Spark Connection Refused - apache-spark

We use GraphX library of Spark to calculate PageRank for our graph which contains 50M vertices and 100M edges, and when we use YARN for scheduling the job, the job fails in the middle of the same stage, and it runs like charm when use use standalone scheduler. The config is following:
We have 4 machines, running Spark 1.2.1 on Cloudera's Hadoop 2.4
each has 12 cores and 64 Gb of RAM, 1Gb eth.
we launch the app with following config:
/opt/spark/bin/spark-submit --class com.test.analytics.spark.graphx.TestPageRank --master yarn-cluster --num-executors 4 --executor-memory 20g --executor-cores 12 --queue root.pagerank /tmp/mvn-spark_2.10-1.0-SNAPSHOT.jar
Here's the stacktrace
org.apache.spark.shuffle.FetchFailedException: Failed to connect to cluster3/192.168.10.3:43492
at org.apache.spark.shuffle.hash.BlockStoreShuffleFetcher$.org$apache$spark$shuffle$hash$BlockStoreShuffleFetcher$$unpackBlock$1(BlockStoreShuffleFetcher.scala:67)
at org.apache.spark.shuffle.hash.BlockStoreShuffleFetcher$$anonfun$3.apply(BlockStoreShuffleFetcher.scala:83)
at org.apache.spark.shuffle.hash.BlockStoreShuffleFetcher$$anonfun$3.apply(BlockStoreShuffleFetcher.scala:83)
at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
at org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:32)
at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:39)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
at org.apache.spark.graphx.impl.EdgePartition.updateVertices(EdgePartition.scala:89)
at org.apache.spark.graphx.impl.ReplicatedVertexView$$anonfun$2$$anonfun$apply$1.apply(ReplicatedVertexView.scala:75)
at org.apache.spark.graphx.impl.ReplicatedVertexView$$anonfun$2$$anonfun$apply$1.apply(ReplicatedVertexView.scala:73)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
at org.apache.spark.graphx.impl.EdgeRDDImpl$$anonfun$mapEdgePartitions$1.apply(EdgeRDDImpl.scala:110)
at org.apache.spark.graphx.impl.EdgeRDDImpl$$anonfun$mapEdgePartitions$1.apply(EdgeRDDImpl.scala:108)
at org.apache.spark.rdd.RDD$$anonfun$14.apply(RDD.scala:618)
at org.apache.spark.rdd.RDD$$anonfun$14.apply(RDD.scala:618)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:280)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:247)
at org.apache.spark.graphx.EdgeRDD.compute(EdgeRDD.scala:49)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:280)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:247)
at org.apache.spark.rdd.MappedRDD.compute(MappedRDD.scala:31)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:280)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:247)
...
at java.lang.Thread.run(Thread.java:745)
Caused by: java.io.IOException: Failed to connect to cluster3/192.168.10.3:43492
at org.apache.spark.network.client.TransportClientFactory.createClient(TransportClientFactory.java:191)
at org.apache.spark.network.client.TransportClientFactory.createClient(TransportClientFactory.java:156)
at org.apache.spark.network.netty.NettyBlockTransferService$$anon$1.createAndStart(NettyBlockTransferService.scala:78)
at org.apache.spark.network.shuffle.RetryingBlockFetcher.fetchAllOutstanding(RetryingBlockFetcher.java:140)
at org.apache.spark.network.shuffle.RetryingBlockFetcher.access$200(RetryingBlockFetcher.java:43)
at org.apache.spark.network.shuffle.RetryingBlockFetcher$1.run(RetryingBlockFetcher.java:170)
at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:471)
at java.util.concurrent.FutureTask.run(FutureTask.java:262)
...
Caused by: java.net.ConnectException: Connection refused: cluster3/192.168.10.3:43492
at sun.nio.ch.SocketChannelImpl.checkConnect(Native Method)
at sun.nio.ch.SocketChannelImpl.finishConnect(SocketChannelImpl.java:739)
at io.netty.channel.socket.nio.NioSocketChannel.doFinishConnect(NioSocketChannel.java:208)
at io.netty.channel.nio.AbstractNioChannel$AbstractNioUnsafe.finishConnect(AbstractNioChannel.java:287)
at io.netty.channel.nio.NioEventLoop.processSelectedKey(NioEventLoop.java:528)
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:116)
...

Related

Stop and Restart SparkContext executing in deploy mode "cluster"

In order to fit the efficiency requirements, I am forced to stop SparkContext and restart it with a new configuration more optimal in terms of number of executors, memory per executor, executor memory overhead...
I can achieve this launching my spark-submit in client mode :
spark-submit --num-executors 5 \
--deploy-mode client \
--class className spark.jar
And then within my code executing:
spark.stop()
val spark2 : SparkSession = SparkSession.builder
.config("spark.submit.deployMode", "client")
.config("spark.executor.instances", "8")
.getOrCreate()
And everything works OK.
However, when launching in client mode, stopping the SparkContext and restarting sparkContext in cluster mode, I get the following error:
20/05/28 18:05:24 ERROR spark.SparkContext: Error initializing SparkContext.
org.apache.spark.SparkException: Detected yarn cluster mode, but isn't running on a cluster. Deployment to YARN is not supported directly by SparkContext. Please use spark-submit.
at org.apache.spark.SparkContext.<init>(SparkContext.scala:379)
at org.apache.spark.SparkContext$.getOrCreate(SparkContext.scala:2520)
at org.apache.spark.sql.SparkSession$Builder$$anonfun$7.apply(SparkSession.scala:935)
at org.apache.spark.sql.SparkSession$Builder$$anonfun$7.apply(SparkSession.scala:926)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.sql.SparkSession$Builder.getOrCreate(SparkSession.scala:926)
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 org.apache.spark.deploy.JavaMainApplication.start(SparkApplication.scala:52)
at org.apache.spark.deploy.SparkSubmit.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:849)
at org.apache.spark.deploy.SparkSubmit.doRunMain$1(SparkSubmit.scala:167)
at org.apache.spark.deploy.SparkSubmit.submit(SparkSubmit.scala:195)
at org.apache.spark.deploy.SparkSubmit.doSubmit(SparkSubmit.scala:86)
at org.apache.spark.deploy.SparkSubmit$$anon$2.doSubmit(SparkSubmit.scala:924)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:933)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
20/05/28 18:05:24 ERROR util.Utils: Uncaught exception in thread main
java.lang.NullPointerException
at org.apache.spark.SparkContext.org$apache$spark$SparkContext$$postApplicationEnd(SparkContext.scala:2416)
at org.apache.spark.SparkContext$$anonfun$stop$1.apply$mcV$sp(SparkContext.scala:1931)
at org.apache.spark.util.Utils$.tryLogNonFatalError(Utils.scala:1385)
at org.apache.spark.SparkContext.stop(SparkContext.scala:1930)
at org.apache.spark.SparkContext.<init>(SparkContext.scala:585)
at org.apache.spark.SparkContext$.getOrCreate(SparkContext.scala:2520)
at org.apache.spark.sql.SparkSession$Builder$$anonfun$7.apply(SparkSession.scala:935)
at org.apache.spark.sql.SparkSession$Builder$$anonfun$7.apply(SparkSession.scala:926)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.sql.SparkSession$Builder.getOrCreate(SparkSession.scala:926)
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 org.apache.spark.deploy.JavaMainApplication.start(SparkApplication.scala:52)
at org.apache.spark.deploy.SparkSubmit.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:849)
at org.apache.spark.deploy.SparkSubmit.doRunMain$1(SparkSubmit.scala:167)
at org.apache.spark.deploy.SparkSubmit.submit(SparkSubmit.scala:195)
at org.apache.spark.deploy.SparkSubmit.doSubmit(SparkSubmit.scala:86)
at org.apache.spark.deploy.SparkSubmit$$anon$2.doSubmit(SparkSubmit.scala:924)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:933)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
I have also tried launching spark-submit in cluster mode, stopping SparkCOntext and restarting it again in cluster mode. In this case I get the error:
Exception in thread "main" org.apache.spark.SparkException: Application application_1583287354042_80626 finished with failed status
at org.apache.spark.deploy.yarn.Client.run(Client.scala:1171)
at org.apache.spark.deploy.yarn.YarnClusterApplication.start(Client.scala:1608)
at org.apache.spark.deploy.SparkSubmit.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:849)
at org.apache.spark.deploy.SparkSubmit.doRunMain$1(SparkSubmit.scala:167)
at org.apache.spark.deploy.SparkSubmit.submit(SparkSubmit.scala:195)
at org.apache.spark.deploy.SparkSubmit.doSubmit(SparkSubmit.scala:86)
at org.apache.spark.deploy.SparkSubmit$$anon$2.doSubmit(SparkSubmit.scala:924)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:933)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
I am not sure if it might be related to the fact that the driver is running on the cluster...
I'd be very grateful if someone could provide a solution to achieve these requirements.

How to reference .so files in spark-submit command

I am using TimesTen Database with Spark 2.3.0
I need to refer to .so files in spark-submit command in order to connect to Timesten db.
Is there any option for same in spark-submit?
I tried adding so file in --conf spark.executor.extraLibraryPath still it doesn't resolve the error.
Error I am getting is :
Exception in thread "main" org.apache.spark.SparkException: Job aborted due to stage failure: Task 135 in stage 8.0 failed 4 times, most recent failure: Lost task 135.3 in stage 8.0 (TID 5308, 10.180.25.241, executor 3): java.sql.SQLException: Problems with loading native library/missing methods: no ttJdbcCS in java.library.path
at com.timesten.jdbc.JdbcOdbcConnection.connect(JdbcOdbcConnection.java:1809)
at com.timesten.jdbc.TimesTenDriver.connect(TimesTenDriver.java:305)
at com.timesten.jdbc.TimesTenDriver.connect(TimesTenDriver.java:161)
at org.apache.spark.sql.execution.datasources.jdbc.DriverWrapper.connect(DriverWrapper.scala:45)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anonfun$createConnectionFactory$1.apply(JdbcUtils.scala:63)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anonfun$createConnectionFactory$1.apply(JdbcUtils.scala:54)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.savePartition(JdbcUtils.scala:600)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anonfun$saveTable$1.apply(JdbcUtils.scala:821)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anonfun$saveTable$1.apply(JdbcUtils.scala:821)
at org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1$$anonfun$apply$29.apply(RDD.scala:929)
at org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1$$anonfun$apply$29.apply(RDD.scala:929)
at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2067)
at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2067)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
at org.apache.spark.scheduler.Task.run(Task.scala:109)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:345)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1599)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1587)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1586)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1586)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:831)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:831)
at scala.Option.foreach(Option.scala:257)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:831)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1820)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1769)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1758)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:642)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2027)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2048)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2067)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2092)
at org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1.apply(RDD.scala:929)
at org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1.apply(RDD.scala:927)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:363)
at org.apache.spark.rdd.RDD.foreachPartition(RDD.scala:927)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.saveTable(JdbcUtils.scala:821)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcRelationProvider.createRelation(JdbcRelationProvider.scala:83)
at org.apache.spark.sql.execution.datasources.SaveIntoDataSourceCommand.run(SaveIntoDataSourceCommand.scala:46)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult$lzycompute(commands.scala:70)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult(commands.scala:68)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.doExecute(commands.scala:86)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:131)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:127)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:127)
at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:80)
at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:80)
at org.apache.spark.sql.DataFrameWriter$$anonfun$runCommand$1.apply(DataFrameWriter.scala:654)
at org.apache.spark.sql.DataFrameWriter$$anonfun$runCommand$1.apply(DataFrameWriter.scala:654)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:77)
at org.apache.spark.sql.DataFrameWriter.runCommand(DataFrameWriter.scala:654)
at org.apache.spark.sql.DataFrameWriter.saveToV1Source(DataFrameWriter.scala:273)
at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:267)
at com.sample.Transformation.main(Transformation.java:195)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at org.apache.spark.deploy.JavaMainApplication.start(SparkApplication.scala:52)
at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:879)
at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:197)
at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:227)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:136)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
Caused by: java.sql.SQLException: Problems with loading native library/missing methods: no ttJdbcCS in java.library.path
at com.timesten.jdbc.JdbcOdbcConnection.connect(JdbcOdbcConnection.java:1809)
at com.timesten.jdbc.TimesTenDriver.connect(TimesTenDriver.java:305)
at com.timesten.jdbc.TimesTenDriver.connect(TimesTenDriver.java:161)
at org.apache.spark.sql.execution.datasources.jdbc.DriverWrapper.connect(DriverWrapper.scala:45)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anonfun$createConnectionFactory$1.apply(JdbcUtils.scala:63)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anonfun$createConnectionFactory$1.apply(JdbcUtils.scala:54)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.savePartition(JdbcUtils.scala:600)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anonfun$saveTable$1.apply(JdbcUtils.scala:821)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anonfun$saveTable$1.apply(JdbcUtils.scala:821)
at org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1$$anonfun$apply$29.apply(RDD.scala:929)
at org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1$$anonfun$apply$29.apply(RDD.scala:929)
at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2067)
at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2067)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
at org.apache.spark.scheduler.Task.run(Task.scala:109)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:345)
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)
My spark-submit command
./spark-submit \
--class com.sample.Transformation \
--conf spark.sql.shuffle.partitions=5001 \
--conf spark.yarn.executor.memoryOverhead=11264 \
--conf spark.executor.extraLibraryPath=/scratch/rmbbuild/Timesten/TimesTen/tt1122/lib \
--executor-memory=91GB \
--conf spark.yarn.driver.memoryOverhead=11264 \
--driver-memory=91G \
--executor-cores=17 \
--driver-cores=17 \
--conf spark.default.parallelism=306 \
--jars /scratch/rmbbuild/spark_ormb/drools-jars/ojdbc6.jar,/scratch/rmbbuild/spark_ormb/drools-jars/kie-api-7.7.0.Final.jar,/scratch/rmbbuild/spark_ormb/drools-jars/drools-core-7.7.0.Final.jar,/scratch/rmbbuild/spark_ormb/drools-jars/drools-compiler-7.7.0.Final.jar,/scratch/rmbbuild/spark_ormb/drools-jars/kie-soup-maven-support-7.7.0.Final.jar,/scratch/rmbbuild/spark_ormb/drools-jars/kie-internal-7.7.0.Final.jar,/scratch/rmbbuild/spark_ormb/drools-jars/xstream-1.4.10.jar,/scratch/rmbbuild/spark_ormb/drools-jars/kie-soup-commons-7.7.0.Final.jar,/scratch/rmbbuild/spark_ormb/drools-jars/ecj-4.4.2.jar,/scratch/rmbbuild/spark_ormb/drools-jars/mvel2-2.4.0.Final.jar,/scratch/rmbbuild/spark_ormb/drools-jars/kie-soup-project-datamodel-commons-7.7.0.Final.jar,/scratch/rmbbuild/spark_ormb/drools-jars/kie-soup-project-datamodel-api-7.7.0.Final.jar,/scratch/rmbbuild/Timesten/TimesTen/tt1122/lib/ttjdbc8.jar --driver-class-path /scratch/rmbbuild/spark_ormb/drools-jars/ojdbc6.jar:/scratch/rmbbuild/Timesten/TimesTen/tt1122/lib/ttjdbc8.jar \
--master spark://10.180.181.189:7077 \
"/scratch/rmbbuild/spark_ormb/POC-jar/Transformation-0.0.1-SNAPSHOT.jar" \
> /scratch/rmbbuild/spark_ormb/POC-jar/logs/logstt21.txt
The spark.executor.extraLibraryPath is a path used on the executors, as the name suggests, so your .so must be available at that location on all of the executors.
Either it must be installed at your specified absolute path on all executor nodes (i.e. at /scratch/rmbbuild/Timesten/TimesTen/tt1122/lib), or it can be uploaded to the executors using the --files option of spark-submit, where it will be available to your job in the working directory of the executor.
See also this question:
Loading shared libraries (.so) distributed by --files argument with spark

Py4JJavaError: org.apache.spark.SparkException: Exception thrown in awaitResult

I did create notebook in jupyter
SPARK_MAJOR_VERSION=2 PYSPARK_DRIVER_PYTHON=jupyter PYSPARK_DRIVER_PYTHON_OPTS='notebook --ip=hadoop-edge-001 --no-browser --port=8888' pyspark --master yarn-client --driver-memory 25g --executor-memory 50g --num-executors 100 --conf "spark.executor.cores=10" --conf "spark.ui.port=8072" --conf "spark.driver.maxResultSize=0" --conf "spark.serializer=org.apache.spark.serializer.KryoSerializer" --conf "spark.kryoserializer.buffer.max=1024m" --conf "spark.shuffle.service.enabled=true" --conf "spark.dynamicAllocation.enabled=true" --conf "spark.dynamicAllocation.minExecutors=2" --conf "spark.dynamicAllocation.initialExecutors=100"
After in notebook did execute:
import pyspark
sc = pyspark.SparkContext(aplicationName="aerobus")
did return error:
Py4JJavaError: An error occurred while calling None.org.apache.spark.api.java.JavaSparkContext.
: org.apache.spark.SparkException: Exception thrown in awaitResult
at org.apache.spark.rpc.RpcTimeout$$anonfun$1.applyOrElse(RpcTimeout.scala:77)
at org.apache.spark.rpc.RpcTimeout$$anonfun$1.applyOrElse(RpcTimeout.scala:75)
at scala.runtime.AbstractPartialFunction.apply(AbstractPartialFunction.scala:36)
at org.apache.spark.rpc.RpcTimeout$$anonfun$addMessageIfTimeout$1.applyOrElse(RpcTimeout.scala:59)
at org.apache.spark.rpc.RpcTimeout$$anonfun$addMessageIfTimeout$1.applyOrElse(RpcTimeout.scala:59)
at scala.PartialFunction$OrElse.apply(PartialFunction.scala:167)
at org.apache.spark.rpc.RpcTimeout.awaitResult(RpcTimeout.scala:83)
at org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend.requestTotalExecutors(CoarseGrainedSchedulerBackend.scala:512)
at org.apache.spark.ExecutorAllocationManager.start(ExecutorAllocationManager.scala:236)
at org.apache.spark.SparkContext$$anonfun$21.apply(SparkContext.scala:552)
at org.apache.spark.SparkContext$$anonfun$21.apply(SparkContext.scala:552)
at scala.Option.foreach(Option.scala:257)
at org.apache.spark.SparkContext.<init>(SparkContext.scala:552)
at org.apache.spark.api.java.JavaSparkContext.<init>(JavaSparkContext.scala:58)
at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
at sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)
at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)
at java.lang.reflect.Constructor.newInstance(Constructor.java:423)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:247)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
at py4j.Gateway.invoke(Gateway.java:236)
at py4j.commands.ConstructorCommand.invokeConstructor(ConstructorCommand.java:80)
at py4j.commands.ConstructorCommand.execute(ConstructorCommand.java:69)
at py4j.GatewayConnection.run(GatewayConnection.java:214)
at java.lang.Thread.run(Thread.java:745)
Caused by: java.io.IOException: Failed to send RPC 5088920142760340842 to /192.168.1.64:54215: java.nio.channels.ClosedChannelException
at org.apache.spark.network.client.TransportClient$3.operationComplete(TransportClient.java:249)
at org.apache.spark.network.client.TransportClient$3.operationComplete(TransportClient.java:233)
at io.netty.util.concurrent.DefaultPromise.notifyListener0(DefaultPromise.java:514)
at io.netty.util.concurrent.DefaultPromise.notifyListenersNow(DefaultPromise.java:488)
at io.netty.util.concurrent.DefaultPromise.access$000(DefaultPromise.java:34)
at io.netty.util.concurrent.DefaultPromise$1.run(DefaultPromise.java:438)
at io.netty.util.concurrent.SingleThreadEventExecutor.runAllTasks(SingleThreadEventExecutor.java:408)
at io.netty.channel.nio.NioEventLoop.run(NioEventLoop.java:455)
at io.netty.util.concurrent.SingleThreadEventExecutor$2.run(SingleThreadEventExecutor.java:140)
at io.netty.util.concurrent.DefaultThreadFactory$DefaultRunnableDecorator.run(DefaultThreadFactory.java:144)
... 1 more
Caused by: java.nio.channels.ClosedChannelException
at io.netty.channel.AbstractChannel$AbstractUnsafe.write(...)(Unknown Source)
How solve this problem?

Spark shuffle error org.apache.spark.shuffle.FetchFailedException: FAILED_TO_UNCOMPRESS(5)

I have a job which processes large volumes of data. This job frequently runs without any error but occasionally it throws this error. I am using Kyro Serializer.
I am running Spark 1.2.0 with yarn cluster.
Full stacktrace here:
org.apache.spark.shuffle.FetchFailedException: FAILED_TO_UNCOMPRESS(5)
at org.apache.spark.shuffle.hash.BlockStoreShuffleFetcher$.org$apache$spark$shuffle$hash$BlockStoreShuffleFetcher$$unpackBlock$1(BlockStoreShuffleFetcher.scala:67)
at org.apache.spark.shuffle.hash.BlockStoreShuffleFetcher$$anonfun$3.apply(BlockStoreShuffleFetcher.scala:83)
at org.apache.spark.shuffle.hash.BlockStoreShuffleFetcher$$anonfun$3.apply(BlockStoreShuffleFetcher.scala:83)
at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
at org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:32)
at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:39)
at org.apache.spark.Aggregator.combineCombinersByKey(Aggregator.scala:89)
at org.apache.spark.shuffle.hash.HashShuffleReader.read(HashShuffleReader.scala:44)
at org.apache.spark.rdd.ShuffledRDD.compute(ShuffledRDD.scala:92)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:263)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:230)
at org.apache.spark.rdd.MappedValuesRDD.compute(MappedValuesRDD.scala:31)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:263)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:230)
at org.apache.spark.rdd.MappedRDD.compute(MappedRDD.scala:31)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:263)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:230)
at org.apache.spark.rdd.MappedRDD.compute(MappedRDD.scala:31)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:263)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:230)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:68)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
at org.apache.spark.scheduler.Task.run(Task.scala:56)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:196)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)
Caused by: java.io.IOException: FAILED_TO_UNCOMPRESS(5)
at org.xerial.snappy.SnappyNative.throw_error(SnappyNative.java:84)
at org.xerial.snappy.SnappyNative.rawUncompress(Native Method)
at org.xerial.snappy.Snappy.rawUncompress(Snappy.java:444)
at org.xerial.snappy.Snappy.uncompress(Snappy.java:480)
at org.xerial.snappy.SnappyInputStream.readFully(SnappyInputStream.java:135)
at org.xerial.snappy.SnappyInputStream.readHeader(SnappyInputStream.java:92)
at org.xerial.snappy.SnappyInputStream.<init>(SnappyInputStream.java:58)
at org.apache.spark.io.SnappyCompressionCodec.compressedInputStream(CompressionCodec.scala:128)
at org.apache.spark.storage.BlockManager.wrapForCompression(BlockManager.scala:1164)
at org.apache.spark.storage.ShuffleBlockFetcherIterator$$anonfun$4.apply(ShuffleBlockFetcherIterator.scala:300)
at org.apache.spark.storage.ShuffleBlockFetcherIterator$$anonfun$4.apply(ShuffleBlockFetcherIterator.scala:299)
at scala.util.Success$$anonfun$map$1.apply(Try.scala:206)
at scala.util.Try$.apply(Try.scala:161)
at scala.util.Success.map(Try.scala:206)
at org.apache.spark.storage.ShuffleBlockFetcherIterator.next(ShuffleBlockFetcherIterator.scala:299)
at org.apache.spark.storage.ShuffleBlockFetcherIterator.next(ShuffleBlockFetcherIterator.scala:53)
... 24 more
I think it is better that you use another compression codec like lz4. To do so in conf/spark-defaults.conf add this a new line: spark.io.compression.codec lz4
to change compression codec from snappy (default) to lz4
However, this problem reported as a bug and has been reopened in the Apache Jira: https://issues.apache.org/jira/browse/SPARK-4105
Check if you executor also have java.lang.OutOfMemoryError: Java heap space or high GC pressure? Having no memory might have caused snappy to fail due to not able to acquire memory. In any case, increasing executor memory allocation and specially spark.memory.shuffleFraction should help

Spark 1.1.0 on cdh5.1.3 does not work in yarn-cluster mode

I am having CDH 5.1 (Hadoop 2.3.0-cdh5.1.3) installed on my cluster, version:
I have installed and configured a prebuilt version of Spark 1.1.0 (Apache Version), built for hadoop 2.3 on my cluster.
when I run the Pi example in the ‘client mode’, it runs successfully, but it fails in the ‘yarn-cluster’ mode. The spark job is successfully submitted, but fails after polling the application master for sometime:
More Logs:
Application application_1415193640322_0016 failed 2 times due to Error launching appattempt_1415193640322_0016_000002. Got exception: org.apache.hadoop.yarn.exceptions.YarnException: java.io.EOFException
at org.apache.hadoop.yarn.ipc.RPCUtil.getRemoteException(RPCUtil.java:38)
at org.apache.hadoop.yarn.server.nodemanager.containermanager.ContainerManagerImpl.startContainers(ContainerManagerImpl.java:710)
at org.apache.hadoop.yarn.api.impl.pb.service.ContainerManagementProtocolPBServiceImpl.startContainers(ContainerManagementProtocolPBServiceImpl.java:60)
at org.apache.hadoop.yarn.proto.ContainerManagementProtocol$ContainerManagementProtocolService$2.callBlockingMethod(ContainerManagementProtocol.java:95)
at org.apache.hadoop.ipc.ProtobufRpcEngine$Server$ProtoBufRpcInvoker.call(ProtobufRpcEngine.java:587)
at org.apache.hadoop.ipc.RPC$Server.call(RPC.java:1026)
at org.apache.hadoop.ipc.Server$Handler$1.run(Server.java:2013)
at org.apache.hadoop.ipc.Server$Handler$1.run(Server.java:2009)
at java.security.AccessController.doPrivileged(Native Method)
at javax.security.auth.Subject.doAs(Subject.java:415)
at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1614)
at org.apache.hadoop.ipc.Server$Handler.run(Server.java:2007)
Caused by: java.io.EOFException
at java.io.DataInputStream.readFully(DataInputStream.java:197)
at java.io.DataInputStream.readUTF(DataInputStream.java:609)
at java.io.DataInputStream.readUTF(DataInputStream.java:564)
at org.apache.hadoop.yarn.security.ContainerTokenIdentifier.readFields(ContainerTokenIdentifier.java:151)
at org.apache.hadoop.security.token.Token.decodeIdentifier(Token.java:142)
at org.apache.hadoop.yarn.server.utils.BuilderUtils.newContainerTokenIdentifier(BuilderUtils.java:262)
at org.apache.hadoop.yarn.server.nodemanager.containermanager.ContainerManagerImpl.startContainers(ContainerManagerImpl.java:696)
... 10 more
at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
at sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:57)
at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)
at java.lang.reflect.Constructor.newInstance(Constructor.java:526)
at org.apache.hadoop.yarn.ipc.RPCUtil.instantiateException(RPCUtil.java:53)
at org.apache.hadoop.yarn.ipc.RPCUtil.unwrapAndThrowException(RPCUtil.java:101)
at org.apache.hadoop.yarn.api.impl.pb.client.ContainerManagementProtocolPBClientImpl.startContainers(ContainerManagementProtocolPBClientImpl.java:99)
at org.apache.hadoop.yarn.server.resourcemanager.amlauncher.AMLauncher.launch(AMLauncher.java:118)
at org.apache.hadoop.yarn.server.resourcemanager.amlauncher.AMLauncher.run(AMLauncher.java:249)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:744)
Caused by: org.apache.hadoop.ipc.RemoteException(org.apache.hadoop.yarn.exceptions.YarnException): java.io.EOFException
at org.apache.hadoop.yarn.ipc.RPCUtil.getRemoteException(RPCUtil.java:38)
at org.apache.hadoop.yarn.server.nodemanager.containermanager.ContainerManagerImpl.startContainers(ContainerManagerImpl.java:710)
at org.apache.hadoop.yarn.api.impl.pb.service.ContainerManagementProtocolPBServiceImpl.startContainers(ContainerManagementProtocolPBServiceImpl.java:60)
at org.apache.hadoop.yarn.proto.ContainerManagementProtocol$ContainerManagementProtocolService$2.callBlockingMethod(ContainerManagementProtocol.java:95)
at org.apache.hadoop.ipc.ProtobufRpcEngine$Server$ProtoBufRpcInvoker.call(ProtobufRpcEngine.java:587)
at org.apache.hadoop.ipc.RPC$Server.call(RPC.java:1026)
at org.apache.hadoop.ipc.Server$Handler$1.run(Server.java:2013)
at org.apache.hadoop.ipc.Server$Handler$1.run(Server.java:2009)
at java.security.AccessController.doPrivileged(Native Method)
at javax.security.auth.Subject.doAs(Subject.java:415)
at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1614)
at org.apache.hadoop.ipc.Server$Handler.run(Server.java:2007)
Caused by: java.io.EOFException
at java.io.DataInputStream.readFully(DataInputStream.java:197)
at java.io.DataInputStream.readUTF(DataInputStream.java:609)
at java.io.DataInputStream.readUTF(DataInputStream.java:564)
at org.apache.hadoop.yarn.security.ContainerTokenIdentifier.readFields(ContainerTokenIdentifier.java:151)
at org.apache.hadoop.security.token.Token.decodeIdentifier(Token.java:142)
at org.apache.hadoop.yarn.server.utils.BuilderUtils.newContainerTokenIdentifier(BuilderUtils.java:262)
at org.apache.hadoop.yarn.server.nodemanager.containermanager.ContainerManagerImpl.startContainers(ContainerManagerImpl.java:696)
... 10 more
at org.apache.hadoop.ipc.Client.call(Client.java:1409)
at org.apache.hadoop.ipc.Client.call(Client.java:1362)
at org.apache.hadoop.ipc.ProtobufRpcEngine$Invoker.invoke(ProtobufRpcEngine.java:206)
at com.sun.proxy.$Proxy69.startContainers(Unknown Source)
at org.apache.hadoop.yarn.api.impl.pb.client.ContainerManagementProtocolPBClientImpl.startContainers(ContainerManagementProtocolPBClientImpl.java:96)
... 5 more
. Failing the application.
When I go to node Manager logs:
Log Type: stderr
Log Length: 87
Error: Could not find or load main class org.apache.spark.deploy.yarn.ExecutorLauncher
Can you please suggest any solution.Do you think I should compile the spark code on my cluster. Or should I use Spark provided with CDH5.1.
Any help will be appreciated!
spark-shell does not work with spark yarn-cluster mode. You should add --master yarn-client
Example:
path/to/pyspark --master yarn-client

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