diff --git src/main/docbkx/book.xml src/main/docbkx/book.xml
index 92c372e..d90d1bc 100644
--- src/main/docbkx/book.xml
+++ src/main/docbkx/book.xml
@@ -4387,230 +4387,442 @@ This option should not normally be used, and it is not in -fixAll.
-
+
- Compression In HBaseCompression
+ Compression and Data Block Encoding In
+ HBaseCompressionData Block
+ Encodingcodecs
- Codecs mentioned in this section are for encoding and decoding data blocks. For
- information about replication codecs, see Codecs mentioned in this section are for encoding and decoding data blocks or row keys.
+ For information about replication codecs, see .
- There are a bunch of compression options in HBase. Some codecs come with java --
- e.g. gzip -- and so require no additional installations. Others require native
- libraries. The native libraries may be available in your hadoop as is the case
- with lz4 and it is just a matter of making sure the hadoop native .so is available
- to HBase. You may have to do extra work to make the codec accessible; for example,
- if the codec has an apache-incompatible license that makes it so hadoop cannot bundle
- the library.
- Below we
- discuss what is necessary for the common codecs. Whatever codec you use, be sure
- to test it is installed properly and is available on all nodes that make up your cluster.
- Add any necessary operational step that will ensure checking the codec present when you
- happen to add new nodes to your cluster. The
- discussed below can help check the codec is properly install.
- As to which codec to use, there is some helpful discussion
- to be found in Documenting Guidance on compression and codecs.
-
+ Some of the information in this section is pulled from a discussion on the
+ HBase Development mailing list.
+ HBase supports several different compression algorithms which can be enabled on a
+ ColumnFamily. Data block encoding attempts to limit duplication of information in keys, taking
+ advantage of some of the fundamental designs and patterns of HBase, such as sorted row keys
+ and the schema of a given table. Compressors reduce the size of large, opaque byte arrays in
+ cells, and can significantly reduce the storage space needed to store uncompressed
+ data.
+ Compressors and data block encoding can be used together on the same ColumnFamily.
+
+
+ Changes Take Effect Upon Compaction
+ If you change compression or encoding for a ColumnFamily, the changes take effect during
+ compaction.
+
+
+ Some codecs take advantage of cvapabilities built into Java, such as GZip compression. Others rely on native libraries. Native libraries may be available as part of Hadoop, such as LZ4. In this case, HBase only needs access to the appropriate shared library. Other codecs, such as Google Snappy, need to be installed first. Some codecs are licensed in ways that conflict with HBase's license and cannot be shipped as part of HBase.
+
+ This section discusses common codecs that are used and tested with HBase. No matter what codec you use, be sure to test that it is installed correctly and is available on all nodes in your cluster. Extra operational steps may be necessary to be sure that codecs are available on newly-deployed nodes. You can use the utility to check that a given codec is correctly installed.
+
+ To configure HBase to use a compressor, see . To enable a compressor for a ColumnFamily, see . To enable data block encoding for a ColumnFamily, see
+ .
+
+ Block Compressors
+
+ none
+
+
+ Snappy
+
+
+ LZO
+
+
+ LZ4
+
+
+ GZ
+
+
-
- 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.
-
- You need to restart regionserver for it to pick up changes!
- Be aware that the regionserver caches the result of the compression check it runs
- ahead of each region open. This means that you will have to restart the regionserver
- for it to notice that you have fixed any codec issues; e.g. changed symlinks or
- moved lib locations under HBase.
-
- On the location of native libraries
- Hadoop looks in lib/native for .so files. HBase looks in
- lib/native/PLATFORM. See the bin/hbase.
- View the file and look for native. See how we
- do the work to find out what platform we are running on running a little java program
- org.apache.hadoop.util.PlatformName to figure it out.
- We'll then add ./lib/native/PLATFORM to the
- LD_LIBRARY_PATH environment for when the JVM starts.
- The JVM will look in here (as well as in any other dirs specified on LD_LIBRARY_PATH)
- for codec native libs. If you are unable to figure your 'platform', do:
- $ ./bin/hbase org.apache.hadoop.util.PlatformName.
- An example platform would be Linux-amd64-64.
-
-
-
-
-
-
- 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.
-
-
+
+ Data Block Encoding Types
+
+ Prefix - Often, keys are very similar. Specifically, keys often share a common prefix
+ and only differ near the end. For instance, one key might be
+ RowKey:Family:Qualifier0 and the next key might be
+ RowKey:Family:Qualifier1. In Prefix encoding, an extra column is
+ added which holds the length of the prefix shared between the current key and the previous
+ key. Assuming the first key here is totally different from the key before, its prefix
+ length is 0. The second key's prefix length is 23, since they have the
+ first 23 characters in common.
+ Obviously if the keys tend to have nothing in common, Prefix will not provide much
+ benefit.
+ The following image shows a hypothetical ColumnFamily with no data block encoding.
+
+ ColumnFamily with No Encoding
+
+
+
+
+
+
+
+
+ Here is the same data with prefix data encoding.
+
+ ColumnFamily with Prefix Encoding
+
+
+
+
+
+
+
+
+
+
+ Diff - Diff encoding expands upon Prefix encoding. Instead of considering the key
+ sequentially as a monolithic series of bytes, each key field is split so that each part of
+ the key can be compressed more efficiently. Two new fields are added: timestamp and type.
+ If the ColumnFamily is the same as the previous row, it is omitted from the current row.
+ If the key length, value length or type are the same as the previous row, the field is
+ omitted. In addition, for increased compression, the timestamp is stored as a Diff from
+ the previous row's timestamp, rather than being stored in full. Given the two row keys in
+ the Prefix example, and given an exact match on timestamp and the same type, neither the
+ value length, or type needs to be stored for the second row, and the timestamp value for
+ the second row is just 0, rather than a full timestamp.
+ Diff encoding is disabled by default because writing and scanning are slower but more
+ data is cached.
+ This image shows the same ColumnFamily from the previous images, with Diff encoding.
+
+ ColumnFamily with Diff Encoding
+
+
+
+
+
+
+
+
+
+
+ Fast Diff - Fast Diff works similar to Diff, but uses a faster implementation. It also
+ adds another field which stores a single bit to track whether the data itself is the same
+ as the previous row. If it is, the data is not stored again. Fast Diff is the recommended
+ codec to use if you have long keys or many columns. The data format is nearly identical to
+ Diff encoding, so there is not an image to illustrate it.
+
+
+ Prefix Tree encoding was introduced as an experimental feature in HBase 0.96. It
+ provides similar memory savings to the Prefix, Diff, and Fast Diff encoder, but provides
+ faster random access at a cost of slower encoding speed. Prefix Tree may be appropriate
+ for applications that have high block cache hit ratios. It introduces new 'tree' fields
+ for the row and column. The row tree field contains a list of offsets/references
+ corresponding to the cells in that row. This allows for a good deal of compression. For
+ more details about Prefix Tree encoding, see HBASE-4676. It is
+ difficult to graphically illustrate a prefix tree, so no image is included. See the
+ Wikipedia article for Trie for more general information
+ about this data structure.
+
+
-
-
- GZIP
-
-
- GZIP will generally compress better than LZO but it will run slower.
- For some setups, better compression may be preferred ('cold' data).
- 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 ).
-
+
+ Which Compressor or Data Block Encoder To Use
+ The compression or codec type to use depends on the characteristics of your data.
+ Choosing the wrong type could cause your data to take more space rather than less, and can
+ have performance implications. In general, you need to weigh your options between smaller
+ size and faster compression/decompression. Following are some general guidelines, expanded from a discussion at Documenting Guidance on compression and codecs.
+
+
+ If you have long keys (compared to the values) or many columns, use a prefix
+ encoder. FAST_DIFF is recommended, as more testing is needed for Prefix Tree
+ encoding.
+
+
+ If the values are large (and not precompressed, such as images), use a data block
+ compressor.
+
+
+ Use GZIP for cold data, which is accessed infrequently. GZIP
+ compression uses more CPU resources than Snappy or LZO, but provides a higher
+ compression ratio.
+
+
+ Use Snappy or LZO for hot data, which is accessed
+ frequently. Snappy and LZO use fewer CPU resources than GZIP, but do not provide as high
+ of a compression ratio.
+
+
+ In most cases, enabling Snappy or LZO by default is a good choice, because they have
+ a low performance overhead and provide space savings.
+
+
+ Before Snappy became available by Google in 2011, LZO was the default. Snappy has
+ similar qualities as LZO but has been shown to perform better.
+
+
-
-
- LZ4
-
-
- LZ4 is bundled with Hadoop. Make sure the hadoop .so is
- accessible when you start HBase. One means of doing this is after figuring your
- platform, see , make a symlink from HBase
- to the native Hadoop libraries presuming the two software installs are colocated.
- For example, if my 'platform' is Linux-amd64-64:
- $ cd $HBASE_HOME
+
+ Compressor Configuration, Installation, and Use
+
+ Configure HBase For Compressors
+ Before HBase can use a given compressor, its libraries need to be available. Due to
+ licensing issues, only GZ compression is available to HBase (via native Java libraries) in
+ a default installation.
+
+ Compressor Support On the Master
+ A new configuration setting was introduced in HBase 0.95, to check the Master to
+ determine which data block encoders are installed and configured on it, and assume that
+ the entire cluster is configured the same. This option,
+ hbase.master.check.compression, defaults to true. This
+ prevents the situation described in HBASE-6370, where
+ a table is created or modified to support a codec that a region server does not support,
+ leading to failures that take a long time to occur and are difficult to debug.
+ If hbase.master.check.compression is enabled, libraries for all desired
+ compressors need to be installed and configured on the Master, even if the Master does
+ not run a region server.
+
+
+ Install GZ Support Via Native Libraries
+ HBase uses Java's built-in GZip support unless the native Hadoop libraries are
+ available on the CLASSPATH. The recommended way to add libraries to the CLASSPATH is to
+ set the environment variable HBASE_LIBRARY_PATH for the user running
+ HBase. If native libraries are not available and Java's GZIP is used, Got
+ brand-new compressor reports will be present in the logs. See ).
+
+
+ Install LZO Support
+ HBase cannot ship with LZO because of incompatibility between HBase, which uses an
+ Apache Software License (ASL) and LZO, which uses a GPL license. See the Using LZO
+ Compression wiki page for information on configuring LZO support for HBase.
+ If you depend upon LZO compression, consider configuring your RegionServers to fail
+ to start if LZO is not available. See .
+
+
+ Configure LZ4 Support
+ LZ4 support is bundled with Hadoop. Make sure the hadoop shared library
+ (libhadoop.so) is accessible when you start
+ HBase. After configuring your platform (see ), you can make a symbolic link from HBase to the native Hadoop
+ libraries. This assumes the two software installs are colocated. For example, if my
+ 'platform' is Linux-amd64-64:
+ $ cd $HBASE_HOME
$ mkdir lib/native
$ ln -s $HADOOP_HOME/lib/native lib/native/Linux-amd64-64
- Use the compression tool to check lz4 installed on all nodes.
- Start up (or restart) hbase. From here on out you will be able to create
- and alter tables to enable LZ4 as a compression codec. E.g.:
- hbase(main):003:0> alter 'TestTable', {NAME => 'info', COMPRESSION => 'LZ4'}
-
-
-
-
-
- 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.
-
+ Use the compression tool to check that LZ4 is installed on all nodes. Start up (or restart)
+ HBase. Afterward, you can create and alter tables to enable LZ4 as a
+ compression codec.:
+
+hbase(main):003:0> alter 'TestTable', {NAME => 'info', COMPRESSION => 'LZ4'}
+
+
+
+
+ Install Snappy Support
+ HBase does not ship with Snappy support because of licensing issues. You can install
+ Snappy binaries (for instance, by using yum install snappy on CentOS)
+ or build Snappy from source. After installing Snappy, search for the shared library,
+ which will be called libsnappy.so.X where X is a number. If you
+ built from source, copy the shared library to a known location on your system, such as
+ /opt/snappy/lib/.
+ In addition to the Snappy library, HBase also needs access to the Hadoop shared
+ library, which will be called something like libhadoop.so.X.Y,
+ where X and Y are both numbers. Make note of the location of the Hadoop library, or copy
+ it to the same location as the Snappy library.
+
+ The Snappy and Hadoop libraries need to be available on each node of your cluster.
+ See to find out how to test that this is the case.
+ See to configure your RegionServers to fail to
+ start if a given compressor is not available.
+
+ Each of these library locations need to be added to the environment variable
+ HBASE_LIBRARY_PATH for the operating system user that runs HBase. You
+ need to restart the RegionServer for the changes to take effect.
+
-
-
- 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 (see below). HBase nor Hadoop cannot include snappy because of licensing issues (The
- hadoop libhadoop.so under its native dir does not include snappy; of note, the shipped .so
- may be for 32-bit architectures -- this fact has tripped up folks in the past with them thinking
- it 64-bit). The notes below are about installing snappy for HBase use. You may want snappy
- available in your hadoop context also. That is not covered here.
- HBase and Hadoop find the snappy .so in different locations currently: Hadoop picks those files in
- ./lib while HBase finds the .so in ./lib/[PLATFORM].
-
-
-
-
- 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'"
-
-
+
+ CompressionTest
+ You can use the CompressionTest tool to verify that your compressor is available to
+ HBase:
+
+ $ hbase org.apache.hadoop.hbase.util.CompressionTest hdfs://host/path/to/hbase snappy
+
+
-
-
-
-
- Installation
-
- Snappy is used by hbase to compress HFiles on flush and when compacting.
-
-
- You will find the snappy library file under the .libs directory from your Snappy build (For example
- /home/hbase/snappy-1.0.5/.libs/). The file is called libsnappy.so.1.x.x where 1.x.x is the version of the snappy
- code you are building. You can either copy this file into your hbase lib directory -- under lib/native/PLATFORM --
- naming the file as libsnappy.so,
- or simply create a symbolic link to it (See ./bin/hbase for how it does library path for native libs).
-
+
+ Enforce Compression Settings On a RegionServer
+ You can configure a RegionServer so that it will fail to restart if compression is
+ configured incorrectly, by adding the option hbase.regionserver.codecs to the
+ hbase-site.xml, and setting its value to a comma-separated list
+ of codecs that need to be available. For example, if you set this property to
+ lzo,gz, the RegionServer would fail to start if both compressors
+ were not available. This would prevent a new server from being added to the cluster
+ without having codecs configured properly.
+
+
-
- The second file you need is the hadoop native library. You will find this file in your hadoop installation directory
- under lib/native/Linux-amd64-64/ or lib/native/Linux-i386-32/. The file you are looking for is libhadoop.so.1.x.x.
- Again, you can simply copy this file or link to it from under hbase in lib/native/PLATFORM (e.g. Linux-amd64-64, etc.),
- using the name libhadoop.so.
-
+
+ Enable Compression On a ColumnFamily
+ To enable compression for a ColumnFamily, use an alter command. You do
+ not need to re-create the table or copy data. If you are changing codecs, be sure the old
+ codec is still available until all the old StoreFiles have been compacted.
+
+ Enabling Compression on a ColumnFamily of an Existing Table using HBase
+ Shell
+ disable 'test'
+hbase> alter 'test', {NAME => 'cf', COMPRESSION => 'GZ'}
+hbase> enable 'test']]>
+
+
+
+ Creating a New Table with Compression On a ColumnFamily
+ create 'test2', { NAME => 'cf2', COMPRESSION => 'SNAPPY' }
+ ]]>
+
+
+ Verifying a ColumnFamily's Compression Settings
+ describe 'test'
+DESCRIPTION ENABLED
+ 'test', {NAME => 'cf', DATA_BLOCK_ENCODING => 'NONE false
+ ', BLOOMFILTER => 'ROW', REPLICATION_SCOPE => '0',
+ VERSIONS => '1', COMPRESSION => 'GZ', MIN_VERSIONS
+ => '0', TTL => 'FOREVER', KEEP_DELETED_CELLS => 'fa
+ lse', BLOCKSIZE => '65536', IN_MEMORY => 'false', B
+ LOCKCACHE => 'true'}
+1 row(s) in 0.1070 seconds
+ ]]>
+
+
-
- At the end of the installation, you should have both libsnappy.so and libhadoop.so links or files present into
- lib/native/Linux-amd64-64 or into lib/native/Linux-i386-32 (where the last part of the directory path is the
- PLATFORM you built and rare running the native lib on)
-
- To point hbase at snappy support, in hbase-env.sh set
- export HBASE_LIBRARY_PATH=/pathtoyourhadoop/lib/native/Linux-amd64-64
- In /pathtoyourhadoop/lib/native/Linux-amd64-64 you should have something like:
-
- libsnappy.a
- libsnappy.so
- libsnappy.so.1
- libsnappy.so.1.1.2
-
-
-
+
+ Testing Compression Performance
+ HBase includes a tool called LoadTestTool which provides mechanisms to test your
+ compression performance. You must specify either -write or
+ -update-read as your first parameter, and if you do not specify another
+ parameter, usage advice is printed for each option.
+
+ LoadTestTool Usage
+
+Options:
+ -batchupdate Whether to use batch as opposed to separate
+ updates for every column in a row
+ -bloom Bloom filter type, one of [NONE, ROW, ROWCOL]
+ -compression Compression type, one of [LZO, GZ, NONE, SNAPPY,
+ LZ4]
+ -data_block_encoding Encoding algorithm (e.g. prefix compression) to
+ use for data blocks in the test column family, one
+ of [NONE, PREFIX, DIFF, FAST_DIFF, PREFIX_TREE].
+ -encryption Enables transparent encryption on the test table,
+ one of [AES]
+ -generator The class which generates load for the tool. Any
+ args for this class can be passed as colon
+ separated after class name
+ -h,--help Show usage
+ -in_memory Tries to keep the HFiles of the CF inmemory as far
+ as possible. Not guaranteed that reads are always
+ served from inmemory
+ -init_only Initialize the test table only, don't do any
+ loading
+ -key_window The 'key window' to maintain between reads and
+ writes for concurrent write/read workload. The
+ default is 0.
+ -max_read_errors The maximum number of read errors to tolerate
+ before terminating all reader threads. The default
+ is 10.
+ -multiput Whether to use multi-puts as opposed to separate
+ puts for every column in a row
+ -num_keys The number of keys to read/write
+ -num_tables A positive integer number. When a number n is
+ speicfied, load test tool will load n table
+ parallely. -tn parameter value becomes table name
+ prefix. Each table name is in format
+ _1..._n
+ -read [:<#threads=20>]
+ -regions_per_server A positive integer number. When a number n is
+ specified, load test tool will create the test
+ table with n regions per server
+ -skip_init Skip the initialization; assume test table already
+ exists
+ -start_key The first key to read/write (a 0-based index). The
+ default value is 0.
+ -tn The name of the table to read or write
+ -update [:<#threads=20>][:<#whether to
+ ignore nonce collisions=0>]
+ -write :[:<#threads=20>]
+ -zk ZK quorum as comma-separated host names without
+ port numbers
+ -zk_root name of parent znode in zookeeper
+ ]]>
+
+
+ Example Usage of LoadTestTool
+
+$ hbase org.apache.hadoop.hbase.util.LoadTestTool -write 1:10:100 -num_keys 1000000
+ -read 100:30 -num_tables 1 -data_block_encoding NONE -tn load_test_tool_NONE
+
+
+
-
- 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.
-
+
+
+ Enable Data Block Encoding
+ Codecs are built into HBase so no extra configuration is needed. Codecs are enabled on a
+ table by setting the DATA_BLOCK_ENCODING property. Disable the table before
+ altering its DATA_BLOCK_ENCODING setting. Following is an example using HBase Shell:
+
+ Enable Data Block Encoding On a Table
+ disable 'test'
+hbase> alter 'test', { NAME => 'cf', DATA_BLOCK_ENCODING => 'FAST_DIFF' }
+Updating all regions with the new schema...
+0/1 regions updated.
+1/1 regions updated.
+Done.
+0 row(s) in 2.2820 seconds
+hbase> enable 'test'
+0 row(s) in 0.1580 seconds
+ ]]>
+
+
+ Verifying a ColumnFamily's Data Block Encoding
+ describe 'test'
+DESCRIPTION ENABLED
+ 'test', {NAME => 'cf', DATA_BLOCK_ENCODING => 'FAST true
+ _DIFF', BLOOMFILTER => 'ROW', REPLICATION_SCOPE =>
+ '0', VERSIONS => '1', COMPRESSION => 'GZ', MIN_VERS
+ IONS => '0', TTL => 'FOREVER', KEEP_DELETED_CELLS =
+ > 'false', BLOCKSIZE => '65536', IN_MEMORY => 'fals
+ e', BLOCKCACHE => 'true'}
+1 row(s) in 0.0650 seconds
+ ]]>
+
+
YCSB: The Yahoo! Cloud Serving Benchmark and HBaseTODO: Describe how YCSB is poor for putting up a decent cluster load.
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