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Posted to issues@spark.apache.org by "Jerry Lam (JIRA)" <ji...@apache.org> on 2015/10/20 20:26:28 UTC
[jira] [Comment Edited] (SPARK-10309) Some tasks failed with Unable
to acquire memory
[ https://issues.apache.org/jira/browse/SPARK-10309?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14965510#comment-14965510 ]
Jerry Lam edited comment on SPARK-10309 at 10/20/15 6:25 PM:
-------------------------------------------------------------
Same issue, I got the following stacktrace:
{noformat}
15/10/20 18:20:43 INFO UnsafeExternalSorter: Thread 64 spilling sort data of 64.0 KB to disk (0 time so far)
15/10/20 18:20:43 ERROR Executor: Exception in task 11.3 in stage 2.0 (TID 514)
java.io.IOException: Unable to acquire 67108864 bytes of memory
at org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.acquireNewPage(UnsafeExternalSorter.java:351)
at org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.acquireNewPageIfNecessary(UnsafeExternalSorter.java:332)
at org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.insertKVRecord(UnsafeExternalSorter.java:461)
at org.apache.spark.sql.execution.UnsafeKVExternalSorter.insertKV(UnsafeKVExternalSorter.java:139)
at org.apache.spark.sql.execution.aggregate.TungstenAggregationIterator.switchToSortBasedAggregation(TungstenAggregationIterator.scala:489)
at org.apache.spark.sql.execution.aggregate.TungstenAggregationIterator.processInputs(TungstenAggregationIterator.scala:379)
at org.apache.spark.sql.execution.aggregate.TungstenAggregationIterator.start(TungstenAggregationIterator.scala:622)
at org.apache.spark.sql.execution.aggregate.TungstenAggregate$$anonfun$doExecute$1.org$apache$spark$sql$execution$aggregate$TungstenAggregate$$anonfun$$executePartition$1(TungstenAggregate.scala:110)
at org.apache.spark.sql.execution.aggregate.TungstenAggregate$$anonfun$doExecute$1$$anonfun$2.apply(TungstenAggregate.scala:119)
at org.apache.spark.sql.execution.aggregate.TungstenAggregate$$anonfun$doExecute$1$$anonfun$2.apply(TungstenAggregate.scala:119)
at org.apache.spark.rdd.MapPartitionsWithPreparationRDD.compute(MapPartitionsWithPreparationRDD.scala:64)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:73)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
at org.apache.spark.scheduler.Task.run(Task.scala:88)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)
{noformat}
was (Author: superwai):
Same issue, I got the following stacktrace:
15/10/20 18:20:43 INFO UnsafeExternalSorter: Thread 64 spilling sort data of 64.0 KB to disk (0 time so far)
15/10/20 18:20:43 ERROR Executor: Exception in task 11.3 in stage 2.0 (TID 514)
java.io.IOException: Unable to acquire 67108864 bytes of memory
at org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.acquireNewPage(UnsafeExternalSorter.java:351)
at org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.acquireNewPageIfNecessary(UnsafeExternalSorter.java:332)
at org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.insertKVRecord(UnsafeExternalSorter.java:461)
at org.apache.spark.sql.execution.UnsafeKVExternalSorter.insertKV(UnsafeKVExternalSorter.java:139)
at org.apache.spark.sql.execution.aggregate.TungstenAggregationIterator.switchToSortBasedAggregation(TungstenAggregationIterator.scala:489)
at org.apache.spark.sql.execution.aggregate.TungstenAggregationIterator.processInputs(TungstenAggregationIterator.scala:379)
at org.apache.spark.sql.execution.aggregate.TungstenAggregationIterator.start(TungstenAggregationIterator.scala:622)
at org.apache.spark.sql.execution.aggregate.TungstenAggregate$$anonfun$doExecute$1.org$apache$spark$sql$execution$aggregate$TungstenAggregate$$anonfun$$executePartition$1(TungstenAggregate.scala:110)
at org.apache.spark.sql.execution.aggregate.TungstenAggregate$$anonfun$doExecute$1$$anonfun$2.apply(TungstenAggregate.scala:119)
at org.apache.spark.sql.execution.aggregate.TungstenAggregate$$anonfun$doExecute$1$$anonfun$2.apply(TungstenAggregate.scala:119)
at org.apache.spark.rdd.MapPartitionsWithPreparationRDD.compute(MapPartitionsWithPreparationRDD.scala:64)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:73)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
at org.apache.spark.scheduler.Task.run(Task.scala:88)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)
> Some tasks failed with Unable to acquire memory
> -----------------------------------------------
>
> Key: SPARK-10309
> URL: https://issues.apache.org/jira/browse/SPARK-10309
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 1.5.0
> Reporter: Davies Liu
>
> *=== Update ===*
> This is caused by a mismatch between `Runtime.getRuntime.availableProcessors()` and the number of active tasks in `ShuffleMemoryManager`. A quick reproduction is the following:
> {code}
> // My machine only has 8 cores
> $ bin/spark-shell --master local[32]
> scala> val df = sc.parallelize(Seq((1, 1), (2, 2))).toDF("a", "b")
> scala> df.as("x").join(df.as("y"), $"x.a" === $"y.a").count()
> Caused by: java.io.IOException: Unable to acquire 2097152 bytes of memory
> at org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.acquireNewPage(UnsafeExternalSorter.java:351)
> at org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.<init>(UnsafeExternalSorter.java:138)
> at org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.create(UnsafeExternalSorter.java:106)
> at org.apache.spark.sql.execution.UnsafeExternalRowSorter.<init>(UnsafeExternalRowSorter.java:68)
> at org.apache.spark.sql.execution.TungstenSort.org$apache$spark$sql$execution$TungstenSort$$preparePartition$1(sort.scala:120)
> at org.apache.spark.sql.execution.TungstenSort$$anonfun$doExecute$2.apply(sort.scala:143)
> at org.apache.spark.sql.execution.TungstenSort$$anonfun$doExecute$2.apply(sort.scala:143)
> at org.apache.spark.rdd.MapPartitionsWithPreparationRDD.prepare(MapPartitionsWithPreparationRDD.scala:50)
> {code}
> *=== Original ===*
> While running Q53 of TPCDS (scale = 1500) on 24 nodes cluster (12G memory on executor):
> {code}
> java.io.IOException: Unable to acquire 33554432 bytes of memory
> at org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.acquireNewPage(UnsafeExternalSorter.java:368)
> at org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.<init>(UnsafeExternalSorter.java:138)
> at org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.create(UnsafeExternalSorter.java:106)
> at org.apache.spark.sql.execution.UnsafeExternalRowSorter.<init>(UnsafeExternalRowSorter.java:68)
> at org.apache.spark.sql.execution.TungstenSort.org$apache$spark$sql$execution$TungstenSort$$preparePartition$1(sort.scala:146)
> at org.apache.spark.sql.execution.TungstenSort$$anonfun$doExecute$3.apply(sort.scala:169)
> at org.apache.spark.sql.execution.TungstenSort$$anonfun$doExecute$3.apply(sort.scala:169)
> at org.apache.spark.rdd.MapPartitionsWithPreparationRDD.compute(MapPartitionsWithPreparationRDD.scala:45)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
> at org.apache.spark.rdd.ZippedPartitionsRDD2.compute(ZippedPartitionsRDD.scala:88)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
> at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
> at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
> at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:73)
> at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
> at org.apache.spark.scheduler.Task.run(Task.scala:88)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
> at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
> at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
> at java.lang.Thread.run(Thread.java:745)
> {code}
> The task could finished after retry.
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