java.io.IOException: Stream is corrupted

[SathishKumarAna]

I am trying to join 4 tables in pyspark but i am getting error as java.io.IOException: Stream is corrupted. I made some configuration changes then also the same error is getting reported.

Currently the LZ4 Compression code i am using is lz4-java-1.5.0. What might be the issue of stream is getting corrupted.

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.se.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)

[odaira]

You may want to read this discussion https://issues.apache.org/jira/browse/SPARK-18105. It seems to me that there is an issue in the way Spark shuffles and merges data.

[odaira]

Would you still need help for this?

[odaira]

Closing this. Please reopen it if you need help.