Spark: LeaseExpiredException while writing large dataframe to parquet files - scala

I have a large dataframe which I am writing to parquet files in HDFS. Getting the below exception from logs :
2018-10-15 18:31:32 ERROR Executor:91 - Exception in task 41.0 in stage 0.0 (TID 1321)
org.apache.spark.SparkException: Task failed while writing rows.
at org.apache.spark.sql.execution.datasources.FileFormatWriter$.org$apache$spark$sql$execution$datasources$FileFormatWriter$$executeTask(FileFormatWriter.scala:285)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply(FileFormatWriter.scala:197)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply(FileFormatWriter.scala:196)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
at org.apache.spark.scheduler.Task.run(Task.scala:109)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:369)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
Caused by: org.apache.hadoop.ipc.RemoteException(org.apache.hadoop.hdfs.server.namenode.LeaseExpiredException): No lease on /home/prod_out/20181007/_temporary/0/_temporary/attempt_20181015183108_0000_m_000041_0/part-00041-1185b10b-bcb1-4b7e-b732-dd6f71322b7d-c000.snappy.parquet (inode 33628528083): File does not exist. Holder DFSClient_NONMAPREDUCE_179567941_77 does not have any open files.
at org.apache.hadoop.hdfs.server.namenode.FSNamesystem.checkLease(FSNamesystem.java:3481)
at org.apache.hadoop.hdfs.server.namenode.FSNamesystem.analyzeFileState(FSNamesystem.java:3284)
at org.apache.hadoop.hdfs.server.namenode.FSNamesystem.getNewBlockTargets(FSNamesystem.java:3122)
at org.apache.hadoop.hdfs.server.namenode.FSNamesystem.getAdditionalBlock(FSNamesystem.java:3082)
at org.apache.hadoop.hdfs.server.namenode.NameNodeRpcServer.addBlock(NameNodeRpcServer.java:822)
at org.apache.hadoop.hdfs.protocolPB.ClientNamenodeProtocolServerSideTranslatorPB.addBlock(ClientNamenodeProtocolServerSideTranslatorPB.java:500)
at org.apache.hadoop.hdfs.protocol.proto.ClientNamenodeProtocolProtos$ClientNamenodeProtocol$2.callBlockingMethod(ClientNamenodeProtocolProtos.java)
at org.apache.hadoop.ipc.ProtobufRpcEngine$Server$ProtoBufRpcInvoker.call(ProtobufRpcEngine.java:616)
at org.apache.hadoop.ipc.RPC$Server.call(RPC.java:969)
at org.apache.hadoop.ipc.Server$Handler$1.run(Server.java:2206)
at org.apache.hadoop.ipc.Server$Handler$1.run(Server.java:2202)
at java.security.AccessController.doPrivileged(Native Method)
at javax.security.auth.Subject.doAs(Subject.java:422)
at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1709)
at org.apache.hadoop.ipc.Server$Handler.run(Server.java:2200)
at org.apache.hadoop.ipc.Client.call(Client.java:1475)
at org.apache.hadoop.ipc.Client.call(Client.java:1412)
at org.apache.hadoop.ipc.ProtobufRpcEngine$Invoker.invoke(ProtobufRpcEngine.java:229)
at com.sun.proxy.$Proxy18.addBlock(Unknown Source)
at org.apache.hadoop.hdfs.protocolPB.ClientNamenodeProtocolTranslatorPB.addBlock(ClientNamenodeProtocolTranslatorPB.java:418)
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.hadoop.io.retry.RetryInvocationHandler.invokeMethod(RetryInvocationHandler.java:191)
at org.apache.hadoop.io.retry.RetryInvocationHandler.invoke(RetryInvocationHandler.java:102)
at com.sun.proxy.$Proxy19.addBlock(Unknown Source)
at org.apache.hadoop.hdfs.DFSOutputStream$DataStreamer.locateFollowingBlock(DFSOutputStream.java:1455)
at org.apache.hadoop.hdfs.DFSOutputStream$DataStreamer.nextBlockOutputStream(DFSOutputStream.java:1251)
at org.apache.hadoop.hdfs.DFSOutputStream$DataStreamer.run(DFSOutputStream.java:448)
2018-10-15 18:32:06 INFO CoarseGrainedExecutorBackend:54 - Got assigned task 2189
Googled about it but couldn't find any concrete solution. Set the speculation false:
conf.set("spark.speculation","false")
But still didn't help.
It's finishing few tasks, generating few part files and then abruptly stops with this error.
Details:
Spark version : 2.3.1 (This was not happening in 1.6x).
There is only one session running, which rules out the possibility of the same location being accessed by a different session.
Any pointers?
Thanks!

Actually the issue is because before spark writes the data into specified hdfs location, it uploads the data into temporary location.This two stage mechanism is the used to ensure consistency of the final data set when working with file systems. In case of successful write the data is moved from temporary location. And in case of unsuccessful write the data is removed from the temporary location. In your case there might be a different executor thread making changes to the temporary location. And once the original executor thread looks to the temporary location, it is not available and hdfs lease exception is thrown.
In order to avoid this exception,
Make sure you are not using any parallel collections.
Avoid multi-threading if applicable
spark.conf.set("spark.speculation","false")

It may be useful to you this solution: java.lang.OutOfMemoryError: Unable to acquire 100 bytes of memory, got 0
In my case I couldn't write orc files. I removed coalesce option and then it worked!

Related

Spark streaming job in scala doesn't run on Airflow

I usually work with Pyspark but I had to deal with a spark streaming job written in Scala. I am running the spark-submit on EMR directly it works but running the same through Airflow throws me the following error. I don't even to where to start debugging the issue. Any ideas would be greatly appreciated.
org.apache.spark.SparkException: Exception thrown in awaitResult:
at org.apache.spark.util.ThreadUtils$.awaitResult(ThreadUtils.scala:226)
at org.apache.spark.deploy.yarn.ApplicationMaster.runDriver(ApplicationMaster.scala:468)
at org.apache.spark.deploy.yarn.ApplicationMaster.org$apache$spark$deploy$yarn$ApplicationMaster$$runImpl(ApplicationMaster.scala:305)
at org.apache.spark.deploy.yarn.ApplicationMaster$$anonfun$run$1.apply$mcV$sp(ApplicationMaster.scala:245)
at org.apache.spark.deploy.yarn.ApplicationMaster$$anonfun$run$1.apply(ApplicationMaster.scala:245)
at org.apache.spark.deploy.yarn.ApplicationMaster$$anonfun$run$1.apply(ApplicationMaster.scala:245)
at org.apache.spark.deploy.yarn.ApplicationMaster$$anon$3.run(ApplicationMaster.scala:779)
at java.security.AccessController.doPrivileged(Native Method)
at javax.security.auth.Subject.doAs(Subject.java:422)
at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1844)
at org.apache.spark.deploy.yarn.ApplicationMaster.doAsUser(ApplicationMaster.scala:778)
at org.apache.spark.deploy.yarn.ApplicationMaster.run(ApplicationMaster.scala:244)
at org.apache.spark.deploy.yarn.ApplicationMaster$.main(ApplicationMaster.scala:803)
at org.apache.spark.deploy.yarn.ApplicationMaster.main(ApplicationMaster.scala)
Caused by: com.typesafe.config.ConfigException$IO: available_application.properties -Dlog4j.configuration=log4j-yarn.properties: java.io.FileNotFoundException: available_application.properties -Dlog4j.configuration=log4j-yarn.properties (No such file or directory)
at com.typesafe.config.impl.Parseable.parseValue(Parseable.java:183)
at com.typesafe.config.impl.Parseable.parseValue(Parseable.java:170)
at com.typesafe.config.impl.Parseable.parse(Parseable.java:227)
at com.typesafe.config.ConfigFactory.parseFile(ConfigFactory.java:595)
at com.typesafe.config.ConfigFactory.loadDefaultConfig(ConfigFactory.java:244)
at com.typesafe.config.ConfigFactory.access$000(ConfigFactory.java:38)
at com.typesafe.config.ConfigFactory$1.call(ConfigFactory.java:378)
at com.typesafe.config.ConfigFactory$1.call(ConfigFactory.java:375)
at com.typesafe.config.impl.ConfigImpl$LoaderCache.getOrElseUpdate(ConfigImpl.java:58)
at com.typesafe.config.impl.ConfigImpl.computeCachedConfig(ConfigImpl.java:86)
at com.typesafe.config.ConfigFactory.load(ConfigFactory.java:375)
at com.typesafe.config.ConfigFactory.load(ConfigFactory.java:299)
at com.typesafe.config.ConfigFactory.load(ConfigFactory.java:288)
at com.nike.tdp.AvailabilityKafkaEvents$.main(AvailabilityKafkaEvents.scala:101)
at com.nike.tdp.AvailabilityKafkaEvents.main(AvailabilityKafkaEvents.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.yarn.ApplicationMaster$$anon$2.run(ApplicationMaster.scala:684)
Caused by: java.io.FileNotFoundException: available_application.properties -Dlog4j.configuration=log4j-yarn.properties (No such file or directory)
at java.io.FileInputStream.open0(Native Method)
at java.io.FileInputStream.open(FileInputStream.java:195)
at java.io.FileInputStream.<init>(FileInputStream.java:138)
at com.typesafe.config.impl.Parseable$ParseableFile.reader(Parseable.java:512)
at com.typesafe.config.impl.Parseable.rawParseValue(Parseable.java:193)
at com.typesafe.config.impl.Parseable.parseValue(Parseable.java:176)
... 19 more
22/10/26 19:14:02 INFO ShutdownHookManager: Shutdown hook called
Caused by: java.io.FileNotFoundException: available_application.properties -Dlog4j.configuration=log4j-yarn.properties
is the main piece of information in the error you've shown.
It looks like you've made a typo in the parameters for running the app and available_application.properties -Dlog4j.configuration=log4j-yarn.properties is interpreted as the configuration file name instead of only available_application.properties (I assume).
Check the parameters used to run your app, maybe quotes in wrong place or missing? Maybe extra whitespace? ...

NiFi Writing to HDFS Error: java.lang.IllegalArgumentException: Can not create a Path from an empty string

I am facing a problem with NiFi writing to HDFS. I am getting an error:
ERROR [Timer-Driven Process Thread-10] o.apache.nifi.processors.hadoop.PutHDFS PutHDFS[id=4af43efa-a8ff-18ac-0000-00002377fba5] Failed to properly initialize Processor. If still scheduled to run, NiFi will attempt to initialize and run the Processor again after the 'Administrative Yield Duration' has elapsed. Failure is due to java.lang.reflect.InvocationTargetException: java.lang.reflect.InvocationTargetException
java.lang.reflect.InvocationTargetException: null
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.nifi.util.ReflectionUtils.invokeMethodsWithAnnotations(ReflectionUtils.java:137)
at org.apache.nifi.util.ReflectionUtils.invokeMethodsWithAnnotations(ReflectionUtils.java:125)
at org.apache.nifi.util.ReflectionUtils.invokeMethodsWithAnnotations(ReflectionUtils.java:70)
at org.apache.nifi.util.ReflectionUtils.invokeMethodsWithAnnotation(ReflectionUtils.java:47)
at org.apache.nifi.controller.StandardProcessorNode.lambda$initiateStart$1(StandardProcessorNode.java:1364)
at java.util.concurrent.FutureTask.run(FutureTask.java:266)
at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.access$201(ScheduledThreadPoolExecutor.java:180)
at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:293)
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.IllegalArgumentException: Can not create a Path from an empty string
at org.apache.hadoop.fs.Path.checkPathArg(Path.java:126)
at org.apache.hadoop.fs.Path.<init>(Path.java:134)
at org.apache.nifi.processors.hadoop.AbstractHadoopProcessor.getConfigurationFromResources(AbstractHadoopProcessor.java:225)
at org.apache.nifi.processors.hadoop.AbstractHadoopProcessor.resetHDFSResources(AbstractHadoopProcessor.java:254)
at org.apache.nifi.processors.hadoop.AbstractHadoopProcessor.abstractOnScheduled(AbstractHadoopProcessor.java:205)
... 15 common frames omitted
My HDFS configuration is:
Note: same configuration were applied on PutFile and it worked perfectly (Kafka.topic was not empty)
It seems when PutHDFS processor is trying to save the file looking for kafka.topic attribute associated with the flowfile but attribute not having any value to it.
Make sure you are having kafka.topic attribute having some value associate to it, we can use UpdataAttribute processor before PutHDFS to add the attribute.

Getting com.gemstone.gemfire.cache.LockTimeoutException after creating 20-30 tables in Snappydata

Spark job fetches the data from hbase and ingests the data to snappydata 1.1.0. Spark which is packaged with Snappydata 1.1.0 is launched as standalone cluster (snappy and spark share the cluster) and jobs are submitted to the Spark via spark restAPI.
Snappydata 1.1.0 cluster will be stable for a week for so. Once; number of columnar tables reaches 20-30; ingestion job fails with below mentioned Exception. Total resources used does not reach 50%. At the peak; each table could be of size 10GB (1 Billion rows and 25 columns).
Exception details:
Caused by: java.sql.SQLException: (SQLState=40XL1 Severity=30000) (Server=sw4/10.49.2.117[1527] Thread=ThriftProcessor-57) A lock could not be obtained within the time requested
at io.snappydata.thrift.SnappyDataService$executeUpdate_result$executeUpdate_resultStandardScheme.read(SnappyDataService.java:8244)
at io.snappydata.thrift.SnappyDataService$executeUpdate_result$executeUpdate_resultStandardScheme.read(SnappyDataService.java:8221)
at io.snappydata.thrift.SnappyDataService$executeUpdate_result.read(SnappyDataService.java:8160)
at org.apache.thrift.TServiceClient.receiveBase(TServiceClient.java:86)
at io.snappydata.thrift.SnappyDataService$Client.recv_executeUpdate(SnappyDataService.java:285)
at io.snappydata.thrift.SnappyDataService$Client.executeUpdate(SnappyDataService.java:269)
at io.snappydata.thrift.internal.ClientService.executeUpdate(ClientService.java:976)
at io.snappydata.thrift.internal.ClientStatement.executeUpdate(ClientStatement.java:687)
at io.snappydata.thrift.internal.ClientStatement.executeUpdate(ClientStatement.java:221)
at org.apache.spark.sql.sources.JdbcExtendedUtils$.executeUpdate(jdbcExtensions.scala:84)
at org.apache.spark.sql.execution.columnar.impl.BaseColumnFormatRelation.createActualTables(ColumnFormatRelation.scala:376)
at org.apache.spark.sql.sources.NativeTableRowLevelSecurityRelation$class.createTable(interfaces.scala:444)
at org.apache.spark.sql.execution.columnar.JDBCAppendableRelation.createTable(JDBCAppendableRelation.scala:46)
at org.apache.spark.sql.execution.columnar.impl.DefaultSource.createRelation(DefaultSource.scala:191)
at org.apache.spark.sql.execution.columnar.impl.DefaultSource.createRelation(DefaultSource.scala:71)
at org.apache.spark.sql.execution.columnar.impl.DefaultSource.createRelation(DefaultSource.scala:41)
at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:328)
at org.apache.spark.sql.execution.command.CreateDataSourceTableCommand.run(createDataSourceTables.scala:73)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult$lzycompute(commands.scala:58)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult(commands.scala:56)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.doExecute(commands.scala:74)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:114)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:114)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:135)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:132)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:113)
at org.apache.spark.sql.execution.CodegenSparkFallback$$anonfun$doExecute$1.apply(CodegenSparkFallback.scala:175)
at org.apache.spark.sql.execution.CodegenSparkFallback$$anonfun$doExecute$1.apply(CodegenSparkFallback.scala:175)
at org.apache.spark.sql.execution.CodegenSparkFallback.executeWithFallback(CodegenSparkFallback.scala:113)
at org.apache.spark.sql.execution.CodegenSparkFallback.doExecute(CodegenSparkFallback.scala:175)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:114)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:114)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:135)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:132)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:113)
at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:92)
at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:92)
at org.apache.spark.sql.SnappySession.createTableInternal(SnappySession.scala:1259)
at org.apache.spark.sql.SnappySession.createTable(SnappySession.scala:990)
at com.pw.smp.csa.SuspiciousActivityDetection$.runjob(SuspiciousActivityDetection.scala:318)
at com.pw.smp.csa.SuspiciousActivityDetection$.main(SuspiciousActivityDetection.scala:142)
at com.pw.smp.csa.SuspiciousActivityDetection.main(SuspiciousActivityDetection.scala)
... 6 more
Caused by: java.rmi.ServerException: Server STACK: java.sql.SQLTransactionRollbackException(40XL1): A lock could not be obtained within the time requested
at com.pivotal.gemfirexd.internal.iapi.error.StandardException.newException(StandardException.java:456)
at com.pivotal.gemfirexd.internal.engine.locks.GfxdLocalLockService.getLockTimeoutException(GfxdLocalLockService.java:295)
at com.pivotal.gemfirexd.internal.engine.locks.GfxdDRWLockService.getLockTimeoutException(GfxdDRWLockService.java:727)
at com.pivotal.gemfirexd.internal.engine.distributed.utils.GemFireXDUtils.lockObject(GemFireXDUtils.java:1350)
at com.pivotal.gemfirexd.internal.impl.sql.catalog.GfxdDataDictionary.lockForWriting(GfxdDataDictionary.java:632)
at com.pivotal.gemfirexd.internal.impl.sql.catalog.GfxdDataDictionary.startWriting(GfxdDataDictionary.java:562)
at com.pivotal.gemfirexd.internal.impl.sql.catalog.GfxdDataDictionary.startWriting(GfxdDataDictionary.java:507)
at com.pivotal.gemfirexd.internal.impl.sql.execute.CreateTableConstantAction.executeConstantAction(CreateTableConstantAction.java:297)
at com.pivotal.gemfirexd.internal.impl.sql.execute.MiscResultSet.open(MiscResultSet.java:64)
at com.pivotal.gemfirexd.internal.impl.sql.GenericPreparedStatement.execute(GenericPreparedStatement.java:593)
at com.pivotal.gemfirexd.internal.impl.jdbc.EmbedStatement.executeStatement(EmbedStatement.java:2179)
at com.pivotal.gemfirexd.internal.impl.jdbc.EmbedStatement.execute(EmbedStatement.java:1289)
at com.pivotal.gemfirexd.internal.impl.jdbc.EmbedStatement.execute(EmbedStatement.java:1006)
at com.pivotal.gemfirexd.internal.impl.jdbc.EmbedStatement.executeUpdate(EmbedStatement.java:503)
at io.snappydata.thrift.server.SnappyDataServiceImpl.executeUpdate(SnappyDataServiceImpl.java:1794)
at io.snappydata.thrift.SnappyDataService$Processor$executeUpdate.getResult(SnappyDataService.java:1535)
at io.snappydata.thrift.SnappyDataService$Processor$executeUpdate.getResult(SnappyDataService.java:1519)
at org.apache.thrift.ProcessFunction.process(ProcessFunction.java:39)
at io.snappydata.thrift.server.SnappyDataServiceImpl$Processor.process(SnappyDataServiceImpl.java:201)
at io.snappydata.thrift.server.SnappyThriftServerThreadPool$WorkerProcess.run(SnappyThriftServerThreadPool.java:270)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at io.snappydata.thrift.server.SnappyThriftServer$1.lambda$newThread$0(SnappyThriftServer.java:143)
at java.lang.Thread.run(Thread.java:748)
Caused by: com.gemstone.gemfire.cache.LockTimeoutException: lock timeout for object: DefaultGfxdLockable#a534854:GfxdDataDictionary, for lock: GfxdReentrantReadWriteLock#77629235,QSync#3630b21a[name=GfxdDataDictionary] [readers=0], requested for owner: DistributedTXLockOwner(member=10.49.2.117(29205):5551,XID=2667,ownerThread=Thread[ThriftProcessor-57,5,SnappyThriftServer Threads],vmCreatorThread=Thread[ThriftProcessor-57,5,SnappyThriftServer Threads])
at com.pivotal.gemfirexd.internal.engine.locks.GfxdLocalLockService.getLockTimeoutRuntimeException(GfxdLocalLockService.java:290)
at com.pivotal.gemfirexd.internal.engine.locks.GfxdLocalLockService.getLockTimeoutException(GfxdLocalLockService.java:296)
... 22 more
at io.snappydata.thrift.common.ThriftExceptionUtil.newSQLException(ThriftExceptionUtil.java:109)
at io.snappydata.thrift.internal.ClientStatement.executeUpdate(ClientStatement.java:696)
... 42 more
Looks like your App is trying to create a table when the data dictionary is locked. Is your app concurrently doing other work?

java.lang.IllegalStateException: Error reading delta file, spark structured streaming with kafka

I am using Structured Streaming + Kafka for realtime data analytics in our project. I am using Spark 2.2, kafka 0.10.2.
I am facing an issue during streaming query recovery from checkpoint at application startup. As there are multiple streaming queries derived from a single kafka streaming point and there are different checkpint directories for every streaming query. So in case of job failure, when we restart the job there are some streaming queries which fails to recover from checkpoint location hence throw an exception of Error reading delta file. Here are the logs :
Job aborted due to stage failure: Task 2 in stage 13.0 failed 4 times, most recent failure: Lost task 2.3 in stage 13.0 (TID 831, ip-172-31-10-246.us-west-2.compute.internal, executor 3): java.lang.IllegalStateException: Error reading delta file /checkpointing/wifiHealthPerUserPerMinute/state/0/2/1.delta of HDFSStateStoreProvider[id = (op=0, part=2), dir = /checkpointing/wifiHealthPerUserPerMinute/state/0/2]: /checkpointing/wifiHealthPerUserPerMinute/state/0/2/1.delta does not exist
at org.apache.spark.sql.execution.streaming.state.HDFSBackedStateStoreProvider.org$apache$spark$sql$execution$streaming$state$HDFSBackedStateStoreProvider$$updateFromDeltaFile(HDFSBackedStateStoreProvider.scala:410)
at org.apache.spark.sql.execution.streaming.state.HDFSBackedStateStoreProvider$$anonfun$org$apache$spark$sql$execution$streaming$state$HDFSBackedStateStoreProvider$$loadMap$1$$anonfun$6.apply(HDFSBackedStateStoreProvider.scala:362)
at org.apache.spark.sql.execution.streaming.state.HDFSBackedStateStoreProvider$$anonfun$org$apache$spark$sql$execution$streaming$state$HDFSBackedStateStoreProvider$$loadMap$1$$anonfun$6.apply(HDFSBackedStateStoreProvider.scala:359)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.sql.execution.streaming.state.HDFSBackedStateStoreProvider$$anonfun$org$apache$spark$sql$execution$streaming$state$HDFSBackedStateStoreProvider$$loadMap$1.apply(HDFSBackedStateStoreProvider.scala:359)
at org.apache.spark.sql.execution.streaming.state.HDFSBackedStateStoreProvider$$anonfun$org$apache$spark$sql$execution$streaming$state$HDFSBackedStateStoreProvider$$loadMap$1.apply(HDFSBackedStateStoreProvider.scala:358)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.sql.execution.streaming.state.HDFSBackedStateStoreProvider.org$apache$spark$sql$execution$streaming$state$HDFSBackedStateStoreProvider$$loadMap(HDFSBackedStateStoreProvider.scala:358)
at org.apache.spark.sql.execution.streaming.state.HDFSBackedStateStoreProvider$$anonfun$org$apache$spark$sql$execution$streaming$state$HDFSBackedStateStoreProvider$$loadMap$1$$anonfun$6.apply(HDFSBackedStateStoreProvider.scala:360)
at org.apache.spark.sql.execution.streaming.state.HDFSBackedStateStoreProvider$$anonfun$org$apache$spark$sql$execution$streaming$state$HDFSBackedStateStoreProvider$$loadMap$1$$anonfun$6.apply(HDFSBackedStateStoreProvider.scala:359)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.sql.execution.streaming.state.HDFSBackedStateStoreProvider$$anonfun$org$apache$spark$sql$execution$streaming$state$HDFSBackedStateStoreProvider$$loadMap$1.apply(HDFSBackedStateStoreProvider.scala:359)
at org.apache.spark.sql.execution.streaming.state.HDFSBackedStateStoreProvider$$anonfun$org$apache$spark$sql$execution$streaming$state$HDFSBackedStateStoreProvider$$loadMap$1.apply(HDFSBackedStateStoreProvider.scala:358)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.sql.execution.streaming.state.HDFSBackedStateStoreProvider.org$apache$spark$sql$execution$streaming$state$HDFSBackedStateStoreProvider$$loadMap(HDFSBackedStateStoreProvider.scala:358)
at org.apache.spark.sql.execution.streaming.state.HDFSBackedStateStoreProvider$$anonfun$org$apache$spark$sql$execution$streaming$state$HDFSBackedStateStoreProvider$$loadMap$1$$anonfun$6.apply(HDFSBackedStateStoreProvider.scala:360)
at org.apache.spark.sql.execution.streaming.state.HDFSBackedStateStoreProvider$$anonfun$org$apache$spark$sql$execution$streaming$state$HDFSBackedStateStoreProvider$$loadMap$1$$anonfun$6.apply(HDFSBackedStateStoreProvider.scala:359)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.sql.execution.streaming.state.HDFSBackedStateStoreProvider$$anonfun$org$apache$spark$sql$execution$streaming$state$HDFSBackedStateStoreProvider$$loadMap$1.apply(HDFSBackedStateStoreProvider.scala:359)
at org.apache.spark.sql.execution.streaming.state.HDFSBackedStateStoreProvider$$anonfun$org$apache$spark$sql$execution$streaming$state$HDFSBackedStateStoreProvider$$loadMap$1.apply(HDFSBackedStateStoreProvider.scala:358)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.sql.execution.streaming.state.HDFSBackedStateStoreProvider.org$apache$spark$sql$execution$streaming$state$HDFSBackedStateStoreProvider$$loadMap(HDFSBackedStateStoreProvider.scala:358)
at org.apache.spark.sql.execution.streaming.state.HDFSBackedStateStoreProvider$$anonfun$org$apache$spark$sql$execution$streaming$state$HDFSBackedStateStoreProvider$$loadMap$1$$anonfun$6.apply(HDFSBackedStateStoreProvider.scala:360)
at org.apache.spark.sql.execution.streaming.state.HDFSBackedStateStoreProvider$$anonfun$org$apache$spark$sql$execution$streaming$state$HDFSBackedStateStoreProvider$$loadMap$1$$anonfun$6.apply(HDFSBackedStateStoreProvider.scala:359)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.sql.execution.streaming.state.HDFSBackedStateStoreProvider$$anonfun$org$apache$spark$sql$execution$streaming$state$HDFSBackedStateStoreProvider$$loadMap$1.apply(HDFSBackedStateStoreProvider.scala:359)
at org.apache.spark.sql.execution.streaming.state.HDFSBackedStateStoreProvider$$anonfun$org$apache$spark$sql$execution$streaming$state$HDFSBackedStateStoreProvider$$loadMap$1.apply(HDFSBackedStateStoreProvider.scala:358)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.sql.execution.streaming.state.HDFSBackedStateStoreProvider.org$apache$spark$sql$execution$streaming$state$HDFSBackedStateStoreProvider$$loadMap(HDFSBackedStateStoreProvider.scala:358)
at org.apache.spark.sql.execution.streaming.state.HDFSBackedStateStoreProvider$$anonfun$org$apache$spark$sql$execution$streaming$state$HDFSBackedStateStoreProvider$$loadMap$1$$anonfun$6.apply(HDFSBackedStateStoreProvider.scala:360)
at org.apache.spark.sql.execution.streaming.state.HDFSBackedStateStoreProvider$$anonfun$org$apache$spark$sql$execution$streaming$state$HDFSBackedStateStoreProvider$$loadMap$1$$anonfun$6.apply(HDFSBackedStateStoreProvider.scala:359)
Please help me out for the same. There may be workarounds for this issue, please suggest me if any, or may be it is a bug.
What's your checkpoint location? This is usually because you are using the local file system to store checkpoints. Make sure you set the "checkpointLocation" option and it points to a distributed file system (such as HDFS) that can be accessed by all nodes. [1]
[1] http://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#recovering-from-failures-with-checkpointing

Spark job failing in YARN mode

I have a Spark program written in Scala that read a CSV file from HDFS, compute a new column and save it as a parquet file. I am running the program in a YARN cluster. But every time I try to launch it the executors fails at some point with this error.
Could you help me to find what might cause this error ?
Log from on executor
16/10/27 15:58:10 WARN storage.BlockManager: Putting block rdd_12_225 failed due to an exception
16/10/27 15:58:10 WARN storage.BlockManager: Block rdd_12_225 could not be removed as it was not found on disk or in memory
16/10/27 15:58:10 ERROR executor.Executor: Exception in task 225.0 in stage 4.0 (TID 465)
java.io.IOException: Stream is corrupted
at org.apache.spark.io.LZ4BlockInputStream.refill(LZ4BlockInputStream.java:211)
at org.apache.spark.io.LZ4BlockInputStream.read(LZ4BlockInputStream.java:125)
at java.io.BufferedInputStream.fill(BufferedInputStream.java:246)
at java.io.BufferedInputStream.read(BufferedInputStream.java:265)
at java.io.DataInputStream.readInt(DataInputStream.java:387)
at org.apache.spark.sql.execution.UnsafeRowSerializerInstance$$anon$3$$anon$1.readSize(UnsafeRowSerializer.scala:113)
at org.apache.spark.sql.execution.UnsafeRowSerializerInstance$$anon$3$$anon$1.<init>(UnsafeRowSerializer.scala:120)
at org.apache.spark.sql.execution.UnsafeRowSerializerInstance$$anon$3.asKeyValueIterator(UnsafeRowSerializer.scala:110)
at org.apache.spark.shuffle.BlockStoreShuffleReader$$anonfun$3.apply(BlockStoreShuffleReader.scala:66)
at org.apache.spark.shuffle.BlockStoreShuffleReader$$anonfun$3.apply(BlockStoreShuffleReader.scala:62)
at scala.collection.Iterator$$anon$12.nextCur(Iterator.scala:434)
at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:440)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
at org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:32)
at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:39)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:370)
at org.apache.spark.sql.execution.columnar.InMemoryRelation$$anonfun$3$$anon$1.next(InMemoryRelation.scala:118)
at org.apache.spark.sql.execution.columnar.InMemoryRelation$$anonfun$3$$anon$1.next(InMemoryRelation.scala:110)
at org.apache.spark.storage.memory.MemoryStore.putIteratorAsValues(MemoryStore.scala:214)
at org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:935)
at org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:926)
at org.apache.spark.storage.BlockManager.doPut(BlockManager.scala:866)
at org.apache.spark.storage.BlockManager.doPutIterator(BlockManager.scala:926)
at org.apache.spark.storage.BlockManager.getOrElseUpdate(BlockManager.scala:670)
at org.apache.spark.rdd.RDD.getOrCompute(RDD.scala:330)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:281)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:283)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:283)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:79)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:47)
at org.apache.spark.scheduler.Task.run(Task.scala:86)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274)
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: net.jpountz.lz4.LZ4Exception: Error decoding offset 15385 of input buffer
at net.jpountz.lz4.LZ4JNIFastDecompressor.decompress(LZ4JNIFastDecompressor.java:39)
at org.apache.spark.io.LZ4BlockInputStream.refill(LZ4BlockInputStream.java:205)
... 41 more
EDIT :
There is the code used
var df = spark.read.option("header", "true").option("inferSchema", "true").option("treatEmptyValuesAsNulls", "true").csv(hdfsFileURLIn).repartition(nPartitions)
df.printSchema()
df = df.withColumn("ipix", a2p(df.col(deName), df.col(raName))).persist(StorageLevel.MEMORY_AND_DISK)
df.repartition(nPartitions, $"ipix").write.mode("overwrite").option("spark.hadoop.dfs.replication", 1).parquet(hdfsFileURLOut)
the user function a2p is just taking two Double and return an other double
I need to say that this worked well with relatively small CSV (~1Go) but this error happen every times with bigger ones (~15Go)
EDIT 2:
Following the suggestions I disabled the repartition and I used StorageLevel.DISK_ONLY
With this I don't get the Putting block rdd_***** failed due to an exception but there is still an exception related to LZ4 (Stream is corrupted):
16/10/28 07:53:00 ERROR util.Utils: Aborting task
java.io.IOException: Stream is corrupted
at org.apache.spark.io.LZ4BlockInputStream.refill(LZ4BlockInputStream.java:211)
at org.apache.spark.io.LZ4BlockInputStream.available(LZ4BlockInputStream.java:109)
at java.io.BufferedInputStream.read(BufferedInputStream.java:353)
at java.io.DataInputStream.read(DataInputStream.java:149)
at org.spark_project.guava.io.ByteStreams.read(ByteStreams.java:899)
at org.spark_project.guava.io.ByteStreams.readFully(ByteStreams.java:733)
at org.apache.spark.sql.execution.UnsafeRowSerializerInstance$$anon$3$$anon$1.next(UnsafeRowSerializer.scala:127)
at org.apache.spark.sql.execution.UnsafeRowSerializerInstance$$anon$3$$anon$1.next(UnsafeRowSerializer.scala:110)
at scala.collection.Iterator$$anon$12.next(Iterator.scala:444)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
at org.apache.spark.util.CompletionIterator.next(CompletionIterator.scala:30)
at org.apache.spark.InterruptibleIterator.next(InterruptibleIterator.scala:43)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
at org.apache.spark.sql.execution.datasources.DefaultWriterContainer$$anonfun$writeRows$1.apply$mcV$sp(WriterContainer.scala:254)
at org.apache.spark.sql.execution.datasources.DefaultWriterContainer$$anonfun$writeRows$1.apply(WriterContainer.scala:252)
at org.apache.spark.sql.execution.datasources.DefaultWriterContainer$$anonfun$writeRows$1.apply(WriterContainer.scala:252)
at org.apache.spark.util.Utils$.tryWithSafeFinallyAndFailureCallbacks(Utils.scala:1345)
at org.apache.spark.sql.execution.datasources.DefaultWriterContainer.writeRows(WriterContainer.scala:258)
at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand$$anonfun$run$1$$anonfun$apply$mcV$sp$1.apply(InsertIntoHadoopFsRelationCommand.scala:143)
at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand$$anonfun$run$1$$anonfun$apply$mcV$sp$1.apply(InsertIntoHadoopFsRelationCommand.scala:143)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:70)
at org.apache.spark.scheduler.Task.run(Task.scala:86)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274)
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: net.jpountz.lz4.LZ4Exception: Error decoding offset 12966 of input buffer
at net.jpountz.lz4.LZ4JNIFastDecompressor.decompress(LZ4JNIFastDecompressor.java:39)
at org.apache.spark.io.LZ4BlockInputStream.refill(LZ4BlockInputStream.java:205)
... 25 more
EDIT 3 : I managed to launch it without any errors by removing also the second repartition (the one that repartition using the column ipix) I will look further in the documentation of this method
EDIT 4 : This is strange, occasionally some executors fail with a segmentation fault :
#
# A fatal error has been detected by the Java Runtime Environment:
#
# SIGSEGV (0xb) at pc=0x00007f48d8a47f2c, pid=3501, tid=0x00007f48cc60c700
#
# JRE version: Java(TM) SE Runtime Environment (8.0_102-b14) (build 1.8.0_102-b14)
# Java VM: Java HotSpot(TM) 64-Bit Server VM (25.102-b14 mixed mode linux-amd64 compressed oops)
# Problematic frame:
# J 4713 C2 org.apache.spark.unsafe.types.UTF8String.hashCode()I (18 bytes) # 0x00007f48d8a47f2c [0x00007f48d8a47e60+0xcc]
#
# Core dump written. Default location: /tmp/hadoop-root/nm-local-dir/usercache/root/appcache/application_1477580152295_0008/container_1477580152295_0008_01_000006/core or core.3501
#
# An error report file with more information is saved as:
# /tmp/hadoop-root/nm-local-dir/usercache/root/appcache/application_1477580152295_0008/container_1477580152295_0008_01_000006/hs_err_pid3501.log
#
# If you would like to submit a bug report, please visit:
# http://bugreport.java.com/bugreport/crash.jsp
#
I checked the memory and all my executors always have plenty of free memory (at least 6Go)
EDIT 4 : So I tested with multiple files and the execution always succeed but sometime some executors fails (with the error above) and are started again by YARN
Which version of lz4-java are you using? This may be related to the problem that was fixed in version 1.1.2 -- see this bug report
Also, I am curious about your function a2p. It should ideally take two Column objects as input, and not just Doubles (unless you registered it as a UDF).
Ran into the same issue.
Symptoms look exactly like this problem: SPARK-18105.
As of 1/29/17 it is not fixed yet.
I replaced lz4-java jar to it's latest version (lz4-java-1.5.0.jar) in jars directory of inside SPARK_HOME path. This worked for me.