You are viewing a plain text version of this content. The canonical link for it is here.
Posted to issues@spark.apache.org by "Hyukjin Kwon (Jira)" <ji...@apache.org> on 2019/11/21 07:17:00 UTC
[jira] [Resolved] (SPARK-29885) Improve the exception message when
reading the daemon port
[ https://issues.apache.org/jira/browse/SPARK-29885?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Hyukjin Kwon resolved SPARK-29885.
----------------------------------
Fix Version/s: 3.0.0
Resolution: Fixed
Issue resolved by pull request 26510
[https://github.com/apache/spark/pull/26510]
> Improve the exception message when reading the daemon port
> ----------------------------------------------------------
>
> Key: SPARK-29885
> URL: https://issues.apache.org/jira/browse/SPARK-29885
> Project: Spark
> Issue Type: Improvement
> Components: Spark Core
> Affects Versions: 2.4.0
> Reporter: jiaan.geng
> Assignee: jiaan.geng
> Priority: Major
> Fix For: 3.0.0
>
>
> In production environment, my pyspark application occurs an exception and it's message as below:
> {code:java}
> 19/10/28 16:15:03 ERROR Executor: Exception in task 0.0 in stage 0.0 (TID 0)
> org.apache.spark.SparkException: No port number in pyspark.daemon's stdout
> at org.apache.spark.api.python.PythonWorkerFactory.startDaemon(PythonWorkerFactory.scala:204)
> at org.apache.spark.api.python.PythonWorkerFactory.createThroughDaemon(PythonWorkerFactory.scala:122)
> at org.apache.spark.api.python.PythonWorkerFactory.create(PythonWorkerFactory.scala:95)
> at org.apache.spark.SparkEnv.createPythonWorker(SparkEnv.scala:117)
> at org.apache.spark.api.python.BasePythonRunner.compute(PythonRunner.scala:108)
> at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:65)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
> at org.apache.spark.rdd.RDD$$anonfun$7.apply(RDD.scala:337)
> at org.apache.spark.rdd.RDD$$anonfun$7.apply(RDD.scala:335)
> at org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:1182)
> at org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:1156)
> at org.apache.spark.storage.BlockManager.doPut(BlockManager.scala:1091)
> at org.apache.spark.storage.BlockManager.doPutIterator(BlockManager.scala:1156)
> at org.apache.spark.storage.BlockManager.getOrElseUpdate(BlockManager.scala:882)
> at org.apache.spark.rdd.RDD.getOrCompute(RDD.scala:335)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:286)
> at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:65)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
> at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
> at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
> at org.apache.spark.scheduler.Task.run(Task.scala:121)
> at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)
> at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)
> 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){code}
>
> At first, I think a physical node has many ports are occupied by a large number of processes.
> But I found the total number of ports in use is only 671.
>
> {code:java}
> [yarn@r1115 ~]$ netstat -a | wc -l
> 671
> {code}
> I checked the code of PythonWorkerFactory in line 204 and found:
> {code:java}
> daemon = pb.start()
> val in = new DataInputStream(daemon.getInputStream)
> try {
> daemonPort = in.readInt()
> } catch {
> case _: EOFException =>
> throw new SparkException(s"No port number in $daemonModule's stdout")
> }
> {code}
> I added some code here:
> {code:java}
> logError("Meet EOFException, daemon is alive: ${daemon.isAlive()}")
> logError("Exit value: ${daemon.exitValue()}")
> {code}
> Then I recurrent the exception and it's message as below:
> {code:java}
> 19/10/28 16:15:03 ERROR PythonWorkerFactory: Meet EOFException, daemon is alive: false
> 19/10/28 16:15:03 ERROR PythonWorkerFactory: Exit value: 139
> 19/10/28 16:15:03 ERROR Executor: Exception in task 0.0 in stage 0.0 (TID 0)
> org.apache.spark.SparkException: No port number in pyspark.daemon's stdout
> at org.apache.spark.api.python.PythonWorkerFactory.startDaemon(PythonWorkerFactory.scala:206)
> at org.apache.spark.api.python.PythonWorkerFactory.createThroughDaemon(PythonWorkerFactory.scala:122)
> at org.apache.spark.api.python.PythonWorkerFactory.create(PythonWorkerFactory.scala:95)
> at org.apache.spark.SparkEnv.createPythonWorker(SparkEnv.scala:117)
> at org.apache.spark.api.python.BasePythonRunner.compute(PythonRunner.scala:108)
> at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:65)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
> at org.apache.spark.rdd.RDD$$anonfun$7.apply(RDD.scala:337)
> at org.apache.spark.rdd.RDD$$anonfun$7.apply(RDD.scala:335)
> at org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:1182)
> at org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:1156)
> at org.apache.spark.storage.BlockManager.doPut(BlockManager.scala:1091)
> at org.apache.spark.storage.BlockManager.doPutIterator(BlockManager.scala:1156)
> at org.apache.spark.storage.BlockManager.getOrElseUpdate(BlockManager.scala:882)
> at org.apache.spark.rdd.RDD.getOrCompute(RDD.scala:335)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:286)
> at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:65)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
> at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
> at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
> at org.apache.spark.scheduler.Task.run(Task.scala:121)
> at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)
> at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)
> at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
> at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
> at java.lang.Thread.run(Thread.java:745){code}
> I think the exception message has caused me a lot of confusion.
--
This message was sent by Atlassian Jira
(v8.3.4#803005)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org