在IDEA中编写Spark的WordCount程序

1：spark shell仅在测试和验证我们的程序时使用的较多，在生产环境中，通常会在IDE中编制程序，然后打成jar包，然后提交到集群，最常用的是创建一个Maven项目，利用Maven来管理jar包的依赖。



2：配置Maven的pom.xml：

<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd"> <modelVersion>4.0.0</modelVersion>

<groupId>com.bie</groupId>
<artifactId>sparkWordCount</artifactId>
<version>1.0-SNAPSHOT</version>

<properties>
<maven.compiler.source>1.7</maven.compiler.source>
<maven.compiler.target>1.7</maven.compiler.target>
<encoding>UTF-8</encoding>
<scala.version>2.10.6</scala.version>
<scala.compat.version>2.10</scala.compat.version>
</properties>

<dependencies>
<dependency>
<groupId>org.scala-lang</groupId>
<artifactId>scala-library</artifactId>
<version>${scala.version}</version>
</dependency>

<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-core_2.10</artifactId>
<version>1.5.2</version>
</dependency>

<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-streaming_2.10</artifactId>
<version>1.5.2</version>
</dependency>

<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-client</artifactId>
<version>2.6.2</version>
</dependency>
</dependencies>

<build>
<sourceDirectory>src/main/scala</sourceDirectory>
<testSourceDirectory>src/test/scala</testSourceDirectory>
<plugins>
<plugin>
<groupId>net.alchim31.maven</groupId>
<artifactId>scala-maven-plugin</artifactId>
<version>3.2.0</version>
<executions>
<execution>
<goals>
<goal>compile</goal>
<goal>testCompile</goal>
</goals>
<configuration>
<args>
<arg>-make:transitive</arg>
<arg>-dependencyfile</arg>
<arg>${project.build.directory}/.scala_dependencies</arg>
</args>
</configuration>
</execution>
</executions>
</plugin>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-surefire-plugin</artifactId>
<version>2.18.1</version>
<configuration>
<useFile>false</useFile>
<disableXmlReport>true</disableXmlReport>
<includes>
<include>**/*Test.*</include>
<include>**/*Suite.*</include>
</includes>
</configuration>
</plugin>

<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-shade-plugin</artifactId>
<version>2.3</version>
<executions>
<execution>
<phase>package</phase>
<goals>
<goal>shade</goal>
</goals>
<configuration>
<filters>
<filter>
<artifact>*:*</artifact>
<excludes>
<exclude>META-INF/*.SF</exclude>
<exclude>META-INF/*.DSA</exclude>
<exclude>META-INF/*.RSA</exclude>
</excludes>
</filter>
</filters>
<transformers>
<transformer implementation="org.apache.maven.plugins.shade.resource.ManifestResourceTransformer">
<mainClass>com.bie.WordCount</mainClass>
</transformer>
</transformers>
</configuration>
</execution>
</executions>
</plugin>
</plugins>
</build>
</project>

注意：配置好pom.xml以后，点击Enable Auto-Import即可;

3：将src/main/java和src/test/java分别修改成src/main/scala和src/test/scala，与pom.xml中的配置保持一致（）；



4：新建一个scala class，类型为Object，然后编写spark程序，如下所示：

import org.apache.spark.{SparkConf, SparkContext}

object WordCount {

def main(args: Array[String]): Unit = {
//创建SparkConf()并且设置App的名称
val conf = new SparkConf().setAppName("wordCount");
//创建SparkContext,该对象是提交spark app的入口
val sc = new SparkContext(conf);
//使用sc创建rdd,并且执行相应的transformation和action
sc.textFile(args(0)).flatMap(_.split(" ")).map((_ ,1)).reduceByKey(_ + _,1).sortBy(_._2,false).saveAsTextFile(args(1));
//停止sc，结束该任务
sc.stop();
}
}

5：使用Maven打包：首先修改pom.xml中的mainClass，使其和自己的类路径对应起来：



然后，点击idea右侧的Maven Project选项，点击Lifecycle,选择clean和package，然后点击Run Maven Build：



等待编译完成，选择编译成功的jar包，并将该jar上传到Spark集群中的某个节点上：



记得，启动你的hdfs和Spark集群，然后使用spark-submit命令提交Spark应用（注意参数的顺序）：

可以看下简单的几行代码，但是打成的包就将近百兆，都是封装好的啊，感觉牛人太多了。



然后开始进行Spark Submit提交操作，命令如下所示：

[root@master spark-1.6.1-bin-hadoop2.6]# bin/spark-submit \
> --class com.bie.WordCount \
> --master spark://master:7077 \
> --executor-memory 512M \
> --total-executor-cores 2 \
> /home/hadoop/data_hadoop/sparkWordCount-1.0-SNAPSHOT.jar \
> hdfs://master:9000/wordcount.txt \
> hdfs://master:9000/output

或者如下：
bin/spark-submit --class com.bie.WordCount --master spark://master:7077 --executor-memory 512M --total-executor-cores 2 /home/hadoop/data_hadoop/sparkWordCount-1.0-SNAPSHOT.jar hdfs://master:9000/wordcount.txt hdfs://master:9000/outpu

操作如下所示：



可以在图形化页面看到多了一个Application：



然后呢，就出错了，学知识，不出点错，感觉都不正常：

org.apache.spark.rpc.RpcTimeoutException: Futures timed out after [120 seconds]. This timeout is controlled by spark.rpc.askTimeout
at org.apache.spark.rpc.RpcTimeout.org$apache$spark$rpc$RpcTimeout$$createRpcTimeoutException(RpcTimeout.scala:48)
at org.apache.spark.rpc.RpcTimeout$$anonfun$addMessageIfTimeout$1.applyOrElse(RpcTimeout.scala:63)
at org.apache.spark.rpc.RpcTimeout$$anonfun$addMessageIfTimeout$1.applyOrElse(RpcTimeout.scala:59)
at scala.runtime.AbstractPartialFunction.apply(AbstractPartialFunction.scala:33)
at org.apache.spark.rpc.RpcTimeout.awaitResult(RpcTimeout.scala:76)
at org.apache.spark.rpc.RpcEndpointRef.askWithRetry(RpcEndpointRef.scala:101)
at org.apache.spark.rpc.RpcEndpointRef.askWithRetry(RpcEndpointRef.scala:77)
at org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend.removeExecutor(CoarseGrainedSchedulerBackend.scala:359)
at org.apache.spark.scheduler.cluster.SparkDeploySchedulerBackend.executorRemoved(SparkDeploySchedulerBackend.scala:144)
at org.apache.spark.deploy.client.AppClient$ClientEndpoint$$anonfun$receive$1.applyOrElse(AppClient.scala:186)
at org.apache.spark.rpc.netty.Inbox$$anonfun$process$1.apply$mcV$sp(Inbox.scala:116)
at org.apache.spark.rpc.netty.Inbox.safelyCall(Inbox.scala:204)
at org.apache.spark.rpc.netty.Inbox.process(Inbox.scala:100)
at org.apache.spark.rpc.netty.Dispatcher$MessageLoop.run(Dispatcher.scala:215)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)
Caused by: java.util.concurrent.TimeoutException: Futures timed out after [120 seconds]
at scala.concurrent.impl.Promise$DefaultPromise.ready(Promise.scala:219)
at scala.concurrent.impl.Promise$DefaultPromise.result(Promise.scala:223)
at scala.concurrent.Await$$anonfun$result$1.apply(package.scala:107)
at scala.concurrent.BlockContext$DefaultBlockContext$.blockOn(BlockContext.scala:53)
at scala.concurrent.Await$.result(package.scala:107)
at org.apache.spark.rpc.RpcTimeout.awaitResult(RpcTimeout.scala:75)
... 12 more
18/02/23 01:28:46 WARN NettyRpcEndpointRef: Error sending message [message = UpdateBlockInfo(BlockManagerId(driver, 192.168.3.129, 60565),broadcast_1_piece0,StorageLevel(false, true, false, false, 1),2358,0,0)] in 1 attempts
org.apache.spark.rpc.RpcTimeoutException: Cannot receive any reply in 120 seconds. This timeout is controlled by spark.rpc.askTimeout
at org.apache.spark.rpc.RpcTimeout.org$apache$spark$rpc$RpcTimeout$$createRpcTimeoutException(RpcTimeout.scala:48)
at org.apache.spark.rpc.RpcTimeout$$anonfun$addMessageIfTimeout$1.applyOrElse(RpcTimeout.scala:63)
at org.apache.spark.rpc.RpcTimeout$$anonfun$addMessageIfTimeout$1.applyOrElse(RpcTimeout.scala:59)
at scala.runtime.AbstractPartialFunction.apply(AbstractPartialFunction.scala:33)
at scala.util.Failure$$anonfun$recover$1.apply(Try.scala:185)
at scala.util.Try$.apply(Try.scala:161)
at scala.util.Failure.recover(Try.scala:185)
at scala.concurrent.Future$$anonfun$recover$1.apply(Future.scala:324)
at scala.concurrent.Future$$anonfun$recover$1.apply(Future.scala:324)
at scala.concurrent.impl.CallbackRunnable.run(Promise.scala:32)
at org.spark-project.guava.util.concurrent.MoreExecutors$SameThreadExecutorService.execute(MoreExecutors.java:293)
at scala.concurrent.impl.ExecutionContextImpl$$anon$1.execute(ExecutionContextImpl.scala:133)
at scala.concurrent.impl.CallbackRunnable.executeWithValue(Promise.scala:40)
at scala.concurrent.impl.Promise$DefaultPromise.tryComplete(Promise.scala:248)
at scala.concurrent.Promise$class.complete(Promise.scala:55)
at scala.concurrent.impl.Promise$DefaultPromise.complete(Promise.scala:153)
at scala.concurrent.Future$$anonfun$map$1.apply(Future.scala:235)
at scala.concurrent.Future$$anonfun$map$1.apply(Future.scala:235)
at scala.concurrent.impl.CallbackRunnable.run(Promise.scala:32)
at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.processBatch$1(Future.scala:643)
at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.apply$mcV$sp(Future.scala:658)
at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.apply(Future.scala:635)
at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.apply(Future.scala:635)
at scala.concurrent.BlockContext$.withBlockContext(BlockContext.scala:72)
at scala.concurrent.Future$InternalCallbackExecutor$Batch.run(Future.scala:634)
at scala.concurrent.Future$InternalCallbackExecutor$.scala$concurrent$Future$InternalCallbackExecutor$$unbatchedExecute(Future.scala:694)
at scala.concurrent.Future$InternalCallbackExecutor$.execute(Future.scala:685)
at scala.concurrent.impl.CallbackRunnable.executeWithValue(Promise.scala:40)
at scala.concurrent.impl.Promise$DefaultPromise.tryComplete(Promise.scala:248)
at scala.concurrent.Promise$class.tryFailure(Promise.scala:112)
at scala.concurrent.impl.Promise$DefaultPromise.tryFailure(Promise.scala:153)
at org.apache.spark.rpc.netty.NettyRpcEnv$$anon$1.run(NettyRpcEnv.scala:241)
at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:471)
at java.util.concurrent.FutureTask.run(FutureTask.java:262)
at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.access$201(ScheduledThreadPoolExecutor.java:178)
at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:292)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)
Caused by: java.util.concurrent.TimeoutException: Cannot receive any reply in 120 seconds
at org.apache.spark.rpc.netty.NettyRpcEnv$$anon$1.run(NettyRpcEnv.scala:242)
... 7 more

18/02/23 01:30:48 WARN NettyRpcEndpointRef: Error sending message [message = RemoveExecutor(0,Command exited with code 1)] in 2 attempts
org.apache.spark.rpc.RpcTimeoutException: Cannot receive any reply in 120 seconds. This timeout is controlled by spark.rpc.askTimeout
at org.apache.spark.rpc.RpcTimeout.org$apache$spark$rpc$RpcTimeout$$createRpcTimeoutException(RpcTimeout.scala:48)
at org.apache.spark.rpc.RpcTimeout$$anonfun$addMessageIfTimeout$1.applyOrElse(RpcTimeout.scala:63)
at org.apache.spark.rpc.RpcTimeout$$anonfun$addMessageIfTimeout$1.applyOrElse(RpcTimeout.scala:59)
at scala.runtime.AbstractPartialFunction.apply(AbstractPartialFunction.scala:33)
at scala.util.Failure$$anonfun$recover$1.apply(Try.scala:185)
at scala.util.Try$.apply(Try.scala:161)
at scala.util.Failure.recover(Try.scala:185)
at scala.concurrent.Future$$anonfun$recover$1.apply(Future.scala:324)
at scala.concurrent.Future$$anonfun$recover$1.apply(Future.scala:324)
at scala.concurrent.impl.CallbackRunnable.run(Promise.scala:32)
at org.spark-project.guava.util.concurrent.MoreExecutors$SameThreadExecutorService.execute(MoreExecutors.java:293)
at scala.concurrent.impl.ExecutionContextImpl$$anon$1.execute(ExecutionContextImpl.scala:133)
at scala.concurrent.impl.CallbackRunnable.executeWithValue(Promise.scala:40)
at scala.concurrent.impl.Promise$DefaultPromise.tryComplete(Promise.scala:248)
at scala.concurrent.Promise$class.complete(Promise.scala:55)
at scala.concurrent.impl.Promise$DefaultPromise.complete(Promise.scala:153)
at scala.concurrent.Future$$anonfun$map$1.apply(Future.scala:235)
at scala.concurrent.Future$$anonfun$map$1.apply(Future.scala:235)
at scala.concurrent.impl.CallbackRunnable.run(Promise.scala:32)
at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.processBatch$1(Future.scala:643)
at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.apply$mcV$sp(Future.scala:658)
at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.apply(Future.scala:635)
at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.apply(Future.scala:635)
at scala.concurrent.BlockContext$.withBlockContext(BlockContext.scala:72)
at scala.concurrent.Future$InternalCallbackExecutor$Batch.run(Future.scala:634)
at scala.concurrent.Future$InternalCallbackExecutor$.scala$concurrent$Future$InternalCallbackExecutor$$unbatchedExecute(Future.scala:694)
at scala.concurrent.Future$InternalCallbackExecutor$.execute(Future.scala:685)
at scala.concurrent.impl.CallbackRunnable.executeWithValue(Promise.scala:40)
at scala.concurrent.impl.Promise$DefaultPromise.tryComplete(Promise.scala:248)
at scala.concurrent.Promise$class.tryFailure(Promise.scala:112)
at scala.concurrent.impl.Promise$DefaultPromise.tryFailure(Promise.scala:153)
at org.apache.spark.rpc.netty.NettyRpcEnv$$anon$1.run(NettyRpcEnv.scala:241)
at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:471)
at java.util.concurrent.FutureTask.run(FutureTask.java:262)
at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.access$201(ScheduledThreadPoolExecutor.java:178)
at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:292)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)
Caused by: java.util.concurrent.TimeoutException: Cannot receive any reply in 120 seconds
at org.apache.spark.rpc.netty.NettyRpcEnv$$anon$1.run(NettyRpcEnv.scala:242)
... 7 more
18/02/23 01:30:49 WARN NettyRpcEndpointRef: Error sending message [message = UpdateBlockInfo(BlockManagerId(driver, 192.168.3.129, 60565),broadcast_1_piece0,StorageLevel(false, true, false, false, 1),2358,0,0)] in 2 attempts
org.apache.spark.rpc.RpcTimeoutException: Cannot receive any reply in 120 seconds. This timeout is controlled by spark.rpc.askTimeout
at org.apache.spark.rpc.RpcTimeout.org$apache$spark$rpc$RpcTimeout$$createRpcTimeoutException(RpcTimeout.scala:48)
at org.apache.spark.rpc.RpcTimeout$$anonfun$addMessageIfTimeout$1.applyOrElse(RpcTimeout.scala:63)
at org.apache.spark.rpc.RpcTimeout$$anonfun$addMessageIfTimeout$1.applyOrElse(RpcTimeout.scala:59)
at scala.runtime.AbstractPartialFunction.apply(AbstractPartialFunction.scala:33)
at scala.util.Failure$$anonfun$recover$1.apply(Try.scala:185)
at scala.util.Try$.apply(Try.scala:161)
at scala.util.Failure.recover(Try.scala:185)
at scala.concurrent.Future$$anonfun$recover$1.apply(Future.scala:324)
at scala.concurrent.Future$$anonfun$recover$1.apply(Future.scala:324)
at scala.concurrent.impl.CallbackRunnable.run(Promise.scala:32)
at org.spark-project.guava.util.concurrent.MoreExecutors$SameThreadExecutorService.execute(MoreExecutors.java:293)
at scala.concurrent.impl.ExecutionContextImpl$$anon$1.execute(ExecutionContextImpl.scala:133)
at scala.concurrent.impl.CallbackRunnable.executeWithValue(Promise.scala:40)
at scala.concurrent.impl.Promise$DefaultPromise.tryComplete(Promise.scala:248)
at scala.concurrent.Promise$class.complete(Promise.scala:55)
at scala.concurrent.impl.Promise$DefaultPromise.complete(Promise.scala:153)
at scala.concurrent.Future$$anonfun$map$1.apply(Future.scala:235)
at scala.concurrent.Future$$anonfun$map$1.apply(Future.scala:235)
at scala.concurrent.impl.CallbackRunnable.run(Promise.scala:32)
at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.processBatch$1(Future.scala:643)
at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.apply$mcV$sp(Future.scala:658)
at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.apply(Future.scala:635)
at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.apply(Future.scala:635)
at scala.concurrent.BlockContext$.withBlockContext(BlockContext.scala:72)
at scala.concurrent.Future$InternalCallbackExecutor$Batch.run(Future.scala:634)
at scala.concurrent.Future$InternalCallbackExecutor$.scala$concurrent$Future$InternalCallbackExecutor$$unbatchedExecute(Future.scala:694)
at scala.concurrent.Future$InternalCallbackExecutor$.execute(Future.scala:685)
at scala.concurrent.impl.CallbackRunnable.executeWithValue(Promise.scala:40)
at scala.concurrent.impl.Promise$DefaultPromise.tryComplete(Promise.scala:248)
at scala.concurrent.Promise$class.tryFailure(Promise.scala:112)
at scala.concurrent.impl.Promise$DefaultPromise.tryFailure(Promise.scala:153)
at org.apache.spark.rpc.netty.NettyRpcEnv$$anon$1.run(NettyRpcEnv.scala:241)
at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:471)
at java.util.concurrent.FutureTask.run(FutureTask.java:262)
at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.access$201(ScheduledThreadPoolExecutor.java:178)
at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:292)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)
Caused by: java.util.concurrent.TimeoutException: Cannot receive any reply in 120 seconds
at org.apache.spark.rpc.netty.NettyRpcEnv$$anon$1.run(NettyRpcEnv.scala:242)
... 7 more

解决思路，百度了一下，也没缕出思路，就只知道是连接超时了，超过了120s，然后呢，我感觉是自己内存设置小了，因为开的虚拟机，主机8G，三台虚拟机，每台分了1G内存，然后设置Spark可以占用800M，跑程序的时候，第一次设置为512M，就连接超时了，第二次设置为了700M，顺利跑完，可以看看跑的过程，还是很有意思的：

[root@master hadoop]# bin/spark-submit --class com.bie.WordCount --master spark://master:7077 --executor-memory 700M --total-executor-cores 2 /home/hadoop/data_hadoop/sparkWordCount-1.0-SNAPSHOT.jar hdfs://master:9000/wordcount.txt hdfs://master:9000/output
bash: bin/spark-submit: No such file or directory
[root@master hadoop]# cd spark-1.6.1-bin-hadoop2.6/
[root@master spark-1.6.1-bin-hadoop2.6]# bin/spark-submit --class com.bie.WordCount --master spark://master:7077 --executor-memory 700M --total-executor-cores 2 /home/hadoop/data_hadoop/sparkWordCount-1.0-SNAPSHOT.jar hdfs://master:9000/wordcount.txt hdfs://master:9000/output
18/02/23 01:45:46 INFO SparkContext: Running Spark version 1.6.1
18/02/23 01:45:47 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
18/02/23 01:45:47 INFO SecurityManager: Changing view acls to: root
18/02/23 01:45:47 INFO SecurityManager: Changing modify acls to: root
18/02/23 01:45:47 INFO SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(root); users with modify permissions: Set(root)
18/02/23 01:45:48 INFO Utils: Successfully started service 'sparkDriver' on port 60097.
18/02/23 01:45:49 INFO Slf4jLogger: Slf4jLogger started
18/02/23 01:45:49 INFO Remoting: Starting remoting
18/02/23 01:45:49 INFO Remoting: Remoting started; listening on addresses :[akka.tcp://sparkDriverActorSystem@192.168.3.129:55353]
18/02/23 01:45:49 INFO Utils: Successfully started service 'sparkDriverActorSystem' on port 55353.
18/02/23 01:45:49 INFO SparkEnv: Registering MapOutputTracker
18/02/23 01:45:49 INFO SparkEnv: Registering BlockManagerMaster
18/02/23 01:45:49 INFO DiskBlockManager: Created local directory at /tmp/blockmgr-17e41a67-b880-4c06-95eb-a0f64928f668
18/02/23 01:45:49 INFO MemoryStore: MemoryStore started with capacity 517.4 MB
18/02/23 01:45:49 INFO SparkEnv: Registering OutputCommitCoordinator
18/02/23 01:45:50 INFO Utils: Successfully started service 'SparkUI' on port 4040.
18/02/23 01:45:50 INFO SparkUI: Started SparkUI at http://192.168.3.129:4040 18/02/23 01:45:50 INFO HttpFileServer: HTTP File server directory is /tmp/spark-99c897ab-ea17-4797-8695-3a5df89ed490/httpd-f346e1dd-642d-437d-8947-6190f2e83065
18/02/23 01:45:50 INFO HttpServer: Starting HTTP Server
18/02/23 01:45:50 INFO Utils: Successfully started service 'HTTP file server' on port 35900.
18/02/23 01:45:51 INFO SparkContext: Added JAR file:/home/hadoop/data_hadoop/sparkWordCount-1.0-SNAPSHOT.jar at http://192.168.3.129:35900/jars/sparkWordCount-1.0-SNAPSHOT.jar with timestamp 1519379151547
18/02/23 01:45:52 INFO AppClient$ClientEndpoint: Connecting to master spark://master:7077...
18/02/23 01:45:52 INFO SparkDeploySchedulerBackend: Connected to Spark cluster with app ID app-20180223014552-0004
18/02/23 01:45:52 INFO Utils: Successfully started service 'org.apache.spark.network.netty.NettyBlockTransferService' on port 37128.
18/02/23 01:45:52 INFO NettyBlockTransferService: Server created on 37128
18/02/23 01:45:52 INFO BlockManagerMaster: Trying to register BlockManager
18/02/23 01:45:52 INFO AppClient$ClientEndpoint: Executor added: app-20180223014552-0004/0 on worker-20180223110813-192.168.3.131-55934 (192.168.3.131:55934) with 1 cores
18/02/23 01:45:52 INFO SparkDeploySchedulerBackend: Granted executor ID app-20180223014552-0004/0 on hostPort 192.168.3.131:55934 with 1 cores, 700.0 MB RAM
18/02/23 01:45:52 INFO AppClient$ClientEndpoint: Executor added: app-20180223014552-0004/1 on worker-20180223110811-192.168.3.130-40991 (192.168.3.130:40991) with 1 cores
18/02/23 01:45:52 INFO SparkDeploySchedulerBackend: Granted executor ID app-20180223014552-0004/1 on hostPort 192.168.3.130:40991 with 1 cores, 700.0 MB RAM
18/02/23 01:45:52 INFO BlockManagerMasterEndpoint: Registering block manager 192.168.3.129:37128 with 517.4 MB RAM, BlockManagerId(driver, 192.168.3.129, 37128)
18/02/23 01:45:52 INFO BlockManagerMaster: Registered BlockManager
18/02/23 01:45:52 INFO AppClient$ClientEndpoint: Executor updated: app-20180223014552-0004/1 is now RUNNING
18/02/23 01:45:52 INFO AppClient$ClientEndpoint: Executor updated: app-20180223014552-0004/0 is now RUNNING
18/02/23 01:45:53 INFO SparkDeploySchedulerBackend: SchedulerBackend is ready for scheduling beginning after reached minRegisteredResourcesRatio: 0.0
18/02/23 01:45:53 WARN SizeEstimator: Failed to check whether UseCompressedOops is set; assuming yes
18/02/23 01:45:53 INFO MemoryStore: Block broadcast_0 stored as values in memory (estimated size 146.7 KB, free 146.7 KB)
18/02/23 01:45:53 INFO MemoryStore: Block broadcast_0_piece0 stored as bytes in memory (estimated size 13.9 KB, free 160.6 KB)
18/02/23 01:45:53 INFO BlockManagerInfo: Added broadcast_0_piece0 in memory on 192.168.3.129:37128 (size: 13.9 KB, free: 517.4 MB)
18/02/23 01:45:53 INFO SparkContext: Created broadcast 0 from textFile at WordCount.scala:13
Java HotSpot(TM) Client VM warning: You have loaded library /tmp/libnetty-transport-native-epoll4006421548933729587.so which might have disabled stack guard. The VM will try to fix the stack guard now.
It's highly recommended that you fix the library with 'execstack -c <libfile>', or link it with '-z noexecstack'.
18/02/23 01:45:55 INFO SparkDeploySchedulerBackend: Registered executor NettyRpcEndpointRef(null) (slaver1:60082) with ID 1
18/02/23 01:45:55 INFO BlockManagerMasterEndpoint: Registering block manager slaver1:56633 with 282.5 MB RAM, BlockManagerId(1, slaver1, 56633)
18/02/23 01:45:56 INFO deprecation: mapred.tip.id is deprecated. Instead, use mapreduce.task.id
18/02/23 01:45:56 INFO deprecation: mapred.task.id is deprecated. Instead, use mapreduce.task.attempt.id
18/02/23 01:45:56 INFO deprecation: mapred.task.is.map is deprecated. Instead, use mapreduce.task.ismap
18/02/23 01:45:56 INFO deprecation: mapred.task.partition is deprecated. Instead, use mapreduce.task.partition
18/02/23 01:45:56 INFO deprecation: mapred.job.id is deprecated. Instead, use mapreduce.job.id
18/02/23 01:45:57 INFO SparkDeploySchedulerBackend: Registered executor NettyRpcEndpointRef(null) (slaver2:46908) with ID 0
18/02/23 01:45:57 INFO SparkContext: Starting job: saveAsTextFile at WordCount.scala:13
18/02/23 01:45:58 INFO FileInputFormat: Total input paths to process : 1
18/02/23 01:45:58 INFO BlockManagerMasterEndpoint: Registering block manager slaver2:36572 with 282.5 MB RAM, BlockManagerId(0, slaver2, 36572)
18/02/23 01:45:58 INFO DAGScheduler: Registering RDD 3 (map at WordCount.scala:13)
18/02/23 01:45:58 INFO DAGScheduler: Registering RDD 5 (sortBy at WordCount.scala:13)
18/02/23 01:45:58 INFO DAGScheduler: Got job 0 (saveAsTextFile at WordCount.scala:13) with 1 output partitions
18/02/23 01:45:58 INFO DAGScheduler: Final stage: ResultStage 2 (saveAsTextFile at WordCount.scala:13)
18/02/23 01:45:58 INFO DAGScheduler: Parents of final stage: List(ShuffleMapStage 1)
18/02/23 01:45:58 INFO DAGScheduler: Missing parents: List(ShuffleMapStage 1)
18/02/23 01:45:58 INFO DAGScheduler: Submitting ShuffleMapStage 0 (MapPartitionsRDD[3] at map at WordCount.scala:13), which has no missing parents
18/02/23 01:45:59 INFO MemoryStore: Block broadcast_1 stored as values in memory (estimated size 4.1 KB, free 164.7 KB)
18/02/23 01:45:59 INFO MemoryStore: Block broadcast_1_piece0 stored as bytes in memory (estimated size 2.3 KB, free 167.0 KB)
18/02/23 01:45:59 INFO BlockManagerInfo: Added broadcast_1_piece0 in memory on 192.168.3.129:37128 (size: 2.3 KB, free: 517.4 MB)
18/02/23 01:45:59 INFO SparkContext: Created broadcast 1 from broadcast at DAGScheduler.scala:1006
18/02/23 01:45:59 INFO DAGScheduler: Submitting 2 missing tasks from ShuffleMapStage 0 (MapPartitionsRDD[3] at map at WordCount.scala:13)
18/02/23 01:45:59 INFO TaskSchedulerImpl: Adding task set 0.0 with 2 tasks
18/02/23 01:45:59 INFO TaskSetManager: Starting task 0.0 in stage 0.0 (TID 0, slaver2, partition 0,NODE_LOCAL, 2196 bytes)
18/02/23 01:45:59 INFO TaskSetManager: Starting task 1.0 in stage 0.0 (TID 1, slaver1, partition 1,NODE_LOCAL, 2196 bytes)
18/02/23 01:47:19 INFO BlockManagerInfo: Added broadcast_1_piece0 in memory on slaver1:56633 (size: 2.3 KB, free: 282.5 MB)
18/02/23 01:47:21 INFO BlockManagerInfo: Added broadcast_0_piece0 in memory on slaver1:56633 (size: 13.9 KB, free: 282.5 MB)
18/02/23 01:47:22 INFO BlockManagerInfo: Added broadcast_1_piece0 in memory on slaver2:36572 (size: 2.3 KB, free: 282.5 MB)
18/02/23 01:47:29 INFO BlockManagerInfo: Added broadcast_0_piece0 in memory on slaver2:36572 (size: 13.9 KB, free: 282.5 MB)
18/02/23 01:47:36 INFO TaskSetManager: Finished task 1.0 in stage 0.0 (TID 1) in 97388 ms on slaver1 (1/2)
18/02/23 01:48:19 INFO DAGScheduler: ShuffleMapStage 0 (map at WordCount.scala:13) finished in 140.390 s
18/02/23 01:48:19 INFO DAGScheduler: looking for newly runnable stages
18/02/23 01:48:19 INFO DAGScheduler: running: Set()
18/02/23 01:48:19 INFO TaskSetManager: Finished task 0.0 in stage 0.0 (TID 0) in 140387 ms on slaver2 (2/2)
18/02/23 01:48:19 INFO TaskSchedulerImpl: Removed TaskSet 0.0, whose tasks have all completed, from pool
18/02/23 01:48:19 INFO DAGScheduler: waiting: Set(ShuffleMapStage 1, ResultStage 2)
18/02/23 01:48:19 INFO DAGScheduler: failed: Set()
18/02/23 01:48:19 INFO DAGScheduler: Submitting ShuffleMapStage 1 (MapPartitionsRDD[5] at sortBy at WordCount.scala:13), which has no missing parents
18/02/23 01:48:19 INFO MemoryStore: Block broadcast_2 stored as values in memory (estimated size 3.5 KB, free 170.5 KB)
18/02/23 01:48:19 INFO MemoryStore: Block broadcast_2_piece0 stored as bytes in memory (estimated size 2.0 KB, free 172.5 KB)
18/02/23 01:48:19 INFO BlockManagerInfo: Added broadcast_2_piece0 in memory on 192.168.3.129:37128 (size: 2.0 KB, free: 517.4 MB)
18/02/23 01:48:19 INFO SparkContext: Created broadcast 2 from broadcast at DAGScheduler.scala:1006
18/02/23 01:48:19 INFO DAGScheduler: Submitting 1 missing tasks from ShuffleMapStage 1 (MapPartitionsRDD[5] at sortBy at WordCount.scala:13)
18/02/23 01:48:19 INFO TaskSchedulerImpl: Adding task set 1.0 with 1 tasks
18/02/23 01:48:19 INFO TaskSetManager: Starting task 0.0 in stage 1.0 (TID 2, slaver2, partition 0,NODE_LOCAL, 1956 bytes)
18/02/23 01:48:19 INFO BlockManagerInfo: Added broadcast_2_piece0 in memory on slaver2:36572 (size: 2.0 KB, free: 282.5 MB)
18/02/23 01:48:19 INFO MapOutputTrackerMasterEndpoint: Asked to send map output locations for shuffle 1 to slaver2:46908
18/02/23 01:48:19 INFO MapOutputTrackerMaster: Size of output statuses for shuffle 1 is 156 bytes
18/02/23 01:48:20 INFO DAGScheduler: ShuffleMapStage 1 (sortBy at WordCount.scala:13) finished in 0.708 s
18/02/23 01:48:20 INFO DAGScheduler: looking for newly runnable stages
18/02/23 01:48:20 INFO DAGScheduler: running: Set()
18/02/23 01:48:20 INFO DAGScheduler: waiting: Set(ResultStage 2)
18/02/23 01:48:20 INFO DAGScheduler: failed: Set()
18/02/23 01:48:20 INFO DAGScheduler: Submitting ResultStage 2 (MapPartitionsRDD[8] at saveAsTextFile at WordCount.scala:13), which has no missing parents
18/02/23 01:48:20 INFO TaskSetManager: Finished task 0.0 in stage 1.0 (TID 2) in 711 ms on slaver2 (1/1)
18/02/23 01:48:20 INFO TaskSchedulerImpl: Removed TaskSet 1.0, whose tasks have all completed, from pool
18/02/23 01:48:20 INFO MemoryStore: Block broadcast_3 stored as values in memory (estimated size 65.1 KB, free 237.6 KB)
18/02/23 01:48:20 INFO MemoryStore: Block broadcast_3_piece0 stored as bytes in memory (estimated size 22.6 KB, free 260.2 KB)
18/02/23 01:48:20 INFO BlockManagerInfo: Added broadcast_3_piece0 in memory on 192.168.3.129:37128 (size: 22.6 KB, free: 517.4 MB)
18/02/23 01:48:20 INFO SparkContext: Created broadcast 3 from broadcast at DAGScheduler.scala:1006
18/02/23 01:48:20 INFO DAGScheduler: Submitting 1 missing tasks from ResultStage 2 (MapPartitionsRDD[8] at saveAsTextFile at WordCount.scala:13)
18/02/23 01:48:20 INFO TaskSchedulerImpl: Adding task set 2.0 with 1 tasks
18/02/23 01:48:20 INFO TaskSetManager: Starting task 0.0 in stage 2.0 (TID 3, slaver2, partition 0,NODE_LOCAL, 1967 bytes)
18/02/23 01:48:20 INFO BlockManagerInfo: Added broadcast_3_piece0 in memory on slaver2:36572 (size: 22.6 KB, free: 282.5 MB)
18/02/23 01:48:20 INFO MapOutputTrackerMasterEndpoint: Asked to send map output locations for shuffle 0 to slaver2:46908
18/02/23 01:48:20 INFO MapOutputTrackerMaster: Size of output statuses for shuffle 0 is 136 bytes
18/02/23 01:48:25 INFO DAGScheduler: ResultStage 2 (saveAsTextFile at WordCount.scala:13) finished in 5.008 s
18/02/23 01:48:25 INFO TaskSetManager: Finished task 0.0 in stage 2.0 (TID 3) in 5012 ms on slaver2 (1/1)
18/02/23 01:48:25 INFO TaskSchedulerImpl: Removed TaskSet 2.0, whose tasks have all completed, from pool
18/02/23 01:48:25 INFO DAGScheduler: Job 0 finished: saveAsTextFile at WordCount.scala:13, took 147.737606 s
18/02/23 01:48:26 INFO SparkUI: Stopped Spark web UI at http://192.168.3.129:4040 18/02/23 01:48:26 INFO SparkDeploySchedulerBackend: Shutting down all executors
18/02/23 01:48:26 INFO SparkDeploySchedulerBackend: Asking each executor to shut down
18/02/23 01:48:26 INFO MapOutputTrackerMasterEndpoint: MapOutputTrackerMasterEndpoint stopped!
18/02/23 01:48:26 INFO MemoryStore: MemoryStore cleared
18/02/23 01:48:26 INFO BlockManager: BlockManager stopped
18/02/23 01:48:26 INFO BlockManagerMaster: BlockManagerMaster stopped
18/02/23 01:48:26 INFO OutputCommitCoordinator$OutputCommitCoordinatorEndpoint: OutputCommitCoordinator stopped!
18/02/23 01:48:26 INFO SparkContext: Successfully stopped SparkContext
18/02/23 01:48:26 INFO ShutdownHookManager: Shutdown hook called
18/02/23 01:48:26 INFO ShutdownHookManager: Deleting directory /tmp/spark-99c897ab-ea17-4797-8695-3a5df89ed490
18/02/23 01:48:26 INFO RemoteActorRefProvider$RemotingTerminator: Shutting down remote daemon.
18/02/23 01:48:26 INFO RemoteActorRefProvider$RemotingTerminator: Remote daemon shut down; proceeding with flushing remote transports.
18/02/23 01:48:26 INFO ShutdownHookManager: Deleting directory /tmp/spark-99c897ab-ea17-4797-8695-3a5df89ed490/httpd-f346e1dd-642d-437d-8947-6190f2e83065
[root@master spark-1.6.1-bin-hadoop2.6]#

最后查看执行结果即可（由于第一次跑失败了，作为强迫症的我就把第一次的输出结果文件删除了）：