java - 运行约 1 小时后，Spark Streaming 作业被终止

Question

我有一个 spark streaming 作业，它从 gnip 读取推文流并将其写入 Kafak。

Spark 和 kafka 在同一个集群上运行。

我的集群由 5 个节点组成。 Kafka-b01 ... Kafka-b05

Spark master 在 Kafak-b05 上运行。

下面是我们如何提交 spark 作业

nohup sh $SPZRK_HOME/bin/spark-submit --total-executor-cores 5 --class com.test.java.gnipStreaming.GnipSparkStreamer --master spark://kafka-b05:7077 GnipStreamContainer。 jar powertrack kafka-b01,kafka-b02,kafka-b03,kafka-b04,kafka-b05 gnip_live_stream 2 &

大约 1 小时后，spark 作业被杀死

nohub文件中的日志显示如下异常

org.apache.spark.storage.BlockFetchException: Failed to fetch block from 2 locations. Most recent failure cause: 
        at org.apache.spark.storage.BlockManager$$anonfun$doGetRemote$2.apply(BlockManager.scala:595) 
        at org.apache.spark.storage.BlockManager$$anonfun$doGetRemote$2.apply(BlockManager.scala:585) 
        at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) 
        at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47) 
        at org.apache.spark.storage.BlockManager.doGetRemote(BlockManager.scala:585) 
        at org.apache.spark.storage.BlockManager.getRemote(BlockManager.scala:570) 
        at org.apache.spark.storage.BlockManager.get(BlockManager.scala:630) 
        at org.apache.spark.rdd.BlockRDD.compute(BlockRDD.scala:48) 
        at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306) 
        at org.apache.spark.rdd.RDD.iterator(RDD.scala:270) 
        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$TaskRunner.run(Executor.scala:214) 
        at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) 
        at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) 
        at java.lang.Thread.run(Thread.java:745) 
Caused by: io.netty.channel.ChannelException: Unable to create Channel from class class io.netty.channel.socket.nio.NioSocketChannel 
        at io.netty.bootstrap.AbstractBootstrap$BootstrapChannelFactory.newChannel(AbstractBootstrap.java:455) 
        at io.netty.bootstrap.AbstractBootstrap.initAndRegister(AbstractBootstrap.java:306) 
        at io.netty.bootstrap.Bootstrap.doConnect(Bootstrap.java:134) 
        at io.netty.bootstrap.Bootstrap.connect(Bootstrap.java:116) 
        at org.apache.spark.network.client.TransportClientFactory.createClient(TransportClientFactory.java:211) 
        at org.apache.spark.network.client.TransportClientFactory.createClient(TransportClientFactory.java:167) 
        at org.apache.spark.network.netty.NettyBlockTransferService$$anon$1.createAndStart(NettyBlockTransferService.scala:90) 
        at org.apache.spark.network.shuffle.RetryingBlockFetcher.fetchAllOutstanding(RetryingBlockFetcher.java:140) 
        at org.apache.spark.network.shuffle.RetryingBlockFetcher.start(RetryingBlockFetcher.java:120) 
        at org.apache.spark.network.netty.NettyBlockTransferService.fetchBlocks(NettyBlockTransferService.scala:99) 
        at org.apache.spark.network.BlockTransferService.fetchBlockSync(BlockTransferService.scala:89) 
        at org.apache.spark.storage.BlockManager$$anonfun$doGetRemote$2.apply(BlockManager.scala:588) 
        ... 15 more 
Caused by: io.netty.channel.ChannelException: Failed to open a socket. 
        at io.netty.channel.socket.nio.NioSocketChannel.newSocket(NioSocketChannel.java:62) 
        at io.netty.channel.socket.nio.NioSocketChannel.<init>(NioSocketChannel.java:72) 
        at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method) 
        at sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62) 
        at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45) 
        at java.lang.reflect.Constructor.newInstance(Constructor.java:423) 
        at java.lang.Class.newInstance(Class.java:442) 
        at io.netty.bootstrap.AbstractBootstrap$BootstrapChannelFactory.newChannel(AbstractBootstrap.java:453) 
        ... 26 more 
Caused by: java.net.SocketException: Too many open files 
        at sun.nio.ch.Net.socket0(Native Method) 
        at sun.nio.ch.Net.socket(Net.java:411) 
        at sun.nio.ch.Net.socket(Net.java:404) 
        at sun.nio.ch.SocketChannelImpl.<init>(SocketChannelImpl.java:105) 
        at sun.nio.ch.SelectorProviderImpl.openSocketChannel(SelectorProviderImpl.java:60) 
        at io.netty.channel.socket.nio.NioSocketChannel.newSocket(NioSocketChannel.java:60) 
        ... 33 more

我已将打开文件的最大数量增加到 3275782(旧数量几乎是这个数量的一半)，但我仍然面临同样的问题。

当我从 spark web 界面检查工作人员的 stderr 日志时，我发现了另一个异常。

java.nio.channels.ClosedChannelException 
        at kafka.network.BlockingChannel.send(BlockingChannel.scala:110) 
        at kafka.producer.SyncProducer.liftedTree1$1(SyncProducer.scala:75) 
        at kafka.producer.SyncProducer.kafka$producer$SyncProducer$$doSend(SyncProducer.scala:74) 
        at kafka.producer.SyncProducer.send(SyncProducer.scala:119) 
        at kafka.client.ClientUtils$.fetchTopicMetadata(ClientUtils.scala:59) 
        at kafka.producer.BrokerPartitionInfo.updateInfo(BrokerPartitionInfo.scala:82) 
        at kafka.producer.BrokerPartitionInfo.getBrokerPartitionInfo(BrokerPartitionInfo.scala:49) 
        at kafka.producer.async.DefaultEventHandler.kafka$producer$async$DefaultEventHandler$$getPartitionListForTopic(DefaultEventHandler.scala:188) 
        at kafka.producer.async.DefaultEventHandler$$anonfun$partitionAndCollate$1.apply(DefaultEventHandler.scala:152) 
        at kafka.producer.async.DefaultEventHandler$$anonfun$partitionAndCollate$1.apply(DefaultEventHandler.scala:151) 
        at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) 
        at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47) 
        at kafka.producer.async.DefaultEventHandler.partitionAndCollate(DefaultEventHandler.scala:151) 
        at kafka.producer.async.DefaultEventHandler.dispatchSerializedData(DefaultEventHandler.scala:96) 
        at kafka.producer.async.DefaultEventHandler.handle(DefaultEventHandler.scala:73) 
        at kafka.producer.Producer.send(Producer.scala:77) 
        at kafka.javaapi.producer.Producer.send(Producer.scala:33) 
        at com.test.java.gnipStreaming.GnipSparkStreamer$1$1.call(GnipSparkStreamer.java:59) 
        at com.test.java.gnipStreaming.GnipSparkStreamer$1$1.call(GnipSparkStreamer.java:51) 
        at org.apache.spark.api.java.JavaRDDLike$$anonfun$foreachPartition$1.apply(JavaRDDLike.scala:225) 
        at org.apache.spark.api.java.JavaRDDLike$$anonfun$foreachPartition$1.apply(JavaRDDLike.scala:225) 
        at org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1$$anonfun$apply$33.apply(RDD.scala:920) 
        at org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1$$anonfun$apply$33.apply(RDD.scala:920) 
        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:214) 
        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)

第二个异常(看起来)与 Kafka 有关，而不是 spark。

您认为问题出在哪里？

编辑

基于 Yuval Itzchakov 的评论这是主播的代码

主类http://pastebin.com/EcbnQQ3a

客户接收类http://pastebin.com/3UFPktKR

Answer 1

问题是您在 DStream.foreachPartition 的迭代中实例化了 Producer 的新实例。如果您有数据密集型流，这可能会导致分配大量生产者并尝试连接到 Kafka。

我要确保的第一件事是，在使用 finally block 发送完数据并调用 producer.close 后，您正确地关闭了流。 :

public void call(JavaRDD<String> rdd) throws Exception {
    rdd.foreachPartition(new VoidFunction<Iterator<String>>() {
       
        @Override
        public void call(Iterator<String> itr) throws Exception {
            try {
                Producer<String, String> producer = getProducer(hosts);
                while(itr.hasNext()) {
                    try {
                        KeyedMessage<String, String> message = 
                        new KeyedMessage<String, String>(topic, itr.next());
                        producer.send(message);
                    } catch (Exception e) {
                          e.printStackTrace();
                    }
            } finally {
                  producer.close()
            }
        }
    });
    return null;
}

如果这仍然不起作用并且您看到太多连接，我会为 Kafka 生产者创建一个对象池，您可以按需对其进行池化。这样，您就可以明确控制正在使用的可用生产者的数量以及您打开的套接字的数量。