Dr. Michael Menzel created SPARK-18737:
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Summary: Serialization setting "spark.serializer" ignored in Spark
2.x
Key: SPARK-18737
URL: https://issues.apache.org/jira/browse/SPARK-18737
Project: Spark
Issue Type: Bug
Affects Versions: 2.0.1, 2.0.0
Reporter: Dr. Michael Menzel
The following exception occurs although the JavaSerializer has been activated:
16/11/22 10:49:24 INFO TaskSetManager: Starting task 0.0 in stage 9.0 (TID 77,
ip-10-121-14-147.eu-central-1.compute.internal, partition 1, RACK_LOCAL, 5621
bytes)
16/11/22 10:49:24 INFO YarnSchedulerBackend$YarnDriverEndpoint: Launching task
77 on executor id: 2 hostname: ip-10-121-14-147.eu-central-1.compute.internal.
16/11/22 10:49:24 INFO BlockManagerInfo: Added broadcast_11_piece0 in memory on
ip-10-121-14-147.eu-central-1.compute.internal:45059 (size: 879.0 B, free:
410.4 MB)
16/11/22 10:49:24 WARN TaskSetManager: Lost task 0.0 in stage 9.0 (TID 77,
ip-10-121-14-147.eu-central-1.compute.internal):
com.esotericsoftware.kryo.KryoException: Encountered unregistered class ID:
13994
at
com.esotericsoftware.kryo.util.DefaultClassResolver.readClass(DefaultClassResolver.java:137)
at com.esotericsoftware.kryo.Kryo.readClass(Kryo.java:670)
at com.esotericsoftware.kryo.Kryo.readClassAndObject(Kryo.java:781)
at
org.apache.spark.serializer.KryoDeserializationStream.readObject(KryoSerializer.scala:229)
at
org.apache.spark.serializer.DeserializationStream$$anon$1.getNext(Serializer.scala:169)
at org.apache.spark.util.NextIterator.hasNext(NextIterator.scala:73)
at scala.collection.Iterator$class.foreach(Iterator.scala:893)
at org.apache.spark.util.NextIterator.foreach(NextIterator.scala:21)
at
scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:59)
at
scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:104)
at
scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:48)
at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:310)
at org.apache.spark.util.NextIterator.to(NextIterator.scala:21)
at
scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:302)
at org.apache.spark.util.NextIterator.toBuffer(NextIterator.scala:21)
at
scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:289)
at org.apache.spark.util.NextIterator.toArray(NextIterator.scala:21)
at
org.apache.spark.rdd.RDD$$anonfun$toLocalIterator$1$$anonfun$org$apache$spark$rdd$RDD$$anonfun$$collectPartition$1$1.apply(RDD.scala:927)
at
org.apache.spark.rdd.RDD$$anonfun$toLocalIterator$1$$anonfun$org$apache$spark$rdd$RDD$$anonfun$$collectPartition$1$1.apply(RDD.scala:927)
at
org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1916)
at
org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1916)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:70)
at org.apache.spark.scheduler.Task.run(Task.scala:86)
at org.apache.spark.executor.Executor$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)
The code runs perfectly with Spark 1.6.0. Since we moved to 2.0.0 and now
2.0.1, we see the Kyro deserialization exception and over time the Spark
streaming job stops processing since too many tasks failed.
Our action was to use conf.set("spark.serializer",
"org.apache.spark.serializer.JavaSerializer") and to disable Kryo class
registration with conf.set("spark.kryo.registrationRequired", false). We hope
to identify the root cause of the exception.
However, setting the serializer to JavaSerializer is oviously ignored by the
Spark-internals. Despite the setting we still see the exception printed in the
log and tasks fail. The occurence seems to be non-deterministic, but to become
more frequent over time.
Several questions we could not answer during our troubleshooting:
1. How can the debug log for Kryo be enabled? -- We tried following the minilog
documentation, but no output can be found.
2. Is the serializer setting effective for Spark internal serializations? How
can the JavaSerialize be forced on internal serializations for worker to driver
communication?
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