[jira] [Created] (SPARK-24912) Broadcast join OutOfMemory stack trace obscures actual cause of OOM

["Bruce Robbins (JIRA)" <ji...@apache.org>]

Bruce Robbins created SPARK-24912:
-------------------------------------

             Summary: Broadcast join OutOfMemory stack trace obscures actual cause of OOM
                 Key: SPARK-24912
                 URL: https://issues.apache.org/jira/browse/SPARK-24912
             Project: Spark
          Issue Type: Bug
          Components: SQL
    Affects Versions: 2.4.0
            Reporter: Bruce Robbins


When the Spark driver suffers an OutOfMemoryError while attempting to broadcast a table for a broadcast join, the resulting stack trace obscures the actual cause of the OOM. For e.g.:
{noformat}
[GC (Allocation Failure)  585453K->585453K(928768K), 0.0060025 secs]
[Full GC (Allocation Failure)  585453K->582524K(928768K), 0.4019639 secs]
java.lang.OutOfMemoryError: Java heap space
Dumping heap to java_pid12446.hprof ...
Heap dump file created [632701033 bytes in 1.016 secs]
Exception in thread "main" java.lang.OutOfMemoryError: Not enough memory to build and broadcast the table to all worker nodes. As a workaround, you can either disable broadcast by setting spark.sql.autoBroadcastJoinThreshold to -1 or increase the spark driver memory by setting spark.driver.memory to a higher value
	at org.apache.spark.sql.execution.exchange.BroadcastExchangeExec$$anonfun$relationFuture$1$$anonfun$apply$1.apply(BroadcastExchangeExec.scala:122)
	at org.apache.spark.sql.execution.exchange.BroadcastExchangeExec$$anonfun$relationFuture$1$$anonfun$apply$1.apply(BroadcastExchangeExec.scala:76)
	at org.apache.spark.sql.execution.SQLExecution$$anonfun$withExecutionId$1.apply(SQLExecution.scala:101)
	at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:125)
	at org.apache.spark.sql.execution.SQLExecution$.withExecutionId(SQLExecution.scala:98)
	at org.apache.spark.sql.execution.exchange.BroadcastExchangeExec$$anonfun$relationFuture$1.apply(BroadcastExchangeExec.scala:75)
	at org.apache.spark.sql.execution.exchange.BroadcastExchangeExec$$anonfun$relationFuture$1.apply(BroadcastExchangeExec.scala:75)
	at scala.concurrent.impl.Future$PromiseCompletingRunnable.liftedTree1$1(Future.scala:24)
	at scala.concurrent.impl.Future$PromiseCompletingRunnable.run(Future.scala:24)
	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)
18/07/24 14:29:58 INFO ContextCleaner: Cleaned accumulator 30
18/07/24 14:29:58 INFO ContextCleaner: Cleaned accumulator 35
{noformat}
The above stack trace blames BroadcastExchangeExec. However, the given line is actually where the original OutOfMemoryError was caught and a new one was created and wrapped by a SparkException. The actual location where the OOM occurred was in LongToUnsafeRowMap#grow, at this line:
{noformat}
val newPage = new Array[Long](newNumWords.toInt)
{noformat}
Sometimes it is helpful to know the actual location from which an OOM is thrown. In the above case, the location indicated that Spark underestimated the size of a large-ish table and ran out of memory trying to load it into memory.





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