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Posted to issues@spark.apache.org by "Hyukjin Kwon (Jira)" <ji...@apache.org> on 2020/08/31 06:00:19 UTC
[jira] [Commented] (SPARK-32724) java.io.IOException: Stream is
corrupted when tried to inner join 4 huge tables. Currently using pyspark
version 2.4.0-cdh6.3.1
[ https://issues.apache.org/jira/browse/SPARK-32724?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17187449#comment-17187449 ]
Hyukjin Kwon commented on SPARK-32724:
--------------------------------------
Let's ask questions to the mailing list (https://spark.apache.org/community.html) before filing it as an issue.
> java.io.IOException: Stream is corrupted when tried to inner join 4 huge tables. Currently using pyspark version 2.4.0-cdh6.3.1
> ---------------------------------------------------------------------------------------------------------------------------------
>
> Key: SPARK-32724
> URL: https://issues.apache.org/jira/browse/SPARK-32724
> Project: Spark
> Issue Type: Question
> Components: PySpark, Spark Core
> Affects Versions: 2.4.0
> Reporter: Kannan
> Priority: Major
>
> When i try to join the 4 tables with 1M data i am getting below error.
> Py4JJavaError: An error occurred while calling o453.count. : org.apache.spark.SparkException: Job aborted due to stage failure: Aborting TaskSet 27.0 because task 9 (partition 9) cannot run anywhere due to node and executor blacklist. Most recent failure: Lost task 9.1 in stage 27.0 (TID 267, si-159l.de.des.com, executor 17): java.io.IOException: Stream is corrupted at net.jpountz.lz4.LZ4BlockInputStream.refill(LZ4BlockInputStream.java:202) at net.jpountz.lz4.LZ4BlockInputStream.refill(LZ4BlockInputStream.java:228) at net.jpountz.lz4.LZ4BlockInputStream.read(LZ4BlockInputStream.java:157) at org.apache.spark.io.ReadAheadInputStream$1.run(ReadAheadInputStream.java:168) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) at java.lang.Thread.run(Thread.java:748) Blacklisting behavior can be configured via spark.blacklist.*. at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1890) at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1878) at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1877) at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48) at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1877) at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:929) at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:929) at scala.Option.foreach(Option.scala:257) at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:929) at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2111) at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2060) at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2049) at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49) at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:740) at org.apache.spark.SparkContext.runJob(SparkContext.scala:2081) at org.apache.spark.SparkContext.runJob(SparkContext.scala:2102) at org.apache.spark.SparkContext.runJob(SparkContext.scala:2121) at org.apache.spark.SparkContext.runJob(SparkContext.scala:2146) at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:945) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112) at org.apache.spark.rdd.RDD.withScope(RDD.scala:363) at org.apache.spark.rdd.RDD.collect(RDD.scala:944) at org.apache.spark.sql.execution.SparkPlan.executeCollect(SparkPlan.scala:299) at org.apache.spark.sql.Dataset$$anonfun$count$1.apply(Dataset.scala:2830) at org.apache.spark.sql.Dataset$$anonfun$count$1.apply(Dataset.scala:2829) at org.apache.spark.sql.Dataset$$anonfun$53.apply(Dataset.scala:3364) at org.apache.spark.sql.execution.SQLExecution$$anonfun$withNewExecutionId$1.apply(SQLExecution.scala:78) at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:125) at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:73) at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3363) at org.apache.spark.sql.Dataset.count(Dataset.scala:2829) at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke(Method.java:498) at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244) at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357) at py4j.Gateway.invoke(Gateway.java:282) at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132) at py4j.commands.CallCommand.execute(CallCommand.java:79) at py4j.GatewayConnection.run(GatewayConnection.java:238) at java.lang.Thread.run(Thread.java:748)
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