[SPARK-24922] Iterative rdd union + reduceByKey operations on small dataset leads to "No space left on device" error on account of lot of shuffle spill.

• Type: Bug
• Status: Resolved
• Priority: Major
• Resolution: Incomplete
• Affects Version/s: 2.3.0
• Component/s: Shuffle, Spark Core
• Labels: bulk-closed

Environment

Java 8, Scala 2.11.8, Spark 2.3.0, sbt 0.13.16

 

• Assignee: Unassigned
• Reporter: Dinesh Dharme
• Created: 25/Jul/18 17:41
• Updated: 17/May/20 18:31
• Resolved: 08/Oct/19 05:43

Description

I am trying to do few (union + reduceByKey) operations on a hiearchical dataset in a iterative fashion in rdd. The first few loops run fine but on the subsequent loops, the operations ends up using the whole scratch space provided to it. 

I have set the scratch directory, i.e. SPARK_LOCAL_DIRS , to be one having 100 GB space.

The heirarchical dataset, whose size is (< 400kB), remains constant throughout the iterations.

 I have tried the worker cleanup flag but it has no effect i.e. "spark.worker.cleanup.enabled=true"

 

Error : 

 

Caused by: java.io.IOException: No space left on device
at java.io.FileOutputStream.writeBytes(Native Method)
at java.io.FileOutputStream.write(FileOutputStream.java:326)
at java.io.BufferedOutputStream.flushBuffer(BufferedOutputStream.java:82)
at java.io.BufferedOutputStream.write(BufferedOutputStream.java:126)
at java.io.DataOutputStream.writeLong(DataOutputStream.java:224)
at org.apache.spark.shuffle.IndexShuffleBlockResolver$$anonfun$writeIndexFileAndCommit$1$$anonfun$apply$mcV$sp$1.apply$mcVJ$sp(IndexShuffleBlockResolver.scala:151)
at org.apache.spark.shuffle.IndexShuffleBlockResolver$$anonfun$writeIndexFileAndCommit$1$$anonfun$apply$mcV$sp$1.apply(IndexShuffleBlockResolver.scala:149)
at org.apache.spark.shuffle.IndexShuffleBlockResolver$$anonfun$writeIndexFileAndCommit$1$$anonfun$apply$mcV$sp$1.apply(IndexShuffleBlockResolver.scala:149)
at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
at scala.collection.mutable.ArrayOps$ofLong.foreach(ArrayOps.scala:246)
at org.apache.spark.shuffle.IndexShuffleBlockResolver$$anonfun$writeIndexFileAndCommit$1.apply$mcV$sp(IndexShuffleBlockResolver.scala:149)
at org.apache.spark.shuffle.IndexShuffleBlockResolver$$anonfun$writeIndexFileAndCommit$1.apply(IndexShuffleBlockResolver.scala:145)
at org.apache.spark.shuffle.IndexShuffleBlockResolver$$anonfun$writeIndexFileAndCommit$1.apply(IndexShuffleBlockResolver.scala:145)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1377)
at org.apache.spark.shuffle.IndexShuffleBlockResolver.writeIndexFileAndCommit(IndexShuffleBlockResolver.scala:153)
at org.apache.spark.shuffle.sort.SortShuffleWriter.write(SortShuffleWriter.scala:73)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:96)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:53)
at org.apache.spark.scheduler.Task.run(Task.scala:109)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:345)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)

 

What I am trying to do (High Level):

I have a dataset of 5 different csv ( Parent, Child1, Child2, Child21, Child22 ) which are related in a hierarchical fashion as shown below. 

Parent-> Child1 -> Child2  -> Child21 

Parent-> Child1 -> Child2  -> Child22 

Each element in the tree has 14 columns (elementid, parentelement_id, cat1, cat2, num1, num2,....., num10)

I am trying to aggregate the values of one column of Child21 into Child1 (i.e. 2 levels up). I am doing the same for another column value of Child22 into Child1. Then I am merging these aggregated values at the same Child1 level.

This is present in the code at location : 

spark.rddexample.dummyrdd.tree.child1.events.Function1

 

 

Code which replicates the issue: 

1] https://github.com/dineshdharme/SparkRddShuffleIssue

 

Steps to reproduce the issue : 

1] Clone the above repository.

2] Put the csvs in the "issue-data" folder in the above repository at a hadoop location "hdfs:///tree/dummy/data/"

3] Set the spark scratch directory (SPARK_LOCAL_DIRS) to a folder which has large space. (> 100 GB)

4] Run "sbt assembly"

5] Run the following command at the project location : 

/path/to/spark-2.3.0-bin-hadoop2.7/bin/spark-submit \
--class spark.rddexample.dummyrdd.FunctionExecutor \
--master local[2] \
--deploy-mode client \
--executor-memory 2G \
--driver-memory 2G \
target/scala-2.11/rdd-shuffle-assembly-0.1.0.jar \
20 \
hdfs:///tree/dummy/data/ \
hdfs:///tree/dummy/results/