Static method collectAndServe failed for class org.apache.spark.api.python.PythonRDD

[k2moz]

Please help me. What's wrong?

I cant call Reduce in "Pi Example"

I did everything as a guide - https://www.ics.uci.edu/~shantas/Install_Spark_on_Windows10.pdf

##List of my components

• Windows10
• JDK:8u60
• Spark: spark-2.0.2-bin-hadoop2.6
• Win Utils: Uzip all in %SPARK_HOME%/bin |
• Mobius: v2.0.200

##All variables been regitred

• HADOOP_HOME - C:\spark-2.0.2-bin-hadoop2.6
• JAVA_HOME - C:\Java
• SCALA_HOME - C:\Program Files (x86)\scala
• SPARK_HOME - C:\spark-2.0.2-bin-hadoop2.6
• SPARKCLR_HOME - C:\Mobius\runtime
• TMP - C:\tmp (i change defoult path like this https://github.com/Microsoft/Mobius/blob/6ed8316625118e273576360f1112526eeb49b1c4/notes/troubleshooting-mobius.md)
• PATHs been registrated too

Run in VisualStudio

1. I change Java running settings for heap size in sparkclr-submit.cmd file
   (

...
:debugmode
"%JAVA_HOME%\bin\java" -Xms512m -Xmx1024m -cp "%LAUNCH_CLASSPATH%" org.apache.spark.deploy.csharp.CSharpRunner debug
goto :eof
...

)
2. call sparkclr-submit debugg in command line
3. Load VisualStudio Exaples and Run "Pi " project with params

<add key="CSharpBackendPortNumber" value="5567"/>
<add key="CSharpWorkerPath" value="C:/Mobius/runtime/bin/CSharpWorker.exe"/>

and SparkContext: var _conf = new SparkConf().Set("spark.local.dir", "C:\\tmp\\SparkCLRTemp");

programm hase down on Reduce part of function CalculatePiUsingAnonymousMethod

private static void CalculatePiUsingAnonymousMethod(int n, RDD<int> rdd)
        {
            var _preCount = rdd
                            .Map(i =>
                            {
                                var random = new Random();
                                var x = random.NextDouble() * 2 - 1;
                                var y = random.NextDouble() * 2 - 1;

                                return (x * x + y * y) < 1 ? 1 : 0;
                            });
            var _count = _preCount.Reduce((a,b)=> {
                return a + b;
            });
        }

And show this

[2018-11-23 15:18:20,447] [1] [INFO ] [Microsoft.Spark.CSharp.Configuration.ConfigurationService] - ConfigurationService runMode is DEBUG
[2018-11-23 15:18:20,456] [1] [INFO ] [Microsoft.Spark.CSharp.Configuration.ConfigurationService+SparkCLRDebugConfiguration] - CSharpBackend port number read from app config 5567. Using it to connect to CSharpBackend
[2018-11-23 15:18:20,456] [1] [INFO ] [Microsoft.Spark.CSharp.Proxy.Ipc.SparkCLRIpcProxy] - CSharpBackend port number to be used in JvMBridge is 5567
[2018-11-23 15:18:20,545] [1] [INFO ] [Microsoft.Spark.CSharp.Core.SparkConf] - spark.master not set. Assuming debug mode.
[2018-11-23 15:18:20,548] [1] [INFO ] [Microsoft.Spark.CSharp.Core.SparkConf] - Spark master set to local
[2018-11-23 15:18:20,550] [1] [INFO ] [Microsoft.Spark.CSharp.Core.SparkConf] - spark.app.name not set. Assuming debug mode
[2018-11-23 15:18:20,551] [1] [INFO ] [Microsoft.Spark.CSharp.Core.SparkConf] - Spark app name set to debug app
[2018-11-23 15:18:20,552] [1] [INFO ] [Microsoft.Spark.CSharp.Core.SparkConf] - Spark configuration key-value set to spark.local.dir=C:\tmp\SparkCLRTemp
[2018-11-23 15:18:22,063] [1] [INFO ] [Microsoft.Spark.CSharp.Core.SparkContext] - Parallelizing 300001 items to form RDD in the cluster with 3 partitions
[2018-11-23 15:18:22,790] [1] [INFO ] [Microsoft.Spark.CSharp.Core.RDD`1[[System.Int32, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089]]] - Executing Map operation on RDD (preservesPartitioning=False)
[2018-11-23 15:20:14,247] [1] [INFO ] [Microsoft.Spark.CSharp.Core.RDD`1[[System.Int32, mscorlib, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089]]] - Executing Reduce operation on RDD
[2018-11-23 15:20:14,258] [1] [INFO ] [Microsoft.Spark.CSharp.Configuration.ConfigurationService+SparkCLRDebugConfiguration] - Worker path read from setting CSharpWorkerPath in app config
[2018-11-23 15:20:15,011] [1] [ERROR] [Microsoft.Spark.CSharp.Interop.Ipc.JvmBridge] - JVM method execution failed: Static method collectAndServe failed for class org.apache.spark.api.python.PythonRDD when called with 1 parameters ([Index=1, Type=JvmObjectReference, Value=14], )
[2018-11-23 15:20:15,011] [1] [ERROR] [Microsoft.Spark.CSharp.Interop.Ipc.JvmBridge] - org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 0.0 failed 1 times, most recent failure: Lost task 0.0 in stage 0.0 (TID 0, localhost): java.net.SocketException: Connection reset by peer: socket write error
        at java.net.SocketOutputStream.socketWrite0(Native Method)
        at java.net.SocketOutputStream.socketWrite(SocketOutputStream.java:109)
        at java.net.SocketOutputStream.write(SocketOutputStream.java:153)
        at java.io.BufferedOutputStream.flushBuffer(BufferedOutputStream.java:82)
        at java.io.BufferedOutputStream.write(BufferedOutputStream.java:126)
        at java.io.DataOutputStream.write(DataOutputStream.java:107)
        at java.io.FilterOutputStream.write(FilterOutputStream.java:97)
        at org.apache.spark.api.python.PythonRDD$.org$apache$spark$api$python$PythonRDD$$write$1(PythonRDD.scala:492)
        at org.apache.spark.api.python.PythonRDD$$anonfun$writeIteratorToStream$1.apply(PythonRDD.scala:504)
        at org.apache.spark.api.python.PythonRDD$$anonfun$writeIteratorToStream$1.apply(PythonRDD.scala:504)
        at scala.collection.Iterator$class.foreach(Iterator.scala:893)
        at org.apache.spark.InterruptibleIterator.foreach(InterruptibleIterator.scala:28)
        at org.apache.spark.api.python.PythonRDD$.writeIteratorToStream(PythonRDD.scala:504)
        at org.apache.spark.api.python.PythonRunner$WriterThread$$anonfun$run$3.apply(PythonRDD.scala:328)
        at org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1953)
        at org.apache.spark.api.python.PythonRunner$WriterThread.run(PythonRDD.scala:269)

Driver stacktrace:
        at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1454)
        at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1442)
        at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1441)
        at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
        at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
        at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1441)
        at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:811)
        at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:811)
        at scala.Option.foreach(Option.scala:257)
        at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:811)
        at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1667)
        at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1622)
        at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1611)
        at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
        at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:632)
        at org.apache.spark.SparkContext.runJob(SparkContext.scala:1873)
        at org.apache.spark.SparkContext.runJob(SparkContext.scala:1886)
        at org.apache.spark.SparkContext.runJob(SparkContext.scala:1899)
        at org.apache.spark.SparkContext.runJob(SparkContext.scala:1913)
        at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:912)
        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:358)
        at org.apache.spark.rdd.RDD.collect(RDD.scala:911)
        at org.apache.spark.api.python.PythonRDD$.collectAndServe(PythonRDD.scala:453)
        at org.apache.spark.api.python.PythonRDD.collectAndServe(PythonRDD.scala)
        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:497)
        at org.apache.spark.api.csharp.CSharpBackendHandler.handleMethodCall(CSharpBackendHandler.scala:156)
        at org.apache.spark.api.csharp.CSharpBackendHandler.handleBackendRequest(CSharpBackendHandler.scala:106)
        at org.apache.spark.api.csharp.CSharpBackendHandler.channelRead0(CSharpBackendHandler.scala:32)
        at org.apache.spark.api.csharp.CSharpBackendHandler.channelRead0(CSharpBackendHandler.scala:28)
        at io.netty.channel.SimpleChannelInboundHandler.channelRead(SimpleChannelInboundHandler.java:105)
        at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:308)
        at io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:294)
        at io.netty.handler.codec.MessageToMessageDecoder.channelRead(MessageToMessageDecoder.java:103)
        at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:308)
        at io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:294)
        at io.netty.handler.codec.ByteToMessageDecoder.channelRead(ByteToMessageDecoder.java:244)
        at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:308)
        at io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:294)
        at io.netty.channel.DefaultChannelPipeline.fireChannelRead(DefaultChannelPipeline.java:846)
        at io.netty.channel.nio.AbstractNioByteChannel$NioByteUnsafe.read(AbstractNioByteChannel.java:131)
        at io.netty.channel.nio.NioEventLoop.processSelectedKey(NioEventLoop.java:511)
        at io.netty.channel.nio.NioEventLoop.processSelectedKeysOptimized(NioEventLoop.java:468)
        at io.netty.channel.nio.NioEventLoop.processSelectedKeys(NioEventLoop.java:382)
        at io.netty.channel.nio.NioEventLoop.run(NioEventLoop.java:354)
        at io.netty.util.concurrent.SingleThreadEventExecutor$2.run(SingleThreadEventExecutor.java:111)
        at io.netty.util.concurrent.DefaultThreadFactory$DefaultRunnableDecorator.run(DefaultThreadFactory.java:137)
        at java.lang.Thread.run(Thread.java:745)
Caused by: java.net.SocketException: Connection reset by peer: socket write error
        at java.net.SocketOutputStream.socketWrite0(Native Method)
        at java.net.SocketOutputStream.socketWrite(SocketOutputStream.java:109)
        at java.net.SocketOutputStream.write(SocketOutputStream.java:153)
        at java.io.BufferedOutputStream.flushBuffer(BufferedOutputStream.java:82)
        at java.io.BufferedOutputStream.write(BufferedOutputStream.java:126)
        at java.io.DataOutputStream.write(DataOutputStream.java:107)
        at java.io.FilterOutputStream.write(FilterOutputStream.java:97)
        at org.apache.spark.api.python.PythonRDD$.org$apache$spark$api$python$PythonRDD$$write$1(PythonRDD.scala:492)
        at org.apache.spark.api.python.PythonRDD$$anonfun$writeIteratorToStream$1.apply(PythonRDD.scala:504)
        at org.apache.spark.api.python.PythonRDD$$anonfun$writeIteratorToStream$1.apply(PythonRDD.scala:504)
        at scala.collection.Iterator$class.foreach(Iterator.scala:893)
        at org.apache.spark.InterruptibleIterator.foreach(InterruptibleIterator.scala:28)
        at org.apache.spark.api.python.PythonRDD$.writeIteratorToStream(PythonRDD.scala:504)
        at org.apache.spark.api.python.PythonRunner$WriterThread$$anonfun$run$3.apply(PythonRDD.scala:328)
        at org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1953)
        at org.apache.spark.api.python.PythonRunner$WriterThread.run(PythonRDD.scala:269)

[2018-11-23 15:20:15,021] [1] [ERROR] [Microsoft.Spark.CSharp.Interop.Ipc.JvmBridge] - JVM method execution failed: Static method collectAndServe failed for class org.apache.spark.api.python.PythonRDD when called with 1 parameters ([Index=1, Type=JvmObjectReference, Value=14], )
[2018-11-23 15:20:15,022] [1] [ERROR] [Microsoft.Spark.CSharp.Interop.Ipc.JvmBridge] -
*******************************************************************************************************************************
   в Microsoft.Spark.CSharp.Interop.Ipc.JvmBridge.CallJavaMethod(Boolean isStatic, Object classNameOrJvmObjectReference, String methodName, Object[] parameters)
*******************************************************************************************************************************

[sharpeye096]

I encountered similar issue with you. There is a commit that upgrade it to Spark 2.3.1.
9aa97b9

It should be some version related issue, please make sure your local spark version is consistent with the one on clustered. I somehow mitigated it by a workaround but now stuck by another issue

[k2moz]

I encountered similar issue with you. There is a commit that upgrade it to Spark 2.3.1.
9aa97b9

It should be some version related issue, please make sure your local spark version is consistent with the one on clustered. I somehow mitigated it by a workaround but now stuck by another issue

I could not solve the problem =(

I had to use Phyton scripts inside my c# code for use pyspark api (Spark 2.4.0)
Phyton Scripts running in c# code with IronPhyton (http://ironpython.net/)