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)
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