[SPARK-18737] Serialization setting "spark.serializer" ignored in Spark 2.x

• Type: Bug
• Status: Resolved
• Priority: Major
• Resolution: Duplicate
• Affects Version/s: 2.0.0, 2.0.1
• Assignee: Unassigned
• Reporter: Dr. Michael Menzel
• Created: 06/Dec/16 07:32
• Updated: 04/Apr/17 20:27
• Resolved: 30/Dec/16 10:18

Description

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?

Issue Links

• duplicates: SPARK-18560 Receiver data can not be dataSerialized properly.