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Spark Error - Caused by: org.apache.spark.SparkException: Could not execute broadcast in 300 secs

By Dinesh

 

While running Spark applications you would have seen below error - 

Caused by: org.apache.spark.SparkException: Could not execute broadcast in 300 secs. You can increase the timeout for broadcasts via spark.sql.broadcastTimeout or disable broadcast join by setting spark.sql.autoBroadcastJoinThreshold to -1
	at org.apache.spark.sql.execution.exchange.BroadcastExchangeExec.doExecuteBroadcast(BroadcastExchangeExec.scala:150)
	at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeBroadcast$1.apply(SparkPlan.scala:154)
	at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeBroadcast$1.apply(SparkPlan.scala:150)
	at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:165)
	at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
	at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:162)
	at org.apache.spark.sql.execution.SparkPlan.executeBroadcast(SparkPlan.scala:150)
	at org.apache.spark.sql.execution.joins.BroadcastNestedLoopJoinExec.doExecute(BroadcastNestedLoopJoinExec.scala:343)

Mostly, When people receive this error they try to set spark.sql.autoBroadcastJoinThreshold to -1
, which will actually turn off BroadcastJoins resulting poor performance of Spark Jobs. 

Ideally, we should analyze why Spark is not able broadcast Data in 300 Seconds / 5 minutes which is less in size i.e spark.sql.autoBroadcastJoinThreshold - 10 MB


A few situation that we have seen is - 
  • Network slowness which is hindering data broadcast. In enterprise applications, we see this behavior rarely.
  • For one of the application- we analyzed that tasks are taking time in scanning files from HDFS/ Hive. There were 612 Tasks which needs to complete to broadcast the data.

  • So, we analyzed that Spark Stage fails after completing 300 Tasks out of 617 in 5 minutes. We had been running with 5 executors, each with 3 cores.
    • Thus, we had two options - 
      • First, Increase spark.sql.broadcastTimeout to 600 Seconds.
      • Second, Increase Number of Cores/ Executors. So, as to increase parallelism. Thus, we increased executors to 10 and each with 4 cores.
Option Second worked for us, providing best performance and SLA at dispense of providing more resources to Job. Also, avoiding delay introduced by SortMegerJoin due to disabled Broadcast join.

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