spark-streaming - spark streaming 集成 kafka，使用window时出现错误

问题描述

问 题

当使用 spark streaming 2.0.0 集成 kafka 0.10.0时出现 KafkaConsumer 多线程争用的问题。
部分代码如下：

  val ssc = new StreamingContext(sc, Seconds(5))
  val stream = KafkaUtils.createDirectStream(ssc,
    PreferConsistent,
    Assign[String, String](fromOffsets.keys.toList, kafkaParams, fromOffsets))
   
  val data = stream.map(_.value())
  val word = data.map(word => (word,1))
  val result = data.reduceByKeyAndWindow({ (x, y) => x + y }, { (x, y) => x - y }, Minutes(2),Minutes(1))
  word.print()
  result.print()
  

Exception:

Exception in thread "main" org.apache.spark.SparkException: Job aborted due to stage failure: Task 1 in stage 34.0 failed 1 times, most recent failure: Lost task 1.0 in stage 34.0 (TID 126, localhost): java.util.ConcurrentModificationException: KafkaConsumer is not safe for multi-threaded access
    at org.apache.kafka.clients.consumer.KafkaConsumer.acquire(KafkaConsumer.java:1430)
    at org.apache.kafka.clients.consumer.KafkaConsumer.seek(KafkaConsumer.java:1131)
    at org.apache.spark.streaming.kafka010.CachedKafkaConsumer.seek(CachedKafkaConsumer.scala:95)
    at org.apache.spark.streaming.kafka010.CachedKafkaConsumer.get(CachedKafkaConsumer.scala:69)
    at org.apache.spark.streaming.kafka010.KafkaRDD$KafkaRDDIterator.next(KafkaRDD.scala:227)
    at org.apache.spark.streaming.kafka010.KafkaRDD$KafkaRDDIterator.next(KafkaRDD.scala:193)
    at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
    at scala.collection.Iterator$$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 scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:439)
    at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
    at org.apache.spark.util.collection.ExternalSorter.insertAll(ExternalSorter.scala:192)
    at org.apache.spark.shuffle.sort.SortShuffleWriter.write(SortShuffleWriter.scala:63)
    at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:79)
    at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:47)
    at org.apache.spark.scheduler.Task.run(Task.scala:85)
    at org.apache.spark.executor.Executor$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)

Driver stacktrace:
    at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1450)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1438)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1437)
    at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
    at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
    at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1437)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:811)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:811)
    at scala.Option.foreach(Option.scala:257)
    at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:811)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1659)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1618)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1607)
    at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
    at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:632)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:1871)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:1884)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:1897)
    at org.apache.spark.rdd.RDD$$anonfun$take$1.apply(RDD.scala:1305)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
    at org.apache.spark.rdd.RDD.withScope(RDD.scala:358)
    at org.apache.spark.rdd.RDD.take(RDD.scala:1279)
    at org.apache.spark.streaming.dstream.DStream$$anonfun$print$2$$anonfun$foreachFunc$3$1.apply(DStream.scala:734)
    at org.apache.spark.streaming.dstream.DStream$$anonfun$print$2$$anonfun$foreachFunc$3$1.apply(DStream.scala:733)
    at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply$mcV$sp(ForEachDStream.scala:51)
    at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply(ForEachDStream.scala:51)
    at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply(ForEachDStream.scala:51)
    at org.apache.spark.streaming.dstream.DStream.createRDDWithLocalProperties(DStream.scala:415)
    at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply$mcV$sp(ForEachDStream.scala:50)
    at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:50)
    at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:50)
    at scala.util.Try$.apply(Try.scala:192)
    at org.apache.spark.streaming.scheduler.Job.run(Job.scala:39)
    at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply$mcV$sp(JobScheduler.scala:245)
    at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:245)
    at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:245)
    at scala.util.DynamicVariable.withValue(DynamicVariable.scala:58)
    at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler.run(JobScheduler.scala:244)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
    at java.lang.Thread.run(Thread.java:745)
Caused by: java.util.ConcurrentModificationException: KafkaConsumer is not safe for multi-threaded access
    at org.apache.kafka.clients.consumer.KafkaConsumer.acquire(KafkaConsumer.java:1430)
    at org.apache.kafka.clients.consumer.KafkaConsumer.seek(KafkaConsumer.java:1131)
    at org.apache.spark.streaming.kafka010.CachedKafkaConsumer.seek(CachedKafkaConsumer.scala:95)
    at org.apache.spark.streaming.kafka010.CachedKafkaConsumer.get(CachedKafkaConsumer.scala:69)
    at org.apache.spark.streaming.kafka010.KafkaRDD$KafkaRDDIterator.next(KafkaRDD.scala:227)
    at org.apache.spark.streaming.kafka010.KafkaRDD$KafkaRDDIterator.next(KafkaRDD.scala:193)
    at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
    at scala.collection.Iterator$$anon$12.nextCur(Iterator.scala:434)
    at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:440)
    at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
    at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:439)
    at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
    at org.apache.spark.util.collection.ExternalSorter.insertAll(ExternalSorter.scala:192)
    at org.apache.spark.shuffle.sort.SortShuffleWriter.write(SortShuffleWriter.scala:63)
    at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:79)
    at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:47)
    at org.apache.spark.scheduler.Task.run(Task.scala:85)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274)
    ... 3 more

只有当 slideDuration 比 batchDuration大很多时才会发生。比如：
当 batchDuration 为 5S，slideDuration 为 60S 时会发生错误
当 batchDuration 为 5s, slideDuration 为 10S 时不会发生错误

解决方案

对使用window操作的DStream在调用window之前先调用checkpoint方法，可以截断lineage，从而避免这个问题。

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