[jira] [Created] (HIVE-9781) Utilize spark.kryo.registrator [Spark Branch]

["Brock Noland (JIRA)" <ji...@apache.org>]

Brock Noland created HIVE-9781:
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             Summary: Utilize spark.kryo.registrator [Spark Branch]
                 Key: HIVE-9781
                 URL: https://issues.apache.org/jira/browse/HIVE-9781
             Project: Hive
          Issue Type: Sub-task
            Reporter: Brock Noland


I noticed in several thread dumps that it appears kyro is serializing the class names associated with our keys and values.

Kyro supports pre-registering classes so that you don't have to serialize the class name and spark supports this via the {{spark.kryo.registrator}} property. We should do this so we don't have to serialize class names.

{noformat}
Thread 12154: (state = BLOCKED)
 - java.lang.Object.hashCode() @bci=0 (Compiled frame; information may be imprecise)
 - com.esotericsoftware.kryo.util.ObjectMap.get(java.lang.Object) @bci=1, line=265 (Compiled frame)
 - com.esotericsoftware.kryo.util.DefaultClassResolver.getRegistration(java.lang.Class) @bci=18, line=61 (Compiled frame)
 - com.esotericsoftware.kryo.Kryo.getRegistration(java.lang.Class) @bci=20, line=429 (Compiled frame)
 - com.esotericsoftware.kryo.util.DefaultClassResolver.readName(com.esotericsoftware.kryo.io.Input) @bci=242, line=148 (Compiled frame)
 - com.esotericsoftware.kryo.util.DefaultClassResolver.readClass(com.esotericsoftware.kryo.io.Input) @bci=65, line=115 (Compiled frame)
 - com.esotericsoftware.kryo.Kryo.readClass(com.esotericsoftware.kryo.io.Input) @bci=20, line=610 (Compiled frame)
 - com.esotericsoftware.kryo.Kryo.readClassAndObject(com.esotericsoftware.kryo.io.Input) @bci=21, line=721 (Compiled frame)
 - com.twitter.chill.Tuple2Serializer.read(com.esotericsoftware.kryo.Kryo, com.esotericsoftware.kryo.io.Input, java.lang.Class) @bci=6, line=41 (Compiled frame)
 - com.twitter.chill.Tuple2Serializer.read(com.esotericsoftware.kryo.Kryo, com.esotericsoftware.kryo.io.Input, java.lang.Class) @bci=4, line=33 (Compiled frame)
 - com.esotericsoftware.kryo.Kryo.readClassAndObject(com.esotericsoftware.kryo.io.Input) @bci=126, line=729 (Compiled frame)
 - org.apache.spark.serializer.KryoDeserializationStream.readObject(scala.reflect.ClassTag) @bci=8, line=142 (Compiled frame)
 - org.apache.spark.serializer.DeserializationStream$$anon$1.getNext() @bci=10, line=133 (Compiled frame)
 - org.apache.spark.util.NextIterator.hasNext() @bci=16, line=71 (Compiled frame)
 - org.apache.spark.util.CompletionIterator.hasNext() @bci=4, line=32 (Compiled frame)
 - scala.collection.Iterator$$anon$13.hasNext() @bci=4, line=371 (Compiled frame)
 - org.apache.spark.util.CompletionIterator.hasNext() @bci=4, line=32 (Compiled frame)
 - org.apache.spark.InterruptibleIterator.hasNext() @bci=22, line=39 (Compiled frame)
 - scala.collection.Iterator$$anon$11.hasNext() @bci=4, line=327 (Compiled frame)
 - org.apache.spark.util.collection.ExternalSorter.insertAll(scala.collection.Iterator) @bci=191, line=217 (Compiled frame)
 - org.apache.spark.shuffle.hash.HashShuffleReader.read() @bci=278, line=61 (Interpreted frame)
 - org.apache.spark.rdd.ShuffledRDD.compute(org.apache.spark.Partition, org.apache.spark.TaskContext) @bci=46, line=92 (Interpreted frame)
 - org.apache.spark.rdd.RDD.computeOrReadCheckpoint(org.apache.spark.Partition, org.apache.spark.TaskContext) @bci=26, line=263 (Interpreted frame)
 - org.apache.spark.rdd.RDD.iterator(org.apache.spark.Partition, org.apache.spark.TaskContext) @bci=33, line=230 (Interpreted frame)
 - org.apache.spark.rdd.MapPartitionsRDD.compute(org.apache.spark.Partition, org.apache.spark.TaskContext) @bci=24, line=35 (Interpreted frame)
 - org.apache.spark.rdd.RDD.computeOrReadCheckpoint(org.apache.spark.Partition, org.apache.spark.TaskContext) @bci=26, line=263 (Interpreted frame)
 - org.apache.spark.rdd.RDD.iterator(org.apache.spark.Partition, org.apache.spark.TaskContext) @bci=33, line=230 (Interpreted frame)
 - org.apache.spark.rdd.MapPartitionsRDD.compute(org.apache.spark.Partition, org.apache.spark.TaskContext) @bci=24, line=35 (Interpreted frame)
 - org.apache.spark.rdd.RDD.computeOrReadCheckpoint(org.apache.spark.Partition, org.apache.spark.TaskContext) @bci=26, line=263 (Interpreted frame)
 - org.apache.spark.rdd.RDD.iterator(org.apache.spark.Partition, org.apache.spark.TaskContext) @bci=33, line=230 (Interpreted frame)
 - org.apache.spark.rdd.UnionRDD.compute(org.apache.spark.Partition, org.apache.spark.TaskContext) @bci=22, line=87 (Interpreted frame)
 - org.apache.spark.rdd.RDD.computeOrReadCheckpoint(org.apache.spark.Partition, org.apache.spark.TaskContext) @bci=26, line=263 (Interpreted frame)
 - org.apache.spark.rdd.RDD.iterator(org.apache.spark.Partition, org.apache.spark.TaskContext) @bci=33, line=230 (Interpreted frame)
 - org.apache.spark.scheduler.ShuffleMapTask.runTask(org.apache.spark.TaskContext) @bci=166, line=68 (Interpreted frame)
 - org.apache.spark.scheduler.ShuffleMapTask.runTask(org.apache.spark.TaskContext) @bci=2, line=41 (Interpreted frame)
 - org.apache.spark.scheduler.Task.run(long) @bci=77, line=56 (Interpreted frame)
 - org.apache.spark.executor.Executor$TaskRunner.run() @bci=310, line=196 (Interpreted frame)
 - java.util.concurrent.ThreadPoolExecutor.runWorker(java.util.concurrent.ThreadPoolExecutor$Worker) @bci=95, line=1145 (Interpreted frame)
 - java.util.concurrent.ThreadPoolExecutor$Worker.run() @bci=5, line=615 (Interpreted frame)
 - java.lang.Thread.run() @bci=11, line=745 (Interpreted frame)
{noformat}



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