Carlos Balduz created PIG-4323:
----------------------------------
Summary: PackageConverter hanging in Spark
Key: PIG-4323
URL: https://issues.apache.org/jira/browse/PIG-4323
Project: Pig
Issue Type: Bug
Components: spark
Reporter: Carlos Balduz
Assignee: Carlos Balduz
After the patch introduced in PIG-4237, PackageConverter hangs for large jobs,
since it has to deserialize JobConfiguration each time apply is called, making
it way too slow:
{code:java}
public Tuple apply(final Tuple t) {
initializeJobConf();
...
{code}
After having a job stuck for 30 minutes, I made a thread dump and saw that the
main issue was:
{code:java}
Thread 11128: (state = IN_JAVA)
- java.util.zip.InflaterInputStream.read(byte[], int, int) @bci=53, line=152
(Compiled frame; information may be imprecise)
- java.util.zip.GZIPInputStream.read(byte[], int, int) @bci=17, line=116
(Compiled frame)
-
org.apache.hadoop.io.WritableUtils.readCompressedByteArray(java.io.DataInput)
@bci=65, line=44 (Compiled frame)
- org.apache.hadoop.io.WritableUtils.readCompressedString(java.io.DataInput)
@bci=1, line=87 (Compiled frame)
-
org.apache.hadoop.io.WritableUtils.readCompressedStringArray(java.io.DataInput)
@bci=29, line=185 (Compiled frame)
- org.apache.hadoop.conf.Configuration.readFields(java.io.DataInput) @bci=37,
line=2564 (Compiled frame)
-
org.apache.pig.backend.hadoop.executionengine.spark.KryoSerializer.deserializeJobConf(byte[])
@bci=24, line=80 (Compiled frame)
-
org.apache.pig.backend.hadoop.executionengine.spark.converter.PackageConverter$PackageFunction.initializeJobConf()
@bci=4, line=60 (Compiled frame)
-
org.apache.pig.backend.hadoop.executionengine.spark.converter.PackageConverter$PackageFunction.apply(org.apache.pig.data.Tuple)
@bci=1, line=67 (Compiled frame)
-
org.apache.pig.backend.hadoop.executionengine.spark.converter.PackageConverter$PackageFunction.apply(java.lang.Object)
@bci=5, line=48 (Compiled frame)
- scala.collection.Iterator$$anon$11.next() @bci=13, line=328 (Compiled frame)
- scala.collection.convert.Wrappers$IteratorWrapper.next() @bci=4, line=30
(Compiled frame)
-
org.apache.pig.backend.hadoop.executionengine.spark.converter.POOutputConsumerIterator.readNext()
@bci=44, line=61 (Compiled frame)
-
org.apache.pig.backend.hadoop.executionengine.spark.converter.POOutputConsumerIterator.readNext()
@bci=162, line=78 (Compiled frame)
-
org.apache.pig.backend.hadoop.executionengine.spark.converter.POOutputConsumerIterator.hasNext()
@bci=1, line=91 (Compiled frame)
-
org.apache.pig.backend.hadoop.executionengine.spark.converter.POOutputConsumerIterator.readNext()
@bci=133, line=75 (Compiled frame)
-
org.apache.pig.backend.hadoop.executionengine.spark.converter.POOutputConsumerIterator.hasNext()
@bci=1, line=91 (Compiled frame)
- scala.collection.convert.Wrappers$JIteratorWrapper.hasNext() @bci=4, line=41
(Compiled frame)
- scala.collection.Iterator$$anon$11.hasNext() @bci=4, line=327 (Compiled
frame)
- scala.collection.Iterator$$anon$11.hasNext() @bci=4, line=327 (Compiled
frame)
- scala.collection.Iterator$class.foreach(scala.collection.Iterator,
scala.Function1) @bci=1, line=727 (Compiled frame)
- scala.collection.AbstractIterator.foreach(scala.Function1) @bci=2, line=1157
(Interpreted frame)
-
org.apache.spark.shuffle.hash.HashShuffleWriter.write(scala.collection.Iterator)
@bci=95, line=65 (Interpreted frame)
-
org.apache.spark.scheduler.ShuffleMapTask.runTask(org.apache.spark.TaskContext)
@bci=170, 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=67, line=54 (Interpreted
frame)
- org.apache.spark.executor.Executor$TaskRunner.run() @bci=336, line=177
(Interpreted frame)
-
java.util.concurrent.ThreadPoolExecutor.runWorker(java.util.concurrent.ThreadPoolExecutor$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)
{code}
I have added a condition that checks whether it has already been initialized or
not, making it work again.
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