next 30 values, starting with valueid Y". Is that right? And these
-values should be returned sorted by valueid?
-
-
-The tl;dr version is that you should probably go with one row per
-user+value, and not build a complicated intra-row pagination scheme on
-your own unless you're really sure it is needed.
-
-
-Your two options mirror a common question people have when designing
-HBase schemas: should I go "tall" or "wide"? Your first schema is
-"tall": each row represents one value for one user, and so there are
-many rows in the table for each user; the row key is user + valueid,
-and there would be (presumably) a single column qualifier that means
-"the value". This is great if you want to scan over rows in sorted
-order by row key (thus my question above, about whether these ids are
-sorted correctly). You can start a scan at any user+valueid, read the
-next 30, and be done. What you're giving up is the ability to have
-transactional guarantees around all the rows for one user, but it
-doesn't sound like you need that. Doing it this way is generally
-recommended (see
-here http://hbase.apache.org/book.html#schema.smackdown).
-
-
-Your second option is "wide": you store a bunch of values in one row,
-using different qualifiers (where the qualifier is the valueid). The
-simple way to do that would be to just store ALL values for one user
-in a single row. I'm guessing you jumped to the "paginated" version
-because you're assuming that storing millions of columns in a single
-row would be bad for performance, which may or may not be true; as
-long as you're not trying to do too much in a single request, or do
-things like scanning over and returning all of the cells in the row,
-it shouldn't be fundamentally worse. The client has methods that allow
-you to get specific slices of columns.
-
-
-Note that neither case fundamentally uses more disk space than the
-other; you're just "shifting" part of the identifying information for
-a value either to the left (into the row key, in option one) or to the
-right (into the column qualifiers in option 2). Under the covers,
-every key/value still stores the whole row key, and column family
-name. (If this is a bit confusing, take an hour and watch Lars
-George's excellent video about understanding HBase schema design:
-http://www.youtube.com/watch?v=_HLoH_PgrLk).
-
-
-A manually paginated version has lots more complexities, as you note,
-like having to keep track of how many things are in each page,
-re-shuffling if new values are inserted, etc. That seems significantly
-more complex. It might have some slight speed advantages (or
-disadvantages!) at extremely high throughput, and the only way to
-really know that would be to try it out. If you don't have time to
-build it both ways and compare, my advice would be to start with the
-simplest option (one row per user+value). Start simple and iterate! :)
-
-
-
-
-
-
-
-
- Performance/Troubleshooting
-
-
- Case Study #1 (Performance Issue On A Single Node)
- Scenario
- Following a scheduled reboot, one data node began exhibiting unusual behavior. Routine MapReduce
- jobs run against HBase tables which regularly completed in five or six minutes began taking 30 or 40 minutes
- to finish. These jobs were consistently found to be waiting on map and reduce tasks assigned to the troubled data node
- (e.g., the slow map tasks all had the same Input Split).
- The situation came to a head during a distributed copy, when the copy was severely prolonged by the lagging node.
-
-
- Hardware
- Datanodes:
-
- Two 12-core processors
- Six Enerprise SATA disks
- 24GB of RAM
- Two bonded gigabit NICs
-
-
- Network:
-
- 10 Gigabit top-of-rack switches
- 20 Gigabit bonded interconnects between racks.
-
-
-
- Hypotheses
- HBase "Hot Spot" Region
- We hypothesized that we were experiencing a familiar point of pain: a "hot spot" region in an HBase table,
- where uneven key-space distribution can funnel a huge number of requests to a single HBase region, bombarding the RegionServer
- process and cause slow response time. Examination of the HBase Master status page showed that the number of HBase requests to the
- troubled node was almost zero. Further, examination of the HBase logs showed that there were no region splits, compactions, or other region transitions
- in progress. This effectively ruled out a "hot spot" as the root cause of the observed slowness.
-
-
- HBase Region With Non-Local Data
- Our next hypothesis was that one of the MapReduce tasks was requesting data from HBase that was not local to the datanode, thus
- forcing HDFS to request data blocks from other servers over the network. Examination of the datanode logs showed that there were very
- few blocks being requested over the network, indicating that the HBase region was correctly assigned, and that the majority of the necessary
- data was located on the node. This ruled out the possibility of non-local data causing a slowdown.
-
-
- Excessive I/O Wait Due To Swapping Or An Over-Worked Or Failing Hard Disk
- After concluding that the Hadoop and HBase were not likely to be the culprits, we moved on to troubleshooting the datanode's hardware.
- Java, by design, will periodically scan its entire memory space to do garbage collection. If system memory is heavily overcommitted, the Linux
- kernel may enter a vicious cycle, using up all of its resources swapping Java heap back and forth from disk to RAM as Java tries to run garbage
- collection. Further, a failing hard disk will often retry reads and/or writes many times before giving up and returning an error. This can manifest
- as high iowait, as running processes wait for reads and writes to complete. Finally, a disk nearing the upper edge of its performance envelope will
- begin to cause iowait as it informs the kernel that it cannot accept any more data, and the kernel queues incoming data into the dirty write pool in memory.
- However, using vmstat(1) and free(1), we could see that no swap was being used, and the amount of disk IO was only a few kilobytes per second.
-
-
- Slowness Due To High Processor Usage
- Next, we checked to see whether the system was performing slowly simply due to very high computational load. top(1) showed that the system load
- was higher than normal, but vmstat(1) and mpstat(1) showed that the amount of processor being used for actual computation was low.
-
-
- Network Saturation (The Winner)
- Since neither the disks nor the processors were being utilized heavily, we moved on to the performance of the network interfaces. The datanode had two
- gigabit ethernet adapters, bonded to form an active-standby interface. ifconfig(8) showed some unusual anomalies, namely interface errors, overruns, framing errors.
- While not unheard of, these kinds of errors are exceedingly rare on modern hardware which is operating as it should:
-
-$ /sbin/ifconfig bond0
-bond0 Link encap:Ethernet HWaddr 00:00:00:00:00:00
-inet addr:10.x.x.x Bcast:10.x.x.255 Mask:255.255.255.0
-UP BROADCAST RUNNING MASTER MULTICAST MTU:1500 Metric:1
-RX packets:2990700159 errors:12 dropped:0 overruns:1 frame:6 <--- Look Here! Errors!
-TX packets:3443518196 errors:0 dropped:0 overruns:0 carrier:0
-collisions:0 txqueuelen:0
-RX bytes:2416328868676 (2.4 TB) TX bytes:3464991094001 (3.4 TB)
-
-
- These errors immediately lead us to suspect that one or more of the ethernet interfaces might have negotiated the wrong line speed. This was confirmed both by running an ICMP ping
- from an external host and observing round-trip-time in excess of 700ms, and by running ethtool(8) on the members of the bond interface and discovering that the active interface
- was operating at 100Mbs/, full duplex.
-
-$ sudo ethtool eth0
-Settings for eth0:
-Supported ports: [ TP ]
-Supported link modes: 10baseT/Half 10baseT/Full
- 100baseT/Half 100baseT/Full
- 1000baseT/Full
-Supports auto-negotiation: Yes
-Advertised link modes: 10baseT/Half 10baseT/Full
- 100baseT/Half 100baseT/Full
- 1000baseT/Full
-Advertised pause frame use: No
-Advertised auto-negotiation: Yes
-Link partner advertised link modes: Not reported
-Link partner advertised pause frame use: No
-Link partner advertised auto-negotiation: No
-Speed: 100Mb/s <--- Look Here! Should say 1000Mb/s!
-Duplex: Full
-Port: Twisted Pair
-PHYAD: 1
-Transceiver: internal
-Auto-negotiation: on
-MDI-X: Unknown
-Supports Wake-on: umbg
-Wake-on: g
-Current message level: 0x00000003 (3)
-Link detected: yes
-
-
- In normal operation, the ICMP ping round trip time should be around 20ms, and the interface speed and duplex should read, "1000MB/s", and, "Full", respectively.
-
-
-
- Resolution
- After determining that the active ethernet adapter was at the incorrect speed, we used the ifenslave(8) command to make the standby interface
- the active interface, which yielded an immediate improvement in MapReduce performance, and a 10 times improvement in network throughput:
-
- On the next trip to the datacenter, we determined that the line speed issue was ultimately caused by a bad network cable, which was replaced.
-
-
-
-
- Case Study #2 (Performance Research 2012)
- Investigation results of a self-described "we're not sure what's wrong, but it seems slow" problem.
- http://gbif.blogspot.com/2012/03/hbase-performance-evaluation-continued.html
-
-
-
-
- Case Study #3 (Performance Research 2010))
-
- Investigation results of general cluster performance from 2010. Although this research is on an older version of the codebase, this writeup
- is still very useful in terms of approach.
- http://hstack.org/hbase-performance-testing/
-
-
-
-
- Case Study #4 (xcievers Config)
- Case study of configuring xceivers, and diagnosing errors from mis-configurations.
- http://www.larsgeorge.com/2012/03/hadoop-hbase-and-xceivers.html
-
- See also .
-
-
-
-
-
-
diff --git hbase-site/src/docbkx/configuration.xml hbase-site/src/docbkx/configuration.xml
deleted file mode 100644
index 35a7a32..0000000
--- hbase-site/src/docbkx/configuration.xml
+++ /dev/null
@@ -1,1709 +0,0 @@
-
-
-
- Configuration
- This chapter is the Not-So-Quick start guide to HBase configuration.
- Please read this chapter carefully and ensure that all requirements have
- been satisfied. Failure to do so will cause you (and us) grief debugging strange errors
- and/or data loss.
-
-
- HBase uses the same configuration system as Hadoop.
- To configure a deploy, edit a file of environment variables
- in conf/hbase-env.sh -- this configuration
- is used mostly by the launcher shell scripts getting the cluster
- off the ground -- and then add configuration to an XML file to
- do things like override HBase defaults, tell HBase what Filesystem to
- use, and the location of the ZooKeeper ensemble
-
-
-Be careful editing XML. Make sure you close all elements.
-Run your file through xmllint or similar
-to ensure well-formedness of your document after an edit session.
-
-
- .
-
-
- When running in distributed mode, after you make
- an edit to an HBase configuration, make sure you copy the
- content of the conf directory to
- all nodes of the cluster. HBase will not do this for you.
- Use rsync.
-
-
- Java
-
- Just like Hadoop, HBase requires java 6 from Oracle. Usually
- you'll want to use the latest version available except the problematic
- u18 (u24 is the latest version as of this writing).
-
-
- Operating System
-
- ssh
-
- ssh must be installed and
- sshd must be running to use Hadoop's scripts to
- manage remote Hadoop and HBase daemons. You must be able to ssh to all
- nodes, including your local node, using passwordless login (Google
- "ssh passwordless login").
-
-
-
- DNS
-
- HBase uses the local hostname to self-report it's IP address.
- Both forward and reverse DNS resolving must work in versions of
- HBase previous to 0.92.0
- The hadoop-dns-checker tool can be used to verify
- DNS is working correctly on the cluster. The project README file provides detailed instructions on usage.
-.
-
- If your machine has multiple interfaces, HBase will use the
- interface that the primary hostname resolves to.
-
- If this is insufficient, you can set
- hbase.regionserver.dns.interface to indicate the
- primary interface. This only works if your cluster configuration is
- consistent and every host has the same network interface
- configuration.
-
- Another alternative is setting
- hbase.regionserver.dns.nameserver to choose a
- different nameserver than the system wide default.
-
-
- Loopback IP
- HBase expects the loopback IP address to be 127.0.0.1. Ubuntu and some other distributions,
- for example, will default to 127.0.1.1 and this will cause problems for you.
-
- /etc/hosts should look something like this:
-
- 127.0.0.1 localhost
- 127.0.0.1 ubuntu.ubuntu-domain ubuntu
-
-
-
-
-
- NTP
-
- The clocks on cluster members should be in basic alignments.
- Some skew is tolerable but wild skew could generate odd behaviors. Run
- NTP
- on your cluster, or an equivalent.
-
- If you are having problems querying data, or "weird" cluster
- operations, check system time!
-
-
-
-
- ulimit
- ulimit
-
- and
- nproc
- nproc
-
-
-
- HBase is a database. It uses a lot of files all at the same time.
- The default ulimit -n -- i.e. user file limit -- of 1024 on most *nix systems
- is insufficient (On mac os x its 256). Any significant amount of loading will
- lead you to .
- You may also notice errors such as...
- 2010-04-06 03:04:37,542 INFO org.apache.hadoop.hdfs.DFSClient: Exception increateBlockOutputStream java.io.EOFException
- 2010-04-06 03:04:37,542 INFO org.apache.hadoop.hdfs.DFSClient: Abandoning block blk_-6935524980745310745_1391901
- Do yourself a favor and change the upper bound on the
- number of file descriptors. Set it to north of 10k. The math runs roughly as follows: per ColumnFamily
- there is at least one StoreFile and possibly up to 5 or 6 if the region is under load. Multiply the
- average number of StoreFiles per ColumnFamily times the number of regions per RegionServer. For example, assuming
- that a schema had 3 ColumnFamilies per region with an average of 3 StoreFiles per ColumnFamily,
- and there are 100 regions per RegionServer, the JVM will open 3 * 3 * 100 = 900 file descriptors
- (not counting open jar files, config files, etc.)
-
- You should also up the hbase users'
- nproc setting; under load, a low-nproc
- setting could manifest as OutOfMemoryError
- See Jack Levin's major hdfs issues
- note up on the user list.
- The requirement that a database requires upping of system limits
- is not peculiar to HBase. See for example the section
- Setting Shell Limits for the Oracle User in
-
- Short Guide to install Oracle 10 on Linux..
-
-
- To be clear, upping the file descriptors and nproc for the user who is
- running the HBase process is an operating system configuration, not an
- HBase configuration. Also, a common mistake is that administrators
- will up the file descriptors for a particular user but for whatever
- reason, HBase will be running as some one else. HBase prints in its
- logs as the first line the ulimit its seeing. Ensure its correct.
-
- A useful read setting config on you hadoop cluster is Aaron
- Kimballs' Configuration
- Parameters: What can you just ignore?
-
-
-
- ulimit on Ubuntu
-
- If you are on Ubuntu you will need to make the following
- changes:
-
- In the file /etc/security/limits.conf add
- a line like: hadoop - nofile 32768
- Replace hadoop with whatever user is running
- Hadoop and HBase. If you have separate users, you will need 2
- entries, one for each user. In the same file set nproc hard and soft
- limits. For example: hadoop soft/hard nproc 32000.
-
- In the file /etc/pam.d/common-session add
- as the last line in the file: session required pam_limits.so
- Otherwise the changes in /etc/security/limits.conf won't be
- applied.
-
- Don't forget to log out and back in again for the changes to
- take effect!
-
-
-
-
- Windows
-
- HBase has been little tested running on Windows. Running a
- production install of HBase on top of Windows is not
- recommended.
-
- If you are running HBase on Windows, you must install Cygwin to have a *nix-like
- environment for the shell scripts. The full details are explained in
- the Windows
- Installation guide. Also
- search our user mailing list to pick
- up latest fixes figured by Windows users.
-
-
-
-
-
- Hadoop
- Hadoop
-
- Please read all of this section
- Please read this section to the end. Up front we
- wade through the weeds of Hadoop versions. Later we talk of what you must do in HBase
- to make it work w/ a particular Hadoop version.
-
-
-
- HBase will lose data unless it is running on an HDFS that has a durable
- sync implementation. Hadoop 0.20.2, Hadoop 0.20.203.0, and Hadoop 0.20.204.0
- DO NOT have this attribute.
- Currently only Hadoop versions 0.20.205.x or any release in excess of this
- version -- this includes hadoop 1.0.0 -- have a working, durable sync
-
- On Hadoop Versions
- The Cloudera blog post An update on Apache Hadoop 1.0
- by Charles Zedlweski has a nice exposition on how all the Hadoop versions relate.
- Its worth checking out if you are having trouble making sense of the
- Hadoop version morass.
-
- . Sync has to be explicitly enabled by setting
- dfs.support.append equal
- to true on both the client side -- in hbase-site.xml
- -- and on the serverside in hdfs-site.xml (The sync
- facility HBase needs is a subset of the append code path).
-
- <property>
- <name>dfs.support.append</name>
- <value>true</value>
- </property>
-
- You will have to restart your cluster after making this edit. Ignore the chicken-little
- comment you'll find in the hdfs-default.xml in the
- description for the dfs.support.append configuration; it says it is not enabled because there
- are ... bugs in the 'append code' and is not supported in any production
- cluster.. This comment is stale, from another era, and while I'm sure there
- are bugs, the sync/append code has been running
- in production at large scale deploys and is on
- by default in the offerings of hadoop by commercial vendors
- Until recently only the
- branch-0.20-append
- branch had a working sync but no official release was ever made from this branch.
- You had to build it yourself. Michael Noll wrote a detailed blog,
- Building
- an Hadoop 0.20.x version for HBase 0.90.2, on how to build an
- Hadoop from branch-0.20-append. Recommended.
- Praveen Kumar has written
- a complimentary article,
- Building Hadoop and HBase for HBase Maven application development.
-Cloudera have dfs.support.append set to true by default..
-
-Or use the
- Cloudera or
- MapR distributions.
- Cloudera' CDH3
- is Apache Hadoop 0.20.x plus patches including all of the
- branch-0.20-append
- additions needed to add a durable sync. Use the released, most recent version of CDH3.
-
- MapR
- includes a commercial, reimplementation of HDFS.
- It has a durable sync as well as some other interesting features that are not
- yet in Apache Hadoop. Their M3
- product is free to use and unlimited.
-
-
- Because HBase depends on Hadoop, it bundles an instance of the
- Hadoop jar under its lib directory. The bundled jar is ONLY for use in standalone mode.
- In distributed mode, it is critical that the version of Hadoop that is out
- on your cluster match what is under HBase. Replace the hadoop jar found in the HBase
- lib directory with the hadoop jar you are running on
- your cluster to avoid version mismatch issues. Make sure you
- replace the jar in HBase everywhere on your cluster. Hadoop version
- mismatch issues have various manifestations but often all looks like
- its hung up.
-
-
- Hadoop Security
- HBase will run on any Hadoop 0.20.x that incorporates Hadoop
- security features -- e.g. Y! 0.20S or CDH3B3 -- as long as you do as
- suggested above and replace the Hadoop jar that ships with HBase
- with the secure version.
-
-
-
- dfs.datanode.max.xcievers
- xcievers
-
-
- An Hadoop HDFS datanode has an upper bound on the number of
- files that it will serve at any one time. The upper bound parameter is
- called xcievers (yes, this is misspelled). Again,
- before doing any loading, make sure you have configured Hadoop's
- conf/hdfs-site.xml setting the
- xceivers value to at least the following:
-
- <property>
- <name>dfs.datanode.max.xcievers</name>
- <value>4096</value>
- </property>
-
-
- Be sure to restart your HDFS after making the above
- configuration.
-
- Not having this configuration in place makes for strange looking
- failures. Eventually you'll see a complain in the datanode logs
- complaining about the xcievers exceeded, but on the run up to this one
- manifestation is complaint about missing blocks. For example:
- 10/12/08 20:10:31 INFO hdfs.DFSClient: Could not obtain block
- blk_XXXXXXXXXXXXXXXXXXXXXX_YYYYYYYY from any node:
- java.io.IOException: No live nodes contain current block. Will get new
- block locations from namenode and retry...
- See Hadoop HDFS: Deceived by Xciever for an informative rant on xceivering.
- See also
-
-
-
-
-
-
- HBase run modes: Standalone and Distributed
-
- HBase has two run modes: and . Out of the box, HBase runs in
- standalone mode. To set up a distributed deploy, you will need to
- configure HBase by editing files in the HBase conf
- directory.
-
- Whatever your mode, you will need to edit
- conf/hbase-env.sh to tell HBase which
- java to use. In this file you set HBase environment
- variables such as the heapsize and other options for the
- JVM, the preferred location for log files,
- etc. Set JAVA_HOME to point at the root of your
- java install.
-
-
- Standalone HBase
-
- This is the default mode. Standalone mode is what is described
- in the section. In
- standalone mode, HBase does not use HDFS -- it uses the local
- filesystem instead -- and it runs all HBase daemons and a local
- ZooKeeper all up in the same JVM. Zookeeper binds to a well known port
- so clients may talk to HBase.
-
-
-
- Distributed
-
- Distributed mode can be subdivided into distributed but all
- daemons run on a single node -- a.k.a
- pseudo-distributed-- and
- fully-distributed where the daemons are spread
- across all nodes in the cluster
- The pseudo-distributed vs fully-distributed nomenclature
- comes from Hadoop.
- .
-
- Distributed modes require an instance of the Hadoop
- Distributed File System (HDFS). See the Hadoop
- requirements and instructions for how to set up a HDFS. Before
- proceeding, ensure you have an appropriate, working HDFS.
-
- Below we describe the different distributed setups. Starting,
- verification and exploration of your install, whether a
- pseudo-distributed or
- fully-distributed configuration is described in a
- section that follows, . The same verification script applies to both
- deploy types.
-
-
- Pseudo-distributed
-
- A pseudo-distributed mode is simply a distributed mode run on
- a single host. Use this configuration testing and prototyping on
- HBase. Do not use this configuration for production nor for
- evaluating HBase performance.
-
- First, confirm your local HDFS setup. Below is an example conf/hdfs-site.xml. Note
- that the replication is set to 1 because this is a pseudo-distributed setup. The properties dfs.name.dir
- and dfs.data.dir are being set explicitly, the latter being where HDFS data will exist on your machine.
-
- Next, configure HBase for usage. Below is an example conf/hbase-site.xml. Note
- that the hbase.rootdir property points to the local HDFS instance. This is the file into
- which you add local customizations and overrides for
- and
-
-
- Now skip to for how to start and verify your
- pseudo-distributed install.
- See Pseudo-distributed
- mode extras for notes on how to start extra Masters and
- RegionServers when running pseudo-distributed.
-
-
-
- Let HBase create the hbase.rootdir
- directory. If you don't, you'll get warning saying HBase needs a
- migration run because the directory is missing files expected by
- HBase (it'll create them if you let it).
-
-
-
- Above we bind to localhost. This means
- that a remote client cannot connect. Amend accordingly, if you
- want to connect from a remote location.
-
-
-
-
- Pseudo-distributed Configuration Files
- The following are exmaple configuration files from a pseudo-distributed setup.
-
-hdfs-site.xml
-
-<configuration>
- ...
- <property>
- <name>dfs.name.dir</name>
- <value>/Users/local/user.name/hdfs-data-name</value>
- </property>
- <property>
- <name>dfs.data.dir</name>
- <value>/Users/local/user.name/hdfs-data</value>
- </property>
- <property>
- <name>dfs.replication</name>
- <value>1</value>
- </property>
- ...
-</configuration>
-
-
-hbase-site.xml
-
-<configuration>
- ...
- <property>
- <name>hbase.rootdir</name>
- <value>hdfs://localhost:8020/hbase</value>
- </property>
- <property>
- <name>hbase.zookeeper.quorum</name>
- <value>localhost</value>
- </property>
- <property>
- <name>hbase.cluster.distributed</name>
- <value>true</value>
- </property>
- ...
-</configuration>
-
-
-
-
-
- Pseudo-distributed Extras
-
-
- Startup
- To start up the initial HBase cluster...
- % bin/start-hbase.sh
-
- To start up an extra backup master(s) on the same server run...
- % bin/local-master-backup.sh start 1
- ... the '1' means use ports 60001 & 60011, and this backup master's logfile will be at logs/hbase-${USER}-1-master-${HOSTNAME}.log.
-
- To startup multiple backup masters run... % bin/local-master-backup.sh start 2 3 You can start up to 9 backup masters (10 total).
-
- To start up more regionservers...
- % bin/local-regionservers.sh start 1
- where '1' means use ports 60201 & 60301 and its logfile will be at logs/hbase-${USER}-1-regionserver-${HOSTNAME}.log.
-
- To add 4 more regionservers in addition to the one you just started by running... % bin/local-regionservers.sh start 2 3 4 5
- This supports up to 99 extra regionservers (100 total).
-
-
-
- Stop
- Assuming you want to stop master backup # 1, run...
- % cat /tmp/hbase-${USER}-1-master.pid |xargs kill -9
- Note that bin/local-master-backup.sh stop 1 will try to stop the cluster along with the master.
-
- To stop an individual regionserver, run...
- % bin/local-regionservers.sh stop 1
-
-
-
-
-
-
-
-
-
- Fully-distributed
-
- For running a fully-distributed operation on more than one
- host, make the following configurations. In
- hbase-site.xml, add the property
- hbase.cluster.distributed and set it to
- true and point the HBase
- hbase.rootdir at the appropriate HDFS NameNode
- and location in HDFS where you would like HBase to write data. For
- example, if you namenode were running at namenode.example.org on
- port 8020 and you wanted to home your HBase in HDFS at
- /hbase, make the following
- configuration.
-
-
-<configuration>
- ...
- <property>
- <name>hbase.rootdir</name>
- <value>hdfs://namenode.example.org:8020/hbase</value>
- <description>The directory shared by RegionServers.
- </description>
- </property>
- <property>
- <name>hbase.cluster.distributed</name>
- <value>true</value>
- <description>The mode the cluster will be in. Possible values are
- false: standalone and pseudo-distributed setups with managed Zookeeper
- true: fully-distributed with unmanaged Zookeeper Quorum (see hbase-env.sh)
- </description>
- </property>
- ...
-</configuration>
-
-
-
- regionservers
-
- In addition, a fully-distributed mode requires that you
- modify conf/regionservers. The
- file
- lists all hosts that you would have running
- HRegionServers, one host per line (This
- file in HBase is like the Hadoop slaves
- file). All servers listed in this file will be started and stopped
- when HBase cluster start or stop is run.
-
-
-
- ZooKeeper and HBase
- See section for ZooKeeper setup for HBase.
-
-
-
- HDFS Client Configuration
-
- Of note, if you have made HDFS client
- configuration on your Hadoop cluster -- i.e.
- configuration you want HDFS clients to use as opposed to
- server-side configurations -- HBase will not see this
- configuration unless you do one of the following:
-
-
-
- Add a pointer to your HADOOP_CONF_DIR
- to the HBASE_CLASSPATH environment variable
- in hbase-env.sh.
-
-
-
- Add a copy of hdfs-site.xml (or
- hadoop-site.xml) or, better, symlinks,
- under ${HBASE_HOME}/conf, or
-
-
-
- if only a small set of HDFS client configurations, add
- them to hbase-site.xml.
-
-
-
- An example of such an HDFS client configuration is
- dfs.replication. If for example, you want to
- run with a replication factor of 5, hbase will create files with
- the default of 3 unless you do the above to make the configuration
- available to HBase.
-
-
-
-
-
- Running and Confirming Your Installation
-
-
-
- Make sure HDFS is running first. Start and stop the Hadoop HDFS
- daemons by running bin/start-hdfs.sh over in the
- HADOOP_HOME directory. You can ensure it started
- properly by testing the put and
- get of files into the Hadoop filesystem. HBase does
- not normally use the mapreduce daemons. These do not need to be
- started.
-
-
-
- If you are managing your own ZooKeeper,
- start it and confirm its running else, HBase will start up ZooKeeper
- for you as part of its start process.
-
-
-
- Start HBase with the following command:
-
-
-
- bin/start-hbase.sh
-
- Run the above from the
-
- HBASE_HOME
-
- directory.
-
- You should now have a running HBase instance. HBase logs can be
- found in the logs subdirectory. Check them out
- especially if HBase had trouble starting.
-
-
-
- HBase also puts up a UI listing vital attributes. By default its
- deployed on the Master host at port 60010 (HBase RegionServers listen
- on port 60020 by default and put up an informational http server at
- 60030). If the Master were running on a host named
- master.example.org on the default port, to see the
- Master's homepage you'd point your browser at
- http://master.example.org:60010.
-
-
-
- Once HBase has started, see the for how to
- create tables, add data, scan your insertions, and finally disable and
- drop your tables.
-
-
-
- To stop HBase after exiting the HBase shell enter
- $ ./bin/stop-hbase.sh
-stopping hbase............... Shutdown can take a moment to
- complete. It can take longer if your cluster is comprised of many
- machines. If you are running a distributed operation, be sure to wait
- until HBase has shut down completely before stopping the Hadoop
- daemons.
-
-
-
-
-
-
- ZooKeeper
- ZooKeeper
-
-
- A distributed HBase depends on a running ZooKeeper cluster.
- All participating nodes and clients need to be able to access the
- running ZooKeeper ensemble. HBase by default manages a ZooKeeper
- "cluster" for you. It will start and stop the ZooKeeper ensemble
- as part of the HBase start/stop process. You can also manage the
- ZooKeeper ensemble independent of HBase and just point HBase at
- the cluster it should use. To toggle HBase management of
- ZooKeeper, use the HBASE_MANAGES_ZK variable in
- conf/hbase-env.sh. This variable, which
- defaults to true, tells HBase whether to
- start/stop the ZooKeeper ensemble servers as part of HBase
- start/stop.
-
- When HBase manages the ZooKeeper ensemble, you can specify
- ZooKeeper configuration using its native
- zoo.cfg file, or, the easier option is to
- just specify ZooKeeper options directly in
- conf/hbase-site.xml. A ZooKeeper
- configuration option can be set as a property in the HBase
- hbase-site.xml XML configuration file by
- prefacing the ZooKeeper option name with
- hbase.zookeeper.property. For example, the
- clientPort setting in ZooKeeper can be changed
- by setting the
- hbase.zookeeper.property.clientPort property.
- For all default values used by HBase, including ZooKeeper
- configuration, see . Look for the
- hbase.zookeeper.property prefix
- For the full list of ZooKeeper configurations, see
- ZooKeeper's zoo.cfg. HBase does not ship
- with a zoo.cfg so you will need to browse
- the conf directory in an appropriate
- ZooKeeper download.
-
-
- You must at least list the ensemble servers in
- hbase-site.xml using the
- hbase.zookeeper.quorum property. This property
- defaults to a single ensemble member at
- localhost which is not suitable for a fully
- distributed HBase. (It binds to the local machine only and remote
- clients will not be able to connect).
- How many ZooKeepers should I run?
-
- You can run a ZooKeeper ensemble that comprises 1 node
- only but in production it is recommended that you run a
- ZooKeeper ensemble of 3, 5 or 7 machines; the more members an
- ensemble has, the more tolerant the ensemble is of host
- failures. Also, run an odd number of machines. In ZooKeeper,
- an even number of peers is supported, but it is normally not used
- because an even sized ensemble requires, proportionally, more peers
- to form a quorum than an odd sized ensemble requires. For example, an
- ensemble with 4 peers requires 3 to form a quorum, while an ensemble with
- 5 also requires 3 to form a quorum. Thus, an ensemble of 5 allows 2 peers to
- fail, and thus is more fault tolerant than the ensemble of 4, which allows
- only 1 down peer.
-
- Give each ZooKeeper server around 1GB of RAM, and if possible, its own
- dedicated disk (A dedicated disk is the best thing you can do
- to ensure a performant ZooKeeper ensemble). For very heavily
- loaded clusters, run ZooKeeper servers on separate machines
- from RegionServers (DataNodes and TaskTrackers).
-
-
- For example, to have HBase manage a ZooKeeper quorum on
- nodes rs{1,2,3,4,5}.example.com, bound to
- port 2222 (the default is 2181) ensure
- HBASE_MANAGE_ZK is commented out or set to
- true in conf/hbase-env.sh
- and then edit conf/hbase-site.xml and set
- hbase.zookeeper.property.clientPort and
- hbase.zookeeper.quorum. You should also set
- hbase.zookeeper.property.dataDir to other than
- the default as the default has ZooKeeper persist data under
- /tmp which is often cleared on system
- restart. In the example below we have ZooKeeper persist to
- /user/local/zookeeper.
- <configuration>
- ...
- <property>
- <name>hbase.zookeeper.property.clientPort</name>
- <value>2222</value>
- <description>Property from ZooKeeper's config zoo.cfg.
- The port at which the clients will connect.
- </description>
- </property>
- <property>
- <name>hbase.zookeeper.quorum</name>
- <value>rs1.example.com,rs2.example.com,rs3.example.com,rs4.example.com,rs5.example.com</value>
- <description>Comma separated list of servers in the ZooKeeper Quorum.
- For example, "host1.mydomain.com,host2.mydomain.com,host3.mydomain.com".
- By default this is set to localhost for local and pseudo-distributed modes
- of operation. For a fully-distributed setup, this should be set to a full
- list of ZooKeeper quorum servers. If HBASE_MANAGES_ZK is set in hbase-env.sh
- this is the list of servers which we will start/stop ZooKeeper on.
- </description>
- </property>
- <property>
- <name>hbase.zookeeper.property.dataDir</name>
- <value>/usr/local/zookeeper</value>
- <description>Property from ZooKeeper's config zoo.cfg.
- The directory where the snapshot is stored.
- </description>
- </property>
- ...
- </configuration>
-
-
- Using existing ZooKeeper ensemble
-
- To point HBase at an existing ZooKeeper cluster, one that
- is not managed by HBase, set HBASE_MANAGES_ZK
- in conf/hbase-env.sh to false
-
- ...
- # Tell HBase whether it should manage it's own instance of Zookeeper or not.
- export HBASE_MANAGES_ZK=false Next set ensemble locations
- and client port, if non-standard, in
- hbase-site.xml, or add a suitably
- configured zoo.cfg to HBase's
- CLASSPATH. HBase will prefer the
- configuration found in zoo.cfg over any
- settings in hbase-site.xml.
-
- When HBase manages ZooKeeper, it will start/stop the
- ZooKeeper servers as a part of the regular start/stop scripts.
- If you would like to run ZooKeeper yourself, independent of
- HBase start/stop, you would do the following
-
-
-${HBASE_HOME}/bin/hbase-daemons.sh {start,stop} zookeeper
-
-
- Note that you can use HBase in this manner to spin up a
- ZooKeeper cluster, unrelated to HBase. Just make sure to set
- HBASE_MANAGES_ZK to false
- if you want it to stay up across HBase restarts so that when
- HBase shuts down, it doesn't take ZooKeeper down with it.
-
- For more information about running a distinct ZooKeeper
- cluster, see the ZooKeeper Getting
- Started Guide. Additionally, see the ZooKeeper Wiki or the
- ZooKeeper documentation
- for more information on ZooKeeper sizing.
-
-
-
-
-
- SASL Authentication with ZooKeeper
- Newer releases of HBase (>= 0.92) will
- support connecting to a ZooKeeper Quorum that supports
- SASL authentication (which is available in Zookeeper
- versions 3.4.0 or later).
-
- This describes how to set up HBase to mutually
- authenticate with a ZooKeeper Quorum. ZooKeeper/HBase
- mutual authentication (HBASE-2418)
- is required as part of a complete secure HBase configuration
- (HBASE-3025).
-
- For simplicity of explication, this section ignores
- additional configuration required (Secure HDFS and Coprocessor
- configuration). It's recommended to begin with an
- HBase-managed Zookeeper configuration (as opposed to a
- standalone Zookeeper quorum) for ease of learning.
-
-
- Operating System Prerequisites
-
-
- You need to have a working Kerberos KDC setup. For
- each $HOST that will run a ZooKeeper
- server, you should have a principle
- zookeeper/$HOST. For each such host,
- add a service key (using the kadmin or
- kadmin.local tool's ktadd
- command) for zookeeper/$HOST and copy
- this file to $HOST, and make it
- readable only to the user that will run zookeeper on
- $HOST. Note the location of this file,
- which we will use below as
- $PATH_TO_ZOOKEEPER_KEYTAB.
-
-
-
- Similarly, for each $HOST that will run
- an HBase server (master or regionserver), you should
- have a principle: hbase/$HOST. For each
- host, add a keytab file called
- hbase.keytab containing a service
- key for hbase/$HOST, copy this file to
- $HOST, and make it readable only to the
- user that will run an HBase service on
- $HOST. Note the location of this file,
- which we will use below as
- $PATH_TO_HBASE_KEYTAB.
-
-
-
- Each user who will be an HBase client should also be
- given a Kerberos principal. This principal should
- usually have a password assigned to it (as opposed to,
- as with the HBase servers, a keytab file) which only
- this user knows. The client's principal's
- maxrenewlife should be set so that it can
- be renewed enough so that the user can complete their
- HBase client processes. For example, if a user runs a
- long-running HBase client process that takes at most 3
- days, we might create this user's principal within
- kadmin with: addprinc -maxrenewlife
- 3days. The Zookeeper client and server
- libraries manage their own ticket refreshment by
- running threads that wake up periodically to do the
- refreshment.
-
-
- On each host that will run an HBase client
- (e.g. hbase shell), add the following
- file to the HBase home directory's conf
- directory:
-
-
- Client {
- com.sun.security.auth.module.Krb5LoginModule required
- useKeyTab=false
- useTicketCache=true;
- };
-
-
- We'll refer to this JAAS configuration file as
- $CLIENT_CONF below.
-
-
- HBase-managed Zookeeper Configuration
-
- On each node that will run a zookeeper, a
- master, or a regionserver, create a JAAS
- configuration file in the conf directory of the node's
- HBASE_HOME directory that looks like the
- following:
-
-
- Server {
- com.sun.security.auth.module.Krb5LoginModule required
- useKeyTab=true
- keyTab="$PATH_TO_ZOOKEEPER_KEYTAB"
- storeKey=true
- useTicketCache=false
- principal="zookeeper/$HOST";
- };
- Client {
- com.sun.security.auth.module.Krb5LoginModule required
- useKeyTab=true
- useTicketCache=false
- keyTab="$PATH_TO_HBASE_KEYTAB"
- principal="hbase/$HOST";
- };
-
-
- where the $PATH_TO_HBASE_KEYTAB and
- $PATH_TO_ZOOKEEPER_KEYTAB files are what
- you created above, and $HOST is the hostname for that
- node.
-
- The Server section will be used by
- the Zookeeper quorum server, while the
- Client section will be used by the HBase
- master and regionservers. The path to this file should
- be substituted for the text $HBASE_SERVER_CONF
- in the hbase-env.sh
- listing below.
-
-
- The path to this file should be substituted for the
- text $CLIENT_CONF in the
- hbase-env.sh listing below.
-
-
- Modify your hbase-env.sh to include the
- following:
-
-
- export HBASE_OPTS="-Djava.security.auth.login.config=$CLIENT_CONF"
- export HBASE_MANAGES_ZK=true
- export HBASE_ZOOKEEPER_OPTS="-Djava.security.auth.login.config=$HBASE_SERVER_CONF"
- export HBASE_MASTER_OPTS="-Djava.security.auth.login.config=$HBASE_SERVER_CONF"
- export HBASE_REGIONSERVER_OPTS="-Djava.security.auth.login.config=$HBASE_SERVER_CONF"
-
-
- where $HBASE_SERVER_CONF and
- $CLIENT_CONF are the full paths to the
- JAAS configuration files created above.
-
- Modify your hbase-site.xml on each node
- that will run zookeeper, master or regionserver to contain:
-
-
-
- hbase.zookeeper.quorum
- $ZK_NODES
-
-
- hbase.cluster.distributed
- true
-
-
- hbase.zookeeper.property.authProvider.1
- org.apache.zookeeper.server.auth.SASLAuthenticationProvider
-
-
- hbase.zookeeper.property.kerberos.removeHostFromPrincipal
- true
-
-
- hbase.zookeeper.property.kerberos.removeRealmFromPrincipal
- true
-
-
- ]]>
-
- where $ZK_NODES is the
- comma-separated list of hostnames of the Zookeeper
- Quorum hosts.
-
- Start your hbase cluster by running one or more
- of the following set of commands on the appropriate
- hosts:
-
-
-
- bin/hbase zookeeper start
- bin/hbase master start
- bin/hbase regionserver start
-
-
-
-
- External Zookeeper Configuration
- Add a JAAS configuration file that looks like:
-
-
- Client {
- com.sun.security.auth.module.Krb5LoginModule required
- useKeyTab=true
- useTicketCache=false
- keyTab="$PATH_TO_HBASE_KEYTAB"
- principal="hbase/$HOST";
- };
-
-
- where the $PATH_TO_HBASE_KEYTAB is the keytab
- created above for HBase services to run on this host, and $HOST is the
- hostname for that node. Put this in the HBase home's
- configuration directory. We'll refer to this file's
- full pathname as $HBASE_SERVER_CONF below.
-
- Modify your hbase-env.sh to include the following:
-
-
- export HBASE_OPTS="-Djava.security.auth.login.config=$CLIENT_CONF"
- export HBASE_MANAGES_ZK=false
- export HBASE_MASTER_OPTS="-Djava.security.auth.login.config=$HBASE_SERVER_CONF"
- export HBASE_REGIONSERVER_OPTS="-Djava.security.auth.login.config=$HBASE_SERVER_CONF"
-
-
-
- Modify your hbase-site.xml on each node
- that will run a master or regionserver to contain:
-
-
-
- hbase.zookeeper.quorum
- $ZK_NODES
-
-
- hbase.cluster.distributed
- true
-
-
- ]]>
-
-
- where $ZK_NODES is the
- comma-separated list of hostnames of the Zookeeper
- Quorum hosts.
-
-
- Add a zoo.cfg for each Zookeeper Quorum host containing:
-
- authProvider.1=org.apache.zookeeper.server.auth.SASLAuthenticationProvider
- kerberos.removeHostFromPrincipal=true
- kerberos.removeRealmFromPrincipal=true
-
-
- Also on each of these hosts, create a JAAS configuration file containing:
-
-
- Server {
- com.sun.security.auth.module.Krb5LoginModule required
- useKeyTab=true
- keyTab="$PATH_TO_ZOOKEEPER_KEYTAB"
- storeKey=true
- useTicketCache=false
- principal="zookeeper/$HOST";
- };
-
-
- where $HOST is the hostname of each
- Quorum host. We will refer to the full pathname of
- this file as $ZK_SERVER_CONF below.
-
-
-
-
- Start your Zookeepers on each Zookeeper Quorum host with:
-
-
- SERVER_JVMFLAGS="-Djava.security.auth.login.config=$ZK_SERVER_CONF" bin/zkServer start
-
-
-
-
-
- Start your HBase cluster by running one or more of the following set of commands on the appropriate nodes:
-
-
-
- bin/hbase master start
- bin/hbase regionserver start
-
-
-
-
-
-
- Zookeeper Server Authentication Log Output
- If the configuration above is successful,
- you should see something similar to the following in
- your Zookeeper server logs:
-
-11/12/05 22:43:39 INFO zookeeper.Login: successfully logged in.
-11/12/05 22:43:39 INFO server.NIOServerCnxnFactory: binding to port 0.0.0.0/0.0.0.0:2181
-11/12/05 22:43:39 INFO zookeeper.Login: TGT refresh thread started.
-11/12/05 22:43:39 INFO zookeeper.Login: TGT valid starting at: Mon Dec 05 22:43:39 UTC 2011
-11/12/05 22:43:39 INFO zookeeper.Login: TGT expires: Tue Dec 06 22:43:39 UTC 2011
-11/12/05 22:43:39 INFO zookeeper.Login: TGT refresh sleeping until: Tue Dec 06 18:36:42 UTC 2011
-..
-11/12/05 22:43:59 INFO auth.SaslServerCallbackHandler:
- Successfully authenticated client: authenticationID=hbase/ip-10-166-175-249.us-west-1.compute.internal@HADOOP.LOCALDOMAIN;
- authorizationID=hbase/ip-10-166-175-249.us-west-1.compute.internal@HADOOP.LOCALDOMAIN.
-11/12/05 22:43:59 INFO auth.SaslServerCallbackHandler: Setting authorizedID: hbase
-11/12/05 22:43:59 INFO server.ZooKeeperServer: adding SASL authorization for authorizationID: hbase
-
-
-
-
-
-
-
- Zookeeper Client Authentication Log Output
- On the Zookeeper client side (HBase master or regionserver),
- you should see something similar to the following:
-
-
-11/12/05 22:43:59 INFO zookeeper.ZooKeeper: Initiating client connection, connectString=ip-10-166-175-249.us-west-1.compute.internal:2181 sessionTimeout=180000 watcher=master:60000
-11/12/05 22:43:59 INFO zookeeper.ClientCnxn: Opening socket connection to server /10.166.175.249:2181
-11/12/05 22:43:59 INFO zookeeper.RecoverableZooKeeper: The identifier of this process is 14851@ip-10-166-175-249
-11/12/05 22:43:59 INFO zookeeper.Login: successfully logged in.
-11/12/05 22:43:59 INFO client.ZooKeeperSaslClient: Client will use GSSAPI as SASL mechanism.
-11/12/05 22:43:59 INFO zookeeper.Login: TGT refresh thread started.
-11/12/05 22:43:59 INFO zookeeper.ClientCnxn: Socket connection established to ip-10-166-175-249.us-west-1.compute.internal/10.166.175.249:2181, initiating session
-11/12/05 22:43:59 INFO zookeeper.Login: TGT valid starting at: Mon Dec 05 22:43:59 UTC 2011
-11/12/05 22:43:59 INFO zookeeper.Login: TGT expires: Tue Dec 06 22:43:59 UTC 2011
-11/12/05 22:43:59 INFO zookeeper.Login: TGT refresh sleeping until: Tue Dec 06 18:30:37 UTC 2011
-11/12/05 22:43:59 INFO zookeeper.ClientCnxn: Session establishment complete on server ip-10-166-175-249.us-west-1.compute.internal/10.166.175.249:2181, sessionid = 0x134106594320000, negotiated timeout = 180000
-
-
-
-
-
- Configuration from Scratch
-
- This has been tested on the current standard Amazon
- Linux AMI. First setup KDC and principals as
- described above. Next checkout code and run a sanity
- check.
-
-
- git clone git://git.apache.org/hbase.git
- cd hbase
- mvn -Psecurity,localTests clean test -Dtest=TestZooKeeperACL
-
-
- Then configure HBase as described above.
- Manually edit target/cached_classpath.txt (see below)..
-
-
- bin/hbase zookeeper &
- bin/hbase master &
- bin/hbase regionserver &
-
-
-
-
-
- Future improvements
-
- Fix target/cached_classpath.txt
-
- You must override the standard hadoop-core jar file from the
- target/cached_classpath.txt
- file with the version containing the HADOOP-7070 fix. You can use the following script to do this:
-
-
- echo `find ~/.m2 -name "*hadoop-core*7070*SNAPSHOT.jar"` ':' `cat target/cached_classpath.txt` | sed 's/ //g' > target/tmp.txt
- mv target/tmp.txt target/cached_classpath.txt
-
-
-
-
-
-
-
- Set JAAS configuration
- programmatically
-
-
- This would avoid the need for a separate Hadoop jar
- that fixes HADOOP-7070.
-
-
-
- Elimination of
- kerberos.removeHostFromPrincipal and
- kerberos.removeRealmFromPrincipal
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- Configuration Files
-
-
- hbase-site.xml and hbase-default.xml
- Just as in Hadoop where you add site-specific HDFS configuration
- to the hdfs-site.xml file,
- for HBase, site specific customizations go into
- the file conf/hbase-site.xml.
- For the list of configurable properties, see
-
- below or view the raw hbase-default.xml
- source file in the HBase source code at
- src/main/resources.
-
-
- Not all configuration options make it out to
- hbase-default.xml. Configuration
- that it is thought rare anyone would change can exist only
- in code; the only way to turn up such configurations is
- via a reading of the source code itself.
-
-
- Currently, changes here will require a cluster restart for HBase to notice the change.
-
-
-
-
-
-
- hbase-env.sh
- Set HBase environment variables in this file.
- Examples include options to pass the JVM on start of
- an HBase daemon such as heap size and garbarge collector configs.
- You can also set configurations for HBase configuration, log directories,
- niceness, ssh options, where to locate process pid files,
- etc. Open the file at
- conf/hbase-env.sh and peruse its content.
- Each option is fairly well documented. Add your own environment
- variables here if you want them read by HBase daemons on startup.
-
- Changes here will require a cluster restart for HBase to notice the change.
-
-
-
-
- log4j.properties
- Edit this file to change rate at which HBase files
- are rolled and to change the level at which HBase logs messages.
-
-
- Changes here will require a cluster restart for HBase to notice the change
- though log levels can be changed for particular daemons via the HBase UI.
-
-
-
- Client configuration and dependencies connecting to an HBase cluster
-
- Since the HBase Master may move around, clients bootstrap by looking to ZooKeeper for
- current critical locations. ZooKeeper is where all these values are kept. Thus clients
- require the location of the ZooKeeper ensemble information before they can do anything else.
- Usually this the ensemble location is kept out in the hbase-site.xml and
- is picked up by the client from the CLASSPATH.
-
- If you are configuring an IDE to run a HBase client, you should
- include the conf/ directory on your classpath so
- hbase-site.xml settings can be found (or
- add src/test/resources to pick up the hbase-site.xml
- used by tests).
-
-
- Minimally, a client of HBase needs the hbase, hadoop, log4j, commons-logging, commons-lang,
- and ZooKeeper jars in its CLASSPATH connecting to a cluster.
-
-
- An example basic hbase-site.xml for client only
- might look as follows:
-
-
-
-
- hbase.zookeeper.quorum
- example1,example2,example3
- The directory shared by region servers.
-
-
-
-]]>
-
-
-
- Java client configuration
- The configuration used by a Java client is kept
- in an HBaseConfiguration instance.
- The factory method on HBaseConfiguration, HBaseConfiguration.create();,
- on invocation, will read in the content of the first hbase-site.xml found on
- the client's CLASSPATH, if one is present
- (Invocation will also factor in any hbase-default.xml found;
- an hbase-default.xml ships inside the hbase.X.X.X.jar).
- It is also possible to specify configuration directly without having to read from a
- hbase-site.xml. For example, to set the ZooKeeper
- ensemble for the cluster programmatically do as follows:
- Configuration config = HBaseConfiguration.create();
-config.set("hbase.zookeeper.quorum", "localhost"); // Here we are running zookeeper locally
- If multiple ZooKeeper instances make up your ZooKeeper ensemble,
- they may be specified in a comma-separated list (just as in the hbase-site.xml file).
- This populated Configuration instance can then be passed to an
- HTable,
- and so on.
-
-
-
-
-
-
-
- Example Configurations
-
-
- Basic Distributed HBase Install
-
- Here is an example basic configuration for a distributed ten
- node cluster. The nodes are named example0,
- example1, etc., through node
- example9 in this example. The HBase Master and the
- HDFS namenode are running on the node example0.
- RegionServers run on nodes
- example1-example9. A 3-node
- ZooKeeper ensemble runs on example1,
- example2, and example3 on the
- default ports. ZooKeeper data is persisted to the directory
- /export/zookeeper. Below we show what the main
- configuration files -- hbase-site.xml,
- regionservers, and
- hbase-env.sh -- found in the HBase
- conf directory might look like.
-
-
- hbase-site.xml
-
-
-
-<?xml version="1.0"?>
-<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
-<configuration>
- <property>
- <name>hbase.zookeeper.quorum</name>
- <value>example1,example2,example3</value>
- <description>The directory shared by RegionServers.
- </description>
- </property>
- <property>
- <name>hbase.zookeeper.property.dataDir</name>
- <value>/export/zookeeper</value>
- <description>Property from ZooKeeper's config zoo.cfg.
- The directory where the snapshot is stored.
- </description>
- </property>
- <property>
- <name>hbase.rootdir</name>
- <value>hdfs://example0:8020/hbase</value>
- <description>The directory shared by RegionServers.
- </description>
- </property>
- <property>
- <name>hbase.cluster.distributed</name>
- <value>true</value>
- <description>The mode the cluster will be in. Possible values are
- false: standalone and pseudo-distributed setups with managed Zookeeper
- true: fully-distributed with unmanaged Zookeeper Quorum (see hbase-env.sh)
- </description>
- </property>
-</configuration>
-
-
-
-
-
- regionservers
-
- In this file you list the nodes that will run RegionServers.
- In our case we run RegionServers on all but the head node
- example1 which is carrying the HBase Master and
- the HDFS namenode
-
-
- example1
- example3
- example4
- example5
- example6
- example7
- example8
- example9
-
-
-
-
- hbase-env.sh
-
- Below we use a diff to show the differences
- from default in the hbase-env.sh file. Here we
- are setting the HBase heap to be 4G instead of the default
- 1G.
-
-
-
-$ git diff hbase-env.sh
-diff --git a/conf/hbase-env.sh b/conf/hbase-env.sh
-index e70ebc6..96f8c27 100644
---- a/conf/hbase-env.sh
-+++ b/conf/hbase-env.sh
-@@ -31,7 +31,7 @@ export JAVA_HOME=/usr/lib//jvm/java-6-sun/
- # export HBASE_CLASSPATH=
-
- # The maximum amount of heap to use, in MB. Default is 1000.
--# export HBASE_HEAPSIZE=1000
-+export HBASE_HEAPSIZE=4096
-
- # Extra Java runtime options.
- # Below are what we set by default. May only work with SUN JVM.
-
-
-
- Use rsync to copy the content of the
- conf directory to all nodes of the
- cluster.
-
-
-
-
-
-
- The Important Configurations
- Below we list what the important
- Configurations. We've divided this section into
- required configuration and worth-a-look recommended configs.
-
-
-
- Required Configurations
- Review the and sections.
-
-
-
- Recommended Configurations
- zookeeper.session.timeout
- The default timeout is three minutes (specified in milliseconds). This means
- that if a server crashes, it will be three minutes before the Master notices
- the crash and starts recovery. You might like to tune the timeout down to
- a minute or even less so the Master notices failures the sooner.
- Before changing this value, be sure you have your JVM garbage collection
- configuration under control otherwise, a long garbage collection that lasts
- beyond the ZooKeeper session timeout will take out
- your RegionServer (You might be fine with this -- you probably want recovery to start
- on the server if a RegionServer has been in GC for a long period of time).
-
- To change this configuration, edit hbase-site.xml,
- copy the changed file around the cluster and restart.
-
- We set this value high to save our having to field noob questions up on the mailing lists asking
- why a RegionServer went down during a massive import. The usual cause is that their JVM is untuned and
- they are running into long GC pauses. Our thinking is that
- while users are getting familiar with HBase, we'd save them having to know all of its
- intricacies. Later when they've built some confidence, then they can play
- with configuration such as this.
-
-
- Number of ZooKeeper Instances
- See .
-
-
- hbase.regionserver.handler.count
-
- This setting defines the number of threads that are kept open to answer
- incoming requests to user tables. The default of 10 is rather low in order to
- prevent users from killing their region servers when using large write buffers
- with a high number of concurrent clients. The rule of thumb is to keep this
- number low when the payload per request approaches the MB (big puts, scans using
- a large cache) and high when the payload is small (gets, small puts, ICVs, deletes).
-
-
- It is safe to set that number to the
- maximum number of incoming clients if their payload is small, the typical example
- being a cluster that serves a website since puts aren't typically buffered
- and most of the operations are gets.
-
-
- The reason why it is dangerous to keep this setting high is that the aggregate
- size of all the puts that are currently happening in a region server may impose
- too much pressure on its memory, or even trigger an OutOfMemoryError. A region server
- running on low memory will trigger its JVM's garbage collector to run more frequently
- up to a point where GC pauses become noticeable (the reason being that all the memory
- used to keep all the requests' payloads cannot be trashed, no matter how hard the
- garbage collector tries). After some time, the overall cluster
- throughput is affected since every request that hits that region server will take longer,
- which exacerbates the problem even more.
-
- You can get a sense of whether you have too little or too many handlers by
-
- on an individual RegionServer then tailing its logs (Queued requests
- consume memory).
-
-
-
- Configuration for large memory machines
-
- HBase ships with a reasonable, conservative configuration that will
- work on nearly all
- machine types that people might want to test with. If you have larger
- machines -- HBase has 8G and larger heap -- you might the following configuration options helpful.
- TODO.
-
-
-
-
-
- Compression
- You should consider enabling ColumnFamily compression. There are several options that are near-frictionless and in most all cases boost
- performance by reducing the size of StoreFiles and thus reducing I/O.
-
- See for more information.
-
-
- Bigger Regions
-
- Consider going to larger regions to cut down on the total number of regions
- on your cluster. Generally less Regions to manage makes for a smoother running
- cluster (You can always later manually split the big Regions should one prove
- hot and you want to spread the request load over the cluster). A lower number of regions is
- preferred, generally in the range of 20 to low-hundreds
- per RegionServer. Adjust the regionsize as appropriate to achieve this number.
-
- For the 0.90.x codebase, the upper-bound of regionsize is about 4Gb, with a default of 256Mb.
- For 0.92.x codebase, due to the HFile v2 change much larger regionsizes can be supported (e.g., 20Gb).
-
- You may need to experiment with this setting based on your hardware configuration and application needs.
-
- Adjust hbase.hregion.max.filesize in your hbase-site.xml.
- RegionSize can also be set on a per-table basis via
- HTableDescriptor.
-
-
-
-
- Managed Splitting
-
- Rather than let HBase auto-split your Regions, manage the splitting manually
- What follows is taken from the javadoc at the head of
- the org.apache.hadoop.hbase.util.RegionSplitter tool
- added to HBase post-0.90.0 release.
-
- .
- With growing amounts of data, splits will continually be needed. Since
- you always know exactly what regions you have, long-term debugging and
- profiling is much easier with manual splits. It is hard to trace the logs to
- understand region level problems if it keeps splitting and getting renamed.
- Data offlining bugs + unknown number of split regions == oh crap! If an
- HLog or StoreFile
- was mistakenly unprocessed by HBase due to a weird bug and
- you notice it a day or so later, you can be assured that the regions
- specified in these files are the same as the current regions and you have
- less headaches trying to restore/replay your data.
- You can finely tune your compaction algorithm. With roughly uniform data
- growth, it's easy to cause split / compaction storms as the regions all
- roughly hit the same data size at the same time. With manual splits, you can
- let staggered, time-based major compactions spread out your network IO load.
-
-
- How do I turn off automatic splitting? Automatic splitting is determined by the configuration value
- hbase.hregion.max.filesize. It is not recommended that you set this
- to Long.MAX_VALUE in case you forget about manual splits. A suggested setting
- is 100GB, which would result in > 1hr major compactions if reached.
-
- What's the optimal number of pre-split regions to create?
- Mileage will vary depending upon your application.
- You could start low with 10 pre-split regions / server and watch as data grows
- over time. It's better to err on the side of too little regions and rolling split later.
- A more complicated answer is that this depends upon the largest storefile
- in your region. With a growing data size, this will get larger over time. You
- want the largest region to be just big enough that the Store compact
- selection algorithm only compacts it due to a timed major. If you don't, your
- cluster can be prone to compaction storms as the algorithm decides to run
- major compactions on a large series of regions all at once. Note that
- compaction storms are due to the uniform data growth, not the manual split
- decision.
-
- If you pre-split your regions too thin, you can increase the major compaction
-interval by configuring HConstants.MAJOR_COMPACTION_PERIOD. If your data size
-grows too large, use the (post-0.90.0 HBase) org.apache.hadoop.hbase.util.RegionSplitter
-script to perform a network IO safe rolling split
-of all regions.
-
-
- Managed Compactions
- A common administrative technique is to manage major compactions manually, rather than letting
- HBase do it. By default, HConstants.MAJOR_COMPACTION_PERIOD is one day and major compactions
- may kick in when you least desire it - especially on a busy system. To turn off automatic major compactions set
- the value to 0.
-
- It is important to stress that major compactions are absolutely necessary for StoreFile cleanup, the only variant is when
- they occur. They can be administered through the HBase shell, or via
- HBaseAdmin.
-
- For more information about compactions and the compaction file selection process, see
-
-
- Speculative Execution
- Speculative Execution of MapReduce tasks is on by default, and for HBase clusters it is generally advised to turn off
- Speculative Execution at a system-level unless you need it for a specific case, where it can be configured per-job.
- Set the properties mapred.map.tasks.speculative.execution and
- mapred.reduce.tasks.speculative.execution to false.
-
-
-
-
- Other Configurations
- Balancer
- The balancer is periodic operation run on the master to redistribute regions on the cluster. It is configured via
- hbase.balancer.period and defaults to 300000 (5 minutes).
- See for more information on the LoadBalancer.
-
-
- Disabling Blockcache
- Do not turn off block cache (You'd do it by setting hbase.block.cache.size to zero).
- Currently we do not do well if you do this because the regionserver will spend all its time loading hfile
- indices over and over again. If your working set it such that block cache does you no good, at least
- size the block cache such that hfile indices will stay up in the cache (you can get a rough idea
- on the size you need by surveying regionserver UIs; you'll see index block size accounted near the
- top of the webpage).
-
-
-
-
-
-
-
- Bloom Filter Configuration
-
- io.hfile.bloom.enabled global kill
- switch
-
- io.hfile.bloom.enabled in
- Configuration serves as the kill switch in case
- something goes wrong. Default = true.
-
-
-
- io.hfile.bloom.error.rate
-
- io.hfile.bloom.error.rate = average false
- positive rate. Default = 1%. Decrease rate by ½ (e.g. to .5%) == +1
- bit per bloom entry.
-
-
-
- io.hfile.bloom.max.fold
-
- io.hfile.bloom.max.fold = guaranteed minimum
- fold rate. Most people should leave this alone. Default = 7, or can
- collapse to at least 1/128th of original size. See the
- Development Process section of the document BloomFilters
- in HBase for more on what this option means.
-
-
-
diff --git hbase-site/src/docbkx/customization.xsl hbase-site/src/docbkx/customization.xsl
deleted file mode 100644
index d80a2b5..0000000
--- hbase-site/src/docbkx/customization.xsl
+++ /dev/null
@@ -1,34 +0,0 @@
-
-
-
-
-
-
-
-
-
-
-
-
diff --git hbase-site/src/docbkx/developer.xml hbase-site/src/docbkx/developer.xml
deleted file mode 100644
index 6d6e7d1..0000000
--- hbase-site/src/docbkx/developer.xml
+++ /dev/null
@@ -1,821 +0,0 @@
-
-
-
- Building and Developing HBase
- This chapter will be of interest only to those building and developing HBase (i.e., as opposed to
- just downloading the latest distribution).
-
-
- HBase Repositories
- There are two different repositories for HBase: Subversion (SVN) and Git. The former is the system of record for committers, but the latter is easier to work with to build and contribute. SVN updates get automatically propagated to the Git repo.
-
- SVN
-
-svn co http://svn.apache.org/repos/asf/hbase/trunk hbase-core-trunk
-
-
-
- Git
-
-git clone git://git.apache.org/hbase.git
-
-
-
-
-
- IDEs
-
- Eclipse
-
- Code Formatting
- See HBASE-3678 Add Eclipse-based Apache Formatter to HBase Wiki
- for an Eclipse formatter to help ensure your code conforms to HBase'y coding convention.
- The issue includes instructions for loading the attached formatter.
- In addition to the automatic formatting, make sure you follow the style guidelines explained in
- Also, no @author tags - that's a rule. Quality Javadoc comments are appreciated. And include the Apache license.
-
-
- Subversive Plugin
- Download and install the Subversive plugin.
- Set up an SVN Repository target from , then check out the code.
-
-
- Git Plugin
- If you cloned the project via git, download and install the Git plugin (EGit). Attach to your local git repo (via the Git Repositories window) and you'll be able to see file revision history, generate patches, etc.
-
-
- HBase Project Setup in Eclipse
- The easiest way is to use the m2eclipse plugin for Eclipse. Eclipse Indigo or newer has m2eclipse built-in, or it can be found here:http://www.eclipse.org/m2e/. M2Eclipse provides Maven integration for Eclipse - it even lets you use the direct Maven commands from within Eclipse to compile and test your project.
- To import the project, you merely need to go to File->Import...Maven->Existing Maven Projects and then point Eclipse at the HBase root directory; m2eclipse will automatically find all the hbase modules for you.
- If you install m2eclipse and import HBase in your workspace, you will have to fix your eclipse Build Path.
- Remove target folder, add target/generated-jamon
- and target/generated-sources/java folders. You may also remove from your Build Path
- the exclusions on the src/main/resources and src/test/resources
- to avoid error message in the console 'Failed to execute goal org.apache.maven.plugins:maven-antrun-plugin:1.6:run (default) on project hbase:
- 'An Ant BuildException has occured: Replace: source file .../target/classes/hbase-default.xml doesn't exist'. This will also
- reduce the eclipse build cycles and make your life easier when developing.
-
-
- Import into eclipse with the command line
- For those not inclined to use m2eclipse, you can generate the Eclipse files from the command line. First, run (you should only have to do this once):
- mvn clean install -DskipTests
- and then close Eclipse and execute...
- mvn eclipse:eclipse
- ... from your local HBase project directory in your workspace to generate some new .project
- and .classpathfiles. Then reopen Eclipse, and import the .project file in the HBase directory to a workspace.
-
-
-
- Maven Classpath Variable
- The M2_REPO classpath variable needs to be set up for the project. This needs to be set to
- your local Maven repository, which is usually ~/.m2/repository
- If this classpath variable is not configured, you will see compile errors in Eclipse like this...
-
-Description Resource Path Location Type
-The project cannot be built until build path errors are resolved hbase Unknown Java Problem
-Unbound classpath variable: 'M2_REPO/asm/asm/3.1/asm-3.1.jar' in project 'hbase' hbase Build path Build Path Problem
-Unbound classpath variable: 'M2_REPO/com/github/stephenc/high-scale-lib/high-scale-lib/1.1.1/high-scale-lib-1.1.1.jar' in project 'hbase' hbase Build path Build Path Problem
-Unbound classpath variable: 'M2_REPO/com/google/guava/guava/r09/guava-r09.jar' in project 'hbase' hbase Build path Build Path Problem
-Unbound classpath variable: 'M2_REPO/com/google/protobuf/protobuf-java/2.3.0/protobuf-java-2.3.0.jar' in project 'hbase' hbase Build path Build Path Problem Unbound classpath variable:
-
-
-
- Eclipse Known Issues
- Eclipse will currently complain about Bytes.java. It is not possible to turn these errors off.
-
-Description Resource Path Location Type
-Access restriction: The method arrayBaseOffset(Class) from the type Unsafe is not accessible due to restriction on required library /System/Library/Java/JavaVirtualMachines/1.6.0.jdk/Contents/Classes/classes.jar Bytes.java /hbase/src/main/java/org/apache/hadoop/hbase/util line 1061 Java Problem
-Access restriction: The method arrayIndexScale(Class) from the type Unsafe is not accessible due to restriction on required library /System/Library/Java/JavaVirtualMachines/1.6.0.jdk/Contents/Classes/classes.jar Bytes.java /hbase/src/main/java/org/apache/hadoop/hbase/util line 1064 Java Problem
-Access restriction: The method getLong(Object, long) from the type Unsafe is not accessible due to restriction on required library /System/Library/Java/JavaVirtualMachines/1.6.0.jdk/Contents/Classes/classes.jar Bytes.java /hbase/src/main/java/org/apache/hadoop/hbase/util line 1111 Java Problem
-
-
-
- Eclipse - More Information
- For additional information on setting up Eclipse for HBase development on Windows, see
- Michael Morello's blog on the topic.
-
-
-
-
-
-
- Building HBase
-
- Basic Compile
- Thanks to maven, building HBase is easy. You can read about the various maven commands in , but the simplest command to compile HBase from its java source code is:
-
-mvn compile
-
- Or, to clean up before compiling:
-
-mvn clean compile
-
- With Eclipse set up as explained above in , you can also simply use the build command in Eclipse. To create the full installable HBase package takes a little bit more work, so read on.
-
-
-
- Building in snappy compression support
- Pass -Dsnappy to trigger the snappy maven profile for building
- snappy native libs into hbase.
-
-
-
- Building the HBase tarball
- Do the following to build the HBase tarball.
- Passing the -Drelease will generate javadoc and run the RAT plugin to verify licenses on source.
- % MAVEN_OPTS="-Xmx2g" mvn clean site install assembly:single -DskipTests -Prelease
-
-
-
-
- Adding an HBase release to Apache's Maven Repository
- Follow the instructions at
- Publishing Maven Artifacts.
- The 'trick' to making it all work is answering the questions put to you by the mvn release plugin properly,
- making sure it is using the actual branch AND before doing the mvn release:perform step,
- VERY IMPORTANT, check and if necessary hand edit the release.properties file that was put under ${HBASE_HOME}
- by the previous step, release:perform. You need to edit it to make it point at
- right locations in SVN.
-
- Use maven 3.0.x.
-
- At the mvn release:perform step, before starting, if you are for example
- releasing hbase 0.92.0, you need to make sure the pom.xml version is 0.92.0-SNAPSHOT. This needs
- to be checked in. Since we do the maven release after actual release, I've been doing this
- checkin into a particular tag rather than into the actual release tag. So, say we released
- hbase 0.92.0 and now we want to do the release to the maven repository, in svn, the 0.92.0
- release will be tagged 0.92.0. Making the maven release, copy the 0.92.0 tag to 0.92.0mvn.
- Check out this tag and change the version therein and commit.
-
- Here is how I'd answer the questions at release:prepare time:
- What is the release version for "HBase"? (org.apache.hbase:hbase) 0.92.0: :
-What is SCM release tag or label for "HBase"? (org.apache.hbase:hbase) hbase-0.92.0: : 0.92.0mvnrelease
-What is the new development version for "HBase"? (org.apache.hbase:hbase) 0.92.1-SNAPSHOT: :
-[INFO] Transforming 'HBase'...
-
- A strange issue I ran into was the one where the upload into the apache
- repository was being sprayed across multiple apache machines making it so I could
- not release. See INFRA-4482 Why is my upload to mvn spread across multiple repositories?.
-
- Here is my ~/.m2/settings.xml.
- <settings xmlns="http://maven.apache.org/SETTINGS/1.0.0"
- xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
- xsi:schemaLocation="http://maven.apache.org/SETTINGS/1.0.0
- http://maven.apache.org/xsd/settings-1.0.0.xsd">
- <servers>
- <!- To publish a snapshot of some part of Maven -->
- <server>
- <id>apache.snapshots.https</id>
- <username>YOUR_APACHE_ID
- </username>
- <password>YOUR_APACHE_PASSWORD
- </password>
- </server>
- <!-- To publish a website using Maven -->
- <!-- To stage a release of some part of Maven -->
- <server>
- <id>apache.releases.https</id>
- <username>YOUR_APACHE_ID
- </username>
- <password>YOUR_APACHE_PASSWORD
- </password>
- </server>
- </servers>
- <profiles>
- <profile>
- <id>apache-release</id>
- <properties>
- <gpg.keyname>YOUR_KEYNAME</gpg.keyname>
- <!--Keyname is something like this ... 00A5F21E... do gpg --list-keys to find it-->
- <gpg.passphrase>YOUR_KEY_PASSWORD
- </gpg.passphrase>
- </properties>
- </profile>
- </profiles>
-</settings>
-
-
- When you run release:perform, pass -Papache-release
- else it will not 'sign' the artifacts it uploads.
-
- If you see run into the below, its because you need to edit version in the pom.xml and add
- -SNAPSHOT to the version (and commit).
- [INFO] Scanning for projects...
-[INFO] Searching repository for plugin with prefix: 'release'.
-[INFO] ------------------------------------------------------------------------
-[INFO] Building HBase
-[INFO] task-segment: [release:prepare] (aggregator-style)
-[INFO] ------------------------------------------------------------------------
-[INFO] [release:prepare {execution: default-cli}]
-[INFO] ------------------------------------------------------------------------
-[ERROR] BUILD FAILURE
-[INFO] ------------------------------------------------------------------------
-[INFO] You don't have a SNAPSHOT project in the reactor projects list.
-[INFO] ------------------------------------------------------------------------
-[INFO] For more information, run Maven with the -e switch
-[INFO] ------------------------------------------------------------------------
-[INFO] Total time: 3 seconds
-[INFO] Finished at: Sat Mar 26 18:11:07 PDT 2011
-[INFO] Final Memory: 35M/423M
-[INFO] -----------------------------------------------------------------------
-
-
-
- Build Gotchas
- If you see Unable to find resource 'VM_global_library.vm', ignore it.
- Its not an error. It is officially ugly though.
-
-
-
-
- Updating hbase.apache.org
-
- Contributing to hbase.apache.org
- The HBase apache web site (including this reference guide) is maintained as part of the main HBase source tree, under /src/docbkx and /src/site. The former is this reference guide; the latter, in most cases, are legacy pages that are in the process of being merged into the docbkx tree.
- To contribute to the reference guide, edit these files and submit them as a patch (see ). Your Jira should contain a summary of the changes in each section (see HBASE-6081 for an example).
- To generate the site locally while you're working on it, run:
- mvn site
- Then you can load up the generated HTML files in your browser (file are under /target/site).
-
-
- Publishing hbase.apache.org
- If you're a committer with rights to publish the site artifacts: set up your apache credentials and the target site location locally in a place and
- form that maven can pick it up, in ~/.m2/settings.xml. See for an example.
- Next, run the following:
- $ mvn -DskipTests -Papache-release site site:deploy
- You will be asked for your password. It can take a little time.
- Remember that it can take a few hours for your site changes to show up.
-
-
-
-
- Tests
-
- Developers, at a minimum, should familiarize themselves with the unit test detail; unit tests in
-HBase have a character not usually seen in other projects.
-
-
-HBase Modules
-As of 0.96, HBase is split into multiple modules which creates "interesting" rules for
-how and where tests are written. If you are writting code for hbase-server, see
- for how to write your tests; these tests can spin
-up a minicluster and will need to be categorized. For any other module, for example
-hbase-common, the tests must be strict unit tests and just test the class
-under test - no use of the HBaseTestingUtility or minicluster is allowed (or even possible
-given the dependency tree).
-
- Running Tests in other Modules
- If the module you are developing in has no other dependencies on other HBase modules, then
- you can cd into that module and just run:
- mvn test
- which will just run the tests IN THAT MODULE. If there are other dependencies on other modules,
- then you will have run the command from the ROOT HBASE DIRECTORY. This will run the tests in the other
- modules, unless you specify to skip the tests in that module. For instance, to skip the tests in the hbase-server module,
- you would run:
- mvn clean test -Dskip-server-tests
- from the top level directory to run all the tests in modules other than hbase-server. Note that you
- can specify to skip tests in multiple modules as well as just for a single module. For example, to skip
- the tests in hbase-server and hbase-common, you would run:
- mvn clean test -Dskip-server-tests -Dskip-common-tests
- Also, keep in mind that if you are running tests in the hbase-server module you will need to
- apply the maven profiles discussed in to get the tests to run properly.
-
-
-
-
-Unit Tests
-HBase unit tests are subdivided into three categories: small, medium and large, with
-corresponding JUnit categories:
-SmallTests, MediumTests,
-LargeTests. JUnit categories are denoted using java annotations
-and look like this in your unit test code.
-...
-@Category(SmallTests.class)
-public class TestHRegionInfo {
-
- @Test
- public void testCreateHRegionInfoName() throws Exception {
- // ...
- }
-...
- @org.junit.Rule
- public org.apache.hadoop.hbase.ResourceCheckerJUnitRule cu =
- new org.apache.hadoop.hbase.ResourceCheckerJUnitRule();
-}
-The above example shows how to mark a test as belonging to the small category. The @org.junit.Rule
-lines on the end are also necessary. Add them to each new unit test file. They are needed by the categorization process.
-HBase uses a patched maven surefire plugin and maven profiles to implement its unit test characterizations.
-
-
-
-SmallTests
-
-Small tests are executed in a shared JVM. We put in this category all the tests that can
-be executed quickly in a shared JVM. The maximum execution time for a small test is 15 seconds,
-and small tests should not use a (mini)cluster.
-
-
-
-MediumTests
-Medium tests represent tests that must be executed
-before proposing a patch. They are designed to run in less than 30 minutes altogether,
-and are quite stable in their results. They are designed to last less than 50 seconds
-individually. They can use a cluster, and each of them is executed in a separate JVM.
-
-
-
-
-LargeTests
-Large tests are everything else. They are typically integration-like
-tests, regression tests for specific bugs, timeout tests, performance tests.
-They are executed before a commit on the pre-integration machines. They can be run on
-the developer machine as well.
-
-
-
-
-Running tests
-Below we describe how to run the HBase junit categories.
-
-
-Default: small and medium category tests
-
-Running mvn test will execute all small tests in a single JVM
-(no fork) and then medium tests in a separate JVM for each test instance.
-Medium tests are NOT executed if there is an error in a small test.
-Large tests are NOT executed. There is one report for small tests, and one report for
-medium tests if they are executed. To run small and medium tests with the security
-profile enabled, do mvn test -P security
-
-
-
-
-Running all tests
-Running mvn test -P runAllTests
-will execute small tests in a single JVM then medium and large tests in a separate JVM for each test.
-Medium and large tests are NOT executed if there is an error in a small test.
-Large tests are NOT executed if there is an error in a small or medium test.
-There is one report for small tests, and one report for medium and large tests if they are executed
-
-
-
-
-Running a single test or all tests in a package
-To run an individual test, e.g. MyTest, do
-mvn test -P localTests -Dtest=MyTest You can also
-pass multiple, individual tests as a comma-delimited list:
-mvn test -P localTests -Dtest=MyTest1,MyTest2,MyTest3
-You can also pass a package, which will run all tests under the package:
-mvn test -P localTests -Dtest=org.apache.hadoop.hbase.client.*
-To run a single test with the security profile enabled:
-mvn test -P security,localTests -Dtest=TestGet
-
-
-
-The -P localTests will remove the JUnit category effect (without this specific profile,
-the categories are taken into account). It will actually use the official release of surefire
-and the old connector (The HBase build uses a patched version of the maven surefire plugin).
-Each junit tests is executed in a separate JVM (A fork per test class). There is no
-parallelization when localTests profile is set. You will see a new message at the end of the
-report: "[INFO] Tests are skipped". It's harmless.
-
-
-
-
-Other test invocation permutations
-Running mvn test -P runSmallTests will execute small tests only, in a single JVM.
-
-Running mvn test -P runMediumTests will execute medium tests in a single JVM.
-
-Running mvn test -P runLargeTests execute medium tests in a single JVM.
-
-
-
-
-hbasetests.sh
-It's also possible to use the script hbasetests.sh. This script runs the medium and
-large tests in parallel with two maven instances, and provides a single report. This script does not use
-the hbase version of surefire so no parallelization is being done other than the two maven instances the
-script sets up.
-It must be executed from the directory which contains the pom.xml.
-For example running
-./dev-support/hbasetests.sh will execute small and medium tests.
-Running ./dev-support/hbasetests.sh runAllTests will execute all tests.
-Running ./dev-support/hbasetests.sh replayFailed will rerun the failed tests a
-second time, in a separate jvm and without parallelisation.
-
-
-
-
-
-Writing Tests
-
-General rules
-
-
-As much as possible, tests should be written as category small tests.
-
-
-All tests must be written to support parallel execution on the same machine, hence they should not use shared resources as fixed ports or fixed file names.
-
-
-Tests should not overlog. More than 100 lines/second makes the logs complex to read and use i/o that are hence not available for the other tests.
-
-
-Tests can be written with HBaseTestingUtility.
-This class offers helper functions to create a temp directory and do the cleanup, or to start a cluster.
-Categories and execution time
-
-
-All tests must be categorized, if not they could be skipped.
-
-
-All tests should be written to be as fast as possible.
-
-
-Small category tests should last less than 15 seconds, and must not have any side effect.
-
-
-Medium category tests should last less than 50 seconds.
-
-
-Large category tests should last less than 3 minutes. This should ensure a good parallelization for people using it, and ease the analysis when the test fails.
-
-
-
-
-Sleeps in tests
-Whenever possible, tests should not use Thread.sleep, but rather waiting for the real event they need. This is faster and clearer for the reader.
-Tests should not do a Thread.sleep without testing an ending condition. This allows understanding what the test is waiting for. Moreover, the test will work whatever the machine performance is.
-Sleep should be minimal to be as fast as possible. Waiting for a variable should be done in a 40ms sleep loop. Waiting for a socket operation should be done in a 200 ms sleep loop.
-
-
-
-
-Tests using a cluster
-
-
-Tests using a HRegion do not have to start a cluster: A region can use the local file system.
-Start/stopping a cluster cost around 10 seconds. They should not be started per test method but per test class.
-Started cluster must be shutdown using HBaseTestingUtility#shutdownMiniCluster, which cleans the directories.
-As most as possible, tests should use the default settings for the cluster. When they don't, they should document it. This will allow to share the cluster later.
-
-
-
-
-
-
-
- Maven Build Commands
- All commands executed from the local HBase project directory.
-
- Note: use Maven 3 (Maven 2 may work but we suggest you use Maven 3).
-
-
- Compile
-
-mvn compile
-
-
-
-
- Running all or individual Unit Tests
- See the section
- above in
-
-
-
- Building against various hadoop versions.
- As of 0.96, HBase supports building against hadoop versions: 1.0.3, 2.0.0-alpha and 3.0.0-SNAPSHOT.
- By default, we will build with Hadoop-1.0.3. To change the version to run with Hadoop-2.0.0-alpha, you would run:
- mvn -Dhadoop.profile=2.0 ...
-
- That is, designate build with hadoop.profile 2.0. Pass 2.0 for hadoop.profile to build against hadoop 2.0.
- Tests may not all pass as of this writing so you may need to pass -DskipTests unless you are inclined
- to fix the failing tests.
-
- Similarly, for 3.0, you would just replace the profile value. Note that Hadoop-3.0.0-SNAPSHOT does not currently have a deployed maven artificat - you will need to build and install your own in your local maven repository if you want to run against this profile.
-
-
- In earilier verions of HBase, you can build against older versions of hadoop, notably, Hadoop 0.22.x and 0.23.x.
- If you are running, for example HBase-0.94 and wanted to build against Hadoop 0.23.x, you would run with:
- mvn -Dhadoop.profile=22 ...
-
-
-
-
- Getting Involved
- HBase gets better only when people contribute!
-
- As HBase is an Apache Software Foundation project, see for more information about how the ASF functions.
-
-
- Mailing Lists
- Sign up for the dev-list and the user-list. See the
- mailing lists page.
- Posing questions - and helping to answer other people's questions - is encouraged!
- There are varying levels of experience on both lists so patience and politeness are encouraged (and please
- stay on topic.)
-
-
-
- Jira
- Check for existing issues in Jira.
- If it's either a new feature request, enhancement, or a bug, file a ticket.
-
- Jira Priorities
- The following is a guideline on setting Jira issue priorities:
-
- Blocker: Should only be used if the issue WILL cause data loss or cluster instability reliably.
- Critical: The issue described can cause data loss or cluster instability in some cases.
- Major: Important but not tragic issues, like updates to the client API that will add a lot of much-needed functionality or significant
- bugs that need to be fixed but that don't cause data loss.
- Minor: Useful enhancements and annoying but not damaging bugs.
- Trivial: Useful enhancements but generally cosmetic.
-
-
-
-
- Code Blocks in Jira Comments
- A commonly used macro in Jira is {code}. If you do this in a Jira comment...
-
-{code}
- code snippet
-{code}
-
- ... Jira will format the code snippet like code, instead of a regular comment. It improves readability.
-
-
-
-
-
-
- Developing
- Codelines
- Most development is done on TRUNK. However, there are branches for minor releases (e.g., 0.90.1, 0.90.2, and 0.90.3 are on the 0.90 branch).
- If you have any questions on this just send an email to the dev dist-list.
-
-
-
- Unit Tests
- In HBase we use JUnit 4.
- If you need to run miniclusters of HDFS, ZooKeeper, HBase, or MapReduce testing,
- be sure to checkout the HBaseTestingUtility.
- Alex Baranau of Sematext describes how it can be used in
- HBase Case-Study: Using HBaseTestingUtility for Local Testing and Development (2010).
-
-
- Mockito
- Sometimes you don't need a full running server
- unit testing. For example, some methods can make do with a
- a org.apache.hadoop.hbase.Server instance
- or a org.apache.hadoop.hbase.master.MasterServices
- Interface reference rather than a full-blown
- org.apache.hadoop.hbase.master.HMaster.
- In these cases, you maybe able to get away with a mocked
- Server instance. For example:
-
- TODO...
-
-
-
-
-
-
- Code Standards
- See and .
-
- Also, please pay attention to the interface stability/audience classifications that you
- will see all over our code base. They look like this at the head of the class:
- @InterfaceAudience.Public
-@InterfaceStability.Stable
-
- If the InterfaceAudience is Private,
- we can change the class (and we do not need to include a InterfaceStability mark).
- If a class is marked Public but its InterfaceStability
- is marked Unstable, we can change it. If it's
- marked Public/Evolving, we're allowed to change it
- but should try not to. If it's Public and Stable
- we can't change it without a deprecation path or with a really GREAT reason.
- When you add new classes, mark them with the annotations above if publically accessible.
- If you are not cleared on how to mark your additions, ask up on the dev list.
-
- This convention comes from our parent project Hadoop.
-
-
-
- Running In-Situ
- If you are developing HBase, frequently it is useful to test your changes against a more-real cluster than what you find in unit tests. In this case, HBase can be run directly from the source in local-mode.
- All you need to do is run:
-
- ${HBASE_HOME}/bin/start-hbase.sh
-
- This will spin up a full local-cluster, just as if you had packaged up HBase and installed it on your machine.
-
- Keep in mind that you will need to have installed HBase into your local maven repository for the in-situ cluster to work properly. That is, you will need to run:
- mvn clean install -DskipTests
- to ensure that maven can find the correct classpath and dependencies. Generally, the above command
- is just a good thing to try running first, if maven is acting oddly.
-
-
-
-
-
- Submitting Patches
-
- Create Patch
- Patch files can be easily generated from Eclipse, for example by selecting "Team -> Create Patch".
- Patches can also be created by git diff and svn diff.
-
- Please submit one patch-file per Jira. For example, if multiple files are changed make sure the
- selected resource when generating the patch is a directory. Patch files can reflect changes in multiple files.
- Make sure you review for code style.
-
-
- Patch File Naming
- The patch file should have the HBase Jira ticket in the name. For example, if a patch was submitted for Foo.java, then
- a patch file called Foo_HBASE_XXXX.patch would be acceptable where XXXX is the HBase Jira number.
-
- If you generating from a branch, then including the target branch in the filename is advised, e.g., HBASE-XXXX-0.90.patch.
-
-
-
- Unit Tests
- Yes, please. Please try to include unit tests with every code patch (and especially new classes and large changes).
- Make sure unit tests pass locally before submitting the patch.
- Also, see .
- If you are creating a new unit test class, notice how other unit test classes have classification/sizing
- annotations at the top and a static method on the end. Be sure to include these in any new unit test files
- you generate. See for more on how the annotations work.
-
-
-
- Attach Patch to Jira
- The patch should be attached to the associated Jira ticket "More Actions -> Attach Files". Make sure you click the
- ASF license inclusion, otherwise the patch can't be considered for inclusion.
-
- Once attached to the ticket, click "Submit Patch" and
- the status of the ticket will change. Committers will review submitted patches for inclusion into the codebase. Please
- understand that not every patch may get committed, and that feedback will likely be provided on the patch. Fear not, though,
- because the HBase community is helpful!
-
-
-
-
- Common Patch Feedback
- The following items are representative of common patch feedback. Your patch process will go faster if these are
- taken into account before submission.
-
-
- See the Java coding standards
- for more information on coding conventions in Java.
-
-
- Space Invaders
- Rather than do this...
-
-if ( foo.equals( bar ) ) { // don't do this
-
- ... do this instead...
-
-if (foo.equals(bar)) {
-
-
- Also, rather than do this...
-
-foo = barArray[ i ]; // don't do this
-
- ... do this instead...
-
-foo = barArray[i];
-
-
-
-
- Auto Generated Code
- Auto-generated code in Eclipse often looks like this...
-
- public void readFields(DataInput arg0) throws IOException { // don't do this
- foo = arg0.readUTF(); // don't do this
-
- ... do this instead ...
-
- public void readFields(DataInput di) throws IOException {
- foo = di.readUTF();
-
- See the difference? 'arg0' is what Eclipse uses for arguments by default.
-
-
-
- Long Lines
-
- Keep lines less than 100 characters.
-
-Bar bar = foo.veryLongMethodWithManyArguments(argument1, argument2, argument3, argument4, argument5, argument6, argument7, argument8, argument9); // don't do this
-
- ... do something like this instead ...
-
-Bar bar = foo.veryLongMethodWithManyArguments(
- argument1, argument2, argument3,argument4, argument5, argument6, argument7, argument8, argument9);
-
-
-
-
- Trailing Spaces
-
- This happens more than people would imagine.
-
-Bar bar = foo.getBar(); <--- imagine there's an extra space(s) after the semicolon instead of a line break.
-
- Make sure there's a line-break after the end of your code, and also avoid lines that have nothing
- but whitespace.
-
-
-
- Implementing Writable
- Every class returned by RegionServers must implement Writable. If you
- are creating a new class that needs to implement this interface, don't forget the default constructor.
-
-
-
- Javadoc
- This is also a very common feedback item. Don't forget Javadoc!
-
-
-
- Javadoc - Useless Defaults
- Don't just leave the @param arguments the way your IDE generated them. Don't do this...
-
- /**
- *
- * @param bar <---- don't do this!!!!
- * @return <---- or this!!!!
- */
- public Foo getFoo(Bar bar);
-
- ... either add something descriptive to the @param and @return lines, or just remove them.
- But the preference is to add something descriptive and useful.
-
-
-
- One Thing At A Time, Folks
- If you submit a patch for one thing, don't do auto-reformatting or unrelated reformatting of code on a completely
- different area of code.
-
- Likewise, don't add unrelated cleanup or refactorings outside the scope of your Jira.
-
-
-
- Ambigious Unit Tests
- Make sure that you're clear about what you are testing in your unit tests and why.
-
-
-
-
-
-
- ReviewBoard
- Larger patches should go through ReviewBoard.
-
- For more information on how to use ReviewBoard, see
- the ReviewBoard documentation.
-
-
-
- Committing Patches
-
- Committers do this. See How To Commit in the HBase wiki.
-
- Commiters will also resolve the Jira, typically after the patch passes a build.
-
-
-
-
-
-
-
diff --git hbase-site/src/docbkx/external_apis.xml hbase-site/src/docbkx/external_apis.xml
deleted file mode 100644
index da03f4c..0000000
--- hbase-site/src/docbkx/external_apis.xml
+++ /dev/null
@@ -1,417 +0,0 @@
-
-
-
- External APIs
- This chapter will cover access to HBase either through non-Java languages, or through custom protocols.
-
-
- Non-Java Languages Talking to the JVM
- Currently the documentation on this topic in the
- HBase Wiki.
- See also the Thrift API Javadoc.
-
-
-
-
- REST
- Currently most of the documentation on REST exists in the
- HBase Wiki on REST.
-
-
-
-
- Thrift
- Currently most of the documentation on Thrift exists in the
- HBase Wiki on Thrift.
-
- Filter Language
- Use Case
- Note: this feature was introduced in HBase 0.92
- This allows the user to perform server-side filtering when accessing HBase over Thrift. The user specifies a filter via a string. The string is parsed on the server to construct the filter
-
-
- General Filter String Syntax
- A simple filter expression is expressed as: “FilterName (argument, argument, ... , argument)”
- You must specify the name of the filter followed by the argument list in parenthesis. Commas separate the individual arguments
- If the argument represents a string, it should be enclosed in single quotes.
- If it represents a boolean, an integer or a comparison operator like <,
- >, != etc. it should not be enclosed in quotes
- The filter name must be one word. All ASCII characters are allowed except for whitespace, single quotes and parenthesis.
- The filter’s arguments can contain any ASCII character. If single quotes are present in the argument, they must be escaped by a
- preceding single quote
-
-
- Compound Filters and Operators
- Currently, two binary operators – AND/OR and two unary operators – WHILE/SKIP are supported.
- Note: the operators are all in uppercase
- AND – as the name suggests, if this
- operator is used, the key-value must pass both the filters
- OR – as the name suggests, if this operator
- is used, the key-value must pass at least one of the filters
- SKIP – For a particular row, if any of the
- key-values don’t pass the filter condition, the entire row is skipped
- WHILE - For a particular row, it continues
- to emit key-values until a key-value is reached that fails the filter condition
- Compound Filters: Using these operators, a
- hierarchy of filters can be created. For example: “(Filter1 AND Filter2) OR (Filter3 AND Filter4)”
-
-
- Order of Evaluation
- Parenthesis have the highest precedence. The SKIP and WHILE operators are next and have the same precedence.The AND operator has the next highest precedence followed by the OR operator.
- For example:
- A filter string of the form:“Filter1 AND Filter2 OR Filter3”
- will be evaluated as:“(Filter1 AND Filter2) OR Filter3”
- A filter string of the form:“Filter1 AND SKIP Filter2 OR Filter3”
- will be evaluated as:“(Filter1 AND (SKIP Filter2)) OR Filter3”
-
-
- Compare Operator
- A compare operator can be any of the following:
-
-
- LESS (<)
-
-
- LESS_OR_EQUAL (<=)
-
-
- EQUAL (=)
-
-
- NOT_EQUAL (!=)
-
-
- GREATER_OR_EQUAL (>=)
-
-
- GREATER (>)
-
-
- NO_OP (no operation)
-
-
- The client should use the symbols (<, <=, =, !=, >, >=) to express
- compare operators.
-
-
- Comparator
- A comparator can be any of the following:
-
-
- BinaryComparator - This
- lexicographically compares against the specified byte array using
- Bytes.compareTo(byte[], byte[])
-
-
- BinaryPrefixComparator - This
- lexicographically compares against a specified byte array. It only compares up to
- the length of this byte array.
-
-
- RegexStringComparator - This compares
- against the specified byte array using the given regular expression. Only EQUAL
- and NOT_EQUAL comparisons are valid with this comparator
-
-
- SubStringComparator - This tests if
- the given substring appears in a specified byte array. The comparison is case
- insensitive. Only EQUAL and NOT_EQUAL comparisons are valid with this
- comparator
-
-
- The general syntax of a comparator is: ComparatorType:ComparatorValue
- The ComparatorType for the various comparators is as follows:
-
-
- BinaryComparator - binary
-
-
- BinaryPrefixComparator - binaryprefix
-
-
- RegexStringComparator - regexstring
-
-
- SubStringComparator - substring
-
-
- The ComparatorValue can be any value.
- Example1: >, 'binary:abc' will match everything that is lexicographically greater than "abc"
- Example2: =, 'binaryprefix:abc' will match everything whose first 3 characters are lexicographically equal to "abc"
- Example3: !=, 'regexstring:ab*yz' will match everything that doesn't begin with "ab" and ends with "yz"
- Example4: =, 'substring:abc123' will match everything that begins with the substring "abc123"
-
-
- Example PHP Client Program that uses the Filter Language
-
-<? $_SERVER['PHP_ROOT'] = realpath(dirname(__FILE__).'/..');
- require_once $_SERVER['PHP_ROOT'].'/flib/__flib.php';
- flib_init(FLIB_CONTEXT_SCRIPT);
- require_module('storage/hbase');
- $hbase = new HBase('<server_name_running_thrift_server>', <port on which thrift server is running>);
- $hbase->open();
- $client = $hbase->getClient();
- $result = $client->scannerOpenWithFilterString('table_name', "(PrefixFilter ('row2') AND (QualifierFilter (>=, 'binary:xyz'))) AND (TimestampsFilter ( 123, 456))");
- $to_print = $client->scannerGetList($result,1);
- while ($to_print) {
- print_r($to_print);
- $to_print = $client->scannerGetList($result,1);
- }
- $client->scannerClose($result);
-?>
-
-
-
- Example Filter Strings
-
-
-
- “PrefixFilter (‘Row’) AND PageFilter (1) AND FirstKeyOnlyFilter ()” will return all key-value pairs that match the following conditions:
- 1) The row containing the key-value should have prefix “Row”
- 2) The key-value must be located in the first row of the table
- 3) The key-value pair must be the first key-value in the row
-
-
-
-
-
-
-
-
- “(RowFilter (=, ‘binary:Row 1’) AND TimeStampsFilter (74689, 89734)) OR
- ColumnRangeFilter (‘abc’, true, ‘xyz’, false))” will return all key-value pairs that match both the following conditions:
- 1) The key-value is in a row having row key “Row 1”
- 2) The key-value must have a timestamp of either 74689 or 89734.
- Or it must match the following condition:
- 1) The key-value pair must be in a column that is lexicographically >= abc and < xyz
-
-
-
-
-
-
-
-
- “SKIP ValueFilter (0)” will skip the entire row if any of the values in the row is not 0
-
-
-
-
-
- Individual Filter Syntax
-
-
- KeyOnlyFilter
- Description: This filter doesn’t take any
- arguments. It returns only the key component of each key-value.
- Syntax: KeyOnlyFilter ()
- Example: "KeyOnlyFilter ()"
-
-
-
- FirstKeyOnlyFilter
- Description: This filter doesn’t take any
- arguments. It returns only the first key-value from each row.
- Syntax: FirstKeyOnlyFilter ()
- Example: "FirstKeyOnlyFilter ()"
-
-
-
- PrefixFilter
- Description: This filter takes one argument – a prefix of a
- row key. It returns only those key-values present in a row that starts with the
- specified row prefix
- Syntax: PrefixFilter (‘<row_prefix>’)
- Example: "PrefixFilter (‘Row’)"
-
-
-
-
- ColumnPrefixFilter
- Description: This filter takes one argument
- – a column prefix. It returns only those key-values present in a column that starts
- with the specified column prefix. The column prefix must be of the form: “qualifier”
- Syntax:ColumnPrefixFilter(‘<column_prefix>’)
- Example: "ColumnPrefixFilter(‘Col’)"
-
-
-
- MultipleColumnPrefixFilter
- Description: This filter takes a list of
- column prefixes. It returns key-values that are present in a column that starts with
- any of the specified column prefixes. Each of the column prefixes must be of the form: “qualifier”
- Syntax:MultipleColumnPrefixFilter(‘<column_prefix>’, ‘<column_prefix>’, …, ‘<column_prefix>’)
- Example: "MultipleColumnPrefixFilter(‘Col1’, ‘Col2’)"
-
-
-
- ColumnCountGetFilter
- Description: This filter takes one argument
- – a limit. It returns the first limit number of columns in the table
- Syntax: ColumnCountGetFilter (‘<limit>’)
- Example: "ColumnCountGetFilter (4)"
-
-
-
- PageFilter
- Description: This filter takes one argument
- – a page size. It returns page size number of rows from the table.
- Syntax: PageFilter (‘<page_size>’)
- Example: "PageFilter (2)"
-
-
-
- ColumnPaginationFilter
- Description: This filter takes two
- arguments – a limit and offset. It returns limit number of columns after offset number
- of columns. It does this for all the rows
- Syntax: ColumnPaginationFilter(‘<limit>’, ‘<offest>’)
- Example: "ColumnPaginationFilter (3, 5)"
-
-
-
- InclusiveStopFilter
- Description: This filter takes one argument
- – a row key on which to stop scanning. It returns all key-values present in rows up to
- and including the specified row
- Syntax: InclusiveStopFilter(‘<stop_row_key>’)
- Example: "InclusiveStopFilter ('Row2')"
-
-
-
- TimeStampsFilter
- Description: This filter takes a list of
- timestamps. It returns those key-values whose timestamps matches any of the specified
- timestamps
- Syntax: TimeStampsFilter (<timestamp>, <timestamp>, ... ,<timestamp>)
- Example: "TimeStampsFilter (5985489, 48895495, 58489845945)"
-
-
-
- RowFilter
- Description: This filter takes a compare
- operator and a comparator. It compares each row key with the comparator using the
- compare operator and if the comparison returns true, it returns all the key-values in
- that row
- Syntax: RowFilter (<compareOp>, ‘<row_comparator>’)
- Example: "RowFilter (<=, ‘xyz)"
-
-
-
- Family Filter
- Description: This filter takes a compare
- operator and a comparator. It compares each qualifier name with the comparator using
- the compare operator and if the comparison returns true, it returns all the key-values
- in that column
- Syntax: QualifierFilter (<compareOp>, ‘<qualifier_comparator>’)
- Example: "QualifierFilter (=, ‘Column1’)"
-
-
-
- QualifierFilter
- Description: This filter takes a compare
- operator and a comparator. It compares each qualifier name with the comparator using
- the compare operator and if the comparison returns true, it returns all the key-values
- in that column
- Syntax: QualifierFilter (<compareOp>,‘<qualifier_comparator>’)
- Example: "QualifierFilter (=,‘Column1’)"
-
-
-
- ValueFilter
- Description: This filter takes a compare operator and a
- comparator. It compares each value with the comparator using the compare operator and
- if the comparison returns true, it returns that key-value
- Syntax: ValueFilter (<compareOp>,‘<value_comparator>’)
- Example: "ValueFilter (!=, ‘Value’)"
-
-
-
- DependentColumnFilter
- Description: This filter takes two arguments – a family
- and a qualifier. It tries to locate this column in each row and returns all key-values
- in that row that have the same timestamp. If the row doesn’t contain the specified
- column – none of the key-values in that row will be returned.
- The filter can also take an optional boolean argument – dropDependentColumn. If set to true, the column we were depending on doesn’t get returned.
- The filter can also take two more additional optional arguments – a compare operator and a value comparator, which are further checks in addition to the family and qualifier. If the dependent column is found, its value should also pass the value check and then only is its timestamp taken into consideration
- Syntax: DependentColumnFilter (‘<family>’, ‘<qualifier>’, <boolean>, <compare operator>, ‘<value comparator’)
- Syntax: DependentColumnFilter (‘<family>’, ‘<qualifier>’, <boolean>)
- Syntax: DependentColumnFilter (‘<family>’, ‘<qualifier>’)
- Example: "DependentColumnFilter (‘conf’, ‘blacklist’, false, >=, ‘zebra’)"
- Example: "DependentColumnFilter (‘conf’, 'blacklist', true)"
- Example: "DependentColumnFilter (‘conf’, 'blacklist')"
-
-
-
- SingleColumnValueFilter
- Description: This filter takes a column family, a
- qualifier, a compare operator and a comparator. If the specified column is not found –
- all the columns of that row will be emitted. If the column is found and the comparison
- with the comparator returns true, all the columns of the row will be emitted. If the
- condition fails, the row will not be emitted.
- This filter also takes two additional optional boolean arguments – filterIfColumnMissing and setLatestVersionOnly
- If the filterIfColumnMissing flag is set to true the columns of the row will not be emitted if the specified column to check is not found in the row. The default value is false.
- If the setLatestVersionOnly flag is set to false, it will test previous versions (timestamps) too. The default value is true.
- These flags are optional and if you must set neither or both
- Syntax: SingleColumnValueFilter(<compare operator>, ‘<comparator>’, ‘<family>’, ‘<qualifier>’,<filterIfColumnMissing_boolean>, <latest_version_boolean>)
- Syntax: SingleColumnValueFilter(<compare operator>, ‘<comparator>’, ‘<family>’, ‘<qualifier>)
- Example: "SingleColumnValueFilter (<=, ‘abc’,‘FamilyA’, ‘Column1’, true, false)"
- Example: "SingleColumnValueFilter (<=, ‘abc’,‘FamilyA’, ‘Column1’)"
-
-
-
- SingleColumnValueExcludeFilter
- Description: This filter takes the same arguments and
- behaves same as SingleColumnValueFilter – however, if the column is found and the
- condition passes, all the columns of the row will be emitted except for the tested
- column value.
- Syntax: SingleColumnValueExcludeFilter(<compare operator>, '<comparator>', '<family>', '<qualifier>',<latest_version_boolean>, <filterIfColumnMissing_boolean>)
- Syntax: SingleColumnValueExcludeFilter(<compare operator>, '<comparator>', '<family>', '<qualifier>')
- Example: "SingleColumnValueExcludeFilter (‘<=’, ‘abc’,‘FamilyA’, ‘Column1’, ‘false’, ‘true’)"
- Example: "SingleColumnValueExcludeFilter (‘<=’, ‘abc’, ‘FamilyA’, ‘Column1’)"
-
-
-
- ColumnRangeFilter
- Description: This filter is used for selecting only those
- keys with columns that are between minColumn and maxColumn. It also takes two boolean
- variables to indicate whether to include the minColumn and maxColumn or not.
- If you don’t want to set the minColumn or the maxColumn – you can pass in an empty argument.
- Syntax: ColumnRangeFilter (‘<minColumn>’, <minColumnInclusive_bool>, ‘<maxColumn>’, <maxColumnInclusive_bool>)
- Example: "ColumnRangeFilter (‘abc’, true, ‘xyz’, false)"
-
-
-
-
-
-
-
-
-
-
-
diff --git hbase-site/src/docbkx/getting_started.xml hbase-site/src/docbkx/getting_started.xml
deleted file mode 100644
index 3aa392b..0000000
--- hbase-site/src/docbkx/getting_started.xml
+++ /dev/null
@@ -1,207 +0,0 @@
-
-
-
- Getting Started
-
-
- Introduction
-
- will get you up and
- running on a single-node instance of HBase using the local filesystem.
- describes setup
- of HBase in distributed mode running on top of HDFS.
-
-
-
- Quick Start
-
- This guide describes setup of a standalone HBase instance that uses
- the local filesystem. It leads you through creating a table, inserting
- rows via the HBase shell, and then cleaning
- up and shutting down your standalone HBase instance. The below exercise
- should take no more than ten minutes (not including download time).
-
-
- Download and unpack the latest stable release.
-
- Choose a download site from this list of Apache Download
- Mirrors. Click on suggested top link. This will take you to a
- mirror of HBase Releases. Click on the folder named
- stable and then download the file that ends in
- .tar.gz to your local filesystem; e.g.
- hbase-.tar.gz.
-
- Decompress and untar your download and then change into the
- unpacked directory.
-
- $ tar xfz hbase-.tar.gz
-$ cd hbase-
-
-
- At this point, you are ready to start HBase. But before starting
- it, you might want to edit conf/hbase-site.xml and
- set the directory you want HBase to write to,
- hbase.rootdir.
-
-<?xml version="1.0"?>
-<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
-<configuration>
- <property>
- <name>hbase.rootdir</name>
- <value>file:///DIRECTORY/hbase</value>
- </property>
-</configuration>
-
- Replace DIRECTORY in the above with a
- path to a directory where you want HBase to store its data. By default,
- hbase.rootdir is set to
- /tmp/hbase-${user.name} which means you'll lose all
- your data whenever your server reboots (Most operating systems clear
- /tmp on restart).
-
-
-
- Start HBase
-
- Now start HBase:$ ./bin/start-hbase.sh
-starting Master, logging to logs/hbase-user-master-example.org.out
-
- You should now have a running standalone HBase instance. In
- standalone mode, HBase runs all daemons in the the one JVM; i.e. both
- the HBase and ZooKeeper daemons. HBase logs can be found in the
- logs subdirectory. Check them out especially if
- HBase had trouble starting.
-
-
- Is java installed?
-
- All of the above presumes a 1.6 version of Oracle
- java is installed on your machine and
- available on your path; i.e. when you type
- java, you see output that describes the
- options the java program takes (HBase requires java 6). If this is not
- the case, HBase will not start. Install java, edit
- conf/hbase-env.sh, uncommenting the
- JAVA_HOME line pointing it to your java install. Then,
- retry the steps above.
-
-
-
-
- Shell Exercises
-
- Connect to your running HBase via the shell.
-
- $ ./bin/hbase shell
-HBase Shell; enter 'help<RETURN>' for list of supported commands.
-Type "exit<RETURN>" to leave the HBase Shell
-Version: 0.90.0, r1001068, Fri Sep 24 13:55:42 PDT 2010
-
-hbase(main):001:0>
-
- Type help and then
- <RETURN> to see a listing of shell commands and
- options. Browse at least the paragraphs at the end of the help emission
- for the gist of how variables and command arguments are entered into the
- HBase shell; in particular note how table names, rows, and columns,
- etc., must be quoted.
-
- Create a table named test with a single column family named cf.
- Verify its creation by listing all tables and then insert some
- values.
-
- hbase(main):003:0> create 'test', 'cf'
-0 row(s) in 1.2200 seconds
-hbase(main):003:0> list 'test'
-..
-1 row(s) in 0.0550 seconds
-hbase(main):004:0> put 'test', 'row1', 'cf:a', 'value1'
-0 row(s) in 0.0560 seconds
-hbase(main):005:0> put 'test', 'row2', 'cf:b', 'value2'
-0 row(s) in 0.0370 seconds
-hbase(main):006:0> put 'test', 'row3', 'cf:c', 'value3'
-0 row(s) in 0.0450 seconds
-
- Above we inserted 3 values, one at a time. The first insert is at
- row1, column cf:a with a value of
- value1. Columns in HBase are comprised of a column family prefix --
- cf in this example -- followed by a colon and then a
- column qualifier suffix (a in this case).
-
- Verify the data insert.
-
- Run a scan of the table by doing the following
-
- hbase(main):007:0> scan 'test'
-ROW COLUMN+CELL
-row1 column=cf:a, timestamp=1288380727188, value=value1
-row2 column=cf:b, timestamp=1288380738440, value=value2
-row3 column=cf:c, timestamp=1288380747365, value=value3
-3 row(s) in 0.0590 seconds
-
- Get a single row as follows
-
- hbase(main):008:0> get 'test', 'row1'
-COLUMN CELL
-cf:a timestamp=1288380727188, value=value1
-1 row(s) in 0.0400 seconds
-
- Now, disable and drop your table. This will clean up all done
- above.
-
- hbase(main):012:0> disable 'test'
-0 row(s) in 1.0930 seconds
-hbase(main):013:0> drop 'test'
-0 row(s) in 0.0770 seconds
-
- Exit the shell by typing exit.
-
- hbase(main):014:0> exit
-
-
-
- Stopping HBase
-
- Stop your hbase instance by running the stop script.
-
- $ ./bin/stop-hbase.sh
-stopping hbase...............
-
-
-
- Where to go next
-
- The above described standalone setup is good for testing and
- experiments only. Next move on to where we'll go into
- depth on the different HBase run modes, requirements and critical
- configurations needed setting up a distributed HBase deploy.
-
-
-
-
diff --git hbase-site/src/docbkx/ops_mgt.xml hbase-site/src/docbkx/ops_mgt.xml
deleted file mode 100644
index 369febb..0000000
--- hbase-site/src/docbkx/ops_mgt.xml
+++ /dev/null
@@ -1,681 +0,0 @@
-
-
-
- HBase Operational Management
- This chapter will cover operational tools and practices required of a running HBase cluster.
- The subject of operations is related to the topics of , ,
- and but is a distinct topic in itself.
-
-
- HBase Tools and Utilities
-
- Here we list HBase tools for administration, analysis, fixup, and
- debugging.
- Driver
- There is a Driver class that is executed by the HBase jar can be used to invoke frequently accessed utilities. For example,
-HADOOP_CLASSPATH=`${HBASE_HOME}/bin/hbase classpath` ${HADOOP_HOME}/bin/hadoop jar ${HBASE_HOME}/hbase-VERSION.jar
-
-... will return...
-
-An example program must be given as the first argument.
-Valid program names are:
- completebulkload: Complete a bulk data load.
- copytable: Export a table from local cluster to peer cluster
- export: Write table data to HDFS.
- import: Import data written by Export.
- importtsv: Import data in TSV format.
- rowcounter: Count rows in HBase table
- verifyrep: Compare the data from tables in two different clusters. WARNING: It doesn't work for incrementColumnValues'd cells since the timestamp is chan
-
-... for allowable program names.
-
-
-
- HBase hbck
- An fsck for your HBase install
- To run hbck against your HBase cluster run
- $ ./bin/hbase hbck
- At the end of the commands output it prints OK
- or INCONSISTENCY. If your cluster reports
- inconsistencies, pass -details to see more detail emitted.
- If inconsistencies, run hbck a few times because the
- inconsistency may be transient (e.g. cluster is starting up or a region is
- splitting).
- Passing -fix may correct the inconsistency (This latter
- is an experimental feature).
-
- For more information, see .
-
-
- HFile Tool
- See .
-
-
- WAL Tools
-
-
- HLog tool
-
- The main method on HLog offers manual
- split and dump facilities. Pass it WALs or the product of a split, the
- content of the recovered.edits. directory.
-
- You can get a textual dump of a WAL file content by doing the
- following:$ ./bin/hbase org.apache.hadoop.hbase.regionserver.wal.HLog --dump hdfs://example.org:8020/hbase/.logs/example.org,60020,1283516293161/10.10.21.10%3A60020.1283973724012The
- return code will be non-zero if issues with the file so you can test
- wholesomeness of file by redirecting STDOUT to
- /dev/null and testing the program return.
-
- Similarly you can force a split of a log file directory by
- doing: $ ./bin/hbase org.apache.hadoop.hbase.regionserver.wal.HLog --split hdfs://example.org:8020/hbase/.logs/example.org,60020,1283516293161/
-
-
- HLogPrettyPrinter
- HLogPrettyPrinter is a tool with configurable options to print the contents of an HLog.
-
-
-
-
-
- Compression Tool
- See .
-
-
- CopyTable
-
- CopyTable is a utility that can copy part or of all of a table, either to the same cluster or another cluster. The usage is as follows:
-$ bin/hbase org.apache.hadoop.hbase.mapreduce.CopyTable [--starttime=X] [--endtime=Y] [--new.name=NEW] [--peer.adr=ADR] tablename
-
-
-
- Options:
-
- starttime Beginning of the time range. Without endtime means starttime to forever.
- endtime End of the time range. Without endtime means starttime to forever.
- versions Number of cell versions to copy.
- new.name New table's name.
- peer.adr Address of the peer cluster given in the format hbase.zookeeper.quorum:hbase.zookeeper.client.port:zookeeper.znode.parent
- families Comma-separated list of ColumnFamilies to copy.
- all.cells Also copy delete markers and uncollected deleted cells (advanced option).
-
- Args:
-
- tablename Name of table to copy.
-
-
- Example of copying 'TestTable' to a cluster that uses replication for a 1 hour window:
-$ bin/hbase org.apache.hadoop.hbase.mapreduce.CopyTable
---starttime=1265875194289 --endtime=1265878794289
---peer.adr=server1,server2,server3:2181:/hbase TestTable
-
- Note: caching for the input Scan is configured via hbase.client.scanner.caching in the job configuration.
-
-
-
- Export
- Export is a utility that will dump the contents of table to HDFS in a sequence file. Invoke via:
-$ bin/hbase org.apache.hadoop.hbase.mapreduce.Export <tablename> <outputdir> [<versions> [<starttime> [<endtime>]]]
-
-
- Note: caching for the input Scan is configured via hbase.client.scanner.caching in the job configuration.
-
-
-
- Import
- Import is a utility that will load data that has been exported back into HBase. Invoke via:
-$ bin/hbase org.apache.hadoop.hbase.mapreduce.Import <tablename> <inputdir>
-
-
-
-
- ImportTsv
- ImportTsv is a utility that will load data in TSV format into HBase. It has two distinct usages: loading data from TSV format in HDFS
- into HBase via Puts, and preparing StoreFiles to be loaded via the completebulkload.
-
- To load data via Puts (i.e., non-bulk loading):
-$ bin/hbase org.apache.hadoop.hbase.mapreduce.ImportTsv -Dimporttsv.columns=a,b,c <tablename> <hdfs-inputdir>
-
-
- To generate StoreFiles for bulk-loading:
-$ bin/hbase org.apache.hadoop.hbase.mapreduce.ImportTsv -Dimporttsv.columns=a,b,c -Dimporttsv.bulk.output=hdfs://storefile-outputdir <tablename> <hdfs-data-inputdir>
-
-
- These generated StoreFiles can be loaded into HBase via .
-
- ImportTsv Options
- Running ImportTsv with no arguments prints brief usage information:
-
-Usage: importtsv -Dimporttsv.columns=a,b,c <tablename> <inputdir>
-
-Imports the given input directory of TSV data into the specified table.
-
-The column names of the TSV data must be specified using the -Dimporttsv.columns
-option. This option takes the form of comma-separated column names, where each
-column name is either a simple column family, or a columnfamily:qualifier. The special
-column name HBASE_ROW_KEY is used to designate that this column should be used
-as the row key for each imported record. You must specify exactly one column
-to be the row key, and you must specify a column name for every column that exists in the
-input data.
-
-By default importtsv will load data directly into HBase. To instead generate
-HFiles of data to prepare for a bulk data load, pass the option:
- -Dimporttsv.bulk.output=/path/for/output
- Note: if you do not use this option, then the target table must already exist in HBase
-
-Other options that may be specified with -D include:
- -Dimporttsv.skip.bad.lines=false - fail if encountering an invalid line
- '-Dimporttsv.separator=|' - eg separate on pipes instead of tabs
- -Dimporttsv.timestamp=currentTimeAsLong - use the specified timestamp for the import
- -Dimporttsv.mapper.class=my.Mapper - A user-defined Mapper to use instead of org.apache.hadoop.hbase.mapreduce.TsvImporterMapper
-
-
- ImportTsv Example
- For example, assume that we are loading data into a table called 'datatsv' with a ColumnFamily called 'd' with two columns "c1" and "c2".
-
- Assume that an input file exists as follows:
-
-row1 c1 c2
-row2 c1 c2
-row3 c1 c2
-row4 c1 c2
-row5 c1 c2
-row6 c1 c2
-row7 c1 c2
-row8 c1 c2
-row9 c1 c2
-row10 c1 c2
-
-
- For ImportTsv to use this imput file, the command line needs to look like this:
-
- HADOOP_CLASSPATH=`${HBASE_HOME}/bin/hbase classpath` ${HADOOP_HOME}/bin/hadoop jar ${HBASE_HOME}/hbase-VERSION.jar importtsv -Dimporttsv.columns=HBASE_ROW_KEY,d:c1,d:c2 -Dimporttsv.bulk.output=hdfs://storefileoutput datatsv hdfs://inputfile
-
- ... and in this example the first column is the rowkey, which is why the HBASE_ROW_KEY is used. The second and third columns in the file will be imported as "d:c1" and "d:c2", respectively.
-
-
- ImportTsv Warning
- If you have preparing a lot of data for bulk loading, make sure the target HBase table is pre-split appropriately.
-
-
- See Also
- For more information about bulk-loading HFiles into HBase, see
-
-
-
-
- CompleteBulkLoad
- The completebulkload utility will move generated StoreFiles into an HBase table. This utility is often used
- in conjunction with output from .
-
- There are two ways to invoke this utility, with explicit classname and via the driver:
-$ bin/hbase org.apache.hadoop.hbase.mapreduce.LoadIncrementalHFile <hdfs://storefileoutput> <tablename>
-
-.. and via the Driver..
-HADOOP_CLASSPATH=`${HBASE_HOME}/bin/hbase classpath` ${HADOOP_HOME}/bin/hadoop jar ${HBASE_HOME}/hbase-VERSION.jar completebulkload <hdfs://storefileoutput> <tablename>
-
-
- For more information about bulk-loading HFiles into HBase, see .
-
-
-
- WALPlayer
- WALPlayer is a utility to replay WAL files into HBase.
-
- The WAL can be replayed for a set of tables or all tables, and a timerange can be provided (in milliseconds). The WAL is filtered to this set of tables. The output can optionally be mapped to another set of tables.
-
- WALPlayer can also generate HFiles for later bulk importing, in that case only a single table and no mapping can be specified.
-
- Invoke via:
-$ bin/hbase org.apache.hadoop.hbase.mapreduce.WALPlayer [options] <wal inputdir> <tables> [<tableMappings>]>
-
-
- For example:
-$ bin/hbase org.apache.hadoop.hbase.mapreduce.WALPlayer /backuplogdir oldTable1,oldTable2 newTable1,newTable2
-
-
-
-
- RowCounter
- RowCounter is a utility that will count all the rows of a table. This is a good utility to use
- as a sanity check to ensure that HBase can read all the blocks of a table if there are any concerns of metadata inconsistency.
-$ bin/hbase org.apache.hadoop.hbase.mapreduce.RowCounter <tablename> [<column1> <column2>...]
-
-
- Note: caching for the input Scan is configured via hbase.client.scanner.caching in the job configuration.
-
-
-
-
-
-
- Region Management
-
- Major Compaction
- Major compactions can be requested via the HBase shell or HBaseAdmin.majorCompact.
-
- Note: major compactions do NOT do region merges. See for more information about compactions.
-
-
-
-
- Merge
- Merge is a utility that can merge adjoining regions in the same table (see org.apache.hadoop.hbase.util.Merge).
-$ bin/hbase org.apache.hbase.util.Merge <tablename> <region1> <region2>
-
- If you feel you have too many regions and want to consolidate them, Merge is the utility you need. Merge must
- run be done when the cluster is down.
- See the O'Reilly HBase Book for
- an example of usage.
-
- Additionally, there is a Ruby script attached to HBASE-1621
- for region merging.
-
-
-
-
- Node Management
- Node Decommission
- You can stop an individual RegionServer by running the following
- script in the HBase directory on the particular node:
- $ ./bin/hbase-daemon.sh stop regionserver
- The RegionServer will first close all regions and then shut itself down.
- On shutdown, the RegionServer's ephemeral node in ZooKeeper will expire.
- The master will notice the RegionServer gone and will treat it as
- a 'crashed' server; it will reassign the nodes the RegionServer was carrying.
- Disable the Load Balancer before Decommissioning a node
- If the load balancer runs while a node is shutting down, then
- there could be contention between the Load Balancer and the
- Master's recovery of the just decommissioned RegionServer.
- Avoid any problems by disabling the balancer first.
- See below.
-
-
-
-
- A downside to the above stop of a RegionServer is that regions could be offline for
- a good period of time. Regions are closed in order. If many regions on the server, the
- first region to close may not be back online until all regions close and after the master
- notices the RegionServer's znode gone. In HBase 0.90.2, we added facility for having
- a node gradually shed its load and then shutdown itself down. HBase 0.90.2 added the
- graceful_stop.sh script. Here is its usage:
- $ ./bin/graceful_stop.sh
-Usage: graceful_stop.sh [--config &conf-dir>] [--restart] [--reload] [--thrift] [--rest] &hostname>
- thrift If we should stop/start thrift before/after the hbase stop/start
- rest If we should stop/start rest before/after the hbase stop/start
- restart If we should restart after graceful stop
- reload Move offloaded regions back on to the stopped server
- debug Move offloaded regions back on to the stopped server
- hostname Hostname of server we are to stop
-
-
- To decommission a loaded RegionServer, run the following:
- $ ./bin/graceful_stop.sh HOSTNAME
- where HOSTNAME is the host carrying the RegionServer
- you would decommission.
- On HOSTNAME
- The HOSTNAME passed to graceful_stop.sh
- must match the hostname that hbase is using to identify RegionServers.
- Check the list of RegionServers in the master UI for how HBase is
- referring to servers. Its usually hostname but can also be FQDN.
- Whatever HBase is using, this is what you should pass the
- graceful_stop.sh decommission
- script. If you pass IPs, the script is not yet smart enough to make
- a hostname (or FQDN) of it and so it will fail when it checks if server is
- currently running; the graceful unloading of regions will not run.
-
- The graceful_stop.sh script will move the regions off the
- decommissioned RegionServer one at a time to minimize region churn.
- It will verify the region deployed in the new location before it
- will moves the next region and so on until the decommissioned server
- is carrying zero regions. At this point, the graceful_stop.sh
- tells the RegionServer stop. The master will at this point notice the
- RegionServer gone but all regions will have already been redeployed
- and because the RegionServer went down cleanly, there will be no
- WAL logs to split.
- Load Balancer
-
- It is assumed that the Region Load Balancer is disabled while the
- graceful_stop script runs (otherwise the balancer
- and the decommission script will end up fighting over region deployments).
- Use the shell to disable the balancer:
- hbase(main):001:0> balance_switch false
-true
-0 row(s) in 0.3590 seconds
-This turns the balancer OFF. To reenable, do:
- hbase(main):001:0> balance_switch true
-false
-0 row(s) in 0.3590 seconds
-
-
-
-
-
- Rolling Restart
-
- You can also ask this script to restart a RegionServer after the shutdown
- AND move its old regions back into place. The latter you might do to
- retain data locality. A primitive rolling restart might be effected by
- running something like the following:
- $ for i in `cat conf/regionservers|sort`; do ./bin/graceful_stop.sh --restart --reload --debug $i; done &> /tmp/log.txt &
-
- Tail the output of /tmp/log.txt to follow the scripts
- progress. The above does RegionServers only. Be sure to disable the
- load balancer before doing the above. You'd need to do the master
- update separately. Do it before you run the above script.
- Here is a pseudo-script for how you might craft a rolling restart script:
-
- Untar your release, make sure of its configuration and
- then rsync it across the cluster. If this is 0.90.2, patch it
- with HBASE-3744 and HBASE-3756.
-
-
-
- Run hbck to ensure the cluster consistent
- $ ./bin/hbase hbck
- Effect repairs if inconsistent.
-
-
-
- Restart the Master: $ ./bin/hbase-daemon.sh stop master; ./bin/hbase-daemon.sh start master
-
-
-
-
- Disable the region balancer:$ echo "balance_switch false" | ./bin/hbase shell
-
-
-
- Run the graceful_stop.sh script per RegionServer. For example:
- $ for i in `cat conf/regionservers|sort`; do ./bin/graceful_stop.sh --restart --reload --debug $i; done &> /tmp/log.txt &
-
- If you are running thrift or rest servers on the RegionServer, pass --thrift or --rest options (See usage
- for graceful_stop.sh script).
-
-
-
- Restart the Master again. This will clear out dead servers list and reenable the balancer.
-
-
-
- Run hbck to ensure the cluster is consistent.
-
-
-
-
-
-
-
-
- HBase Metrics
-
- Metric Setup
- See Metrics for
- an introduction and how to enable Metrics emission.
-
-
-
- RegionServer Metrics
- hbase.regionserver.blockCacheCount
- Block cache item count in memory. This is the number of blocks of StoreFiles (HFiles) in the cache.
-
- hbase.regionserver.blockCacheEvictedCount
- Number of blocks that had to be evicted from the block cache due to heap size constraints.
-
- hbase.regionserver.blockCacheFree
- Block cache memory available (bytes).
-
- hbase.regionserver.blockCacheHitCachingRatio
- Block cache hit caching ratio (0 to 100). The cache-hit ratio for reads configured to look in the cache (i.e., cacheBlocks=true).
-
- hbase.regionserver.blockCacheHitCount
- Number of blocks of StoreFiles (HFiles) read from the cache.
-
- hbase.regionserver.blockCacheHitRatio
- Block cache hit ratio (0 to 100). Includes all read requests, although those with cacheBlocks=false
- will always read from disk and be counted as a "cache miss".
-
- hbase.regionserver.blockCacheMissCount
- Number of blocks of StoreFiles (HFiles) requested but not read from the cache.
-
- hbase.regionserver.blockCacheSize
- Block cache size in memory (bytes). i.e., memory in use by the BlockCache
-
- hbase.regionserver.compactionQueueSize
- Size of the compaction queue. This is the number of Stores in the RegionServer that have been targeted for compaction.
-
- hbase.regionserver.flushQueueSize
- Number of enqueued regions in the MemStore awaiting flush.
-
- hbase.regionserver.fsReadLatency_avg_time
- Filesystem read latency (ms). This is the average time to read from HDFS.
-
- hbase.regionserver.fsReadLatency_num_ops
- Filesystem read operations.
-
- hbase.regionserver.fsSyncLatency_avg_time
- Filesystem sync latency (ms). Latency to sync the write-ahead log records to the filesystem.
-
- hbase.regionserver.fsSyncLatency_num_ops
- Number of operations to sync the write-ahead log records to the filesystem.
-
- hbase.regionserver.fsWriteLatency_avg_time
- Filesystem write latency (ms). Total latency for all writers, including StoreFiles and write-head log.
-
- hbase.regionserver.fsWriteLatency_num_ops
- Number of filesystem write operations, including StoreFiles and write-ahead log.
-
- hbase.regionserver.memstoreSizeMB
- Sum of all the memstore sizes in this RegionServer (MB)
-
- hbase.regionserver.regions
- Number of regions served by the RegionServer
-
- hbase.regionserver.requests
- Total number of read and write requests. Requests correspond to RegionServer RPC calls, thus a single Get will result in 1 request, but a Scan with caching set to 1000 will result in 1 request for each 'next' call (i.e., not each row). A bulk-load request will constitute 1 request per HFile.
-
- hbase.regionserver.storeFileIndexSizeMB
- Sum of all the StoreFile index sizes in this RegionServer (MB)
-
- hbase.regionserver.stores
- Number of Stores open on the RegionServer. A Store corresponds to a ColumnFamily. For example, if a table (which contains the column family) has 3 regions on a RegionServer, there will be 3 stores open for that column family.
-
- hbase.regionserver.storeFiles
- Number of StoreFiles open on the RegionServer. A store may have more than one StoreFile (HFile).
-
-
-
-
-
- HBase Monitoring
-
- Overview
- The following metrics are arguably the most important to monitor for each RegionServer for
- "macro monitoring", preferably with a system like OpenTSDB.
- If your cluster is having performance issues it's likely that you'll see something unusual with
- this group.
-
- HBase:
-
- Requests
- Compactions queue
-
-
- OS:
-
- IO Wait
- User CPU
-
-
- Java:
-
- GC
-
-
-
-
-
- For more information on HBase metrics, see .
-
-
-
-
- Slow Query Log
-The HBase slow query log consists of parseable JSON structures describing the properties of those client operations (Gets, Puts, Deletes, etc.) that either took too long to run, or produced too much output. The thresholds for "too long to run" and "too much output" are configurable, as described below. The output is produced inline in the main region server logs so that it is easy to discover further details from context with other logged events. It is also prepended with identifying tags (responseTooSlow), (responseTooLarge), (operationTooSlow), and (operationTooLarge) in order to enable easy filtering with grep, in case the user desires to see only slow queries.
-
-
-Configuration
-There are two configuration knobs that can be used to adjust the thresholds for when queries are logged.
-
-
-
-
-hbase.ipc.warn.response.time Maximum number of milliseconds that a query can be run without being logged. Defaults to 10000, or 10 seconds. Can be set to -1 to disable logging by time.
-
-hbase.ipc.warn.response.size Maximum byte size of response that a query can return without being logged. Defaults to 100 megabytes. Can be set to -1 to disable logging by size.
-
-
-
-
-Metrics
-The slow query log exposes to metrics to JMX.
-hadoop.regionserver_rpc_slowResponse a global metric reflecting the durations of all responses that triggered logging.
-hadoop.regionserver_rpc_methodName.aboveOneSec A metric reflecting the durations of all responses that lasted for more than one second.
-
-
-
-
-Output
-The output is tagged with operation e.g. (operationTooSlow) if the call was a client operation, such as a Put, Get, or Delete, which we expose detailed fingerprint information for. If not, it is tagged (responseTooSlow) and still produces parseable JSON output, but with less verbose information solely regarding its duration and size in the RPC itself. TooLarge is substituted for TooSlow if the response size triggered the logging, with TooLarge appearing even in the case that both size and duration triggered logging.
-
-
-Example
-
-2011-09-08 10:01:25,824 WARN org.apache.hadoop.ipc.HBaseServer: (operationTooSlow): {"tables":{"riley2":{"puts":[{"totalColumns":11,"families":{"actions":[{"timestamp":1315501284459,"qualifier":"0","vlen":9667580},{"timestamp":1315501284459,"qualifier":"1","vlen":10122412},{"timestamp":1315501284459,"qualifier":"2","vlen":11104617},{"timestamp":1315501284459,"qualifier":"3","vlen":13430635}]},"row":"cfcd208495d565ef66e7dff9f98764da:0"}],"families":["actions"]}},"processingtimems":956,"client":"10.47.34.63:33623","starttimems":1315501284456,"queuetimems":0,"totalPuts":1,"class":"HRegionServer","responsesize":0,"method":"multiPut"}
-
-
-Note that everything inside the "tables" structure is output produced by MultiPut's fingerprint, while the rest of the information is RPC-specific, such as processing time and client IP/port. Other client operations follow the same pattern and the same general structure, with necessary differences due to the nature of the individual operations. In the case that the call is not a client operation, that detailed fingerprint information will be completely absent.
-
-
-This particular example, for example, would indicate that the likely cause of slowness is simply a very large (on the order of 100MB) multiput, as we can tell by the "vlen," or value length, fields of each put in the multiPut.
-
-
-
-
-
-
-
-
-
- Cluster Replication
- See Cluster Replication.
-
-
-
- HBase Backup
- There are two broad strategies for performing HBase backups: backing up with a full cluster shutdown, and backing up on a live cluster.
- Each approach has pros and cons.
-
- For additional information, see HBase Backup Options over on the Sematext Blog.
-
- Full Shutdown Backup
- Some environments can tolerate a periodic full shutdown of their HBase cluster, for example if it is being used a back-end analytic capacity
- and not serving front-end web-pages. The benefits are that the NameNode/Master are RegionServers are down, so there is no chance of missing
- any in-flight changes to either StoreFiles or metadata. The obvious con is that the cluster is down. The steps include:
-
- Stop HBase
-
-
-
- Distcp
- Distcp could be used to either copy the contents of the HBase directory in HDFS to either the same cluster in another directory, or
- to a different cluster.
-
- Note: Distcp works in this situation because the cluster is down and there are no in-flight edits to files.
- Distcp-ing of files in the HBase directory is not generally recommended on a live cluster.
-
-
- Restore (if needed)
- The backup of the hbase directory from HDFS is copied onto the 'real' hbase directory via distcp. The act of copying these files
- creates new HDFS metadata, which is why a restore of the NameNode edits from the time of the HBase backup isn't required for this kind of
- restore, because it's a restore (via distcp) of a specific HDFS directory (i.e., the HBase part) not the entire HDFS file-system.
-
-
-
- Live Cluster Backup - Replication
- This approach assumes that there is a second cluster.
- See the HBase page on replication for more information.
-
-
- Live Cluster Backup - CopyTable
- The utility could either be used to copy data from one table to another on the
- same cluster, or to copy data to another table on another cluster.
-
- Since the cluster is up, there is a risk that edits could be missed in the copy process.
-
-
- Live Cluster Backup - Export
- The approach dumps the content of a table to HDFS on the same cluster. To restore the data, the
- utility would be used.
-
- Since the cluster is up, there is a risk that edits could be missed in the export process.
-
-
-
- Capacity Planning
- Storage
- A common question for HBase administrators is estimating how much storage will be required for an HBase cluster.
- There are several apsects to consider, the most important of which is what data load into the cluster. Start
- with a solid understanding of how HBase handles data internally (KeyValue).
-
- KeyValue
- HBase storage will be dominated by KeyValues. See and for
- how HBase stores data internally.
-
- It is critical to understand that there is a KeyValue instance for every attribute stored in a row, and the
- rowkey-length, ColumnFamily name-length and attribute lengths will drive the size of the database more than any other
- factor.
-
-
- StoreFiles and Blocks
- KeyValue instances are aggregated into blocks, and the blocksize is configurable on a per-ColumnFamily basis.
- Blocks are aggregated into StoreFile's. See .
-
-
- HDFS Block Replication
- Because HBase runs on top of HDFS, factor in HDFS block replication into storage calculations.
-
-
-
- Regions
- Another common question for HBase administrators is determining the right number of regions per
- RegionServer. This affects both storage and hardware planning. See .
-
-
-
-
-
diff --git hbase-site/src/docbkx/performance.xml hbase-site/src/docbkx/performance.xml
deleted file mode 100644
index 41a8916..0000000
--- hbase-site/src/docbkx/performance.xml
+++ /dev/null
@@ -1,547 +0,0 @@
-
-
-
- Performance Tuning
-
-
- Operating System
-
- Memory
- RAM, RAM, RAM. Don't starve HBase.
-
-
- 64-bit
- Use a 64-bit platform (and 64-bit JVM).
-
-
- Swapping
- Watch out for swapping. Set swappiness to 0.
-
-
-
- Network
-
- Perhaps the most important factor in avoiding network issues degrading Hadoop and HBbase performance is the switching hardware
- that is used, decisions made early in the scope of the project can cause major problems when you double or triple the size of your cluster (or more).
-
-
- Important items to consider:
-
- Switching capacity of the device
- Number of systems connected
- Uplink capacity
-
-
-
- Single Switch
- The single most important factor in this configuration is that the switching capacity of the hardware is capable of
- handling the traffic which can be generated by all systems connected to the switch. Some lower priced commodity hardware
- can have a slower switching capacity than could be utilized by a full switch.
-
-
-
- Multiple Switches
- Multiple switches are a potential pitfall in the architecture. The most common configuration of lower priced hardware is a
- simple 1Gbps uplink from one switch to another. This often overlooked pinch point can easily become a bottleneck for cluster communication.
- Especially with MapReduce jobs that are both reading and writing a lot of data the communication across this uplink could be saturated.
-
- Mitigation of this issue is fairly simple and can be accomplished in multiple ways:
-
- Use appropriate hardware for the scale of the cluster which you're attempting to build.
- Use larger single switch configurations i.e. single 48 port as opposed to 2x 24 port
- Configure port trunking for uplinks to utilize multiple interfaces to increase cross switch bandwidth.
-
-
-
-
- Multiple Racks
- Multiple rack configurations carry the same potential issues as multiple switches, and can suffer performance degradation from two main areas:
-
- Poor switch capacity performance
- Insufficient uplink to another rack
-
- If the the switches in your rack have appropriate switching capacity to handle all the hosts at full speed, the next most likely issue will be caused by homing
- more of your cluster across racks. The easiest way to avoid issues when spanning multiple racks is to use port trunking to create a bonded uplink to other racks.
- The downside of this method however, is in the overhead of ports that could potentially be used. An example of this is, creating an 8Gbps port channel from rack
- A to rack B, using 8 of your 24 ports to communicate between racks gives you a poor ROI, using too few however can mean you're not getting the most out of your cluster.
-
- Using 10Gbe links between racks will greatly increase performance, and assuming your switches support a 10Gbe uplink or allow for an expansion card will allow you to
- save your ports for machines as opposed to uplinks.
-
-
-
- Network Interfaces
- Are all the network interfaces functioning correctly? Are you sure? See the Troubleshooting Case Study in .
-
-
-
-
-
- Java
-
-
- The Garbage Collector and HBase
-
-
- Long GC pauses
-
- In his presentation, Avoiding
- Full GCs with MemStore-Local Allocation Buffers, Todd Lipcon
- describes two cases of stop-the-world garbage collections common in
- HBase, especially during loading; CMS failure modes and old generation
- heap fragmentation brought. To address the first, start the CMS
- earlier than default by adding
- -XX:CMSInitiatingOccupancyFraction and setting it down
- from defaults. Start at 60 or 70 percent (The lower you bring down the
- threshold, the more GCing is done, the more CPU used). To address the
- second fragmentation issue, Todd added an experimental facility,
- MSLAB, that
- must be explicitly enabled in HBase 0.90.x (Its defaulted to be on in
- 0.92.x HBase). See hbase.hregion.memstore.mslab.enabled
- to true in your Configuration. See the cited
- slides for background and detailThe latest jvms do better
- regards fragmentation so make sure you are running a recent release.
- Read down in the message,
- Identifying concurrent mode failures caused by fragmentation..
- For more information about GC logs, see .
-
-
-
-
-
-
- HBase Configurations
-
- See .
-
-
- Number of Regions
-
- The number of regions for an HBase table is driven by the . Also, see the architecture
- section on
-
-
-
- Managing Compactions
-
- For larger systems, managing compactions and splits may be
- something you want to consider.
-
-
-
- hbase.regionserver.handler.count
- See .
-
-
-
- hfile.block.cache.size
- See .
- A memory setting for the RegionServer process.
-
-
-
- hbase.regionserver.global.memstore.upperLimit
- See .
- This memory setting is often adjusted for the RegionServer process depending on needs.
-
-
-
- hbase.regionserver.global.memstore.lowerLimit
- See .
- This memory setting is often adjusted for the RegionServer process depending on needs.
-
-
-
- hbase.hstore.blockingStoreFiles
- See .
- If there is blocking in the RegionServer logs, increasing this can help.
-
-
-
- hbase.hregion.memstore.block.multiplier
- See .
- If there is enough RAM, increasing this can help.
-
-
-
-
-
- ZooKeeper
- See for information on configuring ZooKeeper, and see the part
- about having a dedicated disk.
-
-
-
- Schema Design
-
-
- Number of Column Families
- See .
-
-
- Key and Attribute Lengths
- See . See also for
- compression caveats.
-
- Table RegionSize
- The regionsize can be set on a per-table basis via setFileSize on
- HTableDescriptor in the
- event where certain tables require different regionsizes than the configured default regionsize.
-
- See for more information.
-
-
-
- Bloom Filters
- Bloom Filters can be enabled per-ColumnFamily.
- Use HColumnDescriptor.setBloomFilterType(NONE | ROW |
- ROWCOL) to enable blooms per Column Family. Default =
- NONE for no bloom filters. If
- ROW, the hash of the row will be added to the bloom
- on each insert. If ROWCOL, the hash of the row +
- column family + column family qualifier will be added to the bloom on
- each key insert.
- See HColumnDescriptor and
- for more information.
-
-
- ColumnFamily BlockSize
- The blocksize can be configured for each ColumnFamily in a table, and this defaults to 64k. Larger cell values require larger blocksizes.
- There is an inverse relationship between blocksize and the resulting StoreFile indexes (i.e., if the blocksize is doubled then the resulting
- indexes should be roughly halved).
-
- See HColumnDescriptor
- and for more information.
-
-
-
- In-Memory ColumnFamilies
- ColumnFamilies can optionally be defined as in-memory. Data is still persisted to disk, just like any other ColumnFamily.
- In-memory blocks have the highest priority in the , but it is not a guarantee that the entire table
- will be in memory.
-
- See HColumnDescriptor for more information.
-
-
-
- Compression
- Production systems should use compression with their ColumnFamily definitions. See for more information.
-
- However...
- Compression deflates data on disk. When it's in-memory (e.g., in the
- MemStore) or on the wire (e.g., transferring between RegionServer and Client) it's inflated.
- So while using ColumnFamily compression is a best practice, but it's not going to completely eliminate
- the impact of over-sized Keys, over-sized ColumnFamily names, or over-sized Column names.
-
- See on for schema design tips, and for more information on HBase stores data internally.
-
-
-
-
-
-
- Writing to HBase
-
-
- Batch Loading
- Use the bulk load tool if you can. See
- .
- Otherwise, pay attention to the below.
-
-
-
-
-
- Table Creation: Pre-Creating Regions
-
-
-Tables in HBase are initially created with one region by default. For bulk imports, this means that all clients will write to the same region until it is large enough to split and become distributed across the cluster. A useful pattern to speed up the bulk import process is to pre-create empty regions. Be somewhat conservative in this, because too-many regions can actually degrade performance. An example of pre-creation using hex-keys is as follows (note: this example may need to be tweaked to the individual applications keys):
-
-
-public static boolean createTable(HBaseAdmin admin, HTableDescriptor table, byte[][] splits)
-throws IOException {
- try {
- admin.createTable( table, splits );
- return true;
- } catch (TableExistsException e) {
- logger.info("table " + table.getNameAsString() + " already exists");
- // the table already exists...
- return false;
- }
-}
-
-public static byte[][] getHexSplits(String startKey, String endKey, int numRegions) {
- byte[][] splits = new byte[numRegions-1][];
- BigInteger lowestKey = new BigInteger(startKey, 16);
- BigInteger highestKey = new BigInteger(endKey, 16);
- BigInteger range = highestKey.subtract(lowestKey);
- BigInteger regionIncrement = range.divide(BigInteger.valueOf(numRegions));
- lowestKey = lowestKey.add(regionIncrement);
- for(int i=0; i < numRegions-1;i++) {
- BigInteger key = lowestKey.add(regionIncrement.multiply(BigInteger.valueOf(i)));
- byte[] b = String.format("%016x", key).getBytes();
- splits[i] = b;
- }
- return splits;
-}
-
-
-
-
- Table Creation: Deferred Log Flush
-
-
-The default behavior for Puts using the Write Ahead Log (WAL) is that HLog edits will be written immediately. If deferred log flush is used,
-WAL edits are kept in memory until the flush period. The benefit is aggregated and asynchronous HLog- writes, but the potential downside is that if
- the RegionServer goes down the yet-to-be-flushed edits are lost. This is safer, however, than not using WAL at all with Puts.
-
-
-Deferred log flush can be configured on tables via HTableDescriptor. The default value of hbase.regionserver.optionallogflushinterval is 1000ms.
-
-
-
-
- HBase Client: AutoFlush
-
- When performing a lot of Puts, make sure that setAutoFlush is set
- to false on your HTable
- instance. Otherwise, the Puts will be sent one at a time to the
- RegionServer. Puts added via htable.add(Put) and htable.add( <List> Put)
- wind up in the same write buffer. If autoFlush = false,
- these messages are not sent until the write-buffer is filled. To
- explicitly flush the messages, call flushCommits.
- Calling close on the HTable
- instance will invoke flushCommits.
-
-
- HBase Client: Turn off WAL on Puts
- A frequently discussed option for increasing throughput on Puts is to call writeToWAL(false). Turning this off means
- that the RegionServer will not write the Put to the Write Ahead Log,
- only into the memstore, HOWEVER the consequence is that if there
- is a RegionServer failure there will be data loss.
- If writeToWAL(false) is used, do so with extreme caution. You may find in actuality that
- it makes little difference if your load is well distributed across the cluster.
-
- In general, it is best to use WAL for Puts, and where loading throughput
- is a concern to use bulk loading techniques instead.
-
-
-
- HBase Client: Group Puts by RegionServer
- In addition to using the writeBuffer, grouping Puts by RegionServer can reduce the number of client RPC calls per writeBuffer flush.
- There is a utility HTableUtil currently on TRUNK that does this, but you can either copy that or implement your own verison for
- those still on 0.90.x or earlier.
-
-
-
- MapReduce: Skip The Reducer
- When writing a lot of data to an HBase table from a MR job (e.g., with TableOutputFormat), and specifically where Puts are being emitted
- from the Mapper, skip the Reducer step. When a Reducer step is used, all of the output (Puts) from the Mapper will get spooled to disk, then sorted/shuffled to other
- Reducers that will most likely be off-node. It's far more efficient to just write directly to HBase.
-
- For summary jobs where HBase is used as a source and a sink, then writes will be coming from the Reducer step (e.g., summarize values then write out result).
- This is a different processing problem than from the the above case.
-
-
-
-
- Anti-Pattern: One Hot Region
- If all your data is being written to one region at a time, then re-read the
- section on processing timeseries data.
- Also, if you are pre-splitting regions and all your data is still winding up in a single region even though
- your keys aren't monotonically increasing, confirm that your keyspace actually works with the split strategy. There are a
- variety of reasons that regions may appear "well split" but won't work with your data. As
- the HBase client communicates directly with the RegionServers, this can be obtained via
- HTable.getRegionLocation.
-
- See , as well as
-
-
-
-
-
- Reading from HBase
-
-
- Scan Caching
-
- If HBase is used as an input source for a MapReduce job, for
- example, make sure that the input Scan
- instance to the MapReduce job has setCaching set to something greater
- than the default (which is 1). Using the default value means that the
- map-task will make call back to the region-server for every record
- processed. Setting this value to 500, for example, will transfer 500
- rows at a time to the client to be processed. There is a cost/benefit to
- have the cache value be large because it costs more in memory for both
- client and RegionServer, so bigger isn't always better.
-
- Scan Caching in MapReduce Jobs
- Scan settings in MapReduce jobs deserve special attention. Timeouts can result (e.g., UnknownScannerException)
- in Map tasks if it takes longer to process a batch of records before the client goes back to the RegionServer for the
- next set of data. This problem can occur because there is non-trivial processing occuring per row. If you process
- rows quickly, set caching higher. If you process rows more slowly (e.g., lots of transformations per row, writes),
- then set caching lower.
-
- Timeouts can also happen in a non-MapReduce use case (i.e., single threaded HBase client doing a Scan), but the
- processing that is often performed in MapReduce jobs tends to exacerbate this issue.
-
-
-
-
- Scan Attribute Selection
-
- Whenever a Scan is used to process large numbers of rows (and especially when used
- as a MapReduce source), be aware of which attributes are selected. If scan.addFamily is called
- then all of the attributes in the specified ColumnFamily will be returned to the client.
- If only a small number of the available attributes are to be processed, then only those attributes should be specified
- in the input scan because attribute over-selection is a non-trivial performance penalty over large datasets.
-
-
-
- MapReduce - Input Splits
- For MapReduce jobs that use HBase tables as a source, if there a pattern where the "slow" map tasks seem to
- have the same Input Split (i.e., the RegionServer serving the data), see the
- Troubleshooting Case Study in .
-
-
-
-
- Close ResultScanners
-
- This isn't so much about improving performance but rather
- avoiding performance problems. If you forget to
- close ResultScanners
- you can cause problems on the RegionServers. Always have ResultScanner
- processing enclosed in try/catch blocks...
-Scan scan = new Scan();
-// set attrs...
-ResultScanner rs = htable.getScanner(scan);
-try {
- for (Result r = rs.next(); r != null; r = rs.next()) {
- // process result...
-} finally {
- rs.close(); // always close the ResultScanner!
-}
-htable.close();
-
-
-
- Block Cache
-
- Scan
- instances can be set to use the block cache in the RegionServer via the
- setCacheBlocks method. For input Scans to MapReduce jobs, this should be
- false. For frequently accessed rows, it is advisable to use the block
- cache.
-
-
- Optimal Loading of Row Keys
- When performing a table scan
- where only the row keys are needed (no families, qualifiers, values or timestamps), add a FilterList with a
- MUST_PASS_ALL operator to the scanner using setFilter. The filter list
- should include both a FirstKeyOnlyFilter
- and a KeyOnlyFilter.
- Using this filter combination will result in a worst case scenario of a RegionServer reading a single value from disk
- and minimal network traffic to the client for a single row.
-
-
-
- Concurrency: Monitor Data Spread
- When performing a high number of concurrent reads, monitor the data spread of the target tables. If the target table(s) have
- too few regions then the reads could likely be served from too few nodes.
- See , as well as
-
-
-
-
-
- Deleting from HBase
-
- Using HBase Tables as Queues
- HBase tables are sometimes used as queues. In this case, special care must be taken to regularly perform major compactions on tables used in
- this manner. As is documented in , marking rows as deleted creates additional StoreFiles which then need to be processed
- on reads. Tombstones only get cleaned up with major compactions.
-
- See also and HBaseAdmin.majorCompact.
-
-
-
- Delete RPC Behavior
- Be aware that htable.delete(Delete) doesn't use the writeBuffer. It will execute an RegionServer RPC with each invocation.
- For a large number of deletes, consider htable.delete(List).
-
- See
-
-
-
-
- HDFS
- Because HBase runs on it is important to understand how it works and how it affects
- HBase.
-
- Current Issues With Low-Latency Reads
- The original use-case for HDFS was batch processing. As such, there low-latency reads were historically not a priority.
- With the increased adoption of HBase this is changing, and several improvements are already in development.
- See the
- Umbrella Jira Ticket for HDFS Improvements for HBase.
-
-
- Performance Comparisons of HBase vs. HDFS
- A fairly common question on the dist-list is why HBase isn't as performant as HDFS files in a batch context (e.g., as
- a MapReduce source or sink). The short answer is that HBase is doing a lot more than HDFS (e.g., reading the KeyValues,
- returning the most current row or specified timestamps, etc.), and as such HBase is 4-5 times slower than HDFS in this
- processing context. Not that there isn't room for improvement (and this gap will, over time, be reduced), but HDFS
- will always be faster in this use-case.
-
-
-
-
- Amazon EC2
- Performance questions are common on Amazon EC2 environments because it is a shared environment. You will
- not see the same throughput as a dedicated server. In terms of running tests on EC2, run them several times for the same
- reason (i.e., it's a shared environment and you don't know what else is happening on the server).
-
- If you are running on EC2 and post performance questions on the dist-list, please state this fact up-front that
- because EC2 issues are practically a separate class of performance issues.
-
-
-
- Case Studies
- For Performance and Troubleshooting Case Studies, see .
-
-
-
diff --git hbase-site/src/docbkx/preface.xml hbase-site/src/docbkx/preface.xml
deleted file mode 100644
index af54aa2..0000000
--- hbase-site/src/docbkx/preface.xml
+++ /dev/null
@@ -1,65 +0,0 @@
-
-
-
- Preface
-
- This is the official reference guide for the HBase version it ships with.
- This document describes HBase version .
- Herein you will find either the definitive documentation on an HBase topic
- as of its standing when the referenced HBase version shipped, or it
- will point to the location in javadoc,
- JIRA
- or wiki where
- the pertinent information can be found.
-
- This reference guide is a work in progress. Feel free to add content by adding
- a patch to an issue up in the HBase JIRA.
-
-
- Heads-up
-
- If this is your first foray into the wonderful world of
- Distributed Computing, then you are in for
- some interesting times. First off, distributed systems are
- hard; making a distributed system hum requires a disparate
- skillset that spans systems (hardware and software) and
- networking. Your cluster' operation can hiccup because of any
- of a myriad set of reasons from bugs in HBase itself through misconfigurations
- -- misconfiguration of HBase but also operating system misconfigurations --
- through to hardware problems whether it be a bug in your network card
- drivers or an underprovisioned RAM bus (to mention two recent
- examples of hardware issues that manifested as "HBase is slow").
- You will also need to do a recalibration if up to this your
- computing has been bound to a single box. Here is one good
- starting point:
- Fallacies of Distributed Computing.
-
-
-
diff --git hbase-site/src/docbkx/security.xml hbase-site/src/docbkx/security.xml
deleted file mode 100644
index 8d6a33e..0000000
--- hbase-site/src/docbkx/security.xml
+++ /dev/null
@@ -1,509 +0,0 @@
-
-
-
-Secure HBase
-
- Secure Client Access to HBase
- Newer releases of HBase (>= 0.92) support optional SASL authentication of clients.
- This describes how to set up HBase and HBase clients for connection to secure HBase resources.
-
- Prerequisites
-
- HBase must have been built using the new maven profile for secure Hadoop/HBase: -P security. Secure Hadoop dependent classes are separated under a pseudo-module in the security/ directory and are only included if built with the secure Hadoop profile.
-
-
- You need to have a working Kerberos KDC.
-
-
- A HBase configured for secure client access is expected to be running
- on top of a secured HDFS cluster. HBase must be able to authenticate
- to HDFS services. HBase needs Kerberos credentials to interact with
- the Kerberos-enabled HDFS daemons. Authenticating a service should be
- done using a keytab file. The procedure for creating keytabs for HBase
- service is the same as for creating keytabs for Hadoop. Those steps
- are omitted here. Copy the resulting keytab files to wherever HBase
- Master and RegionServer processes are deployed and make them readable
- only to the user account under which the HBase daemons will run.
-
-
- A Kerberos principal has three parts, with the form
- username/fully.qualified.domain.name@YOUR-REALM.COM. We
- recommend using hbase as the username portion.
-
-
- The following is an example of the configuration properties for
- Kerberos operation that must be added to the
- hbase-site.xml file on every server machine in the
- cluster. Required for even the most basic interactions with a
- secure Hadoop configuration, independent of HBase security.
-
-
- hbase.regionserver.kerberos.principal
- hbase/_HOST@YOUR-REALM.COM
-
-
- hbase.regionserver.keytab.file
- /etc/hbase/conf/keytab.krb5
-
-
- hbase.master.kerberos.principal
- hbase/_HOST@YOUR-REALM.COM
-
-
- hbase.master.keytab.file
- /etc/hbase/conf/keytab.krb5
-
- ]]>
-
- Each HBase client user should also be given a Kerberos principal. This
- principal should have a password assigned to it (as opposed to a
- keytab file). The client principal's maxrenewlife should
- be set so that it can be renewed enough times for the HBase client
- process to complete. For example, if a user runs a long-running HBase
- client process that takes at most 3 days, we might create this user's
- principal within kadmin with: addprinc -maxrenewlife
- 3days
-
-
- Long running daemons with indefinite lifetimes that require client
- access to HBase can instead be configured to log in from a keytab. For
- each host running such daemons, create a keytab with
- kadmin or kadmin.local. The procedure for
- creating keytabs for HBase service is the same as for creating
- keytabs for Hadoop. Those steps are omitted here. Copy the resulting
- keytab files to where the client daemon will execute and make them
- readable only to the user account under which the daemon will run.
-
-
-
- Server-side Configuration for Secure Operation
-
- Add the following to the hbase-site.xml file on every server machine in the cluster:
-
-
- hbase.security.authentication
- kerberos
-
-
- hbase.security.authorization
- true
-
-
- hbase.rpc.engine
- org.apache.hadoop.hbase.ipc.SecureRpcEngine
-
-
- hbase.coprocessor.region.classes
- org.apache.hadoop.hbase.security.token.TokenProvider
-
- ]]>
-
- A full shutdown and restart of HBase service is required when deploying
- these configuration changes.
-
-
-
- Client-side Configuration for Secure Operation
-
- Add the following to the hbase-site.xml file on every client:
-
-
- hbase.security.authentication
- kerberos
-
-
- hbase.rpc.engine
- org.apache.hadoop.hbase.ipc.SecureRpcEngine
-
- ]]>
-
- The client environment must be logged in to Kerberos from KDC or
- keytab via the kinit command before communication with
- the HBase cluster will be possible.
-
-
- Be advised that if the hbase.security.authentication
- and hbase.rpc.engine properties in the client- and
- server-side site files do not match, the client will not be able to
- communicate with the cluster.
-
-
- Once HBase is configured for secure RPC it is possible to optionally
- configure encrypted communication. To do so, add the following to the
- hbase-site.xml file on every client:
-
-
- hbase.rpc.protection
- privacy
-
- ]]>
-
- This configuration property can also be set on a per connection basis.
- Set it in the Configuration supplied to
- HTable:
-
-
- Configuration conf = HBaseConfiguration.create();
- conf.set("hbase.rpc.protection", "privacy");
- HTable table = new HTable(conf, tablename);
-
-
- Expect a ~10% performance penalty for encrypted communication.
-
-
-
- Client-side Configuration for Secure Operation - Thrift Gateway
-
- Add the following to the hbase-site.xml file for every Thrift gateway:
-
- hbase.thrift.keytab.file
- /etc/hbase/conf/hbase.keytab
-
-
- hbase.thrift.kerberos.principal
- $USER/_HOST@HADOOP.LOCALDOMAIN
-
- ]]>
-
-
- Substitute the appropriate credential and keytab for $USER and $KEYTAB
- respectively.
-
-
- The Thrift gateway will authenticate with HBase using the supplied
- credential. No authentication will be performed by the Thrift gateway
- itself. All client access via the Thrift gateway will use the Thrift
- gateway's credential and have its privilege.
-
-
-
- Client-side Configuration for Secure Operation - REST Gateway
-
- Add the following to the hbase-site.xml file for every REST gateway:
-
- hbase.rest.keytab.file
- $KEYTAB
-
-
- hbase.rest.kerberos.principal
- $USER/_HOST@HADOOP.LOCALDOMAIN
-
- ]]>
-
-
- Substitute the appropriate credential and keytab for $USER and $KEYTAB
- respectively.
-
-
- The REST gateway will authenticate with HBase using the supplied
- credential. No authentication will be performed by the REST gateway
- itself. All client access via the REST gateway will use the REST
- gateway's credential and have its privilege.
-
-
- It should be possible for clients to authenticate with the HBase
- cluster through the REST gateway in a pass-through manner via SPEGNO
- HTTP authentication. This is future work.
-
-
-
-
-
-
-
- Access Control
-
- Newer releases of HBase (>= 0.92) support optional access control
- list (ACL-) based protection of resources on a column family and/or
- table basis.
-
-
- This describes how to set up Secure HBase for access control, with an
- example of granting and revoking user permission on table resources
- provided.
-
-
- Prerequisites
-
- You must configure HBase for secure operation. Refer to the section
- "Secure Client Access to HBase" and complete all of the steps described
- there.
-
-
- You must also configure ZooKeeper for secure operation. Changes to ACLs
- are synchronized throughout the cluster using ZooKeeper. Secure
- authentication to ZooKeeper must be enabled or otherwise it will be
- possible to subvert HBase access control via direct client access to
- ZooKeeper. Refer to the section on secure ZooKeeper configuration and
- complete all of the steps described there.
-
-
-
- Overview
-
- With Secure RPC and Access Control enabled, client access to HBase is
- authenticated and user data is private unless access has been
- explicitly granted. Access to data can be granted at a table or per
- column family basis.
-
-
- However, the following items have been left out of the initial
- implementation for simplicity:
-
-
-
- Row-level or per value (cell): This would require broader changes for storing the ACLs inline with rows. It is a future goal.
-
-
- Push down of file ownership to HDFS: HBase is not designed for the case where files may have different permissions than the HBase system principal. Pushing file ownership down into HDFS would necessitate changes to core code. Also, while HDFS file ownership would make applying quotas easy, and possibly make bulk imports more straightforward, it is not clear that it would offer a more secure setup.
-
-
- HBase managed "roles" as collections of permissions: We will not model "roles" internally in HBase to begin with. We instead allow group names to be granted permissions, which allows external modeling of roles via group membership. Groups are created and manipulated externally to HBase, via the Hadoop group mapping service.
-
-
-
-Access control mechanisms are mature and fairly standardized in the relational database world. The HBase implementation approximates current convention, but HBase has a simpler feature set than relational databases, especially in terms of client operations. We don't distinguish between an insert (new record) and update (of existing record), for example, as both collapse down into a Put. Accordingly, the important operations condense to four permissions: READ, WRITE, CREATE, and ADMIN.
-
-
-
- Permissions can be granted in any of the following scopes, though
- CREATE and ADMIN permissions are effective only at table scope.
-
-
-
-
- Table
-
-
- Read: User can read from any column family in table
- Write: User can write to any column family in table
- Create: User can alter table attributes; add, alter, or drop column families; and drop the table.
- Admin: User can alter table attributes; add, alter, or drop column families; and enable, disable, or drop the table. User can also trigger region (re)assignments or relocation.
-
-
-
-
- Column Family
-
-
- Read: User can read from the column family
- Write: User can write to the column family
-
-
-
-
-
-
- There is also an implicit global scope for the superuser.
-
-
- The superuser is a principal, specified in the HBase site configuration
- file, that has equivalent access to HBase as the 'root' user would on a
- UNIX derived system. Normally this is the principal that the HBase
- processes themselves authenticate as. Although future versions of HBase
- Access Control may support multiple superusers, the superuser privilege
- will always include the principal used to run the HMaster process. Only
- the superuser is allowed to create tables, switch the balancer on or
- off, or take other actions with global consequence. Furthermore, the
- superuser has an implicit grant of all permissions to all resources.
-
-
- Tables have a new metadata attribute: OWNER, the user principal who owns
- the table. By default this will be set to the user principal who creates
- the table, though it may be changed at table creation time or during an
- alter operation by setting or changing the OWNER table attribute. Only a
- single user principal can own a table at a given time. A table owner will
- have all permissions over a given table.
-
-
-
- Server-side Configuration for Access Control
-
- Enable the AccessController coprocessor in the cluster configuration
- and restart HBase. The restart can be a rolling one. Complete the
- restart of all Master and RegionServer processes before setting up
- ACLs.
-
-
- To enable the AccessController, modify the hbase-site.xml file on every server machine in the cluster to look like:
-
-
- hbase.coprocessor.master.classes
- org.apache.hadoop.hbase.security.access.AccessController
-
-
- hbase.coprocessor.region.classes
- org.apache.hadoop.hbase.security.token.TokenProvider,
- org.apache.hadoop.hbase.security.access.AccessController
-
- ]]>
-
-
- Shell Enhancements for Access Control
-
-The HBase shell has been extended to provide simple commands for editing and updating user permissions. The following commands have been added for access control list management:
-
- Grant
-
-
- grant <user> <permissions> <table> [ <column family> [ <column qualifier> ] ]
-
-
-
- <permissions> is zero or more letters from the set "RWCA": READ('R'), WRITE('W'), CREATE('C'), ADMIN('A').
-
-
- Note: Grants and revocations of individual permissions on a resource are both accomplished using the grant command. A separate revoke command is also provided by the shell, but this is for fast revocation of all of a user's access rights to a given resource only.
-
-
- Revoke
-
-
-
- revoke <user> <table> [ <column family> [ <column qualifier> ] ]
-
-
-
- Alter
-
-
- The alter command has been extended to allow ownership assignment:
-
- alter 'tablename', {OWNER => 'username'}
-
-
-
- User Permission
-
-
- The user_permission command shows all access permissions for the current user for a given table:
-
- user_permission <table>
-
-
-
-
-
-
diff --git hbase-site/src/docbkx/shell.xml hbase-site/src/docbkx/shell.xml
deleted file mode 100644
index 4fbab08..0000000
--- hbase-site/src/docbkx/shell.xml
+++ /dev/null
@@ -1,108 +0,0 @@
-
-
-
- The HBase Shell
-
-
- The HBase Shell is (J)Ruby's
- IRB with some HBase particular commands added. Anything you can do in
- IRB, you should be able to do in the HBase Shell.
- To run the HBase shell,
- do as follows:
- $ ./bin/hbase shell
-
- Type help and then <RETURN>
- to see a listing of shell
- commands and options. Browse at least the paragraphs at the end of
- the help emission for the gist of how variables and command
- arguments are entered into the
- HBase shell; in particular note how table names, rows, and
- columns, etc., must be quoted.
- See
- for example basic shell operation.
-
- Scripting
- For examples scripting HBase, look in the
- HBase bin directory. Look at the files
- that end in *.rb. To run one of these
- files, do as follows:
- $ ./bin/hbase org.jruby.Main PATH_TO_SCRIPT
-
-
-
- Shell Tricks
- irbrc
- Create an .irbrc file for yourself in your
- home directory. Add customizations. A useful one is
- command history so commands are save across Shell invocations:
-
- $ more .irbrc
- require 'irb/ext/save-history'
- IRB.conf[:SAVE_HISTORY] = 100
- IRB.conf[:HISTORY_FILE] = "#{ENV['HOME']}/.irb-save-history"
- See the ruby documentation of
- .irbrc to learn about other possible
- confiurations.
-
-
- LOG data to timestamp
-
- To convert the date '08/08/16 20:56:29' from an hbase log into a timestamp, do:
-
- hbase(main):021:0> import java.text.SimpleDateFormat
- hbase(main):022:0> import java.text.ParsePosition
- hbase(main):023:0> SimpleDateFormat.new("yy/MM/dd HH:mm:ss").parse("08/08/16 20:56:29", ParsePosition.new(0)).getTime() => 1218920189000
-
-
- To go the other direction:
-
- hbase(main):021:0> import java.util.Date
- hbase(main):022:0> Date.new(1218920189000).toString() => "Sat Aug 16 20:56:29 UTC 2008"
-
-
- To output in a format that is exactly like that of the HBase log format will take a little messing with
- SimpleDateFormat.
-
-
- Debug
- Shell debug switch
- You can set a debug switch in the shell to see more output
- -- e.g. more of the stack trace on exception --
- when you run a command:
- hbase> debug <RETURN>
-
-
- DEBUG log level
- To enable DEBUG level logging in the shell,
- launch it with the -d option.
- $ ./bin/hbase shell -d
-
-
-
-
-
diff --git hbase-site/src/docbkx/troubleshooting.xml hbase-site/src/docbkx/troubleshooting.xml
deleted file mode 100644
index b0c2126..0000000
--- hbase-site/src/docbkx/troubleshooting.xml
+++ /dev/null
@@ -1,1058 +0,0 @@
-
-
-
- Troubleshooting and Debugging HBase
-
- General Guidelines
-
- Always start with the master log (TODO: Which lines?).
- Normally it’s just printing the same lines over and over again.
- If not, then there’s an issue.
- Google or search-hadoop.com
- should return some hits for those exceptions you’re seeing.
-
-
- An error rarely comes alone in HBase, usually when something gets screwed up what will
- follow may be hundreds of exceptions and stack traces coming from all over the place.
- The best way to approach this type of problem is to walk the log up to where it all
- began, for example one trick with RegionServers is that they will print some
- metrics when aborting so grepping for Dump
- should get you around the start of the problem.
-
-
- RegionServer suicides are “normal”, as this is what they do when something goes wrong.
- For example, if ulimit and xcievers (the two most important initial settings, see )
- aren’t changed, it will make it impossible at some point for DataNodes to create new threads
- that from the HBase point of view is seen as if HDFS was gone. Think about what would happen if your
- MySQL database was suddenly unable to access files on your local file system, well it’s the same with
- HBase and HDFS. Another very common reason to see RegionServers committing seppuku is when they enter
- prolonged garbage collection pauses that last longer than the default ZooKeeper session timeout.
- For more information on GC pauses, see the
- 3 part blog post by Todd Lipcon
- and above.
-
-
-
- Logs
-
- The key process logs are as follows... (replace <user> with the user that started the service, and <hostname> for the machine name)
-
-
- NameNode: $HADOOP_HOME/logs/hadoop-<user>-namenode-<hostname>.log
-
-
- DataNode: $HADOOP_HOME/logs/hadoop-<user>-datanode-<hostname>.log
-
-
- JobTracker: $HADOOP_HOME/logs/hadoop-<user>-jobtracker-<hostname>.log
-
-
- TaskTracker: $HADOOP_HOME/logs/hadoop-<user>-jobtracker-<hostname>.log
-
-
- HMaster: $HBASE_HOME/logs/hbase-<user>-master-<hostname>.log
-
-
- RegionServer: $HBASE_HOME/logs/hbase-<user>-regionserver-<hostname>.log
-
-
- ZooKeeper: TODO
-
-
- Log Locations
- For stand-alone deployments the logs are obviously going to be on a single machine, however this is a development configuration only.
- Production deployments need to run on a cluster.
-
- NameNode
- The NameNode log is on the NameNode server. The HBase Master is typically run on the NameNode server, and well as ZooKeeper.
- For smaller clusters the JobTracker is typically run on the NameNode server as well.
-
-
- DataNode
- Each DataNode server will have a DataNode log for HDFS, as well as a RegionServer log for HBase.
- Additionally, each DataNode server will also have a TaskTracker log for MapReduce task execution.
-
-
-
- Log Levels
- Enabling RPC-level logging
- Enabling the RPC-level logging on a RegionServer can often given
- insight on timings at the server. Once enabled, the amount of log
- spewed is voluminous. It is not recommended that you leave this
- logging on for more than short bursts of time. To enable RPC-level
- logging, browse to the RegionServer UI and click on
- Log Level. Set the log level to DEBUG for the package
- org.apache.hadoop.ipc (Thats right, for
- hadoop.ipc, NOT, hbase.ipc). Then tail the RegionServers log. Analyze.
- To disable, set the logging level back to INFO level.
-
-
-
-
- JVM Garbage Collection Logs
- HBase is memory intensive, and using the default GC you can see long pauses in all threads including the Juliet Pause aka "GC of Death".
- To help debug this or confirm this is happening GC logging can be turned on in the Java virtual machine.
-
-
- To enable, in hbase-env.sh add:
-
-export HBASE_OPTS="-XX:+UseConcMarkSweepGC -verbose:gc -XX:+PrintGCDetails -XX:+PrintGCTimeStamps -Xloggc:/home/hadoop/hbase/logs/gc-hbase.log"
-
- Adjust the log directory to wherever you log. Note: The GC log does NOT roll automatically, so you'll have to keep an eye on it so it doesn't fill up the disk.
-
-
- At this point you should see logs like so:
-
-64898.952: [GC [1 CMS-initial-mark: 2811538K(3055704K)] 2812179K(3061272K), 0.0007360 secs] [Times: user=0.00 sys=0.00, real=0.00 secs]
-64898.953: [CMS-concurrent-mark-start]
-64898.971: [GC 64898.971: [ParNew: 5567K->576K(5568K), 0.0101110 secs] 2817105K->2812715K(3061272K), 0.0102200 secs] [Times: user=0.07 sys=0.00, real=0.01 secs]
-
-
-
- In this section, the first line indicates a 0.0007360 second pause for the CMS to initially mark. This pauses the entire VM, all threads for that period of time.
-
-
- The third line indicates a "minor GC", which pauses the VM for 0.0101110 seconds - aka 10 milliseconds. It has reduced the "ParNew" from about 5.5m to 576k.
- Later on in this cycle we see:
-
-64901.445: [CMS-concurrent-mark: 1.542/2.492 secs] [Times: user=10.49 sys=0.33, real=2.49 secs]
-64901.445: [CMS-concurrent-preclean-start]
-64901.453: [GC 64901.453: [ParNew: 5505K->573K(5568K), 0.0062440 secs] 2868746K->2864292K(3061272K), 0.0063360 secs] [Times: user=0.05 sys=0.00, real=0.01 secs]
-64901.476: [GC 64901.476: [ParNew: 5563K->575K(5568K), 0.0072510 secs] 2869283K->2864837K(3061272K), 0.0073320 secs] [Times: user=0.05 sys=0.01, real=0.01 secs]
-64901.500: [GC 64901.500: [ParNew: 5517K->573K(5568K), 0.0120390 secs] 2869780K->2865267K(3061272K), 0.0121150 secs] [Times: user=0.09 sys=0.00, real=0.01 secs]
-64901.529: [GC 64901.529: [ParNew: 5507K->569K(5568K), 0.0086240 secs] 2870200K->2865742K(3061272K), 0.0087180 secs] [Times: user=0.05 sys=0.00, real=0.01 secs]
-64901.554: [GC 64901.555: [ParNew: 5516K->575K(5568K), 0.0107130 secs] 2870689K->2866291K(3061272K), 0.0107820 secs] [Times: user=0.06 sys=0.00, real=0.01 secs]
-64901.578: [CMS-concurrent-preclean: 0.070/0.133 secs] [Times: user=0.48 sys=0.01, real=0.14 secs]
-64901.578: [CMS-concurrent-abortable-preclean-start]
-64901.584: [GC 64901.584: [ParNew: 5504K->571K(5568K), 0.0087270 secs] 2871220K->2866830K(3061272K), 0.0088220 secs] [Times: user=0.05 sys=0.00, real=0.01 secs]
-64901.609: [GC 64901.609: [ParNew: 5512K->569K(5568K), 0.0063370 secs] 2871771K->2867322K(3061272K), 0.0064230 secs] [Times: user=0.06 sys=0.00, real=0.01 secs]
-64901.615: [CMS-concurrent-abortable-preclean: 0.007/0.037 secs] [Times: user=0.13 sys=0.00, real=0.03 secs]
-64901.616: [GC[YG occupancy: 645 K (5568 K)]64901.616: [Rescan (parallel) , 0.0020210 secs]64901.618: [weak refs processing, 0.0027950 secs] [1 CMS-remark: 2866753K(3055704K)] 2867399K(3061272K), 0.0049380 secs] [Times: user=0.00 sys=0.01, real=0.01 secs]
-64901.621: [CMS-concurrent-sweep-start]
-
-
-
- The first line indicates that the CMS concurrent mark (finding garbage) has taken 2.4 seconds. But this is a _concurrent_ 2.4 seconds, Java has not been paused at any point in time.
-
-
- There are a few more minor GCs, then there is a pause at the 2nd last line:
-
-64901.616: [GC[YG occupancy: 645 K (5568 K)]64901.616: [Rescan (parallel) , 0.0020210 secs]64901.618: [weak refs processing, 0.0027950 secs] [1 CMS-remark: 2866753K(3055704K)] 2867399K(3061272K), 0.0049380 secs] [Times: user=0.00 sys=0.01, real=0.01 secs]
-
-
-
- The pause here is 0.0049380 seconds (aka 4.9 milliseconds) to 'remark' the heap.
-
-
- At this point the sweep starts, and you can watch the heap size go down:
-
-64901.637: [GC 64901.637: [ParNew: 5501K->569K(5568K), 0.0097350 secs] 2871958K->2867441K(3061272K), 0.0098370 secs] [Times: user=0.05 sys=0.00, real=0.01 secs]
-... lines removed ...
-64904.936: [GC 64904.936: [ParNew: 5532K->568K(5568K), 0.0070720 secs] 1365024K->1360689K(3061272K), 0.0071930 secs] [Times: user=0.05 sys=0.00, real=0.01 secs]
-64904.953: [CMS-concurrent-sweep: 2.030/3.332 secs] [Times: user=9.57 sys=0.26, real=3.33 secs]
-
- At this point, the CMS sweep took 3.332 seconds, and heap went from about ~ 2.8 GB to 1.3 GB (approximate).
-
-
- The key points here is to keep all these pauses low. CMS pauses are always low, but if your ParNew starts growing, you can see minor GC pauses approach 100ms, exceed 100ms and hit as high at 400ms.
-
-
- This can be due to the size of the ParNew, which should be relatively small. If your ParNew is very large after running HBase for a while, in one example a ParNew was about 150MB, then you might have to constrain the size of ParNew (The larger it is, the longer the collections take but if its too small, objects are promoted to old gen too quickly). In the below we constrain new gen size to 64m.
-
-
- Add this to HBASE_OPTS:
-
-export HBASE_OPTS="-XX:NewSize=64m -XX:MaxNewSize=64m <cms options from above> <gc logging options from above>"
-
-
-
- For more information on GC pauses, see the 3 part blog post by Todd Lipcon
- and above.
-
-
-
-
- Resources
-
- search-hadoop.com
-
- search-hadoop.com indexes all the mailing lists and is great for historical searches.
- Search here first when you have an issue as its more than likely someone has already had your problem.
-
-
-
- Mailing Lists
- Ask a question on the HBase mailing lists.
- The 'dev' mailing list is aimed at the community of developers actually building HBase and for features currently under development, and 'user'
- is generally used for questions on released versions of HBase. Before going to the mailing list, make sure your
- question has not already been answered by searching the mailing list archives first. Use
- .
- Take some time crafting your questionSee Getting Answers; a quality question that includes all context and
- exhibits evidence the author has tried to find answers in the manual and out on lists
- is more likely to get a prompt response.
-
-
-
- IRC
- #hbase on irc.freenode.net
-
-
- JIRA
-
- JIRA is also really helpful when looking for Hadoop/HBase-specific issues.
-
-
-
-
- Tools
-
- Builtin Tools
-
- Master Web Interface
- The Master starts a web-interface on port 60010 by default.
-
- The Master web UI lists created tables and their definition (e.g., ColumnFamilies, blocksize, etc.). Additionally,
- the available RegionServers in the cluster are listed along with selected high-level metrics (requests, number of regions, usedHeap, maxHeap).
- The Master web UI allows navigation to each RegionServer's web UI.
-
-
-
- RegionServer Web Interface
- RegionServers starts a web-interface on port 60030 by default.
-
- The RegionServer web UI lists online regions and their start/end keys, as well as point-in-time RegionServer metrics (requests, regions, storeFileIndexSize, compactionQueueSize, etc.).
-
- See for more information in metric definitions.
-
-
-
- zkcli
- zkcli is a very useful tool for investigating ZooKeeper-related issues. To invoke:
-
-./hbase zkcli -server host:port <cmd> <args>
-
- The commands (and arguments) are:
-
- connect host:port
- get path [watch]
- ls path [watch]
- set path data [version]
- delquota [-n|-b] path
- quit
- printwatches on|off
- create [-s] [-e] path data acl
- stat path [watch]
- close
- ls2 path [watch]
- history
- listquota path
- setAcl path acl
- getAcl path
- sync path
- redo cmdno
- addauth scheme auth
- delete path [version]
- setquota -n|-b val path
-
-
-
-
-
- External Tools
-
- tail
-
- tail is the command line tool that lets you look at the end of a file. Add the “-f” option and it will refresh when new data is available. It’s useful when you are wondering what’s happening, for example, when a cluster is taking a long time to shutdown or startup as you can just fire a new terminal and tail the master log (and maybe a few RegionServers).
-
-
-
- top
-
- top is probably one of the most important tool when first trying to see what’s running on a machine and how the resources are consumed. Here’s an example from production system:
-
-top - 14:46:59 up 39 days, 11:55, 1 user, load average: 3.75, 3.57, 3.84
-Tasks: 309 total, 1 running, 308 sleeping, 0 stopped, 0 zombie
-Cpu(s): 4.5%us, 1.6%sy, 0.0%ni, 91.7%id, 1.4%wa, 0.1%hi, 0.6%si, 0.0%st
-Mem: 24414432k total, 24296956k used, 117476k free, 7196k buffers
-Swap: 16008732k total, 14348k used, 15994384k free, 11106908k cached
-
- PID USER PR NI VIRT RES SHR S %CPU %MEM TIME+ COMMAND
-15558 hadoop 18 -2 3292m 2.4g 3556 S 79 10.4 6523:52 java
-13268 hadoop 18 -2 8967m 8.2g 4104 S 21 35.1 5170:30 java
- 8895 hadoop 18 -2 1581m 497m 3420 S 11 2.1 4002:32 java
-…
-
-
-
- Here we can see that the system load average during the last five minutes is 3.75, which very roughly means that on average 3.75 threads were waiting for CPU time during these 5 minutes. In general, the “perfect” utilization equals to the number of cores, under that number the machine is under utilized and over that the machine is over utilized. This is an important concept, see this article to understand it more: http://www.linuxjournal.com/article/9001.
-
-
- Apart from load, we can see that the system is using almost all its available RAM but most of it is used for the OS cache (which is good). The swap only has a few KBs in it and this is wanted, high numbers would indicate swapping activity which is the nemesis of performance of Java systems. Another way to detect swapping is when the load average goes through the roof (although this could also be caused by things like a dying disk, among others).
-
-
- The list of processes isn’t super useful by default, all we know is that 3 java processes are using about 111% of the CPUs. To know which is which, simply type “c” and each line will be expanded. Typing “1” will give you the detail of how each CPU is used instead of the average for all of them like shown here.
-
-
-
- jps
-
- jps is shipped with every JDK and gives the java process ids for the current user (if root, then it gives the ids for all users). Example:
-
-hadoop@sv4borg12:~$ jps
-1322 TaskTracker
-17789 HRegionServer
-27862 Child
-1158 DataNode
-25115 HQuorumPeer
-2950 Jps
-19750 ThriftServer
-18776 jmx
-
- In order, we see a:
-
- Hadoop TaskTracker, manages the local Childs
- HBase RegionServer, serves regions
- Child, its MapReduce task, cannot tell which type exactly
- Hadoop TaskTracker, manages the local Childs
- Hadoop DataNode, serves blocks
- HQuorumPeer, a ZooKeeper ensemble member
- Jps, well… it’s the current process
- ThriftServer, it’s a special one will be running only if thrift was started
- jmx, this is a local process that’s part of our monitoring platform ( poorly named maybe). You probably don’t have that.
-
-
-
- You can then do stuff like checking out the full command line that started the process:
-
-hadoop@sv4borg12:~$ ps aux | grep HRegionServer
-hadoop 17789 155 35.2 9067824 8604364 ? S<l Mar04 9855:48 /usr/java/jdk1.6.0_14/bin/java -Xmx8000m -XX:+DoEscapeAnalysis -XX:+AggressiveOpts -XX:+UseConcMarkSweepGC -XX:NewSize=64m -XX:MaxNewSize=64m -XX:CMSInitiatingOccupancyFraction=88 -verbose:gc -XX:+PrintGCDetails -XX:+PrintGCTimeStamps -Xloggc:/export1/hadoop/logs/gc-hbase.log -Dcom.sun.management.jmxremote.port=10102 -Dcom.sun.management.jmxremote.authenticate=true -Dcom.sun.management.jmxremote.ssl=false -Dcom.sun.management.jmxremote.password.file=/home/hadoop/hbase/conf/jmxremote.password -Dcom.sun.management.jmxremote -Dhbase.log.dir=/export1/hadoop/logs -Dhbase.log.file=hbase-hadoop-regionserver-sv4borg12.log -Dhbase.home.dir=/home/hadoop/hbase -Dhbase.id.str=hadoop -Dhbase.root.logger=INFO,DRFA -Djava.library.path=/home/hadoop/hbase/lib/native/Linux-amd64-64 -classpath /home/hadoop/hbase/bin/../conf:[many jars]:/home/hadoop/hadoop/conf org.apache.hadoop.hbase.regionserver.HRegionServer start
-
-
-
-
- jstack
-
- jstack is one of the most important tools when trying to figure out what a java process is doing apart from looking at the logs. It has to be used in conjunction with jps in order to give it a process id. It shows a list of threads, each one has a name, and they appear in the order that they were created (so the top ones are the most recent threads). Here’s a few example:
-
-
- The main thread of a RegionServer that’s waiting for something to do from the master:
-
- "regionserver60020" prio=10 tid=0x0000000040ab4000 nid=0x45cf waiting on condition [0x00007f16b6a96000..0x00007f16b6a96a70]
- java.lang.Thread.State: TIMED_WAITING (parking)
- at sun.misc.Unsafe.park(Native Method)
- - parking to wait for <0x00007f16cd5c2f30> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject)
- at java.util.concurrent.locks.LockSupport.parkNanos(LockSupport.java:198)
- at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.awaitNanos(AbstractQueuedSynchronizer.java:1963)
- at java.util.concurrent.LinkedBlockingQueue.poll(LinkedBlockingQueue.java:395)
- at org.apache.hadoop.hbase.regionserver.HRegionServer.run(HRegionServer.java:647)
- at java.lang.Thread.run(Thread.java:619)
-
- The MemStore flusher thread that is currently flushing to a file:
-"regionserver60020.cacheFlusher" daemon prio=10 tid=0x0000000040f4e000 nid=0x45eb in Object.wait() [0x00007f16b5b86000..0x00007f16b5b87af0]
- java.lang.Thread.State: WAITING (on object monitor)
- at java.lang.Object.wait(Native Method)
- at java.lang.Object.wait(Object.java:485)
- at org.apache.hadoop.ipc.Client.call(Client.java:803)
- - locked <0x00007f16cb14b3a8> (a org.apache.hadoop.ipc.Client$Call)
- at org.apache.hadoop.ipc.RPC$Invoker.invoke(RPC.java:221)
- at $Proxy1.complete(Unknown Source)
- at sun.reflect.GeneratedMethodAccessor38.invoke(Unknown Source)
- at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:25)
- at java.lang.reflect.Method.invoke(Method.java:597)
- at org.apache.hadoop.io.retry.RetryInvocationHandler.invokeMethod(RetryInvocationHandler.java:82)
- at org.apache.hadoop.io.retry.RetryInvocationHandler.invoke(RetryInvocationHandler.java:59)
- at $Proxy1.complete(Unknown Source)
- at org.apache.hadoop.hdfs.DFSClient$DFSOutputStream.closeInternal(DFSClient.java:3390)
- - locked <0x00007f16cb14b470> (a org.apache.hadoop.hdfs.DFSClient$DFSOutputStream)
- at org.apache.hadoop.hdfs.DFSClient$DFSOutputStream.close(DFSClient.java:3304)
- at org.apache.hadoop.fs.FSDataOutputStream$PositionCache.close(FSDataOutputStream.java:61)
- at org.apache.hadoop.fs.FSDataOutputStream.close(FSDataOutputStream.java:86)
- at org.apache.hadoop.hbase.io.hfile.HFile$Writer.close(HFile.java:650)
- at org.apache.hadoop.hbase.regionserver.StoreFile$Writer.close(StoreFile.java:853)
- at org.apache.hadoop.hbase.regionserver.Store.internalFlushCache(Store.java:467)
- - locked <0x00007f16d00e6f08> (a java.lang.Object)
- at org.apache.hadoop.hbase.regionserver.Store.flushCache(Store.java:427)
- at org.apache.hadoop.hbase.regionserver.Store.access$100(Store.java:80)
- at org.apache.hadoop.hbase.regionserver.Store$StoreFlusherImpl.flushCache(Store.java:1359)
- at org.apache.hadoop.hbase.regionserver.HRegion.internalFlushcache(HRegion.java:907)
- at org.apache.hadoop.hbase.regionserver.HRegion.internalFlushcache(HRegion.java:834)
- at org.apache.hadoop.hbase.regionserver.HRegion.flushcache(HRegion.java:786)
- at org.apache.hadoop.hbase.regionserver.MemStoreFlusher.flushRegion(MemStoreFlusher.java:250)
- at org.apache.hadoop.hbase.regionserver.MemStoreFlusher.flushRegion(MemStoreFlusher.java:224)
- at org.apache.hadoop.hbase.regionserver.MemStoreFlusher.run(MemStoreFlusher.java:146)
-
-
-
- A handler thread that’s waiting for stuff to do (like put, delete, scan, etc):
-
-"IPC Server handler 16 on 60020" daemon prio=10 tid=0x00007f16b011d800 nid=0x4a5e waiting on condition [0x00007f16afefd000..0x00007f16afefd9f0]
- java.lang.Thread.State: WAITING (parking)
- at sun.misc.Unsafe.park(Native Method)
- - parking to wait for <0x00007f16cd3f8dd8> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject)
- at java.util.concurrent.locks.LockSupport.park(LockSupport.java:158)
- at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:1925)
- at java.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:358)
- at org.apache.hadoop.hbase.ipc.HBaseServer$Handler.run(HBaseServer.java:1013)
-
-
-
- And one that’s busy doing an increment of a counter (it’s in the phase where it’s trying to create a scanner in order to read the last value):
-
-"IPC Server handler 66 on 60020" daemon prio=10 tid=0x00007f16b006e800 nid=0x4a90 runnable [0x00007f16acb77000..0x00007f16acb77cf0]
- java.lang.Thread.State: RUNNABLE
- at org.apache.hadoop.hbase.regionserver.KeyValueHeap.<init>(KeyValueHeap.java:56)
- at org.apache.hadoop.hbase.regionserver.StoreScanner.<init>(StoreScanner.java:79)
- at org.apache.hadoop.hbase.regionserver.Store.getScanner(Store.java:1202)
- at org.apache.hadoop.hbase.regionserver.HRegion$RegionScanner.<init>(HRegion.java:2209)
- at org.apache.hadoop.hbase.regionserver.HRegion.instantiateInternalScanner(HRegion.java:1063)
- at org.apache.hadoop.hbase.regionserver.HRegion.getScanner(HRegion.java:1055)
- at org.apache.hadoop.hbase.regionserver.HRegion.getScanner(HRegion.java:1039)
- at org.apache.hadoop.hbase.regionserver.HRegion.getLastIncrement(HRegion.java:2875)
- at org.apache.hadoop.hbase.regionserver.HRegion.incrementColumnValue(HRegion.java:2978)
- at org.apache.hadoop.hbase.regionserver.HRegionServer.incrementColumnValue(HRegionServer.java:2433)
- at sun.reflect.GeneratedMethodAccessor20.invoke(Unknown Source)
- at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:25)
- at java.lang.reflect.Method.invoke(Method.java:597)
- at org.apache.hadoop.hbase.ipc.HBaseRPC$Server.call(HBaseRPC.java:560)
- at org.apache.hadoop.hbase.ipc.HBaseServer$Handler.run(HBaseServer.java:1027)
-
-
-
- A thread that receives data from HDFS:
-
-"IPC Client (47) connection to sv4borg9/10.4.24.40:9000 from hadoop" daemon prio=10 tid=0x00007f16a02d0000 nid=0x4fa3 runnable [0x00007f16b517d000..0x00007f16b517dbf0]
- java.lang.Thread.State: RUNNABLE
- at sun.nio.ch.EPollArrayWrapper.epollWait(Native Method)
- at sun.nio.ch.EPollArrayWrapper.poll(EPollArrayWrapper.java:215)
- at sun.nio.ch.EPollSelectorImpl.doSelect(EPollSelectorImpl.java:65)
- at sun.nio.ch.SelectorImpl.lockAndDoSelect(SelectorImpl.java:69)
- - locked <0x00007f17d5b68c00> (a sun.nio.ch.Util$1)
- - locked <0x00007f17d5b68be8> (a java.util.Collections$UnmodifiableSet)
- - locked <0x00007f1877959b50> (a sun.nio.ch.EPollSelectorImpl)
- at sun.nio.ch.SelectorImpl.select(SelectorImpl.java:80)
- at org.apache.hadoop.net.SocketIOWithTimeout$SelectorPool.select(SocketIOWithTimeout.java:332)
- at org.apache.hadoop.net.SocketIOWithTimeout.doIO(SocketIOWithTimeout.java:157)
- at org.apache.hadoop.net.SocketInputStream.read(SocketInputStream.java:155)
- at org.apache.hadoop.net.SocketInputStream.read(SocketInputStream.java:128)
- at java.io.FilterInputStream.read(FilterInputStream.java:116)
- at org.apache.hadoop.ipc.Client$Connection$PingInputStream.read(Client.java:304)
- at java.io.BufferedInputStream.fill(BufferedInputStream.java:218)
- at java.io.BufferedInputStream.read(BufferedInputStream.java:237)
- - locked <0x00007f1808539178> (a java.io.BufferedInputStream)
- at java.io.DataInputStream.readInt(DataInputStream.java:370)
- at org.apache.hadoop.ipc.Client$Connection.receiveResponse(Client.java:569)
- at org.apache.hadoop.ipc.Client$Connection.run(Client.java:477)
-
-
-
- And here is a master trying to recover a lease after a RegionServer died:
-
-"LeaseChecker" daemon prio=10 tid=0x00000000407ef800 nid=0x76cd waiting on condition [0x00007f6d0eae2000..0x00007f6d0eae2a70]
---
- java.lang.Thread.State: WAITING (on object monitor)
- at java.lang.Object.wait(Native Method)
- at java.lang.Object.wait(Object.java:485)
- at org.apache.hadoop.ipc.Client.call(Client.java:726)
- - locked <0x00007f6d1cd28f80> (a org.apache.hadoop.ipc.Client$Call)
- at org.apache.hadoop.ipc.RPC$Invoker.invoke(RPC.java:220)
- at $Proxy1.recoverBlock(Unknown Source)
- at org.apache.hadoop.hdfs.DFSClient$DFSOutputStream.processDatanodeError(DFSClient.java:2636)
- at org.apache.hadoop.hdfs.DFSClient$DFSOutputStream.<init>(DFSClient.java:2832)
- at org.apache.hadoop.hdfs.DFSClient.append(DFSClient.java:529)
- at org.apache.hadoop.hdfs.DistributedFileSystem.append(DistributedFileSystem.java:186)
- at org.apache.hadoop.fs.FileSystem.append(FileSystem.java:530)
- at org.apache.hadoop.hbase.util.FSUtils.recoverFileLease(FSUtils.java:619)
- at org.apache.hadoop.hbase.regionserver.wal.HLog.splitLog(HLog.java:1322)
- at org.apache.hadoop.hbase.regionserver.wal.HLog.splitLog(HLog.java:1210)
- at org.apache.hadoop.hbase.master.HMaster.splitLogAfterStartup(HMaster.java:648)
- at org.apache.hadoop.hbase.master.HMaster.joinCluster(HMaster.java:572)
- at org.apache.hadoop.hbase.master.HMaster.run(HMaster.java:503)
-
-
-
-
- OpenTSDB
-
- OpenTSDB is an excellent alternative to Ganglia as it uses HBase to store all the time series and doesn’t have to downsample. Monitoring your own HBase cluster that hosts OpenTSDB is a good exercise.
-
-
- Here’s an example of a cluster that’s suffering from hundreds of compactions launched almost all around the same time, which severely affects the IO performance: (TODO: insert graph plotting compactionQueueSize)
-
-
- It’s a good practice to build dashboards with all the important graphs per machine and per cluster so that debugging issues can be done with a single quick look. For example, at StumbleUpon there’s one dashboard per cluster with the most important metrics from both the OS and HBase. You can then go down at the machine level and get even more detailed metrics.
-
-
-
- clusterssh+top
-
- clusterssh+top, it’s like a poor man’s monitoring system and it can be quite useful when you have only a few machines as it’s very easy to setup. Starting clusterssh will give you one terminal per machine and another terminal in which whatever you type will be retyped in every window. This means that you can type “top” once and it will start it for all of your machines at the same time giving you full view of the current state of your cluster. You can also tail all the logs at the same time, edit files, etc.
-
-
-
-
-
-
- Client
- For more information on the HBase client, see .
-
-
- ScannerTimeoutException or UnknownScannerException
- This is thrown if the time between RPC calls from the client to RegionServer exceeds the scan timeout.
- For example, if Scan.setCaching is set to 500, then there will be an RPC call to fetch the next batch of rows every 500 .next() calls on the ResultScanner
- because data is being transferred in blocks of 500 rows to the client. Reducing the setCaching value may be an option, but setting this value too low makes for inefficient
- processing on numbers of rows.
-
- See .
-
-
-
- Shell or client application throws lots of scary exceptions during normal operation
- Since 0.20.0 the default log level for org.apache.hadoop.hbase.*is DEBUG.
-
- On your clients, edit $HBASE_HOME/conf/log4j.properties and change this: log4j.logger.org.apache.hadoop.hbase=DEBUG to this: log4j.logger.org.apache.hadoop.hbase=INFO, or even log4j.logger.org.apache.hadoop.hbase=WARN.
-
-
-
- Long Client Pauses With Compression
- This is a fairly frequent question on the HBase dist-list. The scenario is that a client is typically inserting a lot of data into a
- relatively un-optimized HBase cluster. Compression can exacerbate the pauses, although it is not the source of the problem.
- See on the pattern for pre-creating regions and confirm that the table isn't starting with a single region.
- See for cluster configuration, particularly hbase.hstore.blockingStoreFiles, hbase.hregion.memstore.block.multiplier,
- MAX_FILESIZE (region size), and MEMSTORE_FLUSHSIZE.
- A slightly longer explanation of why pauses can happen is as follows: Puts are sometimes blocked on the MemStores which are blocked by the flusher thread which is blocked because there are
- too many files to compact because the compactor is given too many small files to compact and has to compact the same data repeatedly. This situation can occur even with minor compactions.
- Compounding this situation, HBase doesn't compress data in memory. Thus, the 64MB that lives in the MemStore could become a 6MB file after compression - which results in a smaller StoreFile. The upside is that
- more data is packed into the same region, but performance is achieved by being able to write larger files - which is why HBase waits until the flushize before writing a new StoreFile. And smaller StoreFiles
- become targets for compaction. Without compression the files are much bigger and don't need as much compaction, however this is at the expense of I/O.
-
-
- For additional information, see this thread on Long client pauses with compression.
-
-
-
-
- ZooKeeper Client Connection Errors
- Errors like this...
-
-11/07/05 11:26:41 WARN zookeeper.ClientCnxn: Session 0x0 for server null,
- unexpected error, closing socket connection and attempting reconnect
- java.net.ConnectException: Connection refused: no further information
- at sun.nio.ch.SocketChannelImpl.checkConnect(Native Method)
- at sun.nio.ch.SocketChannelImpl.finishConnect(Unknown Source)
- at org.apache.zookeeper.ClientCnxn$SendThread.run(ClientCnxn.java:1078)
- 11/07/05 11:26:43 INFO zookeeper.ClientCnxn: Opening socket connection to
- server localhost/127.0.0.1:2181
- 11/07/05 11:26:44 WARN zookeeper.ClientCnxn: Session 0x0 for server null,
- unexpected error, closing socket connection and attempting reconnect
- java.net.ConnectException: Connection refused: no further information
- at sun.nio.ch.SocketChannelImpl.checkConnect(Native Method)
- at sun.nio.ch.SocketChannelImpl.finishConnect(Unknown Source)
- at org.apache.zookeeper.ClientCnxn$SendThread.run(ClientCnxn.java:1078)
- 11/07/05 11:26:45 INFO zookeeper.ClientCnxn: Opening socket connection to
- server localhost/127.0.0.1:2181
-
- ... are either due to ZooKeeper being down, or unreachable due to network issues.
-
- The utility may help investigate ZooKeeper issues.
-
-
-
- Client running out of memory though heap size seems to be stable (but the off-heap/direct heap keeps growing)
-
-You are likely running into the issue that is described and worked through in
-the mail thread HBase, mail # user - Suspected memory leak
-and continued over in HBase, mail # dev - FeedbackRe: Suspected memory leak.
-A workaround is passing your client-side JVM a reasonable value for -XX:MaxDirectMemorySize. By default,
-the MaxDirectMemorySize is equal to your -Xmx max heapsize setting (if -Xmx is set).
-Try seting it to something smaller (for example, one user had success setting it to 1g when
-they had a client-side heap of 12g). If you set it too small, it will bring on FullGCs so keep
-it a bit hefty. You want to make this setting client-side only especially if you are running the new experiemental
-server-side off-heap cache since this feature depends on being able to use big direct buffers (You may have to keep
-separate client-side and server-side config dirs).
-
-
-
- Client Slowdown When Calling Admin Methods (flush, compact, etc.)
-
-This is a client issue fixed by HBASE-5073 in 0.90.6.
-There was a ZooKeeper leak in the client and the client was getting pummeled by ZooKeeper events with each additional
-invocation of the admin API.
-
-
-
-
- Secure Client Cannot Connect ([Caused by GSSException: No valid credentials provided (Mechanism level: Failed to find any Kerberos tgt)])
-
-There can be several causes that produce this symptom.
-
-
-First, check that you have a valid Kerberos ticket. One is required in order to set up communication with a secure HBase cluster. Examine the ticket currently in the credential cache, if any, by running the klist command line utility. If no ticket is listed, you must obtain a ticket by running the kinit command with either a keytab specified, or by interactively entering a password for the desired principal.
-
-
-Then, consult the Java Security Guide troubleshooting section. The most common problem addressed there is resolved by setting javax.security.auth.useSubjectCredsOnly system property value to false.
-
-
-Because of a change in the format in which MIT Kerberos writes its credentials cache, there is a bug in the Oracle JDK 6 Update 26 and earlier that causes Java to be unable to read the Kerberos credentials cache created by versions of MIT Kerberos 1.8.1 or higher. If you have this problematic combination of components in your environment, to work around this problem, first log in with kinit and then immediately refresh the credential cache with kinit -R. The refresh will rewrite the credential cache without the problematic formatting.
-
-
-Finally, depending on your Kerberos configuration, you may need to install the Java Cryptography Extension, or JCE. Insure the JCE jars are on the classpath on both server and client systems.
-
-
-You may also need to download the unlimited strength JCE policy files. Uncompress and extract the downloaded file, and install the policy jars into <java-home>/lib/security.
-
-
-
-
-
-
- MapReduce
-
- You Think You're On The Cluster, But You're Actually Local
- This following stacktrace happened using ImportTsv, but things like this
- can happen on any job with a mis-configuration.
-
- WARN mapred.LocalJobRunner: job_local_0001
-java.lang.IllegalArgumentException: Can't read partitions file
- at org.apache.hadoop.hbase.mapreduce.hadoopbackport.TotalOrderPartitioner.setConf(TotalOrderPartitioner.java:111)
- at org.apache.hadoop.util.ReflectionUtils.setConf(ReflectionUtils.java:62)
- at org.apache.hadoop.util.ReflectionUtils.newInstance(ReflectionUtils.java:117)
- at org.apache.hadoop.mapred.MapTask$NewOutputCollector.<init>(MapTask.java:560)
- at org.apache.hadoop.mapred.MapTask.runNewMapper(MapTask.java:639)
- at org.apache.hadoop.mapred.MapTask.run(MapTask.java:323)
- at org.apache.hadoop.mapred.LocalJobRunner$Job.run(LocalJobRunner.java:210)
-Caused by: java.io.FileNotFoundException: File _partition.lst does not exist.
- at org.apache.hadoop.fs.RawLocalFileSystem.getFileStatus(RawLocalFileSystem.java:383)
- at org.apache.hadoop.fs.FilterFileSystem.getFileStatus(FilterFileSystem.java:251)
- at org.apache.hadoop.fs.FileSystem.getLength(FileSystem.java:776)
- at org.apache.hadoop.io.SequenceFile$Reader.<init>(SequenceFile.java:1424)
- at org.apache.hadoop.io.SequenceFile$Reader.<init>(SequenceFile.java:1419)
- at org.apache.hadoop.hbase.mapreduce.hadoopbackport.TotalOrderPartitioner.readPartitions(TotalOrderPartitioner.java:296)
-
- .. see the critical portion of the stack? It's...
-
- at org.apache.hadoop.mapred.LocalJobRunner$Job.run(LocalJobRunner.java:210)
-
- LocalJobRunner means the job is running locally, not on the cluster.
-
- See
-
- http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/mapreduce/package-summary.html#classpath for more
- information on HBase MapReduce jobs and classpaths.
-
-
-
-
-
- NameNode
- For more information on the NameNode, see .
-
-
- HDFS Utilization of Tables and Regions
- To determine how much space HBase is using on HDFS use the hadoop shell commands from the NameNode. For example...
- hadoop fs -dus /hbase/ ...returns the summarized disk utilization for all HBase objects.
- hadoop fs -dus /hbase/myTable ...returns the summarized disk utilization for the HBase table 'myTable'.
- hadoop fs -du /hbase/myTable ...returns a list of the regions under the HBase table 'myTable' and their disk utilization.
- For more information on HDFS shell commands, see the HDFS FileSystem Shell documentation.
-
-
-
- Browsing HDFS for HBase Objects
- Somtimes it will be necessary to explore the HBase objects that exist on HDFS. These objects could include the WALs (Write Ahead Logs), tables, regions, StoreFiles, etc.
- The easiest way to do this is with the NameNode web application that runs on port 50070. The NameNode web application will provide links to the all the DataNodes in the cluster so that
- they can be browsed seamlessly.
- The HDFS directory structure of HBase tables in the cluster is...
-
-/hbase
- /<Table> (Tables in the cluster)
- /<Region> (Regions for the table)
- /<ColumnFamiy> (ColumnFamilies for the Region for the table)
- /<StoreFile> (StoreFiles for the ColumnFamily for the Regions for the table)
-
-
- The HDFS directory structure of HBase WAL is..
-
-/hbase
- /.logs
- /<RegionServer> (RegionServers)
- /<HLog> (WAL HLog files for the RegionServer)
-
-
- See the HDFS User Guide for other non-shell diagnostic
- utilities like fsck.
-
-
- Use Cases
- Two common use-cases for querying HDFS for HBase objects is research the degree of uncompaction of a table. If there are a large number of StoreFiles for each ColumnFamily it could
- indicate the need for a major compaction. Additionally, after a major compaction if the resulting StoreFile is "small" it could indicate the need for a reduction of ColumnFamilies for
- the table.
-
-
-
-
-
-
-
- Network
-
- Network Spikes
- If you are seeing periodic network spikes you might want to check the compactionQueues to see if major
- compactions are happening.
-
- See for more information on managing compactions.
-
-
-
- Loopback IP
- HBase expects the loopback IP Address to be 127.0.0.1. See the Getting Started section on .
-
-
-
- Network Interfaces
- Are all the network interfaces functioning correctly? Are you sure? See the Troubleshooting Case Study in .
-
-
-
-
-
-
- RegionServer
- For more information on the RegionServers, see .
-
-
- Startup Errors
-
- Master Starts, But RegionServers Do Not
- The Master believes the RegionServers have the IP of 127.0.0.1 - which is localhost and resolves to the master's own localhost.
-
- The RegionServers are erroneously informing the Master that their IP addresses are 127.0.0.1.
-
- Modify /etc/hosts on the region servers, from...
-
-# Do not remove the following line, or various programs
-# that require network functionality will fail.
-127.0.0.1 fully.qualified.regionservername regionservername localhost.localdomain localhost
-::1 localhost6.localdomain6 localhost6
-
- ... to (removing the master node's name from localhost)...
-
-# Do not remove the following line, or various programs
-# that require network functionality will fail.
-127.0.0.1 localhost.localdomain localhost
-::1 localhost6.localdomain6 localhost6
-
-
-
-
-
- Compression Link Errors
-
- Since compression algorithms such as LZO need to be installed and configured on each cluster this is a frequent source of startup error. If you see messages like this...
-
-11/02/20 01:32:15 ERROR lzo.GPLNativeCodeLoader: Could not load native gpl library
-java.lang.UnsatisfiedLinkError: no gplcompression in java.library.path
- at java.lang.ClassLoader.loadLibrary(ClassLoader.java:1734)
- at java.lang.Runtime.loadLibrary0(Runtime.java:823)
- at java.lang.System.loadLibrary(System.java:1028)
-
- .. then there is a path issue with the compression libraries. See the Configuration section on LZO compression configuration.
-
-
-
-
- Runtime Errors
-
-
- RegionServer Hanging
-
- Are you running an old JVM (< 1.6.0_u21?)? When you look at a thread dump,
- does it look like threads are BLOCKED but no one holds the lock all are
- blocked on? See HBASE 3622 Deadlock in HBaseServer (JVM bug?).
- Adding -XX:+UseMembar to the HBase HBASE_OPTS in conf/hbase-env.sh
- may fix it.
-
- Also, are you using ? These are discouraged because they can lock up the
- RegionServers if not managed properly.
-
-
-
- java.io.IOException...(Too many open files)
-
- If you see log messages like this...
-
-2010-09-13 01:24:17,336 WARN org.apache.hadoop.hdfs.server.datanode.DataNode:
-Disk-related IOException in BlockReceiver constructor. Cause is java.io.IOException: Too many open files
- at java.io.UnixFileSystem.createFileExclusively(Native Method)
- at java.io.File.createNewFile(File.java:883)
-
- ... see the Getting Started section on ulimit and nproc configuration.
-
-
-
- xceiverCount 258 exceeds the limit of concurrent xcievers 256
-
- This typically shows up in the DataNode logs.
-
-
- See the Getting Started section on xceivers configuration.
-
-
-
- System instability, and the presence of "java.lang.OutOfMemoryError: unable to create new native thread in exceptions" HDFS DataNode logs or that of any system daemon
-
- See the Getting Started section on ulimit and nproc configuration. The default on recent Linux
- distributions is 1024 - which is far too low for HBase.
-
-
-
- DFS instability and/or RegionServer lease timeouts
-
- If you see warning messages like this...
-
-2009-02-24 10:01:33,516 WARN org.apache.hadoop.hbase.util.Sleeper: We slept xxx ms, ten times longer than scheduled: 10000
-2009-02-24 10:01:33,516 WARN org.apache.hadoop.hbase.util.Sleeper: We slept xxx ms, ten times longer than scheduled: 15000
-2009-02-24 10:01:36,472 WARN org.apache.hadoop.hbase.regionserver.HRegionServer: unable to report to master for xxx milliseconds - retrying
-
- ... or see full GC compactions then you may be experiencing full GC's.
-
-
-
- "No live nodes contain current block" and/or YouAreDeadException
-
- These errors can happen either when running out of OS file handles or in periods of severe network problems where the nodes are unreachable.
-
-
- See the Getting Started section on ulimit and nproc configuration and check your network.
-
-
-
- ZooKeeper SessionExpired events
- Master or RegionServers shutting down with messages like those in the logs:
-
-WARN org.apache.zookeeper.ClientCnxn: Exception
-closing session 0x278bd16a96000f to sun.nio.ch.SelectionKeyImpl@355811ec
-java.io.IOException: TIMED OUT
- at org.apache.zookeeper.ClientCnxn$SendThread.run(ClientCnxn.java:906)
-WARN org.apache.hadoop.hbase.util.Sleeper: We slept 79410ms, ten times longer than scheduled: 5000
-INFO org.apache.zookeeper.ClientCnxn: Attempting connection to server hostname/IP:PORT
-INFO org.apache.zookeeper.ClientCnxn: Priming connection to java.nio.channels.SocketChannel[connected local=/IP:PORT remote=hostname/IP:PORT]
-INFO org.apache.zookeeper.ClientCnxn: Server connection successful
-WARN org.apache.zookeeper.ClientCnxn: Exception closing session 0x278bd16a96000d to sun.nio.ch.SelectionKeyImpl@3544d65e
-java.io.IOException: Session Expired
- at org.apache.zookeeper.ClientCnxn$SendThread.readConnectResult(ClientCnxn.java:589)
- at org.apache.zookeeper.ClientCnxn$SendThread.doIO(ClientCnxn.java:709)
- at org.apache.zookeeper.ClientCnxn$SendThread.run(ClientCnxn.java:945)
-ERROR org.apache.hadoop.hbase.regionserver.HRegionServer: ZooKeeper session expired
-
-
- The JVM is doing a long running garbage collecting which is pausing every threads (aka "stop the world").
- Since the RegionServer's local ZooKeeper client cannot send heartbeats, the session times out.
- By design, we shut down any node that isn't able to contact the ZooKeeper ensemble after getting a timeout so that it stops serving data that may already be assigned elsewhere.
-
-
-
- Make sure you give plenty of RAM (in hbase-env.sh), the default of 1GB won't be able to sustain long running imports.
- Make sure you don't swap, the JVM never behaves well under swapping.
- Make sure you are not CPU starving the RegionServer thread. For example, if you are running a MapReduce job using 6 CPU-intensive tasks on a machine with 4 cores, you are probably starving the RegionServer enough to create longer garbage collection pauses.
- Increase the ZooKeeper session timeout
-
- If you wish to increase the session timeout, add the following to your hbase-site.xml to increase the timeout from the default of 60 seconds to 120 seconds.
-
-<property>
- <name>zookeeper.session.timeout</name>
- <value>1200000</value>
-</property>
-<property>
- <name>hbase.zookeeper.property.tickTime</name>
- <value>6000</value>
-</property>
-
-
-
- Be aware that setting a higher timeout means that the regions served by a failed RegionServer will take at least
- that amount of time to be transfered to another RegionServer. For a production system serving live requests, we would instead
- recommend setting it lower than 1 minute and over-provision your cluster in order the lower the memory load on each machines (hence having
- less garbage to collect per machine).
-
-
- If this is happening during an upload which only happens once (like initially loading all your data into HBase), consider bulk loading.
-
- See for other general information about ZooKeeper troubleshooting.
-
-
- NotServingRegionException
- This exception is "normal" when found in the RegionServer logs at DEBUG level. This exception is returned back to the client
- and then the client goes back to .META. to find the new location of the moved region.
- However, if the NotServingRegionException is logged ERROR, then the client ran out of retries and something probably wrong.
-
-
- Regions listed by domain name, then IP
-
- Fix your DNS. In versions of HBase before 0.92.x, reverse DNS needs to give same answer
- as forward lookup. See HBASE 3431
- RegionServer is not using the name given it by the master; double entry in master listing of servers for gorey details.
-
-
-
- Logs flooded with '2011-01-10 12:40:48,407 INFO org.apache.hadoop.io.compress.CodecPool: Got
- brand-new compressor' messages
- We are not using the native versions of compression
- libraries. See HBASE-1900 Put back native support when hadoop 0.21 is released.
- Copy the native libs from hadoop under hbase lib dir or
- symlink them into place and the message should go away.
-
-
-
- Server handler X on 60020 caught: java.nio.channels.ClosedChannelException
-
- If you see this type of message it means that the region server was trying to read/send data from/to a client but
- it already went away. Typical causes for this are if the client was killed (you see a storm of messages like this when a MapReduce
- job is killed or fails) or if the client receives a SocketTimeoutException. It's harmless, but you should consider digging in
- a bit more if you aren't doing something to trigger them.
-
-
-
-
-
- Shutdown Errors
-
-
-
-
-
-
- Master
- For more information on the Master, see .
-
-
- Startup Errors
-
- Master says that you need to run the hbase migrations script
- Upon running that, the hbase migrations script says no files in root directory.
- HBase expects the root directory to either not exist, or to have already been initialized by hbase running a previous time. If you create a new directory for HBase using Hadoop DFS, this error will occur.
- Make sure the HBase root directory does not currently exist or has been initialized by a previous run of HBase. Sure fire solution is to just use Hadoop dfs to delete the HBase root and let HBase create and initialize the directory itself.
-
-
-
-
-
- Shutdown Errors
-
-
-
-
-
-
- ZooKeeper
-
- Startup Errors
-
- Could not find my address: xyz in list of ZooKeeper quorum servers
- A ZooKeeper server wasn't able to start, throws that error. xyz is the name of your server.
- This is a name lookup problem. HBase tries to start a ZooKeeper server on some machine but that machine isn't able to find itself in the hbase.zookeeper.quorum configuration.
-
- Use the hostname presented in the error message instead of the value you used. If you have a DNS server, you can set hbase.zookeeper.dns.interface and hbase.zookeeper.dns.nameserver in hbase-site.xml to make sure it resolves to the correct FQDN.
-
-
-
-
-
- ZooKeeper, The Cluster Canary
- ZooKeeper is the cluster's "canary in the mineshaft". It'll be the first to notice issues if any so making sure its happy is the short-cut to a humming cluster.
-
-
- See the ZooKeeper Operating Environment Troubleshooting page. It has suggestions and tools for checking disk and networking performance; i.e. the operating environment your ZooKeeper and HBase are running in.
-
- Additionally, the utility may help investigate ZooKeeper issues.
-
-
-
-
-
-
- Amazon EC2
-
- ZooKeeper does not seem to work on Amazon EC2
- HBase does not start when deployed as Amazon EC2 instances. Exceptions like the below appear in the Master and/or RegionServer logs:
-
- 2009-10-19 11:52:27,030 INFO org.apache.zookeeper.ClientCnxn: Attempting
- connection to server ec2-174-129-15-236.compute-1.amazonaws.com/10.244.9.171:2181
- 2009-10-19 11:52:27,032 WARN org.apache.zookeeper.ClientCnxn: Exception
- closing session 0x0 to sun.nio.ch.SelectionKeyImpl@656dc861
- java.net.ConnectException: Connection refused
-
-
- Security group policy is blocking the ZooKeeper port on a public address.
- Use the internal EC2 host names when configuring the ZooKeeper quorum peer list.
-
-
-
- Instability on Amazon EC2
- Questions on HBase and Amazon EC2 come up frequently on the HBase dist-list. Search for old threads using Search Hadoop
-
-
-
- Remote Java Connection into EC2 Cluster Not Working
-
- See Andrew's answer here, up on the user list: Remote Java client connection into EC2 instance.
-
-
-
-
-
-
- HBase and Hadoop version issues
-
- NoClassDefFoundError when trying to run 0.90.x on hadoop-0.20.205.x (or hadoop-1.0.x)
- HBase 0.90.x does not ship with hadoop-0.20.205.x, etc. To make it run, you need to replace the hadoop
- jars that HBase shipped with in its lib directory with those of the Hadoop you want to
- run HBase on. If even after replacing Hadoop jars you get the below exception:
-
-sv4r6s38: Exception in thread "main" java.lang.NoClassDefFoundError: org/apache/commons/configuration/Configuration
-sv4r6s38: at org.apache.hadoop.metrics2.lib.DefaultMetricsSystem.<init>(DefaultMetricsSystem.java:37)
-sv4r6s38: at org.apache.hadoop.metrics2.lib.DefaultMetricsSystem.<clinit>(DefaultMetricsSystem.java:34)
-sv4r6s38: at org.apache.hadoop.security.UgiInstrumentation.create(UgiInstrumentation.java:51)
-sv4r6s38: at org.apache.hadoop.security.UserGroupInformation.initialize(UserGroupInformation.java:209)
-sv4r6s38: at org.apache.hadoop.security.UserGroupInformation.ensureInitialized(UserGroupInformation.java:177)
-sv4r6s38: at org.apache.hadoop.security.UserGroupInformation.isSecurityEnabled(UserGroupInformation.java:229)
-sv4r6s38: at org.apache.hadoop.security.KerberosName.<clinit>(KerberosName.java:83)
-sv4r6s38: at org.apache.hadoop.security.UserGroupInformation.initialize(UserGroupInformation.java:202)
-sv4r6s38: at org.apache.hadoop.security.UserGroupInformation.ensureInitialized(UserGroupInformation.java:177)
-
-you need to copy under hbase/lib, the commons-configuration-X.jar you find
-in your Hadoop's lib directory. That should fix the above complaint.
-
-
-
-
-
- Case Studies
- For Performance and Troubleshooting Case Studies, see .
-
-
-
-
diff --git hbase-site/src/docbkx/upgrading.xml hbase-site/src/docbkx/upgrading.xml
deleted file mode 100644
index 5a18872..0000000
--- hbase-site/src/docbkx/upgrading.xml
+++ /dev/null
@@ -1,201 +0,0 @@
-
-
-
- Upgrading
-
- Review , in particular the section on Hadoop version.
-
-
- Upgrading to HBase 0.90.x from 0.20.x or 0.89.x
- This version of 0.90.x HBase can be started on data written by
- HBase 0.20.x or HBase 0.89.x. There is no need of a migration step.
- HBase 0.89.x and 0.90.x does write out the name of region directories
- differently -- it names them with a md5 hash of the region name rather
- than a jenkins hash -- so this means that once started, there is no
- going back to HBase 0.20.x.
-
-
- Be sure to remove the hbase-default.xml from
- your conf
- directory on upgrade. A 0.20.x version of this file will have
- sub-optimal configurations for 0.90.x HBase. The
- hbase-default.xml file is now bundled into the
- HBase jar and read from there. If you would like to review
- the content of this file, see it in the src tree at
- src/main/resources/hbase-default.xml or
- see .
-
-
- Finally, if upgrading from 0.20.x, check your
- .META. schema in the shell. In the past we would
- recommend that users run with a 16kb
- MEMSTORE_FLUSHSIZE.
- Run hbase> scan '-ROOT-' in the shell. This will output
- the current .META. schema. Check
- MEMSTORE_FLUSHSIZE size. Is it 16kb (16384)? If so, you will
- need to change this (The 'normal'/default value is 64MB (67108864)).
- Run the script bin/set_meta_memstore_size.rb.
- This will make the necessary edit to your .META. schema.
- Failure to run this change will make for a slow cluster
-
- See HBASE-3499 Users upgrading to 0.90.0 need to have their .META. table updated with the right MEMSTORE_SIZE
-
-
- .
-
-
-
-
- Upgrading from 0.90.x to 0.92.x
- Upgrade Guide
-You will find that 0.92.0 runs a little differently to 0.90.x releases. Here are a few things to watch out for upgrading from 0.90.x to 0.92.0.
-tl;dr
-
-If you've not patience, here are the important things to know upgrading.
-
-Once you upgrade, you can’t go back.
-
-
-MSLAB is on by default. Watch that heap usage if you have a lot of regions.
-
-
-Distributed splitting is on by defaul. It should make region server failover faster.
-
-
-There’s a separate tarball for security.
-
-
-If -XX:MaxDirectMemorySize is set in your hbase-env.sh, it’s going to enable the experimental off-heap cache (You may not want this).
-
-
-
-
-
-
-
-You can’t go back!
-
-To move to 0.92.0, all you need to do is shutdown your cluster, replace your hbase 0.90.x with hbase 0.92.0 binaries (be sure you clear out all 0.90.x instances) and restart (You cannot do a rolling restart from 0.90.x to 0.92.x -- you must restart).
-On startup, the .META. table content is rewritten removing the table schema from the info:regioninfo column.
-Also, any flushes done post first startup will write out data in the new 0.92.0 file format, HFile V2.
-This means you cannot go back to 0.90.x once you’ve started HBase 0.92.0 over your HBase data directory.
-
-
-
-
-MSLAB is ON by default
-
-In 0.92.0, the hbase.hregion.memstore.mslab.enabled flag is set to true
-(See ). In 0.90.x it was false. When it is enabled, memstores will step allocate memory in MSLAB 2MB chunks even if the
-memstore has zero or just a few small elements. This is fine usually but if you had lots of regions per regionserver in a 0.90.x cluster (and MSLAB was off),
-you may find yourself OOME'ing on upgrade because the thousands of regions * number of column families * 2MB MSLAB (at a minimum)
-puts your heap over the top. Set hbase.hregion.memstore.mslab.enabled to
-false or set the MSLAB size down from 2MB by setting hbase.hregion.memstore.mslab.chunksize to something less.
-
-
-
-Distributed splitting is on by default
-
-Previous, WAL logs on crash were split by the Master alone. In 0.92.0, log splitting is done by the cluster (See See “HBASE-1364 [performance] Distributed splitting of regionserver commit logs”). This should cut down significantly on the amount of time it takes splitting logs and getting regions back online again.
-
-
-
-Memory accounting is different now
-
-In 0.92.0, indices and bloom filters take up residence in the same LRU used caching blocks that come from the filesystem.
-In 0.90.x, the HFile v1 indices lived outside of the LRU so they took up space even if the index was on a ‘cold’ file, one that wasn’t being actively used. With the indices now in the LRU, you may find you
-have less space for block caching. Adjust your block cache accordingly. See the for more detail.
-The block size default size has been changed in 0.92.0 from 0.2 (20 percent of heap) to 0.25.
-
-
-
-
-On the Hadoop version to use
-
-Run 0.92.0 on Hadoop 1.0.x (or CDH3u3 when it ships). The performance benefits are worth making the move. Otherwise, our Hadoop prescription is as it has been; you need an Hadoop that supports a working sync. See .
-
-
-If running on Hadoop 1.0.x (or CDH3u3), enable local read. See Practical Caching presentation for ruminations on the performance benefits ‘going local’ (and for how to enable local reads).
-
-
-HBase 0.92.0 ships with ZooKeeper 3.4.2
-
-If you can, upgrade your zookeeper. If you can’t, 3.4.2 clients should work against 3.3.X ensembles (HBase makes use of 3.4.2 API).
-
-
-
-Online alter is off by default
-
-In 0.92.0, we’ve added an experimental online schema alter facility (See ). Its off by default. Enable it at your own risk. Online alter and splitting tables do not play well together so be sure your cluster quiescent using this feature (for now).
-
-
-
-WebUI
-
-The webui has had a few additions made in 0.92.0. It now shows a list of the regions currently transitioning, recent compactions/flushes, and a process list of running processes (usually empty if all is well and requests are being handled promptly). Other additions including requests by region, a debugging servlet dump, etc.
-
-
-
-Security tarball
-
-We now ship with two tarballs; secure and insecure HBase. Documentation on how to setup a secure HBase is on the way.
-
-
-
-Experimental off-heap cache
-
-
-A new cache was contributed to 0.92.0 to act as a solution between using the “on-heap” cache which is the current LRU cache the region servers have and the operating system cache which is out of our control.
-To enable, set “-XX:MaxDirectMemorySize” in hbase-env.sh to the value for maximum direct memory size and specify hbase.offheapcache.percentage in hbase-site.xml with the percentage that you want to dedicate to off-heap cache. This should only be set for servers and not for clients. Use at your own risk.
-See this blog post for additional information on this new experimental feature: http://www.cloudera.com/blog/2012/01/caching-in-hbase-slabcache/
-
-
-
-Changes in HBase replication
-
-0.92.0 adds two new features: multi-slave and multi-master replication. The way to enable this is the same as adding a new peer, so in order to have multi-master you would just run add_peer for each cluster that acts as a master to the other slave clusters. Collisions are handled at the timestamp level which may or may not be what you want, this needs to be evaluated on a per use case basis. Replication is still experimental in 0.92 and is disabled by default, run it at your own risk.
-
-
-
-
-RegionServer now aborts if OOME
-
-If an OOME, we now have the JVM kill -9 the regionserver process so it goes down fast. Previous, a RegionServer might stick around after incurring an OOME limping along in some wounded state. To disable this facility, and recommend you leave it in place, you’d need to edit the bin/hbase file. Look for the addition of the -XX:OnOutOfMemoryError="kill -9 %p" arguments (See [HBASE-4769] - ‘Abort RegionServer Immediately on OOME’)
-
-
-
-
-HFile V2 and the “Bigger, Fewer” Tendency
-
-0.92.0 stores data in a new format, . As HBase runs, it will move all your data from HFile v1 to HFile v2 format. This auto-migration will run in the background as flushes and compactions run.
-HFile V2 allows HBase run with larger regions/files. In fact, we encourage that all HBasers going forward tend toward Facebook axiom #1, run with larger, fewer regions.
-If you have lots of regions now -- more than 100s per host -- you should look into setting your region size up after you move to 0.92.0 (In 0.92.0, default size is not 1G, up from 256M), and then running online merge tool (See “HBASE-1621 merge tool should work on online cluster, but disabled table”).
-
-
-
-
diff --git hbase-site/src/site/resources/css/freebsd_docbook.css hbase-site/src/site/resources/css/freebsd_docbook.css
deleted file mode 100644
index 3d40fa7..0000000
--- hbase-site/src/site/resources/css/freebsd_docbook.css
+++ /dev/null
@@ -1,208 +0,0 @@
-/*
- * Copyright (c) 2001, 2003, 2010 The FreeBSD Documentation Project
- * All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions
- * are met:
- * 1. Redistributions of source code must retain the above copyright
- * notice, this list of conditions and the following disclaimer.
- * 2. Redistributions in binary form must reproduce the above copyright
- * notice, this list of conditions and the following disclaimer in the
- * documentation and/or other materials provided with the distribution.
- *
- * THIS SOFTWARE IS PROVIDED BY THE AUTHOR AND CONTRIBUTORS ``AS IS'' AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
- * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
- * ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR OR CONTRIBUTORS BE LIABLE
- * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
- * DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS
- * OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
- * HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
- * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY
- * OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF
- * SUCH DAMAGE.
- *
- * $FreeBSD: doc/share/misc/docbook.css,v 1.15 2010/03/20 04:15:01 hrs Exp $
- */
-
-BODY ADDRESS {
- line-height: 1.3;
- margin: .6em 0;
-}
-
-BODY BLOCKQUOTE {
- margin-top: .75em;
- line-height: 1.5;
- margin-bottom: .75em;
-}
-
-HTML BODY {
- margin: 1em 8% 1em 10%;
- line-height: 1.2;
-}
-
-.LEGALNOTICE {
- font-size: small;
- font-variant: small-caps;
-}
-
-BODY DIV {
- margin: 0;
-}
-
-DL {
- margin: .8em 0;
- line-height: 1.2;
-}
-
-BODY FORM {
- margin: .6em 0;
-}
-
-H1, H2, H3, H4, H5, H6,
-DIV.EXAMPLE P B,
-.QUESTION,
-DIV.TABLE P B,
-DIV.PROCEDURE P B {
- color: #990000;
-}
-
-BODY H1, BODY H2, BODY H3, BODY H4, BODY H5, BODY H6 {
- line-height: 1.3;
- margin-left: 0;
-}
-
-BODY H1, BODY H2 {
- margin: .8em 0 0 -4%;
-}
-
-BODY H3, BODY H4 {
- margin: .8em 0 0 -3%;
-}
-
-BODY H5 {
- margin: .8em 0 0 -2%;
-}
-
-BODY H6 {
- margin: .8em 0 0 -1%;
-}
-
-BODY HR {
- margin: .6em;
- border-width: 0 0 1px 0;
- border-style: solid;
- border-color: #cecece;
-}
-
-BODY IMG.NAVHEADER {
- margin: 0 0 0 -4%;
-}
-
-OL {
- margin: 0 0 0 5%;
- line-height: 1.2;
-}
-
-BODY PRE {
- margin: .75em 0;
- line-height: 1.0;
- font-family: monospace;
-}
-
-BODY TD, BODY TH {
- line-height: 1.2;
-}
-
-UL, BODY DIR, BODY MENU {
- margin: 0 0 0 5%;
- line-height: 1.2;
-}
-
-HTML {
- margin: 0;
- padding: 0;
-}
-
-BODY P B.APPLICATION {
- color: #000000;
-}
-
-.FILENAME {
- color: #007a00;
-}
-
-.GUIMENU, .GUIMENUITEM, .GUISUBMENU,
-.GUILABEL, .INTERFACE,
-.SHORTCUT, .SHORTCUT .KEYCAP {
- font-weight: bold;
-}
-
-.GUIBUTTON {
- background-color: #CFCFCF;
- padding: 2px;
-}
-
-.ACCEL {
- background-color: #F0F0F0;
- text-decoration: underline;
-}
-
-.SCREEN {
- padding: 1ex;
-}
-
-.PROGRAMLISTING {
- padding: 1ex;
- background-color: #eee;
- border: 1px solid #ccc;
-}
-
-@media screen { /* hide from IE3 */
- a[href]:hover { background: #ffa }
-}
-
-BLOCKQUOTE.NOTE {
- color: #222;
- background: #eee;
- border: 1px solid #ccc;
- padding: 0.4em 0.4em;
- width: 85%;
-}
-
-BLOCKQUOTE.TIP {
- color: #004F00;
- background: #d8ecd6;
- border: 1px solid green;
- padding: 0.2em 2em;
- width: 85%;
-}
-
-BLOCKQUOTE.IMPORTANT {
- font-style:italic;
- border: 1px solid #a00;
- border-left: 12px solid #c00;
- padding: 0.1em 1em;
-}
-
-BLOCKQUOTE.WARNING {
- color: #9F1313;
- background: #f8e8e8;
- border: 1px solid #e59595;
- padding: 0.2em 2em;
- width: 85%;
-}
-
-.EXAMPLE {
- background: #fefde6;
- border: 1px solid #f1bb16;
- margin: 1em 0;
- padding: 0.2em 2em;
- width: 90%;
-}
-
-.INFORMALTABLE TABLE.CALSTABLE TR TD {
- padding-left: 1em;
- padding-right: 1em;
-}
diff --git hbase-site/src/site/resources/css/site.css hbase-site/src/site/resources/css/site.css
deleted file mode 100644
index f26d03c..0000000
--- hbase-site/src/site/resources/css/site.css
+++ /dev/null
@@ -1,127 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements. See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership. The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License. You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing,
- * software distributed under the License is distributed on an
- * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- * KIND, either express or implied. See the License for the
- * specific language governing permissions and limitations
- * under the License.
- */
-
-a.externalLink, a.externalLink:link, a.externalLink:visited, a.externalLink:active, a.externalLink:hover {
- background: none;
- padding-right: 0;
-}
-
-/*
-body ul {
- list-style-type: square;
-}
-*/
-
-#downloadbox {
- float: right;
- margin: 0 10px 20px 20px;
- padding: 5px;
- border: 1px solid #999;
- background-color: #eee;
-}
-
-#downloadbox h5 {
- color: #000;
- margin: 0;
- border-bottom: 1px solid #aaaaaa;
- font-size: smaller;
- padding: 0;
-}
-
-#downloadbox p {
- margin-top: 1em;
- margin-bottom: 0;
-}
-
-#downloadbox ul {
- margin-top: 0;
- margin-bottom: 1em;
- list-style-type: disc;
-}
-
-#downloadbox li {
- font-size: smaller;
-}
-
-/*
-h4 {
- padding: 0;
- border: none;
- color: #000;
- margin: 0;
- font-size: larger;
- font-weight: bold;
-}
-*/
-
-#banner {
- background: none;
-}
-
-#banner img {
- padding: 10px;
- margin: auto;
- display: block;
- background: none;
- float: center;
- height:;
-}
-
-#breadcrumbs {
- background-image: url();
-}
-
-#footer {
- border-top: 0px;
-}
-
-.frontpagebox {
- float: left;
- text-align: center;
- width: 15em;
- margin-left: 0.5em;
- margin-right: 0.5em;
- margin-top: 2em;
-}
-
-.headline {
- font-size: 120%;
- font-weight: bold;
- padding-top: 1px;
- padding-bottom: 5px;
- background-image: url(../images/breadcrumbs.jpg);
- background-repeat: repeat-x;
-}
-
-.section {
- padding-bottom: 0;
- padding-top: 0;
-}
-
-/*
-#leftColumn {
- display: none !important
-}
-
-#bodyColumn {
- margin-left: 1.5em;
-}
-*/
-
-
diff --git hbase-site/src/site/resources/doap_Hbase.rdf hbase-site/src/site/resources/doap_Hbase.rdf
deleted file mode 100644
index 08e9bc0..0000000
--- hbase-site/src/site/resources/doap_Hbase.rdf
+++ /dev/null
@@ -1,57 +0,0 @@
-
-
-
-
-
- 2012-04-14
-
- Apache HBase
-
-
- Apache HBase software is the Hadoop database. Think of it as a distributed, scalable, big data store.
- Use Apache HBase software when you need random, realtime read/write access to your Big Data. This project's goal is the hosting of very large tables -- billions of rows X millions of columns -- atop clusters of commodity hardware. HBase is an open-source, distributed, versioned, column-oriented store modeled after Google's Bigtable: A Distributed Storage System for Structured Data by Chang et al. Just as Bigtable leverages the distributed data storage provided by the Google File System, HBase provides Bigtable-like capabilities on top of Hadoop and HDFS.
-
-
-
- Java
-
-
-
- Apache hbase 0.92.1
- 2012-03-19
- 0.92.1
-
-
-
-
-
-
-
-
-
-
- Apache HBase PMC
-
-
-
-
-
diff --git hbase-site/src/site/resources/images/architecture.gif hbase-site/src/site/resources/images/architecture.gif
deleted file mode 100644
index 8d84a23..0000000
Binary files hbase-site/src/site/resources/images/architecture.gif and /dev/null differ
diff --git hbase-site/src/site/resources/images/favicon.ico hbase-site/src/site/resources/images/favicon.ico
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index 6e4d0f7..0000000
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diff --git hbase-site/src/site/resources/images/hadoop-logo.jpg hbase-site/src/site/resources/images/hadoop-logo.jpg
deleted file mode 100644
index 809525d..0000000
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diff --git hbase-site/src/site/resources/images/hbase_logo.png hbase-site/src/site/resources/images/hbase_logo.png
deleted file mode 100644
index 615b0a8..0000000
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diff --git hbase-site/src/site/resources/images/hbase_logo.svg hbase-site/src/site/resources/images/hbase_logo.svg
deleted file mode 100644
index c4b3343..0000000
--- hbase-site/src/site/resources/images/hbase_logo.svg
+++ /dev/null
@@ -1,41 +0,0 @@
-
-
-
-
diff --git hbase-site/src/site/resources/images/hfile.png hbase-site/src/site/resources/images/hfile.png
deleted file mode 100644
index 5762970..0000000
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diff --git hbase-site/src/site/resources/images/hfilev2.png hbase-site/src/site/resources/images/hfilev2.png
deleted file mode 100644
index 54cc0cf..0000000
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diff --git hbase-site/src/site/resources/images/replication_overview.png hbase-site/src/site/resources/images/replication_overview.png
deleted file mode 100644
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diff --git hbase-site/src/site/site.vm hbase-site/src/site/site.vm
deleted file mode 100644
index 0a478e4..0000000
--- hbase-site/src/site/site.vm
+++ /dev/null
@@ -1,544 +0,0 @@
-
-#*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements. See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership. The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License. You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
-*#
-
-#macro ( link $href $name $target $img $position $alt $border $width $height )
- #set ( $linkTitle = ' title="' + $name + '"' )
- #if( $target )
- #set ( $linkTarget = ' target="' + $target + '"' )
- #else
- #set ( $linkTarget = "" )
- #end
- #if ( ( $href.toLowerCase().startsWith("http") || $href.toLowerCase().startsWith("https") ) )
- #set ( $linkClass = ' class="externalLink"' )
- #else
- #set ( $linkClass = "" )
- #end
- #if ( $img )
- #if ( $position == "left" )
- #image($img $alt $border $width $height)$name
- #else
- $name #image($img $alt $border $width $height)
- #end
- #else
- $name
- #end
-#end
-##
-#macro ( image $img $alt $border $width $height )
- #if( $img )
- #if ( ! ( $img.toLowerCase().startsWith("http") || $img.toLowerCase().startsWith("https") ) )
- #set ( $imgSrc = $PathTool.calculateLink( $img, $relativePath ) )
- #set ( $imgSrc = $imgSrc.replaceAll( "\\", "/" ) )
- #set ( $imgSrc = ' src="' + $imgSrc + '"' )
- #else
- #set ( $imgSrc = ' src="' + $img + '"' )
- #end
- #if( $alt )
- #set ( $imgAlt = ' alt="' + $alt + '"' )
- #else
- #set ( $imgAlt = ' alt=""' )
- #end
- #if( $border )
- #set ( $imgBorder = ' border="' + $border + '"' )
- #else
- #set ( $imgBorder = "" )
- #end
- #if( $width )
- #set ( $imgWidth = ' width="' + $width + '"' )
- #else
- #set ( $imgWidth = "" )
- #end
- #if( $height )
- #set ( $imgHeight = ' height="' + $height + '"' )
- #else
- #set ( $imgHeight = "" )
- #end
-
- #end
-#end
-#macro ( banner $banner $id )
- #if ( $banner )
- #if( $banner.href )
-
- #else
-
HBase is not an ACID compliant database. However, it does guarantee certain specific
- properties.
-
This specification enumerates the ACID properties of HBase.
-
-
-
For the sake of common vocabulary, we define the following terms:
-
-
Atomicity
-
an operation is atomic if it either completes entirely or not at all
-
-
Consistency
-
- all actions cause the table to transition from one valid state directly to another
- (eg a row will not disappear during an update, etc)
-
-
-
Isolation
-
- an operation is isolated if it appears to complete independently of any other concurrent transaction
-
-
-
Durability
-
any update that reports "successful" to the client will not be lost
-
-
Visibility
-
an update is considered visible if any subsequent read will see the update as having been committed
-
-
- The terms must and may are used as specified by RFC 2119.
- In short, the word "must" implies that, if some case exists where the statement
- is not true, it is a bug. The word "may" implies that, even if the guarantee
- is provided in a current release, users should not rely on it.
-
-
-
-
-
Read APIs
-
-
get
-
scan
-
-
-
Write APIs
-
-
put
-
batch put
-
delete
-
-
Combination (read-modify-write) APIs
-
-
incrementColumnValue
-
checkAndPut
-
-
-
-
-
-
-
-
-
-
All mutations are atomic within a row. Any put will either wholely succeed or wholely fail.[3]
-
-
An operation that returns a "success" code has completely succeeded.
-
An operation that returns a "failure" code has completely failed.
-
An operation that times out may have succeeded and may have failed. However,
- it will not have partially succeeded or failed.
-
-
This is true even if the mutation crosses multiple column families within a row.
-
APIs that mutate several rows will _not_ be atomic across the multiple rows.
- For example, a multiput that operates on rows 'a','b', and 'c' may return having
- mutated some but not all of the rows. In such cases, these APIs will return a list
- of success codes, each of which may be succeeded, failed, or timed out as described above.
-
The checkAndPut API happens atomically like the typical compareAndSet (CAS) operation
- found in many hardware architectures.
-
The order of mutations is seen to happen in a well-defined order for each row, with no
- interleaving. For example, if one writer issues the mutation "a=1,b=1,c=1" and
- another writer issues the mutation "a=2,b=2,c=2", the row must either
- be "a=1,b=1,c=1" or "a=2,b=2,c=2" and must not be something
- like "a=1,b=2,c=1".
-
-
Please note that this is not true _across rows_ for multirow batch mutations.
-
-
-
-
-
-
All rows returned via any access API will consist of a complete row that existed at
- some point in the table's history.
-
This is true across column families - i.e a get of a full row that occurs concurrent
- with some mutations 1,2,3,4,5 will return a complete row that existed at some point in time
- between mutation i and i+1 for some i between 1 and 5.
-
The state of a row will only move forward through the history of edits to it.
-
-
-
-
- A scan is not a consistent view of a table. Scans do
- not exhibit snapshot isolation.
-
-
- Rather, scans have the following properties:
-
-
-
-
- Any row returned by the scan will be a consistent view (i.e. that version
- of the complete row existed at some point in time) [1]
-
-
- A scan will always reflect a view of the data at least as new as
- the beginning of the scan. This satisfies the visibility guarantees
- enumerated below.
-
-
For example, if client A writes data X and then communicates via a side
- channel to client B, any scans started by client B will contain data at least
- as new as X.
-
A scan _must_ reflect all mutations committed prior to the construction
- of the scanner, and _may_ reflect some mutations committed subsequent to the
- construction of the scanner.
-
Scans must include all data written prior to the scan (except in
- the case where data is subsequently mutated, in which case it _may_ reflect
- the mutation)
-
-
-
- Those familiar with relational databases will recognize this isolation level as "read committed".
-
-
- Please note that the guarantees listed above regarding scanner consistency
- are referring to "transaction commit time", not the "timestamp"
- field of each cell. That is to say, a scanner started at time t may see edits
- with a timestamp value greater than t, if those edits were committed with a
- "forward dated" timestamp before the scanner was constructed.
-
-
-
-
-
-
When a client receives a "success" response for any mutation, that
- mutation is immediately visible to both that client and any client with whom it
- later communicates through side channels. [3]
-
A row must never exhibit so-called "time-travel" properties. That
- is to say, if a series of mutations moves a row sequentially through a series of
- states, any sequence of concurrent reads will return a subsequence of those states.
-
-
For example, if a row's cells are mutated using the "incrementColumnValue"
- API, a client must never see the value of any cell decrease.
-
This is true regardless of which read API is used to read back the mutation.
-
-
Any version of a cell that has been returned to a read operation is guaranteed to
- be durably stored.
-
-
-
-
-
-
All visible data is also durable data. That is to say, a read will never return
- data that has not been made durable on disk[2]
-
Any operation that returns a "success" code (eg does not throw an exception)
- will be made durable.[3]
-
Any operation that returns a "failure" code will not be made durable
- (subject to the Atomicity guarantees above)
-
All reasonable failure scenarios will not affect any of the guarantees of this document.
-
-
-
-
-
All of the above guarantees must be possible within HBase. For users who would like to trade
- off some guarantees for performance, HBase may offer several tuning options. For example:
-
-
Visibility may be tuned on a per-read basis to allow stale reads or time travel.
-
Durability may be tuned to only flush data to disk on a periodic basis
[1] A consistent view is not guaranteed intra-row scanning -- i.e. fetching a portion of
- a row in one RPC then going back to fetch another portion of the row in a subsequent RPC.
- Intra-row scanning happens when you set a limit on how many values to return per Scan#next
- (See Scan#setBatch(int)).
-
-
-
[2] In the context of HBase, "durably on disk" implies an hflush() call on the transaction
- log. This does not actually imply an fsync() to magnetic media, but rather just that the data has been
- written to the OS cache on all replicas of the log. In the case of a full datacenter power loss, it is
- possible that the edits are not truly durable.
-
[3] Puts will either wholely succeed or wholely fail, provided that they are actually sent
- to the RegionServer. If the writebuffer is used, Puts will not be sent until the writebuffer is filled
- or it is explicitly flushed.
As being distributed, large scale platforms, the Hadoop and HBase projects mainly focus on *nix environments for production installations. However, being developed in Java, both projects are fully portable across platforms and, hence, also to the Windows operating system. For ease of development the projects rely on Cygwin to have a *nix-like environment on Windows to run the shell scripts.
-
-
-
This document explains the intricacies of running HBase on Windows using Cygwin as an all-in-one single-node installation for testing and development. The HBase Overview and QuickStart guides on the other hand go a long way in explaning how to setup HBase in more complex deployment scenario's.
-
-
-
-
For running HBase on Windows, 3 technologies are required: Java, Cygwin and SSH. The following paragraphs detail the installation of each of the aforementioned technologies.
-
-
HBase depends on the Java Platform, Standard Edition, 6 Release. So the target system has to be provided with at least the Java Runtime Environment (JRE); however if the system will also be used for development, the Jave Development Kit (JDK) is preferred. You can download the latest versions for both from Sun's download page. Installation is a simple GUI wizard that guides you through the process.
-
-
-
Cygwin is probably the oddest technology in this solution stack. It provides a dynamic link library that emulates most of a *nix environment on Windows. On top of that a whole bunch of the most common *nix tools are supplied. Combined, the DLL with the tools form a very *nix-alike environment on Windows.
-
-
For installation, Cygwin provides the setup.exe utility that tracks the versions of all installed components on the target system and provides the mechanism for installing or updating everything from the mirror sites of Cygwin.
-
-
To support installation, the setup.exe utility uses 2 directories on the target system. The Root directory for Cygwin (defaults to C:\cygwin) which will become / within the eventual Cygwin installation; and the Local Package directory (e.g. C:\cygsetup that is the cache where setup.exe stores the packages before they are installed. The cache must not be the same folder as the Cygwin root.
Make sure you have Administrator privileges on the target system.
-
Choose and create you Root and Local Package directories. A good suggestion is to use C:\cygwin\root and C:\cygwin\setup folders.
-
Download the setup.exe utility and save it to the Local Package directory.
-
Run the setup.exe utility,
-
-
Choose the Install from Internet option,
-
Choose your Root and Local Package folders
-
and select an appropriate mirror.
-
Don't select any additional packages yet, as we only want to install Cygwin for now.
-
Wait for download and install
-
Finish the installation
-
-
-
Optionally, you can now also add a shortcut to your Start menu pointing to the setup.exe utility in the Local Package folder.
-
Add CYGWIN_HOME system-wide environment variable that points to your Root directory.
-
Add %CYGWIN_HOME%\bin to the end of your PATH environment variable.
-
Reboot the sytem after making changes to the environment variables otherwise the OS will not be able to find the Cygwin utilities.
-
Test your installation by running your freshly created shortcuts or the Cygwin.bat command in the Root folder. You should end up in a terminal window that is running a Bash shell. Test the shell by issuing following commands:
-
-
cd / should take you to thr Root directory in Cygwin;
-
the LS commands that should list all files and folders in the current directory.
-
Use the exit command to end the terminal.
-
-
-
When needed, to uninstall Cygwin you can simply delete the Root and Local Package directory, and the shortcuts that were created during installation.
-
-
-
-
HBase (and Hadoop) rely on SSH for interprocess/-node communication and launching remote commands. SSH will be provisioned on the target system via Cygwin, which supports running Cygwin programs as Windows services!
-
-
-
Rerun the setup.exe utility.
-
Leave all parameters as is, skipping through the wizard using the Next button until the Select Packages panel is shown.
-
Maximize the window and click the View button to toggle to the list view, which is ordered alfabetically on Package, making it easier to find the packages we'll need.
-
Select the following packages by clicking the status word (normally Skip) so it's marked for installation. Use the Next button to download and install the packages.
-
-
OpenSSH
-
tcp_wrappers
-
diffutils
-
zlib
-
-
-
Wait for the install to complete and finish the installation.
-
-
-
-
Download the latest release of HBase from the website. As the HBase distributable is just a zipped archive, installation is as simple as unpacking the archive so it ends up in its final installation directory. Notice that HBase has to be installed in Cygwin and a good directory suggestion is to use /usr/local/ (or [Root directory]\usr\local in Windows slang). You should end up with a /usr/local/hbase-<version> installation in Cygwin.
-
-This finishes installation. We go on with the configuration.
-
-
-
-
There are 3 parts left to configure: Java, SSH and HBase itself. Following paragraphs explain eacht topic in detail.
-
-
One important thing to remember in shell scripting in general (i.e. *nix and Windows) is that managing, manipulating and assembling path names that contains spaces can be very hard, due to the need to escape and quote those characters and strings. So we try to stay away from spaces in path names. *nix environments can help us out here very easily by using symbolic links.
-
-
-
Create a link in /usr/local to the Java home directory by using the following command and substituting the name of your chosen Java environment:
-
Test your java installation by changing directories to your Java folder CD /usr/local/<jre name> and issueing the command ./bin/java -version. This should output your version of the chosen JRE.
-
-
-
-SSH
-
Configuring SSH is quite elaborate, but primarily a question of launching it by default as a Windows service.
-
-
-
On Windows Vista and above make sure you run the Cygwin shell with elevated privileges, by right-clicking on the shortcut an using Run as Administrator.
-
First of all, we have to make sure the rights on some crucial files are correct. Use the commands underneath. You can verify all rights by using the LS -L command on the different files. Also, notice the auto-completion feature in the shell using <TAB> is extremely handy in these situations.
-
-
chmod +r /etc/passwd to make the passwords file readable for all
-
chmod u+w /etc/passwd to make the passwords file writable for the owner
-
chmod +r /etc/group to make the groups file readable for all
-
-
-
chmod u+w /etc/group to make the groups file writable for the owner
-
-
-
chmod 755 /var to make the var folder writable to owner and readable and executable to all
-
-
-
Edit the /etc/hosts.allow file using your favorite editor (why not VI in the shell!) and make sure the following two lines are in there before the PARANOID line:
-
-
ALL : localhost 127.0.0.1/32 : allow
-
ALL : [::1]/128 : allow
-
-
-
Next we have to configure SSH by using the script ssh-host-config
-
-
If this script asks to overwrite an existing /etc/ssh_config, answer yes.
-
If this script asks to overwrite an existing /etc/sshd_config, answer yes.
-
If this script asks to use privilege separation, answer yes.
-
If this script asks to install sshd as a service, answer yes. Make sure you started your shell as Adminstrator!
-
If this script asks for the CYGWIN value, just <enter> as the default is ntsec.
-
If this script asks to create the sshd account, answer yes.
-
If this script asks to use a different user name as service account, answer no as the default will suffice.
-
If this script asks to create the cyg_server account, answer yes. Enter a password for the account.
-
-
-
Start the SSH service using net start sshd or cygrunsrv --start sshd. Notice that cygrunsrv is the utility that make the process run as a Windows service. Confirm that you see a message stating that the CYGWIN sshd service was started succesfully.
-
Harmonize Windows and Cygwin user account by using the commands:
-
-
mkpasswd -cl > /etc/passwd
-
mkgroup --local > /etc/group
-
-
-
Test the installation of SSH:
-
-
Open a new Cygwin terminal
-
Use the command whoami to verify your userID
-
Issue an ssh localhost to connect to the system itself
-
-
Answer yes when presented with the server's fingerprint
-
Issue your password when prompted
-
test a few commands in the remote session
-
The exit command should take you back to your first shell in Cygwin
-
-
-
Exit should terminate the Cygwin shell.
-
-
-
-
-
-If all previous configurations are working properly, we just need some tinkering at the HBase config files to properly resolve on Windows/Cygwin. All files and paths referenced here start from the HBase [installation directory] as working directory.
-
-
HBase uses the ./conf/hbase-env.sh to configure its dependencies on the runtime environment. Copy and uncomment following lines just underneath their original, change them to fit your environemnt. They should read something like:
-
-
export JAVA_HOME=/usr/local/<jre name>
-
export HBASE_IDENT_STRING=$HOSTNAME as this most likely does not inlcude spaces.
-
-
-
HBase uses the ./conf/hbase-default.xml file for configuration. Some properties do not resolve to existing directories because the JVM runs on Windows. This is the major issue to keep in mind when working with Cygwin: within the shell all paths are *nix-alike, hence relative to the root /. However, every parameter that is to be consumed within the windows processes themself, need to be Windows settings, hence C:\-alike. Change following propeties in the configuration file, adjusting paths where necessary to conform with your own installation:
-
-
hbase.rootdir must read e.g. file:///C:/cygwin/root/tmp/hbase/data
-
hbase.tmp.dir must read C:/cygwin/root/tmp/hbase/tmp
-
hbase.zookeeper.quorum must read 127.0.0.1 because for some reason localhost doesn't seem to resolve properly on Cygwin.
-
-
-
Make sure the configured hbase.rootdir and hbase.tmp.dirdirectories exist and have the proper rights set up e.g. by issuing a chmod 777 on them.
-
-
-
-
-Testing
-
-This should conclude the installation and configuration of HBase on Windows using Cygwin. So it's time to test it.
-
-
Start a Cygwin terminal, if you haven't already.
-
Change directory to HBase installation using CD /usr/local/hbase-<version>, preferably using auto-completion.
-
Start HBase using the command ./bin/start-hbase.sh
-
-
When prompted to accept the SSH fingerprint, answer yes.
-
When prompted, provide your password. Maybe multiple times.
-
When the command completes, the HBase server should have started.
-
However, to be absolutely certain, check the logs in the ./logs directory for any exceptions.
-
-
-
Next we start the HBase shell using the command ./bin/hbase shell
-
We run some simple test commands
-
-
Create a simple table using command create 'test', 'data'
List all rows in the table using the command scan 'test' that should list all the rows previously inserted. Notice how 3 new columns where added without changing the schema!
-
Finally we get rid of the table by issuing disable 'test' followed by drop 'test' and verified by list which should give an empty listing.
-
-
-
Leave the shell by exit
-
To stop the HBase server issue the ./bin/stop-hbase.sh command. And wait for it to complete!!! Killing the process might corrupt your data on disk.
-
In case of problems,
-
-
verify the HBase logs in the ./logs directory.
-
Try to fix the problem
-
Get help on the forums or IRC (#hbase@freenode.net). People are very active and keen to help out!
-
Stopr, restart and retest the server.
-
-
-
-
-
-
-
-
-Now your HBase server is running, start coding and build that next killer app on this particular, but scalable datastore!
-
HBase is the Hadoop database. Think of it as a distributed, scalable, big data store.
-
-
When Would I Use HBase?
-
- Use HBase when you need random, realtime read/write access to your Big Data.
- This project's goal is the hosting of very large tables -- billions of rows X millions of columns -- atop clusters of commodity hardware.
-HBase is an open-source, distributed, versioned, column-oriented store modeled after Google's Bigtable: A Distributed Storage System for Structured Data by Chang et al.
- Just as Bigtable leverages the distributed data storage provided by the Google File System, HBase provides Bigtable-like capabilities on top of Hadoop and HDFS.
-
-
Features
-
-
-
Linear and modular scalability.
-
-
Strictly consistent reads and writes.
-
-
Automatic and configurable sharding of tables
-
-
Automatic failover support between RegionServers.
-
-
Convenient base classes for backing Hadoop MapReduce jobs with HBase tables.
-
-
Easy to use Java API for client access.
-
-
Block cache and Bloom Filters for real-time queries.
-
-
Query predicate push down via server side Filters
-
-
Thrift gateway and a REST-ful Web service that supports XML, Protobuf, and binary data encoding options
-
-
Extensible jruby-based (JIRB) shell
-
-
Support for exporting metrics via the Hadoop metrics subsystem to files or Ganglia; or via JMX
-
First read up on Hadoop metrics.
- If you are using ganglia, the GangliaMetrics
- wiki page is useful read.
-
To have HBase emit metrics, edit $HBASE_HOME/conf/hadoop-metrics.properties
- and enable metric 'contexts' per plugin. As of this writing, hadoop supports
- file and ganglia plugins.
- Yes, the hbase metrics files is named hadoop-metrics rather than
- hbase-metrics because currently at least the hadoop metrics system has the
- properties filename hardcoded. Per metrics context,
- comment out the NullContext and enable one or more plugins instead.
-
-
- If you enable the hbase context, on regionservers you'll see total requests since last
- metric emission, count of regions and storefiles as well as a count of memstore size.
- On the master, you'll see a count of the cluster's requests.
-
-
- Enabling the rpc context is good if you are interested in seeing
- metrics on each hbase rpc method invocation (counts and time taken).
-
-
- The jvm context is
- useful for long-term stats on running hbase jvms -- memory used, thread counts, etc.
- As of this writing, if more than one jvm is running emitting metrics, at least
- in ganglia, the stats are aggregated rather than reported per instance.
-
-
-
-
-
- In addition to the standard output contexts supported by the Hadoop
- metrics package, you can also export HBase metrics via Java Management
- Extensions (JMX). This will allow viewing HBase stats in JConsole or
- any other JMX client.
-
-
-
- To enable JMX support in HBase, first edit
- $HBASE_HOME/conf/hadoop-metrics.properties to support
- metrics refreshing. (If you've running 0.94.1 and above, or have already configured
- hadoop-metrics.properties for another output context,
- you can skip this step).
-
-
-# Configuration of the "hbase" context for null
-hbase.class=org.apache.hadoop.metrics.spi.NullContextWithUpdateThread
-hbase.period=60
-
-# Configuration of the "jvm" context for null
-jvm.class=org.apache.hadoop.metrics.spi.NullContextWithUpdateThread
-jvm.period=60
-
-# Configuration of the "rpc" context for null
-rpc.class=org.apache.hadoop.metrics.spi.NullContextWithUpdateThread
-rpc.period=60
-
-
-
-
- For remote access, you will need to configure JMX remote passwords
- and access profiles. Create the files:
-
-
-
$HBASE_HOME/conf/jmxremote.passwd (set permissions
- to 600)
- After restarting the processes you want to monitor, you should now be
- able to run JConsole (included with the JDK since JDK 5.0) to view
- the statistics via JMX. HBase MBeans are exported under the
- hadoop domain in JMX.
-
-
-
-
- For more information on understanding HBase metrics, see the metrics section in the HBase Reference Guide.
-
September 8th, 2010: HBase 0.20.0 is faster, stronger, slimmer, and sweeter tasting than any previous HBase release. Get it off the Releases page.
-
ApacheCon in Oakland: November 2-6th, 2009:
- The Apache Foundation will be celebrating its 10th anniversary in beautiful Oakland by the Bay. Lots of good talks and meetups including an HBase presentation by a couple of the lads.
-
HBase at Hadoop World in NYC: October 2nd, 2009: A few of us will be talking on Practical HBase out east at Hadoop World: NYC.
-
HUG7 and HBase Hackathon: August 7th-9th, 2009 at StumbleUpon in SF: Sign up for the HBase User Group Meeting, HUG7 or for the Hackathon or for both (all are welcome!).
-
June, 2009 -- HBase at HadoopSummit2009 and at NOSQL: See the presentations
- HBase replication is a way to copy data between HBase deployments. It
- can serve as a disaster recovery solution and can contribute to provide
- higher availability at the HBase layer. It can also serve more practically;
- for example, as a way to easily copy edits from a web-facing cluster to a "MapReduce"
- cluster which will process old and new data and ship back the results
- automatically.
-
-
- The basic architecture pattern used for HBase replication is (HBase cluster) master-push;
- it is much easier to keep track of what’s currently being replicated since
- each region server has its own write-ahead-log (aka WAL or HLog), just like
- other well known solutions like MySQL master/slave replication where
- there’s only one bin log to keep track of. One master cluster can
- replicate to any number of slave clusters, and each region server will
- participate to replicate their own stream of edits. For more information
- on the different properties of master/slave replication and other types
- of replication, please consult
- How Google Serves Data From Multiple Datacenters.
-
-
- The replication is done asynchronously, meaning that the clusters can
- be geographically distant, the links between them can be offline for
- some time, and rows inserted on the master cluster won’t be
- available at the same time on the slave clusters (eventual consistency).
-
-
- The replication format used in this design is conceptually the same as
-
- MySQL’s statement-based replication . Instead of SQL statements, whole
- WALEdits (consisting of multiple cell inserts coming from the clients'
- Put and Delete) are replicated in order to maintain atomicity.
-
-
- The HLogs from each region server are the basis of HBase replication,
- and must be kept in HDFS as long as they are needed to replicate data
- to any slave cluster. Each RS reads from the oldest log it needs to
- replicate and keeps the current position inside ZooKeeper to simplify
- failure recovery. That position can be different for every slave
- cluster, same for the queue of HLogs to process.
-
-
- The clusters participating in replication can be of asymmetric sizes
- and the master cluster will do its “best effort” to balance the stream
- of replication on the slave clusters by relying on randomization.
-
-
- As of version 0.92 HBase supports master/master and cyclic replication as
- well as replication to multiple slaves.
-
-
-
-
-
- The guide on enabling and using cluster replication is contained
- in the API documentation shipped with your HBase distribution.
-
- The following sections describe the life of a single edit going from a
- client that communicates with a master cluster all the way to a single
- slave cluster.
-
-
-
- The client uses a HBase API that sends a Put, Delete or ICV to a region
- server. The key values are transformed into a WALEdit by the region
- server and is inspected by the replication code that, for each family
- that is scoped for replication, adds the scope to the edit. The edit
- is appended to the current WAL and is then applied to its MemStore.
-
-
- In a separate thread, the edit is read from the log (as part of a batch)
- and only the KVs that are replicable are kept (that is, that they are part
- of a family scoped GLOBAL in the family's schema, non-catalog so not
- .META. or -ROOT-, and did not originate in the target slave cluster - in
- case of cyclic replication).
-
-
- The edit is then tagged with the master's cluster UUID.
- When the buffer is filled, or the reader hits the end of the file,
- the buffer is sent to a random region server on the slave cluster.
-
-
- Synchronously, the region server that receives the edits reads them
- sequentially and separates each of them into buffers, one per table.
- Once all edits are read, each buffer is flushed using the normal HBase
- client (HTables managed by a HTablePool). This is done in order to
- leverage parallel insertion (MultiPut).
- The master's cluster UUID is retained in the edits applied at the
- slave cluster in order to allow cyclic replication.
-
-
- Back in the master cluster's region server, the offset for the current
- WAL that's being replicated is registered in ZooKeeper.
-
-
-
-
- The edit is inserted in the same way.
-
-
- In the separate thread, the region server reads, filters and buffers
- the log edits the same way as during normal processing. The slave
- region server that's contacted doesn't answer to the RPC, so the master
- region server will sleep and retry up to a configured number of times.
- If the slave RS still isn't available, the master cluster RS will select a
- new subset of RS to replicate to and will retry sending the buffer of
- edits.
-
-
- In the mean time, the WALs will be rolled and stored in a queue in
- ZooKeeper. Logs that are archived by their region server (archiving is
- basically moving a log from the region server's logs directory to a
- central logs archive directory) will update their paths in the in-memory
- queue of the replicating thread.
-
-
- When the slave cluster is finally available, the buffer will be applied
- the same way as during normal processing. The master cluster RS will then
- replicate the backlog of logs.
-
-
-
-
-
- This section describes in depth how each of replication's internal
- features operate.
-
-
-
- When a master cluster RS initiates a replication source to a slave cluster,
- it first connects to the slave's ZooKeeper ensemble using the provided
- cluster key (that key is composed of the value of hbase.zookeeper.quorum,
- zookeeper.znode.parent and hbase.zookeeper.property.clientPort). It
- then scans the "rs" directory to discover all the available sinks
- (region servers that are accepting incoming streams of edits to replicate)
- and will randomly choose a subset of them using a configured
- ratio (which has a default value of 10%). For example, if a slave
- cluster has 150 machines, 15 will be chosen as potential recipient for
- edits that this master cluster RS will be sending. Since this is done by all
- master cluster RSs, the probability that all slave RSs are used is very high,
- and this method works for clusters of any size. For example, a master cluster
- of 10 machines replicating to a slave cluster of 5 machines with a ratio
- of 10% means that the master cluster RSs will choose one machine each
- at random, thus the chance of overlapping and full usage of the slave
- cluster is higher.
-
-
-
-
- Every master cluster RS has its own znode in the replication znodes hierarchy.
- It contains one znode per peer cluster (if 5 slave clusters, 5 znodes
- are created), and each of these contain a queue
- of HLogs to process. Each of these queues will track the HLogs created
- by that RS, but they can differ in size. For example, if one slave
- cluster becomes unavailable for some time then the HLogs should not be deleted,
- thus they need to stay in the queue (while the others are processed).
- See the section named "Region server failover" for an example.
-
-
- When a source is instantiated, it contains the current HLog that the
- region server is writing to. During log rolling, the new file is added
- to the queue of each slave cluster's znode just before it's made available.
- This ensures that all the sources are aware that a new log exists
- before HLog is able to append edits into it, but this operations is
- now more expensive.
- The queue items are discarded when the replication thread cannot read
- more entries from a file (because it reached the end of the last block)
- and that there are other files in the queue.
- This means that if a source is up-to-date and replicates from the log
- that the region server writes to, reading up to the "end" of the
- current file won't delete the item in the queue.
-
-
- When a log is archived (because it's not used anymore or because there's
- too many of them per hbase.regionserver.maxlogs typically because insertion
- rate is faster than region flushing), it will notify the source threads that the path
- for that log changed. If the a particular source was already done with
- it, it will just ignore the message. If it's in the queue, the path
- will be updated in memory. If the log is currently being replicated,
- the change will be done atomically so that the reader doesn't try to
- open the file when it's already moved. Also, moving a file is a NameNode
- operation so, if the reader is currently reading the log, it won't
- generate any exception.
-
-
-
-
- By default, a source will try to read from a log file and ship log
- entries as fast as possible to a sink. This is first limited by the
- filtering of log entries; only KeyValues that are scoped GLOBAL and
- that don't belong to catalog tables will be retained. A second limit
- is imposed on the total size of the list of edits to replicate per slave,
- which by default is 64MB. This means that a master cluster RS with 3 slaves
- will use at most 192MB to store data to replicate. This doesn't account
- the data filtered that wasn't garbage collected.
-
-
- Once the maximum size of edits was buffered or the reader hits the end
- of the log file, the source thread will stop reading and will choose
- at random a sink to replicate to (from the list that was generated by
- keeping only a subset of slave RSs). It will directly issue a RPC to
- the chosen machine and will wait for the method to return. If it's
- successful, the source will determine if the current file is emptied
- or if it should continue to read from it. If the former, it will delete
- the znode in the queue. If the latter, it will register the new offset
- in the log's znode. If the RPC threw an exception, the source will retry
- 10 times until trying to find a different sink.
-
-
-
-
- If replication isn't enabled, the master's logs cleaning thread will
- delete old logs using a configured TTL. This doesn't work well with
- replication since archived logs passed their TTL may still be in a
- queue. Thus, the default behavior is augmented so that if a log is
- passed its TTL, the cleaning thread will lookup every queue until it
- finds the log (while caching the ones it finds). If it's not found,
- the log will be deleted. The next time it has to look for a log,
- it will first use its cache.
-
-
-
-
- As long as region servers don't fail, keeping track of the logs in ZK
- doesn't add any value. Unfortunately, they do fail, so since ZooKeeper
- is highly available we can count on it and its semantics to help us
- managing the transfer of the queues.
-
-
- All the master cluster RSs keep a watcher on every other one of them to be
- notified when one dies (just like the master does). When it happens,
- they all race to create a znode called "lock" inside the dead RS' znode
- that contains its queues. The one that creates it successfully will
- proceed by transferring all the queues to its own znode (one by one
- since ZK doesn't support the rename operation) and will delete all the
- old ones when it's done. The recovered queues' znodes will be named
- with the id of the slave cluster appended with the name of the dead
- server.
-
-
- Once that is done, the master cluster RS will create one new source thread per
- copied queue, and each of them will follow the read/filter/ship pattern.
- The main difference is that those queues will never have new data since
- they don't belong to their new region server, which means that when
- the reader hits the end of the last log, the queue's znode will be
- deleted and the master cluster RS will close that replication source.
-
-
- For example, consider a master cluster with 3 region servers that's
- replicating to a single slave with id '2'. The following hierarchy
- represents what the znodes layout could be at some point in time. We
- can see the RSs' znodes all contain a "peers" znode that contains a
- single queue. The znode names in the queues represent the actual file
- names on HDFS in the form "address,port.timestamp".
-
- Now let's say that 1.1.1.2 loses its ZK session. The survivors will race
- to create a lock, and for some reasons 1.1.1.3 wins. It will then start
- transferring all the queues to its local peers znode by appending the
- name of the dead server. Right before 1.1.1.3 is able to clean up the
- old znodes, the layout will look like the following:
-
- Some time later, but before 1.1.1.3 is able to finish replicating the
- last HLog from 1.1.1.2, let's say that it dies too (also some new logs
- were created in the normal queues). The last RS will then try to lock
- 1.1.1.3's znode and will begin transferring all the queues. The new
- layout will be:
-
-
-/hbase/replication/rs/
- 1.1.1.1,60020,123456780/
- 2/
- 1.1.1.1,60020.1378 (Contains a position)
-
- 2-1.1.1.3,60020,123456630/
- 1.1.1.3,60020.1325 (Contains a position)
- 1.1.1.3,60020.1401
-
- 2-1.1.1.2,60020,123456790-1.1.1.3,60020,123456630/
- 1.1.1.2,60020.1312 (Contains a position)
- 1.1.1.3,60020,123456630/
- lock
- 2/
- 1.1.1.3,60020.1325 (Contains a position)
- 1.1.1.3,60020.1401
-
- 2-1.1.1.2,60020,123456790/
- 1.1.1.2,60020.1312 (Contains a position)
-
-
-
-
-
-
- Yes, this is for much later.
-
-
-
-
- You can use the HBase-provided utility called CopyTable from the package
- org.apache.hadoop.hbase.mapreduce in order to have a discp-like tool to
- bulk copy data.
-
-
-
-
- Yes, this behavior would help a lot but it's not currently available
- in HBase (BatchUpdate had that, but it was lost in the new API).
-
-
-
-
-
- Here's a list of all the jiras that relate to major issues or missing
- features in the replication implementation.
-
-
-
- HBASE-2611, basically if a region server dies while recovering the
- queues of another dead RS, we will miss the data from the queues
- that weren't copied.
-
-
-
-
-
diff --git hbase-site/src/site/xdoc/sponsors.xml hbase-site/src/site/xdoc/sponsors.xml
deleted file mode 100644
index e39730b..0000000
--- hbase-site/src/site/xdoc/sponsors.xml
+++ /dev/null
@@ -1,35 +0,0 @@
-
-
-
-
- Installing HBase on Windows using Cygwin
-
-
-
-
-
The below companies have been gracious enough to provide their commerical tool offerings free of charge to the Apache HBase project.
-
+
+
+ Physical View
+
+ Although at a conceptual level tables may be viewed as a sparse set of rows.
+ Physically they are stored on a per-column family basis. New columns
+ (i.e., columnfamily:column) can be added to any
+ column family without pre-announcing them.
+
+ It is important to note in the diagram above that the empty cells shown in the
+ conceptual view are not stored since they need not be in a column-oriented
+ storage format. Thus a request for the value of the contents:html
+ column at time stamp t8 would return no value. Similarly, a
+ request for an anchor:my.look.ca value at time stamp
+ t9 would return no value. However, if no timestamp is
+ supplied, the most recent value for a particular column would be returned
+ and would also be the first one found since timestamps are stored in
+ descending order. Thus a request for the values of all columns in the row
+ com.cnn.www if no timestamp is specified would be:
+ the value of contents:html from time stamp
+ t6, the value of anchor:cnnsi.com
+ from time stamp t9, the value of
+ anchor:my.look.ca from time stamp t8.
+
+ For more information about the internals of how HBase stores data, see .
+
+
+
+
+ Table
+
+ Tables are declared up front at schema definition time.
+
+
+
+
+ Row
+ Row keys are uninterrpreted bytes. Rows are
+ lexicographically sorted with the lowest order appearing first
+ in a table. The empty byte array is used to denote both the
+ start and end of a tables' namespace.
+
+
+
+ Column FamilyColumn Family
+
+ Columns in HBase are grouped into column families.
+ All column members of a column family have the same prefix. For example, the
+ columns courses:history and
+ courses:math are both members of the
+ courses column family.
+ The colon character (:) delimits the column family from the
+ column family qualifierColumn Family Qualifier.
+ The column family prefix must be composed of
+ printable characters. The qualifying tail, the
+ column family qualifier, can be made of any
+ arbitrary bytes. Column families must be declared up front
+ at schema definition time whereas columns do not need to be
+ defined at schema time but can be conjured on the fly while
+ the table is up an running.
+ Physically, all column family members are stored together on the
+ filesystem. Because tunings and
+ storage specifications are done at the column family level, it is
+ advised that all column family members have the same general access
+ pattern and size characteristics.
+
+
+
+
+ CellsCells
+ A {row, column, version} tuple exactly
+ specifies a cell in HBase.
+ Cell content is uninterrpreted bytes
+
+
+ Data Model Operations
+ The four primary data model operations are Get, Put, Scan, and Delete. Operations are applied via
+ HTable instances.
+
+
+ Get
+ Get returns
+ attributes for a specified row. Gets are executed via
+
+ HTable.get.
+
+
+
+ Put
+ Put either
+ adds new rows to a table (if the key is new) or can update existing rows (if the key already exists). Puts are executed via
+
+ HTable.put (writeBuffer) or
+ HTable.batch (non-writeBuffer).
+
+
+
+ Scans
+ Scan allow
+ iteration over multiple rows for specified attributes.
+
+ The following is an example of a
+ on an HTable table instance. Assume that a table is populated with rows with keys "row1", "row2", "row3",
+ and then another set of rows with the keys "abc1", "abc2", and "abc3". The following example shows how startRow and stopRow
+ can be applied to a Scan instance to return the rows beginning with "row".
+
+HTable htable = ... // instantiate HTable
+
+Scan scan = new Scan();
+scan.addColumn(Bytes.toBytes("cf"),Bytes.toBytes("attr"));
+scan.setStartRow( Bytes.toBytes("row")); // start key is inclusive
+scan.setStopRow( Bytes.toBytes("row" + (char)0)); // stop key is exclusive
+ResultScanner rs = htable.getScanner(scan);
+try {
+ for (Result r = rs.next(); r != null; r = rs.next()) {
+ // process result...
+} finally {
+ rs.close(); // always close the ResultScanner!
+}
+
+
+
+
+ Delete
+ Delete removes
+ a row from a table. Deletes are executed via
+
+ HTable.delete.
+
+ HBase does not modify data in place, and so deletes are handled by creating new markers called tombstones.
+ These tombstones, along with the dead values, are cleaned up on major compactions.
+
+ See for more information on deleting versions of columns, and see
+ for more information on compactions.
+
+
+
+
+
+
+
+
+ VersionsVersions
+
+ A {row, column, version} tuple exactly
+ specifies a cell in HBase. Its possible to have an
+ unbounded number of cells where the row and column are the same but the
+ cell address differs only in its version dimension.
+
+ While rows and column keys are expressed as bytes, the version is
+ specified using a long integer. Typically this long contains time
+ instances such as those returned by
+ java.util.Date.getTime() or
+ System.currentTimeMillis(), that is: the difference,
+ measured in milliseconds, between the current time and midnight, January
+ 1, 1970 UTC.
+
+ The HBase version dimension is stored in decreasing order, so that
+ when reading from a store file, the most recent values are found
+ first.
+
+ There is a lot of confusion over the semantics of
+ cell versions, in HBase. In particular, a couple
+ questions that often come up are:
+
+ If multiple writes to a cell have the same version, are all
+ versions maintained or just the last?
+ Currently, only the last written is fetchable.
+
+
+
+
+ Is it OK to write cells in a non-increasing version
+ order?
+ Yes
+
+
+
+
+ Below we describe how the version dimension in HBase currently
+ works
+ See HBASE-2406
+ for discussion of HBase versions. Bending time
+ in HBase makes for a good read on the version, or time,
+ dimension in HBase. It has more detail on versioning than is
+ provided here. As of this writing, the limiitation
+ Overwriting values at existing timestamps
+ mentioned in the article no longer holds in HBase. This section is
+ basically a synopsis of this article by Bruno Dumon.
+ .
+
+
+ Versions and HBase Operations
+
+ In this section we look at the behavior of the version dimension
+ for each of the core HBase operations.
+
+
+ Get/Scan
+
+ Gets are implemented on top of Scans. The below discussion of
+ Get applies equally to Scans.
+
+ By default, i.e. if you specify no explicit version, when
+ doing a get, the cell whose version has the
+ largest value is returned (which may or may not be the latest one
+ written, see later). The default behavior can be modified in the
+ following ways:
+
+
+
+ to return more than one version, see Get.setMaxVersions()
+
+
+
+ to return versions other than the latest, see Get.setTimeRange()
+
+ To retrieve the latest version that is less than or equal
+ to a given value, thus giving the 'latest' state of the record
+ at a certain point in time, just use a range from 0 to the
+ desired version and set the max versions to 1.
+
+
+
+
+
+ Default Get Example
+ The following Get will only retrieve the current version of the row
+
+Get get = new Get(Bytes.toBytes("row1"));
+Result r = htable.get(get);
+byte[] b = r.getValue(Bytes.toBytes("cf"), Bytes.toBytes("attr")); // returns current version of value
+
+
+
+
+ Versioned Get Example
+ The following Get will return the last 3 versions of the row.
+
+Get get = new Get(Bytes.toBytes("row1"));
+get.setMaxVersions(3); // will return last 3 versions of row
+Result r = htable.get(get);
+byte[] b = r.getValue(Bytes.toBytes("cf"), Bytes.toBytes("attr")); // returns current version of value
+List<KeyValue> kv = r.getColumn(Bytes.toBytes("cf"), Bytes.toBytes("attr")); // returns all versions of this column
+
+
+
+
+
+ Put
+
+ Doing a put always creates a new version of a
+ cell, at a certain timestamp. By default the
+ system uses the server's currentTimeMillis, but
+ you can specify the version (= the long integer) yourself, on a
+ per-column level. This means you could assign a time in the past or
+ the future, or use the long value for non-time purposes.
+
+ To overwrite an existing value, do a put at exactly the same
+ row, column, and version as that of the cell you would
+ overshadow.
+
+ Implicit Version Example
+ The following Put will be implicitly versioned by HBase with the current time.
+
+Put put = new Put(Bytes.toBytes(row));
+put.add(Bytes.toBytes("cf"), Bytes.toBytes("attr1"), Bytes.toBytes( data));
+htable.put(put);
+
+
+
+
+ Explicit Version Example
+ The following Put has the version timestamp explicitly set.
+
+Put put = new Put( Bytes.toBytes(row));
+long explicitTimeInMs = 555; // just an example
+put.add(Bytes.toBytes("cf"), Bytes.toBytes("attr1"), explicitTimeInMs, Bytes.toBytes(data));
+htable.put(put);
+
+ Caution: the version timestamp is internally by HBase for things like time-to-live calculations.
+ It's usually best to avoid setting this timestamp yourself. Prefer using a separate
+ timestamp attribute of the row, or have the timestamp a part of the rowkey, or both.
+
+
+
+
+
+
+ Delete
+
+ There are three different types of internal delete markers
+ See Lars Hofhansl's blog for discussion of his attempt
+ adding another, Scanning in HBase: Prefix Delete Marker:
+
+ Delete: for a specific version of a column.
+
+ Delete column: for all versions of a column.
+
+ Delete family: for all columns of a particular ColumnFamily
+
+
+ When deleting an entire row, HBase will internally create a tombstone for each ColumnFamily (i.e., not each individual column).
+
+ Deletes work by creating tombstone
+ markers. For example, let's suppose we want to delete a row. For
+ this you can specify a version, or else by default the
+ currentTimeMillis is used. What this means is
+ delete all cells where the version is less than or equal to
+ this version. HBase never modifies data in place, so for
+ example a delete will not immediately delete (or mark as deleted)
+ the entries in the storage file that correspond to the delete
+ condition. Rather, a so-called tombstone is
+ written, which will mask the deleted values
+ When HBase does a major compaction, the tombstones are
+ processed to actually remove the dead values, together with the
+ tombstones themselves.
+ . If the version you specified when deleting a row is
+ larger than the version of any value in the row, then you can
+ consider the complete row to be deleted.
+ Also see for more information on the internal KeyValue format.
+
+
+
+
+
+ Current Limitations
+
+ There are still some bugs (or at least 'undecided behavior')
+ with the version dimension that will be addressed by later HBase
+ releases.
+
+
+ Deletes mask Puts
+
+ Deletes mask puts, even puts that happened after the delete
+ was entered
+ HBASE-2256
+ . Remember that a delete writes a tombstone, which only
+ disappears after then next major compaction has run. Suppose you do
+ a delete of everything <= T. After this you do a new put with a
+ timestamp <= T. This put, even if it happened after the delete,
+ will be masked by the delete tombstone. Performing the put will not
+ fail, but when you do a get you will notice the put did have no
+ effect. It will start working again after the major compaction has
+ run. These issues should not be a problem if you use
+ always-increasing versions for new puts to a row. But they can occur
+ even if you do not care about time: just do delete and put
+ immediately after each other, and there is some chance they happen
+ within the same millisecond.
+
+
+
+ Major compactions change query results
+
+ ...create three cell versions at t1, t2 and t3, with a
+ maximum-versions setting of 2. So when getting all versions, only
+ the values at t2 and t3 will be returned. But if you delete the
+ version at t2 or t3, the one at t1 will appear again. Obviously,
+ once a major compaction has run, such behavior will not be the case
+ anymore...
+ See Garbage Collection in Bending
+ time in HBase
+
+
+
+
+
+ Sort Order
+ All data model operations HBase return data in sorted order. First by row,
+ then by ColumnFamily, followed by column qualifier, and finally timestamp (sorted
+ in reverse, so newest records are returned first).
+
+
+
+ Column Metadata
+ There is no store of column metadata outside of the internal KeyValue instances for a ColumnFamily.
+ Thus, while HBase can support not only a wide number of columns per row, but a heterogenous set of columns
+ between rows as well, it is your responsibility to keep track of the column names.
+
+ The only way to get a complete set of columns that exist for a ColumnFamily is to process all the rows.
+ For more information about how HBase stores data internally, see .
+
+
+ Joins
+ Whether HBase supports joins is a common question on the dist-list, and there is a simple answer: it doesn't,
+ at not least in the way that RDBMS' support them (e.g., with equi-joins or outer-joins in SQL). As has been illustrated
+ in this chapter, the read data model operations in HBase are Get and Scan.
+
+ However, that doesn't mean that equivalent join functionality can't be supported in your application, but
+ you have to do it yourself. The two primary strategies are either denormalizing the data upon writing to HBase,
+ or to have lookup tables and do the join between HBase tables in your application or MapReduce code (and as RDBMS'
+ demonstrate, there are several strategies for this depending on the size of the tables, e.g., nested loops vs.
+ hash-joins). So which is the best approach? It depends on what you are trying to do, and as such there isn't a single
+ answer that works for every use case.
+
+
+
+
+
+ HBase and Schema Design
+ A good general introduction on the strength and weaknesses modelling on
+ the various non-rdbms datastores is Ian Varleys' Master thesis,
+ No Relation: The Mixed Blessings of Non-Relational Databases.
+ Recommended. Also, read for how HBase stores data internally.
+
+
+
+ Schema Creation
+
+ HBase schemas can be created or updated with
+ or by using HBaseAdmin in the Java API.
+
+ Tables must be disabled when making ColumnFamily modifications, for example..
+
+Configuration config = HBaseConfiguration.create();
+HBaseAdmin admin = new HBaseAdmin(conf);
+String table = "myTable";
+
+admin.disableTable(table);
+
+HColumnDescriptor cf1 = ...;
+admin.addColumn(table, cf1); // adding new ColumnFamily
+HColumnDescriptor cf2 = ...;
+admin.modifyColumn(table, cf2); // modifying existing ColumnFamily
+
+admin.enableTable(table);
+
+ See for more information about configuring client connections.
+ Note: online schema changes are supported in the 0.92.x codebase, but the 0.90.x codebase requires the table
+ to be disabled.
+
+ Schema Updates
+ When changes are made to either Tables or ColumnFamilies (e.g., region size, block size), these changes
+ take effect the next time there is a major compaction and the StoreFiles get re-written.
+
+ See for more information on StoreFiles.
+
+
+
+
+
+ On the number of column families
+
+
+ HBase currently does not do well with anything above two or three column families so keep the number
+ of column families in your schema low. Currently, flushing and compactions are done on a per Region basis so
+ if one column family is carrying the bulk of the data bringing on flushes, the adjacent families
+ will also be flushed though the amount of data they carry is small. When many column families the
+ flushing and compaction interaction can make for a bunch of needless i/o loading (To be addressed by
+ changing flushing and compaction to work on a per column family basis). For more information
+ on compactions, see .
+
+ Try to make do with one column family if you can in your schemas. Only introduce a
+ second and third column family in the case where data access is usually column scoped;
+ i.e. you query one column family or the other but usually not both at the one time.
+
+ Cardinality of ColumnFamilies
+ Where multiple ColumnFamilies exist in a single table, be aware of the cardinality (i.e., number of rows).
+ If ColumnFamilyA has 1 million rows and ColumnFamilyB has 1 billion rows, ColumnFamilyA's data will likely be spread
+ across many, many regions (and RegionServers). This makes mass scans for ColumnFamilyA less efficient.
+
+
+
+ Rowkey Design
+
+
+ Monotonically Increasing Row Keys/Timeseries Data
+
+
+ In the HBase chapter of Tom White's book Hadoop: The Definitive Guide (O'Reilly) there is a an optimization note on watching out for a phenomenon where an import process walks in lock-step with all clients in concert pounding one of the table's regions (and thus, a single node), then moving onto the next region, etc. With monotonically increasing row-keys (i.e., using a timestamp), this will happen. See this comic by IKai Lan on why monotonically increasing row keys are problematic in BigTable-like datastores:
+ monotonically increasing values are bad. The pile-up on a single region brought on
+ by monotonically increasing keys can be mitigated by randomizing the input records to not be in sorted order, but in general its best to avoid using a timestamp or a sequence (e.g. 1, 2, 3) as the row-key.
+
+
+
+ If you do need to upload time series data into HBase, you should
+ study OpenTSDB as a
+ successful example. It has a page describing the schema it uses in
+ HBase. The key format in OpenTSDB is effectively [metric_type][event_timestamp], which would appear at first glance to contradict the previous advice about not using a timestamp as the key. However, the difference is that the timestamp is not in the lead position of the key, and the design assumption is that there are dozens or hundreds (or more) of different metric types. Thus, even with a continual stream of input data with a mix of metric types, the Puts are distributed across various points of regions in the table.
+
+
+
+ Try to minimize row and column sizes
+ Or why are my StoreFile indices large?
+ In HBase, values are always freighted with their coordinates; as a
+ cell value passes through the system, it'll be accompanied by its
+ row, column name, and timestamp - always. If your rows and column names
+ are large, especially compared to the size of the cell value, then
+ you may run up against some interesting scenarios. One such is
+ the case described by Marc Limotte at the tail of
+ HBASE-3551
+ (recommended!).
+ Therein, the indices that are kept on HBase storefiles ()
+ to facilitate random access may end up occupyng large chunks of the HBase
+ allotted RAM because the cell value coordinates are large.
+ Mark in the above cited comment suggests upping the block size so
+ entries in the store file index happen at a larger interval or
+ modify the table schema so it makes for smaller rows and column
+ names.
+ Compression will also make for larger indices. See
+ the thread a question storefileIndexSize
+ up on the user mailing list.
+
+ Most of the time small inefficiencies don't matter all that much. Unfortunately,
+ this is a case where they do. Whatever patterns are selected for ColumnFamilies, attributes, and rowkeys they could be repeated
+ several billion times in your data.
+ See for more information on HBase stores data internally to see why this is important.
+ Column Families
+ Try to keep the ColumnFamily names as small as possible, preferably one character (e.g. "d" for data/default).
+
+ See for more information on HBase stores data internally to see why this is important.
+
+ Attributes
+ Although verbose attribute names (e.g., "myVeryImportantAttribute") are easier to read, prefer shorter attribute names (e.g., "via")
+ to store in HBase.
+
+ See for more information on HBase stores data internally to see why this is important.
+
+ Rowkey Length
+ Keep them as short as is reasonable such that they can still be useful for required data access (e.g., Get vs. Scan).
+ A short key that is useless for data access is not better than a longer key with better get/scan properties. Expect tradeoffs
+ when designing rowkeys.
+
+
+ Byte Patterns
+ A long is 8 bytes. You can store an unsigned number up to 18,446,744,073,709,551,615 in those eight bytes.
+ If you stored this number as a String -- presuming a byte per character -- you need nearly 3x the bytes.
+
+ Not convinced? Below is some sample code that you can run on your own.
+
+// long
+//
+long l = 1234567890L;
+byte[] lb = Bytes.toBytes(l);
+System.out.println("long bytes length: " + lb.length); // returns 8
+
+String s = "" + l;
+byte[] sb = Bytes.toBytes(s);
+System.out.println("long as string length: " + sb.length); // returns 10
+
+// hash
+//
+MessageDigest md = MessageDigest.getInstance("MD5");
+byte[] digest = md.digest(Bytes.toBytes(s));
+System.out.println("md5 digest bytes length: " + digest.length); // returns 16
+
+String sDigest = new String(digest);
+byte[] sbDigest = Bytes.toBytes(sDigest);
+System.out.println("md5 digest as string length: " + sbDigest.length); // returns 26
+
+
+
+
+
+ Reverse Timestamps
+ A common problem in database processing is quickly finding the most recent version of a value. A technique using reverse timestamps
+ as a part of the key can help greatly with a special case of this problem. Also found in the HBase chapter of Tom White's book Hadoop: The Definitive Guide (O'Reilly),
+ the technique involves appending (Long.MAX_VALUE - timestamp) to the end of any key, e.g., [key][reverse_timestamp].
+
+ The most recent value for [key] in a table can be found by performing a Scan for [key] and obtaining the first record. Since HBase keys
+ are in sorted order, this key sorts before any older row-keys for [key] and thus is first.
+
+ This technique would be used instead of using HBase Versioning where the intent is to hold onto all versions
+ "forever" (or a very long time) and at the same time quickly obtain access to any other version by using the same Scan technique.
+
+
+
+ Rowkeys and ColumnFamilies
+ Rowkeys are scoped to ColumnFamilies. Thus, the same rowkey could exist in each ColumnFamily that exists in a table without collision.
+
+
+ Immutability of Rowkeys
+ Rowkeys cannot be changed. The only way they can be "changed" in a table is if the row is deleted and then re-inserted.
+ This is a fairly common question on the HBase dist-list so it pays to get the rowkeys right the first time (and/or before you've
+ inserted a lot of data).
+
+
+
+
+
+ Number of Versions
+
+ Maximum Number of Versions
+ The maximum number of row versions to store is configured per column
+ family via HColumnDescriptor.
+ The default for max versions is 3.
+ This is an important parameter because as described in
+ section HBase does not overwrite row values, but rather
+ stores different values per row by time (and qualifier). Excess versions are removed during major
+ compactions. The number of max versions may need to be increased or decreased depending on application needs.
+
+ It is not recommended setting the number of max versions to an exceedingly high level (e.g., hundreds or more) unless those old values are
+ very dear to you because this will greatly increase StoreFile size.
+
+
+
+
+ Minimum Number of Versions
+
+ Like maximum number of row versions, the minimum number of row versions to keep is configured per column
+ family via HColumnDescriptor.
+ The default for min versions is 0, which means the feature is disabled.
+ The minimum number of row versions parameter is used together with the time-to-live parameter and can be combined with the
+ number of row versions parameter to allow configurations such as
+ "keep the last T minutes worth of data, at most N versions, but keep at least M versions around"
+ (where M is the value for minimum number of row versions, M<N).
+ This parameter should only be set when time-to-live is enabled for a column family and must be less than the
+ number of row versions.
+
+
+
+
+
+ Supported Datatypes
+
+ HBase supports a "bytes-in/bytes-out" interface via Put and
+ Result, so anything that can be
+ converted to an array of bytes can be stored as a value. Input could be strings, numbers, complex objects, or even images as long as they can rendered as bytes.
+
+ There are practical limits to the size of values (e.g., storing 10-50MB objects in HBase would probably be too much to ask);
+ search the mailling list for conversations on this topic. All rows in HBase conform to the datamodel, and
+ that includes versioning. Take that into consideration when making your design, as well as block size for the ColumnFamily.
+
+
+ Counters
+
+ One supported datatype that deserves special mention are "counters" (i.e., the ability to do atomic increments of numbers). See
+ Increment in HTable.
+
+ Synchronization on counters are done on the RegionServer, not in the client.
+
+
+
+ Joins
+ If you have multiple tables, don't forget to factor in the potential for into the schema design.
+
+
+
+ Time To Live (TTL)
+ ColumnFamilies can set a TTL length in seconds, and HBase will automatically delete rows once the expiration time is reached.
+ This applies to all versions of a row - even the current one. The TTL time encoded in the HBase for the row is specified in UTC.
+
+ See HColumnDescriptor for more information.
+
+
+
+
+ Keeping Deleted Cells
+
+ ColumnFamilies can optionally keep deleted cells. That means deleted cells can still be retrieved with
+ Get or
+ Scan operations,
+ as long these operations have a time range specified that ends before the timestamp of any delete that would affect the cells.
+ This allows for point in time queries even in the presence of deletes.
+
+
+ Deleted cells are still subject to TTL and there will never be more than "maximum number of versions" deleted cells.
+ A new "raw" scan options returns all deleted rows and the delete markers.
+
+ See HColumnDescriptor for more information.
+
+
+
+
+ Secondary Indexes and Alternate Query Paths
+
+ This section could also be titled "what if my table rowkey looks like this but I also want to query my table like that."
+ A common example on the dist-list is where a row-key is of the format "user-timestamp" but there are are reporting requirements on activity across users for certain
+ time ranges. Thus, selecting by user is easy because it is in the lead position of the key, but time is not.
+
+ There is no single answer on the best way to handle this because it depends on...
+
+ Number of users
+ Data size and data arrival rate
+ Flexibility of reporting requirements (e.g., completely ad-hoc date selection vs. pre-configured ranges)
+ Desired execution speed of query (e.g., 90 seconds may be reasonable to some for an ad-hoc report, whereas it may be too long for others)
+
+ ... and solutions are also influenced by the size of the cluster and how much processing power you have to throw at the solution.
+ Common techniques are in sub-sections below. This is a comprehensive, but not exhaustive, list of approaches.
+
+ It should not be a surprise that secondary indexes require additional cluster space and processing.
+ This is precisely what happens in an RDBMS because the act of creating an alternate index requires both space and processing cycles to update. RBDMS products
+ are more advanced in this regard to handle alternative index management out of the box. However, HBase scales better at larger data volumes, so this is a feature trade-off.
+
+ Pay attention to when implementing any of these approaches.
+ Additionally, see the David Butler response in this dist-list thread HBase, mail # user - Stargate+hbase
+
+
+
+ Filter Query
+
+ Depending on the case, it may be appropriate to use . In this case, no secondary index is created.
+ However, don't try a full-scan on a large table like this from an application (i.e., single-threaded client).
+
+
+
+
+ Periodic-Update Secondary Index
+
+ A secondary index could be created in an other table which is periodically updated via a MapReduce job. The job could be executed intra-day, but depending on
+ load-strategy it could still potentially be out of sync with the main data table.
+ See for more information.
+
+
+
+ Dual-Write Secondary Index
+
+ Another strategy is to build the secondary index while publishing data to the cluster (e.g., write to data table, write to index table).
+ If this is approach is taken after a data table already exists, then bootstrapping will be needed for the secondary index with a MapReduce job (see ).
+
+
+
+ Summary Tables
+
+ Where time-ranges are very wide (e.g., year-long report) and where the data is voluminous, summary tables are a common approach.
+ These would be generated with MapReduce jobs into another table.
+ See for more information.
+
+
+
+ Coprocessor Secondary Index
+
+ Coprocessors act like RDBMS triggers. These were added in 0.92. For more information, see
+
+
+
+ Schema Design Smackdown
+ This section will describe common schema design questions that appear on the dist-list. These are
+ general guidelines and not laws - each application must consider it's own needs.
+
+ Rows vs. Versions
+ A common question is whether one should prefer rows or HBase's built-in-versioning. The context is typically where there are
+ "a lot" of versions of a row to be retained (e.g., where it is significantly above the HBase default of 3 max versions). The
+ rows-approach would require storing a timstamp in some portion of the rowkey so that they would not overwite with each successive update.
+
+ Preference: Rows (generally speaking).
+
+
+ Rows vs. Columns
+ Another common question is whether one should prefer rows or columns. The context is typically in extreme cases of wide
+ tables, such as having 1 row with 1 million attributes, or 1 million rows with 1 columns apiece.
+
+ Preference: Rows (generally speaking). To be clear, this guideline is in the context is in extremely wide cases, not in the
+ standard use-case where one needs to store a few dozen or hundred columns.
+
+
+
+ Operational and Performance Configuration Options
+ See the Performance section for more information operational and performance
+ schema design options, such as Bloom Filters, Table-configured regionsizes, compression, and blocksizes.
+
+
+
+ Constraints
+ HBase currently supports 'constraints' in traditional (SQL) database parlance. The advised usage for Constraints is in enforcing business rules for attributes in the table (eg. make sure values are in the range 1-10).
+ Constraints could also be used to enforce referential integrity, but this is strongly discouraged as it will dramatically decrease the write throughput of the tables where integrity checking is enabled.
+ Extensive documentation on using Constraints can be found at: Constraint since version 0.94.
+
+
+
+
+
+
+ HBase and MapReduce
+ See
+ HBase and MapReduce up in javadocs.
+ Start there. Below is some additional help.
+ For more information about MapReduce (i.e., the framework in general), see the
+ Hadoop MapReduce Tutorial.
+
+ Map-Task Spitting
+
+ The Default HBase MapReduce Splitter
+ When TableInputFormat
+ is used to source an HBase table in a MapReduce job,
+ its splitter will make a map task for each region of the table.
+ Thus, if there are 100 regions in the table, there will be
+ 100 map-tasks for the job - regardless of how many column families are selected in the Scan.
+
+
+ Custom Splitters
+ For those interested in implementing custom splitters, see the method getSplits in
+ TableInputFormatBase.
+ That is where the logic for map-task assignment resides.
+
+
+
+
+ HBase MapReduce Examples
+
+ HBase MapReduce Read Example
+ The following is an example of using HBase as a MapReduce source in read-only manner. Specifically,
+ there is a Mapper instance but no Reducer, and nothing is being emitted from the Mapper. There job would be defined
+ as follows...
+
+Configuration config = HBaseConfiguration.create();
+Job job = new Job(config, "ExampleRead");
+job.setJarByClass(MyReadJob.class); // class that contains mapper
+
+Scan scan = new Scan();
+scan.setCaching(500); // 1 is the default in Scan, which will be bad for MapReduce jobs
+scan.setCacheBlocks(false); // don't set to true for MR jobs
+// set other scan attrs
+...
+
+TableMapReduceUtil.initTableMapperJob(
+ tableName, // input HBase table name
+ scan, // Scan instance to control CF and attribute selection
+ MyMapper.class, // mapper
+ null, // mapper output key
+ null, // mapper output value
+ job);
+job.setOutputFormatClass(NullOutputFormat.class); // because we aren't emitting anything from mapper
+
+boolean b = job.waitForCompletion(true);
+if (!b) {
+ throw new IOException("error with job!");
+}
+
+ ...and the mapper instance would extend TableMapper...
+
+public static class MyMapper extends TableMapper<Text, Text> {
+
+ public void map(ImmutableBytesWritable row, Result value, Context context) throws InterruptedException, IOException {
+ // process data for the row from the Result instance.
+ }
+}
+
+
+
+
+ HBase MapReduce Read/Write Example
+ The following is an example of using HBase both as a source and as a sink with MapReduce.
+ This example will simply copy data from one table to another.
+
+Configuration config = HBaseConfiguration.create();
+Job job = new Job(config,"ExampleReadWrite");
+job.setJarByClass(MyReadWriteJob.class); // class that contains mapper
+
+Scan scan = new Scan();
+scan.setCaching(500); // 1 is the default in Scan, which will be bad for MapReduce jobs
+scan.setCacheBlocks(false); // don't set to true for MR jobs
+// set other scan attrs
+
+TableMapReduceUtil.initTableMapperJob(
+ sourceTable, // input table
+ scan, // Scan instance to control CF and attribute selection
+ MyMapper.class, // mapper class
+ null, // mapper output key
+ null, // mapper output value
+ job);
+TableMapReduceUtil.initTableReducerJob(
+ targetTable, // output table
+ null, // reducer class
+ job);
+job.setNumReduceTasks(0);
+
+boolean b = job.waitForCompletion(true);
+if (!b) {
+ throw new IOException("error with job!");
+}
+
+ An explanation is required of what TableMapReduceUtil is doing, especially with the reducer.
+ TableOutputFormat is being used
+ as the outputFormat class, and several parameters are being set on the config (e.g., TableOutputFormat.OUTPUT_TABLE), as
+ well as setting the reducer output key to ImmutableBytesWritable and reducer value to Writable.
+ These could be set by the programmer on the job and conf, but TableMapReduceUtil tries to make things easier.
+ The following is the example mapper, which will create a Put and matching the input Result
+ and emit it. Note: this is what the CopyTable utility does.
+
+
+public static class MyMapper extends TableMapper<ImmutableBytesWritable, Put> {
+
+ public void map(ImmutableBytesWritable row, Result value, Context context) throws IOException, InterruptedException {
+ // this example is just copying the data from the source table...
+ context.write(row, resultToPut(row,value));
+ }
+
+ private static Put resultToPut(ImmutableBytesWritable key, Result result) throws IOException {
+ Put put = new Put(key.get());
+ for (KeyValue kv : result.raw()) {
+ put.add(kv);
+ }
+ return put;
+ }
+}
+
+ There isn't actually a reducer step, so TableOutputFormat takes care of sending the Put
+ to the target table.
+
+ This is just an example, developers could choose not to use TableOutputFormat and connect to the
+ target table themselves.
+
+
+
+
+ HBase MapReduce Read/Write Example With Multi-Table Output
+ TODO: example for MultiTableOutputFormat.
+
+
+
+ HBase MapReduce Summary to HBase Example
+ The following example uses HBase as a MapReduce source and sink with a summarization step. This example will
+ count the number of distinct instances of a value in a table and write those summarized counts in another table.
+
+Configuration config = HBaseConfiguration.create();
+Job job = new Job(config,"ExampleSummary");
+job.setJarByClass(MySummaryJob.class); // class that contains mapper and reducer
+
+Scan scan = new Scan();
+scan.setCaching(500); // 1 is the default in Scan, which will be bad for MapReduce jobs
+scan.setCacheBlocks(false); // don't set to true for MR jobs
+// set other scan attrs
+
+TableMapReduceUtil.initTableMapperJob(
+ sourceTable, // input table
+ scan, // Scan instance to control CF and attribute selection
+ MyMapper.class, // mapper class
+ Text.class, // mapper output key
+ IntWritable.class, // mapper output value
+ job);
+TableMapReduceUtil.initTableReducerJob(
+ targetTable, // output table
+ MyTableReducer.class, // reducer class
+ job);
+job.setNumReduceTasks(1); // at least one, adjust as required
+
+boolean b = job.waitForCompletion(true);
+if (!b) {
+ throw new IOException("error with job!");
+}
+
+ In this example mapper a column with a String-value is chosen as the value to summarize upon.
+ This value is used as the key to emit from the mapper, and an IntWritable represents an instance counter.
+
+public static class MyMapper extends TableMapper<Text, IntWritable> {
+
+ private final IntWritable ONE = new IntWritable(1);
+ private Text text = new Text();
+
+ public void map(ImmutableBytesWritable row, Result value, Context context) throws IOException, InterruptedException {
+ String val = new String(value.getValue(Bytes.toBytes("cf"), Bytes.toBytes("attr1")));
+ text.set(val); // we can only emit Writables...
+
+ context.write(text, ONE);
+ }
+}
+
+ In the reducer, the "ones" are counted (just like any other MR example that does this), and then emits a Put.
+
+public static class MyTableReducer extends TableReducer<Text, IntWritable, ImmutableBytesWritable> {
+
+ public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
+ int i = 0;
+ for (IntWritable val : values) {
+ i += val.get();
+ }
+ Put put = new Put(Bytes.toBytes(key.toString()));
+ put.add(Bytes.toBytes("cf"), Bytes.toBytes("count"), Bytes.toBytes(i));
+
+ context.write(null, put);
+ }
+}
+
+
+
+
+ HBase MapReduce Summary to File Example
+ This very similar to the summary example above, with exception that this is using HBase as a MapReduce source
+ but HDFS as the sink. The differences are in the job setup and in the reducer. The mapper remains the same.
+
+
+Configuration config = HBaseConfiguration.create();
+Job job = new Job(config,"ExampleSummaryToFile");
+job.setJarByClass(MySummaryFileJob.class); // class that contains mapper and reducer
+
+Scan scan = new Scan();
+scan.setCaching(500); // 1 is the default in Scan, which will be bad for MapReduce jobs
+scan.setCacheBlocks(false); // don't set to true for MR jobs
+// set other scan attrs
+
+TableMapReduceUtil.initTableMapperJob(
+ sourceTable, // input table
+ scan, // Scan instance to control CF and attribute selection
+ MyMapper.class, // mapper class
+ Text.class, // mapper output key
+ IntWritable.class, // mapper output value
+ job);
+job.setReducerClass(MyReducer.class); // reducer class
+job.setNumReduceTasks(1); // at least one, adjust as required
+FileOutputFormat.setOutputPath(job, new Path("/tmp/mr/mySummaryFile")); // adjust directories as required
+
+boolean b = job.waitForCompletion(true);
+if (!b) {
+ throw new IOException("error with job!");
+}
+
+ As stated above, the previous Mapper can run unchanged with this example.
+ As for the Reducer, it is a "generic" Reducer instead of extending TableMapper and emitting Puts.
+
+ public static class MyReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
+
+ public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
+ int i = 0;
+ for (IntWritable val : values) {
+ i += val.get();
+ }
+ context.write(key, new IntWritable(i));
+ }
+}
+
+
+
+ HBase MapReduce Summary to HBase Without Reducer
+ It is also possible to perform summaries without a reducer - if you use HBase as the reducer.
+
+ An HBase target table would need to exist for the job summary. The HTable method incrementColumnValue
+ would be used to atomically increment values. From a performance perspective, it might make sense to keep a Map
+ of values with their values to be incremeneted for each map-task, and make one update per key at during the
+ cleanup method of the mapper. However, your milage may vary depending on the number of rows to be processed and
+ unique keys.
+
+ In the end, the summary results are in HBase.
+
+
+
+ HBase MapReduce Summary to RDBMS
+ Sometimes it is more appropriate to generate summaries to an RDBMS. For these cases, it is possible
+ to generate summaries directly to an RDBMS via a custom reducer. The setup method
+ can connect to an RDBMS (the connection information can be passed via custom parameters in the context) and the
+ cleanup method can close the connection.
+
+ It is critical to understand that number of reducers for the job affects the summarization implementation, and
+ you'll have to design this into your reducer. Specifically, whether it is designed to run as a singleton (one reducer)
+ or multiple reducers. Neither is right or wrong, it depends on your use-case. Recognize that the more reducers that
+ are assigned to the job, the more simultaneous connections to the RDBMS will be created - this will scale, but only to a point.
+
+
+ public static class MyRdbmsReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
+
+ private Connection c = null;
+
+ public void setup(Context context) {
+ // create DB connection...
+ }
+
+ public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
+ // do summarization
+ // in this example the keys are Text, but this is just an example
+ }
+
+ public void cleanup(Context context) {
+ // close db connection
+ }
+
+}
+
+ In the end, the summary results are written to your RDBMS table/s.
+
+
+
+
+
+ Accessing Other HBase Tables in a MapReduce Job
+ Although the framework currently allows one HBase table as input to a
+ MapReduce job, other HBase tables can
+ be accessed as lookup tables, etc., in a
+ MapReduce job via creating an HTable instance in the setup method of the Mapper.
+ public class MyMapper extends TableMapper<Text, LongWritable> {
+ private HTable myOtherTable;
+
+ public void setup(Context context) {
+ myOtherTable = new HTable("myOtherTable");
+ }
+
+ public void map(ImmutableBytesWritable row, Result value, Context context) throws IOException, InterruptedException {
+ // process Result...
+ // use 'myOtherTable' for lookups
+ }
+
+
+
+
+
+ Speculative Execution
+ It is generally advisable to turn off speculative execution for
+ MapReduce jobs that use HBase as a source. This can either be done on a
+ per-Job basis through properties, on on the entire cluster. Especially
+ for longer running jobs, speculative execution will create duplicate
+ map-tasks which will double-write your data to HBase; this is probably
+ not what you want.
+
+ See for more information.
+
+
+
+
+
+
+
+ Architecture
+
+ Overview
+
+ NoSQL?
+ HBase is a type of "NoSQL" database. "NoSQL" is a general term meaning that the database isn't an RDBMS which
+ supports SQL as it's primary access language, but there are many types of NoSQL databases: BerkeleyDB is an
+ example of a local NoSQL database, whereas HBase is very much a distributed database. Technically speaking,
+ HBase is really more a "Data Store" than "Data Base" because it lacks many of the features you find in an RDBMS,
+ such as typed columns, secondary indexes, triggers, and advanced query languages, etc.
+
+ However, HBase has many features which supports both linear and modular scaling. HBase clusters expand
+ by adding RegionServers that are hosted on commodity class servers. If a cluster expands from 10 to 20
+ RegionServers, for example, it doubles both in terms of storage and as well as processing capacity.
+ RDBMS can scale well, but only up to a point - specifically, the size of a single database server - and for the best
+ performance requires specialized hardware and storage devices. HBase features of note are:
+
+ Strongly consistent reads/writes: HBase is not an "eventually consistent" DataStore. This
+ makes it very suitable for tasks such as high-speed counter aggregation.
+ Automatic sharding: HBase tables are distributed on the cluster via regions, and regions are
+ automatically split and re-distributed as your data grows.
+ Automatic RegionServer failover
+ Hadoop/HDFS Integration: HBase supports HDFS out of the box as it's distributed file system.
+ MapReduce: HBase supports massively parallelized processing via MapReduce for using HBase as both
+ source and sink.
+ Java Client API: HBase supports an easy to use Java API for programmatic access.
+ Thrift/REST API: HBase also supports Thrift and REST for non-Java front-ends.
+ Block Cache and Bloom Filters: HBase supports a Block Cache and Bloom Filters for high volume query optimization.
+ Operational Management: HBase provides build-in web-pages for operational insight as well as JMX metrics.
+
+
+
+
+
+ When Should I Use HBase?
+ HBase isn't suitable for every problem.
+ First, make sure you have enough data. If you have hundreds of millions or billions of rows, then
+ HBase is a good candidate. If you only have a few thousand/million rows, then using a traditional RDBMS
+ might be a better choice due to the fact that all of your data might wind up on a single node (or two) and
+ the rest of the cluster may be sitting idle.
+
+ Second, make sure you can live without all the extra features that an RDBMS provides (e.g., typed columns,
+ secondary indexes, transactions, advanced query languages, etc.) An application built against an RDBMS cannot be
+ "ported" to HBase by simply changing a JDBC driver, for example. Consider moving from an RDBMS to HBase as a
+ complete redesign as opposed to a port.
+
+ Third, make sure you have enough hardware. Even HDFS doesn't do well with anything less than
+ 5 DataNodes (due to things such as HDFS block replication which has a default of 3), plus a NameNode.
+
+ HBase can run quite well stand-alone on a laptop - but this should be considered a development
+ configuration only.
+
+
+
+ What Is The Difference Between HBase and Hadoop/HDFS?
+ HDFS is a distributed file system that is well suited for the storage of large files.
+ It's documentation states that it is not, however, a general purpose file system, and does not provide fast individual record lookups in files.
+ HBase, on the other hand, is built on top of HDFS and provides fast record lookups (and updates) for large tables.
+ This can sometimes be a point of conceptual confusion. HBase internally puts your data in indexed "StoreFiles" that exist
+ on HDFS for high-speed lookups. See the and the rest of this chapter for more information on how HBase achieves its goals.
+
+
+
+
+
+ Catalog Tables
+ The catalog tables -ROOT- and .META. exist as HBase tables. They are are filtered out
+ of the HBase shell's list command, but they are in fact tables just like any other.
+
+
+ ROOT
+ -ROOT- keeps track of where the .META. table is. The -ROOT- table structure is as follows:
+
+ Key:
+
+ .META. region key (.META.,,1)
+
+
+ Values:
+
+ info:regioninfo (serialized HRegionInfo
+ instance of .META.)
+ info:server (server:port of the RegionServer holding .META.)
+ info:serverstartcode (start-time of the RegionServer process holding .META.)
+
+
+
+
+ META
+ The .META. table keeps a list of all regions in the system. The .META. table structure is as follows:
+
+ Key:
+
+ Region key of the format ([table],[region start key],[region id])
+
+
+ Values:
+
+ info:regioninfo (serialized
+ HRegionInfo instance for this region)
+
+ info:server (server:port of the RegionServer containing this region)
+ info:serverstartcode (start-time of the RegionServer process containing this region)
+
+
+ When a table is in the process of splitting two other columns will be created, info:splitA and info:splitB
+ which represent the two daughter regions. The values for these columns are also serialized HRegionInfo instances.
+ After the region has been split eventually this row will be deleted.
+
+ Notes on HRegionInfo: the empty key is used to denote table start and table end. A region with an empty start key
+ is the first region in a table. If region has both an empty start and an empty end key, its the only region in the table
+
+ In the (hopefully unlikely) event that programmatic processing of catalog metadata is required, see the
+ Writables utility.
+
+
+
+ Startup Sequencing
+ The META location is set in ROOT first. Then META is updated with server and startcode values.
+
+ For information on region-RegionServer assignment, see .
+
+
+
+
+
+ Client
+ The HBase client
+ HTable
+ is responsible for finding RegionServers that are serving the
+ particular row range of interest. It does this by querying
+ the .META. and -ROOT- catalog tables
+ (TODO: Explain). After locating the required
+ region(s), the client directly contacts
+ the RegionServer serving that region (i.e., it does not go
+ through the master) and issues the read or write request.
+ This information is cached in the client so that subsequent requests
+ need not go through the lookup process. Should a region be reassigned
+ either by the master load balancer or because a RegionServer has died,
+ the client will requery the catalog tables to determine the new
+ location of the user region.
+
+ See for more information about the impact of the Master on HBase Client
+ communication.
+
+ Administrative functions are handled through HBaseAdmin
+
+ Connections
+ For connection configuration information, see .
+
+ HTable
+instances are not thread-safe. When creating HTable instances, it is advisable to use the same HBaseConfiguration
+instance. This will ensure sharing of ZooKeeper and socket instances to the RegionServers
+which is usually what you want. For example, this is preferred:
+ HBaseConfiguration conf = HBaseConfiguration.create();
+HTable table1 = new HTable(conf, "myTable");
+HTable table2 = new HTable(conf, "myTable");
+ as opposed to this:
+ HBaseConfiguration conf1 = HBaseConfiguration.create();
+HTable table1 = new HTable(conf1, "myTable");
+HBaseConfiguration conf2 = HBaseConfiguration.create();
+HTable table2 = new HTable(conf2, "myTable");
+ For more information about how connections are handled in the HBase client,
+ see HConnectionManager.
+
+ Connection Pooling
+ For applications which require high-end multithreaded access (e.g., web-servers or application servers that may serve many application threads
+ in a single JVM), see HTablePool.
+
+
+
+ WriteBuffer and Batch Methods
+ If is turned off on
+ HTable,
+ Puts are sent to RegionServers when the writebuffer
+ is filled. The writebuffer is 2MB by default. Before an HTable instance is
+ discarded, either close() or
+ flushCommits() should be invoked so Puts
+ will not be lost.
+
+ Note: htable.delete(Delete); does not go in the writebuffer! This only applies to Puts.
+
+ For additional information on write durability, review the ACID semantics page.
+
+ For fine-grained control of batching of
+ Puts or Deletes,
+ see the batch methods on HTable.
+
+
+ External Clients
+ Information on non-Java clients and custom protocols is covered in
+
+
+ RowLocks
+ RowLocks are still
+ in the client API however they are discouraged because if not managed properly these can
+ lock up the RegionServers.
+
+ There is an oustanding ticket HBASE-2332 to
+ remove this feature from the client.
+
+
+
+
+ Client Request Filters
+ Get and Scan instances can be
+ optionally configured with filters which are applied on the RegionServer.
+
+ Filters can be confusing because there are many different types, and it is best to approach them by understanding the groups
+ of Filter functionality.
+
+ Structural
+ Structural Filters contain other Filters.
+ FilterList
+ FilterList
+ represents a list of Filters with a relationship of FilterList.Operator.MUST_PASS_ALL or
+ FilterList.Operator.MUST_PASS_ONE between the Filters. The following example shows an 'or' between two
+ Filters (checking for either 'my value' or 'my other value' on the same attribute).
+
+FilterList list = new FilterList(FilterList.Operator.MUST_PASS_ONE);
+SingleColumnValueFilter filter1 = new SingleColumnValueFilter(
+ cf,
+ column,
+ CompareOp.EQUAL,
+ Bytes.toBytes("my value")
+ );
+list.add(filter1);
+SingleColumnValueFilter filter2 = new SingleColumnValueFilter(
+ cf,
+ column,
+ CompareOp.EQUAL,
+ Bytes.toBytes("my other value")
+ );
+list.add(filter2);
+scan.setFilter(list);
+
+
+
+
+ Column Value
+ SingleColumnValueFilter
+ SingleColumnValueFilter
+ can be used to test column values for equivalence (CompareOp.EQUAL
+ ), inequality (CompareOp.NOT_EQUAL), or ranges
+ (e.g., CompareOp.GREATER). The folowing is example of testing equivalence a column to a String value "my value"...
+
+SingleColumnValueFilter filter = new SingleColumnValueFilter(
+ cf,
+ column,
+ CompareOp.EQUAL,
+ Bytes.toBytes("my value")
+ );
+scan.setFilter(filter);
+
+
+
+
+ Column Value Comparators
+ There are several Comparator classes in the Filter package that deserve special mention.
+ These Comparators are used in concert with other Filters, such as .
+
+ RegexStringComparator
+ RegexStringComparator
+ supports regular expressions for value comparisons.
+
+RegexStringComparator comp = new RegexStringComparator("my."); // any value that starts with 'my'
+SingleColumnValueFilter filter = new SingleColumnValueFilter(
+ cf,
+ column,
+ CompareOp.EQUAL,
+ comp
+ );
+scan.setFilter(filter);
+
+ See the Oracle JavaDoc for supported RegEx patterns in Java.
+
+
+ SubstringComparator
+ SubstringComparator
+ can be used to determine if a given substring exists in a value. The comparison is case-insensitive.
+
+
+SubstringComparator comp = new SubstringComparator("y val"); // looking for 'my value'
+SingleColumnValueFilter filter = new SingleColumnValueFilter(
+ cf,
+ column,
+ CompareOp.EQUAL,
+ comp
+ );
+scan.setFilter(filter);
+
+
+ BinaryPrefixComparator
+ See BinaryPrefixComparator.
+
+ BinaryComparator
+ See BinaryComparator.
+
+
+ KeyValue Metadata
+ As HBase stores data internally as KeyValue pairs, KeyValue Metadata Filters evaluate the existence of keys (i.e., ColumnFamily:Column qualifiers)
+ for a row, as opposed to values the previous section.
+
+ FamilyFilter
+ FamilyFilter can be used
+ to filter on the ColumnFamily. It is generally a better idea to select ColumnFamilies in the Scan than to do it with a Filter.
+
+ QualifierFilter
+ QualifierFilter can be used
+ to filter based on Column (aka Qualifier) name.
+
+
+ ColumnPrefixFilter
+ ColumnPrefixFilter can be used
+ to filter based on the lead portion of Column (aka Qualifier) names.
+
+ A ColumnPrefixFilter seeks ahead to the first column matching the prefix in each row and for each involved column family. It can be used to efficiently
+ get a subset of the columns in very wide rows.
+
+ Note: The same column qualifier can be used in different column families. This filter returns all matching columns.
+
+ Example: Find all columns in a row and family that start with "abc"
+
+HTableInterface t = ...;
+byte[] row = ...;
+byte[] family = ...;
+byte[] prefix = Bytes.toBytes("abc");
+Scan scan = new Scan(row, row); // (optional) limit to one row
+scan.addFamily(family); // (optional) limit to one family
+Filter f = new ColumnPrefixFilter(prefix);
+scan.setFilter(f);
+scan.setBatch(10); // set this if there could be many columns returned
+ResultScanner rs = t.getScanner(scan);
+for (Result r = rs.next(); r != null; r = rs.next()) {
+ for (KeyValue kv : r.raw()) {
+ // each kv represents a column
+ }
+}
+rs.close();
+
+
+
+ MultipleColumnPrefixFilter
+ MultipleColumnPrefixFilter behaves like ColumnPrefixFilter
+ but allows specifying multiple prefixes.
+
+ Like ColumnPrefixFilter, MultipleColumnPrefixFilter efficiently seeks ahead to the first column matching the lowest prefix and also seeks past ranges of columns between prefixes.
+ It can be used to efficiently get discontinuous sets of columns from very wide rows.
+
+ Example: Find all columns in a row and family that start with "abc" or "xyz"
+
+HTableInterface t = ...;
+byte[] row = ...;
+byte[] family = ...;
+byte[][] prefixes = new byte[][] {Bytes.toBytes("abc"), Bytes.toBytes("xyz")};
+Scan scan = new Scan(row, row); // (optional) limit to one row
+scan.addFamily(family); // (optional) limit to one family
+Filter f = new MultipleColumnPrefixFilter(prefixes);
+scan.setFilter(f);
+scan.setBatch(10); // set this if there could be many columns returned
+ResultScanner rs = t.getScanner(scan);
+for (Result r = rs.next(); r != null; r = rs.next()) {
+ for (KeyValue kv : r.raw()) {
+ // each kv represents a column
+ }
+}
+rs.close();
+
+
+
+ ColumnRangeFilter
+ A ColumnRangeFilter allows efficient intra row scanning.
+
+ A ColumnRangeFilter can seek ahead to the first matching column for each involved column family. It can be used to efficiently
+ get a 'slice' of the columns of a very wide row.
+ i.e. you have a million columns in a row but you only want to look at columns bbbb-bbdd.
+
+ Note: The same column qualifier can be used in different column families. This filter returns all matching columns.
+
+ Example: Find all columns in a row and family between "bbbb" (inclusive) and "bbdd" (inclusive)
+
+HTableInterface t = ...;
+byte[] row = ...;
+byte[] family = ...;
+byte[] startColumn = Bytes.toBytes("bbbb");
+byte[] endColumn = Bytes.toBytes("bbdd");
+Scan scan = new Scan(row, row); // (optional) limit to one row
+scan.addFamily(family); // (optional) limit to one family
+Filter f = new ColumnRangeFilter(startColumn, true, endColumn, true);
+scan.setFilter(f);
+scan.setBatch(10); // set this if there could be many columns returned
+ResultScanner rs = t.getScanner(scan);
+for (Result r = rs.next(); r != null; r = rs.next()) {
+ for (KeyValue kv : r.raw()) {
+ // each kv represents a column
+ }
+}
+rs.close();
+
+
+ Note: Introduced in HBase 0.92
+
+
+ RowKey
+ RowFilter
+ It is generally a better idea to use the startRow/stopRow methods on Scan for row selection, however
+ RowFilter can also be used.
+
+
+ Utility
+ FirstKeyOnlyFilter
+ This is primarily used for rowcount jobs.
+ See FirstKeyOnlyFilter.
+
+
+
+
+ Master
+ HMaster is the implementation of the Master Server. The Master server
+ is responsible for monitoring all RegionServer instances in the cluster, and is
+ the interface for all metadata changes. In a distributed cluster, the Master typically runs on the .
+
+ Startup Behavior
+ If run in a multi-Master environment, all Masters compete to run the cluster. If the active
+ Master loses it's lease in ZooKeeper (or the Master shuts down), then then the remaining Masters jostle to
+ take over the Master role.
+
+
+ Runtime Impact
+ A common dist-list question is what happens to an HBase cluster when the Master goes down. Because the
+ HBase client talks directly to the RegionServers, the cluster can still function in a "steady
+ state." Additionally, per ROOT and META exist as HBase tables (i.e., are
+ not resident in the Master). However, the Master controls critical functions such as RegionServer failover and
+ completing region splits. So while the cluster can still run for a time without the Master,
+ the Master should be restarted as soon as possible.
+
+
+ Interface
+ The methods exposed by HMasterInterface are primarily metadata-oriented methods:
+
+ Table (createTable, modifyTable, removeTable, enable, disable)
+
+ ColumnFamily (addColumn, modifyColumn, removeColumn)
+
+ Region (move, assign, unassign)
+
+
+ For example, when the HBaseAdmin method disableTable is invoked, it is serviced by the Master server.
+
+
+ Processes
+ The Master runs several background threads:
+
+ LoadBalancer
+ Periodically, and when there are not any regions in transition,
+ a load balancer will run and move regions around to balance cluster load.
+ See for configuring this property.
+ See for more information on region assignment.
+
+
+ CatalogJanitor
+ Periodically checks and cleans up the .META. table. See for more information on META.
+
+
+
+
+ RegionServer
+ HRegionServer is the RegionServer implementation. It is responsible for serving and managing regions.
+ In a distributed cluster, a RegionServer runs on a .
+
+ Interface
+ The methods exposed by HRegionRegionInterface contain both data-oriented and region-maintenance methods:
+
+ Data (get, put, delete, next, etc.)
+
+ Region (splitRegion, compactRegion, etc.)
+
+
+ For example, when the HBaseAdmin method majorCompact is invoked on a table, the client is actually iterating through
+ all regions for the specified table and requesting a major compaction directly to each region.
+
+
+ Processes
+ The RegionServer runs a variety of background threads:
+ CompactSplitThread
+ Checks for splits and handle minor compactions.
+
+ MajorCompactionChecker
+ Checks for major compactions.
+
+ MemStoreFlusher
+ Periodically flushes in-memory writes in the MemStore to StoreFiles.
+
+ LogRoller
+ Periodically checks the RegionServer's HLog.
+
+
+
+ Coprocessors
+ Coprocessors were added in 0.92. There is a thorough Blog Overview of CoProcessors
+ posted. Documentation will eventually move to this reference guide, but the blog is the most current information available at this time.
+
+
+
+
+ Block Cache
+
+ Design
+ The Block Cache is an LRU cache that contains three levels of block priority to allow for scan-resistance and in-memory ColumnFamilies:
+
+
+ Single access priority: The first time a block is loaded from HDFS it normally has this priority and it will be part of the first group to be considered
+ during evictions. The advantage is that scanned blocks are more likely to get evicted than blocks that are getting more usage.
+
+ Mutli access priority: If a block in the previous priority group is accessed again, it upgrades to this priority. It is thus part of the second group
+ considered during evictions.
+
+ In-memory access priority: If the block's family was configured to be "in-memory", it will be part of this priority disregarding the number of times it
+ was accessed. Catalog tables are configured like this. This group is the last one considered during evictions.
+
+
+
+ For more information, see the LruBlockCache source
+
+
+
+ Usage
+ Block caching is enabled by default for all the user tables which means that any read operation will load the LRU cache. This might be good for a large number of use cases,
+ but further tunings are usually required in order to achieve better performance. An important concept is the
+ working set size, or WSS, which is: "the amount of memory needed to compute the answer to a problem".
+ For a website, this would be the data that's needed to answer the queries over a short amount of time.
+
+ The way to calculate how much memory is available in HBase for caching is:
+
+
+ number of region servers * heap size * hfile.block.cache.size * 0.85
+
+ The default value for the block cache is 0.25 which represents 25% of the available heap. The last value (85%) is the default acceptable loading factor in the LRU cache after
+ which eviction is started. The reason it is included in this equation is that it would be unrealistic to say that it is possible to use 100% of the available memory since this would
+ make the process blocking from the point where it loads new blocks. Here are some examples:
+
+
+ One region server with the default heap size (1GB) and the default block cache size will have 217MB of block cache available.
+
+ 20 region servers with the heap size set to 8GB and a default block cache size will have 34GB of block cache.
+
+ 100 region servers with the heap size set to 24GB and a block cache size of 0.5 will have about 1TB of block cache.
+
+
+ Your data isn't the only resident of the block cache, here are others that you may have to take into account:
+
+
+ Catalog tables: The -ROOT- and .META. tables are forced into the block cache and have the in-memory priority which means that they are harder to evict. The former never uses
+ more than a few hundreds of bytes while the latter can occupy a few MBs (depending on the number of regions).
+
+ HFiles indexes: HFile is the file format that HBase uses to store data in HDFS and it contains a multi-layered index in order seek to the data without having to read the whole file.
+ The size of those indexes is a factor of the block size (64KB by default), the size of your keys and the amount of data you are storing. For big data sets it's not unusual to see numbers around
+ 1GB per region server, although not all of it will be in cache because the LRU will evict indexes that aren't used.
+
+ Keys: Taking into account only the values that are being stored is missing half the picture since every value is stored along with its keys
+ (row key, family, qualifier, and timestamp). See .
+
+ Bloom filters: Just like the HFile indexes, those data structures (when enabled) are stored in the LRU.
+
+
+ Currently the recommended way to measure HFile indexes and bloom filters sizes is to look at the region server web UI and checkout the relevant metrics. For keys,
+ sampling can be done by using the HFile command line tool and look for the average key size metric.
+
+ It's generally bad to use block caching when the WSS doesn't fit in memory. This is the case when you have for example 40GB available across all your region servers' block caches
+ but you need to process 1TB of data. One of the reasons is that the churn generated by the evictions will trigger more garbage collections unnecessarily. Here are two use cases:
+
+
+ Fully random reading pattern: This is a case where you almost never access the same row twice within a short amount of time such that the chance of hitting a cached block is close
+ to 0. Setting block caching on such a table is a waste of memory and CPU cycles, more so that it will generate more garbage to pick up by the JVM. For more information on monitoring GC,
+ see .
+
+ Mapping a table: In a typical MapReduce job that takes a table in input, every row will be read only once so there's no need to put them into the block cache. The Scan object has
+ the option of turning this off via the setCaching method (set it to false). You can still keep block caching turned on on this table if you need fast random read access. An example would be
+ counting the number of rows in a table that serves live traffic, caching every block of that table would create massive churn and would surely evict data that's currently in use.
+
+
+
+
+
+
+ Write Ahead Log (WAL)
+
+
+ Purpose
+
+ Each RegionServer adds updates (Puts, Deletes) to its write-ahead log (WAL)
+ first, and then to the for the affected .
+ This ensures that HBase has durable writes. Without WAL, there is the possibility of data loss in the case of a RegionServer failure
+ before each MemStore is flushed and new StoreFiles are written. HLog
+ is the HBase WAL implementation, and there is one HLog instance per RegionServer.
+ The WAL is in HDFS in /hbase/.logs/ with subdirectories per region.
+
+ For more general information about the concept of write ahead logs, see the Wikipedia
+ Write-Ahead Log article.
+
+
+
+ WAL Flushing
+ TODO (describe).
+
+
+
+
+ WAL Splitting
+
+ How edits are recovered from a crashed RegionServer
+ When a RegionServer crashes, it will lose its ephemeral lease in
+ ZooKeeper...TODO
+
+
+ hbase.hlog.split.skip.errors
+
+ When set to true, the default, any error
+ encountered splitting will be logged, the problematic WAL will be
+ moved into the .corrupt directory under the hbase
+ rootdir, and processing will continue. If set to
+ false, the exception will be propagated and the
+ split logged as failed.
+ See HBASE-2958
+ When hbase.hlog.split.skip.errors is set to false, we fail the
+ split but thats it. We need to do more than just fail split
+ if this flag is set.
+
+
+
+
+ How EOFExceptions are treated when splitting a crashed
+ RegionServers' WALs
+
+ If we get an EOF while splitting logs, we proceed with the split
+ even when hbase.hlog.split.skip.errors ==
+ false. An EOF while reading the last log in the
+ set of files to split is near-guaranteed since the RegionServer likely
+ crashed mid-write of a record. But we'll continue even if we got an
+ EOF reading other than the last file in the set.
+ For background, see HBASE-2643
+ Figure how to deal with eof splitting logs
+
+
+
+
+
+
+
+
+ Regions
+ Regions are the basic element of availability and
+ distribution for tables, and are comprised of a Store per Column Family. The heirarchy of objects
+ is as follows:
+
+Table (HBase table)
+ Region (Regions for the table)
+ Store (Store per ColumnFamily for each Region for the table)
+ MemStore (MemStore for each Store for each Region for the table)
+ StoreFile (StoreFiles for each Store for each Region for the table)
+ Block (Blocks within a StoreFile within a Store for each Region for the table)
+
+ For a description of what HBase files look like when written to HDFS, see .
+
+
+
+ Region Size
+
+ Determining the "right" region size can be tricky, and there are a few factors
+ to consider:
+
+
+
+ HBase scales by having regions across many servers. Thus if
+ you have 2 regions for 16GB data, on a 20 node machine your data
+ will be concentrated on just a few machines - nearly the entire
+ cluster will be idle. This really cant be stressed enough, since a
+ common problem is loading 200MB data into HBase then wondering why
+ your awesome 10 node cluster isn't doing anything.
+
+
+
+ On the other hand, high region count has been known to make things slow.
+ This is getting better with each release of HBase, but it is probably better to have
+ 700 regions than 3000 for the same amount of data.
+
+
+
+ There is not much memory footprint difference between 1 region
+ and 10 in terms of indexes, etc, held by the RegionServer.
+
+
+
+ When starting off, its probably best to stick to the default region-size, perhaps going
+ smaller for hot tables (or manually split hot regions to spread the load over
+ the cluster), or go with larger region sizes if your cell sizes tend to be
+ largish (100k and up).
+ See for more information on configuration.
+
+
+
+
+ Region-RegionServer Assignment
+ This section describes how Regions are assigned to RegionServers.
+
+
+
+ Startup
+ When HBase starts regions are assigned as follows (short version):
+
+ The Master invokes the AssignmentManager upon startup.
+
+ The AssignmentManager looks at the existing region assignments in META.
+
+ If the region assignment is still valid (i.e., if the RegionServer is still online)
+ then the assignment is kept.
+
+ If the assignment is invalid, then the LoadBalancerFactory is invoked to assign the
+ region. The DefaultLoadBalancer will randomly assign the region to a RegionServer.
+
+ META is updated with the RegionServer assignment (if needed) and the RegionServer start codes
+ (start time of the RegionServer process) upon region opening by the RegionServer.
+
+
+
+
+
+
+ Failover
+ When a RegionServer fails (short version):
+
+ The regions immediately become unavailable because the RegionServer is down.
+
+ The Master will detect that the RegionServer has failed.
+
+ The region assignments will be considered invalid and will be re-assigned just
+ like the startup sequence.
+
+
+
+
+
+
+ Region Load Balancing
+
+ Regions can be periodically moved by the .
+
+
+
+
+
+
+ Region-RegionServer Locality
+ Over time, Region-RegionServer locality is achieved via HDFS block replication.
+ The HDFS client does the following by default when choosing locations to write replicas:
+
+ First replica is written to local node
+
+ Second replica is written to another node in same rack
+
+ Third replica is written to a node in another rack (if sufficient nodes)
+
+
+ Thus, HBase eventually achieves locality for a region after a flush or a compaction.
+ In a RegionServer failover situation a RegionServer may be assigned regions with non-local
+ StoreFiles (because none of the replicas are local), however as new data is written
+ in the region, or the table is compacted and StoreFiles are re-written, they will become "local"
+ to the RegionServer.
+
+ For more information, see HDFS Design on Replica Placement
+ and also Lars George's blog on HBase and HDFS locality.
+
+
+
+
+ Region Splits
+
+ Splits run unaided on the RegionServer; i.e. the Master does not
+ participate. The RegionServer splits a region, offlines the split
+ region and then adds the daughter regions to META, opens daughters on
+ the parent's hosting RegionServer and then reports the split to the
+ Master. See for how to manually manage
+ splits (and for why you might do this)
+
+ Custom Split Policies
+ The default split policy can be overwritten using a custom RegionSplitPolicy (HBase 0.94+).
+ Typically a custom split policy should extend HBase's default split policy: ConstantSizeRegionSplitPolicy.
+
+ The policy can set globally through the HBaseConfiguration used or on a per table basis:
+
+HTableDescriptor myHtd = ...;
+myHtd.setValue(HTableDescriptor.SPLIT_POLICY, MyCustomSplitPolicy.class.getName());
+
+
+
+
+
+
+ Store
+ A Store hosts a MemStore and 0 or more StoreFiles (HFiles). A Store corresponds to a column family for a table for a given region.
+
+
+ MemStore
+ The MemStore holds in-memory modifications to the Store. Modifications are KeyValues.
+ When asked to flush, current memstore is moved to snapshot and is cleared.
+ HBase continues to serve edits out of new memstore and backing snapshot until flusher reports in that the
+ flush succeeded. At this point the snapshot is let go.
+
+
+ StoreFile (HFile)
+ StoreFiles are where your data lives.
+
+ HFile Format
+ The hfile file format is based on
+ the SSTable file described in the BigTable [2006] paper and on
+ Hadoop's tfile
+ (The unit test suite and the compression harness were taken directly from tfile).
+ Schubert Zhang's blog post on HFile: A Block-Indexed File Format to Store Sorted Key-Value Pairs makes for a thorough introduction to HBase's hfile. Matteo Bertozzi has also put up a
+ helpful description, HBase I/O: HFile.
+
+ For more information, see the HFile source code.
+ Also see for information about the HFile v2 format that was included in 0.92.
+
+
+
+ HFile Tool
+
+ To view a textualized version of hfile content, you can do use
+ the org.apache.hadoop.hbase.io.hfile.HFile
+ tool. Type the following to see usage:$ ${HBASE_HOME}/bin/hbase org.apache.hadoop.hbase.io.hfile.HFile For
+ example, to view the content of the file
+ hdfs://10.81.47.41:8020/hbase/TEST/1418428042/DSMP/4759508618286845475,
+ type the following:$ ${HBASE_HOME}/bin/hbase org.apache.hadoop.hbase.io.hfile.HFile -v -f hdfs://10.81.47.41:8020/hbase/TEST/1418428042/DSMP/4759508618286845475 If
+ you leave off the option -v to see just a summary on the hfile. See
+ usage for other things to do with the HFile
+ tool.
+
+
+ StoreFile Directory Structure on HDFS
+ For more information of what StoreFiles look like on HDFS with respect to the directory structure, see .
+
+
+
+
+
+ Blocks
+ StoreFiles are composed of blocks. The blocksize is configured on a per-ColumnFamily basis.
+
+ Compression happens at the block level within StoreFiles. For more information on compression, see .
+
+ For more information on blocks, see the HFileBlock source code.
+
+
+
+ KeyValue
+ The KeyValue class is the heart of data storage in HBase. KeyValue wraps a byte array and takes offsets and lengths into passed array
+ at where to start interpreting the content as KeyValue.
+
+ The KeyValue format inside a byte array is:
+
+ keylength
+ valuelength
+ key
+ value
+
+
+ The Key is further decomposed as:
+
+ rowlength
+ row (i.e., the rowkey)
+ columnfamilylength
+ columnfamily
+ columnqualifier
+ timestamp
+ keytype (e.g., Put, Delete, DeleteColumn, DeleteFamily)
+
+
+ KeyValue instances are not split across blocks.
+ For example, if there is an 8 MB KeyValue, even if the block-size is 64kb this KeyValue will be read
+ in as a coherent block. For more information, see the KeyValue source code.
+
+ Example
+ To emphasize the points above, examine what happens with two Puts for two different columns for the same row:
+
+ Put #1: rowkey=row1, cf:attr1=value1
+ Put #2: rowkey=row1, cf:attr2=value2
+
+ Even though these are for the same row, a KeyValue is created for each column:
+ Key portion for Put #1:
+
+ rowlength ------------> 4
+ row -----------------> row1
+ columnfamilylength ---> 2
+ columnfamily --------> cf
+ columnqualifier ------> attr1
+ timestamp -----------> server time of Put
+ keytype -------------> Put
+
+
+ Key portion for Put #2:
+
+ rowlength ------------> 4
+ row -----------------> row1
+ columnfamilylength ---> 2
+ columnfamily --------> cf
+ columnqualifier ------> attr2
+ timestamp -----------> server time of Put
+ keytype -------------> Put
+
+
+
+
+ It is critical to understand that the rowkey, ColumnFamily, and column (aka columnqualifier) are embedded within
+ the KeyValue instance. The longer these identifiers are, the bigger the KeyValue is.
+
+
+ Compaction
+ There are two types of compactions: minor and major. Minor compactions will usually pick up a couple of the smaller adjacent
+ StoreFiles and rewrite them as one. Minors do not drop deletes or expired cells, only major compactions do this. Sometimes a minor compaction
+ will pick up all the StoreFiles in the Store and in this case it actually promotes itself to being a major compaction.
+
+ After a major compaction runs there will be a single StoreFile per Store, and this will help performance usually. Caution: major compactions rewrite all of the Stores data and on a loaded system, this may not be tenable;
+ major compactions will usually have to be done manually on large systems. See .
+
+ Compactions will not perform region merges. See for more information on region merging.
+
+
+ Compaction File Selection
+ To understand the core algorithm for StoreFile selection, there is some ASCII-art in the Store source code that
+ will serve as useful reference. It has been copied below:
+
+/* normal skew:
+ *
+ * older ----> newer
+ * _
+ * | | _
+ * | | | | _
+ * --|-|- |-|- |-|---_-------_------- minCompactSize
+ * | | | | | | | | _ | |
+ * | | | | | | | | | | | |
+ * | | | | | | | | | | | |
+ */
+
+ Important knobs:
+
+ hbase.store.compaction.ratio Ratio used in compaction
+ file selection algorithm (default 1.2f).
+ hbase.hstore.compaction.min (.90 hbase.hstore.compactionThreshold) (files) Minimum number
+ of StoreFiles per Store to be selected for a compaction to occur (default 2).
+ hbase.hstore.compaction.max (files) Maximum number of StoreFiles to compact per minor compaction (default 10).
+ hbase.hstore.compaction.min.size (bytes)
+ Any StoreFile smaller than this setting with automatically be a candidate for compaction. Defaults to
+ hbase.hregion.memstore.flush.size (128 mb).
+ hbase.hstore.compaction.max.size (.92) (bytes)
+ Any StoreFile larger than this setting with automatically be excluded from compaction (default Long.MAX_VALUE).
+
+
+ The minor compaction StoreFile selection logic is size based, and selects a file for compaction when the file
+ <= sum(smaller_files) * hbase.hstore.compaction.ratio.
+
+
+
+ Minor Compaction File Selection - Example #1 (Basic Example)
+ This example mirrors an example from the unit test TestCompactSelection.
+
+ hbase.store.compaction.ratio = 1.0f
+ hbase.hstore.compaction.min = 3 (files) >
+ hbase.hstore.compaction.max = 5 (files) >
+ hbase.hstore.compaction.min.size = 10 (bytes) >
+ hbase.hstore.compaction.max.size = 1000 (bytes) >
+
+ The following StoreFiles exist: 100, 50, 23, 12, and 12 bytes apiece (oldest to newest).
+ With the above parameters, the files that would be selected for minor compaction are 23, 12, and 12.
+
+ Why?
+
+ 100 --> No, because sum(50, 23, 12, 12) * 1.0 = 97.
+ 50 --> No, because sum(23, 12, 12) * 1.0 = 47.
+ 23 --> Yes, because sum(12, 12) * 1.0 = 24.
+ 12 --> Yes, because the previous file has been included, and because this
+ does not exceed the the max-file limit of 5
+ 12 --> Yes, because the previous file had been included, and because this
+ does not exceed the the max-file limit of 5.
+
+
+
+
+ Minor Compaction File Selection - Example #2 (Not Enough Files To Compact)
+ This example mirrors an example from the unit test TestCompactSelection.
+
+ hbase.store.compaction.ratio = 1.0f
+ hbase.hstore.compaction.min = 3 (files) >
+ hbase.hstore.compaction.max = 5 (files) >
+ hbase.hstore.compaction.min.size = 10 (bytes) >
+ hbase.hstore.compaction.max.size = 1000 (bytes) >
+
+
+ The following StoreFiles exist: 100, 25, 12, and 12 bytes apiece (oldest to newest).
+ With the above parameters, the files that would be selected for minor compaction are 23, 12, and 12.
+
+ Why?
+
+ 100 --> No, because sum(25, 12, 12) * 1.0 = 47
+ 25 --> No, because sum(12, 12) * 1.0 = 24
+ 12 --> No. Candidate because sum(12) * 1.0 = 12, there are only 2 files to compact and that is less than the threshold of 3
+ 12 --> No. Candidate because the previous StoreFile was, but there are not enough files to compact
+
+
+
+
+ Minor Compaction File Selection - Example #3 (Limiting Files To Compact)
+ This example mirrors an example from the unit test TestCompactSelection.
+
+ hbase.store.compaction.ratio = 1.0f
+ hbase.hstore.compaction.min = 3 (files) >
+ hbase.hstore.compaction.max = 5 (files) >
+ hbase.hstore.compaction.min.size = 10 (bytes) >
+ hbase.hstore.compaction.max.size = 1000 (bytes) >
+
+ The following StoreFiles exist: 7, 6, 5, 4, 3, 2, and 1 bytes apiece (oldest to newest).
+ With the above parameters, the files that would be selected for minor compaction are 7, 6, 5, 4, 3.
+
+ Why?
+
+ 7 --> Yes, because sum(6, 5, 4, 3, 2, 1) * 1.0 = 21. Also, 7 is less than the min-size
+ 6 --> Yes, because sum(5, 4, 3, 2, 1) * 1.0 = 15. Also, 6 is less than the min-size.
+ 5 --> Yes, because sum(4, 3, 2, 1) * 1.0 = 10. Also, 5 is less than the min-size.
+ 4 --> Yes, because sum(3, 2, 1) * 1.0 = 6. Also, 4 is less than the min-size.
+ 3 --> Yes, because sum(2, 1) * 1.0 = 3. Also, 3 is less than the min-size.
+ 2 --> No. Candidate because previous file was selected and 2 is less than the min-size, but the max-number of files to compact has been reached.
+ 1 --> No. Candidate because previous file was selected and 1 is less than the min-size, but max-number of files to compact has been reached.
+
+
+
+
+ Impact of Key Configuration Options
+ hbase.store.compaction.ratio. A large ratio (e.g., 10) will produce a single giant file. Conversely, a value of .25 will
+ produce behavior similar to the BigTable compaction algorithm - resulting in 4 StoreFiles.
+
+ hbase.hstore.compaction.min.size. Because
+ this limit represents the "automatic include" limit for all StoreFiles smaller than this value, this value may need to
+ be adjusted downwards in write-heavy environments where many 1 or 2 mb StoreFiles are being flushed, because every file
+ will be targeted for compaction and the resulting files may still be under the min-size and require further compaction, etc.
+
+
+
+
+
+
+
+ Bloom Filters
+ Bloom filters were developed over in HBase-1200
+ Add bloomfilters.
+ For description of the development process -- why static blooms
+ rather than dynamic -- and for an overview of the unique properties
+ that pertain to blooms in HBase, as well as possible future
+ directions, see the Development Process section
+ of the document BloomFilters
+ in HBase attached to HBase-1200.
+
+ The bloom filters described here are actually version two of
+ blooms in HBase. In versions up to 0.19.x, HBase had a dynamic bloom
+ option based on work done by the European Commission One-Lab
+ Project 034819. The core of the HBase bloom work was later
+ pulled up into Hadoop to implement org.apache.hadoop.io.BloomMapFile.
+ Version 1 of HBase blooms never worked that well. Version 2 is a
+ rewrite from scratch though again it starts with the one-lab
+ work.
+
+ See also and .
+
+
+
+ Bloom StoreFile footprint
+
+ Bloom filters add an entry to the StoreFile
+ general FileInfo data structure and then two
+ extra entries to the StoreFile metadata
+ section.
+
+
+ BloomFilter in the StoreFile
+ FileInfo data structure
+
+ FileInfo has a
+ BLOOM_FILTER_TYPE entry which is set to
+ NONE, ROW or
+ ROWCOL.
+
+
+
+ BloomFilter entries in StoreFile
+ metadata
+
+ BLOOM_FILTER_META holds Bloom Size, Hash
+ Function used, etc. Its small in size and is cached on
+ StoreFile.Reader load
+ BLOOM_FILTER_DATA is the actual bloomfilter
+ data. Obtained on-demand. Stored in the LRU cache, if it is enabled
+ (Its enabled by default).
+
+
+
+
+
+
+ Bulk Loading
+ Overview
+
+ HBase includes several methods of loading data into tables.
+ The most straightforward method is to either use the TableOutputFormat
+ class from a MapReduce job, or use the normal client APIs; however,
+ these are not always the most efficient methods.
+
+
+ The bulk load feature uses a MapReduce job to output table data in HBase's internal
+ data format, and then directly loads the generated StoreFiles into a running
+ cluster. Using bulk load will use less CPU and network resources than
+ simply using the HBase API.
+
+
+ Bulk Load Architecture
+
+ The HBase bulk load process consists of two main steps.
+
+ Preparing data via a MapReduce job
+
+ The first step of a bulk load is to generate HBase data files (StoreFiles) from
+ a MapReduce job using HFileOutputFormat. This output format writes
+ out data in HBase's internal storage format so that they can be
+ later loaded very efficiently into the cluster.
+
+
+ In order to function efficiently, HFileOutputFormat must be
+ configured such that each output HFile fits within a single region.
+ In order to do this, jobs whose output will be bulk loaded into HBase
+ use Hadoop's TotalOrderPartitioner class to partition the map output
+ into disjoint ranges of the key space, corresponding to the key
+ ranges of the regions in the table.
+
+
+ HFileOutputFormat includes a convenience function,
+ configureIncrementalLoad(), which automatically sets up
+ a TotalOrderPartitioner based on the current region boundaries of a
+ table.
+
+
+ Completing the data load
+
+ After the data has been prepared using
+ HFileOutputFormat, it is loaded into the cluster using
+ completebulkload. This command line tool iterates
+ through the prepared data files, and for each one determines the
+ region the file belongs to. It then contacts the appropriate Region
+ Server which adopts the HFile, moving it into its storage directory
+ and making the data available to clients.
+
+
+ If the region boundaries have changed during the course of bulk load
+ preparation, or between the preparation and completion steps, the
+ completebulkloads utility will automatically split the
+ data files into pieces corresponding to the new boundaries. This
+ process is not optimally efficient, so users should take care to
+ minimize the delay between preparing a bulk load and importing it
+ into the cluster, especially if other clients are simultaneously
+ loading data through other means.
+
+
+
+ Importing the prepared data using the completebulkload tool
+
+ After a data import has been prepared, either by using the
+ importtsv tool with the
+ "importtsv.bulk.output" option or by some other MapReduce
+ job using the HFileOutputFormat, the
+ completebulkload tool is used to import the data into the
+ running cluster.
+
+
+ The completebulkload tool simply takes the output path
+ where importtsv or your MapReduce job put its results, and
+ the table name to import into. For example:
+
+ $ hadoop jar hbase-VERSION.jar completebulkload [-c /path/to/hbase/config/hbase-site.xml] /user/todd/myoutput mytable
+
+ The -c config-file option can be used to specify a file
+ containing the appropriate hbase parameters (e.g., hbase-site.xml) if
+ not supplied already on the CLASSPATH (In addition, the CLASSPATH must
+ contain the directory that has the zookeeper configuration file if
+ zookeeper is NOT managed by HBase).
+
+
+ Note: If the target table does not already exist in HBase, this
+ tool will create the table automatically.
+
+ This tool will run quickly, after which point the new data will be visible in
+ the cluster.
+
+
+ See Also
+ For more information about the referenced utilities, see and .
+
+
+ Advanced Usage
+
+ Although the importtsv tool is useful in many cases, advanced users may
+ want to generate data programatically, or import data from other formats. To get
+ started doing so, dig into ImportTsv.java and check the JavaDoc for
+ HFileOutputFormat.
+
+
+ The import step of the bulk load can also be done programatically. See the
+ LoadIncrementalHFiles class for more information.
+
+
+
+
+ HDFS
+ As HBase runs on HDFS (and each StoreFile is written as a file on HDFS),
+ it is important to have an understanding of the HDFS Architecture
+ especially in terms of how it stores files, handles failovers, and replicates blocks.
+
+ See the Hadoop documentation on HDFS Architecture
+ for more information.
+
+ NameNode
+ The NameNode is responsible for maintaining the filesystem metadata. See the above HDFS Architecture link
+ for more information.
+
+
+ DataNode
+ The DataNodes are responsible for storing HDFS blocks. See the above HDFS Architecture link
+ for more information.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ FAQ
+
+ General
+
+ When should I use HBase?
+
+ See the in the Architecture chapter.
+
+
+
+
+ Are there other HBase FAQs?
+
+
+ See the FAQ that is up on the wiki, HBase Wiki FAQ.
+
+
+
+
+ Does HBase support SQL?
+
+
+ Not really. SQL-ish support for HBase via Hive is in development, however Hive is based on MapReduce which is not generally suitable for low-latency requests.
+ See the section for examples on the HBase client.
+
+
+
+
+ How can I find examples of NoSQL/HBase?
+
+ See the link to the BigTable paper in in the appendix, as
+ well as the other papers.
+
+
+
+
+ What is the history of HBase?
+
+ See .
+
+
+
+
+ Architecture
+
+ How does HBase handle Region-RegionServer assignment and locality?
+
+
+ See .
+
+
+
+
+ Configuration
+
+ How can I get started with my first cluster?
+
+
+ See .
+
+
+
+
+ Where can I learn about the rest of the configuration options?
+
+
+ See .
+
+
+
+
+ Schema Design / Data Access
+
+ How should I design my schema in HBase?
+
+
+ See and
+
+
+
+
+
+ How can I store (fill in the blank) in HBase?
+
+
+
+ See .
+
+
+
+
+
+ How can I handle secondary indexes in HBase?
+
+
+
+ See
+
+
+
+
+ Can I change a table's rowkeys?
+
+
+ This is a very common quesiton. You can't. See .
+
+
+
+
+ What APIs does HBase support?
+
+
+ See , and .
+
+
+
+
+ MapReduce
+
+ How can I use MapReduce with HBase?
+
+
+ See
+
+
+
+
+ Performance and Troubleshooting
+
+
+ How can I improve HBase cluster performance?
+
+
+
+ See .
+
+
+
+
+
+ How can I troubleshoot my HBase cluster?
+
+
+
+ See .
+
+
+
+
+ Amazon EC2
+
+
+ I am running HBase on Amazon EC2 and...
+
+
+
+ EC2 issues are a special case. See Troubleshooting and Performance sections.
+
+
+
+
+ Operations
+
+
+ How do I manage my HBase cluster?
+
+
+
+ See
+
+
+
+
+
+ How do I back up my HBase cluster?
+
+
+
+ See
+
+
+
+
+ HBase in Action
+
+ Where can I find interesting videos and presentations on HBase?
+
+
+ See
+
+
+
+
+
+
+
+
+ hbck In Depth
+ HBaseFsck (hbck) is a tool for checking for region consistency and table integrity problems
+and repairing a corrupted HBase. It works in two basic modes -- a read-only inconsistency
+identifying mode and a multi-phase read-write repair mode.
+
+
+ Running hbck to identify inconsistencies
+To check to see if your HBase cluster has corruptions, run hbck against your HBase cluster:
+
+$ ./bin/hbase hbck
+
+
+At the end of the commands output it prints OK or tells you the number of INCONSISTENCIES
+present. You may also want to run run hbck a few times because some inconsistencies can be
+transient (e.g. cluster is starting up or a region is splitting). Operationally you may want to run
+hbck regularly and setup alert (e.g. via nagios) if it repeatedly reports inconsistencies .
+A run of hbck will report a list of inconsistencies along with a brief description of the regions and
+tables affected. The using the -details option will report more details including a representative
+listing of all the splits present in all the tables.
+
+
+$ ./bin/hbase hbck -details
+
+
+ Inconsistencies
+
+ If after several runs, inconsistencies continue to be reported, you may have encountered a
+corruption. These should be rare, but in the event they occur newer versions of HBase include
+the hbck tool enabled with automatic repair options.
+
+
+ There are two invariants that when violated create inconsistencies in HBase:
+
+
+ HBase’s region consistency invariant is satisfied if every region is assigned and
+deployed on exactly one region server, and all places where this state kept is in
+accordance.
+
+ HBase’s table integrity invariant is satisfied if for each table, every possible row key
+resolves to exactly one region.
+
+
+
+Repairs generally work in three phases -- a read-only information gathering phase that identifies
+inconsistencies, a table integrity repair phase that restores the table integrity invariant, and then
+finally a region consistency repair phase that restores the region consistency invariant.
+Starting from version 0.90.0, hbck could detect region consistency problems report on a subset
+of possible table integrity problems. It also included the ability to automatically fix the most
+common inconsistency, region assignment and deployment consistency problems. This repair
+could be done by using the -fix command line option. These problems close regions if they are
+open on the wrong server or on multiple region servers and also assigns regions to region
+servers if they are not open.
+
+
+Starting from HBase versions 0.90.7, 0.92.2 and 0.94.0, several new command line options are
+introduced to aid repairing a corrupted HBase. This hbck sometimes goes by the nickname
+“uberhbck”. Each particular version of uber hbck is compatible with the HBase’s of the same
+major version (0.90.7 uberhbck can repair a 0.90.4). However, versions <=0.90.6 and versions
+<=0.92.1 may require restarting the master or failing over to a backup master.
+
+
+ Localized repairs
+
+ When repairing a corrupted HBase, it is best to repair the lowest risk inconsistencies first.
+These are generally region consistency repairs -- localized single region repairs, that only modify
+in-memory data, ephemeral zookeeper data, or patch holes in the META table.
+Region consistency requires that the HBase instance has the state of the region’s data in HDFS
+(.regioninfo files), the region’s row in the .META. table., and region’s deployment/assignments on
+region servers and the master in accordance. Options for repairing region consistency include:
+
+ -fixAssignments (equivalent to the 0.90 -fix option) repairs unassigned, incorrectly
+assigned or multiply assigned regions.
+
+ -fixMeta which removes meta rows when corresponding regions are not present in
+HDFS and adds new meta rows if they regions are present in HDFS while not in META.
+
+
+ To fix deployment and assignment problems you can run this command:
+
+
+$ ./bin/hbase hbck -fixAssignments
+
+To fix deployment and assignment problems as well as repairing incorrect meta rows you can
+run this command:.
+
+$ ./bin/hbase hbck -fixAssignments -fixMeta
+
+There are a few classes of table integrity problems that are low risk repairs. The first two are
+degenerate (startkey == endkey) regions and backwards regions (startkey > endkey). These are
+automatically handled by sidelining the data to a temporary directory (/hbck/xxxx).
+The third low-risk class is hdfs region holes. This can be repaired by using the:
+
+ -fixHdfsHoles option for fabricating new empty regions on the file system.
+If holes are detected you can use -fixHdfsHoles and should include -fixMeta and -fixAssignments to make the new region consistent.
+
+
+
+$ ./bin/hbase hbck -fixAssignments -fixMeta -fixHdfsHoles
+
+Since this is a common operation, we’ve added a the -repairHoles flag that is equivalent to the
+previous command:
+
+$ ./bin/hbase hbck -repairHoles
+
+If inconsistencies still remain after these steps, you most likely have table integrity problems
+related to orphaned or overlapping regions.
+
+ Region Overlap Repairs
+Table integrity problems can require repairs that deal with overlaps. This is a riskier operation
+because it requires modifications to the file system, requires some decision making, and may
+require some manual steps. For these repairs it is best to analyze the output of a hbck -details
+run so that you isolate repairs attempts only upon problems the checks identify. Because this is
+riskier, there are safeguard that should be used to limit the scope of the repairs.
+WARNING: This is a relatively new and have only been tested on online but idle HBase instances
+(no reads/writes). Use at your own risk in an active production environment!
+The options for repairing table integrity violations include:
+
+ -fixHdfsOrphans option for “adopting” a region directory that is missing a region
+metadata file (the .regioninfo file).
+
+ -fixHdfsOverlaps ability for fixing overlapping regions
+
+
+When repairing overlapping regions, a region’s data can be modified on the file system in two
+ways: 1) by merging regions into a larger region or 2) by sidelining regions by moving data to
+“sideline” directory where data could be restored later. Merging a large number of regions is
+technically correct but could result in an extremely large region that requires series of costly
+compactions and splitting operations. In these cases, it is probably better to sideline the regions
+that overlap with the most other regions (likely the largest ranges) so that merges can happen on
+a more reasonable scale. Since these sidelined regions are already laid out in HBase’s native
+directory and HFile format, they can be restored by using HBase’s bulk load mechanism.
+The default safeguard thresholds are conservative. These options let you override the default
+thresholds and to enable the large region sidelining feature.
+
+ -maxMerge <n> maximum number of overlapping regions to merge
+
+ -sidelineBigOverlaps if more than maxMerge regions are overlapping, sideline attempt
+to sideline the regions overlapping with the most other regions.
+
+ -maxOverlapsToSideline <n> if sidelining large overlapping regions, sideline at most n
+regions.
+
+
+
+Since often times you would just want to get the tables repaired, you can use this option to turn
+on all repair options:
+
+ -repair includes all the region consistency options and only the hole repairing table
+integrity options.
+
+
+Finally, there are safeguards to limit repairs to only specific tables. For example the following
+command would only attempt to repair table TableFoo and TableBar.
+
+$ ./bin/hbase/ hbck -repair TableFoo TableBar
+
+ Special cases: Meta is not properly assigned
+There are a few special cases that hbck can handle as well.
+Sometimes the meta table’s only region is inconsistently assigned or deployed. In this case
+there is a special -fixMetaOnly option that can try to fix meta assignments.
+
+$ ./bin/hbase hbck -fixMetaOnly -fixAssignments
+
+
+ Special cases: HBase version file is missing
+HBase’s data on the file system requires a version file in order to start. If this flie is missing, you
+can use the -fixVersionFile option to fabricating a new HBase version file. This assumes that
+the version of hbck you are running is the appropriate version for the HBase cluster.
+
+ Special case: Root and META are corrupt.
+The most drastic corruption scenario is the case where the ROOT or META is corrupted and
+HBase will not start. In this case you can use the OfflineMetaRepair tool create new ROOT
+and META regions and tables.
+This tool assumes that HBase is offline. It then marches through the existing HBase home
+directory, loads as much information from region metadata files (.regioninfo files) as possible
+from the file system. If the region metadata has proper table integrity, it sidelines the original root
+and meta table directories, and builds new ones with pointers to the region directories and their
+data.
+
+$ ./bin/hbase org.apache.hadoop.hbase.util.OfflineMetaRepair
+
+NOTE: This tool is not as clever as uberhbck but can be used to bootstrap repairs that uberhbck
+can complete.
+If the tool succeeds you should be able to start hbase and run online repairs if necessary.
+
+
+
+
+
+
+ Compression In HBaseCompression
+
+
+ CompressionTest Tool
+
+ HBase includes a tool to test compression is set up properly.
+ To run it, type /bin/hbase org.apache.hadoop.hbase.util.CompressionTest.
+ This will emit usage on how to run the tool.
+
+
+
+
+
+
+ hbase.regionserver.codecs
+
+
+
+ To have a RegionServer test a set of codecs and fail-to-start if any
+ code is missing or misinstalled, add the configuration
+
+ hbase.regionserver.codecs
+
+ to your hbase-site.xml with a value of
+ codecs to test on startup. For example if the
+
+ hbase.regionserver.codecs
+ value is lzo,gz and if lzo is not present
+ or improperly installed, the misconfigured RegionServer will fail
+ to start.
+
+
+ Administrators might make use of this facility to guard against
+ the case where a new server is added to cluster but the cluster
+ requires install of a particular coded.
+
+
+
+
+
+ LZO
+
+ Unfortunately, HBase cannot ship with LZO because of
+ the licensing issues; HBase is Apache-licensed, LZO is GPL.
+ Therefore LZO install is to be done post-HBase install.
+ See the Using LZO Compression
+ wiki page for how to make LZO work with HBase.
+
+ A common problem users run into when using LZO is that while initial
+ setup of the cluster runs smooth, a month goes by and some sysadmin goes to
+ add a machine to the cluster only they'll have forgotten to do the LZO
+ fixup on the new machine. In versions since HBase 0.90.0, we should
+ fail in a way that makes it plain what the problem is, but maybe not.
+ See
+ for a feature to help protect against failed LZO install.
+
+
+
+
+ GZIP
+
+
+ GZIP will generally compress better than LZO though slower.
+ For some setups, better compression may be preferred.
+ Java will use java's GZIP unless the native Hadoop libs are
+ available on the CLASSPATH; in this case it will use native
+ compressors instead (If the native libs are NOT present,
+ you will see lots of Got brand-new compressor
+ reports in your logs; see ).
+
+
+
+
+ SNAPPY
+
+
+ If snappy is installed, HBase can make use of it (courtesy of
+ hadoop-snappy
+ See Alejandro's note up on the list on difference between Snappy in Hadoop
+ and Snappy in HBase).
+
+
+
+
+ Build and install snappy on all nodes
+ of your cluster.
+
+
+
+
+ Use CompressionTest to verify snappy support is enabled and the libs can be loaded ON ALL NODES of your cluster:
+ $ hbase org.apache.hadoop.hbase.util.CompressionTest hdfs://host/path/to/hbase snappy
+
+
+
+
+ Create a column family with snappy compression and verify it in the hbase shell:
+ $ hbase> create 't1', { NAME => 'cf1', COMPRESSION => 'SNAPPY' }
+hbase> describe 't1'
+ In the output of the "describe" command, you need to ensure it lists "COMPRESSION => 'SNAPPY'"
+
+
+
+
+
+
+
+
+ Changing Compression Schemes
+ A frequent question on the dist-list is how to change compression schemes for ColumnFamilies. This is actually quite simple,
+ and can be done via an alter command. Because the compression scheme is encoded at the block-level in StoreFiles, the table does
+ not need to be re-created and the data does not copied somewhere else. Just make sure
+ the old codec is still available until you are sure that all of the old StoreFiles have been compacted.
+
+
+
+
+
+ YCSB: The Yahoo! Cloud Serving Benchmark and HBase
+ TODO: Describe how YCSB is poor for putting up a decent cluster load.
+ TODO: Describe setup of YCSB for HBase
+ Ted Dunning redid YCSB so its mavenized and added facility for verifying workloads. See Ted Dunning's YCSB.
+
+
+
+
+ HFile format version 2
+
+ Motivation
+ Note: this feature was introduced in HBase 0.92
+ We found it necessary to revise the HFile format after encountering high memory usage and slow startup times caused by large Bloom filters and block indexes in the region server. Bloom filters can get as large as 100 MB per HFile, which adds up to 2 GB when aggregated over 20 regions. Block indexes can grow as large as 6 GB in aggregate size over the same set of regions. A region is not considered opened until all of its block index data is loaded. Large Bloom filters produce a different performance problem: the first get request that requires a Bloom filter lookup will incur the latency of loading the entire Bloom filter bit array.
+ To speed up region server startup we break Bloom filters and block indexes into multiple blocks and write those blocks out as they fill up, which also reduces the HFile writer’s memory footprint. In the Bloom filter case, “filling up a block” means accumulating enough keys to efficiently utilize a fixed-size bit array, and in the block index case we accumulate an “index block” of the desired size. Bloom filter blocks and index blocks (we call these “inline blocks”) become interspersed with data blocks, and as a side effect we can no longer rely on the difference between block offsets to determine data block length, as it was done in version 1.
+ HFile is a low-level file format by design, and it should not deal with application-specific details such as Bloom filters, which are handled at StoreFile level. Therefore, we call Bloom filter blocks in an HFile "inline" blocks. We also supply HFile with an interface to write those inline blocks.
+ Another format modification aimed at reducing the region server startup time is to use a contiguous “load-on-open” section that has to be loaded in memory at the time an HFile is being opened. Currently, as an HFile opens, there are separate seek operations to read the trailer, data/meta indexes, and file info. To read the Bloom filter, there are two more seek operations for its “data” and “meta” portions. In version 2, we seek once to read the trailer and seek again to read everything else we need to open the file from a contiguous block.
+ HFile format version 1 overview As we will be discussing the changes we are making to the HFile format, it is useful to give a short overview of the previous (HFile version 1) format. An HFile in the existing format is structured as follows:
+
+
+
+
+
+ HFile Version 1
+
+
+ HFile Version 1
+
+
+
+ Image courtesy of Lars George, hbase-architecture-101-storage.html.
+
+ Block index format in version 1
+ The block index in version 1 is very straightforward. For each entry, it contains:
+
+
+ Offset (long)
+
+
+ Uncompressed size (int)
+
+
+ Key (a serialized byte array written using Bytes.writeByteArray)
+
+
+ Key length as a variable-length integer (VInt)
+
+
+
+
+ Key bytes
+
+
+
+
+
+ The number of entries in the block index is stored in the fixed file trailer, and has to be passed in to the method that reads the block index. One of the limitations of the block index in version 1 is that it does not provide the compressed size of a block, which turns out to be necessary for decompression. Therefore, the HFile reader has to infer this compressed size from the offset difference between blocks. We fix this limitation in version 2, where we store on-disk block size instead of uncompressed size, and get uncompressed size from the block header.
+ HBase file format with inline blocks (version 2)
+
+ Overview
+ The version of HBase introducing the above features reads both version 1 and 2 HFiles, but only writes version 2 HFiles. A version 2 HFile is structured as follows:
+
+
+
+
+
+ HFile Version 2
+
+
+ HFile Version 2
+
+
+
+
+
+
+ Unified version 2 block format
+ In the version 2 every block in the data section contains the following fields:
+
+
+ 8 bytes: Block type, a sequence of bytes equivalent to version 1's "magic records". Supported block types are:
+
+
+ DATA – data blocks
+
+
+
+
+ LEAF_INDEX – leaf-level index blocks in a multi-level-block-index
+
+
+
+
+ BLOOM_CHUNK – Bloom filter chunks
+
+
+
+
+ META – meta blocks (not used for Bloom filters in version 2 anymore)
+
+
+
+
+ INTERMEDIATE_INDEX – intermediate-level index blocks in a multi-level blockindex
+
+
+
+
+ ROOT_INDEX – root>level index blocks in a multi>level block index
+
+
+
+
+ FILE_INFO – the “file info” block, a small key>value map of metadata
+
+
+
+
+ BLOOM_META – a Bloom filter metadata block in the load>on>open section
+
+
+
+
+ TRAILER – a fixed>size file trailer. As opposed to the above, this is not an
+ HFile v2 block but a fixed>size (for each HFile version) data structure
+
+
+
+
+ INDEX_V1 – this block type is only used for legacy HFile v1 block
+
+
+
+
+
+ Compressed size of the block's data, not including the header (int).
+
+
+Can be used for skipping the current data block when scanning HFile data.
+
+
+
+ Uncompressed size of the block's data, not including the header (int)
+
+ This is equal to the compressed size if the compression algorithm is NON
+
+
+
+ File offset of the previous block of the same type (long)
+
+ Can be used for seeking to the previous data/index block
+
+
+
+ Compressed data (or uncompressed data if the compression algorithm is NONE).
+
+
+ The above format of blocks is used in the following HFile sections:
+
+
+ Scanned block section. The section is named so because it contains all data blocks that need to be read when an HFile is scanned sequentially. Also contains leaf block index and Bloom chunk blocks.
+
+
+ Non-scanned block section. This section still contains unified-format v2 blocks but it does not have to be read when doing a sequential scan. This section contains “meta” blocks and intermediate-level index blocks.
+
+
+
+ We are supporting “meta” blocks in version 2 the same way they were supported in version 1, even though we do not store Bloom filter data in these blocks anymore.
+
+ Block index in version 2
+ There are three types of block indexes in HFile version 2, stored in two different formats (root and non-root):
+
+
+ Data index — version 2 multi-level block index, consisting of:
+
+
+
+ Version 2 root index, stored in the data block index section of the file
+
+
+
+
+Optionally, version 2 intermediate levels, stored in the non%root format in the data index section of the file. Intermediate levels can only be present if leaf level blocks are present
+
+
+
+
+Optionally, version 2 leaf levels, stored in the non%root format inline with data blocks
+
+
+
+
+
+ Meta index — version 2 root index format only, stored in the meta index section of the file
+
+
+ Bloom index — version 2 root index format only, stored in the “load-on-open” section as part of Bloom filter metadata.
+
+
+
+ Root block index format in version 2
+ This format applies to:
+
+
+ Root level of the version 2 data index
+
+
+ Entire meta and Bloom indexes in version 2, which are always single-level.
+
+
+ A version 2 root index block is a sequence of entries of the following format, similar to entries of a version 1 block index, but storing on-disk size instead of uncompressed size.
+
+
+ Offset (long)
+
+This offset may point to a data block or to a deeper>level index block.
+
+
+
+ On-disk size (int)
+
+
+ Key (a serialized byte array stored using Bytes.writeByteArray)
+
+
+ Key (VInt)
+
+
+
+ Key bytes
+
+
+
+
+
+ A single-level version 2 block index consists of just a single root index block. To read a root index block of version 2, one needs to know the number of entries. For the data index and the meta index the number of entries is stored in the trailer, and for the Bloom index it is stored in the compound Bloom filter metadata.
+
+ For a multi-level block index we also store the following fields in the root index block in the load-on-open section of the HFile, in addition to the data structure described above:
+
+
+ Middle leaf index block offset
+
+
+ Middle leaf block on-disk size (meaning the leaf index block containing the reference to the “middle” data block of the file)
+
+
+ The index of the mid-key (defined below) in the middle leaf-level block.
+
+
+
+ These additional fields are used to efficiently retrieve the mid-key of the HFile used in HFile splits, which we define as the first key of the block with a zero-based index of (n – 1) / 2, if the total number of blocks in the HFile is n. This definition is consistent with how the mid-key was determined in HFile version 1, and is reasonable in general, because blocks are likely to be the same size on average, but we don’t have any estimates on individual key/value pair sizes.
+
+ When writing a version 2 HFile, the total number of data blocks pointed to by every leaf-level index block is kept track of. When we finish writing and the total number of leaf-level blocks is determined, it is clear which leaf-level block contains the mid-key, and the fields listed above are computed. When reading the HFile and the mid-key is requested, we retrieve the middle leaf index block (potentially from the block cache) and get the mid-key value from the appropriate position inside that leaf block.
+
+ Non-root block index format in version 2
+ This format applies to intermediate-level and leaf index blocks of a version 2 multi-level data block index. Every non-root index block is structured as follows.
+
+
+ numEntries: the number of entries (int).
+
+
+ entryOffsets: the “secondary index” of offsets of entries in the block, to facilitate a quick binary search on the key (numEntries + 1 int values). The last value is the total length of all entries in this index block. For example, in a non-root index block with entry sizes 60, 80, 50 the “secondary index” will contain the following int array: {0, 60, 140, 190}.
+
+
+ Entries. Each entry contains:
+
+
+
+Offset of the block referenced by this entry in the file (long)
+
+
+
+
+On>disk size of the referenced block (int)
+
+
+
+
+Key. The length can be calculated from entryOffsets.
+
+
+
+
+
+
+ Bloom filters in version 2
+ In contrast with version 1, in a version 2 HFile Bloom filter metadata is stored in the load-on-open section of the HFile for quick startup.
+
+
+ A compound Bloom filter.
+
+
+
+ Bloom filter version = 3 (int). There used to be a DynamicByteBloomFilter class that had the Bloom filter version number 2
+
+
+
+
+The total byte size of all compound Bloom filter chunks (long)
+
+
+
+
+ Number of hash functions (int
+
+
+
+
+Type of hash functions (int)
+
+
+
+
+The total key count inserted into the Bloom filter (long)
+
+
+
+
+The maximum total number of keys in the Bloom filter (long)
+
+
+
+
+The number of chunks (int)
+
+
+
+
+Comparator class used for Bloom filter keys, a UTF>8 encoded string stored using Bytes.writeByteArray
+
+
+
+
+ Bloom block index in the version 2 root block index format
+
+
+
+
+ File Info format in versions 1 and 2
+ The file info block is a serialized HbaseMapWritable (essentially a map from byte arrays to byte arrays) with the following keys, among others. StoreFile-level logic adds more keys to this.
+
+
+
+ hfile.LASTKEY
+
+
+ The last key of the file (byte array)
+
+
+
+
+ hfile.AVG_KEY_LEN
+
+
+ The average key length in the file (int)
+
+
+
+
+ hfile.AVG_VALUE_LEN
+
+
+ The average value length in the file (int)
+
+
+
+ File info format did not change in version 2. However, we moved the file info to the final section of the file, which can be loaded as one block at the time the HFile is being opened. Also, we do not store comparator in the version 2 file info anymore. Instead, we store it in the fixed file trailer. This is because we need to know the comparator at the time of parsing the load-on-open section of the HFile.
+ Fixed file trailer format differences between versions 1 and 2
+ The following table shows common and different fields between fixed file trailers in versions 1 and 2. Note that the size of the trailer is different depending on the version, so it is “fixed” only within one version. However, the version is always stored as the last four-byte integer in the file.
+
+
+
+
+
+
+
+
+ Version 1
+
+
+ Version 2
+
+
+
+
+ File info offset (long)
+
+
+
+
+ Data index offset (long)
+
+
+ loadOnOpenOffset (long)
+ The offset of the section that we need toload when opening the file.
+
+
+
+
+ Number of data index entries (int)
+
+
+
+
+ metaIndexOffset (long)
+ This field is not being used by the version 1 reader, so we removed it from version 2.
+
+
+ uncompressedDataIndexSize (long)
+ The total uncompressed size of the whole data block index, including root-level, intermediate-level, and leaf-level blocks.
+
+
+
+
+ Number of meta index entries (int)
+
+
+
+
+ Total uncompressed bytes (long)
+
+
+
+
+ numEntries (int)
+
+
+ numEntries (long)
+
+
+
+
+ Compression codec: 0 = LZO, 1 = GZ, 2 = NONE (int)
+
+
+
+
+
+
+
+ The number of levels in the data block index (int)
+
+
+
+
+
+
+
+ firstDataBlockOffset (long)
+ The offset of the first first data block. Used when scanning.
+
+
+
+
+
+
+
+ lastDataBlockEnd (long)
+ The offset of the first byte after the last key/value data block. We don't need to go beyond this offset when scanning.
+
+
+
+
+ Version: 1 (int)
+
+
+ Version: 2 (int)
+
+
+
+
+
+
+ Other Information About HBase
+ HBase Videos
+ Introduction to HBase
+
+ Introduction to HBase by Todd Lipcon (Chicago Data Summit 2011).
+
+ Introduction to HBase by Todd Lipcon (2010).
+
+
+
+ Building Real Time Services at Facebook with HBase by Jonathan Gray (Hadoop World 2011).
+
+ HBase and Hadoop, Mixing Real-Time and Batch Processing at StumbleUpon by JD Cryans (Hadoop World 2010).
+
+
+ HBase Presentations (Slides)
+ Advanced HBase Schema Design by Lars George (Hadoop World 2011).
+
+ Introduction to HBase by Todd Lipcon (Chicago Data Summit 2011).
+
+ Getting The Most From Your HBase Install by Ryan Rawson, Jonathan Gray (Hadoop World 2009).
+
+
+ HBase Papers
+ BigTable by Google (2006).
+
+ HBase and HDFS Locality by Lars George (2010).
+
+ No Relation: The Mixed Blessings of Non-Relational Databases by Ian Varley (2009).
+
+
+ HBase Sites
+ Cloudera's HBase Blog has a lot of links to useful HBase information.
+
+ CAP Confusion is a relevant entry for background information on
+ distributed storage systems.
+
+
+
+ HBase Wiki has a page with a number of presentations.
+
+
+ HBase Books
+ HBase: The Definitive Guide by Lars George.
+
+
+ Hadoop Books
+ Hadoop: The Definitive Guide by Tom White.
+
+
+
+
+
+ HBase History
+
+ 2006: BigTable paper published by Google.
+
+ 2006 (end of year): HBase development starts.
+
+ 2008: HBase becomes Hadoop sub-project.
+
+ 2010: HBase becomes Apache top-level project.
+
+
+
+
+ HBase and the Apache Software Foundation
+ HBase is a project in the Apache Software Foundation and as such there are responsibilities to the ASF to ensure
+ a healthy project.
+ ASF Development Process
+ See the Apache Development Process page
+ for all sorts of information on how the ASF is structured (e.g., PMC, committers, contributors), to tips on contributing
+ and getting involved, and how open-source works at ASF.
+
+
+ ASF Board Reporting
+ Once a quarter, each project in the ASF portfolio submits a report to the ASF board. This is done by the HBase project
+ lead and the committers. See ASF board reporting for more information.
+
+
+
+
+
+ Index
+
+
diff --git src/docbkx/case_studies.xml src/docbkx/case_studies.xml
new file mode 100644
index 0000000..a10f53c
--- /dev/null
+++ src/docbkx/case_studies.xml
@@ -0,0 +1,324 @@
+
+
+
+ Case Studies
+
+ Overview
+ This chapter will describe a variety of performance and troubleshooting case studies that can
+ provide a useful blueprint on diagnosing cluster issues.
+ For more information on Performance and Troubleshooting, see and .
+
+
+
+
+ Schema Design
+
+
+ List Data
+ The following is an exchange from the user dist-list regarding a fairly common question:
+ how to handle per-user list data in HBase.
+
+ *** QUESTION ***
+
+ We're looking at how to store a large amount of (per-user) list data in
+HBase, and we were trying to figure out what kind of access pattern made
+the most sense. One option is store the majority of the data in a key, so
+we could have something like:
+
+
+
+<FixedWidthUserName><FixedWidthValueId1>:"" (no value)
+<FixedWidthUserName><FixedWidthValueId2>:"" (no value)
+<FixedWidthUserName><FixedWidthValueId3>:"" (no value)
+
+
+The other option we had was to do this entirely using:
+
+<FixedWidthUserName><FixedWidthPageNum0>:<FixedWidthLength><FixedIdNextPageNum><ValueId1><ValueId2><ValueId3>...
+<FixedWidthUserName><FixedWidthPageNum1>:<FixedWidthLength><FixedIdNextPageNum><ValueId1><ValueId2><ValueId3>...
+
+
+where each row would contain multiple values.
+So in one case reading the first thirty values would be:
+
+
+scan { STARTROW => 'FixedWidthUsername' LIMIT => 30}
+
+And in the second case it would be
+
+get 'FixedWidthUserName\x00\x00\x00\x00'
+
+
+The general usage pattern would be to read only the first 30 values of
+these lists, with infrequent access reading deeper into the lists. Some
+users would have <= 30 total values in these lists, and some users would
+have millions (i.e. power-law distribution)
+
+
+ The single-value format seems like it would take up more space on HBase,
+but would offer some improved retrieval / pagination flexibility. Would
+there be any significant performance advantages to be able to paginate via
+gets vs paginating with scans?
+
+
+ My initial understanding was that doing a scan should be faster if our
+paging size is unknown (and caching is set appropriately), but that gets
+should be faster if we'll always need the same page size. I've ended up
+hearing different people tell me opposite things about performance. I
+assume the page sizes would be relatively consistent, so for most use cases
+we could guarantee that we only wanted one page of data in the
+fixed-page-length case. I would also assume that we would have infrequent
+updates, but may have inserts into the middle of these lists (meaning we'd
+need to update all subsequent rows).
+
+
+Thanks for help / suggestions / follow-up questions.
+
+ *** ANSWER ***
+
+If I understand you correctly, you're ultimately trying to store
+triples in the form "user, valueid, value", right? E.g., something
+like:
+
+
+"user123, firstname, Paul",
+"user234, lastname, Smith"
+
+
+(But the usernames are fixed width, and the valueids are fixed width).
+
+
+And, your access pattern is along the lines of: "for user X, list the
+next 30 values, starting with valueid Y". Is that right? And these
+values should be returned sorted by valueid?
+
+
+The tl;dr version is that you should probably go with one row per
+user+value, and not build a complicated intra-row pagination scheme on
+your own unless you're really sure it is needed.
+
+
+Your two options mirror a common question people have when designing
+HBase schemas: should I go "tall" or "wide"? Your first schema is
+"tall": each row represents one value for one user, and so there are
+many rows in the table for each user; the row key is user + valueid,
+and there would be (presumably) a single column qualifier that means
+"the value". This is great if you want to scan over rows in sorted
+order by row key (thus my question above, about whether these ids are
+sorted correctly). You can start a scan at any user+valueid, read the
+next 30, and be done. What you're giving up is the ability to have
+transactional guarantees around all the rows for one user, but it
+doesn't sound like you need that. Doing it this way is generally
+recommended (see
+here http://hbase.apache.org/book.html#schema.smackdown).
+
+
+Your second option is "wide": you store a bunch of values in one row,
+using different qualifiers (where the qualifier is the valueid). The
+simple way to do that would be to just store ALL values for one user
+in a single row. I'm guessing you jumped to the "paginated" version
+because you're assuming that storing millions of columns in a single
+row would be bad for performance, which may or may not be true; as
+long as you're not trying to do too much in a single request, or do
+things like scanning over and returning all of the cells in the row,
+it shouldn't be fundamentally worse. The client has methods that allow
+you to get specific slices of columns.
+
+
+Note that neither case fundamentally uses more disk space than the
+other; you're just "shifting" part of the identifying information for
+a value either to the left (into the row key, in option one) or to the
+right (into the column qualifiers in option 2). Under the covers,
+every key/value still stores the whole row key, and column family
+name. (If this is a bit confusing, take an hour and watch Lars
+George's excellent video about understanding HBase schema design:
+http://www.youtube.com/watch?v=_HLoH_PgrLk).
+
+
+A manually paginated version has lots more complexities, as you note,
+like having to keep track of how many things are in each page,
+re-shuffling if new values are inserted, etc. That seems significantly
+more complex. It might have some slight speed advantages (or
+disadvantages!) at extremely high throughput, and the only way to
+really know that would be to try it out. If you don't have time to
+build it both ways and compare, my advice would be to start with the
+simplest option (one row per user+value). Start simple and iterate! :)
+
+
+
+
+
+
+
+
+ Performance/Troubleshooting
+
+
+ Case Study #1 (Performance Issue On A Single Node)
+ Scenario
+ Following a scheduled reboot, one data node began exhibiting unusual behavior. Routine MapReduce
+ jobs run against HBase tables which regularly completed in five or six minutes began taking 30 or 40 minutes
+ to finish. These jobs were consistently found to be waiting on map and reduce tasks assigned to the troubled data node
+ (e.g., the slow map tasks all had the same Input Split).
+ The situation came to a head during a distributed copy, when the copy was severely prolonged by the lagging node.
+
+
+ Hardware
+ Datanodes:
+
+ Two 12-core processors
+ Six Enerprise SATA disks
+ 24GB of RAM
+ Two bonded gigabit NICs
+
+
+ Network:
+
+ 10 Gigabit top-of-rack switches
+ 20 Gigabit bonded interconnects between racks.
+
+
+
+ Hypotheses
+ HBase "Hot Spot" Region
+ We hypothesized that we were experiencing a familiar point of pain: a "hot spot" region in an HBase table,
+ where uneven key-space distribution can funnel a huge number of requests to a single HBase region, bombarding the RegionServer
+ process and cause slow response time. Examination of the HBase Master status page showed that the number of HBase requests to the
+ troubled node was almost zero. Further, examination of the HBase logs showed that there were no region splits, compactions, or other region transitions
+ in progress. This effectively ruled out a "hot spot" as the root cause of the observed slowness.
+
+
+ HBase Region With Non-Local Data
+ Our next hypothesis was that one of the MapReduce tasks was requesting data from HBase that was not local to the datanode, thus
+ forcing HDFS to request data blocks from other servers over the network. Examination of the datanode logs showed that there were very
+ few blocks being requested over the network, indicating that the HBase region was correctly assigned, and that the majority of the necessary
+ data was located on the node. This ruled out the possibility of non-local data causing a slowdown.
+
+
+ Excessive I/O Wait Due To Swapping Or An Over-Worked Or Failing Hard Disk
+ After concluding that the Hadoop and HBase were not likely to be the culprits, we moved on to troubleshooting the datanode's hardware.
+ Java, by design, will periodically scan its entire memory space to do garbage collection. If system memory is heavily overcommitted, the Linux
+ kernel may enter a vicious cycle, using up all of its resources swapping Java heap back and forth from disk to RAM as Java tries to run garbage
+ collection. Further, a failing hard disk will often retry reads and/or writes many times before giving up and returning an error. This can manifest
+ as high iowait, as running processes wait for reads and writes to complete. Finally, a disk nearing the upper edge of its performance envelope will
+ begin to cause iowait as it informs the kernel that it cannot accept any more data, and the kernel queues incoming data into the dirty write pool in memory.
+ However, using vmstat(1) and free(1), we could see that no swap was being used, and the amount of disk IO was only a few kilobytes per second.
+
+
+ Slowness Due To High Processor Usage
+ Next, we checked to see whether the system was performing slowly simply due to very high computational load. top(1) showed that the system load
+ was higher than normal, but vmstat(1) and mpstat(1) showed that the amount of processor being used for actual computation was low.
+
+
+ Network Saturation (The Winner)
+ Since neither the disks nor the processors were being utilized heavily, we moved on to the performance of the network interfaces. The datanode had two
+ gigabit ethernet adapters, bonded to form an active-standby interface. ifconfig(8) showed some unusual anomalies, namely interface errors, overruns, framing errors.
+ While not unheard of, these kinds of errors are exceedingly rare on modern hardware which is operating as it should:
+
+$ /sbin/ifconfig bond0
+bond0 Link encap:Ethernet HWaddr 00:00:00:00:00:00
+inet addr:10.x.x.x Bcast:10.x.x.255 Mask:255.255.255.0
+UP BROADCAST RUNNING MASTER MULTICAST MTU:1500 Metric:1
+RX packets:2990700159 errors:12 dropped:0 overruns:1 frame:6 <--- Look Here! Errors!
+TX packets:3443518196 errors:0 dropped:0 overruns:0 carrier:0
+collisions:0 txqueuelen:0
+RX bytes:2416328868676 (2.4 TB) TX bytes:3464991094001 (3.4 TB)
+
+
+ These errors immediately lead us to suspect that one or more of the ethernet interfaces might have negotiated the wrong line speed. This was confirmed both by running an ICMP ping
+ from an external host and observing round-trip-time in excess of 700ms, and by running ethtool(8) on the members of the bond interface and discovering that the active interface
+ was operating at 100Mbs/, full duplex.
+
+$ sudo ethtool eth0
+Settings for eth0:
+Supported ports: [ TP ]
+Supported link modes: 10baseT/Half 10baseT/Full
+ 100baseT/Half 100baseT/Full
+ 1000baseT/Full
+Supports auto-negotiation: Yes
+Advertised link modes: 10baseT/Half 10baseT/Full
+ 100baseT/Half 100baseT/Full
+ 1000baseT/Full
+Advertised pause frame use: No
+Advertised auto-negotiation: Yes
+Link partner advertised link modes: Not reported
+Link partner advertised pause frame use: No
+Link partner advertised auto-negotiation: No
+Speed: 100Mb/s <--- Look Here! Should say 1000Mb/s!
+Duplex: Full
+Port: Twisted Pair
+PHYAD: 1
+Transceiver: internal
+Auto-negotiation: on
+MDI-X: Unknown
+Supports Wake-on: umbg
+Wake-on: g
+Current message level: 0x00000003 (3)
+Link detected: yes
+
+
+ In normal operation, the ICMP ping round trip time should be around 20ms, and the interface speed and duplex should read, "1000MB/s", and, "Full", respectively.
+
+
+
+ Resolution
+ After determining that the active ethernet adapter was at the incorrect speed, we used the ifenslave(8) command to make the standby interface
+ the active interface, which yielded an immediate improvement in MapReduce performance, and a 10 times improvement in network throughput:
+
+ On the next trip to the datacenter, we determined that the line speed issue was ultimately caused by a bad network cable, which was replaced.
+
+
+
+
+ Case Study #2 (Performance Research 2012)
+ Investigation results of a self-described "we're not sure what's wrong, but it seems slow" problem.
+ http://gbif.blogspot.com/2012/03/hbase-performance-evaluation-continued.html
+
+
+
+
+ Case Study #3 (Performance Research 2010))
+
+ Investigation results of general cluster performance from 2010. Although this research is on an older version of the codebase, this writeup
+ is still very useful in terms of approach.
+ http://hstack.org/hbase-performance-testing/
+
+
+
+
+ Case Study #4 (xcievers Config)
+ Case study of configuring xceivers, and diagnosing errors from mis-configurations.
+ http://www.larsgeorge.com/2012/03/hadoop-hbase-and-xceivers.html
+
+ See also .
+
+
+
+
+
+
diff --git src/docbkx/configuration.xml src/docbkx/configuration.xml
new file mode 100644
index 0000000..35a7a32
--- /dev/null
+++ src/docbkx/configuration.xml
@@ -0,0 +1,1709 @@
+
+
+
+ Configuration
+ This chapter is the Not-So-Quick start guide to HBase configuration.
+ Please read this chapter carefully and ensure that all requirements have
+ been satisfied. Failure to do so will cause you (and us) grief debugging strange errors
+ and/or data loss.
+
+
+ HBase uses the same configuration system as Hadoop.
+ To configure a deploy, edit a file of environment variables
+ in conf/hbase-env.sh -- this configuration
+ is used mostly by the launcher shell scripts getting the cluster
+ off the ground -- and then add configuration to an XML file to
+ do things like override HBase defaults, tell HBase what Filesystem to
+ use, and the location of the ZooKeeper ensemble
+
+
+Be careful editing XML. Make sure you close all elements.
+Run your file through xmllint or similar
+to ensure well-formedness of your document after an edit session.
+
+
+ .
+
+
+ When running in distributed mode, after you make
+ an edit to an HBase configuration, make sure you copy the
+ content of the conf directory to
+ all nodes of the cluster. HBase will not do this for you.
+ Use rsync.
+
+
+ Java
+
+ Just like Hadoop, HBase requires java 6 from Oracle. Usually
+ you'll want to use the latest version available except the problematic
+ u18 (u24 is the latest version as of this writing).
+
+
+ Operating System
+
+ ssh
+
+ ssh must be installed and
+ sshd must be running to use Hadoop's scripts to
+ manage remote Hadoop and HBase daemons. You must be able to ssh to all
+ nodes, including your local node, using passwordless login (Google
+ "ssh passwordless login").
+
+
+
+ DNS
+
+ HBase uses the local hostname to self-report it's IP address.
+ Both forward and reverse DNS resolving must work in versions of
+ HBase previous to 0.92.0
+ The hadoop-dns-checker tool can be used to verify
+ DNS is working correctly on the cluster. The project README file provides detailed instructions on usage.
+.
+
+ If your machine has multiple interfaces, HBase will use the
+ interface that the primary hostname resolves to.
+
+ If this is insufficient, you can set
+ hbase.regionserver.dns.interface to indicate the
+ primary interface. This only works if your cluster configuration is
+ consistent and every host has the same network interface
+ configuration.
+
+ Another alternative is setting
+ hbase.regionserver.dns.nameserver to choose a
+ different nameserver than the system wide default.
+
+
+ Loopback IP
+ HBase expects the loopback IP address to be 127.0.0.1. Ubuntu and some other distributions,
+ for example, will default to 127.0.1.1 and this will cause problems for you.
+
+ /etc/hosts should look something like this:
+
+ 127.0.0.1 localhost
+ 127.0.0.1 ubuntu.ubuntu-domain ubuntu
+
+
+
+
+
+ NTP
+
+ The clocks on cluster members should be in basic alignments.
+ Some skew is tolerable but wild skew could generate odd behaviors. Run
+ NTP
+ on your cluster, or an equivalent.
+
+ If you are having problems querying data, or "weird" cluster
+ operations, check system time!
+
+
+
+
+ ulimit
+ ulimit
+
+ and
+ nproc
+ nproc
+
+
+
+ HBase is a database. It uses a lot of files all at the same time.
+ The default ulimit -n -- i.e. user file limit -- of 1024 on most *nix systems
+ is insufficient (On mac os x its 256). Any significant amount of loading will
+ lead you to .
+ You may also notice errors such as...
+ 2010-04-06 03:04:37,542 INFO org.apache.hadoop.hdfs.DFSClient: Exception increateBlockOutputStream java.io.EOFException
+ 2010-04-06 03:04:37,542 INFO org.apache.hadoop.hdfs.DFSClient: Abandoning block blk_-6935524980745310745_1391901
+ Do yourself a favor and change the upper bound on the
+ number of file descriptors. Set it to north of 10k. The math runs roughly as follows: per ColumnFamily
+ there is at least one StoreFile and possibly up to 5 or 6 if the region is under load. Multiply the
+ average number of StoreFiles per ColumnFamily times the number of regions per RegionServer. For example, assuming
+ that a schema had 3 ColumnFamilies per region with an average of 3 StoreFiles per ColumnFamily,
+ and there are 100 regions per RegionServer, the JVM will open 3 * 3 * 100 = 900 file descriptors
+ (not counting open jar files, config files, etc.)
+
+ You should also up the hbase users'
+ nproc setting; under load, a low-nproc
+ setting could manifest as OutOfMemoryError
+ See Jack Levin's major hdfs issues
+ note up on the user list.
+ The requirement that a database requires upping of system limits
+ is not peculiar to HBase. See for example the section
+ Setting Shell Limits for the Oracle User in
+
+ Short Guide to install Oracle 10 on Linux..
+
+
+ To be clear, upping the file descriptors and nproc for the user who is
+ running the HBase process is an operating system configuration, not an
+ HBase configuration. Also, a common mistake is that administrators
+ will up the file descriptors for a particular user but for whatever
+ reason, HBase will be running as some one else. HBase prints in its
+ logs as the first line the ulimit its seeing. Ensure its correct.
+
+ A useful read setting config on you hadoop cluster is Aaron
+ Kimballs' Configuration
+ Parameters: What can you just ignore?
+
+
+
+ ulimit on Ubuntu
+
+ If you are on Ubuntu you will need to make the following
+ changes:
+
+ In the file /etc/security/limits.conf add
+ a line like: hadoop - nofile 32768
+ Replace hadoop with whatever user is running
+ Hadoop and HBase. If you have separate users, you will need 2
+ entries, one for each user. In the same file set nproc hard and soft
+ limits. For example: hadoop soft/hard nproc 32000.
+
+ In the file /etc/pam.d/common-session add
+ as the last line in the file: session required pam_limits.so
+ Otherwise the changes in /etc/security/limits.conf won't be
+ applied.
+
+ Don't forget to log out and back in again for the changes to
+ take effect!
+
+
+
+
+ Windows
+
+ HBase has been little tested running on Windows. Running a
+ production install of HBase on top of Windows is not
+ recommended.
+
+ If you are running HBase on Windows, you must install Cygwin to have a *nix-like
+ environment for the shell scripts. The full details are explained in
+ the Windows
+ Installation guide. Also
+ search our user mailing list to pick
+ up latest fixes figured by Windows users.
+
+
+
+
+
+ Hadoop
+ Hadoop
+
+ Please read all of this section
+ Please read this section to the end. Up front we
+ wade through the weeds of Hadoop versions. Later we talk of what you must do in HBase
+ to make it work w/ a particular Hadoop version.
+
+
+
+ HBase will lose data unless it is running on an HDFS that has a durable
+ sync implementation. Hadoop 0.20.2, Hadoop 0.20.203.0, and Hadoop 0.20.204.0
+ DO NOT have this attribute.
+ Currently only Hadoop versions 0.20.205.x or any release in excess of this
+ version -- this includes hadoop 1.0.0 -- have a working, durable sync
+
+ On Hadoop Versions
+ The Cloudera blog post An update on Apache Hadoop 1.0
+ by Charles Zedlweski has a nice exposition on how all the Hadoop versions relate.
+ Its worth checking out if you are having trouble making sense of the
+ Hadoop version morass.
+
+ . Sync has to be explicitly enabled by setting
+ dfs.support.append equal
+ to true on both the client side -- in hbase-site.xml
+ -- and on the serverside in hdfs-site.xml (The sync
+ facility HBase needs is a subset of the append code path).
+
+ <property>
+ <name>dfs.support.append</name>
+ <value>true</value>
+ </property>
+
+ You will have to restart your cluster after making this edit. Ignore the chicken-little
+ comment you'll find in the hdfs-default.xml in the
+ description for the dfs.support.append configuration; it says it is not enabled because there
+ are ... bugs in the 'append code' and is not supported in any production
+ cluster.. This comment is stale, from another era, and while I'm sure there
+ are bugs, the sync/append code has been running
+ in production at large scale deploys and is on
+ by default in the offerings of hadoop by commercial vendors
+ Until recently only the
+ branch-0.20-append
+ branch had a working sync but no official release was ever made from this branch.
+ You had to build it yourself. Michael Noll wrote a detailed blog,
+ Building
+ an Hadoop 0.20.x version for HBase 0.90.2, on how to build an
+ Hadoop from branch-0.20-append. Recommended.
+ Praveen Kumar has written
+ a complimentary article,
+ Building Hadoop and HBase for HBase Maven application development.
+Cloudera have dfs.support.append set to true by default..
+
+Or use the
+ Cloudera or
+ MapR distributions.
+ Cloudera' CDH3
+ is Apache Hadoop 0.20.x plus patches including all of the
+ branch-0.20-append
+ additions needed to add a durable sync. Use the released, most recent version of CDH3.
+
+ MapR
+ includes a commercial, reimplementation of HDFS.
+ It has a durable sync as well as some other interesting features that are not
+ yet in Apache Hadoop. Their M3
+ product is free to use and unlimited.
+
+
+ Because HBase depends on Hadoop, it bundles an instance of the
+ Hadoop jar under its lib directory. The bundled jar is ONLY for use in standalone mode.
+ In distributed mode, it is critical that the version of Hadoop that is out
+ on your cluster match what is under HBase. Replace the hadoop jar found in the HBase
+ lib directory with the hadoop jar you are running on
+ your cluster to avoid version mismatch issues. Make sure you
+ replace the jar in HBase everywhere on your cluster. Hadoop version
+ mismatch issues have various manifestations but often all looks like
+ its hung up.
+
+
+ Hadoop Security
+ HBase will run on any Hadoop 0.20.x that incorporates Hadoop
+ security features -- e.g. Y! 0.20S or CDH3B3 -- as long as you do as
+ suggested above and replace the Hadoop jar that ships with HBase
+ with the secure version.
+
+
+
+ dfs.datanode.max.xcievers
+ xcievers
+
+
+ An Hadoop HDFS datanode has an upper bound on the number of
+ files that it will serve at any one time. The upper bound parameter is
+ called xcievers (yes, this is misspelled). Again,
+ before doing any loading, make sure you have configured Hadoop's
+ conf/hdfs-site.xml setting the
+ xceivers value to at least the following:
+
+ <property>
+ <name>dfs.datanode.max.xcievers</name>
+ <value>4096</value>
+ </property>
+
+
+ Be sure to restart your HDFS after making the above
+ configuration.
+
+ Not having this configuration in place makes for strange looking
+ failures. Eventually you'll see a complain in the datanode logs
+ complaining about the xcievers exceeded, but on the run up to this one
+ manifestation is complaint about missing blocks. For example:
+ 10/12/08 20:10:31 INFO hdfs.DFSClient: Could not obtain block
+ blk_XXXXXXXXXXXXXXXXXXXXXX_YYYYYYYY from any node:
+ java.io.IOException: No live nodes contain current block. Will get new
+ block locations from namenode and retry...
+ See Hadoop HDFS: Deceived by Xciever for an informative rant on xceivering.
+ See also
+
+
+
+
+
+
+ HBase run modes: Standalone and Distributed
+
+ HBase has two run modes: and . Out of the box, HBase runs in
+ standalone mode. To set up a distributed deploy, you will need to
+ configure HBase by editing files in the HBase conf
+ directory.
+
+ Whatever your mode, you will need to edit
+ conf/hbase-env.sh to tell HBase which
+ java to use. In this file you set HBase environment
+ variables such as the heapsize and other options for the
+ JVM, the preferred location for log files,
+ etc. Set JAVA_HOME to point at the root of your
+ java install.
+
+
+ Standalone HBase
+
+ This is the default mode. Standalone mode is what is described
+ in the section. In
+ standalone mode, HBase does not use HDFS -- it uses the local
+ filesystem instead -- and it runs all HBase daemons and a local
+ ZooKeeper all up in the same JVM. Zookeeper binds to a well known port
+ so clients may talk to HBase.
+
+
+
+ Distributed
+
+ Distributed mode can be subdivided into distributed but all
+ daemons run on a single node -- a.k.a
+ pseudo-distributed-- and
+ fully-distributed where the daemons are spread
+ across all nodes in the cluster
+ The pseudo-distributed vs fully-distributed nomenclature
+ comes from Hadoop.
+ .
+
+ Distributed modes require an instance of the Hadoop
+ Distributed File System (HDFS). See the Hadoop
+ requirements and instructions for how to set up a HDFS. Before
+ proceeding, ensure you have an appropriate, working HDFS.
+
+ Below we describe the different distributed setups. Starting,
+ verification and exploration of your install, whether a
+ pseudo-distributed or
+ fully-distributed configuration is described in a
+ section that follows, . The same verification script applies to both
+ deploy types.
+
+
+ Pseudo-distributed
+
+ A pseudo-distributed mode is simply a distributed mode run on
+ a single host. Use this configuration testing and prototyping on
+ HBase. Do not use this configuration for production nor for
+ evaluating HBase performance.
+
+ First, confirm your local HDFS setup. Below is an example conf/hdfs-site.xml. Note
+ that the replication is set to 1 because this is a pseudo-distributed setup. The properties dfs.name.dir
+ and dfs.data.dir are being set explicitly, the latter being where HDFS data will exist on your machine.
+
+ Next, configure HBase for usage. Below is an example conf/hbase-site.xml. Note
+ that the hbase.rootdir property points to the local HDFS instance. This is the file into
+ which you add local customizations and overrides for
+ and
+
+
+ Now skip to for how to start and verify your
+ pseudo-distributed install.
+ See Pseudo-distributed
+ mode extras for notes on how to start extra Masters and
+ RegionServers when running pseudo-distributed.
+
+
+
+ Let HBase create the hbase.rootdir
+ directory. If you don't, you'll get warning saying HBase needs a
+ migration run because the directory is missing files expected by
+ HBase (it'll create them if you let it).
+
+
+
+ Above we bind to localhost. This means
+ that a remote client cannot connect. Amend accordingly, if you
+ want to connect from a remote location.
+
+
+
+
+ Pseudo-distributed Configuration Files
+ The following are exmaple configuration files from a pseudo-distributed setup.
+
+hdfs-site.xml
+
+<configuration>
+ ...
+ <property>
+ <name>dfs.name.dir</name>
+ <value>/Users/local/user.name/hdfs-data-name</value>
+ </property>
+ <property>
+ <name>dfs.data.dir</name>
+ <value>/Users/local/user.name/hdfs-data</value>
+ </property>
+ <property>
+ <name>dfs.replication</name>
+ <value>1</value>
+ </property>
+ ...
+</configuration>
+
+
+hbase-site.xml
+
+<configuration>
+ ...
+ <property>
+ <name>hbase.rootdir</name>
+ <value>hdfs://localhost:8020/hbase</value>
+ </property>
+ <property>
+ <name>hbase.zookeeper.quorum</name>
+ <value>localhost</value>
+ </property>
+ <property>
+ <name>hbase.cluster.distributed</name>
+ <value>true</value>
+ </property>
+ ...
+</configuration>
+
+
+
+
+
+ Pseudo-distributed Extras
+
+
+ Startup
+ To start up the initial HBase cluster...
+ % bin/start-hbase.sh
+
+ To start up an extra backup master(s) on the same server run...
+ % bin/local-master-backup.sh start 1
+ ... the '1' means use ports 60001 & 60011, and this backup master's logfile will be at logs/hbase-${USER}-1-master-${HOSTNAME}.log.
+
+ To startup multiple backup masters run... % bin/local-master-backup.sh start 2 3 You can start up to 9 backup masters (10 total).
+
+ To start up more regionservers...
+ % bin/local-regionservers.sh start 1
+ where '1' means use ports 60201 & 60301 and its logfile will be at logs/hbase-${USER}-1-regionserver-${HOSTNAME}.log.
+
+ To add 4 more regionservers in addition to the one you just started by running... % bin/local-regionservers.sh start 2 3 4 5
+ This supports up to 99 extra regionservers (100 total).
+
+
+
+ Stop
+ Assuming you want to stop master backup # 1, run...
+ % cat /tmp/hbase-${USER}-1-master.pid |xargs kill -9
+ Note that bin/local-master-backup.sh stop 1 will try to stop the cluster along with the master.
+
+ To stop an individual regionserver, run...
+ % bin/local-regionservers.sh stop 1
+
+
+
+
+
+
+
+
+
+ Fully-distributed
+
+ For running a fully-distributed operation on more than one
+ host, make the following configurations. In
+ hbase-site.xml, add the property
+ hbase.cluster.distributed and set it to
+ true and point the HBase
+ hbase.rootdir at the appropriate HDFS NameNode
+ and location in HDFS where you would like HBase to write data. For
+ example, if you namenode were running at namenode.example.org on
+ port 8020 and you wanted to home your HBase in HDFS at
+ /hbase, make the following
+ configuration.
+
+
+<configuration>
+ ...
+ <property>
+ <name>hbase.rootdir</name>
+ <value>hdfs://namenode.example.org:8020/hbase</value>
+ <description>The directory shared by RegionServers.
+ </description>
+ </property>
+ <property>
+ <name>hbase.cluster.distributed</name>
+ <value>true</value>
+ <description>The mode the cluster will be in. Possible values are
+ false: standalone and pseudo-distributed setups with managed Zookeeper
+ true: fully-distributed with unmanaged Zookeeper Quorum (see hbase-env.sh)
+ </description>
+ </property>
+ ...
+</configuration>
+
+
+
+ regionservers
+
+ In addition, a fully-distributed mode requires that you
+ modify conf/regionservers. The
+ file
+ lists all hosts that you would have running
+ HRegionServers, one host per line (This
+ file in HBase is like the Hadoop slaves
+ file). All servers listed in this file will be started and stopped
+ when HBase cluster start or stop is run.
+
+
+
+ ZooKeeper and HBase
+ See section for ZooKeeper setup for HBase.
+
+
+
+ HDFS Client Configuration
+
+ Of note, if you have made HDFS client
+ configuration on your Hadoop cluster -- i.e.
+ configuration you want HDFS clients to use as opposed to
+ server-side configurations -- HBase will not see this
+ configuration unless you do one of the following:
+
+
+
+ Add a pointer to your HADOOP_CONF_DIR
+ to the HBASE_CLASSPATH environment variable
+ in hbase-env.sh.
+
+
+
+ Add a copy of hdfs-site.xml (or
+ hadoop-site.xml) or, better, symlinks,
+ under ${HBASE_HOME}/conf, or
+
+
+
+ if only a small set of HDFS client configurations, add
+ them to hbase-site.xml.
+
+
+
+ An example of such an HDFS client configuration is
+ dfs.replication. If for example, you want to
+ run with a replication factor of 5, hbase will create files with
+ the default of 3 unless you do the above to make the configuration
+ available to HBase.
+
+
+
+
+
+ Running and Confirming Your Installation
+
+
+
+ Make sure HDFS is running first. Start and stop the Hadoop HDFS
+ daemons by running bin/start-hdfs.sh over in the
+ HADOOP_HOME directory. You can ensure it started
+ properly by testing the put and
+ get of files into the Hadoop filesystem. HBase does
+ not normally use the mapreduce daemons. These do not need to be
+ started.
+
+
+
+ If you are managing your own ZooKeeper,
+ start it and confirm its running else, HBase will start up ZooKeeper
+ for you as part of its start process.
+
+
+
+ Start HBase with the following command:
+
+
+
+ bin/start-hbase.sh
+
+ Run the above from the
+
+ HBASE_HOME
+
+ directory.
+
+ You should now have a running HBase instance. HBase logs can be
+ found in the logs subdirectory. Check them out
+ especially if HBase had trouble starting.
+
+
+
+ HBase also puts up a UI listing vital attributes. By default its
+ deployed on the Master host at port 60010 (HBase RegionServers listen
+ on port 60020 by default and put up an informational http server at
+ 60030). If the Master were running on a host named
+ master.example.org on the default port, to see the
+ Master's homepage you'd point your browser at
+ http://master.example.org:60010.
+
+
+
+ Once HBase has started, see the for how to
+ create tables, add data, scan your insertions, and finally disable and
+ drop your tables.
+
+
+
+ To stop HBase after exiting the HBase shell enter
+ $ ./bin/stop-hbase.sh
+stopping hbase............... Shutdown can take a moment to
+ complete. It can take longer if your cluster is comprised of many
+ machines. If you are running a distributed operation, be sure to wait
+ until HBase has shut down completely before stopping the Hadoop
+ daemons.
+
+
+
+
+
+
+ ZooKeeper
+ ZooKeeper
+
+
+ A distributed HBase depends on a running ZooKeeper cluster.
+ All participating nodes and clients need to be able to access the
+ running ZooKeeper ensemble. HBase by default manages a ZooKeeper
+ "cluster" for you. It will start and stop the ZooKeeper ensemble
+ as part of the HBase start/stop process. You can also manage the
+ ZooKeeper ensemble independent of HBase and just point HBase at
+ the cluster it should use. To toggle HBase management of
+ ZooKeeper, use the HBASE_MANAGES_ZK variable in
+ conf/hbase-env.sh. This variable, which
+ defaults to true, tells HBase whether to
+ start/stop the ZooKeeper ensemble servers as part of HBase
+ start/stop.
+
+ When HBase manages the ZooKeeper ensemble, you can specify
+ ZooKeeper configuration using its native
+ zoo.cfg file, or, the easier option is to
+ just specify ZooKeeper options directly in
+ conf/hbase-site.xml. A ZooKeeper
+ configuration option can be set as a property in the HBase
+ hbase-site.xml XML configuration file by
+ prefacing the ZooKeeper option name with
+ hbase.zookeeper.property. For example, the
+ clientPort setting in ZooKeeper can be changed
+ by setting the
+ hbase.zookeeper.property.clientPort property.
+ For all default values used by HBase, including ZooKeeper
+ configuration, see . Look for the
+ hbase.zookeeper.property prefix
+ For the full list of ZooKeeper configurations, see
+ ZooKeeper's zoo.cfg. HBase does not ship
+ with a zoo.cfg so you will need to browse
+ the conf directory in an appropriate
+ ZooKeeper download.
+
+
+ You must at least list the ensemble servers in
+ hbase-site.xml using the
+ hbase.zookeeper.quorum property. This property
+ defaults to a single ensemble member at
+ localhost which is not suitable for a fully
+ distributed HBase. (It binds to the local machine only and remote
+ clients will not be able to connect).
+ How many ZooKeepers should I run?
+
+ You can run a ZooKeeper ensemble that comprises 1 node
+ only but in production it is recommended that you run a
+ ZooKeeper ensemble of 3, 5 or 7 machines; the more members an
+ ensemble has, the more tolerant the ensemble is of host
+ failures. Also, run an odd number of machines. In ZooKeeper,
+ an even number of peers is supported, but it is normally not used
+ because an even sized ensemble requires, proportionally, more peers
+ to form a quorum than an odd sized ensemble requires. For example, an
+ ensemble with 4 peers requires 3 to form a quorum, while an ensemble with
+ 5 also requires 3 to form a quorum. Thus, an ensemble of 5 allows 2 peers to
+ fail, and thus is more fault tolerant than the ensemble of 4, which allows
+ only 1 down peer.
+
+ Give each ZooKeeper server around 1GB of RAM, and if possible, its own
+ dedicated disk (A dedicated disk is the best thing you can do
+ to ensure a performant ZooKeeper ensemble). For very heavily
+ loaded clusters, run ZooKeeper servers on separate machines
+ from RegionServers (DataNodes and TaskTrackers).
+
+
+ For example, to have HBase manage a ZooKeeper quorum on
+ nodes rs{1,2,3,4,5}.example.com, bound to
+ port 2222 (the default is 2181) ensure
+ HBASE_MANAGE_ZK is commented out or set to
+ true in conf/hbase-env.sh
+ and then edit conf/hbase-site.xml and set
+ hbase.zookeeper.property.clientPort and
+ hbase.zookeeper.quorum. You should also set
+ hbase.zookeeper.property.dataDir to other than
+ the default as the default has ZooKeeper persist data under
+ /tmp which is often cleared on system
+ restart. In the example below we have ZooKeeper persist to
+ /user/local/zookeeper.
+ <configuration>
+ ...
+ <property>
+ <name>hbase.zookeeper.property.clientPort</name>
+ <value>2222</value>
+ <description>Property from ZooKeeper's config zoo.cfg.
+ The port at which the clients will connect.
+ </description>
+ </property>
+ <property>
+ <name>hbase.zookeeper.quorum</name>
+ <value>rs1.example.com,rs2.example.com,rs3.example.com,rs4.example.com,rs5.example.com</value>
+ <description>Comma separated list of servers in the ZooKeeper Quorum.
+ For example, "host1.mydomain.com,host2.mydomain.com,host3.mydomain.com".
+ By default this is set to localhost for local and pseudo-distributed modes
+ of operation. For a fully-distributed setup, this should be set to a full
+ list of ZooKeeper quorum servers. If HBASE_MANAGES_ZK is set in hbase-env.sh
+ this is the list of servers which we will start/stop ZooKeeper on.
+ </description>
+ </property>
+ <property>
+ <name>hbase.zookeeper.property.dataDir</name>
+ <value>/usr/local/zookeeper</value>
+ <description>Property from ZooKeeper's config zoo.cfg.
+ The directory where the snapshot is stored.
+ </description>
+ </property>
+ ...
+ </configuration>
+
+
+ Using existing ZooKeeper ensemble
+
+ To point HBase at an existing ZooKeeper cluster, one that
+ is not managed by HBase, set HBASE_MANAGES_ZK
+ in conf/hbase-env.sh to false
+
+ ...
+ # Tell HBase whether it should manage it's own instance of Zookeeper or not.
+ export HBASE_MANAGES_ZK=false Next set ensemble locations
+ and client port, if non-standard, in
+ hbase-site.xml, or add a suitably
+ configured zoo.cfg to HBase's
+ CLASSPATH. HBase will prefer the
+ configuration found in zoo.cfg over any
+ settings in hbase-site.xml.
+
+ When HBase manages ZooKeeper, it will start/stop the
+ ZooKeeper servers as a part of the regular start/stop scripts.
+ If you would like to run ZooKeeper yourself, independent of
+ HBase start/stop, you would do the following
+
+
+${HBASE_HOME}/bin/hbase-daemons.sh {start,stop} zookeeper
+
+
+ Note that you can use HBase in this manner to spin up a
+ ZooKeeper cluster, unrelated to HBase. Just make sure to set
+ HBASE_MANAGES_ZK to false
+ if you want it to stay up across HBase restarts so that when
+ HBase shuts down, it doesn't take ZooKeeper down with it.
+
+ For more information about running a distinct ZooKeeper
+ cluster, see the ZooKeeper Getting
+ Started Guide. Additionally, see the ZooKeeper Wiki or the
+ ZooKeeper documentation
+ for more information on ZooKeeper sizing.
+
+
+
+
+
+ SASL Authentication with ZooKeeper
+ Newer releases of HBase (>= 0.92) will
+ support connecting to a ZooKeeper Quorum that supports
+ SASL authentication (which is available in Zookeeper
+ versions 3.4.0 or later).
+
+ This describes how to set up HBase to mutually
+ authenticate with a ZooKeeper Quorum. ZooKeeper/HBase
+ mutual authentication (HBASE-2418)
+ is required as part of a complete secure HBase configuration
+ (HBASE-3025).
+
+ For simplicity of explication, this section ignores
+ additional configuration required (Secure HDFS and Coprocessor
+ configuration). It's recommended to begin with an
+ HBase-managed Zookeeper configuration (as opposed to a
+ standalone Zookeeper quorum) for ease of learning.
+
+
+ Operating System Prerequisites
+
+
+ You need to have a working Kerberos KDC setup. For
+ each $HOST that will run a ZooKeeper
+ server, you should have a principle
+ zookeeper/$HOST. For each such host,
+ add a service key (using the kadmin or
+ kadmin.local tool's ktadd
+ command) for zookeeper/$HOST and copy
+ this file to $HOST, and make it
+ readable only to the user that will run zookeeper on
+ $HOST. Note the location of this file,
+ which we will use below as
+ $PATH_TO_ZOOKEEPER_KEYTAB.
+
+
+
+ Similarly, for each $HOST that will run
+ an HBase server (master or regionserver), you should
+ have a principle: hbase/$HOST. For each
+ host, add a keytab file called
+ hbase.keytab containing a service
+ key for hbase/$HOST, copy this file to
+ $HOST, and make it readable only to the
+ user that will run an HBase service on
+ $HOST. Note the location of this file,
+ which we will use below as
+ $PATH_TO_HBASE_KEYTAB.
+
+
+
+ Each user who will be an HBase client should also be
+ given a Kerberos principal. This principal should
+ usually have a password assigned to it (as opposed to,
+ as with the HBase servers, a keytab file) which only
+ this user knows. The client's principal's
+ maxrenewlife should be set so that it can
+ be renewed enough so that the user can complete their
+ HBase client processes. For example, if a user runs a
+ long-running HBase client process that takes at most 3
+ days, we might create this user's principal within
+ kadmin with: addprinc -maxrenewlife
+ 3days. The Zookeeper client and server
+ libraries manage their own ticket refreshment by
+ running threads that wake up periodically to do the
+ refreshment.
+
+
+ On each host that will run an HBase client
+ (e.g. hbase shell), add the following
+ file to the HBase home directory's conf
+ directory:
+
+
+ Client {
+ com.sun.security.auth.module.Krb5LoginModule required
+ useKeyTab=false
+ useTicketCache=true;
+ };
+
+
+ We'll refer to this JAAS configuration file as
+ $CLIENT_CONF below.
+
+
+ HBase-managed Zookeeper Configuration
+
+ On each node that will run a zookeeper, a
+ master, or a regionserver, create a JAAS
+ configuration file in the conf directory of the node's
+ HBASE_HOME directory that looks like the
+ following:
+
+
+ Server {
+ com.sun.security.auth.module.Krb5LoginModule required
+ useKeyTab=true
+ keyTab="$PATH_TO_ZOOKEEPER_KEYTAB"
+ storeKey=true
+ useTicketCache=false
+ principal="zookeeper/$HOST";
+ };
+ Client {
+ com.sun.security.auth.module.Krb5LoginModule required
+ useKeyTab=true
+ useTicketCache=false
+ keyTab="$PATH_TO_HBASE_KEYTAB"
+ principal="hbase/$HOST";
+ };
+
+
+ where the $PATH_TO_HBASE_KEYTAB and
+ $PATH_TO_ZOOKEEPER_KEYTAB files are what
+ you created above, and $HOST is the hostname for that
+ node.
+
+ The Server section will be used by
+ the Zookeeper quorum server, while the
+ Client section will be used by the HBase
+ master and regionservers. The path to this file should
+ be substituted for the text $HBASE_SERVER_CONF
+ in the hbase-env.sh
+ listing below.
+
+
+ The path to this file should be substituted for the
+ text $CLIENT_CONF in the
+ hbase-env.sh listing below.
+
+
+ Modify your hbase-env.sh to include the
+ following:
+
+
+ export HBASE_OPTS="-Djava.security.auth.login.config=$CLIENT_CONF"
+ export HBASE_MANAGES_ZK=true
+ export HBASE_ZOOKEEPER_OPTS="-Djava.security.auth.login.config=$HBASE_SERVER_CONF"
+ export HBASE_MASTER_OPTS="-Djava.security.auth.login.config=$HBASE_SERVER_CONF"
+ export HBASE_REGIONSERVER_OPTS="-Djava.security.auth.login.config=$HBASE_SERVER_CONF"
+
+
+ where $HBASE_SERVER_CONF and
+ $CLIENT_CONF are the full paths to the
+ JAAS configuration files created above.
+
+ Modify your hbase-site.xml on each node
+ that will run zookeeper, master or regionserver to contain:
+
+
+
+ hbase.zookeeper.quorum
+ $ZK_NODES
+
+
+ hbase.cluster.distributed
+ true
+
+
+ hbase.zookeeper.property.authProvider.1
+ org.apache.zookeeper.server.auth.SASLAuthenticationProvider
+
+
+ hbase.zookeeper.property.kerberos.removeHostFromPrincipal
+ true
+
+
+ hbase.zookeeper.property.kerberos.removeRealmFromPrincipal
+ true
+
+
+ ]]>
+
+ where $ZK_NODES is the
+ comma-separated list of hostnames of the Zookeeper
+ Quorum hosts.
+
+ Start your hbase cluster by running one or more
+ of the following set of commands on the appropriate
+ hosts:
+
+
+
+ bin/hbase zookeeper start
+ bin/hbase master start
+ bin/hbase regionserver start
+
+
+
+
+ External Zookeeper Configuration
+ Add a JAAS configuration file that looks like:
+
+
+ Client {
+ com.sun.security.auth.module.Krb5LoginModule required
+ useKeyTab=true
+ useTicketCache=false
+ keyTab="$PATH_TO_HBASE_KEYTAB"
+ principal="hbase/$HOST";
+ };
+
+
+ where the $PATH_TO_HBASE_KEYTAB is the keytab
+ created above for HBase services to run on this host, and $HOST is the
+ hostname for that node. Put this in the HBase home's
+ configuration directory. We'll refer to this file's
+ full pathname as $HBASE_SERVER_CONF below.
+
+ Modify your hbase-env.sh to include the following:
+
+
+ export HBASE_OPTS="-Djava.security.auth.login.config=$CLIENT_CONF"
+ export HBASE_MANAGES_ZK=false
+ export HBASE_MASTER_OPTS="-Djava.security.auth.login.config=$HBASE_SERVER_CONF"
+ export HBASE_REGIONSERVER_OPTS="-Djava.security.auth.login.config=$HBASE_SERVER_CONF"
+
+
+
+ Modify your hbase-site.xml on each node
+ that will run a master or regionserver to contain:
+
+
+
+ hbase.zookeeper.quorum
+ $ZK_NODES
+
+
+ hbase.cluster.distributed
+ true
+
+
+ ]]>
+
+
+ where $ZK_NODES is the
+ comma-separated list of hostnames of the Zookeeper
+ Quorum hosts.
+
+
+ Add a zoo.cfg for each Zookeeper Quorum host containing:
+
+ authProvider.1=org.apache.zookeeper.server.auth.SASLAuthenticationProvider
+ kerberos.removeHostFromPrincipal=true
+ kerberos.removeRealmFromPrincipal=true
+
+
+ Also on each of these hosts, create a JAAS configuration file containing:
+
+
+ Server {
+ com.sun.security.auth.module.Krb5LoginModule required
+ useKeyTab=true
+ keyTab="$PATH_TO_ZOOKEEPER_KEYTAB"
+ storeKey=true
+ useTicketCache=false
+ principal="zookeeper/$HOST";
+ };
+
+
+ where $HOST is the hostname of each
+ Quorum host. We will refer to the full pathname of
+ this file as $ZK_SERVER_CONF below.
+
+
+
+
+ Start your Zookeepers on each Zookeeper Quorum host with:
+
+
+ SERVER_JVMFLAGS="-Djava.security.auth.login.config=$ZK_SERVER_CONF" bin/zkServer start
+
+
+
+
+
+ Start your HBase cluster by running one or more of the following set of commands on the appropriate nodes:
+
+
+
+ bin/hbase master start
+ bin/hbase regionserver start
+
+
+
+
+
+
+ Zookeeper Server Authentication Log Output
+ If the configuration above is successful,
+ you should see something similar to the following in
+ your Zookeeper server logs:
+
+11/12/05 22:43:39 INFO zookeeper.Login: successfully logged in.
+11/12/05 22:43:39 INFO server.NIOServerCnxnFactory: binding to port 0.0.0.0/0.0.0.0:2181
+11/12/05 22:43:39 INFO zookeeper.Login: TGT refresh thread started.
+11/12/05 22:43:39 INFO zookeeper.Login: TGT valid starting at: Mon Dec 05 22:43:39 UTC 2011
+11/12/05 22:43:39 INFO zookeeper.Login: TGT expires: Tue Dec 06 22:43:39 UTC 2011
+11/12/05 22:43:39 INFO zookeeper.Login: TGT refresh sleeping until: Tue Dec 06 18:36:42 UTC 2011
+..
+11/12/05 22:43:59 INFO auth.SaslServerCallbackHandler:
+ Successfully authenticated client: authenticationID=hbase/ip-10-166-175-249.us-west-1.compute.internal@HADOOP.LOCALDOMAIN;
+ authorizationID=hbase/ip-10-166-175-249.us-west-1.compute.internal@HADOOP.LOCALDOMAIN.
+11/12/05 22:43:59 INFO auth.SaslServerCallbackHandler: Setting authorizedID: hbase
+11/12/05 22:43:59 INFO server.ZooKeeperServer: adding SASL authorization for authorizationID: hbase
+
+
+
+
+
+
+
+ Zookeeper Client Authentication Log Output
+ On the Zookeeper client side (HBase master or regionserver),
+ you should see something similar to the following:
+
+
+11/12/05 22:43:59 INFO zookeeper.ZooKeeper: Initiating client connection, connectString=ip-10-166-175-249.us-west-1.compute.internal:2181 sessionTimeout=180000 watcher=master:60000
+11/12/05 22:43:59 INFO zookeeper.ClientCnxn: Opening socket connection to server /10.166.175.249:2181
+11/12/05 22:43:59 INFO zookeeper.RecoverableZooKeeper: The identifier of this process is 14851@ip-10-166-175-249
+11/12/05 22:43:59 INFO zookeeper.Login: successfully logged in.
+11/12/05 22:43:59 INFO client.ZooKeeperSaslClient: Client will use GSSAPI as SASL mechanism.
+11/12/05 22:43:59 INFO zookeeper.Login: TGT refresh thread started.
+11/12/05 22:43:59 INFO zookeeper.ClientCnxn: Socket connection established to ip-10-166-175-249.us-west-1.compute.internal/10.166.175.249:2181, initiating session
+11/12/05 22:43:59 INFO zookeeper.Login: TGT valid starting at: Mon Dec 05 22:43:59 UTC 2011
+11/12/05 22:43:59 INFO zookeeper.Login: TGT expires: Tue Dec 06 22:43:59 UTC 2011
+11/12/05 22:43:59 INFO zookeeper.Login: TGT refresh sleeping until: Tue Dec 06 18:30:37 UTC 2011
+11/12/05 22:43:59 INFO zookeeper.ClientCnxn: Session establishment complete on server ip-10-166-175-249.us-west-1.compute.internal/10.166.175.249:2181, sessionid = 0x134106594320000, negotiated timeout = 180000
+
+
+
+
+
+ Configuration from Scratch
+
+ This has been tested on the current standard Amazon
+ Linux AMI. First setup KDC and principals as
+ described above. Next checkout code and run a sanity
+ check.
+
+
+ git clone git://git.apache.org/hbase.git
+ cd hbase
+ mvn -Psecurity,localTests clean test -Dtest=TestZooKeeperACL
+
+
+ Then configure HBase as described above.
+ Manually edit target/cached_classpath.txt (see below)..
+
+
+ bin/hbase zookeeper &
+ bin/hbase master &
+ bin/hbase regionserver &
+
+
+
+
+
+ Future improvements
+
+ Fix target/cached_classpath.txt
+
+ You must override the standard hadoop-core jar file from the
+ target/cached_classpath.txt
+ file with the version containing the HADOOP-7070 fix. You can use the following script to do this:
+
+
+ echo `find ~/.m2 -name "*hadoop-core*7070*SNAPSHOT.jar"` ':' `cat target/cached_classpath.txt` | sed 's/ //g' > target/tmp.txt
+ mv target/tmp.txt target/cached_classpath.txt
+
+
+
+
+
+
+
+ Set JAAS configuration
+ programmatically
+
+
+ This would avoid the need for a separate Hadoop jar
+ that fixes HADOOP-7070.
+
+
+
+ Elimination of
+ kerberos.removeHostFromPrincipal and
+ kerberos.removeRealmFromPrincipal
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Configuration Files
+
+
+ hbase-site.xml and hbase-default.xml
+ Just as in Hadoop where you add site-specific HDFS configuration
+ to the hdfs-site.xml file,
+ for HBase, site specific customizations go into
+ the file conf/hbase-site.xml.
+ For the list of configurable properties, see
+
+ below or view the raw hbase-default.xml
+ source file in the HBase source code at
+ src/main/resources.
+
+
+ Not all configuration options make it out to
+ hbase-default.xml. Configuration
+ that it is thought rare anyone would change can exist only
+ in code; the only way to turn up such configurations is
+ via a reading of the source code itself.
+
+
+ Currently, changes here will require a cluster restart for HBase to notice the change.
+
+
+
+
+
+
+ hbase-env.sh
+ Set HBase environment variables in this file.
+ Examples include options to pass the JVM on start of
+ an HBase daemon such as heap size and garbarge collector configs.
+ You can also set configurations for HBase configuration, log directories,
+ niceness, ssh options, where to locate process pid files,
+ etc. Open the file at
+ conf/hbase-env.sh and peruse its content.
+ Each option is fairly well documented. Add your own environment
+ variables here if you want them read by HBase daemons on startup.
+
+ Changes here will require a cluster restart for HBase to notice the change.
+
+
+
+
+ log4j.properties
+ Edit this file to change rate at which HBase files
+ are rolled and to change the level at which HBase logs messages.
+
+
+ Changes here will require a cluster restart for HBase to notice the change
+ though log levels can be changed for particular daemons via the HBase UI.
+
+
+
+ Client configuration and dependencies connecting to an HBase cluster
+
+ Since the HBase Master may move around, clients bootstrap by looking to ZooKeeper for
+ current critical locations. ZooKeeper is where all these values are kept. Thus clients
+ require the location of the ZooKeeper ensemble information before they can do anything else.
+ Usually this the ensemble location is kept out in the hbase-site.xml and
+ is picked up by the client from the CLASSPATH.
+
+ If you are configuring an IDE to run a HBase client, you should
+ include the conf/ directory on your classpath so
+ hbase-site.xml settings can be found (or
+ add src/test/resources to pick up the hbase-site.xml
+ used by tests).
+
+
+ Minimally, a client of HBase needs the hbase, hadoop, log4j, commons-logging, commons-lang,
+ and ZooKeeper jars in its CLASSPATH connecting to a cluster.
+
+
+ An example basic hbase-site.xml for client only
+ might look as follows:
+
+
+
+
+ hbase.zookeeper.quorum
+ example1,example2,example3
+ The directory shared by region servers.
+
+
+
+]]>
+
+
+
+ Java client configuration
+ The configuration used by a Java client is kept
+ in an HBaseConfiguration instance.
+ The factory method on HBaseConfiguration, HBaseConfiguration.create();,
+ on invocation, will read in the content of the first hbase-site.xml found on
+ the client's CLASSPATH, if one is present
+ (Invocation will also factor in any hbase-default.xml found;
+ an hbase-default.xml ships inside the hbase.X.X.X.jar).
+ It is also possible to specify configuration directly without having to read from a
+ hbase-site.xml. For example, to set the ZooKeeper
+ ensemble for the cluster programmatically do as follows:
+ Configuration config = HBaseConfiguration.create();
+config.set("hbase.zookeeper.quorum", "localhost"); // Here we are running zookeeper locally
+ If multiple ZooKeeper instances make up your ZooKeeper ensemble,
+ they may be specified in a comma-separated list (just as in the hbase-site.xml file).
+ This populated Configuration instance can then be passed to an
+ HTable,
+ and so on.
+
+
+
+
+
+
+
+ Example Configurations
+
+
+ Basic Distributed HBase Install
+
+ Here is an example basic configuration for a distributed ten
+ node cluster. The nodes are named example0,
+ example1, etc., through node
+ example9 in this example. The HBase Master and the
+ HDFS namenode are running on the node example0.
+ RegionServers run on nodes
+ example1-example9. A 3-node
+ ZooKeeper ensemble runs on example1,
+ example2, and example3 on the
+ default ports. ZooKeeper data is persisted to the directory
+ /export/zookeeper. Below we show what the main
+ configuration files -- hbase-site.xml,
+ regionservers, and
+ hbase-env.sh -- found in the HBase
+ conf directory might look like.
+
+
+ hbase-site.xml
+
+
+
+<?xml version="1.0"?>
+<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
+<configuration>
+ <property>
+ <name>hbase.zookeeper.quorum</name>
+ <value>example1,example2,example3</value>
+ <description>The directory shared by RegionServers.
+ </description>
+ </property>
+ <property>
+ <name>hbase.zookeeper.property.dataDir</name>
+ <value>/export/zookeeper</value>
+ <description>Property from ZooKeeper's config zoo.cfg.
+ The directory where the snapshot is stored.
+ </description>
+ </property>
+ <property>
+ <name>hbase.rootdir</name>
+ <value>hdfs://example0:8020/hbase</value>
+ <description>The directory shared by RegionServers.
+ </description>
+ </property>
+ <property>
+ <name>hbase.cluster.distributed</name>
+ <value>true</value>
+ <description>The mode the cluster will be in. Possible values are
+ false: standalone and pseudo-distributed setups with managed Zookeeper
+ true: fully-distributed with unmanaged Zookeeper Quorum (see hbase-env.sh)
+ </description>
+ </property>
+</configuration>
+
+
+
+
+
+ regionservers
+
+ In this file you list the nodes that will run RegionServers.
+ In our case we run RegionServers on all but the head node
+ example1 which is carrying the HBase Master and
+ the HDFS namenode
+
+
+ example1
+ example3
+ example4
+ example5
+ example6
+ example7
+ example8
+ example9
+
+
+
+
+ hbase-env.sh
+
+ Below we use a diff to show the differences
+ from default in the hbase-env.sh file. Here we
+ are setting the HBase heap to be 4G instead of the default
+ 1G.
+
+
+
+$ git diff hbase-env.sh
+diff --git a/conf/hbase-env.sh b/conf/hbase-env.sh
+index e70ebc6..96f8c27 100644
+--- a/conf/hbase-env.sh
++++ b/conf/hbase-env.sh
+@@ -31,7 +31,7 @@ export JAVA_HOME=/usr/lib//jvm/java-6-sun/
+ # export HBASE_CLASSPATH=
+
+ # The maximum amount of heap to use, in MB. Default is 1000.
+-# export HBASE_HEAPSIZE=1000
++export HBASE_HEAPSIZE=4096
+
+ # Extra Java runtime options.
+ # Below are what we set by default. May only work with SUN JVM.
+
+
+
+ Use rsync to copy the content of the
+ conf directory to all nodes of the
+ cluster.
+
+
+
+
+
+
+ The Important Configurations
+ Below we list what the important
+ Configurations. We've divided this section into
+ required configuration and worth-a-look recommended configs.
+
+
+
+ Required Configurations
+ Review the and sections.
+
+
+
+ Recommended Configurations
+ zookeeper.session.timeout
+ The default timeout is three minutes (specified in milliseconds). This means
+ that if a server crashes, it will be three minutes before the Master notices
+ the crash and starts recovery. You might like to tune the timeout down to
+ a minute or even less so the Master notices failures the sooner.
+ Before changing this value, be sure you have your JVM garbage collection
+ configuration under control otherwise, a long garbage collection that lasts
+ beyond the ZooKeeper session timeout will take out
+ your RegionServer (You might be fine with this -- you probably want recovery to start
+ on the server if a RegionServer has been in GC for a long period of time).
+
+ To change this configuration, edit hbase-site.xml,
+ copy the changed file around the cluster and restart.
+
+ We set this value high to save our having to field noob questions up on the mailing lists asking
+ why a RegionServer went down during a massive import. The usual cause is that their JVM is untuned and
+ they are running into long GC pauses. Our thinking is that
+ while users are getting familiar with HBase, we'd save them having to know all of its
+ intricacies. Later when they've built some confidence, then they can play
+ with configuration such as this.
+
+
+ Number of ZooKeeper Instances
+ See .
+
+
+ hbase.regionserver.handler.count
+
+ This setting defines the number of threads that are kept open to answer
+ incoming requests to user tables. The default of 10 is rather low in order to
+ prevent users from killing their region servers when using large write buffers
+ with a high number of concurrent clients. The rule of thumb is to keep this
+ number low when the payload per request approaches the MB (big puts, scans using
+ a large cache) and high when the payload is small (gets, small puts, ICVs, deletes).
+
+
+ It is safe to set that number to the
+ maximum number of incoming clients if their payload is small, the typical example
+ being a cluster that serves a website since puts aren't typically buffered
+ and most of the operations are gets.
+
+
+ The reason why it is dangerous to keep this setting high is that the aggregate
+ size of all the puts that are currently happening in a region server may impose
+ too much pressure on its memory, or even trigger an OutOfMemoryError. A region server
+ running on low memory will trigger its JVM's garbage collector to run more frequently
+ up to a point where GC pauses become noticeable (the reason being that all the memory
+ used to keep all the requests' payloads cannot be trashed, no matter how hard the
+ garbage collector tries). After some time, the overall cluster
+ throughput is affected since every request that hits that region server will take longer,
+ which exacerbates the problem even more.
+
+ You can get a sense of whether you have too little or too many handlers by
+
+ on an individual RegionServer then tailing its logs (Queued requests
+ consume memory).
+
+
+
+ Configuration for large memory machines
+
+ HBase ships with a reasonable, conservative configuration that will
+ work on nearly all
+ machine types that people might want to test with. If you have larger
+ machines -- HBase has 8G and larger heap -- you might the following configuration options helpful.
+ TODO.
+
+
+
+
+
+ Compression
+ You should consider enabling ColumnFamily compression. There are several options that are near-frictionless and in most all cases boost
+ performance by reducing the size of StoreFiles and thus reducing I/O.
+
+ See for more information.
+
+
+ Bigger Regions
+
+ Consider going to larger regions to cut down on the total number of regions
+ on your cluster. Generally less Regions to manage makes for a smoother running
+ cluster (You can always later manually split the big Regions should one prove
+ hot and you want to spread the request load over the cluster). A lower number of regions is
+ preferred, generally in the range of 20 to low-hundreds
+ per RegionServer. Adjust the regionsize as appropriate to achieve this number.
+
+ For the 0.90.x codebase, the upper-bound of regionsize is about 4Gb, with a default of 256Mb.
+ For 0.92.x codebase, due to the HFile v2 change much larger regionsizes can be supported (e.g., 20Gb).
+
+ You may need to experiment with this setting based on your hardware configuration and application needs.
+
+ Adjust hbase.hregion.max.filesize in your hbase-site.xml.
+ RegionSize can also be set on a per-table basis via
+ HTableDescriptor.
+
+
+
+
+ Managed Splitting
+
+ Rather than let HBase auto-split your Regions, manage the splitting manually
+ What follows is taken from the javadoc at the head of
+ the org.apache.hadoop.hbase.util.RegionSplitter tool
+ added to HBase post-0.90.0 release.
+
+ .
+ With growing amounts of data, splits will continually be needed. Since
+ you always know exactly what regions you have, long-term debugging and
+ profiling is much easier with manual splits. It is hard to trace the logs to
+ understand region level problems if it keeps splitting and getting renamed.
+ Data offlining bugs + unknown number of split regions == oh crap! If an
+ HLog or StoreFile
+ was mistakenly unprocessed by HBase due to a weird bug and
+ you notice it a day or so later, you can be assured that the regions
+ specified in these files are the same as the current regions and you have
+ less headaches trying to restore/replay your data.
+ You can finely tune your compaction algorithm. With roughly uniform data
+ growth, it's easy to cause split / compaction storms as the regions all
+ roughly hit the same data size at the same time. With manual splits, you can
+ let staggered, time-based major compactions spread out your network IO load.
+
+
+ How do I turn off automatic splitting? Automatic splitting is determined by the configuration value
+ hbase.hregion.max.filesize. It is not recommended that you set this
+ to Long.MAX_VALUE in case you forget about manual splits. A suggested setting
+ is 100GB, which would result in > 1hr major compactions if reached.
+
+ What's the optimal number of pre-split regions to create?
+ Mileage will vary depending upon your application.
+ You could start low with 10 pre-split regions / server and watch as data grows
+ over time. It's better to err on the side of too little regions and rolling split later.
+ A more complicated answer is that this depends upon the largest storefile
+ in your region. With a growing data size, this will get larger over time. You
+ want the largest region to be just big enough that the Store compact
+ selection algorithm only compacts it due to a timed major. If you don't, your
+ cluster can be prone to compaction storms as the algorithm decides to run
+ major compactions on a large series of regions all at once. Note that
+ compaction storms are due to the uniform data growth, not the manual split
+ decision.
+
+ If you pre-split your regions too thin, you can increase the major compaction
+interval by configuring HConstants.MAJOR_COMPACTION_PERIOD. If your data size
+grows too large, use the (post-0.90.0 HBase) org.apache.hadoop.hbase.util.RegionSplitter
+script to perform a network IO safe rolling split
+of all regions.
+
+
+ Managed Compactions
+ A common administrative technique is to manage major compactions manually, rather than letting
+ HBase do it. By default, HConstants.MAJOR_COMPACTION_PERIOD is one day and major compactions
+ may kick in when you least desire it - especially on a busy system. To turn off automatic major compactions set
+ the value to 0.
+
+ It is important to stress that major compactions are absolutely necessary for StoreFile cleanup, the only variant is when
+ they occur. They can be administered through the HBase shell, or via
+ HBaseAdmin.
+
+ For more information about compactions and the compaction file selection process, see
+
+
+ Speculative Execution
+ Speculative Execution of MapReduce tasks is on by default, and for HBase clusters it is generally advised to turn off
+ Speculative Execution at a system-level unless you need it for a specific case, where it can be configured per-job.
+ Set the properties mapred.map.tasks.speculative.execution and
+ mapred.reduce.tasks.speculative.execution to false.
+
+
+
+
+ Other Configurations
+ Balancer
+ The balancer is periodic operation run on the master to redistribute regions on the cluster. It is configured via
+ hbase.balancer.period and defaults to 300000 (5 minutes).
+ See for more information on the LoadBalancer.
+
+
+ Disabling Blockcache
+ Do not turn off block cache (You'd do it by setting hbase.block.cache.size to zero).
+ Currently we do not do well if you do this because the regionserver will spend all its time loading hfile
+ indices over and over again. If your working set it such that block cache does you no good, at least
+ size the block cache such that hfile indices will stay up in the cache (you can get a rough idea
+ on the size you need by surveying regionserver UIs; you'll see index block size accounted near the
+ top of the webpage).
+
+
+
+
+
+
+
+ Bloom Filter Configuration
+
+ io.hfile.bloom.enabled global kill
+ switch
+
+ io.hfile.bloom.enabled in
+ Configuration serves as the kill switch in case
+ something goes wrong. Default = true.
+
+
+
+ io.hfile.bloom.error.rate
+
+ io.hfile.bloom.error.rate = average false
+ positive rate. Default = 1%. Decrease rate by ½ (e.g. to .5%) == +1
+ bit per bloom entry.
+
+
+
+ io.hfile.bloom.max.fold
+
+ io.hfile.bloom.max.fold = guaranteed minimum
+ fold rate. Most people should leave this alone. Default = 7, or can
+ collapse to at least 1/128th of original size. See the
+ Development Process section of the document BloomFilters
+ in HBase for more on what this option means.
+
+
+
diff --git src/docbkx/customization.xsl src/docbkx/customization.xsl
new file mode 100644
index 0000000..d80a2b5
--- /dev/null
+++ src/docbkx/customization.xsl
@@ -0,0 +1,34 @@
+
+
+
+
+
+
+
+
+
+
+
+
diff --git src/docbkx/developer.xml src/docbkx/developer.xml
new file mode 100644
index 0000000..6d6e7d1
--- /dev/null
+++ src/docbkx/developer.xml
@@ -0,0 +1,821 @@
+
+
+
+ Building and Developing HBase
+ This chapter will be of interest only to those building and developing HBase (i.e., as opposed to
+ just downloading the latest distribution).
+
+
+ HBase Repositories
+ There are two different repositories for HBase: Subversion (SVN) and Git. The former is the system of record for committers, but the latter is easier to work with to build and contribute. SVN updates get automatically propagated to the Git repo.
+
+ SVN
+
+svn co http://svn.apache.org/repos/asf/hbase/trunk hbase-core-trunk
+
+
+
+ Git
+
+git clone git://git.apache.org/hbase.git
+
+
+
+
+
+ IDEs
+
+ Eclipse
+
+ Code Formatting
+ See HBASE-3678 Add Eclipse-based Apache Formatter to HBase Wiki
+ for an Eclipse formatter to help ensure your code conforms to HBase'y coding convention.
+ The issue includes instructions for loading the attached formatter.
+ In addition to the automatic formatting, make sure you follow the style guidelines explained in
+ Also, no @author tags - that's a rule. Quality Javadoc comments are appreciated. And include the Apache license.
+
+
+ Subversive Plugin
+ Download and install the Subversive plugin.
+ Set up an SVN Repository target from , then check out the code.
+
+
+ Git Plugin
+ If you cloned the project via git, download and install the Git plugin (EGit). Attach to your local git repo (via the Git Repositories window) and you'll be able to see file revision history, generate patches, etc.
+
+
+ HBase Project Setup in Eclipse
+ The easiest way is to use the m2eclipse plugin for Eclipse. Eclipse Indigo or newer has m2eclipse built-in, or it can be found here:http://www.eclipse.org/m2e/. M2Eclipse provides Maven integration for Eclipse - it even lets you use the direct Maven commands from within Eclipse to compile and test your project.
+ To import the project, you merely need to go to File->Import...Maven->Existing Maven Projects and then point Eclipse at the HBase root directory; m2eclipse will automatically find all the hbase modules for you.
+ If you install m2eclipse and import HBase in your workspace, you will have to fix your eclipse Build Path.
+ Remove target folder, add target/generated-jamon
+ and target/generated-sources/java folders. You may also remove from your Build Path
+ the exclusions on the src/main/resources and src/test/resources
+ to avoid error message in the console 'Failed to execute goal org.apache.maven.plugins:maven-antrun-plugin:1.6:run (default) on project hbase:
+ 'An Ant BuildException has occured: Replace: source file .../target/classes/hbase-default.xml doesn't exist'. This will also
+ reduce the eclipse build cycles and make your life easier when developing.
+
+
+ Import into eclipse with the command line
+ For those not inclined to use m2eclipse, you can generate the Eclipse files from the command line. First, run (you should only have to do this once):
+ mvn clean install -DskipTests
+ and then close Eclipse and execute...
+ mvn eclipse:eclipse
+ ... from your local HBase project directory in your workspace to generate some new .project
+ and .classpathfiles. Then reopen Eclipse, and import the .project file in the HBase directory to a workspace.
+
+
+
+ Maven Classpath Variable
+ The M2_REPO classpath variable needs to be set up for the project. This needs to be set to
+ your local Maven repository, which is usually ~/.m2/repository
+ If this classpath variable is not configured, you will see compile errors in Eclipse like this...
+
+Description Resource Path Location Type
+The project cannot be built until build path errors are resolved hbase Unknown Java Problem
+Unbound classpath variable: 'M2_REPO/asm/asm/3.1/asm-3.1.jar' in project 'hbase' hbase Build path Build Path Problem
+Unbound classpath variable: 'M2_REPO/com/github/stephenc/high-scale-lib/high-scale-lib/1.1.1/high-scale-lib-1.1.1.jar' in project 'hbase' hbase Build path Build Path Problem
+Unbound classpath variable: 'M2_REPO/com/google/guava/guava/r09/guava-r09.jar' in project 'hbase' hbase Build path Build Path Problem
+Unbound classpath variable: 'M2_REPO/com/google/protobuf/protobuf-java/2.3.0/protobuf-java-2.3.0.jar' in project 'hbase' hbase Build path Build Path Problem Unbound classpath variable:
+
+
+
+ Eclipse Known Issues
+ Eclipse will currently complain about Bytes.java. It is not possible to turn these errors off.
+
+Description Resource Path Location Type
+Access restriction: The method arrayBaseOffset(Class) from the type Unsafe is not accessible due to restriction on required library /System/Library/Java/JavaVirtualMachines/1.6.0.jdk/Contents/Classes/classes.jar Bytes.java /hbase/src/main/java/org/apache/hadoop/hbase/util line 1061 Java Problem
+Access restriction: The method arrayIndexScale(Class) from the type Unsafe is not accessible due to restriction on required library /System/Library/Java/JavaVirtualMachines/1.6.0.jdk/Contents/Classes/classes.jar Bytes.java /hbase/src/main/java/org/apache/hadoop/hbase/util line 1064 Java Problem
+Access restriction: The method getLong(Object, long) from the type Unsafe is not accessible due to restriction on required library /System/Library/Java/JavaVirtualMachines/1.6.0.jdk/Contents/Classes/classes.jar Bytes.java /hbase/src/main/java/org/apache/hadoop/hbase/util line 1111 Java Problem
+
+
+
+ Eclipse - More Information
+ For additional information on setting up Eclipse for HBase development on Windows, see
+ Michael Morello's blog on the topic.
+
+
+
+
+
+
+ Building HBase
+
+ Basic Compile
+ Thanks to maven, building HBase is easy. You can read about the various maven commands in , but the simplest command to compile HBase from its java source code is:
+
+mvn compile
+
+ Or, to clean up before compiling:
+
+mvn clean compile
+
+ With Eclipse set up as explained above in , you can also simply use the build command in Eclipse. To create the full installable HBase package takes a little bit more work, so read on.
+
+
+
+ Building in snappy compression support
+ Pass -Dsnappy to trigger the snappy maven profile for building
+ snappy native libs into hbase.
+
+
+
+ Building the HBase tarball
+ Do the following to build the HBase tarball.
+ Passing the -Drelease will generate javadoc and run the RAT plugin to verify licenses on source.
+ % MAVEN_OPTS="-Xmx2g" mvn clean site install assembly:single -DskipTests -Prelease
+
+
+
+
+ Adding an HBase release to Apache's Maven Repository
+ Follow the instructions at
+ Publishing Maven Artifacts.
+ The 'trick' to making it all work is answering the questions put to you by the mvn release plugin properly,
+ making sure it is using the actual branch AND before doing the mvn release:perform step,
+ VERY IMPORTANT, check and if necessary hand edit the release.properties file that was put under ${HBASE_HOME}
+ by the previous step, release:perform. You need to edit it to make it point at
+ right locations in SVN.
+
+ Use maven 3.0.x.
+
+ At the mvn release:perform step, before starting, if you are for example
+ releasing hbase 0.92.0, you need to make sure the pom.xml version is 0.92.0-SNAPSHOT. This needs
+ to be checked in. Since we do the maven release after actual release, I've been doing this
+ checkin into a particular tag rather than into the actual release tag. So, say we released
+ hbase 0.92.0 and now we want to do the release to the maven repository, in svn, the 0.92.0
+ release will be tagged 0.92.0. Making the maven release, copy the 0.92.0 tag to 0.92.0mvn.
+ Check out this tag and change the version therein and commit.
+
+ Here is how I'd answer the questions at release:prepare time:
+ What is the release version for "HBase"? (org.apache.hbase:hbase) 0.92.0: :
+What is SCM release tag or label for "HBase"? (org.apache.hbase:hbase) hbase-0.92.0: : 0.92.0mvnrelease
+What is the new development version for "HBase"? (org.apache.hbase:hbase) 0.92.1-SNAPSHOT: :
+[INFO] Transforming 'HBase'...
+
+ A strange issue I ran into was the one where the upload into the apache
+ repository was being sprayed across multiple apache machines making it so I could
+ not release. See INFRA-4482 Why is my upload to mvn spread across multiple repositories?.
+
+ Here is my ~/.m2/settings.xml.
+ <settings xmlns="http://maven.apache.org/SETTINGS/1.0.0"
+ xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+ xsi:schemaLocation="http://maven.apache.org/SETTINGS/1.0.0
+ http://maven.apache.org/xsd/settings-1.0.0.xsd">
+ <servers>
+ <!- To publish a snapshot of some part of Maven -->
+ <server>
+ <id>apache.snapshots.https</id>
+ <username>YOUR_APACHE_ID
+ </username>
+ <password>YOUR_APACHE_PASSWORD
+ </password>
+ </server>
+ <!-- To publish a website using Maven -->
+ <!-- To stage a release of some part of Maven -->
+ <server>
+ <id>apache.releases.https</id>
+ <username>YOUR_APACHE_ID
+ </username>
+ <password>YOUR_APACHE_PASSWORD
+ </password>
+ </server>
+ </servers>
+ <profiles>
+ <profile>
+ <id>apache-release</id>
+ <properties>
+ <gpg.keyname>YOUR_KEYNAME</gpg.keyname>
+ <!--Keyname is something like this ... 00A5F21E... do gpg --list-keys to find it-->
+ <gpg.passphrase>YOUR_KEY_PASSWORD
+ </gpg.passphrase>
+ </properties>
+ </profile>
+ </profiles>
+</settings>
+
+
+ When you run release:perform, pass -Papache-release
+ else it will not 'sign' the artifacts it uploads.
+
+ If you see run into the below, its because you need to edit version in the pom.xml and add
+ -SNAPSHOT to the version (and commit).
+ [INFO] Scanning for projects...
+[INFO] Searching repository for plugin with prefix: 'release'.
+[INFO] ------------------------------------------------------------------------
+[INFO] Building HBase
+[INFO] task-segment: [release:prepare] (aggregator-style)
+[INFO] ------------------------------------------------------------------------
+[INFO] [release:prepare {execution: default-cli}]
+[INFO] ------------------------------------------------------------------------
+[ERROR] BUILD FAILURE
+[INFO] ------------------------------------------------------------------------
+[INFO] You don't have a SNAPSHOT project in the reactor projects list.
+[INFO] ------------------------------------------------------------------------
+[INFO] For more information, run Maven with the -e switch
+[INFO] ------------------------------------------------------------------------
+[INFO] Total time: 3 seconds
+[INFO] Finished at: Sat Mar 26 18:11:07 PDT 2011
+[INFO] Final Memory: 35M/423M
+[INFO] -----------------------------------------------------------------------
+
+
+
+ Build Gotchas
+ If you see Unable to find resource 'VM_global_library.vm', ignore it.
+ Its not an error. It is officially ugly though.
+
+
+
+
+ Updating hbase.apache.org
+
+ Contributing to hbase.apache.org
+ The HBase apache web site (including this reference guide) is maintained as part of the main HBase source tree, under /src/docbkx and /src/site. The former is this reference guide; the latter, in most cases, are legacy pages that are in the process of being merged into the docbkx tree.
+ To contribute to the reference guide, edit these files and submit them as a patch (see ). Your Jira should contain a summary of the changes in each section (see HBASE-6081 for an example).
+ To generate the site locally while you're working on it, run:
+ mvn site
+ Then you can load up the generated HTML files in your browser (file are under /target/site).
+
+
+ Publishing hbase.apache.org
+ If you're a committer with rights to publish the site artifacts: set up your apache credentials and the target site location locally in a place and
+ form that maven can pick it up, in ~/.m2/settings.xml. See for an example.
+ Next, run the following:
+ $ mvn -DskipTests -Papache-release site site:deploy
+ You will be asked for your password. It can take a little time.
+ Remember that it can take a few hours for your site changes to show up.
+
+
+
+
+ Tests
+
+ Developers, at a minimum, should familiarize themselves with the unit test detail; unit tests in
+HBase have a character not usually seen in other projects.
+
+
+HBase Modules
+As of 0.96, HBase is split into multiple modules which creates "interesting" rules for
+how and where tests are written. If you are writting code for hbase-server, see
+ for how to write your tests; these tests can spin
+up a minicluster and will need to be categorized. For any other module, for example
+hbase-common, the tests must be strict unit tests and just test the class
+under test - no use of the HBaseTestingUtility or minicluster is allowed (or even possible
+given the dependency tree).
+
+ Running Tests in other Modules
+ If the module you are developing in has no other dependencies on other HBase modules, then
+ you can cd into that module and just run:
+ mvn test
+ which will just run the tests IN THAT MODULE. If there are other dependencies on other modules,
+ then you will have run the command from the ROOT HBASE DIRECTORY. This will run the tests in the other
+ modules, unless you specify to skip the tests in that module. For instance, to skip the tests in the hbase-server module,
+ you would run:
+ mvn clean test -Dskip-server-tests
+ from the top level directory to run all the tests in modules other than hbase-server. Note that you
+ can specify to skip tests in multiple modules as well as just for a single module. For example, to skip
+ the tests in hbase-server and hbase-common, you would run:
+ mvn clean test -Dskip-server-tests -Dskip-common-tests
+ Also, keep in mind that if you are running tests in the hbase-server module you will need to
+ apply the maven profiles discussed in to get the tests to run properly.
+
+
+
+
+Unit Tests
+HBase unit tests are subdivided into three categories: small, medium and large, with
+corresponding JUnit categories:
+SmallTests, MediumTests,
+LargeTests. JUnit categories are denoted using java annotations
+and look like this in your unit test code.
+...
+@Category(SmallTests.class)
+public class TestHRegionInfo {
+
+ @Test
+ public void testCreateHRegionInfoName() throws Exception {
+ // ...
+ }
+...
+ @org.junit.Rule
+ public org.apache.hadoop.hbase.ResourceCheckerJUnitRule cu =
+ new org.apache.hadoop.hbase.ResourceCheckerJUnitRule();
+}
+The above example shows how to mark a test as belonging to the small category. The @org.junit.Rule
+lines on the end are also necessary. Add them to each new unit test file. They are needed by the categorization process.
+HBase uses a patched maven surefire plugin and maven profiles to implement its unit test characterizations.
+
+
+
+SmallTests
+
+Small tests are executed in a shared JVM. We put in this category all the tests that can
+be executed quickly in a shared JVM. The maximum execution time for a small test is 15 seconds,
+and small tests should not use a (mini)cluster.
+
+
+
+MediumTests
+Medium tests represent tests that must be executed
+before proposing a patch. They are designed to run in less than 30 minutes altogether,
+and are quite stable in their results. They are designed to last less than 50 seconds
+individually. They can use a cluster, and each of them is executed in a separate JVM.
+
+
+
+
+LargeTests
+Large tests are everything else. They are typically integration-like
+tests, regression tests for specific bugs, timeout tests, performance tests.
+They are executed before a commit on the pre-integration machines. They can be run on
+the developer machine as well.
+
+
+
+
+Running tests
+Below we describe how to run the HBase junit categories.
+
+
+Default: small and medium category tests
+
+Running mvn test will execute all small tests in a single JVM
+(no fork) and then medium tests in a separate JVM for each test instance.
+Medium tests are NOT executed if there is an error in a small test.
+Large tests are NOT executed. There is one report for small tests, and one report for
+medium tests if they are executed. To run small and medium tests with the security
+profile enabled, do mvn test -P security
+
+
+
+
+Running all tests
+Running mvn test -P runAllTests
+will execute small tests in a single JVM then medium and large tests in a separate JVM for each test.
+Medium and large tests are NOT executed if there is an error in a small test.
+Large tests are NOT executed if there is an error in a small or medium test.
+There is one report for small tests, and one report for medium and large tests if they are executed
+
+
+
+
+Running a single test or all tests in a package
+To run an individual test, e.g. MyTest, do
+mvn test -P localTests -Dtest=MyTest You can also
+pass multiple, individual tests as a comma-delimited list:
+mvn test -P localTests -Dtest=MyTest1,MyTest2,MyTest3
+You can also pass a package, which will run all tests under the package:
+mvn test -P localTests -Dtest=org.apache.hadoop.hbase.client.*
+To run a single test with the security profile enabled:
+mvn test -P security,localTests -Dtest=TestGet
+
+
+
+The -P localTests will remove the JUnit category effect (without this specific profile,
+the categories are taken into account). It will actually use the official release of surefire
+and the old connector (The HBase build uses a patched version of the maven surefire plugin).
+Each junit tests is executed in a separate JVM (A fork per test class). There is no
+parallelization when localTests profile is set. You will see a new message at the end of the
+report: "[INFO] Tests are skipped". It's harmless.
+
+
+
+
+Other test invocation permutations
+Running mvn test -P runSmallTests will execute small tests only, in a single JVM.
+
+Running mvn test -P runMediumTests will execute medium tests in a single JVM.
+
+Running mvn test -P runLargeTests execute medium tests in a single JVM.
+
+
+
+
+hbasetests.sh
+It's also possible to use the script hbasetests.sh. This script runs the medium and
+large tests in parallel with two maven instances, and provides a single report. This script does not use
+the hbase version of surefire so no parallelization is being done other than the two maven instances the
+script sets up.
+It must be executed from the directory which contains the pom.xml.
+For example running
+./dev-support/hbasetests.sh will execute small and medium tests.
+Running ./dev-support/hbasetests.sh runAllTests will execute all tests.
+Running ./dev-support/hbasetests.sh replayFailed will rerun the failed tests a
+second time, in a separate jvm and without parallelisation.
+
+
+
+
+
+Writing Tests
+
+General rules
+
+
+As much as possible, tests should be written as category small tests.
+
+
+All tests must be written to support parallel execution on the same machine, hence they should not use shared resources as fixed ports or fixed file names.
+
+
+Tests should not overlog. More than 100 lines/second makes the logs complex to read and use i/o that are hence not available for the other tests.
+
+
+Tests can be written with HBaseTestingUtility.
+This class offers helper functions to create a temp directory and do the cleanup, or to start a cluster.
+Categories and execution time
+
+
+All tests must be categorized, if not they could be skipped.
+
+
+All tests should be written to be as fast as possible.
+
+
+Small category tests should last less than 15 seconds, and must not have any side effect.
+
+
+Medium category tests should last less than 50 seconds.
+
+
+Large category tests should last less than 3 minutes. This should ensure a good parallelization for people using it, and ease the analysis when the test fails.
+
+
+
+
+Sleeps in tests
+Whenever possible, tests should not use Thread.sleep, but rather waiting for the real event they need. This is faster and clearer for the reader.
+Tests should not do a Thread.sleep without testing an ending condition. This allows understanding what the test is waiting for. Moreover, the test will work whatever the machine performance is.
+Sleep should be minimal to be as fast as possible. Waiting for a variable should be done in a 40ms sleep loop. Waiting for a socket operation should be done in a 200 ms sleep loop.
+
+
+
+
+Tests using a cluster
+
+
+Tests using a HRegion do not have to start a cluster: A region can use the local file system.
+Start/stopping a cluster cost around 10 seconds. They should not be started per test method but per test class.
+Started cluster must be shutdown using HBaseTestingUtility#shutdownMiniCluster, which cleans the directories.
+As most as possible, tests should use the default settings for the cluster. When they don't, they should document it. This will allow to share the cluster later.
+
+
+
+
+
+
+
+ Maven Build Commands
+ All commands executed from the local HBase project directory.
+
+ Note: use Maven 3 (Maven 2 may work but we suggest you use Maven 3).
+
+
+ Compile
+
+mvn compile
+
+
+
+
+ Running all or individual Unit Tests
+ See the section
+ above in
+
+
+
+ Building against various hadoop versions.
+ As of 0.96, HBase supports building against hadoop versions: 1.0.3, 2.0.0-alpha and 3.0.0-SNAPSHOT.
+ By default, we will build with Hadoop-1.0.3. To change the version to run with Hadoop-2.0.0-alpha, you would run:
+ mvn -Dhadoop.profile=2.0 ...
+
+ That is, designate build with hadoop.profile 2.0. Pass 2.0 for hadoop.profile to build against hadoop 2.0.
+ Tests may not all pass as of this writing so you may need to pass -DskipTests unless you are inclined
+ to fix the failing tests.
+
+ Similarly, for 3.0, you would just replace the profile value. Note that Hadoop-3.0.0-SNAPSHOT does not currently have a deployed maven artificat - you will need to build and install your own in your local maven repository if you want to run against this profile.
+
+
+ In earilier verions of HBase, you can build against older versions of hadoop, notably, Hadoop 0.22.x and 0.23.x.
+ If you are running, for example HBase-0.94 and wanted to build against Hadoop 0.23.x, you would run with:
+ mvn -Dhadoop.profile=22 ...
+
+
+
+
+ Getting Involved
+ HBase gets better only when people contribute!
+
+ As HBase is an Apache Software Foundation project, see for more information about how the ASF functions.
+
+
+ Mailing Lists
+ Sign up for the dev-list and the user-list. See the
+ mailing lists page.
+ Posing questions - and helping to answer other people's questions - is encouraged!
+ There are varying levels of experience on both lists so patience and politeness are encouraged (and please
+ stay on topic.)
+
+
+
+ Jira
+ Check for existing issues in Jira.
+ If it's either a new feature request, enhancement, or a bug, file a ticket.
+
+ Jira Priorities
+ The following is a guideline on setting Jira issue priorities:
+
+ Blocker: Should only be used if the issue WILL cause data loss or cluster instability reliably.
+ Critical: The issue described can cause data loss or cluster instability in some cases.
+ Major: Important but not tragic issues, like updates to the client API that will add a lot of much-needed functionality or significant
+ bugs that need to be fixed but that don't cause data loss.
+ Minor: Useful enhancements and annoying but not damaging bugs.
+ Trivial: Useful enhancements but generally cosmetic.
+
+
+
+
+ Code Blocks in Jira Comments
+ A commonly used macro in Jira is {code}. If you do this in a Jira comment...
+
+{code}
+ code snippet
+{code}
+
+ ... Jira will format the code snippet like code, instead of a regular comment. It improves readability.
+
+
+
+
+
+
+ Developing
+ Codelines
+ Most development is done on TRUNK. However, there are branches for minor releases (e.g., 0.90.1, 0.90.2, and 0.90.3 are on the 0.90 branch).
+ If you have any questions on this just send an email to the dev dist-list.
+
+
+
+ Unit Tests
+ In HBase we use JUnit 4.
+ If you need to run miniclusters of HDFS, ZooKeeper, HBase, or MapReduce testing,
+ be sure to checkout the HBaseTestingUtility.
+ Alex Baranau of Sematext describes how it can be used in
+ HBase Case-Study: Using HBaseTestingUtility for Local Testing and Development (2010).
+
+
+ Mockito
+ Sometimes you don't need a full running server
+ unit testing. For example, some methods can make do with a
+ a org.apache.hadoop.hbase.Server instance
+ or a org.apache.hadoop.hbase.master.MasterServices
+ Interface reference rather than a full-blown
+ org.apache.hadoop.hbase.master.HMaster.
+ In these cases, you maybe able to get away with a mocked
+ Server instance. For example:
+
+ TODO...
+
+
+
+
+
+
+ Code Standards
+ See and .
+
+ Also, please pay attention to the interface stability/audience classifications that you
+ will see all over our code base. They look like this at the head of the class:
+ @InterfaceAudience.Public
+@InterfaceStability.Stable
+
+ If the InterfaceAudience is Private,
+ we can change the class (and we do not need to include a InterfaceStability mark).
+ If a class is marked Public but its InterfaceStability
+ is marked Unstable, we can change it. If it's
+ marked Public/Evolving, we're allowed to change it
+ but should try not to. If it's Public and Stable
+ we can't change it without a deprecation path or with a really GREAT reason.
+ When you add new classes, mark them with the annotations above if publically accessible.
+ If you are not cleared on how to mark your additions, ask up on the dev list.
+
+ This convention comes from our parent project Hadoop.
+
+
+
+ Running In-Situ
+ If you are developing HBase, frequently it is useful to test your changes against a more-real cluster than what you find in unit tests. In this case, HBase can be run directly from the source in local-mode.
+ All you need to do is run:
+
+ ${HBASE_HOME}/bin/start-hbase.sh
+
+ This will spin up a full local-cluster, just as if you had packaged up HBase and installed it on your machine.
+
+ Keep in mind that you will need to have installed HBase into your local maven repository for the in-situ cluster to work properly. That is, you will need to run:
+ mvn clean install -DskipTests
+ to ensure that maven can find the correct classpath and dependencies. Generally, the above command
+ is just a good thing to try running first, if maven is acting oddly.
+
+
+
+
+
+ Submitting Patches
+
+ Create Patch
+ Patch files can be easily generated from Eclipse, for example by selecting "Team -> Create Patch".
+ Patches can also be created by git diff and svn diff.
+
+ Please submit one patch-file per Jira. For example, if multiple files are changed make sure the
+ selected resource when generating the patch is a directory. Patch files can reflect changes in multiple files.
+ Make sure you review for code style.
+
+
+ Patch File Naming
+ The patch file should have the HBase Jira ticket in the name. For example, if a patch was submitted for Foo.java, then
+ a patch file called Foo_HBASE_XXXX.patch would be acceptable where XXXX is the HBase Jira number.
+
+ If you generating from a branch, then including the target branch in the filename is advised, e.g., HBASE-XXXX-0.90.patch.
+
+
+
+ Unit Tests
+ Yes, please. Please try to include unit tests with every code patch (and especially new classes and large changes).
+ Make sure unit tests pass locally before submitting the patch.
+ Also, see .
+ If you are creating a new unit test class, notice how other unit test classes have classification/sizing
+ annotations at the top and a static method on the end. Be sure to include these in any new unit test files
+ you generate. See for more on how the annotations work.
+
+
+
+ Attach Patch to Jira
+ The patch should be attached to the associated Jira ticket "More Actions -> Attach Files". Make sure you click the
+ ASF license inclusion, otherwise the patch can't be considered for inclusion.
+
+ Once attached to the ticket, click "Submit Patch" and
+ the status of the ticket will change. Committers will review submitted patches for inclusion into the codebase. Please
+ understand that not every patch may get committed, and that feedback will likely be provided on the patch. Fear not, though,
+ because the HBase community is helpful!
+
+
+
+
+ Common Patch Feedback
+ The following items are representative of common patch feedback. Your patch process will go faster if these are
+ taken into account before submission.
+
+
+ See the Java coding standards
+ for more information on coding conventions in Java.
+
+
+ Space Invaders
+ Rather than do this...
+
+if ( foo.equals( bar ) ) { // don't do this
+
+ ... do this instead...
+
+if (foo.equals(bar)) {
+
+
+ Also, rather than do this...
+
+foo = barArray[ i ]; // don't do this
+
+ ... do this instead...
+
+foo = barArray[i];
+
+
+
+
+ Auto Generated Code
+ Auto-generated code in Eclipse often looks like this...
+
+ public void readFields(DataInput arg0) throws IOException { // don't do this
+ foo = arg0.readUTF(); // don't do this
+
+ ... do this instead ...
+
+ public void readFields(DataInput di) throws IOException {
+ foo = di.readUTF();
+
+ See the difference? 'arg0' is what Eclipse uses for arguments by default.
+
+
+
+ Long Lines
+
+ Keep lines less than 100 characters.
+
+Bar bar = foo.veryLongMethodWithManyArguments(argument1, argument2, argument3, argument4, argument5, argument6, argument7, argument8, argument9); // don't do this
+
+ ... do something like this instead ...
+
+Bar bar = foo.veryLongMethodWithManyArguments(
+ argument1, argument2, argument3,argument4, argument5, argument6, argument7, argument8, argument9);
+
+
+
+
+ Trailing Spaces
+
+ This happens more than people would imagine.
+
+Bar bar = foo.getBar(); <--- imagine there's an extra space(s) after the semicolon instead of a line break.
+
+ Make sure there's a line-break after the end of your code, and also avoid lines that have nothing
+ but whitespace.
+
+
+
+ Implementing Writable
+ Every class returned by RegionServers must implement Writable. If you
+ are creating a new class that needs to implement this interface, don't forget the default constructor.
+
+
+
+ Javadoc
+ This is also a very common feedback item. Don't forget Javadoc!
+
+
+
+ Javadoc - Useless Defaults
+ Don't just leave the @param arguments the way your IDE generated them. Don't do this...
+
+ /**
+ *
+ * @param bar <---- don't do this!!!!
+ * @return <---- or this!!!!
+ */
+ public Foo getFoo(Bar bar);
+
+ ... either add something descriptive to the @param and @return lines, or just remove them.
+ But the preference is to add something descriptive and useful.
+
+
+
+ One Thing At A Time, Folks
+ If you submit a patch for one thing, don't do auto-reformatting or unrelated reformatting of code on a completely
+ different area of code.
+
+ Likewise, don't add unrelated cleanup or refactorings outside the scope of your Jira.
+
+
+
+ Ambigious Unit Tests
+ Make sure that you're clear about what you are testing in your unit tests and why.
+
+
+
+
+
+
+ ReviewBoard
+ Larger patches should go through ReviewBoard.
+
+ For more information on how to use ReviewBoard, see
+ the ReviewBoard documentation.
+
+
+
+ Committing Patches
+
+ Committers do this. See How To Commit in the HBase wiki.
+
+ Commiters will also resolve the Jira, typically after the patch passes a build.
+
+
+
+
+
+
+
diff --git src/docbkx/external_apis.xml src/docbkx/external_apis.xml
new file mode 100644
index 0000000..da03f4c
--- /dev/null
+++ src/docbkx/external_apis.xml
@@ -0,0 +1,417 @@
+
+
+
+ External APIs
+ This chapter will cover access to HBase either through non-Java languages, or through custom protocols.
+
+
+ Non-Java Languages Talking to the JVM
+ Currently the documentation on this topic in the
+ HBase Wiki.
+ See also the Thrift API Javadoc.
+
+
+
+
+ REST
+ Currently most of the documentation on REST exists in the
+ HBase Wiki on REST.
+
+
+
+
+ Thrift
+ Currently most of the documentation on Thrift exists in the
+ HBase Wiki on Thrift.
+
+ Filter Language
+ Use Case
+ Note: this feature was introduced in HBase 0.92
+ This allows the user to perform server-side filtering when accessing HBase over Thrift. The user specifies a filter via a string. The string is parsed on the server to construct the filter
+
+
+ General Filter String Syntax
+ A simple filter expression is expressed as: “FilterName (argument, argument, ... , argument)”
+ You must specify the name of the filter followed by the argument list in parenthesis. Commas separate the individual arguments
+ If the argument represents a string, it should be enclosed in single quotes.
+ If it represents a boolean, an integer or a comparison operator like <,
+ >, != etc. it should not be enclosed in quotes
+ The filter name must be one word. All ASCII characters are allowed except for whitespace, single quotes and parenthesis.
+ The filter’s arguments can contain any ASCII character. If single quotes are present in the argument, they must be escaped by a
+ preceding single quote
+
+
+ Compound Filters and Operators
+ Currently, two binary operators – AND/OR and two unary operators – WHILE/SKIP are supported.
+ Note: the operators are all in uppercase
+ AND – as the name suggests, if this
+ operator is used, the key-value must pass both the filters
+ OR – as the name suggests, if this operator
+ is used, the key-value must pass at least one of the filters
+ SKIP – For a particular row, if any of the
+ key-values don’t pass the filter condition, the entire row is skipped
+ WHILE - For a particular row, it continues
+ to emit key-values until a key-value is reached that fails the filter condition
+ Compound Filters: Using these operators, a
+ hierarchy of filters can be created. For example: “(Filter1 AND Filter2) OR (Filter3 AND Filter4)”
+
+
+ Order of Evaluation
+ Parenthesis have the highest precedence. The SKIP and WHILE operators are next and have the same precedence.The AND operator has the next highest precedence followed by the OR operator.
+ For example:
+ A filter string of the form:“Filter1 AND Filter2 OR Filter3”
+ will be evaluated as:“(Filter1 AND Filter2) OR Filter3”
+ A filter string of the form:“Filter1 AND SKIP Filter2 OR Filter3”
+ will be evaluated as:“(Filter1 AND (SKIP Filter2)) OR Filter3”
+
+
+ Compare Operator
+ A compare operator can be any of the following:
+
+
+ LESS (<)
+
+
+ LESS_OR_EQUAL (<=)
+
+
+ EQUAL (=)
+
+
+ NOT_EQUAL (!=)
+
+
+ GREATER_OR_EQUAL (>=)
+
+
+ GREATER (>)
+
+
+ NO_OP (no operation)
+
+
+ The client should use the symbols (<, <=, =, !=, >, >=) to express
+ compare operators.
+
+
+ Comparator
+ A comparator can be any of the following:
+
+
+ BinaryComparator - This
+ lexicographically compares against the specified byte array using
+ Bytes.compareTo(byte[], byte[])
+
+
+ BinaryPrefixComparator - This
+ lexicographically compares against a specified byte array. It only compares up to
+ the length of this byte array.
+
+
+ RegexStringComparator - This compares
+ against the specified byte array using the given regular expression. Only EQUAL
+ and NOT_EQUAL comparisons are valid with this comparator
+
+
+ SubStringComparator - This tests if
+ the given substring appears in a specified byte array. The comparison is case
+ insensitive. Only EQUAL and NOT_EQUAL comparisons are valid with this
+ comparator
+
+
+ The general syntax of a comparator is: ComparatorType:ComparatorValue
+ The ComparatorType for the various comparators is as follows:
+
+
+ BinaryComparator - binary
+
+
+ BinaryPrefixComparator - binaryprefix
+
+
+ RegexStringComparator - regexstring
+
+
+ SubStringComparator - substring
+
+
+ The ComparatorValue can be any value.
+ Example1: >, 'binary:abc' will match everything that is lexicographically greater than "abc"
+ Example2: =, 'binaryprefix:abc' will match everything whose first 3 characters are lexicographically equal to "abc"
+ Example3: !=, 'regexstring:ab*yz' will match everything that doesn't begin with "ab" and ends with "yz"
+ Example4: =, 'substring:abc123' will match everything that begins with the substring "abc123"
+
+
+ Example PHP Client Program that uses the Filter Language
+
+<? $_SERVER['PHP_ROOT'] = realpath(dirname(__FILE__).'/..');
+ require_once $_SERVER['PHP_ROOT'].'/flib/__flib.php';
+ flib_init(FLIB_CONTEXT_SCRIPT);
+ require_module('storage/hbase');
+ $hbase = new HBase('<server_name_running_thrift_server>', <port on which thrift server is running>);
+ $hbase->open();
+ $client = $hbase->getClient();
+ $result = $client->scannerOpenWithFilterString('table_name', "(PrefixFilter ('row2') AND (QualifierFilter (>=, 'binary:xyz'))) AND (TimestampsFilter ( 123, 456))");
+ $to_print = $client->scannerGetList($result,1);
+ while ($to_print) {
+ print_r($to_print);
+ $to_print = $client->scannerGetList($result,1);
+ }
+ $client->scannerClose($result);
+?>
+
+
+
+ Example Filter Strings
+
+
+
+ “PrefixFilter (‘Row’) AND PageFilter (1) AND FirstKeyOnlyFilter ()” will return all key-value pairs that match the following conditions:
+ 1) The row containing the key-value should have prefix “Row”
+ 2) The key-value must be located in the first row of the table
+ 3) The key-value pair must be the first key-value in the row
+
+
+
+
+
+
+
+
+ “(RowFilter (=, ‘binary:Row 1’) AND TimeStampsFilter (74689, 89734)) OR
+ ColumnRangeFilter (‘abc’, true, ‘xyz’, false))” will return all key-value pairs that match both the following conditions:
+ 1) The key-value is in a row having row key “Row 1”
+ 2) The key-value must have a timestamp of either 74689 or 89734.
+ Or it must match the following condition:
+ 1) The key-value pair must be in a column that is lexicographically >= abc and < xyz
+
+
+
+
+
+
+
+
+ “SKIP ValueFilter (0)” will skip the entire row if any of the values in the row is not 0
+
+
+
+
+
+ Individual Filter Syntax
+
+
+ KeyOnlyFilter
+ Description: This filter doesn’t take any
+ arguments. It returns only the key component of each key-value.
+ Syntax: KeyOnlyFilter ()
+ Example: "KeyOnlyFilter ()"
+
+
+
+ FirstKeyOnlyFilter
+ Description: This filter doesn’t take any
+ arguments. It returns only the first key-value from each row.
+ Syntax: FirstKeyOnlyFilter ()
+ Example: "FirstKeyOnlyFilter ()"
+
+
+
+ PrefixFilter
+ Description: This filter takes one argument – a prefix of a
+ row key. It returns only those key-values present in a row that starts with the
+ specified row prefix
+ Syntax: PrefixFilter (‘<row_prefix>’)
+ Example: "PrefixFilter (‘Row’)"
+
+
+
+
+ ColumnPrefixFilter
+ Description: This filter takes one argument
+ – a column prefix. It returns only those key-values present in a column that starts
+ with the specified column prefix. The column prefix must be of the form: “qualifier”
+ Syntax:ColumnPrefixFilter(‘<column_prefix>’)
+ Example: "ColumnPrefixFilter(‘Col’)"
+
+
+
+ MultipleColumnPrefixFilter
+ Description: This filter takes a list of
+ column prefixes. It returns key-values that are present in a column that starts with
+ any of the specified column prefixes. Each of the column prefixes must be of the form: “qualifier”
+ Syntax:MultipleColumnPrefixFilter(‘<column_prefix>’, ‘<column_prefix>’, …, ‘<column_prefix>’)
+ Example: "MultipleColumnPrefixFilter(‘Col1’, ‘Col2’)"
+
+
+
+ ColumnCountGetFilter
+ Description: This filter takes one argument
+ – a limit. It returns the first limit number of columns in the table
+ Syntax: ColumnCountGetFilter (‘<limit>’)
+ Example: "ColumnCountGetFilter (4)"
+
+
+
+ PageFilter
+ Description: This filter takes one argument
+ – a page size. It returns page size number of rows from the table.
+ Syntax: PageFilter (‘<page_size>’)
+ Example: "PageFilter (2)"
+
+
+
+ ColumnPaginationFilter
+ Description: This filter takes two
+ arguments – a limit and offset. It returns limit number of columns after offset number
+ of columns. It does this for all the rows
+ Syntax: ColumnPaginationFilter(‘<limit>’, ‘<offest>’)
+ Example: "ColumnPaginationFilter (3, 5)"
+
+
+
+ InclusiveStopFilter
+ Description: This filter takes one argument
+ – a row key on which to stop scanning. It returns all key-values present in rows up to
+ and including the specified row
+ Syntax: InclusiveStopFilter(‘<stop_row_key>’)
+ Example: "InclusiveStopFilter ('Row2')"
+
+
+
+ TimeStampsFilter
+ Description: This filter takes a list of
+ timestamps. It returns those key-values whose timestamps matches any of the specified
+ timestamps
+ Syntax: TimeStampsFilter (<timestamp>, <timestamp>, ... ,<timestamp>)
+ Example: "TimeStampsFilter (5985489, 48895495, 58489845945)"
+
+
+
+ RowFilter
+ Description: This filter takes a compare
+ operator and a comparator. It compares each row key with the comparator using the
+ compare operator and if the comparison returns true, it returns all the key-values in
+ that row
+ Syntax: RowFilter (<compareOp>, ‘<row_comparator>’)
+ Example: "RowFilter (<=, ‘xyz)"
+
+
+
+ Family Filter
+ Description: This filter takes a compare
+ operator and a comparator. It compares each qualifier name with the comparator using
+ the compare operator and if the comparison returns true, it returns all the key-values
+ in that column
+ Syntax: QualifierFilter (<compareOp>, ‘<qualifier_comparator>’)
+ Example: "QualifierFilter (=, ‘Column1’)"
+
+
+
+ QualifierFilter
+ Description: This filter takes a compare
+ operator and a comparator. It compares each qualifier name with the comparator using
+ the compare operator and if the comparison returns true, it returns all the key-values
+ in that column
+ Syntax: QualifierFilter (<compareOp>,‘<qualifier_comparator>’)
+ Example: "QualifierFilter (=,‘Column1’)"
+
+
+
+ ValueFilter
+ Description: This filter takes a compare operator and a
+ comparator. It compares each value with the comparator using the compare operator and
+ if the comparison returns true, it returns that key-value
+ Syntax: ValueFilter (<compareOp>,‘<value_comparator>’)
+ Example: "ValueFilter (!=, ‘Value’)"
+
+
+
+ DependentColumnFilter
+ Description: This filter takes two arguments – a family
+ and a qualifier. It tries to locate this column in each row and returns all key-values
+ in that row that have the same timestamp. If the row doesn’t contain the specified
+ column – none of the key-values in that row will be returned.
+ The filter can also take an optional boolean argument – dropDependentColumn. If set to true, the column we were depending on doesn’t get returned.
+ The filter can also take two more additional optional arguments – a compare operator and a value comparator, which are further checks in addition to the family and qualifier. If the dependent column is found, its value should also pass the value check and then only is its timestamp taken into consideration
+ Syntax: DependentColumnFilter (‘<family>’, ‘<qualifier>’, <boolean>, <compare operator>, ‘<value comparator’)
+ Syntax: DependentColumnFilter (‘<family>’, ‘<qualifier>’, <boolean>)
+ Syntax: DependentColumnFilter (‘<family>’, ‘<qualifier>’)
+ Example: "DependentColumnFilter (‘conf’, ‘blacklist’, false, >=, ‘zebra’)"
+ Example: "DependentColumnFilter (‘conf’, 'blacklist', true)"
+ Example: "DependentColumnFilter (‘conf’, 'blacklist')"
+
+
+
+ SingleColumnValueFilter
+ Description: This filter takes a column family, a
+ qualifier, a compare operator and a comparator. If the specified column is not found –
+ all the columns of that row will be emitted. If the column is found and the comparison
+ with the comparator returns true, all the columns of the row will be emitted. If the
+ condition fails, the row will not be emitted.
+ This filter also takes two additional optional boolean arguments – filterIfColumnMissing and setLatestVersionOnly
+ If the filterIfColumnMissing flag is set to true the columns of the row will not be emitted if the specified column to check is not found in the row. The default value is false.
+ If the setLatestVersionOnly flag is set to false, it will test previous versions (timestamps) too. The default value is true.
+ These flags are optional and if you must set neither or both
+ Syntax: SingleColumnValueFilter(<compare operator>, ‘<comparator>’, ‘<family>’, ‘<qualifier>’,<filterIfColumnMissing_boolean>, <latest_version_boolean>)
+ Syntax: SingleColumnValueFilter(<compare operator>, ‘<comparator>’, ‘<family>’, ‘<qualifier>)
+ Example: "SingleColumnValueFilter (<=, ‘abc’,‘FamilyA’, ‘Column1’, true, false)"
+ Example: "SingleColumnValueFilter (<=, ‘abc’,‘FamilyA’, ‘Column1’)"
+
+
+
+ SingleColumnValueExcludeFilter
+ Description: This filter takes the same arguments and
+ behaves same as SingleColumnValueFilter – however, if the column is found and the
+ condition passes, all the columns of the row will be emitted except for the tested
+ column value.
+ Syntax: SingleColumnValueExcludeFilter(<compare operator>, '<comparator>', '<family>', '<qualifier>',<latest_version_boolean>, <filterIfColumnMissing_boolean>)
+ Syntax: SingleColumnValueExcludeFilter(<compare operator>, '<comparator>', '<family>', '<qualifier>')
+ Example: "SingleColumnValueExcludeFilter (‘<=’, ‘abc’,‘FamilyA’, ‘Column1’, ‘false’, ‘true’)"
+ Example: "SingleColumnValueExcludeFilter (‘<=’, ‘abc’, ‘FamilyA’, ‘Column1’)"
+
+
+
+ ColumnRangeFilter
+ Description: This filter is used for selecting only those
+ keys with columns that are between minColumn and maxColumn. It also takes two boolean
+ variables to indicate whether to include the minColumn and maxColumn or not.
+ If you don’t want to set the minColumn or the maxColumn – you can pass in an empty argument.
+ Syntax: ColumnRangeFilter (‘<minColumn>’, <minColumnInclusive_bool>, ‘<maxColumn>’, <maxColumnInclusive_bool>)
+ Example: "ColumnRangeFilter (‘abc’, true, ‘xyz’, false)"
+
+
+
+
+
+
+
+
+
+
+
diff --git src/docbkx/getting_started.xml src/docbkx/getting_started.xml
new file mode 100644
index 0000000..3aa392b
--- /dev/null
+++ src/docbkx/getting_started.xml
@@ -0,0 +1,207 @@
+
+
+
+ Getting Started
+
+
+ Introduction
+
+ will get you up and
+ running on a single-node instance of HBase using the local filesystem.
+ describes setup
+ of HBase in distributed mode running on top of HDFS.
+
+
+
+ Quick Start
+
+ This guide describes setup of a standalone HBase instance that uses
+ the local filesystem. It leads you through creating a table, inserting
+ rows via the HBase shell, and then cleaning
+ up and shutting down your standalone HBase instance. The below exercise
+ should take no more than ten minutes (not including download time).
+
+
+ Download and unpack the latest stable release.
+
+ Choose a download site from this list of Apache Download
+ Mirrors. Click on suggested top link. This will take you to a
+ mirror of HBase Releases. Click on the folder named
+ stable and then download the file that ends in
+ .tar.gz to your local filesystem; e.g.
+ hbase-.tar.gz.
+
+ Decompress and untar your download and then change into the
+ unpacked directory.
+
+ $ tar xfz hbase-.tar.gz
+$ cd hbase-
+
+
+ At this point, you are ready to start HBase. But before starting
+ it, you might want to edit conf/hbase-site.xml and
+ set the directory you want HBase to write to,
+ hbase.rootdir.
+
+<?xml version="1.0"?>
+<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
+<configuration>
+ <property>
+ <name>hbase.rootdir</name>
+ <value>file:///DIRECTORY/hbase</value>
+ </property>
+</configuration>
+
+ Replace DIRECTORY in the above with a
+ path to a directory where you want HBase to store its data. By default,
+ hbase.rootdir is set to
+ /tmp/hbase-${user.name} which means you'll lose all
+ your data whenever your server reboots (Most operating systems clear
+ /tmp on restart).
+
+
+
+ Start HBase
+
+ Now start HBase:$ ./bin/start-hbase.sh
+starting Master, logging to logs/hbase-user-master-example.org.out
+
+ You should now have a running standalone HBase instance. In
+ standalone mode, HBase runs all daemons in the the one JVM; i.e. both
+ the HBase and ZooKeeper daemons. HBase logs can be found in the
+ logs subdirectory. Check them out especially if
+ HBase had trouble starting.
+
+
+ Is java installed?
+
+ All of the above presumes a 1.6 version of Oracle
+ java is installed on your machine and
+ available on your path; i.e. when you type
+ java, you see output that describes the
+ options the java program takes (HBase requires java 6). If this is not
+ the case, HBase will not start. Install java, edit
+ conf/hbase-env.sh, uncommenting the
+ JAVA_HOME line pointing it to your java install. Then,
+ retry the steps above.
+
+
+
+
+ Shell Exercises
+
+ Connect to your running HBase via the shell.
+
+ $ ./bin/hbase shell
+HBase Shell; enter 'help<RETURN>' for list of supported commands.
+Type "exit<RETURN>" to leave the HBase Shell
+Version: 0.90.0, r1001068, Fri Sep 24 13:55:42 PDT 2010
+
+hbase(main):001:0>
+
+ Type help and then
+ <RETURN> to see a listing of shell commands and
+ options. Browse at least the paragraphs at the end of the help emission
+ for the gist of how variables and command arguments are entered into the
+ HBase shell; in particular note how table names, rows, and columns,
+ etc., must be quoted.
+
+ Create a table named test with a single column family named cf.
+ Verify its creation by listing all tables and then insert some
+ values.
+
+ hbase(main):003:0> create 'test', 'cf'
+0 row(s) in 1.2200 seconds
+hbase(main):003:0> list 'test'
+..
+1 row(s) in 0.0550 seconds
+hbase(main):004:0> put 'test', 'row1', 'cf:a', 'value1'
+0 row(s) in 0.0560 seconds
+hbase(main):005:0> put 'test', 'row2', 'cf:b', 'value2'
+0 row(s) in 0.0370 seconds
+hbase(main):006:0> put 'test', 'row3', 'cf:c', 'value3'
+0 row(s) in 0.0450 seconds
+
+ Above we inserted 3 values, one at a time. The first insert is at
+ row1, column cf:a with a value of
+ value1. Columns in HBase are comprised of a column family prefix --
+ cf in this example -- followed by a colon and then a
+ column qualifier suffix (a in this case).
+
+ Verify the data insert.
+
+ Run a scan of the table by doing the following
+
+ hbase(main):007:0> scan 'test'
+ROW COLUMN+CELL
+row1 column=cf:a, timestamp=1288380727188, value=value1
+row2 column=cf:b, timestamp=1288380738440, value=value2
+row3 column=cf:c, timestamp=1288380747365, value=value3
+3 row(s) in 0.0590 seconds
+
+ Get a single row as follows
+
+ hbase(main):008:0> get 'test', 'row1'
+COLUMN CELL
+cf:a timestamp=1288380727188, value=value1
+1 row(s) in 0.0400 seconds
+
+ Now, disable and drop your table. This will clean up all done
+ above.
+
+ hbase(main):012:0> disable 'test'
+0 row(s) in 1.0930 seconds
+hbase(main):013:0> drop 'test'
+0 row(s) in 0.0770 seconds
+
+ Exit the shell by typing exit.
+
+ hbase(main):014:0> exit
+
+
+
+ Stopping HBase
+
+ Stop your hbase instance by running the stop script.
+
+ $ ./bin/stop-hbase.sh
+stopping hbase...............
+
+
+
+ Where to go next
+
+ The above described standalone setup is good for testing and
+ experiments only. Next move on to where we'll go into
+ depth on the different HBase run modes, requirements and critical
+ configurations needed setting up a distributed HBase deploy.
+
+
+
+
diff --git src/docbkx/ops_mgt.xml src/docbkx/ops_mgt.xml
new file mode 100644
index 0000000..369febb
--- /dev/null
+++ src/docbkx/ops_mgt.xml
@@ -0,0 +1,681 @@
+
+
+
+ HBase Operational Management
+ This chapter will cover operational tools and practices required of a running HBase cluster.
+ The subject of operations is related to the topics of , ,
+ and but is a distinct topic in itself.
+
+
+ HBase Tools and Utilities
+
+ Here we list HBase tools for administration, analysis, fixup, and
+ debugging.
+ Driver
+ There is a Driver class that is executed by the HBase jar can be used to invoke frequently accessed utilities. For example,
+HADOOP_CLASSPATH=`${HBASE_HOME}/bin/hbase classpath` ${HADOOP_HOME}/bin/hadoop jar ${HBASE_HOME}/hbase-VERSION.jar
+
+... will return...
+
+An example program must be given as the first argument.
+Valid program names are:
+ completebulkload: Complete a bulk data load.
+ copytable: Export a table from local cluster to peer cluster
+ export: Write table data to HDFS.
+ import: Import data written by Export.
+ importtsv: Import data in TSV format.
+ rowcounter: Count rows in HBase table
+ verifyrep: Compare the data from tables in two different clusters. WARNING: It doesn't work for incrementColumnValues'd cells since the timestamp is chan
+
+... for allowable program names.
+
+
+
+ HBase hbck
+ An fsck for your HBase install
+ To run hbck against your HBase cluster run
+ $ ./bin/hbase hbck
+ At the end of the commands output it prints OK
+ or INCONSISTENCY. If your cluster reports
+ inconsistencies, pass -details to see more detail emitted.
+ If inconsistencies, run hbck a few times because the
+ inconsistency may be transient (e.g. cluster is starting up or a region is
+ splitting).
+ Passing -fix may correct the inconsistency (This latter
+ is an experimental feature).
+
+ For more information, see .
+
+
+ HFile Tool
+ See .
+
+
+ WAL Tools
+
+
+ HLog tool
+
+ The main method on HLog offers manual
+ split and dump facilities. Pass it WALs or the product of a split, the
+ content of the recovered.edits. directory.
+
+ You can get a textual dump of a WAL file content by doing the
+ following:$ ./bin/hbase org.apache.hadoop.hbase.regionserver.wal.HLog --dump hdfs://example.org:8020/hbase/.logs/example.org,60020,1283516293161/10.10.21.10%3A60020.1283973724012The
+ return code will be non-zero if issues with the file so you can test
+ wholesomeness of file by redirecting STDOUT to
+ /dev/null and testing the program return.
+
+ Similarly you can force a split of a log file directory by
+ doing: $ ./bin/hbase org.apache.hadoop.hbase.regionserver.wal.HLog --split hdfs://example.org:8020/hbase/.logs/example.org,60020,1283516293161/
+
+
+ HLogPrettyPrinter
+ HLogPrettyPrinter is a tool with configurable options to print the contents of an HLog.
+
+
+
+
+
+ Compression Tool
+ See .
+
+
+ CopyTable
+
+ CopyTable is a utility that can copy part or of all of a table, either to the same cluster or another cluster. The usage is as follows:
+$ bin/hbase org.apache.hadoop.hbase.mapreduce.CopyTable [--starttime=X] [--endtime=Y] [--new.name=NEW] [--peer.adr=ADR] tablename
+
+
+
+ Options:
+
+ starttime Beginning of the time range. Without endtime means starttime to forever.
+ endtime End of the time range. Without endtime means starttime to forever.
+ versions Number of cell versions to copy.
+ new.name New table's name.
+ peer.adr Address of the peer cluster given in the format hbase.zookeeper.quorum:hbase.zookeeper.client.port:zookeeper.znode.parent
+ families Comma-separated list of ColumnFamilies to copy.
+ all.cells Also copy delete markers and uncollected deleted cells (advanced option).
+
+ Args:
+
+ tablename Name of table to copy.
+
+
+ Example of copying 'TestTable' to a cluster that uses replication for a 1 hour window:
+$ bin/hbase org.apache.hadoop.hbase.mapreduce.CopyTable
+--starttime=1265875194289 --endtime=1265878794289
+--peer.adr=server1,server2,server3:2181:/hbase TestTable
+
+ Note: caching for the input Scan is configured via hbase.client.scanner.caching in the job configuration.
+
+
+
+ Export
+ Export is a utility that will dump the contents of table to HDFS in a sequence file. Invoke via:
+$ bin/hbase org.apache.hadoop.hbase.mapreduce.Export <tablename> <outputdir> [<versions> [<starttime> [<endtime>]]]
+
+
+ Note: caching for the input Scan is configured via hbase.client.scanner.caching in the job configuration.
+
+
+
+ Import
+ Import is a utility that will load data that has been exported back into HBase. Invoke via:
+$ bin/hbase org.apache.hadoop.hbase.mapreduce.Import <tablename> <inputdir>
+
+
+
+
+ ImportTsv
+ ImportTsv is a utility that will load data in TSV format into HBase. It has two distinct usages: loading data from TSV format in HDFS
+ into HBase via Puts, and preparing StoreFiles to be loaded via the completebulkload.
+
+ To load data via Puts (i.e., non-bulk loading):
+$ bin/hbase org.apache.hadoop.hbase.mapreduce.ImportTsv -Dimporttsv.columns=a,b,c <tablename> <hdfs-inputdir>
+
+
+ To generate StoreFiles for bulk-loading:
+$ bin/hbase org.apache.hadoop.hbase.mapreduce.ImportTsv -Dimporttsv.columns=a,b,c -Dimporttsv.bulk.output=hdfs://storefile-outputdir <tablename> <hdfs-data-inputdir>
+
+
+ These generated StoreFiles can be loaded into HBase via .
+
+ ImportTsv Options
+ Running ImportTsv with no arguments prints brief usage information:
+
+Usage: importtsv -Dimporttsv.columns=a,b,c <tablename> <inputdir>
+
+Imports the given input directory of TSV data into the specified table.
+
+The column names of the TSV data must be specified using the -Dimporttsv.columns
+option. This option takes the form of comma-separated column names, where each
+column name is either a simple column family, or a columnfamily:qualifier. The special
+column name HBASE_ROW_KEY is used to designate that this column should be used
+as the row key for each imported record. You must specify exactly one column
+to be the row key, and you must specify a column name for every column that exists in the
+input data.
+
+By default importtsv will load data directly into HBase. To instead generate
+HFiles of data to prepare for a bulk data load, pass the option:
+ -Dimporttsv.bulk.output=/path/for/output
+ Note: if you do not use this option, then the target table must already exist in HBase
+
+Other options that may be specified with -D include:
+ -Dimporttsv.skip.bad.lines=false - fail if encountering an invalid line
+ '-Dimporttsv.separator=|' - eg separate on pipes instead of tabs
+ -Dimporttsv.timestamp=currentTimeAsLong - use the specified timestamp for the import
+ -Dimporttsv.mapper.class=my.Mapper - A user-defined Mapper to use instead of org.apache.hadoop.hbase.mapreduce.TsvImporterMapper
+
+
+ ImportTsv Example
+ For example, assume that we are loading data into a table called 'datatsv' with a ColumnFamily called 'd' with two columns "c1" and "c2".
+
+ Assume that an input file exists as follows:
+
+row1 c1 c2
+row2 c1 c2
+row3 c1 c2
+row4 c1 c2
+row5 c1 c2
+row6 c1 c2
+row7 c1 c2
+row8 c1 c2
+row9 c1 c2
+row10 c1 c2
+
+
+ For ImportTsv to use this imput file, the command line needs to look like this:
+
+ HADOOP_CLASSPATH=`${HBASE_HOME}/bin/hbase classpath` ${HADOOP_HOME}/bin/hadoop jar ${HBASE_HOME}/hbase-VERSION.jar importtsv -Dimporttsv.columns=HBASE_ROW_KEY,d:c1,d:c2 -Dimporttsv.bulk.output=hdfs://storefileoutput datatsv hdfs://inputfile
+
+ ... and in this example the first column is the rowkey, which is why the HBASE_ROW_KEY is used. The second and third columns in the file will be imported as "d:c1" and "d:c2", respectively.
+
+
+ ImportTsv Warning
+ If you have preparing a lot of data for bulk loading, make sure the target HBase table is pre-split appropriately.
+
+
+ See Also
+ For more information about bulk-loading HFiles into HBase, see
+
+
+
+
+ CompleteBulkLoad
+ The completebulkload utility will move generated StoreFiles into an HBase table. This utility is often used
+ in conjunction with output from .
+
+ There are two ways to invoke this utility, with explicit classname and via the driver:
+$ bin/hbase org.apache.hadoop.hbase.mapreduce.LoadIncrementalHFile <hdfs://storefileoutput> <tablename>
+
+.. and via the Driver..
+HADOOP_CLASSPATH=`${HBASE_HOME}/bin/hbase classpath` ${HADOOP_HOME}/bin/hadoop jar ${HBASE_HOME}/hbase-VERSION.jar completebulkload <hdfs://storefileoutput> <tablename>
+
+
+ For more information about bulk-loading HFiles into HBase, see .
+
+
+
+ WALPlayer
+ WALPlayer is a utility to replay WAL files into HBase.
+
+ The WAL can be replayed for a set of tables or all tables, and a timerange can be provided (in milliseconds). The WAL is filtered to this set of tables. The output can optionally be mapped to another set of tables.
+
+ WALPlayer can also generate HFiles for later bulk importing, in that case only a single table and no mapping can be specified.
+
+ Invoke via:
+$ bin/hbase org.apache.hadoop.hbase.mapreduce.WALPlayer [options] <wal inputdir> <tables> [<tableMappings>]>
+
+
+ For example:
+$ bin/hbase org.apache.hadoop.hbase.mapreduce.WALPlayer /backuplogdir oldTable1,oldTable2 newTable1,newTable2
+
+
+
+
+ RowCounter
+ RowCounter is a utility that will count all the rows of a table. This is a good utility to use
+ as a sanity check to ensure that HBase can read all the blocks of a table if there are any concerns of metadata inconsistency.
+$ bin/hbase org.apache.hadoop.hbase.mapreduce.RowCounter <tablename> [<column1> <column2>...]
+
+
+ Note: caching for the input Scan is configured via hbase.client.scanner.caching in the job configuration.
+
+
+
+
+
+
+ Region Management
+
+ Major Compaction
+ Major compactions can be requested via the HBase shell or HBaseAdmin.majorCompact.
+
+ Note: major compactions do NOT do region merges. See for more information about compactions.
+
+
+
+
+ Merge
+ Merge is a utility that can merge adjoining regions in the same table (see org.apache.hadoop.hbase.util.Merge).
+$ bin/hbase org.apache.hbase.util.Merge <tablename> <region1> <region2>
+
+ If you feel you have too many regions and want to consolidate them, Merge is the utility you need. Merge must
+ run be done when the cluster is down.
+ See the O'Reilly HBase Book for
+ an example of usage.
+
+ Additionally, there is a Ruby script attached to HBASE-1621
+ for region merging.
+
+
+
+
+ Node Management
+ Node Decommission
+ You can stop an individual RegionServer by running the following
+ script in the HBase directory on the particular node:
+ $ ./bin/hbase-daemon.sh stop regionserver
+ The RegionServer will first close all regions and then shut itself down.
+ On shutdown, the RegionServer's ephemeral node in ZooKeeper will expire.
+ The master will notice the RegionServer gone and will treat it as
+ a 'crashed' server; it will reassign the nodes the RegionServer was carrying.
+ Disable the Load Balancer before Decommissioning a node
+ If the load balancer runs while a node is shutting down, then
+ there could be contention between the Load Balancer and the
+ Master's recovery of the just decommissioned RegionServer.
+ Avoid any problems by disabling the balancer first.
+ See below.
+
+
+
+
+ A downside to the above stop of a RegionServer is that regions could be offline for
+ a good period of time. Regions are closed in order. If many regions on the server, the
+ first region to close may not be back online until all regions close and after the master
+ notices the RegionServer's znode gone. In HBase 0.90.2, we added facility for having
+ a node gradually shed its load and then shutdown itself down. HBase 0.90.2 added the
+ graceful_stop.sh script. Here is its usage:
+ $ ./bin/graceful_stop.sh
+Usage: graceful_stop.sh [--config &conf-dir>] [--restart] [--reload] [--thrift] [--rest] &hostname>
+ thrift If we should stop/start thrift before/after the hbase stop/start
+ rest If we should stop/start rest before/after the hbase stop/start
+ restart If we should restart after graceful stop
+ reload Move offloaded regions back on to the stopped server
+ debug Move offloaded regions back on to the stopped server
+ hostname Hostname of server we are to stop
+
+
+ To decommission a loaded RegionServer, run the following:
+ $ ./bin/graceful_stop.sh HOSTNAME
+ where HOSTNAME is the host carrying the RegionServer
+ you would decommission.
+ On HOSTNAME
+ The HOSTNAME passed to graceful_stop.sh
+ must match the hostname that hbase is using to identify RegionServers.
+ Check the list of RegionServers in the master UI for how HBase is
+ referring to servers. Its usually hostname but can also be FQDN.
+ Whatever HBase is using, this is what you should pass the
+ graceful_stop.sh decommission
+ script. If you pass IPs, the script is not yet smart enough to make
+ a hostname (or FQDN) of it and so it will fail when it checks if server is
+ currently running; the graceful unloading of regions will not run.
+
+ The graceful_stop.sh script will move the regions off the
+ decommissioned RegionServer one at a time to minimize region churn.
+ It will verify the region deployed in the new location before it
+ will moves the next region and so on until the decommissioned server
+ is carrying zero regions. At this point, the graceful_stop.sh
+ tells the RegionServer stop. The master will at this point notice the
+ RegionServer gone but all regions will have already been redeployed
+ and because the RegionServer went down cleanly, there will be no
+ WAL logs to split.
+ Load Balancer
+
+ It is assumed that the Region Load Balancer is disabled while the
+ graceful_stop script runs (otherwise the balancer
+ and the decommission script will end up fighting over region deployments).
+ Use the shell to disable the balancer:
+ hbase(main):001:0> balance_switch false
+true
+0 row(s) in 0.3590 seconds
+This turns the balancer OFF. To reenable, do:
+ hbase(main):001:0> balance_switch true
+false
+0 row(s) in 0.3590 seconds
+
+
+
+
+
+ Rolling Restart
+
+ You can also ask this script to restart a RegionServer after the shutdown
+ AND move its old regions back into place. The latter you might do to
+ retain data locality. A primitive rolling restart might be effected by
+ running something like the following:
+ $ for i in `cat conf/regionservers|sort`; do ./bin/graceful_stop.sh --restart --reload --debug $i; done &> /tmp/log.txt &
+
+ Tail the output of /tmp/log.txt to follow the scripts
+ progress. The above does RegionServers only. Be sure to disable the
+ load balancer before doing the above. You'd need to do the master
+ update separately. Do it before you run the above script.
+ Here is a pseudo-script for how you might craft a rolling restart script:
+
+ Untar your release, make sure of its configuration and
+ then rsync it across the cluster. If this is 0.90.2, patch it
+ with HBASE-3744 and HBASE-3756.
+
+
+
+ Run hbck to ensure the cluster consistent
+ $ ./bin/hbase hbck
+ Effect repairs if inconsistent.
+
+
+
+ Restart the Master: $ ./bin/hbase-daemon.sh stop master; ./bin/hbase-daemon.sh start master
+
+
+
+
+ Disable the region balancer:$ echo "balance_switch false" | ./bin/hbase shell
+
+
+
+ Run the graceful_stop.sh script per RegionServer. For example:
+ $ for i in `cat conf/regionservers|sort`; do ./bin/graceful_stop.sh --restart --reload --debug $i; done &> /tmp/log.txt &
+
+ If you are running thrift or rest servers on the RegionServer, pass --thrift or --rest options (See usage
+ for graceful_stop.sh script).
+
+
+
+ Restart the Master again. This will clear out dead servers list and reenable the balancer.
+
+
+
+ Run hbck to ensure the cluster is consistent.
+
+
+
+
+
+
+
+
+ HBase Metrics
+
+ Metric Setup
+ See Metrics for
+ an introduction and how to enable Metrics emission.
+
+
+
+ RegionServer Metrics
+ hbase.regionserver.blockCacheCount
+ Block cache item count in memory. This is the number of blocks of StoreFiles (HFiles) in the cache.
+
+ hbase.regionserver.blockCacheEvictedCount
+ Number of blocks that had to be evicted from the block cache due to heap size constraints.
+
+ hbase.regionserver.blockCacheFree
+ Block cache memory available (bytes).
+
+ hbase.regionserver.blockCacheHitCachingRatio
+ Block cache hit caching ratio (0 to 100). The cache-hit ratio for reads configured to look in the cache (i.e., cacheBlocks=true).
+
+ hbase.regionserver.blockCacheHitCount
+ Number of blocks of StoreFiles (HFiles) read from the cache.
+
+ hbase.regionserver.blockCacheHitRatio
+ Block cache hit ratio (0 to 100). Includes all read requests, although those with cacheBlocks=false
+ will always read from disk and be counted as a "cache miss".
+
+ hbase.regionserver.blockCacheMissCount
+ Number of blocks of StoreFiles (HFiles) requested but not read from the cache.
+
+ hbase.regionserver.blockCacheSize
+ Block cache size in memory (bytes). i.e., memory in use by the BlockCache
+
+ hbase.regionserver.compactionQueueSize
+ Size of the compaction queue. This is the number of Stores in the RegionServer that have been targeted for compaction.
+
+ hbase.regionserver.flushQueueSize
+ Number of enqueued regions in the MemStore awaiting flush.
+
+ hbase.regionserver.fsReadLatency_avg_time
+ Filesystem read latency (ms). This is the average time to read from HDFS.
+
+ hbase.regionserver.fsReadLatency_num_ops
+ Filesystem read operations.
+
+ hbase.regionserver.fsSyncLatency_avg_time
+ Filesystem sync latency (ms). Latency to sync the write-ahead log records to the filesystem.
+
+ hbase.regionserver.fsSyncLatency_num_ops
+ Number of operations to sync the write-ahead log records to the filesystem.
+
+ hbase.regionserver.fsWriteLatency_avg_time
+ Filesystem write latency (ms). Total latency for all writers, including StoreFiles and write-head log.
+
+ hbase.regionserver.fsWriteLatency_num_ops
+ Number of filesystem write operations, including StoreFiles and write-ahead log.
+
+ hbase.regionserver.memstoreSizeMB
+ Sum of all the memstore sizes in this RegionServer (MB)
+
+ hbase.regionserver.regions
+ Number of regions served by the RegionServer
+
+ hbase.regionserver.requests
+ Total number of read and write requests. Requests correspond to RegionServer RPC calls, thus a single Get will result in 1 request, but a Scan with caching set to 1000 will result in 1 request for each 'next' call (i.e., not each row). A bulk-load request will constitute 1 request per HFile.
+
+ hbase.regionserver.storeFileIndexSizeMB
+ Sum of all the StoreFile index sizes in this RegionServer (MB)
+
+ hbase.regionserver.stores
+ Number of Stores open on the RegionServer. A Store corresponds to a ColumnFamily. For example, if a table (which contains the column family) has 3 regions on a RegionServer, there will be 3 stores open for that column family.
+
+ hbase.regionserver.storeFiles
+ Number of StoreFiles open on the RegionServer. A store may have more than one StoreFile (HFile).
+
+
+
+
+
+ HBase Monitoring
+
+ Overview
+ The following metrics are arguably the most important to monitor for each RegionServer for
+ "macro monitoring", preferably with a system like OpenTSDB.
+ If your cluster is having performance issues it's likely that you'll see something unusual with
+ this group.
+
+ HBase:
+
+ Requests
+ Compactions queue
+
+
+ OS:
+
+ IO Wait
+ User CPU
+
+
+ Java:
+
+ GC
+
+
+
+
+
+ For more information on HBase metrics, see .
+
+
+
+
+ Slow Query Log
+The HBase slow query log consists of parseable JSON structures describing the properties of those client operations (Gets, Puts, Deletes, etc.) that either took too long to run, or produced too much output. The thresholds for "too long to run" and "too much output" are configurable, as described below. The output is produced inline in the main region server logs so that it is easy to discover further details from context with other logged events. It is also prepended with identifying tags (responseTooSlow), (responseTooLarge), (operationTooSlow), and (operationTooLarge) in order to enable easy filtering with grep, in case the user desires to see only slow queries.
+
+
+Configuration
+There are two configuration knobs that can be used to adjust the thresholds for when queries are logged.
+
+
+
+
+hbase.ipc.warn.response.time Maximum number of milliseconds that a query can be run without being logged. Defaults to 10000, or 10 seconds. Can be set to -1 to disable logging by time.
+
+hbase.ipc.warn.response.size Maximum byte size of response that a query can return without being logged. Defaults to 100 megabytes. Can be set to -1 to disable logging by size.
+
+
+
+
+Metrics
+The slow query log exposes to metrics to JMX.
+hadoop.regionserver_rpc_slowResponse a global metric reflecting the durations of all responses that triggered logging.
+hadoop.regionserver_rpc_methodName.aboveOneSec A metric reflecting the durations of all responses that lasted for more than one second.
+
+
+
+
+Output
+The output is tagged with operation e.g. (operationTooSlow) if the call was a client operation, such as a Put, Get, or Delete, which we expose detailed fingerprint information for. If not, it is tagged (responseTooSlow) and still produces parseable JSON output, but with less verbose information solely regarding its duration and size in the RPC itself. TooLarge is substituted for TooSlow if the response size triggered the logging, with TooLarge appearing even in the case that both size and duration triggered logging.
+
+
+Example
+
+2011-09-08 10:01:25,824 WARN org.apache.hadoop.ipc.HBaseServer: (operationTooSlow): {"tables":{"riley2":{"puts":[{"totalColumns":11,"families":{"actions":[{"timestamp":1315501284459,"qualifier":"0","vlen":9667580},{"timestamp":1315501284459,"qualifier":"1","vlen":10122412},{"timestamp":1315501284459,"qualifier":"2","vlen":11104617},{"timestamp":1315501284459,"qualifier":"3","vlen":13430635}]},"row":"cfcd208495d565ef66e7dff9f98764da:0"}],"families":["actions"]}},"processingtimems":956,"client":"10.47.34.63:33623","starttimems":1315501284456,"queuetimems":0,"totalPuts":1,"class":"HRegionServer","responsesize":0,"method":"multiPut"}
+
+
+Note that everything inside the "tables" structure is output produced by MultiPut's fingerprint, while the rest of the information is RPC-specific, such as processing time and client IP/port. Other client operations follow the same pattern and the same general structure, with necessary differences due to the nature of the individual operations. In the case that the call is not a client operation, that detailed fingerprint information will be completely absent.
+
+
+This particular example, for example, would indicate that the likely cause of slowness is simply a very large (on the order of 100MB) multiput, as we can tell by the "vlen," or value length, fields of each put in the multiPut.
+
+
+
+
+
+
+
+
+
+ Cluster Replication
+ See Cluster Replication.
+
+
+
+ HBase Backup
+ There are two broad strategies for performing HBase backups: backing up with a full cluster shutdown, and backing up on a live cluster.
+ Each approach has pros and cons.
+
+ For additional information, see HBase Backup Options over on the Sematext Blog.
+
+ Full Shutdown Backup
+ Some environments can tolerate a periodic full shutdown of their HBase cluster, for example if it is being used a back-end analytic capacity
+ and not serving front-end web-pages. The benefits are that the NameNode/Master are RegionServers are down, so there is no chance of missing
+ any in-flight changes to either StoreFiles or metadata. The obvious con is that the cluster is down. The steps include:
+
+ Stop HBase
+
+
+
+ Distcp
+ Distcp could be used to either copy the contents of the HBase directory in HDFS to either the same cluster in another directory, or
+ to a different cluster.
+
+ Note: Distcp works in this situation because the cluster is down and there are no in-flight edits to files.
+ Distcp-ing of files in the HBase directory is not generally recommended on a live cluster.
+
+
+ Restore (if needed)
+ The backup of the hbase directory from HDFS is copied onto the 'real' hbase directory via distcp. The act of copying these files
+ creates new HDFS metadata, which is why a restore of the NameNode edits from the time of the HBase backup isn't required for this kind of
+ restore, because it's a restore (via distcp) of a specific HDFS directory (i.e., the HBase part) not the entire HDFS file-system.
+
+
+
+ Live Cluster Backup - Replication
+ This approach assumes that there is a second cluster.
+ See the HBase page on replication for more information.
+
+
+ Live Cluster Backup - CopyTable
+ The utility could either be used to copy data from one table to another on the
+ same cluster, or to copy data to another table on another cluster.
+
+ Since the cluster is up, there is a risk that edits could be missed in the copy process.
+
+
+ Live Cluster Backup - Export
+ The approach dumps the content of a table to HDFS on the same cluster. To restore the data, the
+ utility would be used.
+
+ Since the cluster is up, there is a risk that edits could be missed in the export process.
+
+
+
+ Capacity Planning
+ Storage
+ A common question for HBase administrators is estimating how much storage will be required for an HBase cluster.
+ There are several apsects to consider, the most important of which is what data load into the cluster. Start
+ with a solid understanding of how HBase handles data internally (KeyValue).
+
+ KeyValue
+ HBase storage will be dominated by KeyValues. See and for
+ how HBase stores data internally.
+
+ It is critical to understand that there is a KeyValue instance for every attribute stored in a row, and the
+ rowkey-length, ColumnFamily name-length and attribute lengths will drive the size of the database more than any other
+ factor.
+
+
+ StoreFiles and Blocks
+ KeyValue instances are aggregated into blocks, and the blocksize is configurable on a per-ColumnFamily basis.
+ Blocks are aggregated into StoreFile's. See .
+
+
+ HDFS Block Replication
+ Because HBase runs on top of HDFS, factor in HDFS block replication into storage calculations.
+
+
+
+ Regions
+ Another common question for HBase administrators is determining the right number of regions per
+ RegionServer. This affects both storage and hardware planning. See .
+
+
+
+
+
diff --git src/docbkx/performance.xml src/docbkx/performance.xml
new file mode 100644
index 0000000..41a8916
--- /dev/null
+++ src/docbkx/performance.xml
@@ -0,0 +1,547 @@
+
+
+
+ Performance Tuning
+
+
+ Operating System
+
+ Memory
+ RAM, RAM, RAM. Don't starve HBase.
+
+
+ 64-bit
+ Use a 64-bit platform (and 64-bit JVM).
+
+
+ Swapping
+ Watch out for swapping. Set swappiness to 0.
+
+
+
+ Network
+
+ Perhaps the most important factor in avoiding network issues degrading Hadoop and HBbase performance is the switching hardware
+ that is used, decisions made early in the scope of the project can cause major problems when you double or triple the size of your cluster (or more).
+
+
+ Important items to consider:
+
+ Switching capacity of the device
+ Number of systems connected
+ Uplink capacity
+
+
+
+ Single Switch
+ The single most important factor in this configuration is that the switching capacity of the hardware is capable of
+ handling the traffic which can be generated by all systems connected to the switch. Some lower priced commodity hardware
+ can have a slower switching capacity than could be utilized by a full switch.
+
+
+
+ Multiple Switches
+ Multiple switches are a potential pitfall in the architecture. The most common configuration of lower priced hardware is a
+ simple 1Gbps uplink from one switch to another. This often overlooked pinch point can easily become a bottleneck for cluster communication.
+ Especially with MapReduce jobs that are both reading and writing a lot of data the communication across this uplink could be saturated.
+
+ Mitigation of this issue is fairly simple and can be accomplished in multiple ways:
+
+ Use appropriate hardware for the scale of the cluster which you're attempting to build.
+ Use larger single switch configurations i.e. single 48 port as opposed to 2x 24 port
+ Configure port trunking for uplinks to utilize multiple interfaces to increase cross switch bandwidth.
+
+
+
+
+ Multiple Racks
+ Multiple rack configurations carry the same potential issues as multiple switches, and can suffer performance degradation from two main areas:
+
+ Poor switch capacity performance
+ Insufficient uplink to another rack
+
+ If the the switches in your rack have appropriate switching capacity to handle all the hosts at full speed, the next most likely issue will be caused by homing
+ more of your cluster across racks. The easiest way to avoid issues when spanning multiple racks is to use port trunking to create a bonded uplink to other racks.
+ The downside of this method however, is in the overhead of ports that could potentially be used. An example of this is, creating an 8Gbps port channel from rack
+ A to rack B, using 8 of your 24 ports to communicate between racks gives you a poor ROI, using too few however can mean you're not getting the most out of your cluster.
+
+ Using 10Gbe links between racks will greatly increase performance, and assuming your switches support a 10Gbe uplink or allow for an expansion card will allow you to
+ save your ports for machines as opposed to uplinks.
+
+
+
+ Network Interfaces
+ Are all the network interfaces functioning correctly? Are you sure? See the Troubleshooting Case Study in .
+
+
+
+
+
+ Java
+
+
+ The Garbage Collector and HBase
+
+
+ Long GC pauses
+
+ In his presentation, Avoiding
+ Full GCs with MemStore-Local Allocation Buffers, Todd Lipcon
+ describes two cases of stop-the-world garbage collections common in
+ HBase, especially during loading; CMS failure modes and old generation
+ heap fragmentation brought. To address the first, start the CMS
+ earlier than default by adding
+ -XX:CMSInitiatingOccupancyFraction and setting it down
+ from defaults. Start at 60 or 70 percent (The lower you bring down the
+ threshold, the more GCing is done, the more CPU used). To address the
+ second fragmentation issue, Todd added an experimental facility,
+ MSLAB, that
+ must be explicitly enabled in HBase 0.90.x (Its defaulted to be on in
+ 0.92.x HBase). See hbase.hregion.memstore.mslab.enabled
+ to true in your Configuration. See the cited
+ slides for background and detailThe latest jvms do better
+ regards fragmentation so make sure you are running a recent release.
+ Read down in the message,
+ Identifying concurrent mode failures caused by fragmentation..
+ For more information about GC logs, see .
+
+
+
+
+
+
+ HBase Configurations
+
+ See .
+
+
+ Number of Regions
+
+ The number of regions for an HBase table is driven by the . Also, see the architecture
+ section on
+
+
+
+ Managing Compactions
+
+ For larger systems, managing compactions and splits may be
+ something you want to consider.
+
+
+
+ hbase.regionserver.handler.count
+ See .
+
+
+
+ hfile.block.cache.size
+ See .
+ A memory setting for the RegionServer process.
+
+
+
+ hbase.regionserver.global.memstore.upperLimit
+ See .
+ This memory setting is often adjusted for the RegionServer process depending on needs.
+
+
+
+ hbase.regionserver.global.memstore.lowerLimit
+ See .
+ This memory setting is often adjusted for the RegionServer process depending on needs.
+
+
+
+ hbase.hstore.blockingStoreFiles
+ See .
+ If there is blocking in the RegionServer logs, increasing this can help.
+
+
+
+ hbase.hregion.memstore.block.multiplier
+ See .
+ If there is enough RAM, increasing this can help.
+
+
+
+
+
+ ZooKeeper
+ See for information on configuring ZooKeeper, and see the part
+ about having a dedicated disk.
+
+
+
+ Schema Design
+
+
+ Number of Column Families
+ See .
+
+
+ Key and Attribute Lengths
+ See . See also for
+ compression caveats.
+
+ Table RegionSize
+ The regionsize can be set on a per-table basis via setFileSize on
+ HTableDescriptor in the
+ event where certain tables require different regionsizes than the configured default regionsize.
+
+ See for more information.
+
+
+
+ Bloom Filters
+ Bloom Filters can be enabled per-ColumnFamily.
+ Use HColumnDescriptor.setBloomFilterType(NONE | ROW |
+ ROWCOL) to enable blooms per Column Family. Default =
+ NONE for no bloom filters. If
+ ROW, the hash of the row will be added to the bloom
+ on each insert. If ROWCOL, the hash of the row +
+ column family + column family qualifier will be added to the bloom on
+ each key insert.
+ See HColumnDescriptor and
+ for more information.
+
+
+ ColumnFamily BlockSize
+ The blocksize can be configured for each ColumnFamily in a table, and this defaults to 64k. Larger cell values require larger blocksizes.
+ There is an inverse relationship between blocksize and the resulting StoreFile indexes (i.e., if the blocksize is doubled then the resulting
+ indexes should be roughly halved).
+
+ See HColumnDescriptor
+ and for more information.
+
+
+
+ In-Memory ColumnFamilies
+ ColumnFamilies can optionally be defined as in-memory. Data is still persisted to disk, just like any other ColumnFamily.
+ In-memory blocks have the highest priority in the , but it is not a guarantee that the entire table
+ will be in memory.
+
+ See HColumnDescriptor for more information.
+
+
+
+ Compression
+ Production systems should use compression with their ColumnFamily definitions. See for more information.
+
+ However...
+ Compression deflates data on disk. When it's in-memory (e.g., in the
+ MemStore) or on the wire (e.g., transferring between RegionServer and Client) it's inflated.
+ So while using ColumnFamily compression is a best practice, but it's not going to completely eliminate
+ the impact of over-sized Keys, over-sized ColumnFamily names, or over-sized Column names.
+
+ See on for schema design tips, and for more information on HBase stores data internally.
+
+
+
+
+
+
+ Writing to HBase
+
+
+ Batch Loading
+ Use the bulk load tool if you can. See
+ .
+ Otherwise, pay attention to the below.
+
+
+
+
+
+ Table Creation: Pre-Creating Regions
+
+
+Tables in HBase are initially created with one region by default. For bulk imports, this means that all clients will write to the same region until it is large enough to split and become distributed across the cluster. A useful pattern to speed up the bulk import process is to pre-create empty regions. Be somewhat conservative in this, because too-many regions can actually degrade performance. An example of pre-creation using hex-keys is as follows (note: this example may need to be tweaked to the individual applications keys):
+
+
+public static boolean createTable(HBaseAdmin admin, HTableDescriptor table, byte[][] splits)
+throws IOException {
+ try {
+ admin.createTable( table, splits );
+ return true;
+ } catch (TableExistsException e) {
+ logger.info("table " + table.getNameAsString() + " already exists");
+ // the table already exists...
+ return false;
+ }
+}
+
+public static byte[][] getHexSplits(String startKey, String endKey, int numRegions) {
+ byte[][] splits = new byte[numRegions-1][];
+ BigInteger lowestKey = new BigInteger(startKey, 16);
+ BigInteger highestKey = new BigInteger(endKey, 16);
+ BigInteger range = highestKey.subtract(lowestKey);
+ BigInteger regionIncrement = range.divide(BigInteger.valueOf(numRegions));
+ lowestKey = lowestKey.add(regionIncrement);
+ for(int i=0; i < numRegions-1;i++) {
+ BigInteger key = lowestKey.add(regionIncrement.multiply(BigInteger.valueOf(i)));
+ byte[] b = String.format("%016x", key).getBytes();
+ splits[i] = b;
+ }
+ return splits;
+}
+
+
+
+
+ Table Creation: Deferred Log Flush
+
+
+The default behavior for Puts using the Write Ahead Log (WAL) is that HLog edits will be written immediately. If deferred log flush is used,
+WAL edits are kept in memory until the flush period. The benefit is aggregated and asynchronous HLog- writes, but the potential downside is that if
+ the RegionServer goes down the yet-to-be-flushed edits are lost. This is safer, however, than not using WAL at all with Puts.
+
+
+Deferred log flush can be configured on tables via HTableDescriptor. The default value of hbase.regionserver.optionallogflushinterval is 1000ms.
+
+
+
+
+ HBase Client: AutoFlush
+
+ When performing a lot of Puts, make sure that setAutoFlush is set
+ to false on your HTable
+ instance. Otherwise, the Puts will be sent one at a time to the
+ RegionServer. Puts added via htable.add(Put) and htable.add( <List> Put)
+ wind up in the same write buffer. If autoFlush = false,
+ these messages are not sent until the write-buffer is filled. To
+ explicitly flush the messages, call flushCommits.
+ Calling close on the HTable
+ instance will invoke flushCommits.
+
+
+ HBase Client: Turn off WAL on Puts
+ A frequently discussed option for increasing throughput on Puts is to call writeToWAL(false). Turning this off means
+ that the RegionServer will not write the Put to the Write Ahead Log,
+ only into the memstore, HOWEVER the consequence is that if there
+ is a RegionServer failure there will be data loss.
+ If writeToWAL(false) is used, do so with extreme caution. You may find in actuality that
+ it makes little difference if your load is well distributed across the cluster.
+
+ In general, it is best to use WAL for Puts, and where loading throughput
+ is a concern to use bulk loading techniques instead.
+
+
+
+ HBase Client: Group Puts by RegionServer
+ In addition to using the writeBuffer, grouping Puts by RegionServer can reduce the number of client RPC calls per writeBuffer flush.
+ There is a utility HTableUtil currently on TRUNK that does this, but you can either copy that or implement your own verison for
+ those still on 0.90.x or earlier.
+
+
+
+ MapReduce: Skip The Reducer
+ When writing a lot of data to an HBase table from a MR job (e.g., with TableOutputFormat), and specifically where Puts are being emitted
+ from the Mapper, skip the Reducer step. When a Reducer step is used, all of the output (Puts) from the Mapper will get spooled to disk, then sorted/shuffled to other
+ Reducers that will most likely be off-node. It's far more efficient to just write directly to HBase.
+
+ For summary jobs where HBase is used as a source and a sink, then writes will be coming from the Reducer step (e.g., summarize values then write out result).
+ This is a different processing problem than from the the above case.
+
+
+
+
+ Anti-Pattern: One Hot Region
+ If all your data is being written to one region at a time, then re-read the
+ section on processing timeseries data.
+ Also, if you are pre-splitting regions and all your data is still winding up in a single region even though
+ your keys aren't monotonically increasing, confirm that your keyspace actually works with the split strategy. There are a
+ variety of reasons that regions may appear "well split" but won't work with your data. As
+ the HBase client communicates directly with the RegionServers, this can be obtained via
+ HTable.getRegionLocation.
+
+ See , as well as
+
+
+
+
+
+ Reading from HBase
+
+
+ Scan Caching
+
+ If HBase is used as an input source for a MapReduce job, for
+ example, make sure that the input Scan
+ instance to the MapReduce job has setCaching set to something greater
+ than the default (which is 1). Using the default value means that the
+ map-task will make call back to the region-server for every record
+ processed. Setting this value to 500, for example, will transfer 500
+ rows at a time to the client to be processed. There is a cost/benefit to
+ have the cache value be large because it costs more in memory for both
+ client and RegionServer, so bigger isn't always better.
+
+ Scan Caching in MapReduce Jobs
+ Scan settings in MapReduce jobs deserve special attention. Timeouts can result (e.g., UnknownScannerException)
+ in Map tasks if it takes longer to process a batch of records before the client goes back to the RegionServer for the
+ next set of data. This problem can occur because there is non-trivial processing occuring per row. If you process
+ rows quickly, set caching higher. If you process rows more slowly (e.g., lots of transformations per row, writes),
+ then set caching lower.
+
+ Timeouts can also happen in a non-MapReduce use case (i.e., single threaded HBase client doing a Scan), but the
+ processing that is often performed in MapReduce jobs tends to exacerbate this issue.
+
+
+
+
+ Scan Attribute Selection
+
+ Whenever a Scan is used to process large numbers of rows (and especially when used
+ as a MapReduce source), be aware of which attributes are selected. If scan.addFamily is called
+ then all of the attributes in the specified ColumnFamily will be returned to the client.
+ If only a small number of the available attributes are to be processed, then only those attributes should be specified
+ in the input scan because attribute over-selection is a non-trivial performance penalty over large datasets.
+
+
+
+ MapReduce - Input Splits
+ For MapReduce jobs that use HBase tables as a source, if there a pattern where the "slow" map tasks seem to
+ have the same Input Split (i.e., the RegionServer serving the data), see the
+ Troubleshooting Case Study in .
+
+
+
+
+ Close ResultScanners
+
+ This isn't so much about improving performance but rather
+ avoiding performance problems. If you forget to
+ close ResultScanners
+ you can cause problems on the RegionServers. Always have ResultScanner
+ processing enclosed in try/catch blocks...
+Scan scan = new Scan();
+// set attrs...
+ResultScanner rs = htable.getScanner(scan);
+try {
+ for (Result r = rs.next(); r != null; r = rs.next()) {
+ // process result...
+} finally {
+ rs.close(); // always close the ResultScanner!
+}
+htable.close();
+
+
+
+ Block Cache
+
+ Scan
+ instances can be set to use the block cache in the RegionServer via the
+ setCacheBlocks method. For input Scans to MapReduce jobs, this should be
+ false. For frequently accessed rows, it is advisable to use the block
+ cache.
+
+
+ Optimal Loading of Row Keys
+ When performing a table scan
+ where only the row keys are needed (no families, qualifiers, values or timestamps), add a FilterList with a
+ MUST_PASS_ALL operator to the scanner using setFilter. The filter list
+ should include both a FirstKeyOnlyFilter
+ and a KeyOnlyFilter.
+ Using this filter combination will result in a worst case scenario of a RegionServer reading a single value from disk
+ and minimal network traffic to the client for a single row.
+
+
+
+ Concurrency: Monitor Data Spread
+ When performing a high number of concurrent reads, monitor the data spread of the target tables. If the target table(s) have
+ too few regions then the reads could likely be served from too few nodes.
+ See , as well as
+
+
+
+
+
+ Deleting from HBase
+
+ Using HBase Tables as Queues
+ HBase tables are sometimes used as queues. In this case, special care must be taken to regularly perform major compactions on tables used in
+ this manner. As is documented in , marking rows as deleted creates additional StoreFiles which then need to be processed
+ on reads. Tombstones only get cleaned up with major compactions.
+
+ See also and HBaseAdmin.majorCompact.
+
+
+
+ Delete RPC Behavior
+ Be aware that htable.delete(Delete) doesn't use the writeBuffer. It will execute an RegionServer RPC with each invocation.
+ For a large number of deletes, consider htable.delete(List).
+
+ See
+
+
+
+
+ HDFS
+ Because HBase runs on it is important to understand how it works and how it affects
+ HBase.
+
+ Current Issues With Low-Latency Reads
+ The original use-case for HDFS was batch processing. As such, there low-latency reads were historically not a priority.
+ With the increased adoption of HBase this is changing, and several improvements are already in development.
+ See the
+ Umbrella Jira Ticket for HDFS Improvements for HBase.
+
+
+ Performance Comparisons of HBase vs. HDFS
+ A fairly common question on the dist-list is why HBase isn't as performant as HDFS files in a batch context (e.g., as
+ a MapReduce source or sink). The short answer is that HBase is doing a lot more than HDFS (e.g., reading the KeyValues,
+ returning the most current row or specified timestamps, etc.), and as such HBase is 4-5 times slower than HDFS in this
+ processing context. Not that there isn't room for improvement (and this gap will, over time, be reduced), but HDFS
+ will always be faster in this use-case.
+
+
+
+
+ Amazon EC2
+ Performance questions are common on Amazon EC2 environments because it is a shared environment. You will
+ not see the same throughput as a dedicated server. In terms of running tests on EC2, run them several times for the same
+ reason (i.e., it's a shared environment and you don't know what else is happening on the server).
+
+ If you are running on EC2 and post performance questions on the dist-list, please state this fact up-front that
+ because EC2 issues are practically a separate class of performance issues.
+
+
+
+ Case Studies
+ For Performance and Troubleshooting Case Studies, see .
+
+
+
diff --git src/docbkx/preface.xml src/docbkx/preface.xml
new file mode 100644
index 0000000..af54aa2
--- /dev/null
+++ src/docbkx/preface.xml
@@ -0,0 +1,65 @@
+
+
+
+ Preface
+
+ This is the official reference guide for the HBase version it ships with.
+ This document describes HBase version .
+ Herein you will find either the definitive documentation on an HBase topic
+ as of its standing when the referenced HBase version shipped, or it
+ will point to the location in javadoc,
+ JIRA
+ or wiki where
+ the pertinent information can be found.
+
+ This reference guide is a work in progress. Feel free to add content by adding
+ a patch to an issue up in the HBase JIRA.
+
+
+ Heads-up
+
+ If this is your first foray into the wonderful world of
+ Distributed Computing, then you are in for
+ some interesting times. First off, distributed systems are
+ hard; making a distributed system hum requires a disparate
+ skillset that spans systems (hardware and software) and
+ networking. Your cluster' operation can hiccup because of any
+ of a myriad set of reasons from bugs in HBase itself through misconfigurations
+ -- misconfiguration of HBase but also operating system misconfigurations --
+ through to hardware problems whether it be a bug in your network card
+ drivers or an underprovisioned RAM bus (to mention two recent
+ examples of hardware issues that manifested as "HBase is slow").
+ You will also need to do a recalibration if up to this your
+ computing has been bound to a single box. Here is one good
+ starting point:
+ Fallacies of Distributed Computing.
+
+
+
diff --git src/docbkx/security.xml src/docbkx/security.xml
new file mode 100644
index 0000000..8d6a33e
--- /dev/null
+++ src/docbkx/security.xml
@@ -0,0 +1,509 @@
+
+
+
+Secure HBase
+
+ Secure Client Access to HBase
+ Newer releases of HBase (>= 0.92) support optional SASL authentication of clients.
+ This describes how to set up HBase and HBase clients for connection to secure HBase resources.
+
+ Prerequisites
+
+ HBase must have been built using the new maven profile for secure Hadoop/HBase: -P security. Secure Hadoop dependent classes are separated under a pseudo-module in the security/ directory and are only included if built with the secure Hadoop profile.
+
+
+ You need to have a working Kerberos KDC.
+
+
+ A HBase configured for secure client access is expected to be running
+ on top of a secured HDFS cluster. HBase must be able to authenticate
+ to HDFS services. HBase needs Kerberos credentials to interact with
+ the Kerberos-enabled HDFS daemons. Authenticating a service should be
+ done using a keytab file. The procedure for creating keytabs for HBase
+ service is the same as for creating keytabs for Hadoop. Those steps
+ are omitted here. Copy the resulting keytab files to wherever HBase
+ Master and RegionServer processes are deployed and make them readable
+ only to the user account under which the HBase daemons will run.
+
+
+ A Kerberos principal has three parts, with the form
+ username/fully.qualified.domain.name@YOUR-REALM.COM. We
+ recommend using hbase as the username portion.
+
+
+ The following is an example of the configuration properties for
+ Kerberos operation that must be added to the
+ hbase-site.xml file on every server machine in the
+ cluster. Required for even the most basic interactions with a
+ secure Hadoop configuration, independent of HBase security.
+
+
+ hbase.regionserver.kerberos.principal
+ hbase/_HOST@YOUR-REALM.COM
+
+
+ hbase.regionserver.keytab.file
+ /etc/hbase/conf/keytab.krb5
+
+
+ hbase.master.kerberos.principal
+ hbase/_HOST@YOUR-REALM.COM
+
+
+ hbase.master.keytab.file
+ /etc/hbase/conf/keytab.krb5
+
+ ]]>
+
+ Each HBase client user should also be given a Kerberos principal. This
+ principal should have a password assigned to it (as opposed to a
+ keytab file). The client principal's maxrenewlife should
+ be set so that it can be renewed enough times for the HBase client
+ process to complete. For example, if a user runs a long-running HBase
+ client process that takes at most 3 days, we might create this user's
+ principal within kadmin with: addprinc -maxrenewlife
+ 3days
+
+
+ Long running daemons with indefinite lifetimes that require client
+ access to HBase can instead be configured to log in from a keytab. For
+ each host running such daemons, create a keytab with
+ kadmin or kadmin.local. The procedure for
+ creating keytabs for HBase service is the same as for creating
+ keytabs for Hadoop. Those steps are omitted here. Copy the resulting
+ keytab files to where the client daemon will execute and make them
+ readable only to the user account under which the daemon will run.
+
+
+
+ Server-side Configuration for Secure Operation
+
+ Add the following to the hbase-site.xml file on every server machine in the cluster:
+
+
+ hbase.security.authentication
+ kerberos
+
+
+ hbase.security.authorization
+ true
+
+
+ hbase.rpc.engine
+ org.apache.hadoop.hbase.ipc.SecureRpcEngine
+
+
+ hbase.coprocessor.region.classes
+ org.apache.hadoop.hbase.security.token.TokenProvider
+
+ ]]>
+
+ A full shutdown and restart of HBase service is required when deploying
+ these configuration changes.
+
+
+
+ Client-side Configuration for Secure Operation
+
+ Add the following to the hbase-site.xml file on every client:
+
+
+ hbase.security.authentication
+ kerberos
+
+
+ hbase.rpc.engine
+ org.apache.hadoop.hbase.ipc.SecureRpcEngine
+
+ ]]>
+
+ The client environment must be logged in to Kerberos from KDC or
+ keytab via the kinit command before communication with
+ the HBase cluster will be possible.
+
+
+ Be advised that if the hbase.security.authentication
+ and hbase.rpc.engine properties in the client- and
+ server-side site files do not match, the client will not be able to
+ communicate with the cluster.
+
+
+ Once HBase is configured for secure RPC it is possible to optionally
+ configure encrypted communication. To do so, add the following to the
+ hbase-site.xml file on every client:
+
+
+ hbase.rpc.protection
+ privacy
+
+ ]]>
+
+ This configuration property can also be set on a per connection basis.
+ Set it in the Configuration supplied to
+ HTable:
+
+
+ Configuration conf = HBaseConfiguration.create();
+ conf.set("hbase.rpc.protection", "privacy");
+ HTable table = new HTable(conf, tablename);
+
+
+ Expect a ~10% performance penalty for encrypted communication.
+
+
+
+ Client-side Configuration for Secure Operation - Thrift Gateway
+
+ Add the following to the hbase-site.xml file for every Thrift gateway:
+
+ hbase.thrift.keytab.file
+ /etc/hbase/conf/hbase.keytab
+
+
+ hbase.thrift.kerberos.principal
+ $USER/_HOST@HADOOP.LOCALDOMAIN
+
+ ]]>
+
+
+ Substitute the appropriate credential and keytab for $USER and $KEYTAB
+ respectively.
+
+
+ The Thrift gateway will authenticate with HBase using the supplied
+ credential. No authentication will be performed by the Thrift gateway
+ itself. All client access via the Thrift gateway will use the Thrift
+ gateway's credential and have its privilege.
+
+
+
+ Client-side Configuration for Secure Operation - REST Gateway
+
+ Add the following to the hbase-site.xml file for every REST gateway:
+
+ hbase.rest.keytab.file
+ $KEYTAB
+
+
+ hbase.rest.kerberos.principal
+ $USER/_HOST@HADOOP.LOCALDOMAIN
+
+ ]]>
+
+
+ Substitute the appropriate credential and keytab for $USER and $KEYTAB
+ respectively.
+
+
+ The REST gateway will authenticate with HBase using the supplied
+ credential. No authentication will be performed by the REST gateway
+ itself. All client access via the REST gateway will use the REST
+ gateway's credential and have its privilege.
+
+
+ It should be possible for clients to authenticate with the HBase
+ cluster through the REST gateway in a pass-through manner via SPEGNO
+ HTTP authentication. This is future work.
+
+
+
+
+
+
+
+ Access Control
+
+ Newer releases of HBase (>= 0.92) support optional access control
+ list (ACL-) based protection of resources on a column family and/or
+ table basis.
+
+
+ This describes how to set up Secure HBase for access control, with an
+ example of granting and revoking user permission on table resources
+ provided.
+
+
+ Prerequisites
+
+ You must configure HBase for secure operation. Refer to the section
+ "Secure Client Access to HBase" and complete all of the steps described
+ there.
+
+
+ You must also configure ZooKeeper for secure operation. Changes to ACLs
+ are synchronized throughout the cluster using ZooKeeper. Secure
+ authentication to ZooKeeper must be enabled or otherwise it will be
+ possible to subvert HBase access control via direct client access to
+ ZooKeeper. Refer to the section on secure ZooKeeper configuration and
+ complete all of the steps described there.
+
+
+
+ Overview
+
+ With Secure RPC and Access Control enabled, client access to HBase is
+ authenticated and user data is private unless access has been
+ explicitly granted. Access to data can be granted at a table or per
+ column family basis.
+
+
+ However, the following items have been left out of the initial
+ implementation for simplicity:
+
+
+
+ Row-level or per value (cell): This would require broader changes for storing the ACLs inline with rows. It is a future goal.
+
+
+ Push down of file ownership to HDFS: HBase is not designed for the case where files may have different permissions than the HBase system principal. Pushing file ownership down into HDFS would necessitate changes to core code. Also, while HDFS file ownership would make applying quotas easy, and possibly make bulk imports more straightforward, it is not clear that it would offer a more secure setup.
+
+
+ HBase managed "roles" as collections of permissions: We will not model "roles" internally in HBase to begin with. We instead allow group names to be granted permissions, which allows external modeling of roles via group membership. Groups are created and manipulated externally to HBase, via the Hadoop group mapping service.
+
+
+
+Access control mechanisms are mature and fairly standardized in the relational database world. The HBase implementation approximates current convention, but HBase has a simpler feature set than relational databases, especially in terms of client operations. We don't distinguish between an insert (new record) and update (of existing record), for example, as both collapse down into a Put. Accordingly, the important operations condense to four permissions: READ, WRITE, CREATE, and ADMIN.
+
+
+
+ Permissions can be granted in any of the following scopes, though
+ CREATE and ADMIN permissions are effective only at table scope.
+
+
+
+
+ Table
+
+
+ Read: User can read from any column family in table
+ Write: User can write to any column family in table
+ Create: User can alter table attributes; add, alter, or drop column families; and drop the table.
+ Admin: User can alter table attributes; add, alter, or drop column families; and enable, disable, or drop the table. User can also trigger region (re)assignments or relocation.
+
+
+
+
+ Column Family
+
+
+ Read: User can read from the column family
+ Write: User can write to the column family
+
+
+
+
+
+
+ There is also an implicit global scope for the superuser.
+
+
+ The superuser is a principal, specified in the HBase site configuration
+ file, that has equivalent access to HBase as the 'root' user would on a
+ UNIX derived system. Normally this is the principal that the HBase
+ processes themselves authenticate as. Although future versions of HBase
+ Access Control may support multiple superusers, the superuser privilege
+ will always include the principal used to run the HMaster process. Only
+ the superuser is allowed to create tables, switch the balancer on or
+ off, or take other actions with global consequence. Furthermore, the
+ superuser has an implicit grant of all permissions to all resources.
+
+
+ Tables have a new metadata attribute: OWNER, the user principal who owns
+ the table. By default this will be set to the user principal who creates
+ the table, though it may be changed at table creation time or during an
+ alter operation by setting or changing the OWNER table attribute. Only a
+ single user principal can own a table at a given time. A table owner will
+ have all permissions over a given table.
+
+
+
+ Server-side Configuration for Access Control
+
+ Enable the AccessController coprocessor in the cluster configuration
+ and restart HBase. The restart can be a rolling one. Complete the
+ restart of all Master and RegionServer processes before setting up
+ ACLs.
+
+
+ To enable the AccessController, modify the hbase-site.xml file on every server machine in the cluster to look like:
+
+
+ hbase.coprocessor.master.classes
+ org.apache.hadoop.hbase.security.access.AccessController
+
+
+ hbase.coprocessor.region.classes
+ org.apache.hadoop.hbase.security.token.TokenProvider,
+ org.apache.hadoop.hbase.security.access.AccessController
+
+ ]]>
+
+
+ Shell Enhancements for Access Control
+
+The HBase shell has been extended to provide simple commands for editing and updating user permissions. The following commands have been added for access control list management:
+
+ Grant
+
+
+ grant <user> <permissions> <table> [ <column family> [ <column qualifier> ] ]
+
+
+
+ <permissions> is zero or more letters from the set "RWCA": READ('R'), WRITE('W'), CREATE('C'), ADMIN('A').
+
+
+ Note: Grants and revocations of individual permissions on a resource are both accomplished using the grant command. A separate revoke command is also provided by the shell, but this is for fast revocation of all of a user's access rights to a given resource only.
+
+
+ Revoke
+
+
+
+ revoke <user> <table> [ <column family> [ <column qualifier> ] ]
+
+
+
+ Alter
+
+
+ The alter command has been extended to allow ownership assignment:
+
+ alter 'tablename', {OWNER => 'username'}
+
+
+
+ User Permission
+
+
+ The user_permission command shows all access permissions for the current user for a given table:
+
+ user_permission <table>
+
+
+
+
+
+
diff --git src/docbkx/shell.xml src/docbkx/shell.xml
new file mode 100644
index 0000000..4fbab08
--- /dev/null
+++ src/docbkx/shell.xml
@@ -0,0 +1,108 @@
+
+
+
+ The HBase Shell
+
+
+ The HBase Shell is (J)Ruby's
+ IRB with some HBase particular commands added. Anything you can do in
+ IRB, you should be able to do in the HBase Shell.
+ To run the HBase shell,
+ do as follows:
+ $ ./bin/hbase shell
+
+ Type help and then <RETURN>
+ to see a listing of shell
+ commands and options. Browse at least the paragraphs at the end of
+ the help emission for the gist of how variables and command
+ arguments are entered into the
+ HBase shell; in particular note how table names, rows, and
+ columns, etc., must be quoted.
+ See
+ for example basic shell operation.
+
+ Scripting
+ For examples scripting HBase, look in the
+ HBase bin directory. Look at the files
+ that end in *.rb. To run one of these
+ files, do as follows:
+ $ ./bin/hbase org.jruby.Main PATH_TO_SCRIPT
+
+
+
+ Shell Tricks
+ irbrc
+ Create an .irbrc file for yourself in your
+ home directory. Add customizations. A useful one is
+ command history so commands are save across Shell invocations:
+
+ $ more .irbrc
+ require 'irb/ext/save-history'
+ IRB.conf[:SAVE_HISTORY] = 100
+ IRB.conf[:HISTORY_FILE] = "#{ENV['HOME']}/.irb-save-history"
+ See the ruby documentation of
+ .irbrc to learn about other possible
+ confiurations.
+
+
+ LOG data to timestamp
+
+ To convert the date '08/08/16 20:56:29' from an hbase log into a timestamp, do:
+
+ hbase(main):021:0> import java.text.SimpleDateFormat
+ hbase(main):022:0> import java.text.ParsePosition
+ hbase(main):023:0> SimpleDateFormat.new("yy/MM/dd HH:mm:ss").parse("08/08/16 20:56:29", ParsePosition.new(0)).getTime() => 1218920189000
+
+
+ To go the other direction:
+
+ hbase(main):021:0> import java.util.Date
+ hbase(main):022:0> Date.new(1218920189000).toString() => "Sat Aug 16 20:56:29 UTC 2008"
+
+
+ To output in a format that is exactly like that of the HBase log format will take a little messing with
+ SimpleDateFormat.
+
+
+ Debug
+ Shell debug switch
+ You can set a debug switch in the shell to see more output
+ -- e.g. more of the stack trace on exception --
+ when you run a command:
+ hbase> debug <RETURN>
+
+
+ DEBUG log level
+ To enable DEBUG level logging in the shell,
+ launch it with the -d option.
+ $ ./bin/hbase shell -d
+
+
+
+
+
diff --git src/docbkx/troubleshooting.xml src/docbkx/troubleshooting.xml
new file mode 100644
index 0000000..b0c2126
--- /dev/null
+++ src/docbkx/troubleshooting.xml
@@ -0,0 +1,1058 @@
+
+
+
+ Troubleshooting and Debugging HBase
+
+ General Guidelines
+
+ Always start with the master log (TODO: Which lines?).
+ Normally it’s just printing the same lines over and over again.
+ If not, then there’s an issue.
+ Google or search-hadoop.com
+ should return some hits for those exceptions you’re seeing.
+
+
+ An error rarely comes alone in HBase, usually when something gets screwed up what will
+ follow may be hundreds of exceptions and stack traces coming from all over the place.
+ The best way to approach this type of problem is to walk the log up to where it all
+ began, for example one trick with RegionServers is that they will print some
+ metrics when aborting so grepping for Dump
+ should get you around the start of the problem.
+
+
+ RegionServer suicides are “normal”, as this is what they do when something goes wrong.
+ For example, if ulimit and xcievers (the two most important initial settings, see )
+ aren’t changed, it will make it impossible at some point for DataNodes to create new threads
+ that from the HBase point of view is seen as if HDFS was gone. Think about what would happen if your
+ MySQL database was suddenly unable to access files on your local file system, well it’s the same with
+ HBase and HDFS. Another very common reason to see RegionServers committing seppuku is when they enter
+ prolonged garbage collection pauses that last longer than the default ZooKeeper session timeout.
+ For more information on GC pauses, see the
+ 3 part blog post by Todd Lipcon
+ and above.
+
+
+
+ Logs
+
+ The key process logs are as follows... (replace <user> with the user that started the service, and <hostname> for the machine name)
+
+
+ NameNode: $HADOOP_HOME/logs/hadoop-<user>-namenode-<hostname>.log
+
+
+ DataNode: $HADOOP_HOME/logs/hadoop-<user>-datanode-<hostname>.log
+
+
+ JobTracker: $HADOOP_HOME/logs/hadoop-<user>-jobtracker-<hostname>.log
+
+
+ TaskTracker: $HADOOP_HOME/logs/hadoop-<user>-jobtracker-<hostname>.log
+
+
+ HMaster: $HBASE_HOME/logs/hbase-<user>-master-<hostname>.log
+
+
+ RegionServer: $HBASE_HOME/logs/hbase-<user>-regionserver-<hostname>.log
+
+
+ ZooKeeper: TODO
+
+
+ Log Locations
+ For stand-alone deployments the logs are obviously going to be on a single machine, however this is a development configuration only.
+ Production deployments need to run on a cluster.
+
+ NameNode
+ The NameNode log is on the NameNode server. The HBase Master is typically run on the NameNode server, and well as ZooKeeper.
+ For smaller clusters the JobTracker is typically run on the NameNode server as well.
+
+
+ DataNode
+ Each DataNode server will have a DataNode log for HDFS, as well as a RegionServer log for HBase.
+ Additionally, each DataNode server will also have a TaskTracker log for MapReduce task execution.
+
+
+
+ Log Levels
+ Enabling RPC-level logging
+ Enabling the RPC-level logging on a RegionServer can often given
+ insight on timings at the server. Once enabled, the amount of log
+ spewed is voluminous. It is not recommended that you leave this
+ logging on for more than short bursts of time. To enable RPC-level
+ logging, browse to the RegionServer UI and click on
+ Log Level. Set the log level to DEBUG for the package
+ org.apache.hadoop.ipc (Thats right, for
+ hadoop.ipc, NOT, hbase.ipc). Then tail the RegionServers log. Analyze.
+ To disable, set the logging level back to INFO level.
+
+
+
+
+ JVM Garbage Collection Logs
+ HBase is memory intensive, and using the default GC you can see long pauses in all threads including the Juliet Pause aka "GC of Death".
+ To help debug this or confirm this is happening GC logging can be turned on in the Java virtual machine.
+
+
+ To enable, in hbase-env.sh add:
+
+export HBASE_OPTS="-XX:+UseConcMarkSweepGC -verbose:gc -XX:+PrintGCDetails -XX:+PrintGCTimeStamps -Xloggc:/home/hadoop/hbase/logs/gc-hbase.log"
+
+ Adjust the log directory to wherever you log. Note: The GC log does NOT roll automatically, so you'll have to keep an eye on it so it doesn't fill up the disk.
+
+
+ At this point you should see logs like so:
+
+64898.952: [GC [1 CMS-initial-mark: 2811538K(3055704K)] 2812179K(3061272K), 0.0007360 secs] [Times: user=0.00 sys=0.00, real=0.00 secs]
+64898.953: [CMS-concurrent-mark-start]
+64898.971: [GC 64898.971: [ParNew: 5567K->576K(5568K), 0.0101110 secs] 2817105K->2812715K(3061272K), 0.0102200 secs] [Times: user=0.07 sys=0.00, real=0.01 secs]
+
+
+
+ In this section, the first line indicates a 0.0007360 second pause for the CMS to initially mark. This pauses the entire VM, all threads for that period of time.
+
+
+ The third line indicates a "minor GC", which pauses the VM for 0.0101110 seconds - aka 10 milliseconds. It has reduced the "ParNew" from about 5.5m to 576k.
+ Later on in this cycle we see:
+
+64901.445: [CMS-concurrent-mark: 1.542/2.492 secs] [Times: user=10.49 sys=0.33, real=2.49 secs]
+64901.445: [CMS-concurrent-preclean-start]
+64901.453: [GC 64901.453: [ParNew: 5505K->573K(5568K), 0.0062440 secs] 2868746K->2864292K(3061272K), 0.0063360 secs] [Times: user=0.05 sys=0.00, real=0.01 secs]
+64901.476: [GC 64901.476: [ParNew: 5563K->575K(5568K), 0.0072510 secs] 2869283K->2864837K(3061272K), 0.0073320 secs] [Times: user=0.05 sys=0.01, real=0.01 secs]
+64901.500: [GC 64901.500: [ParNew: 5517K->573K(5568K), 0.0120390 secs] 2869780K->2865267K(3061272K), 0.0121150 secs] [Times: user=0.09 sys=0.00, real=0.01 secs]
+64901.529: [GC 64901.529: [ParNew: 5507K->569K(5568K), 0.0086240 secs] 2870200K->2865742K(3061272K), 0.0087180 secs] [Times: user=0.05 sys=0.00, real=0.01 secs]
+64901.554: [GC 64901.555: [ParNew: 5516K->575K(5568K), 0.0107130 secs] 2870689K->2866291K(3061272K), 0.0107820 secs] [Times: user=0.06 sys=0.00, real=0.01 secs]
+64901.578: [CMS-concurrent-preclean: 0.070/0.133 secs] [Times: user=0.48 sys=0.01, real=0.14 secs]
+64901.578: [CMS-concurrent-abortable-preclean-start]
+64901.584: [GC 64901.584: [ParNew: 5504K->571K(5568K), 0.0087270 secs] 2871220K->2866830K(3061272K), 0.0088220 secs] [Times: user=0.05 sys=0.00, real=0.01 secs]
+64901.609: [GC 64901.609: [ParNew: 5512K->569K(5568K), 0.0063370 secs] 2871771K->2867322K(3061272K), 0.0064230 secs] [Times: user=0.06 sys=0.00, real=0.01 secs]
+64901.615: [CMS-concurrent-abortable-preclean: 0.007/0.037 secs] [Times: user=0.13 sys=0.00, real=0.03 secs]
+64901.616: [GC[YG occupancy: 645 K (5568 K)]64901.616: [Rescan (parallel) , 0.0020210 secs]64901.618: [weak refs processing, 0.0027950 secs] [1 CMS-remark: 2866753K(3055704K)] 2867399K(3061272K), 0.0049380 secs] [Times: user=0.00 sys=0.01, real=0.01 secs]
+64901.621: [CMS-concurrent-sweep-start]
+
+
+
+ The first line indicates that the CMS concurrent mark (finding garbage) has taken 2.4 seconds. But this is a _concurrent_ 2.4 seconds, Java has not been paused at any point in time.
+
+
+ There are a few more minor GCs, then there is a pause at the 2nd last line:
+
+64901.616: [GC[YG occupancy: 645 K (5568 K)]64901.616: [Rescan (parallel) , 0.0020210 secs]64901.618: [weak refs processing, 0.0027950 secs] [1 CMS-remark: 2866753K(3055704K)] 2867399K(3061272K), 0.0049380 secs] [Times: user=0.00 sys=0.01, real=0.01 secs]
+
+
+
+ The pause here is 0.0049380 seconds (aka 4.9 milliseconds) to 'remark' the heap.
+
+
+ At this point the sweep starts, and you can watch the heap size go down:
+
+64901.637: [GC 64901.637: [ParNew: 5501K->569K(5568K), 0.0097350 secs] 2871958K->2867441K(3061272K), 0.0098370 secs] [Times: user=0.05 sys=0.00, real=0.01 secs]
+... lines removed ...
+64904.936: [GC 64904.936: [ParNew: 5532K->568K(5568K), 0.0070720 secs] 1365024K->1360689K(3061272K), 0.0071930 secs] [Times: user=0.05 sys=0.00, real=0.01 secs]
+64904.953: [CMS-concurrent-sweep: 2.030/3.332 secs] [Times: user=9.57 sys=0.26, real=3.33 secs]
+
+ At this point, the CMS sweep took 3.332 seconds, and heap went from about ~ 2.8 GB to 1.3 GB (approximate).
+
+
+ The key points here is to keep all these pauses low. CMS pauses are always low, but if your ParNew starts growing, you can see minor GC pauses approach 100ms, exceed 100ms and hit as high at 400ms.
+
+
+ This can be due to the size of the ParNew, which should be relatively small. If your ParNew is very large after running HBase for a while, in one example a ParNew was about 150MB, then you might have to constrain the size of ParNew (The larger it is, the longer the collections take but if its too small, objects are promoted to old gen too quickly). In the below we constrain new gen size to 64m.
+
+
+ Add this to HBASE_OPTS:
+
+export HBASE_OPTS="-XX:NewSize=64m -XX:MaxNewSize=64m <cms options from above> <gc logging options from above>"
+
+
+
+ For more information on GC pauses, see the 3 part blog post by Todd Lipcon
+ and above.
+
+
+
+
+ Resources
+
+ search-hadoop.com
+
+ search-hadoop.com indexes all the mailing lists and is great for historical searches.
+ Search here first when you have an issue as its more than likely someone has already had your problem.
+
+
+
+ Mailing Lists
+ Ask a question on the HBase mailing lists.
+ The 'dev' mailing list is aimed at the community of developers actually building HBase and for features currently under development, and 'user'
+ is generally used for questions on released versions of HBase. Before going to the mailing list, make sure your
+ question has not already been answered by searching the mailing list archives first. Use
+ .
+ Take some time crafting your questionSee Getting Answers; a quality question that includes all context and
+ exhibits evidence the author has tried to find answers in the manual and out on lists
+ is more likely to get a prompt response.
+
+
+
+ IRC
+ #hbase on irc.freenode.net
+
+
+ JIRA
+
+ JIRA is also really helpful when looking for Hadoop/HBase-specific issues.
+
+
+
+
+ Tools
+
+ Builtin Tools
+
+ Master Web Interface
+ The Master starts a web-interface on port 60010 by default.
+
+ The Master web UI lists created tables and their definition (e.g., ColumnFamilies, blocksize, etc.). Additionally,
+ the available RegionServers in the cluster are listed along with selected high-level metrics (requests, number of regions, usedHeap, maxHeap).
+ The Master web UI allows navigation to each RegionServer's web UI.
+
+
+
+ RegionServer Web Interface
+ RegionServers starts a web-interface on port 60030 by default.
+
+ The RegionServer web UI lists online regions and their start/end keys, as well as point-in-time RegionServer metrics (requests, regions, storeFileIndexSize, compactionQueueSize, etc.).
+
+ See for more information in metric definitions.
+
+
+
+ zkcli
+ zkcli is a very useful tool for investigating ZooKeeper-related issues. To invoke:
+
+./hbase zkcli -server host:port <cmd> <args>
+
+ The commands (and arguments) are:
+
+ connect host:port
+ get path [watch]
+ ls path [watch]
+ set path data [version]
+ delquota [-n|-b] path
+ quit
+ printwatches on|off
+ create [-s] [-e] path data acl
+ stat path [watch]
+ close
+ ls2 path [watch]
+ history
+ listquota path
+ setAcl path acl
+ getAcl path
+ sync path
+ redo cmdno
+ addauth scheme auth
+ delete path [version]
+ setquota -n|-b val path
+
+
+
+
+
+ External Tools
+
+ tail
+
+ tail is the command line tool that lets you look at the end of a file. Add the “-f” option and it will refresh when new data is available. It’s useful when you are wondering what’s happening, for example, when a cluster is taking a long time to shutdown or startup as you can just fire a new terminal and tail the master log (and maybe a few RegionServers).
+
+
+
+ top
+
+ top is probably one of the most important tool when first trying to see what’s running on a machine and how the resources are consumed. Here’s an example from production system:
+
+top - 14:46:59 up 39 days, 11:55, 1 user, load average: 3.75, 3.57, 3.84
+Tasks: 309 total, 1 running, 308 sleeping, 0 stopped, 0 zombie
+Cpu(s): 4.5%us, 1.6%sy, 0.0%ni, 91.7%id, 1.4%wa, 0.1%hi, 0.6%si, 0.0%st
+Mem: 24414432k total, 24296956k used, 117476k free, 7196k buffers
+Swap: 16008732k total, 14348k used, 15994384k free, 11106908k cached
+
+ PID USER PR NI VIRT RES SHR S %CPU %MEM TIME+ COMMAND
+15558 hadoop 18 -2 3292m 2.4g 3556 S 79 10.4 6523:52 java
+13268 hadoop 18 -2 8967m 8.2g 4104 S 21 35.1 5170:30 java
+ 8895 hadoop 18 -2 1581m 497m 3420 S 11 2.1 4002:32 java
+…
+
+
+
+ Here we can see that the system load average during the last five minutes is 3.75, which very roughly means that on average 3.75 threads were waiting for CPU time during these 5 minutes. In general, the “perfect” utilization equals to the number of cores, under that number the machine is under utilized and over that the machine is over utilized. This is an important concept, see this article to understand it more: http://www.linuxjournal.com/article/9001.
+
+
+ Apart from load, we can see that the system is using almost all its available RAM but most of it is used for the OS cache (which is good). The swap only has a few KBs in it and this is wanted, high numbers would indicate swapping activity which is the nemesis of performance of Java systems. Another way to detect swapping is when the load average goes through the roof (although this could also be caused by things like a dying disk, among others).
+
+
+ The list of processes isn’t super useful by default, all we know is that 3 java processes are using about 111% of the CPUs. To know which is which, simply type “c” and each line will be expanded. Typing “1” will give you the detail of how each CPU is used instead of the average for all of them like shown here.
+
+
+
+ jps
+
+ jps is shipped with every JDK and gives the java process ids for the current user (if root, then it gives the ids for all users). Example:
+
+hadoop@sv4borg12:~$ jps
+1322 TaskTracker
+17789 HRegionServer
+27862 Child
+1158 DataNode
+25115 HQuorumPeer
+2950 Jps
+19750 ThriftServer
+18776 jmx
+
+ In order, we see a:
+
+ Hadoop TaskTracker, manages the local Childs
+ HBase RegionServer, serves regions
+ Child, its MapReduce task, cannot tell which type exactly
+ Hadoop TaskTracker, manages the local Childs
+ Hadoop DataNode, serves blocks
+ HQuorumPeer, a ZooKeeper ensemble member
+ Jps, well… it’s the current process
+ ThriftServer, it’s a special one will be running only if thrift was started
+ jmx, this is a local process that’s part of our monitoring platform ( poorly named maybe). You probably don’t have that.
+
+
+
+ You can then do stuff like checking out the full command line that started the process:
+
+hadoop@sv4borg12:~$ ps aux | grep HRegionServer
+hadoop 17789 155 35.2 9067824 8604364 ? S<l Mar04 9855:48 /usr/java/jdk1.6.0_14/bin/java -Xmx8000m -XX:+DoEscapeAnalysis -XX:+AggressiveOpts -XX:+UseConcMarkSweepGC -XX:NewSize=64m -XX:MaxNewSize=64m -XX:CMSInitiatingOccupancyFraction=88 -verbose:gc -XX:+PrintGCDetails -XX:+PrintGCTimeStamps -Xloggc:/export1/hadoop/logs/gc-hbase.log -Dcom.sun.management.jmxremote.port=10102 -Dcom.sun.management.jmxremote.authenticate=true -Dcom.sun.management.jmxremote.ssl=false -Dcom.sun.management.jmxremote.password.file=/home/hadoop/hbase/conf/jmxremote.password -Dcom.sun.management.jmxremote -Dhbase.log.dir=/export1/hadoop/logs -Dhbase.log.file=hbase-hadoop-regionserver-sv4borg12.log -Dhbase.home.dir=/home/hadoop/hbase -Dhbase.id.str=hadoop -Dhbase.root.logger=INFO,DRFA -Djava.library.path=/home/hadoop/hbase/lib/native/Linux-amd64-64 -classpath /home/hadoop/hbase/bin/../conf:[many jars]:/home/hadoop/hadoop/conf org.apache.hadoop.hbase.regionserver.HRegionServer start
+
+
+
+
+ jstack
+
+ jstack is one of the most important tools when trying to figure out what a java process is doing apart from looking at the logs. It has to be used in conjunction with jps in order to give it a process id. It shows a list of threads, each one has a name, and they appear in the order that they were created (so the top ones are the most recent threads). Here’s a few example:
+
+
+ The main thread of a RegionServer that’s waiting for something to do from the master:
+
+ "regionserver60020" prio=10 tid=0x0000000040ab4000 nid=0x45cf waiting on condition [0x00007f16b6a96000..0x00007f16b6a96a70]
+ java.lang.Thread.State: TIMED_WAITING (parking)
+ at sun.misc.Unsafe.park(Native Method)
+ - parking to wait for <0x00007f16cd5c2f30> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject)
+ at java.util.concurrent.locks.LockSupport.parkNanos(LockSupport.java:198)
+ at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.awaitNanos(AbstractQueuedSynchronizer.java:1963)
+ at java.util.concurrent.LinkedBlockingQueue.poll(LinkedBlockingQueue.java:395)
+ at org.apache.hadoop.hbase.regionserver.HRegionServer.run(HRegionServer.java:647)
+ at java.lang.Thread.run(Thread.java:619)
+
+ The MemStore flusher thread that is currently flushing to a file:
+"regionserver60020.cacheFlusher" daemon prio=10 tid=0x0000000040f4e000 nid=0x45eb in Object.wait() [0x00007f16b5b86000..0x00007f16b5b87af0]
+ java.lang.Thread.State: WAITING (on object monitor)
+ at java.lang.Object.wait(Native Method)
+ at java.lang.Object.wait(Object.java:485)
+ at org.apache.hadoop.ipc.Client.call(Client.java:803)
+ - locked <0x00007f16cb14b3a8> (a org.apache.hadoop.ipc.Client$Call)
+ at org.apache.hadoop.ipc.RPC$Invoker.invoke(RPC.java:221)
+ at $Proxy1.complete(Unknown Source)
+ at sun.reflect.GeneratedMethodAccessor38.invoke(Unknown Source)
+ at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:25)
+ at java.lang.reflect.Method.invoke(Method.java:597)
+ at org.apache.hadoop.io.retry.RetryInvocationHandler.invokeMethod(RetryInvocationHandler.java:82)
+ at org.apache.hadoop.io.retry.RetryInvocationHandler.invoke(RetryInvocationHandler.java:59)
+ at $Proxy1.complete(Unknown Source)
+ at org.apache.hadoop.hdfs.DFSClient$DFSOutputStream.closeInternal(DFSClient.java:3390)
+ - locked <0x00007f16cb14b470> (a org.apache.hadoop.hdfs.DFSClient$DFSOutputStream)
+ at org.apache.hadoop.hdfs.DFSClient$DFSOutputStream.close(DFSClient.java:3304)
+ at org.apache.hadoop.fs.FSDataOutputStream$PositionCache.close(FSDataOutputStream.java:61)
+ at org.apache.hadoop.fs.FSDataOutputStream.close(FSDataOutputStream.java:86)
+ at org.apache.hadoop.hbase.io.hfile.HFile$Writer.close(HFile.java:650)
+ at org.apache.hadoop.hbase.regionserver.StoreFile$Writer.close(StoreFile.java:853)
+ at org.apache.hadoop.hbase.regionserver.Store.internalFlushCache(Store.java:467)
+ - locked <0x00007f16d00e6f08> (a java.lang.Object)
+ at org.apache.hadoop.hbase.regionserver.Store.flushCache(Store.java:427)
+ at org.apache.hadoop.hbase.regionserver.Store.access$100(Store.java:80)
+ at org.apache.hadoop.hbase.regionserver.Store$StoreFlusherImpl.flushCache(Store.java:1359)
+ at org.apache.hadoop.hbase.regionserver.HRegion.internalFlushcache(HRegion.java:907)
+ at org.apache.hadoop.hbase.regionserver.HRegion.internalFlushcache(HRegion.java:834)
+ at org.apache.hadoop.hbase.regionserver.HRegion.flushcache(HRegion.java:786)
+ at org.apache.hadoop.hbase.regionserver.MemStoreFlusher.flushRegion(MemStoreFlusher.java:250)
+ at org.apache.hadoop.hbase.regionserver.MemStoreFlusher.flushRegion(MemStoreFlusher.java:224)
+ at org.apache.hadoop.hbase.regionserver.MemStoreFlusher.run(MemStoreFlusher.java:146)
+
+
+
+ A handler thread that’s waiting for stuff to do (like put, delete, scan, etc):
+
+"IPC Server handler 16 on 60020" daemon prio=10 tid=0x00007f16b011d800 nid=0x4a5e waiting on condition [0x00007f16afefd000..0x00007f16afefd9f0]
+ java.lang.Thread.State: WAITING (parking)
+ at sun.misc.Unsafe.park(Native Method)
+ - parking to wait for <0x00007f16cd3f8dd8> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject)
+ at java.util.concurrent.locks.LockSupport.park(LockSupport.java:158)
+ at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:1925)
+ at java.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:358)
+ at org.apache.hadoop.hbase.ipc.HBaseServer$Handler.run(HBaseServer.java:1013)
+
+
+
+ And one that’s busy doing an increment of a counter (it’s in the phase where it’s trying to create a scanner in order to read the last value):
+
+"IPC Server handler 66 on 60020" daemon prio=10 tid=0x00007f16b006e800 nid=0x4a90 runnable [0x00007f16acb77000..0x00007f16acb77cf0]
+ java.lang.Thread.State: RUNNABLE
+ at org.apache.hadoop.hbase.regionserver.KeyValueHeap.<init>(KeyValueHeap.java:56)
+ at org.apache.hadoop.hbase.regionserver.StoreScanner.<init>(StoreScanner.java:79)
+ at org.apache.hadoop.hbase.regionserver.Store.getScanner(Store.java:1202)
+ at org.apache.hadoop.hbase.regionserver.HRegion$RegionScanner.<init>(HRegion.java:2209)
+ at org.apache.hadoop.hbase.regionserver.HRegion.instantiateInternalScanner(HRegion.java:1063)
+ at org.apache.hadoop.hbase.regionserver.HRegion.getScanner(HRegion.java:1055)
+ at org.apache.hadoop.hbase.regionserver.HRegion.getScanner(HRegion.java:1039)
+ at org.apache.hadoop.hbase.regionserver.HRegion.getLastIncrement(HRegion.java:2875)
+ at org.apache.hadoop.hbase.regionserver.HRegion.incrementColumnValue(HRegion.java:2978)
+ at org.apache.hadoop.hbase.regionserver.HRegionServer.incrementColumnValue(HRegionServer.java:2433)
+ at sun.reflect.GeneratedMethodAccessor20.invoke(Unknown Source)
+ at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:25)
+ at java.lang.reflect.Method.invoke(Method.java:597)
+ at org.apache.hadoop.hbase.ipc.HBaseRPC$Server.call(HBaseRPC.java:560)
+ at org.apache.hadoop.hbase.ipc.HBaseServer$Handler.run(HBaseServer.java:1027)
+
+
+
+ A thread that receives data from HDFS:
+
+"IPC Client (47) connection to sv4borg9/10.4.24.40:9000 from hadoop" daemon prio=10 tid=0x00007f16a02d0000 nid=0x4fa3 runnable [0x00007f16b517d000..0x00007f16b517dbf0]
+ java.lang.Thread.State: RUNNABLE
+ at sun.nio.ch.EPollArrayWrapper.epollWait(Native Method)
+ at sun.nio.ch.EPollArrayWrapper.poll(EPollArrayWrapper.java:215)
+ at sun.nio.ch.EPollSelectorImpl.doSelect(EPollSelectorImpl.java:65)
+ at sun.nio.ch.SelectorImpl.lockAndDoSelect(SelectorImpl.java:69)
+ - locked <0x00007f17d5b68c00> (a sun.nio.ch.Util$1)
+ - locked <0x00007f17d5b68be8> (a java.util.Collections$UnmodifiableSet)
+ - locked <0x00007f1877959b50> (a sun.nio.ch.EPollSelectorImpl)
+ at sun.nio.ch.SelectorImpl.select(SelectorImpl.java:80)
+ at org.apache.hadoop.net.SocketIOWithTimeout$SelectorPool.select(SocketIOWithTimeout.java:332)
+ at org.apache.hadoop.net.SocketIOWithTimeout.doIO(SocketIOWithTimeout.java:157)
+ at org.apache.hadoop.net.SocketInputStream.read(SocketInputStream.java:155)
+ at org.apache.hadoop.net.SocketInputStream.read(SocketInputStream.java:128)
+ at java.io.FilterInputStream.read(FilterInputStream.java:116)
+ at org.apache.hadoop.ipc.Client$Connection$PingInputStream.read(Client.java:304)
+ at java.io.BufferedInputStream.fill(BufferedInputStream.java:218)
+ at java.io.BufferedInputStream.read(BufferedInputStream.java:237)
+ - locked <0x00007f1808539178> (a java.io.BufferedInputStream)
+ at java.io.DataInputStream.readInt(DataInputStream.java:370)
+ at org.apache.hadoop.ipc.Client$Connection.receiveResponse(Client.java:569)
+ at org.apache.hadoop.ipc.Client$Connection.run(Client.java:477)
+
+
+
+ And here is a master trying to recover a lease after a RegionServer died:
+
+"LeaseChecker" daemon prio=10 tid=0x00000000407ef800 nid=0x76cd waiting on condition [0x00007f6d0eae2000..0x00007f6d0eae2a70]
+--
+ java.lang.Thread.State: WAITING (on object monitor)
+ at java.lang.Object.wait(Native Method)
+ at java.lang.Object.wait(Object.java:485)
+ at org.apache.hadoop.ipc.Client.call(Client.java:726)
+ - locked <0x00007f6d1cd28f80> (a org.apache.hadoop.ipc.Client$Call)
+ at org.apache.hadoop.ipc.RPC$Invoker.invoke(RPC.java:220)
+ at $Proxy1.recoverBlock(Unknown Source)
+ at org.apache.hadoop.hdfs.DFSClient$DFSOutputStream.processDatanodeError(DFSClient.java:2636)
+ at org.apache.hadoop.hdfs.DFSClient$DFSOutputStream.<init>(DFSClient.java:2832)
+ at org.apache.hadoop.hdfs.DFSClient.append(DFSClient.java:529)
+ at org.apache.hadoop.hdfs.DistributedFileSystem.append(DistributedFileSystem.java:186)
+ at org.apache.hadoop.fs.FileSystem.append(FileSystem.java:530)
+ at org.apache.hadoop.hbase.util.FSUtils.recoverFileLease(FSUtils.java:619)
+ at org.apache.hadoop.hbase.regionserver.wal.HLog.splitLog(HLog.java:1322)
+ at org.apache.hadoop.hbase.regionserver.wal.HLog.splitLog(HLog.java:1210)
+ at org.apache.hadoop.hbase.master.HMaster.splitLogAfterStartup(HMaster.java:648)
+ at org.apache.hadoop.hbase.master.HMaster.joinCluster(HMaster.java:572)
+ at org.apache.hadoop.hbase.master.HMaster.run(HMaster.java:503)
+
+
+
+
+ OpenTSDB
+
+ OpenTSDB is an excellent alternative to Ganglia as it uses HBase to store all the time series and doesn’t have to downsample. Monitoring your own HBase cluster that hosts OpenTSDB is a good exercise.
+
+
+ Here’s an example of a cluster that’s suffering from hundreds of compactions launched almost all around the same time, which severely affects the IO performance: (TODO: insert graph plotting compactionQueueSize)
+
+
+ It’s a good practice to build dashboards with all the important graphs per machine and per cluster so that debugging issues can be done with a single quick look. For example, at StumbleUpon there’s one dashboard per cluster with the most important metrics from both the OS and HBase. You can then go down at the machine level and get even more detailed metrics.
+
+
+
+ clusterssh+top
+
+ clusterssh+top, it’s like a poor man’s monitoring system and it can be quite useful when you have only a few machines as it’s very easy to setup. Starting clusterssh will give you one terminal per machine and another terminal in which whatever you type will be retyped in every window. This means that you can type “top” once and it will start it for all of your machines at the same time giving you full view of the current state of your cluster. You can also tail all the logs at the same time, edit files, etc.
+
+
+
+
+
+
+ Client
+ For more information on the HBase client, see .
+
+
+ ScannerTimeoutException or UnknownScannerException
+ This is thrown if the time between RPC calls from the client to RegionServer exceeds the scan timeout.
+ For example, if Scan.setCaching is set to 500, then there will be an RPC call to fetch the next batch of rows every 500 .next() calls on the ResultScanner
+ because data is being transferred in blocks of 500 rows to the client. Reducing the setCaching value may be an option, but setting this value too low makes for inefficient
+ processing on numbers of rows.
+
+ See .
+
+
+
+ Shell or client application throws lots of scary exceptions during normal operation
+ Since 0.20.0 the default log level for org.apache.hadoop.hbase.*is DEBUG.
+
+ On your clients, edit $HBASE_HOME/conf/log4j.properties and change this: log4j.logger.org.apache.hadoop.hbase=DEBUG to this: log4j.logger.org.apache.hadoop.hbase=INFO, or even log4j.logger.org.apache.hadoop.hbase=WARN.
+
+
+
+ Long Client Pauses With Compression
+ This is a fairly frequent question on the HBase dist-list. The scenario is that a client is typically inserting a lot of data into a
+ relatively un-optimized HBase cluster. Compression can exacerbate the pauses, although it is not the source of the problem.
+ See on the pattern for pre-creating regions and confirm that the table isn't starting with a single region.
+ See for cluster configuration, particularly hbase.hstore.blockingStoreFiles, hbase.hregion.memstore.block.multiplier,
+ MAX_FILESIZE (region size), and MEMSTORE_FLUSHSIZE.
+ A slightly longer explanation of why pauses can happen is as follows: Puts are sometimes blocked on the MemStores which are blocked by the flusher thread which is blocked because there are
+ too many files to compact because the compactor is given too many small files to compact and has to compact the same data repeatedly. This situation can occur even with minor compactions.
+ Compounding this situation, HBase doesn't compress data in memory. Thus, the 64MB that lives in the MemStore could become a 6MB file after compression - which results in a smaller StoreFile. The upside is that
+ more data is packed into the same region, but performance is achieved by being able to write larger files - which is why HBase waits until the flushize before writing a new StoreFile. And smaller StoreFiles
+ become targets for compaction. Without compression the files are much bigger and don't need as much compaction, however this is at the expense of I/O.
+
+
+ For additional information, see this thread on Long client pauses with compression.
+
+
+
+
+ ZooKeeper Client Connection Errors
+ Errors like this...
+
+11/07/05 11:26:41 WARN zookeeper.ClientCnxn: Session 0x0 for server null,
+ unexpected error, closing socket connection and attempting reconnect
+ java.net.ConnectException: Connection refused: no further information
+ at sun.nio.ch.SocketChannelImpl.checkConnect(Native Method)
+ at sun.nio.ch.SocketChannelImpl.finishConnect(Unknown Source)
+ at org.apache.zookeeper.ClientCnxn$SendThread.run(ClientCnxn.java:1078)
+ 11/07/05 11:26:43 INFO zookeeper.ClientCnxn: Opening socket connection to
+ server localhost/127.0.0.1:2181
+ 11/07/05 11:26:44 WARN zookeeper.ClientCnxn: Session 0x0 for server null,
+ unexpected error, closing socket connection and attempting reconnect
+ java.net.ConnectException: Connection refused: no further information
+ at sun.nio.ch.SocketChannelImpl.checkConnect(Native Method)
+ at sun.nio.ch.SocketChannelImpl.finishConnect(Unknown Source)
+ at org.apache.zookeeper.ClientCnxn$SendThread.run(ClientCnxn.java:1078)
+ 11/07/05 11:26:45 INFO zookeeper.ClientCnxn: Opening socket connection to
+ server localhost/127.0.0.1:2181
+
+ ... are either due to ZooKeeper being down, or unreachable due to network issues.
+
+ The utility may help investigate ZooKeeper issues.
+
+
+
+ Client running out of memory though heap size seems to be stable (but the off-heap/direct heap keeps growing)
+
+You are likely running into the issue that is described and worked through in
+the mail thread HBase, mail # user - Suspected memory leak
+and continued over in HBase, mail # dev - FeedbackRe: Suspected memory leak.
+A workaround is passing your client-side JVM a reasonable value for -XX:MaxDirectMemorySize. By default,
+the MaxDirectMemorySize is equal to your -Xmx max heapsize setting (if -Xmx is set).
+Try seting it to something smaller (for example, one user had success setting it to 1g when
+they had a client-side heap of 12g). If you set it too small, it will bring on FullGCs so keep
+it a bit hefty. You want to make this setting client-side only especially if you are running the new experiemental
+server-side off-heap cache since this feature depends on being able to use big direct buffers (You may have to keep
+separate client-side and server-side config dirs).
+
+
+
+ Client Slowdown When Calling Admin Methods (flush, compact, etc.)
+
+This is a client issue fixed by HBASE-5073 in 0.90.6.
+There was a ZooKeeper leak in the client and the client was getting pummeled by ZooKeeper events with each additional
+invocation of the admin API.
+
+
+
+
+ Secure Client Cannot Connect ([Caused by GSSException: No valid credentials provided (Mechanism level: Failed to find any Kerberos tgt)])
+
+There can be several causes that produce this symptom.
+
+
+First, check that you have a valid Kerberos ticket. One is required in order to set up communication with a secure HBase cluster. Examine the ticket currently in the credential cache, if any, by running the klist command line utility. If no ticket is listed, you must obtain a ticket by running the kinit command with either a keytab specified, or by interactively entering a password for the desired principal.
+
+
+Then, consult the Java Security Guide troubleshooting section. The most common problem addressed there is resolved by setting javax.security.auth.useSubjectCredsOnly system property value to false.
+
+
+Because of a change in the format in which MIT Kerberos writes its credentials cache, there is a bug in the Oracle JDK 6 Update 26 and earlier that causes Java to be unable to read the Kerberos credentials cache created by versions of MIT Kerberos 1.8.1 or higher. If you have this problematic combination of components in your environment, to work around this problem, first log in with kinit and then immediately refresh the credential cache with kinit -R. The refresh will rewrite the credential cache without the problematic formatting.
+
+
+Finally, depending on your Kerberos configuration, you may need to install the Java Cryptography Extension, or JCE. Insure the JCE jars are on the classpath on both server and client systems.
+
+
+You may also need to download the unlimited strength JCE policy files. Uncompress and extract the downloaded file, and install the policy jars into <java-home>/lib/security.
+
+
+
+
+
+
+ MapReduce
+
+ You Think You're On The Cluster, But You're Actually Local
+ This following stacktrace happened using ImportTsv, but things like this
+ can happen on any job with a mis-configuration.
+
+ WARN mapred.LocalJobRunner: job_local_0001
+java.lang.IllegalArgumentException: Can't read partitions file
+ at org.apache.hadoop.hbase.mapreduce.hadoopbackport.TotalOrderPartitioner.setConf(TotalOrderPartitioner.java:111)
+ at org.apache.hadoop.util.ReflectionUtils.setConf(ReflectionUtils.java:62)
+ at org.apache.hadoop.util.ReflectionUtils.newInstance(ReflectionUtils.java:117)
+ at org.apache.hadoop.mapred.MapTask$NewOutputCollector.<init>(MapTask.java:560)
+ at org.apache.hadoop.mapred.MapTask.runNewMapper(MapTask.java:639)
+ at org.apache.hadoop.mapred.MapTask.run(MapTask.java:323)
+ at org.apache.hadoop.mapred.LocalJobRunner$Job.run(LocalJobRunner.java:210)
+Caused by: java.io.FileNotFoundException: File _partition.lst does not exist.
+ at org.apache.hadoop.fs.RawLocalFileSystem.getFileStatus(RawLocalFileSystem.java:383)
+ at org.apache.hadoop.fs.FilterFileSystem.getFileStatus(FilterFileSystem.java:251)
+ at org.apache.hadoop.fs.FileSystem.getLength(FileSystem.java:776)
+ at org.apache.hadoop.io.SequenceFile$Reader.<init>(SequenceFile.java:1424)
+ at org.apache.hadoop.io.SequenceFile$Reader.<init>(SequenceFile.java:1419)
+ at org.apache.hadoop.hbase.mapreduce.hadoopbackport.TotalOrderPartitioner.readPartitions(TotalOrderPartitioner.java:296)
+
+ .. see the critical portion of the stack? It's...
+
+ at org.apache.hadoop.mapred.LocalJobRunner$Job.run(LocalJobRunner.java:210)
+
+ LocalJobRunner means the job is running locally, not on the cluster.
+
+ See
+
+ http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/mapreduce/package-summary.html#classpath for more
+ information on HBase MapReduce jobs and classpaths.
+
+
+
+
+
+ NameNode
+ For more information on the NameNode, see .
+
+
+ HDFS Utilization of Tables and Regions
+ To determine how much space HBase is using on HDFS use the hadoop shell commands from the NameNode. For example...
+ hadoop fs -dus /hbase/ ...returns the summarized disk utilization for all HBase objects.
+ hadoop fs -dus /hbase/myTable ...returns the summarized disk utilization for the HBase table 'myTable'.
+ hadoop fs -du /hbase/myTable ...returns a list of the regions under the HBase table 'myTable' and their disk utilization.
+ For more information on HDFS shell commands, see the HDFS FileSystem Shell documentation.
+
+
+
+ Browsing HDFS for HBase Objects
+ Somtimes it will be necessary to explore the HBase objects that exist on HDFS. These objects could include the WALs (Write Ahead Logs), tables, regions, StoreFiles, etc.
+ The easiest way to do this is with the NameNode web application that runs on port 50070. The NameNode web application will provide links to the all the DataNodes in the cluster so that
+ they can be browsed seamlessly.
+ The HDFS directory structure of HBase tables in the cluster is...
+
+/hbase
+ /<Table> (Tables in the cluster)
+ /<Region> (Regions for the table)
+ /<ColumnFamiy> (ColumnFamilies for the Region for the table)
+ /<StoreFile> (StoreFiles for the ColumnFamily for the Regions for the table)
+
+
+ The HDFS directory structure of HBase WAL is..
+
+/hbase
+ /.logs
+ /<RegionServer> (RegionServers)
+ /<HLog> (WAL HLog files for the RegionServer)
+
+
+ See the HDFS User Guide for other non-shell diagnostic
+ utilities like fsck.
+
+
+ Use Cases
+ Two common use-cases for querying HDFS for HBase objects is research the degree of uncompaction of a table. If there are a large number of StoreFiles for each ColumnFamily it could
+ indicate the need for a major compaction. Additionally, after a major compaction if the resulting StoreFile is "small" it could indicate the need for a reduction of ColumnFamilies for
+ the table.
+
+
+
+
+
+
+
+ Network
+
+ Network Spikes
+ If you are seeing periodic network spikes you might want to check the compactionQueues to see if major
+ compactions are happening.
+
+ See for more information on managing compactions.
+
+
+
+ Loopback IP
+ HBase expects the loopback IP Address to be 127.0.0.1. See the Getting Started section on .
+
+
+
+ Network Interfaces
+ Are all the network interfaces functioning correctly? Are you sure? See the Troubleshooting Case Study in .
+
+
+
+
+
+
+ RegionServer
+ For more information on the RegionServers, see .
+
+
+ Startup Errors
+
+ Master Starts, But RegionServers Do Not
+ The Master believes the RegionServers have the IP of 127.0.0.1 - which is localhost and resolves to the master's own localhost.
+
+ The RegionServers are erroneously informing the Master that their IP addresses are 127.0.0.1.
+
+ Modify /etc/hosts on the region servers, from...
+
+# Do not remove the following line, or various programs
+# that require network functionality will fail.
+127.0.0.1 fully.qualified.regionservername regionservername localhost.localdomain localhost
+::1 localhost6.localdomain6 localhost6
+
+ ... to (removing the master node's name from localhost)...
+
+# Do not remove the following line, or various programs
+# that require network functionality will fail.
+127.0.0.1 localhost.localdomain localhost
+::1 localhost6.localdomain6 localhost6
+
+
+
+
+
+ Compression Link Errors
+
+ Since compression algorithms such as LZO need to be installed and configured on each cluster this is a frequent source of startup error. If you see messages like this...
+
+11/02/20 01:32:15 ERROR lzo.GPLNativeCodeLoader: Could not load native gpl library
+java.lang.UnsatisfiedLinkError: no gplcompression in java.library.path
+ at java.lang.ClassLoader.loadLibrary(ClassLoader.java:1734)
+ at java.lang.Runtime.loadLibrary0(Runtime.java:823)
+ at java.lang.System.loadLibrary(System.java:1028)
+
+ .. then there is a path issue with the compression libraries. See the Configuration section on LZO compression configuration.
+
+
+
+
+ Runtime Errors
+
+
+ RegionServer Hanging
+
+ Are you running an old JVM (< 1.6.0_u21?)? When you look at a thread dump,
+ does it look like threads are BLOCKED but no one holds the lock all are
+ blocked on? See HBASE 3622 Deadlock in HBaseServer (JVM bug?).
+ Adding -XX:+UseMembar to the HBase HBASE_OPTS in conf/hbase-env.sh
+ may fix it.
+
+ Also, are you using ? These are discouraged because they can lock up the
+ RegionServers if not managed properly.
+
+
+
+ java.io.IOException...(Too many open files)
+
+ If you see log messages like this...
+
+2010-09-13 01:24:17,336 WARN org.apache.hadoop.hdfs.server.datanode.DataNode:
+Disk-related IOException in BlockReceiver constructor. Cause is java.io.IOException: Too many open files
+ at java.io.UnixFileSystem.createFileExclusively(Native Method)
+ at java.io.File.createNewFile(File.java:883)
+
+ ... see the Getting Started section on ulimit and nproc configuration.
+
+
+
+ xceiverCount 258 exceeds the limit of concurrent xcievers 256
+
+ This typically shows up in the DataNode logs.
+
+
+ See the Getting Started section on xceivers configuration.
+
+
+
+ System instability, and the presence of "java.lang.OutOfMemoryError: unable to create new native thread in exceptions" HDFS DataNode logs or that of any system daemon
+
+ See the Getting Started section on ulimit and nproc configuration. The default on recent Linux
+ distributions is 1024 - which is far too low for HBase.
+
+
+
+ DFS instability and/or RegionServer lease timeouts
+
+ If you see warning messages like this...
+
+2009-02-24 10:01:33,516 WARN org.apache.hadoop.hbase.util.Sleeper: We slept xxx ms, ten times longer than scheduled: 10000
+2009-02-24 10:01:33,516 WARN org.apache.hadoop.hbase.util.Sleeper: We slept xxx ms, ten times longer than scheduled: 15000
+2009-02-24 10:01:36,472 WARN org.apache.hadoop.hbase.regionserver.HRegionServer: unable to report to master for xxx milliseconds - retrying
+
+ ... or see full GC compactions then you may be experiencing full GC's.
+
+
+
+ "No live nodes contain current block" and/or YouAreDeadException
+
+ These errors can happen either when running out of OS file handles or in periods of severe network problems where the nodes are unreachable.
+
+
+ See the Getting Started section on ulimit and nproc configuration and check your network.
+
+
+
+ ZooKeeper SessionExpired events
+ Master or RegionServers shutting down with messages like those in the logs:
+
+WARN org.apache.zookeeper.ClientCnxn: Exception
+closing session 0x278bd16a96000f to sun.nio.ch.SelectionKeyImpl@355811ec
+java.io.IOException: TIMED OUT
+ at org.apache.zookeeper.ClientCnxn$SendThread.run(ClientCnxn.java:906)
+WARN org.apache.hadoop.hbase.util.Sleeper: We slept 79410ms, ten times longer than scheduled: 5000
+INFO org.apache.zookeeper.ClientCnxn: Attempting connection to server hostname/IP:PORT
+INFO org.apache.zookeeper.ClientCnxn: Priming connection to java.nio.channels.SocketChannel[connected local=/IP:PORT remote=hostname/IP:PORT]
+INFO org.apache.zookeeper.ClientCnxn: Server connection successful
+WARN org.apache.zookeeper.ClientCnxn: Exception closing session 0x278bd16a96000d to sun.nio.ch.SelectionKeyImpl@3544d65e
+java.io.IOException: Session Expired
+ at org.apache.zookeeper.ClientCnxn$SendThread.readConnectResult(ClientCnxn.java:589)
+ at org.apache.zookeeper.ClientCnxn$SendThread.doIO(ClientCnxn.java:709)
+ at org.apache.zookeeper.ClientCnxn$SendThread.run(ClientCnxn.java:945)
+ERROR org.apache.hadoop.hbase.regionserver.HRegionServer: ZooKeeper session expired
+
+
+ The JVM is doing a long running garbage collecting which is pausing every threads (aka "stop the world").
+ Since the RegionServer's local ZooKeeper client cannot send heartbeats, the session times out.
+ By design, we shut down any node that isn't able to contact the ZooKeeper ensemble after getting a timeout so that it stops serving data that may already be assigned elsewhere.
+
+
+
+ Make sure you give plenty of RAM (in hbase-env.sh), the default of 1GB won't be able to sustain long running imports.
+ Make sure you don't swap, the JVM never behaves well under swapping.
+ Make sure you are not CPU starving the RegionServer thread. For example, if you are running a MapReduce job using 6 CPU-intensive tasks on a machine with 4 cores, you are probably starving the RegionServer enough to create longer garbage collection pauses.
+ Increase the ZooKeeper session timeout
+
+ If you wish to increase the session timeout, add the following to your hbase-site.xml to increase the timeout from the default of 60 seconds to 120 seconds.
+
+<property>
+ <name>zookeeper.session.timeout</name>
+ <value>1200000</value>
+</property>
+<property>
+ <name>hbase.zookeeper.property.tickTime</name>
+ <value>6000</value>
+</property>
+
+
+
+ Be aware that setting a higher timeout means that the regions served by a failed RegionServer will take at least
+ that amount of time to be transfered to another RegionServer. For a production system serving live requests, we would instead
+ recommend setting it lower than 1 minute and over-provision your cluster in order the lower the memory load on each machines (hence having
+ less garbage to collect per machine).
+
+
+ If this is happening during an upload which only happens once (like initially loading all your data into HBase), consider bulk loading.
+
+ See for other general information about ZooKeeper troubleshooting.
+
+
+ NotServingRegionException
+ This exception is "normal" when found in the RegionServer logs at DEBUG level. This exception is returned back to the client
+ and then the client goes back to .META. to find the new location of the moved region.
+ However, if the NotServingRegionException is logged ERROR, then the client ran out of retries and something probably wrong.
+
+
+ Regions listed by domain name, then IP
+
+ Fix your DNS. In versions of HBase before 0.92.x, reverse DNS needs to give same answer
+ as forward lookup. See HBASE 3431
+ RegionServer is not using the name given it by the master; double entry in master listing of servers for gorey details.
+
+
+
+ Logs flooded with '2011-01-10 12:40:48,407 INFO org.apache.hadoop.io.compress.CodecPool: Got
+ brand-new compressor' messages
+ We are not using the native versions of compression
+ libraries. See HBASE-1900 Put back native support when hadoop 0.21 is released.
+ Copy the native libs from hadoop under hbase lib dir or
+ symlink them into place and the message should go away.
+
+
+
+ Server handler X on 60020 caught: java.nio.channels.ClosedChannelException
+
+ If you see this type of message it means that the region server was trying to read/send data from/to a client but
+ it already went away. Typical causes for this are if the client was killed (you see a storm of messages like this when a MapReduce
+ job is killed or fails) or if the client receives a SocketTimeoutException. It's harmless, but you should consider digging in
+ a bit more if you aren't doing something to trigger them.
+
+
+
+
+
+ Shutdown Errors
+
+
+
+
+
+
+ Master
+ For more information on the Master, see .
+
+
+ Startup Errors
+
+ Master says that you need to run the hbase migrations script
+ Upon running that, the hbase migrations script says no files in root directory.
+ HBase expects the root directory to either not exist, or to have already been initialized by hbase running a previous time. If you create a new directory for HBase using Hadoop DFS, this error will occur.
+ Make sure the HBase root directory does not currently exist or has been initialized by a previous run of HBase. Sure fire solution is to just use Hadoop dfs to delete the HBase root and let HBase create and initialize the directory itself.
+
+
+
+
+
+ Shutdown Errors
+
+
+
+
+
+
+ ZooKeeper
+
+ Startup Errors
+
+ Could not find my address: xyz in list of ZooKeeper quorum servers
+ A ZooKeeper server wasn't able to start, throws that error. xyz is the name of your server.
+ This is a name lookup problem. HBase tries to start a ZooKeeper server on some machine but that machine isn't able to find itself in the hbase.zookeeper.quorum configuration.
+
+ Use the hostname presented in the error message instead of the value you used. If you have a DNS server, you can set hbase.zookeeper.dns.interface and hbase.zookeeper.dns.nameserver in hbase-site.xml to make sure it resolves to the correct FQDN.
+
+
+
+
+
+ ZooKeeper, The Cluster Canary
+ ZooKeeper is the cluster's "canary in the mineshaft". It'll be the first to notice issues if any so making sure its happy is the short-cut to a humming cluster.
+
+
+ See the ZooKeeper Operating Environment Troubleshooting page. It has suggestions and tools for checking disk and networking performance; i.e. the operating environment your ZooKeeper and HBase are running in.
+
+ Additionally, the utility may help investigate ZooKeeper issues.
+
+
+
+
+
+
+ Amazon EC2
+
+ ZooKeeper does not seem to work on Amazon EC2
+ HBase does not start when deployed as Amazon EC2 instances. Exceptions like the below appear in the Master and/or RegionServer logs:
+
+ 2009-10-19 11:52:27,030 INFO org.apache.zookeeper.ClientCnxn: Attempting
+ connection to server ec2-174-129-15-236.compute-1.amazonaws.com/10.244.9.171:2181
+ 2009-10-19 11:52:27,032 WARN org.apache.zookeeper.ClientCnxn: Exception
+ closing session 0x0 to sun.nio.ch.SelectionKeyImpl@656dc861
+ java.net.ConnectException: Connection refused
+
+
+ Security group policy is blocking the ZooKeeper port on a public address.
+ Use the internal EC2 host names when configuring the ZooKeeper quorum peer list.
+
+
+
+ Instability on Amazon EC2
+ Questions on HBase and Amazon EC2 come up frequently on the HBase dist-list. Search for old threads using Search Hadoop
+
+
+
+ Remote Java Connection into EC2 Cluster Not Working
+
+ See Andrew's answer here, up on the user list: Remote Java client connection into EC2 instance.
+
+
+
+
+
+
+ HBase and Hadoop version issues
+
+ NoClassDefFoundError when trying to run 0.90.x on hadoop-0.20.205.x (or hadoop-1.0.x)
+ HBase 0.90.x does not ship with hadoop-0.20.205.x, etc. To make it run, you need to replace the hadoop
+ jars that HBase shipped with in its lib directory with those of the Hadoop you want to
+ run HBase on. If even after replacing Hadoop jars you get the below exception:
+
+sv4r6s38: Exception in thread "main" java.lang.NoClassDefFoundError: org/apache/commons/configuration/Configuration
+sv4r6s38: at org.apache.hadoop.metrics2.lib.DefaultMetricsSystem.<init>(DefaultMetricsSystem.java:37)
+sv4r6s38: at org.apache.hadoop.metrics2.lib.DefaultMetricsSystem.<clinit>(DefaultMetricsSystem.java:34)
+sv4r6s38: at org.apache.hadoop.security.UgiInstrumentation.create(UgiInstrumentation.java:51)
+sv4r6s38: at org.apache.hadoop.security.UserGroupInformation.initialize(UserGroupInformation.java:209)
+sv4r6s38: at org.apache.hadoop.security.UserGroupInformation.ensureInitialized(UserGroupInformation.java:177)
+sv4r6s38: at org.apache.hadoop.security.UserGroupInformation.isSecurityEnabled(UserGroupInformation.java:229)
+sv4r6s38: at org.apache.hadoop.security.KerberosName.<clinit>(KerberosName.java:83)
+sv4r6s38: at org.apache.hadoop.security.UserGroupInformation.initialize(UserGroupInformation.java:202)
+sv4r6s38: at org.apache.hadoop.security.UserGroupInformation.ensureInitialized(UserGroupInformation.java:177)
+
+you need to copy under hbase/lib, the commons-configuration-X.jar you find
+in your Hadoop's lib directory. That should fix the above complaint.
+
+
+
+
+
+ Case Studies
+ For Performance and Troubleshooting Case Studies, see .
+
+
+
+
diff --git src/docbkx/upgrading.xml src/docbkx/upgrading.xml
new file mode 100644
index 0000000..5a18872
--- /dev/null
+++ src/docbkx/upgrading.xml
@@ -0,0 +1,201 @@
+
+
+
+ Upgrading
+
+ Review , in particular the section on Hadoop version.
+
+
+ Upgrading to HBase 0.90.x from 0.20.x or 0.89.x
+ This version of 0.90.x HBase can be started on data written by
+ HBase 0.20.x or HBase 0.89.x. There is no need of a migration step.
+ HBase 0.89.x and 0.90.x does write out the name of region directories
+ differently -- it names them with a md5 hash of the region name rather
+ than a jenkins hash -- so this means that once started, there is no
+ going back to HBase 0.20.x.
+
+
+ Be sure to remove the hbase-default.xml from
+ your conf
+ directory on upgrade. A 0.20.x version of this file will have
+ sub-optimal configurations for 0.90.x HBase. The
+ hbase-default.xml file is now bundled into the
+ HBase jar and read from there. If you would like to review
+ the content of this file, see it in the src tree at
+ src/main/resources/hbase-default.xml or
+ see .
+
+
+ Finally, if upgrading from 0.20.x, check your
+ .META. schema in the shell. In the past we would
+ recommend that users run with a 16kb
+ MEMSTORE_FLUSHSIZE.
+ Run hbase> scan '-ROOT-' in the shell. This will output
+ the current .META. schema. Check
+ MEMSTORE_FLUSHSIZE size. Is it 16kb (16384)? If so, you will
+ need to change this (The 'normal'/default value is 64MB (67108864)).
+ Run the script bin/set_meta_memstore_size.rb.
+ This will make the necessary edit to your .META. schema.
+ Failure to run this change will make for a slow cluster
+
+ See HBASE-3499 Users upgrading to 0.90.0 need to have their .META. table updated with the right MEMSTORE_SIZE
+
+
+ .
+
+
+
+
+ Upgrading from 0.90.x to 0.92.x
+ Upgrade Guide
+You will find that 0.92.0 runs a little differently to 0.90.x releases. Here are a few things to watch out for upgrading from 0.90.x to 0.92.0.
+tl;dr
+
+If you've not patience, here are the important things to know upgrading.
+
+Once you upgrade, you can’t go back.
+
+
+MSLAB is on by default. Watch that heap usage if you have a lot of regions.
+
+
+Distributed splitting is on by defaul. It should make region server failover faster.
+
+
+There’s a separate tarball for security.
+
+
+If -XX:MaxDirectMemorySize is set in your hbase-env.sh, it’s going to enable the experimental off-heap cache (You may not want this).
+
+
+
+
+
+
+
+You can’t go back!
+
+To move to 0.92.0, all you need to do is shutdown your cluster, replace your hbase 0.90.x with hbase 0.92.0 binaries (be sure you clear out all 0.90.x instances) and restart (You cannot do a rolling restart from 0.90.x to 0.92.x -- you must restart).
+On startup, the .META. table content is rewritten removing the table schema from the info:regioninfo column.
+Also, any flushes done post first startup will write out data in the new 0.92.0 file format, HFile V2.
+This means you cannot go back to 0.90.x once you’ve started HBase 0.92.0 over your HBase data directory.
+
+
+
+
+MSLAB is ON by default
+
+In 0.92.0, the hbase.hregion.memstore.mslab.enabled flag is set to true
+(See ). In 0.90.x it was false. When it is enabled, memstores will step allocate memory in MSLAB 2MB chunks even if the
+memstore has zero or just a few small elements. This is fine usually but if you had lots of regions per regionserver in a 0.90.x cluster (and MSLAB was off),
+you may find yourself OOME'ing on upgrade because the thousands of regions * number of column families * 2MB MSLAB (at a minimum)
+puts your heap over the top. Set hbase.hregion.memstore.mslab.enabled to
+false or set the MSLAB size down from 2MB by setting hbase.hregion.memstore.mslab.chunksize to something less.
+
+
+
+Distributed splitting is on by default
+
+Previous, WAL logs on crash were split by the Master alone. In 0.92.0, log splitting is done by the cluster (See See “HBASE-1364 [performance] Distributed splitting of regionserver commit logs”). This should cut down significantly on the amount of time it takes splitting logs and getting regions back online again.
+
+
+
+Memory accounting is different now
+
+In 0.92.0, indices and bloom filters take up residence in the same LRU used caching blocks that come from the filesystem.
+In 0.90.x, the HFile v1 indices lived outside of the LRU so they took up space even if the index was on a ‘cold’ file, one that wasn’t being actively used. With the indices now in the LRU, you may find you
+have less space for block caching. Adjust your block cache accordingly. See the for more detail.
+The block size default size has been changed in 0.92.0 from 0.2 (20 percent of heap) to 0.25.
+
+
+
+
+On the Hadoop version to use
+
+Run 0.92.0 on Hadoop 1.0.x (or CDH3u3 when it ships). The performance benefits are worth making the move. Otherwise, our Hadoop prescription is as it has been; you need an Hadoop that supports a working sync. See .
+
+
+If running on Hadoop 1.0.x (or CDH3u3), enable local read. See Practical Caching presentation for ruminations on the performance benefits ‘going local’ (and for how to enable local reads).
+
+
+HBase 0.92.0 ships with ZooKeeper 3.4.2
+
+If you can, upgrade your zookeeper. If you can’t, 3.4.2 clients should work against 3.3.X ensembles (HBase makes use of 3.4.2 API).
+
+
+
+Online alter is off by default
+
+In 0.92.0, we’ve added an experimental online schema alter facility (See ). Its off by default. Enable it at your own risk. Online alter and splitting tables do not play well together so be sure your cluster quiescent using this feature (for now).
+
+
+
+WebUI
+
+The webui has had a few additions made in 0.92.0. It now shows a list of the regions currently transitioning, recent compactions/flushes, and a process list of running processes (usually empty if all is well and requests are being handled promptly). Other additions including requests by region, a debugging servlet dump, etc.
+
+
+
+Security tarball
+
+We now ship with two tarballs; secure and insecure HBase. Documentation on how to setup a secure HBase is on the way.
+
+
+
+Experimental off-heap cache
+
+
+A new cache was contributed to 0.92.0 to act as a solution between using the “on-heap” cache which is the current LRU cache the region servers have and the operating system cache which is out of our control.
+To enable, set “-XX:MaxDirectMemorySize” in hbase-env.sh to the value for maximum direct memory size and specify hbase.offheapcache.percentage in hbase-site.xml with the percentage that you want to dedicate to off-heap cache. This should only be set for servers and not for clients. Use at your own risk.
+See this blog post for additional information on this new experimental feature: http://www.cloudera.com/blog/2012/01/caching-in-hbase-slabcache/
+
+
+
+Changes in HBase replication
+
+0.92.0 adds two new features: multi-slave and multi-master replication. The way to enable this is the same as adding a new peer, so in order to have multi-master you would just run add_peer for each cluster that acts as a master to the other slave clusters. Collisions are handled at the timestamp level which may or may not be what you want, this needs to be evaluated on a per use case basis. Replication is still experimental in 0.92 and is disabled by default, run it at your own risk.
+
+
+
+
+RegionServer now aborts if OOME
+
+If an OOME, we now have the JVM kill -9 the regionserver process so it goes down fast. Previous, a RegionServer might stick around after incurring an OOME limping along in some wounded state. To disable this facility, and recommend you leave it in place, you’d need to edit the bin/hbase file. Look for the addition of the -XX:OnOutOfMemoryError="kill -9 %p" arguments (See [HBASE-4769] - ‘Abort RegionServer Immediately on OOME’)
+
+
+
+
+HFile V2 and the “Bigger, Fewer” Tendency
+
+0.92.0 stores data in a new format, . As HBase runs, it will move all your data from HFile v1 to HFile v2 format. This auto-migration will run in the background as flushes and compactions run.
+HFile V2 allows HBase run with larger regions/files. In fact, we encourage that all HBasers going forward tend toward Facebook axiom #1, run with larger, fewer regions.
+If you have lots of regions now -- more than 100s per host -- you should look into setting your region size up after you move to 0.92.0 (In 0.92.0, default size is not 1G, up from 256M), and then running online merge tool (See “HBASE-1621 merge tool should work on online cluster, but disabled table”).
+
+
+
+
diff --git src/site/resources/css/freebsd_docbook.css src/site/resources/css/freebsd_docbook.css
new file mode 100644
index 0000000..3d40fa7
--- /dev/null
+++ src/site/resources/css/freebsd_docbook.css
@@ -0,0 +1,208 @@
+/*
+ * Copyright (c) 2001, 2003, 2010 The FreeBSD Documentation Project
+ * All rights reserved.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ * 1. Redistributions of source code must retain the above copyright
+ * notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ * notice, this list of conditions and the following disclaimer in the
+ * documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR AND CONTRIBUTORS ``AS IS'' AND
+ * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+ * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+ * ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR OR CONTRIBUTORS BE LIABLE
+ * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+ * DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS
+ * OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
+ * HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
+ * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY
+ * OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF
+ * SUCH DAMAGE.
+ *
+ * $FreeBSD: doc/share/misc/docbook.css,v 1.15 2010/03/20 04:15:01 hrs Exp $
+ */
+
+BODY ADDRESS {
+ line-height: 1.3;
+ margin: .6em 0;
+}
+
+BODY BLOCKQUOTE {
+ margin-top: .75em;
+ line-height: 1.5;
+ margin-bottom: .75em;
+}
+
+HTML BODY {
+ margin: 1em 8% 1em 10%;
+ line-height: 1.2;
+}
+
+.LEGALNOTICE {
+ font-size: small;
+ font-variant: small-caps;
+}
+
+BODY DIV {
+ margin: 0;
+}
+
+DL {
+ margin: .8em 0;
+ line-height: 1.2;
+}
+
+BODY FORM {
+ margin: .6em 0;
+}
+
+H1, H2, H3, H4, H5, H6,
+DIV.EXAMPLE P B,
+.QUESTION,
+DIV.TABLE P B,
+DIV.PROCEDURE P B {
+ color: #990000;
+}
+
+BODY H1, BODY H2, BODY H3, BODY H4, BODY H5, BODY H6 {
+ line-height: 1.3;
+ margin-left: 0;
+}
+
+BODY H1, BODY H2 {
+ margin: .8em 0 0 -4%;
+}
+
+BODY H3, BODY H4 {
+ margin: .8em 0 0 -3%;
+}
+
+BODY H5 {
+ margin: .8em 0 0 -2%;
+}
+
+BODY H6 {
+ margin: .8em 0 0 -1%;
+}
+
+BODY HR {
+ margin: .6em;
+ border-width: 0 0 1px 0;
+ border-style: solid;
+ border-color: #cecece;
+}
+
+BODY IMG.NAVHEADER {
+ margin: 0 0 0 -4%;
+}
+
+OL {
+ margin: 0 0 0 5%;
+ line-height: 1.2;
+}
+
+BODY PRE {
+ margin: .75em 0;
+ line-height: 1.0;
+ font-family: monospace;
+}
+
+BODY TD, BODY TH {
+ line-height: 1.2;
+}
+
+UL, BODY DIR, BODY MENU {
+ margin: 0 0 0 5%;
+ line-height: 1.2;
+}
+
+HTML {
+ margin: 0;
+ padding: 0;
+}
+
+BODY P B.APPLICATION {
+ color: #000000;
+}
+
+.FILENAME {
+ color: #007a00;
+}
+
+.GUIMENU, .GUIMENUITEM, .GUISUBMENU,
+.GUILABEL, .INTERFACE,
+.SHORTCUT, .SHORTCUT .KEYCAP {
+ font-weight: bold;
+}
+
+.GUIBUTTON {
+ background-color: #CFCFCF;
+ padding: 2px;
+}
+
+.ACCEL {
+ background-color: #F0F0F0;
+ text-decoration: underline;
+}
+
+.SCREEN {
+ padding: 1ex;
+}
+
+.PROGRAMLISTING {
+ padding: 1ex;
+ background-color: #eee;
+ border: 1px solid #ccc;
+}
+
+@media screen { /* hide from IE3 */
+ a[href]:hover { background: #ffa }
+}
+
+BLOCKQUOTE.NOTE {
+ color: #222;
+ background: #eee;
+ border: 1px solid #ccc;
+ padding: 0.4em 0.4em;
+ width: 85%;
+}
+
+BLOCKQUOTE.TIP {
+ color: #004F00;
+ background: #d8ecd6;
+ border: 1px solid green;
+ padding: 0.2em 2em;
+ width: 85%;
+}
+
+BLOCKQUOTE.IMPORTANT {
+ font-style:italic;
+ border: 1px solid #a00;
+ border-left: 12px solid #c00;
+ padding: 0.1em 1em;
+}
+
+BLOCKQUOTE.WARNING {
+ color: #9F1313;
+ background: #f8e8e8;
+ border: 1px solid #e59595;
+ padding: 0.2em 2em;
+ width: 85%;
+}
+
+.EXAMPLE {
+ background: #fefde6;
+ border: 1px solid #f1bb16;
+ margin: 1em 0;
+ padding: 0.2em 2em;
+ width: 90%;
+}
+
+.INFORMALTABLE TABLE.CALSTABLE TR TD {
+ padding-left: 1em;
+ padding-right: 1em;
+}
diff --git src/site/resources/css/site.css src/site/resources/css/site.css
new file mode 100644
index 0000000..f26d03c
--- /dev/null
+++ src/site/resources/css/site.css
@@ -0,0 +1,127 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied. See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+a.externalLink, a.externalLink:link, a.externalLink:visited, a.externalLink:active, a.externalLink:hover {
+ background: none;
+ padding-right: 0;
+}
+
+/*
+body ul {
+ list-style-type: square;
+}
+*/
+
+#downloadbox {
+ float: right;
+ margin: 0 10px 20px 20px;
+ padding: 5px;
+ border: 1px solid #999;
+ background-color: #eee;
+}
+
+#downloadbox h5 {
+ color: #000;
+ margin: 0;
+ border-bottom: 1px solid #aaaaaa;
+ font-size: smaller;
+ padding: 0;
+}
+
+#downloadbox p {
+ margin-top: 1em;
+ margin-bottom: 0;
+}
+
+#downloadbox ul {
+ margin-top: 0;
+ margin-bottom: 1em;
+ list-style-type: disc;
+}
+
+#downloadbox li {
+ font-size: smaller;
+}
+
+/*
+h4 {
+ padding: 0;
+ border: none;
+ color: #000;
+ margin: 0;
+ font-size: larger;
+ font-weight: bold;
+}
+*/
+
+#banner {
+ background: none;
+}
+
+#banner img {
+ padding: 10px;
+ margin: auto;
+ display: block;
+ background: none;
+ float: center;
+ height:;
+}
+
+#breadcrumbs {
+ background-image: url();
+}
+
+#footer {
+ border-top: 0px;
+}
+
+.frontpagebox {
+ float: left;
+ text-align: center;
+ width: 15em;
+ margin-left: 0.5em;
+ margin-right: 0.5em;
+ margin-top: 2em;
+}
+
+.headline {
+ font-size: 120%;
+ font-weight: bold;
+ padding-top: 1px;
+ padding-bottom: 5px;
+ background-image: url(../images/breadcrumbs.jpg);
+ background-repeat: repeat-x;
+}
+
+.section {
+ padding-bottom: 0;
+ padding-top: 0;
+}
+
+/*
+#leftColumn {
+ display: none !important
+}
+
+#bodyColumn {
+ margin-left: 1.5em;
+}
+*/
+
+
diff --git src/site/resources/doap_Hbase.rdf src/site/resources/doap_Hbase.rdf
new file mode 100644
index 0000000..08e9bc0
--- /dev/null
+++ src/site/resources/doap_Hbase.rdf
@@ -0,0 +1,57 @@
+
+
+
+
+
+ 2012-04-14
+
+ Apache HBase
+
+
+ Apache HBase software is the Hadoop database. Think of it as a distributed, scalable, big data store.
+ Use Apache HBase software when you need random, realtime read/write access to your Big Data. This project's goal is the hosting of very large tables -- billions of rows X millions of columns -- atop clusters of commodity hardware. HBase is an open-source, distributed, versioned, column-oriented store modeled after Google's Bigtable: A Distributed Storage System for Structured Data by Chang et al. Just as Bigtable leverages the distributed data storage provided by the Google File System, HBase provides Bigtable-like capabilities on top of Hadoop and HDFS.
+
+
+
+ Java
+
+
+
+ Apache hbase 0.92.1
+ 2012-03-19
+ 0.92.1
+
+
+
+
+
+
+
+
+
+
+ Apache HBase PMC
+
+
+
+
+
diff --git src/site/site.vm src/site/site.vm
new file mode 100644
index 0000000..0a478e4
--- /dev/null
+++ src/site/site.vm
@@ -0,0 +1,544 @@
+
+#*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+*#
+
+#macro ( link $href $name $target $img $position $alt $border $width $height )
+ #set ( $linkTitle = ' title="' + $name + '"' )
+ #if( $target )
+ #set ( $linkTarget = ' target="' + $target + '"' )
+ #else
+ #set ( $linkTarget = "" )
+ #end
+ #if ( ( $href.toLowerCase().startsWith("http") || $href.toLowerCase().startsWith("https") ) )
+ #set ( $linkClass = ' class="externalLink"' )
+ #else
+ #set ( $linkClass = "" )
+ #end
+ #if ( $img )
+ #if ( $position == "left" )
+ #image($img $alt $border $width $height)$name
+ #else
+ $name #image($img $alt $border $width $height)
+ #end
+ #else
+ $name
+ #end
+#end
+##
+#macro ( image $img $alt $border $width $height )
+ #if( $img )
+ #if ( ! ( $img.toLowerCase().startsWith("http") || $img.toLowerCase().startsWith("https") ) )
+ #set ( $imgSrc = $PathTool.calculateLink( $img, $relativePath ) )
+ #set ( $imgSrc = $imgSrc.replaceAll( "\\", "/" ) )
+ #set ( $imgSrc = ' src="' + $imgSrc + '"' )
+ #else
+ #set ( $imgSrc = ' src="' + $img + '"' )
+ #end
+ #if( $alt )
+ #set ( $imgAlt = ' alt="' + $alt + '"' )
+ #else
+ #set ( $imgAlt = ' alt=""' )
+ #end
+ #if( $border )
+ #set ( $imgBorder = ' border="' + $border + '"' )
+ #else
+ #set ( $imgBorder = "" )
+ #end
+ #if( $width )
+ #set ( $imgWidth = ' width="' + $width + '"' )
+ #else
+ #set ( $imgWidth = "" )
+ #end
+ #if( $height )
+ #set ( $imgHeight = ' height="' + $height + '"' )
+ #else
+ #set ( $imgHeight = "" )
+ #end
+
+ #end
+#end
+#macro ( banner $banner $id )
+ #if ( $banner )
+ #if( $banner.href )
+
+ #else
+
HBase is not an ACID compliant database. However, it does guarantee certain specific
+ properties.
+
This specification enumerates the ACID properties of HBase.
+
+
+
For the sake of common vocabulary, we define the following terms:
+
+
Atomicity
+
an operation is atomic if it either completes entirely or not at all
+
+
Consistency
+
+ all actions cause the table to transition from one valid state directly to another
+ (eg a row will not disappear during an update, etc)
+
+
+
Isolation
+
+ an operation is isolated if it appears to complete independently of any other concurrent transaction
+
+
+
Durability
+
any update that reports "successful" to the client will not be lost
+
+
Visibility
+
an update is considered visible if any subsequent read will see the update as having been committed
+
+
+ The terms must and may are used as specified by RFC 2119.
+ In short, the word "must" implies that, if some case exists where the statement
+ is not true, it is a bug. The word "may" implies that, even if the guarantee
+ is provided in a current release, users should not rely on it.
+
+
+
+
+
Read APIs
+
+
get
+
scan
+
+
+
Write APIs
+
+
put
+
batch put
+
delete
+
+
Combination (read-modify-write) APIs
+
+
incrementColumnValue
+
checkAndPut
+
+
+
+
+
+
+
+
+
+
All mutations are atomic within a row. Any put will either wholely succeed or wholely fail.[3]
+
+
An operation that returns a "success" code has completely succeeded.
+
An operation that returns a "failure" code has completely failed.
+
An operation that times out may have succeeded and may have failed. However,
+ it will not have partially succeeded or failed.
+
+
This is true even if the mutation crosses multiple column families within a row.
+
APIs that mutate several rows will _not_ be atomic across the multiple rows.
+ For example, a multiput that operates on rows 'a','b', and 'c' may return having
+ mutated some but not all of the rows. In such cases, these APIs will return a list
+ of success codes, each of which may be succeeded, failed, or timed out as described above.
+
The checkAndPut API happens atomically like the typical compareAndSet (CAS) operation
+ found in many hardware architectures.
+
The order of mutations is seen to happen in a well-defined order for each row, with no
+ interleaving. For example, if one writer issues the mutation "a=1,b=1,c=1" and
+ another writer issues the mutation "a=2,b=2,c=2", the row must either
+ be "a=1,b=1,c=1" or "a=2,b=2,c=2" and must not be something
+ like "a=1,b=2,c=1".
+
+
Please note that this is not true _across rows_ for multirow batch mutations.
+
+
+
+
+
+
All rows returned via any access API will consist of a complete row that existed at
+ some point in the table's history.
+
This is true across column families - i.e a get of a full row that occurs concurrent
+ with some mutations 1,2,3,4,5 will return a complete row that existed at some point in time
+ between mutation i and i+1 for some i between 1 and 5.
+
The state of a row will only move forward through the history of edits to it.
+
+
+
+
+ A scan is not a consistent view of a table. Scans do
+ not exhibit snapshot isolation.
+
+
+ Rather, scans have the following properties:
+
+
+
+
+ Any row returned by the scan will be a consistent view (i.e. that version
+ of the complete row existed at some point in time) [1]
+
+
+ A scan will always reflect a view of the data at least as new as
+ the beginning of the scan. This satisfies the visibility guarantees
+ enumerated below.
+
+
For example, if client A writes data X and then communicates via a side
+ channel to client B, any scans started by client B will contain data at least
+ as new as X.
+
A scan _must_ reflect all mutations committed prior to the construction
+ of the scanner, and _may_ reflect some mutations committed subsequent to the
+ construction of the scanner.
+
Scans must include all data written prior to the scan (except in
+ the case where data is subsequently mutated, in which case it _may_ reflect
+ the mutation)
+
+
+
+ Those familiar with relational databases will recognize this isolation level as "read committed".
+
+
+ Please note that the guarantees listed above regarding scanner consistency
+ are referring to "transaction commit time", not the "timestamp"
+ field of each cell. That is to say, a scanner started at time t may see edits
+ with a timestamp value greater than t, if those edits were committed with a
+ "forward dated" timestamp before the scanner was constructed.
+
+
+
+
+
+
When a client receives a "success" response for any mutation, that
+ mutation is immediately visible to both that client and any client with whom it
+ later communicates through side channels. [3]
+
A row must never exhibit so-called "time-travel" properties. That
+ is to say, if a series of mutations moves a row sequentially through a series of
+ states, any sequence of concurrent reads will return a subsequence of those states.
+
+
For example, if a row's cells are mutated using the "incrementColumnValue"
+ API, a client must never see the value of any cell decrease.
+
This is true regardless of which read API is used to read back the mutation.
+
+
Any version of a cell that has been returned to a read operation is guaranteed to
+ be durably stored.
+
+
+
+
+
+
All visible data is also durable data. That is to say, a read will never return
+ data that has not been made durable on disk[2]
+
Any operation that returns a "success" code (eg does not throw an exception)
+ will be made durable.[3]
+
Any operation that returns a "failure" code will not be made durable
+ (subject to the Atomicity guarantees above)
+
All reasonable failure scenarios will not affect any of the guarantees of this document.
+
+
+
+
+
All of the above guarantees must be possible within HBase. For users who would like to trade
+ off some guarantees for performance, HBase may offer several tuning options. For example:
+
+
Visibility may be tuned on a per-read basis to allow stale reads or time travel.
+
Durability may be tuned to only flush data to disk on a periodic basis
[1] A consistent view is not guaranteed intra-row scanning -- i.e. fetching a portion of
+ a row in one RPC then going back to fetch another portion of the row in a subsequent RPC.
+ Intra-row scanning happens when you set a limit on how many values to return per Scan#next
+ (See Scan#setBatch(int)).
+
+
+
[2] In the context of HBase, "durably on disk" implies an hflush() call on the transaction
+ log. This does not actually imply an fsync() to magnetic media, but rather just that the data has been
+ written to the OS cache on all replicas of the log. In the case of a full datacenter power loss, it is
+ possible that the edits are not truly durable.
+
[3] Puts will either wholely succeed or wholely fail, provided that they are actually sent
+ to the RegionServer. If the writebuffer is used, Puts will not be sent until the writebuffer is filled
+ or it is explicitly flushed.
As being distributed, large scale platforms, the Hadoop and HBase projects mainly focus on *nix environments for production installations. However, being developed in Java, both projects are fully portable across platforms and, hence, also to the Windows operating system. For ease of development the projects rely on Cygwin to have a *nix-like environment on Windows to run the shell scripts.
+
+
+
This document explains the intricacies of running HBase on Windows using Cygwin as an all-in-one single-node installation for testing and development. The HBase Overview and QuickStart guides on the other hand go a long way in explaning how to setup HBase in more complex deployment scenario's.
+
+
+
+
For running HBase on Windows, 3 technologies are required: Java, Cygwin and SSH. The following paragraphs detail the installation of each of the aforementioned technologies.
+
+
HBase depends on the Java Platform, Standard Edition, 6 Release. So the target system has to be provided with at least the Java Runtime Environment (JRE); however if the system will also be used for development, the Jave Development Kit (JDK) is preferred. You can download the latest versions for both from Sun's download page. Installation is a simple GUI wizard that guides you through the process.
+
+
+
Cygwin is probably the oddest technology in this solution stack. It provides a dynamic link library that emulates most of a *nix environment on Windows. On top of that a whole bunch of the most common *nix tools are supplied. Combined, the DLL with the tools form a very *nix-alike environment on Windows.
+
+
For installation, Cygwin provides the setup.exe utility that tracks the versions of all installed components on the target system and provides the mechanism for installing or updating everything from the mirror sites of Cygwin.
+
+
To support installation, the setup.exe utility uses 2 directories on the target system. The Root directory for Cygwin (defaults to C:\cygwin) which will become / within the eventual Cygwin installation; and the Local Package directory (e.g. C:\cygsetup that is the cache where setup.exe stores the packages before they are installed. The cache must not be the same folder as the Cygwin root.
Make sure you have Administrator privileges on the target system.
+
Choose and create you Root and Local Package directories. A good suggestion is to use C:\cygwin\root and C:\cygwin\setup folders.
+
Download the setup.exe utility and save it to the Local Package directory.
+
Run the setup.exe utility,
+
+
Choose the Install from Internet option,
+
Choose your Root and Local Package folders
+
and select an appropriate mirror.
+
Don't select any additional packages yet, as we only want to install Cygwin for now.
+
Wait for download and install
+
Finish the installation
+
+
+
Optionally, you can now also add a shortcut to your Start menu pointing to the setup.exe utility in the Local Package folder.
+
Add CYGWIN_HOME system-wide environment variable that points to your Root directory.
+
Add %CYGWIN_HOME%\bin to the end of your PATH environment variable.
+
Reboot the sytem after making changes to the environment variables otherwise the OS will not be able to find the Cygwin utilities.
+
Test your installation by running your freshly created shortcuts or the Cygwin.bat command in the Root folder. You should end up in a terminal window that is running a Bash shell. Test the shell by issuing following commands:
+
+
cd / should take you to thr Root directory in Cygwin;
+
the LS commands that should list all files and folders in the current directory.
+
Use the exit command to end the terminal.
+
+
+
When needed, to uninstall Cygwin you can simply delete the Root and Local Package directory, and the shortcuts that were created during installation.
+
+
+
+
HBase (and Hadoop) rely on SSH for interprocess/-node communication and launching remote commands. SSH will be provisioned on the target system via Cygwin, which supports running Cygwin programs as Windows services!
+
+
+
Rerun the setup.exe utility.
+
Leave all parameters as is, skipping through the wizard using the Next button until the Select Packages panel is shown.
+
Maximize the window and click the View button to toggle to the list view, which is ordered alfabetically on Package, making it easier to find the packages we'll need.
+
Select the following packages by clicking the status word (normally Skip) so it's marked for installation. Use the Next button to download and install the packages.
+
+
OpenSSH
+
tcp_wrappers
+
diffutils
+
zlib
+
+
+
Wait for the install to complete and finish the installation.
+
+
+
+
Download the latest release of HBase from the website. As the HBase distributable is just a zipped archive, installation is as simple as unpacking the archive so it ends up in its final installation directory. Notice that HBase has to be installed in Cygwin and a good directory suggestion is to use /usr/local/ (or [Root directory]\usr\local in Windows slang). You should end up with a /usr/local/hbase-<version> installation in Cygwin.
+
+This finishes installation. We go on with the configuration.
+
+
+
+
There are 3 parts left to configure: Java, SSH and HBase itself. Following paragraphs explain eacht topic in detail.
+
+
One important thing to remember in shell scripting in general (i.e. *nix and Windows) is that managing, manipulating and assembling path names that contains spaces can be very hard, due to the need to escape and quote those characters and strings. So we try to stay away from spaces in path names. *nix environments can help us out here very easily by using symbolic links.
+
+
+
Create a link in /usr/local to the Java home directory by using the following command and substituting the name of your chosen Java environment:
+
Test your java installation by changing directories to your Java folder CD /usr/local/<jre name> and issueing the command ./bin/java -version. This should output your version of the chosen JRE.
+
+
+
+SSH
+
Configuring SSH is quite elaborate, but primarily a question of launching it by default as a Windows service.
+
+
+
On Windows Vista and above make sure you run the Cygwin shell with elevated privileges, by right-clicking on the shortcut an using Run as Administrator.
+
First of all, we have to make sure the rights on some crucial files are correct. Use the commands underneath. You can verify all rights by using the LS -L command on the different files. Also, notice the auto-completion feature in the shell using <TAB> is extremely handy in these situations.
+
+
chmod +r /etc/passwd to make the passwords file readable for all
+
chmod u+w /etc/passwd to make the passwords file writable for the owner
+
chmod +r /etc/group to make the groups file readable for all
+
+
+
chmod u+w /etc/group to make the groups file writable for the owner
+
+
+
chmod 755 /var to make the var folder writable to owner and readable and executable to all
+
+
+
Edit the /etc/hosts.allow file using your favorite editor (why not VI in the shell!) and make sure the following two lines are in there before the PARANOID line:
+
+
ALL : localhost 127.0.0.1/32 : allow
+
ALL : [::1]/128 : allow
+
+
+
Next we have to configure SSH by using the script ssh-host-config
+
+
If this script asks to overwrite an existing /etc/ssh_config, answer yes.
+
If this script asks to overwrite an existing /etc/sshd_config, answer yes.
+
If this script asks to use privilege separation, answer yes.
+
If this script asks to install sshd as a service, answer yes. Make sure you started your shell as Adminstrator!
+
If this script asks for the CYGWIN value, just <enter> as the default is ntsec.
+
If this script asks to create the sshd account, answer yes.
+
If this script asks to use a different user name as service account, answer no as the default will suffice.
+
If this script asks to create the cyg_server account, answer yes. Enter a password for the account.
+
+
+
Start the SSH service using net start sshd or cygrunsrv --start sshd. Notice that cygrunsrv is the utility that make the process run as a Windows service. Confirm that you see a message stating that the CYGWIN sshd service was started succesfully.
+
Harmonize Windows and Cygwin user account by using the commands:
+
+
mkpasswd -cl > /etc/passwd
+
mkgroup --local > /etc/group
+
+
+
Test the installation of SSH:
+
+
Open a new Cygwin terminal
+
Use the command whoami to verify your userID
+
Issue an ssh localhost to connect to the system itself
+
+
Answer yes when presented with the server's fingerprint
+
Issue your password when prompted
+
test a few commands in the remote session
+
The exit command should take you back to your first shell in Cygwin
+
+
+
Exit should terminate the Cygwin shell.
+
+
+
+
+
+If all previous configurations are working properly, we just need some tinkering at the HBase config files to properly resolve on Windows/Cygwin. All files and paths referenced here start from the HBase [installation directory] as working directory.
+
+
HBase uses the ./conf/hbase-env.sh to configure its dependencies on the runtime environment. Copy and uncomment following lines just underneath their original, change them to fit your environemnt. They should read something like:
+
+
export JAVA_HOME=/usr/local/<jre name>
+
export HBASE_IDENT_STRING=$HOSTNAME as this most likely does not inlcude spaces.
+
+
+
HBase uses the ./conf/hbase-default.xml file for configuration. Some properties do not resolve to existing directories because the JVM runs on Windows. This is the major issue to keep in mind when working with Cygwin: within the shell all paths are *nix-alike, hence relative to the root /. However, every parameter that is to be consumed within the windows processes themself, need to be Windows settings, hence C:\-alike. Change following propeties in the configuration file, adjusting paths where necessary to conform with your own installation:
+
+
hbase.rootdir must read e.g. file:///C:/cygwin/root/tmp/hbase/data
+
hbase.tmp.dir must read C:/cygwin/root/tmp/hbase/tmp
+
hbase.zookeeper.quorum must read 127.0.0.1 because for some reason localhost doesn't seem to resolve properly on Cygwin.
+
+
+
Make sure the configured hbase.rootdir and hbase.tmp.dirdirectories exist and have the proper rights set up e.g. by issuing a chmod 777 on them.
+
+
+
+
+Testing
+
+This should conclude the installation and configuration of HBase on Windows using Cygwin. So it's time to test it.
+
+
Start a Cygwin terminal, if you haven't already.
+
Change directory to HBase installation using CD /usr/local/hbase-<version>, preferably using auto-completion.
+
Start HBase using the command ./bin/start-hbase.sh
+
+
When prompted to accept the SSH fingerprint, answer yes.
+
When prompted, provide your password. Maybe multiple times.
+
When the command completes, the HBase server should have started.
+
However, to be absolutely certain, check the logs in the ./logs directory for any exceptions.
+
+
+
Next we start the HBase shell using the command ./bin/hbase shell
+
We run some simple test commands
+
+
Create a simple table using command create 'test', 'data'
List all rows in the table using the command scan 'test' that should list all the rows previously inserted. Notice how 3 new columns where added without changing the schema!
+
Finally we get rid of the table by issuing disable 'test' followed by drop 'test' and verified by list which should give an empty listing.
+
+
+
Leave the shell by exit
+
To stop the HBase server issue the ./bin/stop-hbase.sh command. And wait for it to complete!!! Killing the process might corrupt your data on disk.
+
In case of problems,
+
+
verify the HBase logs in the ./logs directory.
+
Try to fix the problem
+
Get help on the forums or IRC (#hbase@freenode.net). People are very active and keen to help out!
+
Stopr, restart and retest the server.
+
+
+
+
+
+
+
+
+Now your HBase server is running, start coding and build that next killer app on this particular, but scalable datastore!
+
HBase is the Hadoop database. Think of it as a distributed, scalable, big data store.
+
+
When Would I Use HBase?
+
+ Use HBase when you need random, realtime read/write access to your Big Data.
+ This project's goal is the hosting of very large tables -- billions of rows X millions of columns -- atop clusters of commodity hardware.
+HBase is an open-source, distributed, versioned, column-oriented store modeled after Google's Bigtable: A Distributed Storage System for Structured Data by Chang et al.
+ Just as Bigtable leverages the distributed data storage provided by the Google File System, HBase provides Bigtable-like capabilities on top of Hadoop and HDFS.
+
+
Features
+
+
+
Linear and modular scalability.
+
+
Strictly consistent reads and writes.
+
+
Automatic and configurable sharding of tables
+
+
Automatic failover support between RegionServers.
+
+
Convenient base classes for backing Hadoop MapReduce jobs with HBase tables.
+
+
Easy to use Java API for client access.
+
+
Block cache and Bloom Filters for real-time queries.
+
+
Query predicate push down via server side Filters
+
+
Thrift gateway and a REST-ful Web service that supports XML, Protobuf, and binary data encoding options
+
+
Extensible jruby-based (JIRB) shell
+
+
Support for exporting metrics via the Hadoop metrics subsystem to files or Ganglia; or via JMX
+
First read up on Hadoop metrics.
+ If you are using ganglia, the GangliaMetrics
+ wiki page is useful read.
+
To have HBase emit metrics, edit $HBASE_HOME/conf/hadoop-metrics.properties
+ and enable metric 'contexts' per plugin. As of this writing, hadoop supports
+ file and ganglia plugins.
+ Yes, the hbase metrics files is named hadoop-metrics rather than
+ hbase-metrics because currently at least the hadoop metrics system has the
+ properties filename hardcoded. Per metrics context,
+ comment out the NullContext and enable one or more plugins instead.
+
+
+ If you enable the hbase context, on regionservers you'll see total requests since last
+ metric emission, count of regions and storefiles as well as a count of memstore size.
+ On the master, you'll see a count of the cluster's requests.
+
+
+ Enabling the rpc context is good if you are interested in seeing
+ metrics on each hbase rpc method invocation (counts and time taken).
+
+
+ The jvm context is
+ useful for long-term stats on running hbase jvms -- memory used, thread counts, etc.
+ As of this writing, if more than one jvm is running emitting metrics, at least
+ in ganglia, the stats are aggregated rather than reported per instance.
+
+
+
+
+
+ In addition to the standard output contexts supported by the Hadoop
+ metrics package, you can also export HBase metrics via Java Management
+ Extensions (JMX). This will allow viewing HBase stats in JConsole or
+ any other JMX client.
+
+
+
+ To enable JMX support in HBase, first edit
+ $HBASE_HOME/conf/hadoop-metrics.properties to support
+ metrics refreshing. (If you've running 0.94.1 and above, or have already configured
+ hadoop-metrics.properties for another output context,
+ you can skip this step).
+
+
+# Configuration of the "hbase" context for null
+hbase.class=org.apache.hadoop.metrics.spi.NullContextWithUpdateThread
+hbase.period=60
+
+# Configuration of the "jvm" context for null
+jvm.class=org.apache.hadoop.metrics.spi.NullContextWithUpdateThread
+jvm.period=60
+
+# Configuration of the "rpc" context for null
+rpc.class=org.apache.hadoop.metrics.spi.NullContextWithUpdateThread
+rpc.period=60
+
+
+
+
+ For remote access, you will need to configure JMX remote passwords
+ and access profiles. Create the files:
+
+
+
$HBASE_HOME/conf/jmxremote.passwd (set permissions
+ to 600)
+ After restarting the processes you want to monitor, you should now be
+ able to run JConsole (included with the JDK since JDK 5.0) to view
+ the statistics via JMX. HBase MBeans are exported under the
+ hadoop domain in JMX.
+
+
+
+
+ For more information on understanding HBase metrics, see the metrics section in the HBase Reference Guide.
+
September 8th, 2010: HBase 0.20.0 is faster, stronger, slimmer, and sweeter tasting than any previous HBase release. Get it off the Releases page.
+
ApacheCon in Oakland: November 2-6th, 2009:
+ The Apache Foundation will be celebrating its 10th anniversary in beautiful Oakland by the Bay. Lots of good talks and meetups including an HBase presentation by a couple of the lads.
+
HBase at Hadoop World in NYC: October 2nd, 2009: A few of us will be talking on Practical HBase out east at Hadoop World: NYC.
+
HUG7 and HBase Hackathon: August 7th-9th, 2009 at StumbleUpon in SF: Sign up for the HBase User Group Meeting, HUG7 or for the Hackathon or for both (all are welcome!).
+
June, 2009 -- HBase at HadoopSummit2009 and at NOSQL: See the presentations
+ HBase replication is a way to copy data between HBase deployments. It
+ can serve as a disaster recovery solution and can contribute to provide
+ higher availability at the HBase layer. It can also serve more practically;
+ for example, as a way to easily copy edits from a web-facing cluster to a "MapReduce"
+ cluster which will process old and new data and ship back the results
+ automatically.
+
+
+ The basic architecture pattern used for HBase replication is (HBase cluster) master-push;
+ it is much easier to keep track of what’s currently being replicated since
+ each region server has its own write-ahead-log (aka WAL or HLog), just like
+ other well known solutions like MySQL master/slave replication where
+ there’s only one bin log to keep track of. One master cluster can
+ replicate to any number of slave clusters, and each region server will
+ participate to replicate their own stream of edits. For more information
+ on the different properties of master/slave replication and other types
+ of replication, please consult
+ How Google Serves Data From Multiple Datacenters.
+
+
+ The replication is done asynchronously, meaning that the clusters can
+ be geographically distant, the links between them can be offline for
+ some time, and rows inserted on the master cluster won’t be
+ available at the same time on the slave clusters (eventual consistency).
+
+
+ The replication format used in this design is conceptually the same as
+
+ MySQL’s statement-based replication . Instead of SQL statements, whole
+ WALEdits (consisting of multiple cell inserts coming from the clients'
+ Put and Delete) are replicated in order to maintain atomicity.
+
+
+ The HLogs from each region server are the basis of HBase replication,
+ and must be kept in HDFS as long as they are needed to replicate data
+ to any slave cluster. Each RS reads from the oldest log it needs to
+ replicate and keeps the current position inside ZooKeeper to simplify
+ failure recovery. That position can be different for every slave
+ cluster, same for the queue of HLogs to process.
+
+
+ The clusters participating in replication can be of asymmetric sizes
+ and the master cluster will do its “best effort” to balance the stream
+ of replication on the slave clusters by relying on randomization.
+
+
+ As of version 0.92 HBase supports master/master and cyclic replication as
+ well as replication to multiple slaves.
+
+
+
+
+
+ The guide on enabling and using cluster replication is contained
+ in the API documentation shipped with your HBase distribution.
+
+ The following sections describe the life of a single edit going from a
+ client that communicates with a master cluster all the way to a single
+ slave cluster.
+
+
+
+ The client uses a HBase API that sends a Put, Delete or ICV to a region
+ server. The key values are transformed into a WALEdit by the region
+ server and is inspected by the replication code that, for each family
+ that is scoped for replication, adds the scope to the edit. The edit
+ is appended to the current WAL and is then applied to its MemStore.
+
+
+ In a separate thread, the edit is read from the log (as part of a batch)
+ and only the KVs that are replicable are kept (that is, that they are part
+ of a family scoped GLOBAL in the family's schema, non-catalog so not
+ .META. or -ROOT-, and did not originate in the target slave cluster - in
+ case of cyclic replication).
+
+
+ The edit is then tagged with the master's cluster UUID.
+ When the buffer is filled, or the reader hits the end of the file,
+ the buffer is sent to a random region server on the slave cluster.
+
+
+ Synchronously, the region server that receives the edits reads them
+ sequentially and separates each of them into buffers, one per table.
+ Once all edits are read, each buffer is flushed using the normal HBase
+ client (HTables managed by a HTablePool). This is done in order to
+ leverage parallel insertion (MultiPut).
+ The master's cluster UUID is retained in the edits applied at the
+ slave cluster in order to allow cyclic replication.
+
+
+ Back in the master cluster's region server, the offset for the current
+ WAL that's being replicated is registered in ZooKeeper.
+
+
+
+
+ The edit is inserted in the same way.
+
+
+ In the separate thread, the region server reads, filters and buffers
+ the log edits the same way as during normal processing. The slave
+ region server that's contacted doesn't answer to the RPC, so the master
+ region server will sleep and retry up to a configured number of times.
+ If the slave RS still isn't available, the master cluster RS will select a
+ new subset of RS to replicate to and will retry sending the buffer of
+ edits.
+
+
+ In the mean time, the WALs will be rolled and stored in a queue in
+ ZooKeeper. Logs that are archived by their region server (archiving is
+ basically moving a log from the region server's logs directory to a
+ central logs archive directory) will update their paths in the in-memory
+ queue of the replicating thread.
+
+
+ When the slave cluster is finally available, the buffer will be applied
+ the same way as during normal processing. The master cluster RS will then
+ replicate the backlog of logs.
+
+
+
+
+
+ This section describes in depth how each of replication's internal
+ features operate.
+
+
+
+ When a master cluster RS initiates a replication source to a slave cluster,
+ it first connects to the slave's ZooKeeper ensemble using the provided
+ cluster key (that key is composed of the value of hbase.zookeeper.quorum,
+ zookeeper.znode.parent and hbase.zookeeper.property.clientPort). It
+ then scans the "rs" directory to discover all the available sinks
+ (region servers that are accepting incoming streams of edits to replicate)
+ and will randomly choose a subset of them using a configured
+ ratio (which has a default value of 10%). For example, if a slave
+ cluster has 150 machines, 15 will be chosen as potential recipient for
+ edits that this master cluster RS will be sending. Since this is done by all
+ master cluster RSs, the probability that all slave RSs are used is very high,
+ and this method works for clusters of any size. For example, a master cluster
+ of 10 machines replicating to a slave cluster of 5 machines with a ratio
+ of 10% means that the master cluster RSs will choose one machine each
+ at random, thus the chance of overlapping and full usage of the slave
+ cluster is higher.
+
+
+
+
+ Every master cluster RS has its own znode in the replication znodes hierarchy.
+ It contains one znode per peer cluster (if 5 slave clusters, 5 znodes
+ are created), and each of these contain a queue
+ of HLogs to process. Each of these queues will track the HLogs created
+ by that RS, but they can differ in size. For example, if one slave
+ cluster becomes unavailable for some time then the HLogs should not be deleted,
+ thus they need to stay in the queue (while the others are processed).
+ See the section named "Region server failover" for an example.
+
+
+ When a source is instantiated, it contains the current HLog that the
+ region server is writing to. During log rolling, the new file is added
+ to the queue of each slave cluster's znode just before it's made available.
+ This ensures that all the sources are aware that a new log exists
+ before HLog is able to append edits into it, but this operations is
+ now more expensive.
+ The queue items are discarded when the replication thread cannot read
+ more entries from a file (because it reached the end of the last block)
+ and that there are other files in the queue.
+ This means that if a source is up-to-date and replicates from the log
+ that the region server writes to, reading up to the "end" of the
+ current file won't delete the item in the queue.
+
+
+ When a log is archived (because it's not used anymore or because there's
+ too many of them per hbase.regionserver.maxlogs typically because insertion
+ rate is faster than region flushing), it will notify the source threads that the path
+ for that log changed. If the a particular source was already done with
+ it, it will just ignore the message. If it's in the queue, the path
+ will be updated in memory. If the log is currently being replicated,
+ the change will be done atomically so that the reader doesn't try to
+ open the file when it's already moved. Also, moving a file is a NameNode
+ operation so, if the reader is currently reading the log, it won't
+ generate any exception.
+
+
+
+
+ By default, a source will try to read from a log file and ship log
+ entries as fast as possible to a sink. This is first limited by the
+ filtering of log entries; only KeyValues that are scoped GLOBAL and
+ that don't belong to catalog tables will be retained. A second limit
+ is imposed on the total size of the list of edits to replicate per slave,
+ which by default is 64MB. This means that a master cluster RS with 3 slaves
+ will use at most 192MB to store data to replicate. This doesn't account
+ the data filtered that wasn't garbage collected.
+
+
+ Once the maximum size of edits was buffered or the reader hits the end
+ of the log file, the source thread will stop reading and will choose
+ at random a sink to replicate to (from the list that was generated by
+ keeping only a subset of slave RSs). It will directly issue a RPC to
+ the chosen machine and will wait for the method to return. If it's
+ successful, the source will determine if the current file is emptied
+ or if it should continue to read from it. If the former, it will delete
+ the znode in the queue. If the latter, it will register the new offset
+ in the log's znode. If the RPC threw an exception, the source will retry
+ 10 times until trying to find a different sink.
+
+
+
+
+ If replication isn't enabled, the master's logs cleaning thread will
+ delete old logs using a configured TTL. This doesn't work well with
+ replication since archived logs passed their TTL may still be in a
+ queue. Thus, the default behavior is augmented so that if a log is
+ passed its TTL, the cleaning thread will lookup every queue until it
+ finds the log (while caching the ones it finds). If it's not found,
+ the log will be deleted. The next time it has to look for a log,
+ it will first use its cache.
+
+
+
+
+ As long as region servers don't fail, keeping track of the logs in ZK
+ doesn't add any value. Unfortunately, they do fail, so since ZooKeeper
+ is highly available we can count on it and its semantics to help us
+ managing the transfer of the queues.
+
+
+ All the master cluster RSs keep a watcher on every other one of them to be
+ notified when one dies (just like the master does). When it happens,
+ they all race to create a znode called "lock" inside the dead RS' znode
+ that contains its queues. The one that creates it successfully will
+ proceed by transferring all the queues to its own znode (one by one
+ since ZK doesn't support the rename operation) and will delete all the
+ old ones when it's done. The recovered queues' znodes will be named
+ with the id of the slave cluster appended with the name of the dead
+ server.
+
+
+ Once that is done, the master cluster RS will create one new source thread per
+ copied queue, and each of them will follow the read/filter/ship pattern.
+ The main difference is that those queues will never have new data since
+ they don't belong to their new region server, which means that when
+ the reader hits the end of the last log, the queue's znode will be
+ deleted and the master cluster RS will close that replication source.
+
+
+ For example, consider a master cluster with 3 region servers that's
+ replicating to a single slave with id '2'. The following hierarchy
+ represents what the znodes layout could be at some point in time. We
+ can see the RSs' znodes all contain a "peers" znode that contains a
+ single queue. The znode names in the queues represent the actual file
+ names on HDFS in the form "address,port.timestamp".
+
+ Now let's say that 1.1.1.2 loses its ZK session. The survivors will race
+ to create a lock, and for some reasons 1.1.1.3 wins. It will then start
+ transferring all the queues to its local peers znode by appending the
+ name of the dead server. Right before 1.1.1.3 is able to clean up the
+ old znodes, the layout will look like the following:
+
+ Some time later, but before 1.1.1.3 is able to finish replicating the
+ last HLog from 1.1.1.2, let's say that it dies too (also some new logs
+ were created in the normal queues). The last RS will then try to lock
+ 1.1.1.3's znode and will begin transferring all the queues. The new
+ layout will be:
+
+
+/hbase/replication/rs/
+ 1.1.1.1,60020,123456780/
+ 2/
+ 1.1.1.1,60020.1378 (Contains a position)
+
+ 2-1.1.1.3,60020,123456630/
+ 1.1.1.3,60020.1325 (Contains a position)
+ 1.1.1.3,60020.1401
+
+ 2-1.1.1.2,60020,123456790-1.1.1.3,60020,123456630/
+ 1.1.1.2,60020.1312 (Contains a position)
+ 1.1.1.3,60020,123456630/
+ lock
+ 2/
+ 1.1.1.3,60020.1325 (Contains a position)
+ 1.1.1.3,60020.1401
+
+ 2-1.1.1.2,60020,123456790/
+ 1.1.1.2,60020.1312 (Contains a position)
+
+
+
+
+
+
+ Yes, this is for much later.
+
+
+
+
+ You can use the HBase-provided utility called CopyTable from the package
+ org.apache.hadoop.hbase.mapreduce in order to have a discp-like tool to
+ bulk copy data.
+
+
+
+
+ Yes, this behavior would help a lot but it's not currently available
+ in HBase (BatchUpdate had that, but it was lost in the new API).
+
+
+
+
+
+ Here's a list of all the jiras that relate to major issues or missing
+ features in the replication implementation.
+
+
+
+ HBASE-2611, basically if a region server dies while recovering the
+ queues of another dead RS, we will miss the data from the queues
+ that weren't copied.
+
+
+
+
+
diff --git src/site/xdoc/sponsors.xml src/site/xdoc/sponsors.xml
new file mode 100644
index 0000000..e39730b
--- /dev/null
+++ src/site/xdoc/sponsors.xml
@@ -0,0 +1,35 @@
+
+
+
+
+ Installing HBase on Windows using Cygwin
+
+
+
+
+
The below companies have been gracious enough to provide their commerical tool offerings free of charge to the Apache HBase project.
+