[jira] [Updated] (SPARK-29619) Add retry times when reading the daemon port.
[ https://issues.apache.org/jira/browse/SPARK-29619?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] jiaan.geng updated SPARK-29619: --- Description: This ticket is related to https://issues.apache.org/jira/browse/SPARK-29885 and add try mechanism. > Add retry times when reading the daemon port. > - > > Key: SPARK-29619 > URL: https://issues.apache.org/jira/browse/SPARK-29619 > Project: Spark > Issue Type: Improvement > Components: Spark Core >Affects Versions: 3.0.0 >Reporter: jiaan.geng >Priority: Major > > This ticket is related to https://issues.apache.org/jira/browse/SPARK-29885 > and add try mechanism. -- This message was sent by Atlassian Jira (v8.3.4#803005) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-29619) Add retry times when reading the daemon port.
[
https://issues.apache.org/jira/browse/SPARK-29619?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
jiaan.geng updated SPARK-29619:
---
Summary: Add retry times when reading the daemon port. (was: Improve the
exception message when reading the daemon port and add retry times.)
> Add retry times when reading the daemon port.
> -
>
> Key: SPARK-29619
> URL: https://issues.apache.org/jira/browse/SPARK-29619
> Project: Spark
> Issue Type: Improvement
> Components: Spark Core
>Affects Versions: 3.0.0
>Reporter: jiaan.geng
>Priority: Major
>
> 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
[jira] [Updated] (SPARK-29619) Add retry times when reading the daemon port.
[
https://issues.apache.org/jira/browse/SPARK-29619?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
jiaan.geng updated SPARK-29619:
---
Description: (was: 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
or
