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Jungtaek Lim commented on SPARK-31460: -------------------------------------- 1. Please check your app / submit phase doesn't override Kafka client version - it should be 2.0.0 AFAIK. 2. I guess you've changed the Kafka consumer config for timeout - 1000 ms (1 second) which is way too short. (In SPARK-23829 you can see it was 120000ms = 2 mins) Please set it reasonably. > spark-sql-kafka source in spark 2.4.4 causes reading stream failure frequently > ------------------------------------------------------------------------------ > > Key: SPARK-31460 > URL: https://issues.apache.org/jira/browse/SPARK-31460 > Project: Spark > Issue Type: Bug > Components: Structured Streaming > Affects Versions: 2.4.4 > Reporter: vinay > Priority: Major > Original Estimate: 24h > Remaining Estimate: 24h > > In spark 2.4.4 , it provides a source "spark-sql-kafka-0-10_2.11". > > When I wanted to read from my kafka-0.10.2.11 cluster, it throws out an error > "*java.util.concurrent.TimeoutException: Cannot fetch record for offset xxxxx > in 1000 milliseconds*" frequently, and the job thus failed. > > I see this issue was seen before in 2.3 according to ticket 23829 and an > upgrade to spark 2.4 was supposed to solve this. > > {code:java} > compile group: 'org.apache.spark', name: 'spark-sql-kafka-0-10_2.11', > version: '2.4.4'{code} > Here is the error stack. > {code:java} > org.apache.spark.SparkException: Writing job aborted. > > org.apache.spark.sql.execution.datasources.v2.WriteToDataSourceV2Exec.doExecute(WriteToDataSourceV2Exec.scala:92) > > org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:131) > > org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:127) > > org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155) > > org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151) > org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152) > org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:127) > org.apache.spark.sql.execution.SparkPlan.getByteArrayRdd(SparkPlan.scala:247) > org.apache.spark.sql.execution.SparkPlan.executeCollect(SparkPlan.scala:296) > > org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$collectFromPlan(Dataset.scala:3389) > org.apache.spark.sql.Dataset$$anonfun$collect$1.apply(Dataset.scala:2788) > org.apache.spark.sql.Dataset$$anonfun$collect$1.apply(Dataset.scala:2788) > org.apache.spark.sql.Dataset$$anonfun$52.apply(Dataset.scala:3370) > > org.apache.spark.sql.execution.SQLExecution$$anonfun$withNewExecutionId$1.apply(SQLExecution.scala:78) > > org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:125) > > org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:73) > org.apache.spark.sql.Dataset.withAction(Dataset.scala:3369) > org.apache.spark.sql.Dataset.collect(Dataset.scala:2788) > org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch$5$$anonfun$apply$17.apply(MicroBatchExecution.scala:540) > > org.apache.spark.sql.execution.SQLExecution$$anonfun$withNewExecutionId$1.apply(SQLExecution.scala:78) > > org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:125) > > org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:73) > org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch$5.apply(MicroBatchExecution.scala:535) > > org.apache.spark.sql.execution.streaming.ProgressReporter$class.reportTimeTaken(ProgressReporter.scala:351) > > org.apache.spark.sql.execution.streaming.StreamExecution.reportTimeTaken(StreamExecution.scala:58) > org.apache.spark.sql.execution.streaming.MicroBatchExecution.org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch(MicroBatchExecution.scala:534) > org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1$$anonfun$apply$mcZ$sp$1.apply$mcV$sp(MicroBatchExecution.scala:198) > org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1$$anonfun$apply$mcZ$sp$1.apply(MicroBatchExecution.scala:166) > org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1$$anonfun$apply$mcZ$sp$1.apply(MicroBatchExecution.scala:166) > > org.apache.spark.sql.execution.streaming.ProgressReporter$class.reportTimeTaken(ProgressReporter.scala:351) > > org.apache.spark.sql.execution.streaming.StreamExecution.reportTimeTaken(StreamExecution.scala:58) > org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1.apply$mcZ$sp(MicroBatchExecution.scala:166) > > org.apache.spark.sql.execution.streaming.ProcessingTimeExecutor.execute(TriggerExecutor.scala:56) > > org.apache.spark.sql.execution.streaming.MicroBatchExecution.runActivatedStream(MicroBatchExecution.scala:160) > org.apache.spark.sql.execution.streaming.StreamExecution.org$apache$spark$sql$execution$streaming$StreamExecution$$runStream(StreamExecution.scala:281) > > org.apache.spark.sql.execution.streaming.StreamExecution$$anon$1.run(StreamExecution.scala:193) > Caused by: org.apache.spark.SparkException: Job aborted due to stage failure: > Task 0 in stage 44426.0 failed 4 times, most recent failure: Lost task 0.3 in > stage 44426.0 (TID 209753, 10.244.3.161, executor 5): > java.util.concurrent.TimeoutException: Cannot fetch record for offset 7700744 > in 1000 milliseconds > > org.apache.spark.sql.kafka010.InternalKafkaConsumer.fetchData(KafkaDataConsumer.scala:488) > org.apache.spark.sql.kafka010.InternalKafkaConsumer.org$apache$spark$sql$kafka010$InternalKafkaConsumer$$fetchRecord(KafkaDataConsumer.scala:371) > > org.apache.spark.sql.kafka010.InternalKafkaConsumer$$anonfun$get$1.apply(KafkaDataConsumer.scala:251) > > org.apache.spark.sql.kafka010.InternalKafkaConsumer$$anonfun$get$1.apply(KafkaDataConsumer.scala:234) > > org.apache.spark.util.UninterruptibleThread.runUninterruptibly(UninterruptibleThread.scala:77) > > org.apache.spark.sql.kafka010.InternalKafkaConsumer.runUninterruptiblyIfPossible(KafkaDataConsumer.scala:209) > > org.apache.spark.sql.kafka010.InternalKafkaConsumer.get(KafkaDataConsumer.scala:234) > > org.apache.spark.sql.kafka010.KafkaDataConsumer$class.get(KafkaDataConsumer.scala:64) > > org.apache.spark.sql.kafka010.KafkaDataConsumer$NonCachedKafkaDataConsumer.get(KafkaDataConsumer.scala:506) > > org.apache.spark.sql.kafka010.KafkaMicroBatchInputPartitionReader.next(KafkaMicroBatchReader.scala:357) > > org.apache.spark.sql.execution.datasources.v2.DataSourceRDD$$anon$1.hasNext(DataSourceRDD.scala:49) > > org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37) > > org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown > Source) > > org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43) > > org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$13$$anon$1.hasNext(WholeStageCodegenExec.scala:636) > > org.apache.spark.sql.execution.datasources.v2.DataWritingSparkTask$$anonfun$run$3.apply(WriteToDataSourceV2Exec.scala:117) > > org.apache.spark.sql.execution.datasources.v2.DataWritingSparkTask$$anonfun$run$3.apply(WriteToDataSourceV2Exec.scala:116) > > org.apache.spark.util.Utils$.tryWithSafeFinallyAndFailureCallbacks(Utils.scala:1394) > > org.apache.spark.sql.execution.datasources.v2.DataWritingSparkTask$.run(WriteToDataSourceV2Exec.scala:146) > > org.apache.spark.sql.execution.datasources.v2.WriteToDataSourceV2Exec$$anonfun$doExecute$2.apply(WriteToDataSourceV2Exec.scala:67) > > org.apache.spark.sql.execution.datasources.v2.WriteToDataSourceV2Exec$$anonfun$doExecute$2.apply(WriteToDataSourceV2Exec.scala:66) > org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90) > org.apache.spark.scheduler.Task.run(Task.scala:123) > > org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408) > org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360) > org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414) > > java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) > > java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) > java.lang.Thread.run(Thread.java:748) > Driver stacktrace: > > org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1889) > > org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1877) > > org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1876) > > scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) > scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48) > org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1876) > > org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:926) > > org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:926) > scala.Option.foreach(Option.scala:257) > > org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:926) > > org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2110) > > org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2059) > > org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2048) > org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49) > org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:737) > org.apache.spark.SparkContext.runJob(SparkContext.scala:2061) > > org.apache.spark.sql.execution.datasources.v2.WriteToDataSourceV2Exec.doExecute(WriteToDataSourceV2Exec.scala:64)... > 35 more > Caused by: java.util.concurrent.TimeoutException: Cannot fetch record for > offset 7700744 in 1000 milliseconds > > org.apache.spark.sql.kafka010.InternalKafkaConsumer.fetchData(KafkaDataConsumer.scala:488) > org.apache.spark.sql.kafka010.InternalKafkaConsumer.org$apache$spark$sql$kafka010$InternalKafkaConsumer$$fetchRecord(KafkaDataConsumer.scala:371) > > org.apache.spark.sql.kafka010.InternalKafkaConsumer$$anonfun$get$1.apply(KafkaDataConsumer.scala:251) > > org.apache.spark.sql.kafka010.InternalKafkaConsumer$$anonfun$get$1.apply(KafkaDataConsumer.scala:234) > > org.apache.spark.util.UninterruptibleThread.runUninterruptibly(UninterruptibleThread.scala:77) > > org.apache.spark.sql.kafka010.InternalKafkaConsumer.runUninterruptiblyIfPossible(KafkaDataConsumer.scala:209) > > org.apache.spark.sql.kafka010.InternalKafkaConsumer.get(KafkaDataConsumer.scala:234) > > org.apache.spark.sql.kafka010.KafkaDataConsumer$class.get(KafkaDataConsumer.scala:64) > > org.apache.spark.sql.kafka010.KafkaDataConsumer$NonCachedKafkaDataConsumer.get(KafkaDataConsumer.scala:506) > > org.apache.spark.sql.kafka010.KafkaMicroBatchInputPartitionReader.next(KafkaMicroBatchReader.scala:357) > > org.apache.spark.sql.execution.datasources.v2.DataSourceRDD$$anon$1.hasNext(DataSourceRDD.scala:49) > > org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37) > > org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown > Source) > > org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43) > > org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$13$$anon$1.hasNext(WholeStageCodegenExec.scala:636) > > org.apache.spark.sql.execution.datasources.v2.DataWritingSparkTask$$anonfun$run$3.apply(WriteToDataSourceV2Exec.scala:117) > > org.apache.spark.sql.execution.datasources.v2.DataWritingSparkTask$$anonfun$run$3.apply(WriteToDataSourceV2Exec.scala:116) > > org.apache.spark.util.Utils$.tryWithSafeFinallyAndFailureCallbacks(Utils.scala:1394) > > org.apache.spark.sql.execution.datasources.v2.DataWritingSparkTask$.run(WriteToDataSourceV2Exec.scala:146) > > org.apache.spark.sql.execution.datasources.v2.WriteToDataSourceV2Exec$$anonfun$doExecute$2.apply(WriteToDataSourceV2Exec.scala:67) > > org.apache.spark.sql.execution.datasources.v2.WriteToDataSourceV2Exec$$anonfun$doExecute$2.apply(WriteToDataSourceV2Exec.scala:66) > org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90) > org.apache.spark.scheduler.Task.run(Task.scala:123) > > org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408) > org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360) > org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414) > > java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) > > java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) > java.lang.Thread.run(Thread.java:748) > {code} -- This message was sent by Atlassian Jira (v8.3.4#803005) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org