[SPARK-17503] Memory leak in Memory store when unable to cache the whole RDD in memory - ASF JIRA

Details

Type: Bug
Status: Resolved
Priority: Major
Resolution: Fixed
Affects Version/s: 2.1.0
Fix Version/s: 2.0.1, 2.1.0
Component/s: Spark Core
Labels:
None

Target Version/s:

2.0.1, 2.1.0

Description

Problem description:

The following query triggers out of memory error.

sc.parallelize(1 to 1000000000, 100).map(x => new Array[Long](1000)).cache().count()

This is not expected, we should fallback to use disk instead if there is not enough memory for cache.

Stacktrace:

scala> sc.parallelize(1 to 1000000000, 100).map(x => new Array[Long](1000)).cache().count()
[Stage 0:>                                                          (0 + 5) / 5]16/09/11 17:27:20 WARN MemoryStore: Not enough space to cache rdd_1_4 in memory! (computed 631.5 MB so far)
16/09/11 17:27:20 WARN MemoryStore: Not enough space to cache rdd_1_0 in memory! (computed 631.5 MB so far)
16/09/11 17:27:20 WARN BlockManager: Putting block rdd_1_0 failed
16/09/11 17:27:20 WARN BlockManager: Putting block rdd_1_4 failed
16/09/11 17:27:21 WARN MemoryStore: Not enough space to cache rdd_1_1 in memory! (computed 947.3 MB so far)
16/09/11 17:27:21 WARN BlockManager: Putting block rdd_1_1 failed
16/09/11 17:27:22 WARN MemoryStore: Not enough space to cache rdd_1_3 in memory! (computed 1423.7 MB so far)
16/09/11 17:27:22 WARN BlockManager: Putting block rdd_1_3 failed

java.lang.OutOfMemoryError: Java heap space
Dumping heap to java_pid26528.hprof ...
Heap dump file created [6551021666 bytes in 9.876 secs]
16/09/11 17:28:15 WARN NettyRpcEnv: Ignored message: HeartbeatResponse(false)
16/09/11 17:28:15 WARN NettyRpcEndpointRef: Error sending message [message = Heartbeat(driver,[Lscala.Tuple2;@46c9ce96,BlockManagerId(driver, 127.0.0.1, 55360))] in 1 attempts
org.apache.spark.rpc.RpcTimeoutException: Futures timed out after [10 seconds]. This timeout is controlled by spark.executor.heartbeatInterval
	at org.apache.spark.rpc.RpcTimeout.org$apache$spark$rpc$RpcTimeout$$createRpcTimeoutException(RpcTimeout.scala:48)
	at org.apache.spark.rpc.RpcTimeout$$anonfun$addMessageIfTimeout$1.applyOrElse(RpcTimeout.scala:63)
	at org.apache.spark.rpc.RpcTimeout$$anonfun$addMessageIfTimeout$1.applyOrElse(RpcTimeout.scala:59)
	at scala.PartialFunction$OrElse.apply(PartialFunction.scala:167)
	at org.apache.spark.rpc.RpcTimeout.awaitResult(RpcTimeout.scala:83)
	at org.apache.spark.rpc.RpcEndpointRef.askWithRetry(RpcEndpointRef.scala:102)
	at org.apache.spark.executor.Executor.org$apache$spark$executor$Executor$$reportHeartBeat(Executor.scala:523)
	at org.apache.spark.executor.Executor$$anon$1$$anonfun$run$1.apply$mcV$sp(Executor.scala:552)
	at org.apache.spark.executor.Executor$$anon$1$$anonfun$run$1.apply(Executor.scala:552)
	at org.apache.spark.executor.Executor$$anon$1$$anonfun$run$1.apply(Executor.scala:552)
	at org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1857)
	at org.apache.spark.executor.Executor$$anon$1.run(Executor.scala:552)
	at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511)
	at java.util.concurrent.FutureTask.runAndReset(FutureTask.java:308)
	at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.access$301(ScheduledThreadPoolExecutor.java:180)
	at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:294)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
	at java.lang.Thread.run(Thread.java:745)
Caused by: java.util.concurrent.TimeoutException: Futures timed out after [10 seconds]
	at scala.concurrent.impl.Promise$DefaultPromise.ready(Promise.scala:219)
	at scala.concurrent.impl.Promise$DefaultPromise.result(Promise.scala:223)
	at scala.concurrent.Await$$anonfun$result$1.apply(package.scala:190)
	at scala.concurrent.BlockContext$DefaultBlockContext$.blockOn(BlockContext.scala:53)
	at scala.concurrent.Await$.result(package.scala:190)
	at org.apache.spark.rpc.RpcTimeout.awaitResult(RpcTimeout.scala:81)
	... 14 more
16/09/11 17:28:15 ERROR Executor: Exception in task 3.0 in stage 0.0 (TID 3)
java.lang.OutOfMemoryError: Java heap space
	at $line14.$read$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$anonfun$1.apply(<console>:24)
	at $line14.$read$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$anonfun$1.apply(<console>:23)
	at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
	at scala.collection.Iterator$JoinIterator.next(Iterator.scala:232)
	at org.apache.spark.storage.memory.PartiallyUnrolledIterator.next(MemoryStore.scala:683)
	at org.apache.spark.InterruptibleIterator.next(InterruptibleIterator.scala:43)
	at org.apache.spark.util.Utils$.getIteratorSize(Utils.scala:1684)
	at org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:1134)
	at org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:1134)
	at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1915)
	at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1915)
	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:70)
	at org.apache.spark.scheduler.Task.run(Task.scala:86)
	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
	at java.lang.Thread.run(Thread.java:745)
16/09/11 17:28:15 ERROR Executor: Exception in task 4.0 in stage 0.0 (TID 4)
java.lang.OutOfMemoryError: Java heap space
	at $line14.$read$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$anonfun$1.apply(<console>:24)
	at $line14.$read$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$anonfun$1.apply(<console>:23)
	at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
	at scala.collection.Iterator$JoinIterator.next(Iterator.scala:232)
	at org.apache.spark.storage.memory.PartiallyUnrolledIterator.next(MemoryStore.scala:683)
	at org.apache.spark.InterruptibleIterator.next(InterruptibleIterator.scala:43)
	at org.apache.spark.util.Utils$.getIteratorSize(Utils.scala:1684)
	at org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:1134)
	at org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:1134)
	at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1915)
	at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1915)
	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:70)
	at org.apache.spark.scheduler.Task.run(Task.scala:86)
	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
	at java.lang.Thread.run(Thread.java:745)
16/09/11 17:28:15 ERROR SparkUncaughtExceptionHandler: Uncaught exception in thread Thread[Executor task launch worker-3,5,main]
java.lang.OutOfMemoryError: Java heap space
	at $line14.$read$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$anonfun$1.apply(<console>:24)
	at $line14.$read$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$anonfun$1.apply(<console>:23)
	at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
	at scala.collection.Iterator$JoinIterator.next(Iterator.scala:232)
	at org.apache.spark.storage.memory.PartiallyUnrolledIterator.next(MemoryStore.scala:683)
	at org.apache.spark.InterruptibleIterator.next(InterruptibleIterator.scala:43)
	at org.apache.spark.util.Utils$.getIteratorSize(Utils.scala:1684)
	at org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:1134)
	at org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:1134)
	at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1915)
	at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1915)
	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:70)
	at org.apache.spark.scheduler.Task.run(Task.scala:86)
	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
	at java.lang.Thread.run(Thread.java:745)
16/09/11 17:28:15 ERROR SparkUncaughtExceptionHandler: Uncaught exception in thread Thread[Executor task launch worker-4,5,main]
java.lang.OutOfMemoryError: Java heap space
	at $line14.$read$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$anonfun$1.apply(<console>:24)
	at $line14.$read$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$anonfun$1.apply(<console>:23)
	at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
	at scala.collection.Iterator$JoinIterator.next(Iterator.scala:232)
	at org.apache.spark.storage.memory.PartiallyUnrolledIterator.next(MemoryStore.scala:683)
	at org.apache.spark.InterruptibleIterator.next(InterruptibleIterator.scala:43)
	at org.apache.spark.util.Utils$.getIteratorSize(Utils.scala:1684)
	at org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:1134)
	at org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:1134)
	at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1915)
	at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1915)
	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:70)
	at org.apache.spark.scheduler.Task.run(Task.scala:86)
	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
	at java.lang.Thread.run(Thread.java:745)
16/09/11 17:28:15 WARN TaskSetManager: Lost task 4.0 in stage 0.0 (TID 4, localhost): java.lang.OutOfMemoryError: Java heap space
	at $line14.$read$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$anonfun$1.apply(<console>:24)
	at $line14.$read$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$anonfun$1.apply(<console>:23)
	at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
	at scala.collection.Iterator$JoinIterator.next(Iterator.scala:232)
	at org.apache.spark.storage.memory.PartiallyUnrolledIterator.next(MemoryStore.scala:683)
	at org.apache.spark.InterruptibleIterator.next(InterruptibleIterator.scala:43)
	at org.apache.spark.util.Utils$.getIteratorSize(Utils.scala:1684)
	at org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:1134)
	at org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:1134)
	at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1915)
	at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1915)
	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:70)
	at org.apache.spark.scheduler.Task.run(Task.scala:86)
	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
	at java.lang.Thread.run(Thread.java:745)

Analysis:

When the RDD is too big to cache, Spark returns a PartiallyUnrolledIterator.

   // line 287, in file MemoryStore.scala 

    } else {
      // We ran out of space while unrolling the values for this block
      logUnrollFailureMessage(blockId, vector.estimateSize())
      Left(new PartiallyUnrolledIterator(
        this, unrollMemoryUsedByThisBlock, unrolled = vector.iterator, rest = values))
    }

Parameter 'unrolled' points to a vector array buffer, which stores all input values we have read so far when trying to cache the RDD. Parameter 'rest' is a iterator over all unread input values.

For example, if the input RDD partition has 100GB bytes, and Spark executor has a 10GB cache, then parameter 'unrolled' will points to a array of 10GB bytes, the parameter 'rest' iterator points to unread 90GB input data.

We expect the 10GB 'unrolled' memory to be garbage collected immediately after all values in 'unrolled' have been consumed by PartiallyUnrolledIterator. But current Spark code will not collect the 10GB 'unrolled' until all 100GB input data has been processed.

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Screen Shot 2016-09-12 at 4.16.15 PM.png
12/Sep/16 08:36
89 kB
Sean Zhong
Screen Shot 2016-09-12 at 4.34.19 PM.png
12/Sep/16 08:36
107 kB
Sean Zhong

Issue Links

links to

[Github] Pull Request #15056 (clockfly)

Memory leak in Memory store when unable to cache the whole RDD in memory

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Problem description:

Analysis:

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