为什么spark流应用程序在从kafka购买Yarn时会暂停？

sqserrrh  于 2021-06-07  发布在  Kafka
关注(0)|答案(1)|浏览(263)

我正在用scala写一个spark流媒体应用程序。该应用程序的目标是消费Kafka的最新记录并将其打印到标准输出。
当我在本地使用 --master local[n] . 但是，当我在yarn中运行应用程序（并生成我正在使用的主题）时，应用程序会陷入：

16/11/18 20:53:05 INFO JobScheduler: Added jobs for time 1479502385000 ms

在多次重复上述行之后，spark会出现以下错误：

16/11/18 20:54:47 WARN TaskSetManager: Lost task 0.0 in stage 9.0 (TID 9, r3d3.hadoop.REDACTED.REDACTED): java.net.ConnectException: Connection timed out
at sun.nio.ch.Net.connect0(Native Method)
at sun.nio.ch.Net.connect(Net.java:454)
at sun.nio.ch.Net.connect(Net.java:446)
at sun.nio.ch.SocketChannelImpl.connect(SocketChannelImpl.java:648)
at kafka.network.BlockingChannel.connect(BlockingChannel.scala:57)
at kafka.consumer.SimpleConsumer.connect(SimpleConsumer.scala:44)
at kafka.consumer.SimpleConsumer.getOrMakeConnection(SimpleConsumer.scala:142)
at kafka.consumer.SimpleConsumer.kafka$consumer$SimpleConsumer$$sendRequest(SimpleConsumer.scala:69)
at kafka.consumer.SimpleConsumer$$anonfun$fetch$1$$anonfun$apply$mcV$sp$1.apply$mcV$sp(SimpleConsumer.scala:109)
at kafka.consumer.SimpleConsumer$$anonfun$fetch$1$$anonfun$apply$mcV$sp$1.apply(SimpleConsumer.scala:109)
at kafka.consumer.SimpleConsumer$$anonfun$fetch$1$$anonfun$apply$mcV$sp$1.apply(SimpleConsumer.scala:109)
at kafka.metrics.KafkaTimer.time(KafkaTimer.scala:33)
at kafka.consumer.SimpleConsumer$$anonfun$fetch$1.apply$mcV$sp(SimpleConsumer.scala:108)
at kafka.consumer.SimpleConsumer$$anonfun$fetch$1.apply(SimpleConsumer.scala:108)
at kafka.consumer.SimpleConsumer$$anonfun$fetch$1.apply(SimpleConsumer.scala:108)
at kafka.metrics.KafkaTimer.time(KafkaTimer.scala:33)
at kafka.consumer.SimpleConsumer.fetch(SimpleConsumer.scala:107)
at org.apache.spark.streaming.kafka.KafkaRDD$KafkaRDDIterator.fetchBatch(KafkaRDD.scala:150)
at org.apache.spark.streaming.kafka.KafkaRDD$KafkaRDDIterator.getNext(KafkaRDD.scala:162)
at org.apache.spark.util.NextIterator.hasNext(NextIterator.scala:73)
at scala.collection.Iterator$class.foreach(Iterator.scala:727)
at org.apache.spark.util.NextIterator.foreach(NextIterator.scala:21)
at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47)
at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273)
at org.apache.spark.util.NextIterator.to(NextIterator.scala:21)
at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265)
at org.apache.spark.util.NextIterator.toBuffer(NextIterator.scala:21)
at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252)
at org.apache.spark.util.NextIterator.toArray(NextIterator.scala:21)
at org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$12.apply(RDD.scala:927)
at org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$12.apply(RDD.scala:927)
at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
at org.apache.spark.scheduler.Task.run(Task.scala:89)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:213)
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)

流式处理ui出错：

org.apache.spark.streaming.dstream.DStream.print(DStream.scala:757)
com.REDACTED.bdp.Main$.main(Main.scala:88)
com.REDACTED.bdp.Main.main(Main.scala)
sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
java.lang.reflect.Method.invoke(Method.java:498)
org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:731)
org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:181)
org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:206)
org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:121)
org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)

Yarn应用日志错误（stdout）：

java.lang.NullPointerException
        at org.apache.spark.streaming.kafka.KafkaRDD$KafkaRDDIterator.close(KafkaRDD.scala:158)
        at org.apache.spark.util.NextIterator.closeIfNeeded(NextIterator.scala:66)
        at org.apache.spark.streaming.kafka.KafkaRDD$KafkaRDDIterator$$anonfun$1.apply(KafkaRDD.scala:101)
        at org.apache.spark.streaming.kafka.KafkaRDD$KafkaRDDIterator$$anonfun$1.apply(KafkaRDD.scala:101)
        at org.apache.spark.TaskContextImpl$$anon$1.onTaskCompletion(TaskContextImpl.scala:60)
        at org.apache.spark.TaskContextImpl$$anonfun$markTaskCompleted$1.apply(TaskContextImpl.scala:79)
        at org.apache.spark.TaskContextImpl$$anonfun$markTaskCompleted$1.apply(TaskContextImpl.scala:77)
        at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
        at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
        at org.apache.spark.TaskContextImpl.markTaskCompleted(TaskContextImpl.scala:77)
        at org.apache.spark.scheduler.Task.run(Task.scala:91)
        at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:213)
        at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
        at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
        at java.lang.Thread.run(Thread.java:745)
[2016-11-21 15:57:49,925] ERROR Exception in task 0.1 in stage 33.0 (TID 34) (org.apache.spark.executor.Executor)
org.apache.spark.util.TaskCompletionListenerException
        at org.apache.spark.TaskContextImpl.markTaskCompleted(TaskContextImpl.scala:87)
        at org.apache.spark.scheduler.Task.run(Task.scala:91)
        at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:213)
        at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
        at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
        at java.lang.Thread.run(Thread.java:745)

应用程序日志中的另一个错误：

[2016-11-21 15:52:32,264] WARN Exception encountered while connecting to the server :  (org.apache.hadoop.ipc.Client)
org.apache.hadoop.ipc.RemoteException(org.apache.hadoop.ipc.StandbyException): Operation category READ is not supported in state standby
        at org.apache.hadoop.security.SaslRpcClient.saslConnect(SaslRpcClient.java:375)
        at org.apache.hadoop.ipc.Client$Connection.setupSaslConnection(Client.java:558)
        at org.apache.hadoop.ipc.Client$Connection.access$1800(Client.java:373)
        at org.apache.hadoop.ipc.Client$Connection$2.run(Client.java:727)
        at org.apache.hadoop.ipc.Client$Connection$2.run(Client.java:723)
        at java.security.AccessController.doPrivileged(Native Method)
        at javax.security.auth.Subject.doAs(Subject.java:422)
        at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1657)
        at org.apache.hadoop.ipc.Client$Connection.setupIOstreams(Client.java:722)
        at org.apache.hadoop.ipc.Client$Connection.access$2800(Client.java:373)
        at org.apache.hadoop.ipc.Client.getConnection(Client.java:1493)
        at org.apache.hadoop.ipc.Client.call(Client.java:1397)
        at org.apache.hadoop.ipc.Client.call(Client.java:1358)
        at org.apache.hadoop.ipc.ProtobufRpcEngine$Invoker.invoke(ProtobufRpcEngine.java:229)
        at com.sun.proxy.$Proxy9.getFileInfo(Unknown Source)
        at org.apache.hadoop.hdfs.protocolPB.ClientNamenodeProtocolTranslatorPB.getFileInfo(ClientNamenodeProtocolTranslatorPB.java:771)
        at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
        at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
        at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
        at java.lang.reflect.Method.invoke(Method.java:498)
        at org.apache.hadoop.io.retry.RetryInvocationHandler.invokeMethod(RetryInvocationHandler.java:252)
        at org.apache.hadoop.io.retry.RetryInvocationHandler.invoke(RetryInvocationHandler.java:104)
        at com.sun.proxy.$Proxy10.getFileInfo(Unknown Source)
        at org.apache.hadoop.hdfs.DFSClient.getFileInfo(DFSClient.java:2116)
        at org.apache.hadoop.hdfs.DistributedFileSystem$22.doCall(DistributedFileSystem.java:1315)
        at org.apache.hadoop.hdfs.DistributedFileSystem$22.doCall(DistributedFileSystem.java:1311)
        at org.apache.hadoop.fs.FileSystemLinkResolver.resolve(FileSystemLinkResolver.java:81)
        at org.apache.hadoop.hdfs.DistributedFileSystem.getFileStatus(DistributedFileSystem.java:1311)
        at org.apache.hadoop.fs.FileSystem.exists(FileSystem.java:1424)
        at org.apache.spark.deploy.yarn.Client$.org$apache$spark$deploy$yarn$Client$$sparkJar(Client.scala:1195)
        at org.apache.spark.deploy.yarn.Client$.populateClasspath(Client.scala:1333)
        at org.apache.spark.deploy.yarn.ExecutorRunnable.prepareEnvironment(ExecutorRunnable.scala:290)
        at org.apache.spark.deploy.yarn.ExecutorRunnable.env$lzycompute(ExecutorRunnable.scala:61)
        at org.apache.spark.deploy.yarn.ExecutorRunnable.env(ExecutorRunnable.scala:61)
        at org.apache.spark.deploy.yarn.ExecutorRunnable.startContainer(ExecutorRunnable.scala:80)
        at org.apache.spark.deploy.yarn.ExecutorRunnable.run(ExecutorRunnable.scala:68)
        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)

奇怪的是，不管出于什么原因，这款应用程序有5%的时间都能成功阅读Kafka的作品。
簇和Yarn似乎运转正常。群集使用kerberos进行保护。
这个错误的来源是什么？

yarnscalaapache-kafkaapache-sparkspark-streaming

1条答案

按热度按时间
balp4ylt

balp4ylt1#

热释光；dr.这个答案并没有给出答案，只是暗示了下一步可能的行动。
我的理解是 Lost task 流式处理作业的事件可能会在作业执行且无法完成时报告，在您的情况下，这是spark执行器和kafka代理之间的连接问题。

16/11/18 20:54:47 WARN TaskSetManager: Lost task 0.0 in stage 9.0 (TID 9, r3d3.hadoop.REDACTED.REDACTED): java.net.ConnectException: Connection timed out
at sun.nio.ch.Net.connect0(Native Method)
at sun.nio.ch.Net.connect(Net.java:454)
at sun.nio.ch.Net.connect(Net.java:446)
at sun.nio.ch.SocketChannelImpl.connect(SocketChannelImpl.java:648)
at kafka.network.BlockingChannel.connect(BlockingChannel.scala:57)
at kafka.consumer.SimpleConsumer.connect(SimpleConsumer.scala:44)
at kafka.consumer.SimpleConsumer.getOrMakeConnection(SimpleConsumer.scala:142)
at kafka.consumer.SimpleConsumer.kafka$consumer$SimpleConsumer$$sendRequest(SimpleConsumer.scala:69)
at kafka.consumer.SimpleConsumer$$anonfun$fetch$1$$anonfun$apply$mcV$sp$1.apply$mcV$sp(SimpleConsumer.scala:109)
at kafka.consumer.SimpleConsumer$$anonfun$fetch$1$$anonfun$apply$mcV$sp$1.apply(SimpleConsumer.scala:109)
at kafka.consumer.SimpleConsumer$$anonfun$fetch$1$$anonfun$apply$mcV$sp$1.apply(SimpleConsumer.scala:109)
at kafka.metrics.KafkaTimer.time(KafkaTimer.scala:33)
at kafka.consumer.SimpleConsumer$$anonfun$fetch$1.apply$mcV$sp(SimpleConsumer.scala:108)
at kafka.consumer.SimpleConsumer$$anonfun$fetch$1.apply(SimpleConsumer.scala:108)
at kafka.consumer.SimpleConsumer$$anonfun$fetch$1.apply(SimpleConsumer.scala:108)
at kafka.metrics.KafkaTimer.time(KafkaTimer.scala:33)
at kafka.consumer.SimpleConsumer.fetch(SimpleConsumer.scala:107)
at org.apache.spark.streaming.kafka.KafkaRDD$KafkaRDDIterator.fetchBatch(KafkaRDD.scala:150)

错误消息的模式如下：

Lost task [id] in stage [taskSetId] (TID [tid], [host], executor [executorId]): [reason]

这可以理解为在主机上运行spark执行器 r3d3.hadoop.REDACTED.REDACTED .
失败的原因如下：

java.net.ConnectException: Connection timed out
at sun.nio.ch.Net.connect0(Native Method)
at sun.nio.ch.Net.connect(Net.java:454)
at sun.nio.ch.Net.connect(Net.java:446)
at sun.nio.ch.SocketChannelImpl.connect(SocketChannelImpl.java:648)
at kafka.network.BlockingChannel.connect(BlockingChannel.scala:57)
at kafka.consumer.SimpleConsumer.connect(SimpleConsumer.scala:44)
at kafka.consumer.SimpleConsumer.getOrMakeConnection(SimpleConsumer.scala:142)
at kafka.consumer.SimpleConsumer.kafka$consumer$SimpleConsumer$$sendRequest(SimpleConsumer.scala:69)
at kafka.consumer.SimpleConsumer$$anonfun$fetch$1$$anonfun$apply$mcV$sp$1.apply$mcV$sp(SimpleConsumer.scala:109)
at kafka.consumer.SimpleConsumer$$anonfun$fetch$1$$anonfun$apply$mcV$sp$1.apply(SimpleConsumer.scala:109)
at kafka.consumer.SimpleConsumer$$anonfun$fetch$1$$anonfun$apply$mcV$sp$1.apply(SimpleConsumer.scala:109)
at kafka.metrics.KafkaTimer.time(KafkaTimer.scala:33)
at kafka.consumer.SimpleConsumer$$anonfun$fetch$1.apply$mcV$sp(SimpleConsumer.scala:108)
at kafka.consumer.SimpleConsumer$$anonfun$fetch$1.apply(SimpleConsumer.scala:108)
at kafka.consumer.SimpleConsumer$$anonfun$fetch$1.apply(SimpleConsumer.scala:108)
at kafka.metrics.KafkaTimer.time(KafkaTimer.scala:33)
at kafka.consumer.SimpleConsumer.fetch(SimpleConsumer.scala:107)

我会问自己，Kafka经纪人什么时候可以为客户提供服务（在您的案例中，这是一个spark流媒体应用程序，可能有助于了解问题的根本原因，也可能没有）。
我认为这可能与apachespark无关，会在Kafka的圈子里寻找更多的答案。

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