I am working with Talend tMatchGroupHadoop component with Amazon EMR cluster,
it is giving an error: "could only be replicated to 0 nodes, instead of 1".
Actually data node is running in the AMR cluster.
hadoop fsck
..............Status: HEALTHY
Total size: 315153 B
Total dirs: 12
Total files: 14 (Files currently being written: 1)
Total blocks (validated): 13 (avg. block size 24242 B)
Minimally replicated blocks: 13 (100.0 %)
Over-replicated blocks: 0 (0.0 %)
Under-replicated blocks: 0 (0.0 %)
Mis-replicated blocks: 0 (0.0 %)
Default replication factor: 1
Average block replication: 1.0
Corrupt blocks: 0
Missing replicas: 0 (0.0 %)
Number of data-nodes: 1
Number of racks: 1
FSCK ended at Mon Sep 08 06:07:58 UTC 2014 in 158 milliseconds
I am getting the following error:
[statistics] connecting to socket on port 3645
[statistics] connected
[INFO ]: org.apache.hadoop.hdfs.DFSClient - Exception in createBlockOutputStream 10.230.30.124:9200 java.net.ConnectException: Connection timed out: no further information
[INFO ]: org.apache.hadoop.hdfs.DFSClient - Abandoning block blk_-3580819895919001579_2135
[INFO ]: org.apache.hadoop.hdfs.DFSClient - Excluding datanode 10.230.30.124:9200
Exception in component tMatchGroupHadoop_2_GroupOut
org.apache.hadoop.ipc.RemoteException: java.io.IOException: File /in could only be replicated to 0 nodes, instead of 1
at org.apache.hadoop.hdfs.server.namenode.FSNamesystem.getAdditionalBlock(FSNamesystem.java:1569)
at org.apache.hadoop.hdfs.server.namenode.NameNode.addBlock(NameNode.java:701)
at sun.reflect.GeneratedMethodAccessor6.invoke(Unknown Source)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:606)
at org.apache.hadoop.ipc.RPC$Server.call(RPC.java:583)
at org.apache.hadoop.ipc.Server$Handler$1.run(Server.java:1393)
at org.apache.hadoop.ipc.Server$Handler$1.run(Server.java:1389)
at java.security.AccessController.doPrivileged(Native Method)
at javax.security.auth.Subject.doAs(Subject.java:415)
at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1140)
at org.apache.hadoop.ipc.Server$Handler.run(Server.java:1387)
at org.apache.hadoop.ipc.Client.call(Client.java:1092)
[WARN ]: org.apache.hadoop.hdfs.DFSClient - DataStreamer Exception: org.apache.hadoop.ipc.RemoteException: java.io.IOException: File /in could only be replicated to 0 nodes, instead of 1
at org.apache.hadoop.hdfs.server.namenode.FSNamesystem.getAdditionalBlock(FSNamesystem.java:1569)
at org.apache.hadoop.hdfs.server.namenode.NameNode.addBlock(NameNode.java:701)
at sun.reflect.GeneratedMethodAccessor6.invoke(Unknown Source)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:606)
at org.apache.hadoop.ipc.RPC$Server.call(RPC.java:583)
at org.apache.hadoop.ipc.Server$Handler$1.run(Server.java:1393)
at org.apache.hadoop.ipc.Server$Handler$1.run(Server.java:1389)
at java.security.AccessController.doPrivileged(Native Method)
at javax.security.auth.Subject.doAs(Subject.java:415)
at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1140)
at org.apache.hadoop.ipc.Server$Handler.run(Server.java:1387)
at org.apache.hadoop.ipc.RPC$Invoker.invoke(RPC.java:229)
at com.sun.proxy.$Proxy1.addBlock(Unknown Source)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:606)
at org.apache.hadoop.io.retry.RetryInvocationHandler.invokeMethod(RetryInvocationHandler.java:85)
at org.apache.hadoop.io.retry.RetryInvocationHandler.invoke(RetryInvocationHandler.java:62)
at com.sun.proxy.$Proxy1.addBlock(Unknown Source)
at org.apache.hadoop.hdfs.DFSClient$DFSOutputStream.locateFollowingBlock(DFSClient.java:3595)
at org.apache.hadoop.hdfs.DFSClient$DFSOutputStream.nextBlockOutputStream(DFSClient.java:3456)
at org.apache.hadoop.hdfs.DFSClient$DFSOutputStream.access$2600(DFSClient.java:2672)
at org.apache.hadoop.hdfs.DFSClient$DFSOutputStream$DataStreamer.run(DFSClient.java:2912)
at org.apache.hadoop.ipc.Client.call(Client.java:1092)
at org.apache.hadoop.ipc.RPC$Invoker.invoke(RPC.java:229)
at com.sun.proxy.$Proxy1.addBlock(Unknown Source)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:606)
at org.apache.hadoop.io.retry.RetryInvocationHandler.invokeMethod(RetryInvocationHandler.java:85)
at org.apache.hadoop.io.retry.RetryInvocationHandler.invoke(RetryInvocationHandler.java:62)
at com.sun.proxy.$Proxy1.addBlock(Unknown Source)
at org.apache.hadoop.hdfs.DFSClient$DFSOutputStream.locateFollowingBlock(DFSClient.java:3595)
at org.apache.hadoop.hdfs.DFSClient$DFSOutputStream.nextBlockOutputStream(DFSClient.java:3456)
at org.apache.hadoop.hdfs.DFSClient$DFSOutputStream.access$2600(DFSClient.java:2672)
at org.apache.hadoop.hdfs.DFSClient$DFSOutputStream$DataStreamer.run(DFSClient.java:2912)
[WARN ]: org.apache.hadoop.hdfs.DFSClient - Error Recovery for block blk_-3580819895919001579_2135 bad datanode[0] nodes == null
[WARN ]: org.apache.hadoop.hdfs.DFSClient - Could not get block locations. Source file "/in" - Aborting...
[statistics] disconnected
[ERROR]: org.apache.hadoop.hdfs.DFSClient - Exception closing file /in : org.apache.hadoop.ipc.RemoteException: java.io.IOException: File /in could only be replicated to 0 nodes, instead of 1
at org.apache.hadoop.hdfs.server.namenode.FSNamesystem.getAdditionalBlock(FSNamesystem.java:1569)
at org.apache.hadoop.hdfs.server.namenode.NameNode.addBlock(NameNode.java:701)
at sun.reflect.GeneratedMethodAccessor6.invoke(Unknown Source)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:606)
at org.apache.hadoop.ipc.RPC$Server.call(RPC.java:583)
at org.apache.hadoop.ipc.Server$Handler$1.run(Server.java:1393)
at org.apache.hadoop.ipc.Server$Handler$1.run(Server.java:1389)
at java.security.AccessController.doPrivileged(Native Method)
at javax.security.auth.Subject.doAs(Subject.java:415)
at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1140)
at org.apache.hadoop.ipc.Server$Handler.run(Server.java:1387)
org.apache.hadoop.ipc.RemoteException: java.io.IOException: File /in could only be replicated to 0 nodes, instead of 1
at org.apache.hadoop.hdfs.server.namenode.FSNamesystem.getAdditionalBlock(FSNamesystem.java:1569)
at org.apache.hadoop.hdfs.server.namenode.NameNode.addBlock(NameNode.java:701)
at sun.reflect.GeneratedMethodAccessor6.invoke(Unknown Source)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:606)
at org.apache.hadoop.ipc.RPC$Server.call(RPC.java:583)
at org.apache.hadoop.ipc.Server$Handler$1.run(Server.java:1393)
at org.apache.hadoop.ipc.Server$Handler$1.run(Server.java:1389)
at java.security.AccessController.doPrivileged(Native Method)
at javax.security.auth.Subject.doAs(Subject.java:415)
at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1140)
at org.apache.hadoop.ipc.Server$Handler.run(Server.java:1387)
at org.apache.hadoop.ipc.Client.call(Client.java:1092)
at org.apache.hadoop.ipc.RPC$Invoker.invoke(RPC.java:229)
at com.sun.proxy.$Proxy1.addBlock(Unknown Source)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:606)
at org.apache.hadoop.io.retry.RetryInvocationHandler.invokeMethod(RetryInvocationHandler.java:85)
at org.apache.hadoop.io.retry.RetryInvocationHandler.invoke(RetryInvocationHandler.java:62)
at com.sun.proxy.$Proxy1.addBlock(Unknown Source)
at org.apache.hadoop.hdfs.DFSClient$DFSOutputStream.locateFollowingBlock(DFSClient.java:3595)
at org.apache.hadoop.hdfs.DFSClient$DFSOutputStream.nextBlockOutputStream(DFSClient.java:3456)
at org.apache.hadoop.hdfs.DFSClient$DFSOutputStream.access$2600(DFSClient.java:2672)
at org.apache.hadoop.hdfs.DFSClient$DFSOutputStream$DataStreamer.run(DFSClient.java:2912)
Job HadoopMatch ended at 17:54 08/09/2014. [exit code=1]
What is wrong here?
to work this, we have to check the option, use datanode hostname.
We have do modification in the windows host file
C:\Windows\System32\Drivers\etc\hosts
EC2PublicIP PrivateDNS
example :
10.210.202.106 ip-101-210-141-188.ec2.internal
then it is working now.
Related
In Apache Spark I do join operation between rate stream and csv file reading streaming operation, both of them are needed for very low intensity data generation. Rate produces increasing ids and limits the generation speed, while the csv reader tends to load all the data without rate limit. So jointing the stream should help with limiting the csv data.
readFromCSVFile(tmpPath.toString).as("csv").join(rate.as("counter")).where("csv.id == counter.value")
Unfortunately, join uses HDFS under the hood, so I'm getting the large error stack:
2021-10-15 14:18:02 ERROR Inbox:94 - Ignoring error
java.util.concurrent.RejectedExecutionException: Task org.apache.spark.executor.Executor$TaskRunner#137b9386 rejected from java.util.concurrent.ThreadPoolExecutor#4cd6112[Shutting down, pool size = 7, active threads = 7, queued tasks = 0, completed tasks = 30]
at java.base/java.util.concurrent.ThreadPoolExecutor$AbortPolicy.rejectedExecution(ThreadPoolExecutor.java:2055)
at java.base/java.util.concurrent.ThreadPoolExecutor.reject(ThreadPoolExecutor.java:825)
at java.base/java.util.concurrent.ThreadPoolExecutor.execute(ThreadPoolExecutor.java:1355)
at org.apache.spark.executor.Executor.launchTask(Executor.scala:230)
at org.apache.spark.scheduler.local.LocalEndpoint.$anonfun$reviveOffers$1(LocalSchedulerBackend.scala:93)
at org.apache.spark.scheduler.local.LocalEndpoint.$anonfun$reviveOffers$1$adapted(LocalSchedulerBackend.scala:91)
at scala.collection.Iterator.foreach(Iterator.scala:941)
at scala.collection.Iterator.foreach$(Iterator.scala:941)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1429)
at scala.collection.IterableLike.foreach(IterableLike.scala:74)
at scala.collection.IterableLike.foreach$(IterableLike.scala:73)
at scala.collection.AbstractIterable.foreach(Iterable.scala:56)
at org.apache.spark.scheduler.local.LocalEndpoint.reviveOffers(LocalSchedulerBackend.scala:91)
at org.apache.spark.scheduler.local.LocalEndpoint$$anonfun$receive$1.applyOrElse(LocalSchedulerBackend.scala:74)
at org.apache.spark.rpc.netty.Inbox.$anonfun$process$1(Inbox.scala:115)
at org.apache.spark.rpc.netty.Inbox.safelyCall(Inbox.scala:203)
at org.apache.spark.rpc.netty.Inbox.process(Inbox.scala:100)
at org.apache.spark.rpc.netty.MessageLoop.org$apache$spark$rpc$netty$MessageLoop$$receiveLoop(MessageLoop.scala:75)
at org.apache.spark.rpc.netty.MessageLoop$$anon$1.run(MessageLoop.scala:41)
at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1128)
at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:628)
at java.base/java.lang.Thread.run(Thread.java:835)
2021-10-15 14:18:02 ERROR WriteToDataSourceV2Exec:73 - Data source write support org.apache.spark.sql.execution.streaming.sources.MicroBatchWrite#2d76d5c5 is aborting.
2021-10-15 14:18:02 ERROR WriteToDataSourceV2Exec:73 - Data source write support org.apache.spark.sql.execution.streaming.sources.MicroBatchWrite#2d76d5c5 aborted.
2021-10-15 14:18:02 ERROR MicroBatchExecution:94 - Query kafkaDataGenerator [id = 23a9869d-913f-4cf6-b0ed-e8149ed149e6, runId = d98459c0-ae1f-44e8-a610-7ee413740880] terminated with error
org.apache.spark.SparkException: Writing job aborted.
at org.apache.spark.sql.execution.datasources.v2.V2TableWriteExec.writeWithV2(WriteToDataSourceV2Exec.scala:413)
at org.apache.spark.sql.execution.datasources.v2.V2TableWriteExec.writeWithV2$(WriteToDataSourceV2Exec.scala:361)
at org.apache.spark.sql.execution.datasources.v2.WriteToDataSourceV2Exec.writeWithV2(WriteToDataSourceV2Exec.scala:322)
at org.apache.spark.sql.execution.datasources.v2.WriteToDataSourceV2Exec.run(WriteToDataSourceV2Exec.scala:329)
at org.apache.spark.sql.execution.datasources.v2.V2CommandExec.result$lzycompute(V2CommandExec.scala:39)
at org.apache.spark.sql.execution.datasources.v2.V2CommandExec.result(V2CommandExec.scala:39)
at org.apache.spark.sql.execution.datasources.v2.V2CommandExec.executeCollect(V2CommandExec.scala:45)
at org.apache.spark.sql.Dataset.collectFromPlan(Dataset.scala:3627)
at org.apache.spark.sql.Dataset.$anonfun$collect$1(Dataset.scala:2940)
at org.apache.spark.sql.Dataset.$anonfun$withAction$1(Dataset.scala:3618)
at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$5(SQLExecution.scala:100)
at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:160)
at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:87)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:764)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:64)
at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3616)
at org.apache.spark.sql.Dataset.collect(Dataset.scala:2940)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution.$anonfun$runBatch$16(MicroBatchExecution.scala:575)
at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$5(SQLExecution.scala:100)
at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:160)
at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:87)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:764)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:64)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution.$anonfun$runBatch$15(MicroBatchExecution.scala:570)
at org.apache.spark.sql.execution.streaming.ProgressReporter.reportTimeTaken(ProgressReporter.scala:352)
at org.apache.spark.sql.execution.streaming.ProgressReporter.reportTimeTaken$(ProgressReporter.scala:350)
at org.apache.spark.sql.execution.streaming.StreamExecution.reportTimeTaken(StreamExecution.scala:69)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution.runBatch(MicroBatchExecution.scala:570)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution.$anonfun$runActivatedStream$2(MicroBatchExecution.scala:223)
at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23)
at org.apache.spark.sql.execution.streaming.ProgressReporter.reportTimeTaken(ProgressReporter.scala:352)
at org.apache.spark.sql.execution.streaming.ProgressReporter.reportTimeTaken$(ProgressReporter.scala:350)
at org.apache.spark.sql.execution.streaming.StreamExecution.reportTimeTaken(StreamExecution.scala:69)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution.$anonfun$runActivatedStream$1(MicroBatchExecution.scala:191)
at org.apache.spark.sql.execution.streaming.ProcessingTimeExecutor.execute(TriggerExecutor.scala:57)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution.runActivatedStream(MicroBatchExecution.scala:185)
at org.apache.spark.sql.execution.streaming.StreamExecution.org$apache$spark$sql$execution$streaming$StreamExecution$$runStream(StreamExecution.scala:334)
at org.apache.spark.sql.execution.streaming.StreamExecution$$anon$1.run(StreamExecution.scala:245)
Caused by: org.apache.spark.SparkException: Job 0 cancelled because SparkContext was shut down
at org.apache.spark.scheduler.DAGScheduler.$anonfun$cleanUpAfterSchedulerStop$1(DAGScheduler.scala:979)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$cleanUpAfterSchedulerStop$1$adapted(DAGScheduler.scala:977)
at scala.collection.mutable.HashSet.foreach(HashSet.scala:79)
at org.apache.spark.scheduler.DAGScheduler.cleanUpAfterSchedulerStop(DAGScheduler.scala:977)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onStop(DAGScheduler.scala:2257)
at org.apache.spark.util.EventLoop.stop(EventLoop.scala:84)
at org.apache.spark.scheduler.DAGScheduler.stop(DAGScheduler.scala:2170)
at org.apache.spark.SparkContext.$anonfun$stop$12(SparkContext.scala:1973)
at org.apache.spark.util.Utils$.tryLogNonFatalError(Utils.scala:1357)
at org.apache.spark.SparkContext.stop(SparkContext.scala:1973)
at org.apache.spark.SparkContext.$anonfun$new$35(SparkContext.scala:631)
at org.apache.spark.util.SparkShutdownHook.run(ShutdownHookManager.scala:214)
at org.apache.spark.util.SparkShutdownHookManager.$anonfun$runAll$2(ShutdownHookManager.scala:188)
at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23)
at org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1932)
at org.apache.spark.util.SparkShutdownHookManager.$anonfun$runAll$1(ShutdownHookManager.scala:188)
at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23)
at scala.util.Try$.apply(Try.scala:213)
at org.apache.spark.util.SparkShutdownHookManager.runAll(ShutdownHookManager.scala:188)
at org.apache.spark.util.SparkShutdownHookManager$$anon$2.run(ShutdownHookManager.scala:178)
at java.base/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:515)
at java.base/java.util.concurrent.FutureTask.run(FutureTask.java:264)
at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1128)
at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:628)
at java.base/java.lang.Thread.run(Thread.java:835)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:775)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2099)
at org.apache.spark.sql.execution.datasources.v2.V2TableWriteExec.writeWithV2(WriteToDataSourceV2Exec.scala:382)
... 37 more
2021-10-15 14:18:03 ERROR Utils:94 - Aborting task
java.lang.NullPointerException
at org.apache.spark.sql.execution.datasources.v2.DataWritingSparkTask$.$anonfun$run$7(WriteToDataSourceV2Exec.scala:445)
at org.apache.spark.util.Utils$.tryWithSafeFinallyAndFailureCallbacks(Utils.scala:1411)
at org.apache.spark.sql.execution.datasources.v2.DataWritingSparkTask$.run(WriteToDataSourceV2Exec.scala:477)
at org.apache.spark.sql.execution.datasources.v2.V2TableWriteExec.$anonfun$writeWithV2$2(WriteToDataSourceV2Exec.scala:385)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:127)
at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:446)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1377)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:449)
at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1128)
at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:628)
at java.base/java.lang.Thread.run(Thread.java:835)
2021-10-15 14:18:03 ERROR DataWritingSparkTask:73 - Aborting commit for partition 30 (task 31, attempt 0, stage 2.0)
and the most important part is there:
java.lang.IllegalStateException: Error committing version 1 into HDFSStateStore[id=(op=0,part=33),dir=file:/C:/Users/eljah32/AppData/Local/Temp/spark-cb8ca918-43cc-43d6-8f36-3bf80d1e7852/kafkaDataGenerator/state/0/33/left-keyWithIndexToValue]
at org.apache.spark.sql.execution.streaming.state.HDFSBackedStateStoreProvider$HDFSBackedStateStore.commit(HDFSBackedStateStoreProvider.scala:139)
It means, that the join operation requires the HDFS to be used. HDFSBackedStateStoreProvider is the only possible implementation, another one known is based on RocksDB. I Haven't found a way is it possible to disable StateStoreProvider for the join operation, if the data amount is too small and we can rely on memory operatons for the particular job? May be there is some option to disable StateStoreProvider usage since there is no pure in memory implementation?
I'm using PySpark (Python 3.5.2 and Spark 2.2.0.2.6.4.0-91) and I have a Dataframe of predicted values (through a random forest model defined with the MLlib library) with the following structure :
DataFrame[id: bigint, features: vector, rawPrediction: vector, probability: vector, prediction: double]
I got it with :
rf_predictions = random_forest_model.transform(dataframe)
But when I want to display the content of it, it only works with the 2 first columns "id" and "features" :
rf_predictions.select("id","features").show()
But when I try :
rf_predictions.select("prediction").show()
In order to display the "prediction" column (same problem with the columns "rawPrediction" or "probability"), it returns me the following bug :
19/09/20 18:33:31 WARN TaskSetManager: Lost task 0.0 in stage 51.0 (TID 169, slmupd5hsn03.zres.ztech, executor 1): org.apache.spark.SparkException: Failed to execute user defined function($anonfun$1: (vector) => vector)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:395)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:234)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:228)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:827)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:827)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
at org.apache.spark.scheduler.Task.run(Task.scala:108)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:338)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
Caused by: java.lang.ArrayIndexOutOfBoundsException
19/09/20 18:33:32 ERROR TaskSetManager: Task 0 in stage 51.0 failed 4 times; aborting job
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/hdp/current/spark2-client/python/pyspark/sql/dataframe.py", line 336, in show
print(self._jdf.showString(n, 20))
File "/usr/hdp/current/spark2-client/python/lib/py4j-0.10.4-src.zip/py4j/java_gateway.py", line 1133, in __call__
File "/usr/hdp/current/spark2-client/python/pyspark/sql/utils.py", line 63, in deco
return f(*a, **kw)
File "/usr/hdp/current/spark2-client/python/lib/py4j-0.10.4-src.zip/py4j/protocol.py", line 319, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling o1887.showString.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 51.0 failed 4 times, most recent failure: Lost task 0.3 in stage 51.0 (TID 172, slmupd5hsn01.zres.ztech, executor 2): org.apache.spark.SparkException: Failed to execute user defined function($anonfun$1: (vector) => vector)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:395)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:234)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:228)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:827)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:827)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
at org.apache.spark.scheduler.Task.run(Task.scala:108)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:338)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
Caused by: java.lang.ArrayIndexOutOfBoundsException
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1517)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1505)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1504)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1504)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:814)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:814)
at scala.Option.foreach(Option.scala:257)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:814)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1732)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1687)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1676)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:630)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2029)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2050)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2069)
at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:336)
at org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:38)
at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$collectFromPlan(Dataset.scala:2861)
at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2150)
at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2150)
at org.apache.spark.sql.Dataset$$anonfun$55.apply(Dataset.scala:2842)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:65)
at org.apache.spark.sql.Dataset.withAction(Dataset.scala:2841)
at org.apache.spark.sql.Dataset.head(Dataset.scala:2150)
at org.apache.spark.sql.Dataset.take(Dataset.scala:2363)
at org.apache.spark.sql.Dataset.showString(Dataset.scala:241)
at sun.reflect.GeneratedMethodAccessor69.invoke(Unknown Source)
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:280)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:214)
at java.lang.Thread.run(Thread.java:745)
Caused by: org.apache.spark.SparkException: Failed to execute user defined function($anonfun$1: (vector) => vector)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:395)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:234)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:228)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:827)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:827)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
at org.apache.spark.scheduler.Task.run(Task.scala:108)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:338)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
... 1 more
Caused by: java.lang.ArrayIndexOutOfBoundsException
And yet I don't use any UDF functions as you can see. Would you know why I get this bug or how I could avoid/fix it please ?
Do you think there is a way to transforming this column to a RDD, List or whatever else and then rebuild it as a dataframe column in order to be able to get the predicted labels ?
Thank you very much in advance.
Best regards
try
rf_predictions.select("rawPrediction").show()
or to map rawPrediction to prediction use Pipeline
try adding Pipeline to your code:
from pyspark.ml import Pipeline
# Chain indexers and forest in a Pipeline
# Train a RandomForest model.
rf = RandomForestClassifier(labelCol="", featuresCol="", numTrees=10)
pipeline = Pipeline(stages=[labelConverter])
# Train model. This also runs the indexers.
model = pipeline.fit(dataframe)
# Make predictions.
predictions = model.transform(testData)
I run pyspark in the local model to learn the wordcount case. I write a spark_test.txt in current file, and run pyspark command , lines = sc.textFile('../spark_test.txt') lines.count(), it throws out a bug:
Input path does not exist: file:/Users/lisl/myproject/spark_test.txt
** I tried:**
sc.textFile('spark_test.txt')
lines = sc.textFile('../spark_test.txt')
lines.count()
i expect the outpub be the number of lines of the file , but the actual output is :
file "/Users/lisl/.pyenv/versions/3.7.1/lib/python3.7/site-packages/pyspark/python/lib/py4j-0.10.7-src.zip/py4j/protocol.py", line 328, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling z:org.apache.spark.api.python.PythonRDD.collectAndServe.
: org.apache.hadoop.mapred.InvalidInputException: Input path does not exist: file:/Users/lisl/myproject/learn_test/spark_test.txt
at org.apache.hadoop.mapred.FileInputFormat.singleThreadedListStatus(FileInputFormat.java:287)
at org.apache.hadoop.mapred.FileInputFormat.listStatus(FileInputFormat.java:229)
at org.apache.hadoop.mapred.FileInputFormat.getSplits(FileInputFormat.java:315)
at org.apache.spark.rdd.HadoopRDD.getPartitions(HadoopRDD.scala:204)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:253)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:251)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:251)
at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:49)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:253)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:251)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:251)
at org.apache.spark.api.python.PythonRDD.getPartitions(PythonRDD.scala:55)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:253)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:251)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:251)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2126)
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.api.python.PythonRDD$.collectAndServe(PythonRDD.scala:166)
at org.apache.spark.api.python.PythonRDD.collectAndServe(PythonRDD.scala)
at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.base/java.lang.reflect.Method.invoke(Method.java:567)
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.base/java.lang.Thread.run(Thread.java:835)
First of all, some lines of code are missing. Please try the code below and make sure that the path to your file exist and you have given read access for all to it (chmod a+r yourFile)
text_file = sc.textFile("path/to/your/input/file")
wordCounts = text_file.flatMap(lambda line: line.split(" ")) \
.map(lambda word: (word, 1)) \
.reduceByKey(lambda a, b: a + b)
wordCounts.saveAsTextFile("path/to/your/output/file")
I don't know where is the problem and how to fix it.The spark version is 2.1.0 and the python version is 3.4.6.
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/__ / .__/\_,_/_/ /_/\_\ version 2.1.0
/_/
Using Python version 3.4.6 (default, Mar 9 2017 19:57:54)
SparkSession available as 'spark'.
##This is the command i code as the official document
>>> input_data = sc.textFile('my python')
>>> input_data.count()
but it is not work.
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/spark/python/pyspark/rdd.py", line 1041, in count
return self.mapPartitions(lambda i: [sum(1 for _ in i)]).sum()
File "/usr/local/spark/python/pyspark/rdd.py", line 1032, in sum
return self.mapPartitions(lambda x: [sum(x)]).fold(0, operator.add)
File "/usr/local/spark/python/pyspark/rdd.py", line 906, in fold
vals = self.mapPartitions(func).collect()
File "/usr/local/spark/python/pyspark/rdd.py", line 809, in collect
port = self.ctx._jvm.PythonRDD.collectAndServe(self._jrdd.rdd())
File "/usr/local/spark/python/lib/py4j-0.10.4-src.zip/py4j/java_gateway.py", line 1133, in __call__
File "/usr/local/spark/python/pyspark/sql/utils.py", line 63, in deco
return f(*a, **kw)
File "/usr/local/spark/python/lib/py4j-0.10.4-src.zip/py4j/protocol.py", line 319, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling z:org.apache.spark.api.python.PythonRDD.collectAndServe.
: java.net.ConnectException: Call From hadoop/192.168.81.129 to localhost:9000 failed on connection exception: java.net.ConnectException: 拒绝连接; For more details see: http://wiki.apache.org/hadoop/ConnectionRefused
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.net.NetUtils.wrapWithMessage(NetUtils.java:792)
at org.apache.hadoop.net.NetUtils.wrapException(NetUtils.java:732)
at org.apache.hadoop.ipc.Client.call(Client.java:1479)
at org.apache.hadoop.ipc.Client.call(Client.java:1412)
at org.apache.hadoop.ipc.ProtobufRpcEngine$Invoker.invoke(ProtobufRpcEngine.java:229)
at com.sun.proxy.$Proxy19.getFileInfo(Unknown Source)
at org.apache.hadoop.hdfs.protocolPB.ClientNamenodeProtocolTranslatorPB.getFileInfo(ClientNamenodeProtocolTranslatorPB.java:771)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:606)
at org.apache.hadoop.io.retry.RetryInvocationHandler.invokeMethod(RetryInvocationHandler.java:191)
at org.apache.hadoop.io.retry.RetryInvocationHandler.invoke(RetryInvocationHandler.java:102)
at com.sun.proxy.$Proxy20.getFileInfo(Unknown Source)
at org.apache.hadoop.hdfs.DFSClient.getFileInfo(DFSClient.java:2108)
at org.apache.hadoop.hdfs.DistributedFileSystem$22.doCall(DistributedFileSystem.java:1305)
at org.apache.hadoop.hdfs.DistributedFileSystem$22.doCall(DistributedFileSystem.java:1301)
at org.apache.hadoop.fs.FileSystemLinkResolver.resolve(FileSystemLinkResolver.java:81)
at org.apache.hadoop.hdfs.DistributedFileSystem.getFileStatus(DistributedFileSystem.java:1301)
at org.apache.hadoop.fs.Globber.getFileStatus(Globber.java:57)
at org.apache.hadoop.fs.Globber.glob(Globber.java:252)
at org.apache.hadoop.fs.FileSystem.globStatus(FileSystem.java:1674)
at org.apache.hadoop.mapred.FileInputFormat.singleThreadedListStatus(FileInputFormat.java:259)
at org.apache.hadoop.mapred.FileInputFormat.listStatus(FileInputFormat.java:229)
at org.apache.hadoop.mapred.FileInputFormat.getSplits(FileInputFormat.java:315)
at org.apache.spark.rdd.HadoopRDD.getPartitions(HadoopRDD.scala:202)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:252)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:250)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:250)
at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:252)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:250)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:250)
at org.apache.spark.api.python.PythonRDD.getPartitions(PythonRDD.scala:53)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:252)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:250)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:250)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1958)
at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:935)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:362)
at org.apache.spark.rdd.RDD.collect(RDD.scala:934)
at org.apache.spark.api.python.PythonRDD$.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:57)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:606)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
at py4j.Gateway.invoke(Gateway.java:280)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:214)
at java.lang.Thread.run(Thread.java:745)
Caused by: java.net.ConnectException: 拒绝连接
at sun.nio.ch.SocketChannelImpl.checkConnect(Native Method)
at sun.nio.ch.SocketChannelImpl.finishConnect(SocketChannelImpl.java:744)
at org.apache.hadoop.net.SocketIOWithTimeout.connect(SocketIOWithTimeout.java:206)
at org.apache.hadoop.net.NetUtils.connect(NetUtils.java:531)
at org.apache.hadoop.net.NetUtils.connect(NetUtils.java:495)
at org.apache.hadoop.ipc.Client$Connection.setupConnection(Client.java:614)
at org.apache.hadoop.ipc.Client$Connection.setupIOstreams(Client.java:712)
at org.apache.hadoop.ipc.Client$Connection.access$2900(Client.java:375)
at org.apache.hadoop.ipc.Client.getConnection(Client.java:1528)
at org.apache.hadoop.ipc.Client.call(Client.java:1451)
... 56 more
>>>
just getting started with Prediction.IO - Getting an exception when performing pio train. I'm using the universal recommender template.
Pio status, and pio-start-all working fine, reporting no errors
Seems " nor Elasticsearch client found" - but curl localhost fine on port 9200.
Does anybody has a clue on what the error message given is referring to?
> [INFO] [URModel] Ready to pass date fields names to closure Some(List(, , ))
> [INFO] [URModel] Converting PropertyMap into Elasticsearch style rdd
> [Stage 41:=============================> (2 + 2) / 4][INFO] [URModel] Grouping all correlators into doc +
> fields for writing to index
> [INFO] [URModel] Finding non-empty RDDs from a list of 2 correlators and 1 properties
> [INFO] [URModel] New data to index, performing a hot swap of the index.
> Exception in thread "main" java.lang.IllegalStateException: No Elasticsearch client configuration detected, check your pio-env.sh
> forproper configuration settings
> at dk.bilzonen.esClient$.client$lzycompute(esClient.scala:58)
> at dk.bilzonen.esClient$.client(esClient.scala:55)
> at dk.bilzonen.esClient$.hotSwap(esClient.scala:169)
> at dk.bilzonen.URModel.save(URModel.scala:147)
> at dk.bilzonen.URModel.save(URModel.scala:38)
> at io.prediction.controller.P2LAlgorithm.makePersistentModel(P2LAlgorithm.scala:111)
> at io.prediction.controller.Engine$$anonfun$makeSerializableModels$2.apply(Engine.scala:294)
> at io.prediction.controller.Engine$$anonfun$makeSerializableModels$2.apply(Engine.scala:293)
> at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
> at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
> at scala.collection.Iterator$class.foreach(Iterator.scala:727)
> at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
> at scala.collection.IterableLike$class.foreach(IterableLike.scala:72)
> at scala.collection.AbstractIterable.foreach(Iterable.scala:54)
> at scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
> at scala.collection.AbstractTraversable.map(Traversable.scala:105)
> at io.prediction.controller.Engine.makeSerializableModels(Engine.scala:293)
> at io.prediction.controller.Engine.train(Engine.scala:185)
> at io.prediction.workflow.CoreWorkflow$.runTrain(CoreWorkflow.scala:65)
> at io.prediction.workflow.CreateWorkflow$.main(CreateWorkflow.scala:247)
> at io.prediction.workflow.CreateWorkflow.main(CreateWorkflow.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:498)
> at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:672)
> at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:180)
> at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:205)
> at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:120)
> at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
The default configuration for PredictionIO (in pio-env.sh in your PredictionIO /conf directory) has ElasticSearch running on port 9300. If you update it to 9200 there does your training step succeed?