Why a Spark Core Application Is Suspended Instead of Being Exited When Driver Memory Is Insufficient to Store Collected Intensive Data?

Question

A Spark Core application is attempting to collect intensive data and store it into the Driver. When the Driver runs out of memory, the Spark Core application is suspended. Why does the Spark Core application not exit?

The following is the log information displayed at the time of out-of-memory (OOM) error.

16/04/19 15:56:22 ERROR Utils: Uncaught exception in thread task-result-getter-2
java.lang.OutOfMemoryError: Java heap space
at java.lang.reflect.Array.newArray(Native Method)
at java.lang.reflect.Array.newInstance(Array.java:75)
at java.io.ObjectInputStream.readArray(ObjectInputStream.java:1671)
at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1345)
at java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:2000)
at java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1924)
at java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1801)
at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1351)
at java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:2000)
at java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1924)
at java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1801)
at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1351)
at java.io.ObjectInputStream.readArray(ObjectInputStream.java:1707)
at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1345)
at java.io.ObjectInputStream.readObject(ObjectInputStream.java:371)
at org.apache.spark.serializer.JavaDeserializationStream.readObject(JavaSerializer.scala:71)
at org.apache.spark.serializer.JavaSerializerInstance.deserialize(JavaSerializer.scala:91)
at org.apache.spark.scheduler.DirectTaskResult.value(TaskResult.scala:94)
at org.apache.spark.scheduler.TaskResultGetter$$anon$3$$anonfun$run$1.apply$mcV$sp(TaskResultGetter.scala:66)
at org.apache.spark.scheduler.TaskResultGetter$$anon$3$$anonfun$run$1.apply(TaskResultGetter.scala:57)
at org.apache.spark.scheduler.TaskResultGetter$$anon$3$$anonfun$run$1.apply(TaskResultGetter.scala:57)
at org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1716)
at org.apache.spark.scheduler.TaskResultGetter$$anon$3.run(TaskResultGetter.scala:56)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
Exception in thread "task-result-getter-2" java.lang.OutOfMemoryError: Java heap space
at java.lang.reflect.Array.newArray(Native Method)
at java.lang.reflect.Array.newInstance(Array.java:75)
at java.io.ObjectInputStream.readArray(ObjectInputStream.java:1671)
at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1345)
at java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:2000)
at java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1924)
at java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1801)
at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1351)
at java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:2000)
at java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1924)
at java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1801)
at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1351)
at java.io.ObjectInputStream.readArray(ObjectInputStream.java:1707)
at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1345)
at java.io.ObjectInputStream.readObject(ObjectInputStream.java:371)
at org.apache.spark.serializer.JavaDeserializationStream.readObject(JavaSerializer.scala:71)
at org.apache.spark.serializer.JavaSerializerInstance.deserialize(JavaSerializer.scala:91)
at org.apache.spark.scheduler.DirectTaskResult.value(TaskResult.scala:94)
at org.apache.spark.scheduler.TaskResultGetter$$anon$3$$anonfun$run$1.apply$mcV$sp(TaskResultGetter.scala:66)
at org.apache.spark.scheduler.TaskResultGetter$$anon$3$$anonfun$run$1.apply(TaskResultGetter.scala:57)
at org.apache.spark.scheduler.TaskResultGetter$$anon$3$$anonfun$run$1.apply(TaskResultGetter.scala:57)
at org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1716)
at org.apache.spark.scheduler.TaskResultGetter$$anon$3.run(TaskResultGetter.scala:56)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)

Answer

If memory of the Driver is insufficient to store the intensive data that has been collected, the OOM error is reported and the Driver performs garbage collection repeatedly to reclaim the memory occupied by garbage. The Spark Core application remains suspended while garbage collection is under way.

If you expect the Spark Core application to exit forcibly in the event of OOM error, add the following information to the configuration option spark.driver.extraJavaOptions in the Spark client configuration file $SPARK_HOME/conf/spark-defaults.conf when you start the Spark Core application for the first time: