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org.apache.spark.SparkException: Kryo serialization failed: Buffer overflow

 

We were running an application which was leading to below error - 

Job aborted due to stage failure: Task 137 in stage 5.0 failed 4 times, most recent failure: Lost task 137.3 in stage 5.0 (TID 2090, ncABC.hadoop.com, executor 1): org.apache.spark.SparkException: Kryo serialization failed: Buffer overflow. Available: 0, required: 59606960. To avoid this, increase spark.kryoserializer.buffer.max value.
	at org.apache.spark.serializer.KryoSerializerInstance.serialize(KryoSerializer.scala:330)
	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:456)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
	at java.lang.Thread.run(Thread.java:748)
Caused by: com.esotericsoftware.kryo.KryoException: Buffer overflow. Available: 0, required: 59606960
	at com.esotericsoftware.kryo.io.Output.require(Output.java:167)
	at com.esotericsoftware.kryo.io.Output.writeBytes(Output.java:251)
	at com.esotericsoftware.kryo.io.Output.writeBytes(Output.java:237)
	at com.esotericsoftware.kryo.serializers.DefaultArraySerializers$ByteArraySerializer.write(DefaultArraySerializers.java:49)
	at com.esotericsoftware.kryo.serializers.DefaultArraySerializers$ByteArraySerializer.write(DefaultArraySerializers.java:38)
	at com.esotericsoftware.kryo.Kryo.writeClassAndObject(Kryo.java:651)
	at com.twitter.chill.Tuple2Serializer.write(TupleSerializers.scala:37)
	at com.twitter.chill.Tuple2Serializer.write(TupleSerializers.scala:33)
	at com.esotericsoftware.kryo.Kryo.writeClassAndObject(Kryo.java:651)


Solution - 

On further analysis, we found that this error was originating from BroadcastNestedLoopJoin

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)


So, we removed BroadcastNestedLoopJoin by updating SQL where clause having NOT IN  to NOT EXISTS. Refer details here - http://techdevins.blogspot.com/2021/06/spark-disable-broadcast-join-not.html


This solved our problem.


Alternative Solution - 

We read this solution at multiple places but we didn't try to set below properties - 

--conf  spark.kryoserializer.buffer.max=1024m  spark.kryoserializer.buffer=512m

And, don't recommend to set it anything other than default values because,

There is a note that there will be one buffer per core on each worker. This buffer will grow up to spark.kryoserializer.buffer.max if needed. 


That said, if you have worker with 4 cores then 4*512m ~ 2GB is taken up for Kryo Buffer, and that seems a good chunk of memory. 


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