具有多个上下文的Spark作业失败

时间：2016-03-11 09:31:22

标签： java apache-spark emr spark-cassandra-connector

在火花应用程序中，我们制作了两个火花上下文，

1）用于从文件系统读取数据。

2）用于连接和加载数据到Cassandra。

在一个应用程序中，我们只能运行一个spark spark上下文，所以我们先停止1st然后开始第二个。

我收到了以下错误。

Error 1) 16/03/10 05:40:44 ERROR Utils: Uncaught exception in thread      Thread-2
java.io.IOException: Target log file already exists        (hdfs:///var/log/spark/apps/application_1457586850134_0001_2)
    at `org.apache.spark.scheduler.EventLoggingListener.stop(EventLoggingListener.scala:225)`

这是因为，在运行作业（第一个上下文）时，在hadoop中不需要存在目录/文件，因此对于运行第二个上下文，存在日志目录/日志文件 我是上面的错误。

我通过设置spark.eventLog.overwrite = true

解决了这个问题 
Error 2) WARN executor.CoarseGrainedExecutorBackend: An unknown (ip-10-93-141-13.ec2.internal:48849) driver disconnected. 16/03/10 06:47:37                                                            ERROR executor.CoarseGrainedExecutorBackend: Driver 10.93.141.13:48849disassociated! Shutting down.

我尝试增加

  

spark.yarn.driver.memoryOverhead = 1024

     

spark.yarn.executor.memoryOverhead = 1024

但问题仍然存在。

错误3）

Exception in thread "main" java.io.IOException: Failed to connect to /10.93.141.13:46008
    at org.apache.spark.network.client.TransportClientFactory.createClient(TransportClientFactory.java:216)
    at org.apache.spark.network.client.TransportClientFactory.createClient(TransportClientFactory.java:167)
    at org.apache.spark.rpc.netty.NettyRpcEnv.createClient(NettyRpcEnv.scala:200)
    at org.apache.spark.rpc.netty.Outbox$$anon$1.call(Outbox.scala:187)
    at org.apache.spark.rpc.netty.Outbox$$anon$1.call(Outbox.scala:183)
    at java.util.concurrent.FutureTask.run(FutureTask.java:262)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
    at java.lang.Thread.run(Thread.java:745)

我已在核心节点中检入，它没有侦听端口46008。

错误4）

WARN YarnAllocator: Container marked as failed:              container_1457586850134_0006_01_000006 on host: ip-10-164-169-         46.ec2.internal. Exit status: 1. Diagnostics: Exception from container-   launch.
Container id: container_1457586850134_0006_01_000006
Exit code: 1
Stack trace: ExitCodeException exitCode=1:
at org.apache.hadoop.util.Shell.runCommand(Shell.java:545)
at org.apache.hadoop.util.Shell.run(Shell.java:456)
at   org.apache.hadoop.util.Shell$ShellCommandExecutor.execute(Shell.java:722)
at org.apache.hadoop.yarn.server.nodemanager.DefaultContainerExecutor.launchContainer(DefaultContainerExecutor.java:211)
at org.apache.hadoop.yarn.server.nodemanager.containermanager.launcher.ContainerLaunch.call(ContainerLaunch.java:302)
at org.apache.hadoop.yarn.server.nodemanager.containermanager.launcher.ContainerLaunch.call(ContainerLaunch.java:82)
at java.util.concurrent.FutureTask.run(FutureTask.java:262)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)


Container exited with a non-zero exit code 1

16/03/10 06:47:17 WARN ApplicationMaster: Reporter thread fails 1 time(s) in a row.
java.lang.IllegalStateException: RpcEnv already stopped.
at org.apache.spark.rpc.netty.Dispatcher.postMessage(Dispatcher.scala:159)
    at org.apache.spark.rpc.netty.Dispatcher.postOneWayMessage(Dispatcher.scala:131)
    at org.apache.spark.rpc.netty.NettyRpcEnv.send(NettyRpcEnv.scala:192)
    at org.apache.spark.rpc.netty.NettyRpcEndpointRef.send(NettyRpcEnv.scala:516)
    at org.apache.spark.deploy.yarn.YarnAllocator$$anonfun$processCompletedContainers$1$$anonfun$apply$7.apply(YarnAllocator.scala:531)
    at org.apache.spark.deploy.yarn.YarnAllocator$$anonfun$processCompletedContainers$1$$anonfun$apply$7.apply(YarnAllocator.scala:512)
    at scala.Option.foreach(Option.scala:236)
    at org.apache.spark.deploy.yarn.YarnAllocator$$anonfun$processCompletedContainers$1.apply(YarnAllocator.scala:512)
    at org.apache.spark.deploy.yarn.YarnAllocator$$anonfun$processCompletedContainers$1.apply(YarnAllocator.scala:442)
    at scala.collection.Iterator$class.foreach(Iterator.scala:727)
    at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
    at scala.collection.IterableLike$class.foreach(IterableLike.scala:72)
    at scala.collection.AbstractIterable.foreach(Iterable.scala:54)
    at org.apache.spark.deploy.yarn.YarnAllocator.processCompletedContainers(YarnAllocator.scala:442)
    at org.apache.spark.deploy.yarn.YarnAllocator.allocateResources(YarnAllocator.scala:242)
    at org.apache.spark.deploy.yarn.ApplicationMaster$$anon$1.run(ApplicationMaster.scala:368)

上述错误是由于容器连续失败。

上面的火花问题似乎是由于邮件列表中提出的多个上下文所致。

https://mail-archives.apache.org/mod_mbox/spark-issues/201602.mbox/%3CJIRA.12936774.1454603470000.316442.1454707659978@Atlassian.JIRA%3E

sparkContext.stop（）无法释放资源。

我正在使用以下选项

--class com.mobi.vserv.driver.Query5kPids                            
  --conf spark.eventLog.overwrite=true  
  --conf spark.yarn.executor.memoryOverhead=1024                       
  --conf spark.yarn.driver.memoryOverhead=1024                             
  --num-executors 4                
  --executor-memory 3g            
  --executor-cores 2                  
  --driver-memory 3g

我在EMR上运行，主机和2个从机节点，主机有8核和16GB内存，每个从机有4核5120 MB可用内存。

以下是我的代码。

    public class Query5kPids implements Serializable{

    static List<UserSetGet> ListFromS3 = new ArrayList<UserSetGet>();

    public static void main(String[] args) throws JSONException, IOException, InterruptedException, URISyntaxException {


    SparkConf conf = new SparkConf();
    conf.setAppName("Spark-Cassandra Integration");
    conf.setMaster("yarn-cluster");
    conf.set("spark.cassandra.connection.host", "12.16.193.19");
    conf.set("spark.cassandra.connection.port", "9042");


    SparkConf conf1 = new SparkConf().setAppName("SparkAutomation").setMaster("yarn-cluster");

    Query5kPids app1 = new Query5kPids(conf1);
    app1.run1(file);

    Query5kPids app = new Query5kPids(conf);
    System.out.println("Both RDD has been generated");
    app.run();

   }

   private void run() throws JSONException, IOException, InterruptedException {

   JavaSparkContext sc = new JavaSparkContext(conf);
   query(sc);
   sc.stop();
   }

   private void run1(File file) throws JSONException, IOException,     InterruptedException {
   JavaSparkContext sc = new JavaSparkContext(conf);
   getData(sc,file);
   sc.stop();

  }

   private void getData(JavaSparkContext sc, File file) {

   JavaRDD<String> Data = sc.textFile(file.toString());
   System.out.println("RDD Count is " + Data.count());

   // Other map opetations to convert to UserSetGet RDD.
   ListFromS3 = Data.collect();

   }
   private void query(JavaSparkContext sc) {

   System.out.prin`enter code here`tln("RDD Count is " +  ListFromS3.size());
   //This gets printed. 
   //Which means it application is coming to the second part of the program.

   for (int i = 0; i < ListFromS3.size(); i++) {

   sb.append(ListFromS3.get(i).getApnid());
   sb.append(',');
   }
   sb.setLength(sb.length() - 3);

   JavaRDD<CassandraRow> cassandraRDD = CassandraJavaUtil.javaFunctions(sc).cassandraTable("dmp", "user_profile_spark_test").select       ("app_day_count", "app_first_seen","app_last_seen", "app_usage_count", "total_day_count", "total_usage_count")
 .where("apnid IN ('" + sb + "')");

   if(cassandraRDD.isEmpty()){

   JavaRDD<UserSetGet> rddFromGz = sc.parallelize(ListFromS3);

   CassandraJavaUtil.javaFunctions(rddFromGz).writerBuilder("dmp", "user_profile_spark_test", mapToRow(UserSetGet.class)).saveToCassand();
        logger.info("DataSaved");
   }
   }

   }

以下是我的POM 

   <dependencies>
   <dependency>
   <groupId>org.apache-extras.cassandra-jdbc</groupId>
   <artifactId>cassandra-jdbc</artifactId>
   <version>1.2.5</version>
   </dependency>

   <dependency>
   <groupId>junit</groupId>
   <artifactId>junit</artifactId>
   <version>3.8.1</version>
   <scope>test</scope>
   </dependency>
   <dependency>
   <groupId>org.codehaus.jettison</groupId>
   <artifactId>jettison</artifactId>
   <version>1.3.7</version>
   </dependency>
   <dependency>
   <groupId>log4j</groupId>
   <artifactId>log4j</artifactId>
   <version>1.2.17</version>
   </dependency>


   <dependency>
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-core_2.10</artifactId>
   <version>1.6.0</version>
   </dependency>

   <dependency>
   <groupId>com.datastax.spark</groupId>
   <artifactId>spark-cassandra-connector_2.10</artifactId>
   <version>1.5.0-M1</version>
   </dependency>

   <dependency>
   <groupId>com.datastax.cassandra</groupId>
   <artifactId>cassandra-driver-core</artifactId>
   <version>2.1.6</version>
   </dependency>

   <dependency>
   <groupId>com.datastax.spark</groupId>
   <artifactId>spark-cassandra-connector-java_2.10</artifactId>
   <version>1.5.0-M3</version>
   </dependency>

   <dependency>
   <groupId>org.apache.commons</groupId>
   <artifactId>commons-collections4</artifactId>
   <version>4.1</version>
   </dependency>
   </dependencies>

1 个答案:

答案 0 :(得分：1)

我们正在使用本地spark上下文运行测试，并使用以下“hack”来解决碰撞问题

sc.stop（） //避免Akka重新绑定到同一个端口，因为它不会在关闭时立即取消绑定

System.clearProperty（ “spark.driver.port”）

您使用2种不同的火花环境的原因是什么？你为什么不能只使用1？

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