eclipse开发spark程序配置在集群上运行

这篇bolg讲一下，IDE开发的spark程序如何提交到集群上运行。
首先保证你的集群是运行成功的，集群搭建可以参考[url]http://kevin12.iteye.com/blog/2273556[/url]

开发集群测试的spark wordcount程序；
[b]1.hdfs数据准备.[/b]
先将README.md文件上传到hdfs上的/library/wordcount/input2目录
root@master1:/usr/local/hadoop/hadoop-2.6.0/sbin# hdfs dfs -mkdir /library/wordcount/input2
root@master1:/usr/local/hadoop/hadoop-2.6.0/sbin# hdfs dfs -put /usr/local/tools/README.md /library/wordcount/input2
查看一下确保文件已经上传到hdfs上。

[img]http://dl2.iteye.com/upload/attachment/0114/8234/7049d283-0838-3f40-a9d6-f087c0fc7899.png[/img]
程序代码：

package com.imf.spark

import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
/**
 * 用户scala开发集群测试的spark wordcount程序
 */
object WordCount_Cluster {
   def main(args: Array[String]): Unit = {
     /**
      * 1.创建Spark的配置对象SparkConf，设置Spark程序的运行时的配置信息，
      * 例如：通过setMaster来设置程序要链接的Spark集群的Master的URL,如果设置为local,
      * 则代表Spark程序在本地运行，特别适合于机器配置条件非常差的情况。
      */
     //创建SparkConf对象
     val conf = new SparkConf()
     //设置应用程序名称，在程序运行的监控界面可以看到名称
     conf.setAppName("My First Spark App!")
     /**
      * 2.创建SparkContext对象
      * SparkContext是spark程序所有功能的唯一入口，无论是采用Scala,java,python,R等都必须有一个SprakContext
      * SparkContext核心作用：初始化spark应用程序运行所需要的核心组件，包括DAGScheduler,TaskScheduler,SchedulerBackend
      * 同时还会负责Spark程序往Master注册程序等；
      * SparkContext是整个应用程序中最为至关重要的一个对象；
      */
     //通过创建SparkContext对象，通过传入SparkConf实例定制Spark运行的具体参数和配置信息
     val sc = new SparkContext(conf)

     /**
      * 3.根据具体数据的来源（HDFS,HBase,Local,FS,DB,S3等）通过SparkContext来创建RDD；
      * RDD的创建基本有三种方式：根据外部的数据来源（例如HDFS）、根据Scala集合、由其他的RDD操作；
      * 数据会被RDD划分成为一系列的Partitions,分配到每个Partition的数据属于一个Task的处理范畴；
      */
     //读取hdfs文件内容并切分成Pratitions
     //val lines = sc.textFile("hdfs://master1:9000/library/wordcount/input2")
     val lines = sc.textFile("/library/wordcount/input2")//这里不设置并行度是spark有自己的算法，暂时不去考虑

     /**
      * 4.对初始的RDD进行Transformation级别的处理，例如map,filter等高阶函数的变成，来进行具体的数据计算
      * 4.1.将每一行的字符串拆分成单个单词
      */
     //对每一行的字符串进行拆分并把所有行的拆分结果通过flat合并成一个大的集合
      val words = lines.flatMap { line => line.split(" ") }
     /**
      * 4.2.在单词拆分的基础上对每个单词实例计数为1，也就是word => (word,1)
      */
     val pairs = words.map{word =>(word,1)}

     /**
      * 4.3.在每个单词实例计数为1基础上统计每个单词在文件中出现的总次数
      */
     //对相同的key进行value的累积（包括Local和Reducer级别同时Reduce）
     val wordCounts = pairs.reduceByKey(_+_)
     //打印输出
     wordCounts.collect.foreach(pair => println(pair._1+":"+pair._2))
     sc.stop()
   }
}

[b]2.打包程序[/b]
将程序打包成jar文件，并将jar文件复制到虚拟机的master1上的/usr/local/sparkApps目录下。

[img]http://dl2.iteye.com/upload/attachment/0114/8236/4b3504f9-708f-354b-89a2-9fddc19a981f.png[/img]

[b]3.提交应用程序到集群中。[/b]
可以参考官方文档[url]http://spark.apache.org/docs/latest/submitting-applications.html[/url]
第一次用的是下面的命令，结果运行不了，提示信息和命令如下：原因没有提交到集群中。

root@master1:/usr/local/spark/spark-1.6.0-bin-hadoop2.6/bin# ./spark-submit \
> --class com.imf.spark.WordCount_Cluster \
> --master master spark://master1:7077 \
> /usr/local/sparkApps/WordCount.jar
Error: Master must start with yarn, spark, mesos, or local
Run with --help for usage help or --verbose for debug output

第二次修改命令如下，运行正常：

./spark-submit \
--class com.imf.spark.WordCount_Cluster \
--master yarn \
/usr/local/sparkApps/WordCount.jar

运行结果如下：

root@master1:/usr/local/spark/spark-1.6.0-bin-hadoop2.6/bin# ./spark-submit \
> --class com.imf.spark.WordCount_Cluster \
> --master yarn \
> /usr/local/sparkApps/WordCount.jar
16/01/27 07:21:55 INFO spark.SparkContext: Running Spark version 1.6.0
16/01/27 07:21:55 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
16/01/27 07:21:56 INFO spark.SecurityManager: Changing view acls to: root
16/01/27 07:21:56 INFO spark.SecurityManager: Changing modify acls to: root
16/01/27 07:21:56 INFO spark.SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(root); users with modify permissions: Set(root)
16/01/27 07:21:57 INFO util.Utils: Successfully started service 'sparkDriver' on port 33500.
16/01/27 07:21:57 INFO slf4j.Slf4jLogger: Slf4jLogger started
16/01/27 07:21:57 INFO Remoting: Starting remoting
16/01/27 07:21:58 INFO Remoting: Remoting started; listening on addresses :[akka.tcp://[email protected]:40488]
16/01/27 07:21:58 INFO util.Utils: Successfully started service 'sparkDriverActorSystem' on port 40488.
16/01/27 07:21:58 INFO spark.SparkEnv: Registering MapOutputTracker
16/01/27 07:21:58 INFO spark.SparkEnv: Registering BlockManagerMaster
16/01/27 07:21:58 INFO storage.DiskBlockManager: Created local directory at /tmp/blockmgr-298b85db-a14e-448b-bf12-a76e7f9d3e61
16/01/27 07:21:58 INFO storage.MemoryStore: MemoryStore started with capacity 517.4 MB
16/01/27 07:21:59 INFO spark.SparkEnv: Registering OutputCommitCoordinator
16/01/27 07:21:59 INFO server.Server: jetty-8.y.z-SNAPSHOT
16/01/27 07:21:59 INFO server.AbstractConnector: Started [email protected]:4040
16/01/27 07:21:59 INFO util.Utils: Successfully started service 'SparkUI' on port 4040.
16/01/27 07:21:59 INFO ui.SparkUI: Started SparkUI at http://192.168.112.130:4040
16/01/27 07:21:59 INFO spark.HttpFileServer: HTTP File server directory is /tmp/spark-1273b284-86a3-45a5-ab5f-7996b7f78a5f/httpd-c0ad1a3a-48fc-4aef-8ac1-ddc28159fe2c
16/01/27 07:21:59 INFO spark.HttpServer: Starting HTTP Server
16/01/27 07:21:59 INFO server.Server: jetty-8.y.z-SNAPSHOT
16/01/27 07:21:59 INFO server.AbstractConnector: Started [email protected]:41722
16/01/27 07:21:59 INFO util.Utils: Successfully started service 'HTTP file server' on port 41722.
16/01/27 07:21:59 INFO spark.SparkContext: Added JAR file:/usr/local/sparkApps/WordCount.jar at http://192.168.112.130:41722/jars/WordCount.jar with timestamp 1453850519749
16/01/27 07:22:00 INFO client.RMProxy: Connecting to ResourceManager at master1/192.168.112.130:8032
16/01/27 07:22:01 INFO yarn.Client: Requesting a new application from cluster with 3 NodeManagers
16/01/27 07:22:01 INFO yarn.Client: Verifying our application has not requested more than the maximum memory capability of the cluster (8192 MB per container)
16/01/27 07:22:01 INFO yarn.Client: Will allocate AM container, with 896 MB memory including 384 MB overhead
16/01/27 07:22:01 INFO yarn.Client: Setting up container launch context for our AM
16/01/27 07:22:01 INFO yarn.Client: Setting up the launch environment for our AM container
16/01/27 07:22:01 INFO yarn.Client: Preparing resources for our AM container
16/01/27 07:22:02 INFO yarn.Client: Uploading resource file:/usr/local/spark/spark-1.6.0-bin-hadoop2.6/lib/spark-assembly-1.6.0-hadoop2.6.0.jar -> hdfs://master1:9000/user/root/.sparkStaging/application_1453847555417_0001/spark-assembly-1.6.0-hadoop2.6.0.jar
16/01/27 07:22:11 INFO yarn.Client: Uploading resource file:/tmp/spark-1273b284-86a3-45a5-ab5f-7996b7f78a5f/__spark_conf__2067657392446030944.zip -> hdfs://master1:9000/user/root/.sparkStaging/application_1453847555417_0001/__spark_conf__2067657392446030944.zip
16/01/27 07:22:11 INFO spark.SecurityManager: Changing view acls to: root
16/01/27 07:22:11 INFO spark.SecurityManager: Changing modify acls to: root
16/01/27 07:22:11 INFO spark.SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(root); users with modify permissions: Set(root)
16/01/27 07:22:12 INFO yarn.Client: Submitting application 1 to ResourceManager
16/01/27 07:22:12 INFO impl.YarnClientImpl: Submitted application application_1453847555417_0001
16/01/27 07:22:14 INFO yarn.Client: Application report for application_1453847555417_0001 (state: ACCEPTED)
16/01/27 07:22:14 INFO yarn.Client: 
     client token: N/A
     diagnostics: N/A
     ApplicationMaster host: N/A
     ApplicationMaster RPC port: -1
     queue: default
     start time: 1453850532502
     final status: UNDEFINED
     tracking URL: http://master1:8088/proxy/application_1453847555417_0001/
     user: root
16/01/27 07:22:15 INFO yarn.Client: Application report for application_1453847555417_0001 (state: ACCEPTED)
16/01/27 07:22:16 INFO yarn.Client: Application report for application_1453847555417_0001 (state: ACCEPTED)
16/01/27 07:22:17 INFO yarn.Client: Application report for application_1453847555417_0001 (state: ACCEPTED)
16/01/27 07:22:18 INFO yarn.Client: Application report for application_1453847555417_0001 (state: ACCEPTED)
16/01/27 07:22:19 INFO yarn.Client: Application report for application_1453847555417_0001 (state: ACCEPTED)
16/01/27 07:22:20 INFO yarn.Client: Application report for application_1453847555417_0001 (state: ACCEPTED)
16/01/27 07:22:21 INFO yarn.Client: Application report for application_1453847555417_0001 (state: ACCEPTED)
16/01/27 07:22:22 INFO yarn.Client: Application report for application_1453847555417_0001 (state: ACCEPTED)
16/01/27 07:22:23 INFO yarn.Client: Application report for application_1453847555417_0001 (state: ACCEPTED)
16/01/27 07:22:24 INFO yarn.Client: Application report for application_1453847555417_0001 (state: ACCEPTED)
16/01/27 07:22:25 INFO yarn.Client: Application report for application_1453847555417_0001 (state: ACCEPTED)
16/01/27 07:22:26 INFO yarn.Client: Application report for application_1453847555417_0001 (state: ACCEPTED)
16/01/27 07:22:27 INFO yarn.Client: Application report for application_1453847555417_0001 (state: ACCEPTED)
16/01/27 07:22:28 INFO yarn.Client: Application report for application_1453847555417_0001 (state: ACCEPTED)
16/01/27 07:22:29 INFO yarn.Client: Application report for application_1453847555417_0001 (state: ACCEPTED)
16/01/27 07:22:30 INFO yarn.Client: Application report for application_1453847555417_0001 (state: ACCEPTED)
16/01/27 07:22:31 INFO yarn.Client: Application report for application_1453847555417_0001 (state: ACCEPTED)
16/01/27 07:22:32 INFO yarn.Client: Application report for application_1453847555417_0001 (state: ACCEPTED)
16/01/27 07:22:32 INFO cluster.YarnSchedulerBackend$YarnSchedulerEndpoint: ApplicationMaster registered as NettyRpcEndpointRef(null)
16/01/27 07:22:32 INFO cluster.YarnClientSchedulerBackend: Add WebUI Filter. org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter, Map(PROXY_HOSTS -> master1, PROXY_URI_BASES -> http://master1:8088/proxy/application_1453847555417_0001), /proxy/application_1453847555417_0001
16/01/27 07:22:32 INFO ui.JettyUtils: Adding filter: org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter
16/01/27 07:22:33 INFO yarn.Client: Application report for application_1453847555417_0001 (state: ACCEPTED)
16/01/27 07:22:34 INFO yarn.Client: Application report for application_1453847555417_0001 (state: RUNNING)
16/01/27 07:22:34 INFO yarn.Client: 
     client token: N/A
     diagnostics: N/A
     ApplicationMaster host: 192.168.112.132
     ApplicationMaster RPC port: 0
     queue: default
     start time: 1453850532502
     final status: UNDEFINED
     tracking URL: http://master1:8088/proxy/application_1453847555417_0001/
     user: root
16/01/27 07:22:34 INFO cluster.YarnClientSchedulerBackend: Application application_1453847555417_0001 has started running.
16/01/27 07:22:34 INFO util.Utils: Successfully started service 'org.apache.spark.network.netty.NettyBlockTransferService' on port 33873.
16/01/27 07:22:34 INFO netty.NettyBlockTransferService: Server created on 33873
16/01/27 07:22:34 INFO storage.BlockManagerMaster: Trying to register BlockManager
16/01/27 07:22:34 INFO storage.BlockManagerMasterEndpoint: Registering block manager 192.168.112.130:33873 with 517.4 MB RAM, BlockManagerId(driver, 192.168.112.130, 33873)
16/01/27 07:22:34 INFO storage.BlockManagerMaster: Registered BlockManager
16/01/27 07:22:37 INFO scheduler.EventLoggingListener: Logging events to hdfs://master1:9000/historyserverforSpark/application_1453847555417_0001
16/01/27 07:22:37 INFO cluster.YarnClientSchedulerBackend: SchedulerBackend is ready for scheduling beginning after waiting maxRegisteredResourcesWaitingTime: 30000(ms)
16/01/27 07:22:46 INFO storage.MemoryStore: Block broadcast_0 stored as values in memory (estimated size 212.9 KB, free 212.9 KB)
16/01/27 07:22:46 INFO storage.MemoryStore: Block broadcast_0_piece0 stored as bytes in memory (estimated size 19.6 KB, free 232.4 KB)
16/01/27 07:22:46 INFO storage.BlockManagerInfo: Added broadcast_0_piece0 in memory on 192.168.112.130:33873 (size: 19.6 KB, free: 517.4 MB)
16/01/27 07:22:46 INFO spark.SparkContext: Created broadcast 0 from textFile at WordCount_Cluster.scala:36
16/01/27 07:22:49 INFO mapred.FileInputFormat: Total input paths to process : 1
16/01/27 07:22:50 INFO spark.SparkContext: Starting job: collect at WordCount_Cluster.scala:55
16/01/27 07:22:51 INFO scheduler.DAGScheduler: Registering RDD 3 (map at WordCount_Cluster.scala:47)
16/01/27 07:22:51 INFO scheduler.DAGScheduler: Got job 0 (collect at WordCount_Cluster.scala:55) with 2 output partitions
16/01/27 07:22:51 INFO scheduler.DAGScheduler: Final stage: ResultStage 1 (collect at WordCount_Cluster.scala:55)
16/01/27 07:22:51 INFO scheduler.DAGScheduler: Parents of final stage: List(ShuffleMapStage 0)
16/01/27 07:22:51 INFO scheduler.DAGScheduler: Missing parents: List(ShuffleMapStage 0)
16/01/27 07:22:51 INFO scheduler.DAGScheduler: Submitting ShuffleMapStage 0 (MapPartitionsRDD[3] at map at WordCount_Cluster.scala:47), which has no missing parents
16/01/27 07:22:52 INFO storage.MemoryStore: Block broadcast_1 stored as values in memory (estimated size 4.1 KB, free 236.5 KB)
16/01/27 07:22:52 INFO storage.MemoryStore: Block broadcast_1_piece0 stored as bytes in memory (estimated size 2.3 KB, free 238.8 KB)
16/01/27 07:22:52 INFO storage.BlockManagerInfo: Added broadcast_1_piece0 in memory on 192.168.112.130:33873 (size: 2.3 KB, free: 517.4 MB)
16/01/27 07:22:52 INFO spark.SparkContext: Created broadcast 1 from broadcast at DAGScheduler.scala:1006
16/01/27 07:22:52 INFO scheduler.DAGScheduler: Submitting 2 missing tasks from ShuffleMapStage 0 (MapPartitionsRDD[3] at map at WordCount_Cluster.scala:47)
16/01/27 07:22:52 INFO cluster.YarnScheduler: Adding task set 0.0 with 2 tasks
16/01/27 07:23:08 WARN cluster.YarnScheduler: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
16/01/27 07:23:22 INFO cluster.YarnClientSchedulerBackend: Registered executor NettyRpcEndpointRef(null) (worker3:56136) with ID 2
16/01/27 07:23:23 INFO scheduler.TaskSetManager: Starting task 0.0 in stage 0.0 (TID 0, worker3, partition 0,NODE_LOCAL, 2202 bytes)
16/01/27 07:23:23 WARN cluster.YarnScheduler: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
16/01/27 07:23:24 INFO storage.BlockManagerMasterEndpoint: Registering block manager worker3:41970 with 517.4 MB RAM, BlockManagerId(2, worker3, 41970)
16/01/27 07:23:33 INFO storage.BlockManagerInfo: Added broadcast_1_piece0 in memory on worker3:41970 (size: 2.3 KB, free: 517.4 MB)
16/01/27 07:23:37 INFO storage.BlockManagerInfo: Added broadcast_0_piece0 in memory on worker3:41970 (size: 19.6 KB, free: 517.4 MB)
16/01/27 07:23:39 INFO cluster.YarnClientSchedulerBackend: Registered executor NettyRpcEndpointRef(null) (worker2:42174) with ID 1
16/01/27 07:23:39 INFO scheduler.TaskSetManager: Starting task 1.0 in stage 0.0 (TID 1, worker2, partition 1,RACK_LOCAL, 2202 bytes)
16/01/27 07:23:40 INFO storage.BlockManagerMasterEndpoint: Registering block manager worker2:44472 with 517.4 MB RAM, BlockManagerId(1, worker2, 44472)
16/01/27 07:23:47 INFO storage.BlockManagerInfo: Added broadcast_1_piece0 in memory on worker2:44472 (size: 2.3 KB, free: 517.4 MB)
16/01/27 07:23:51 INFO storage.BlockManagerInfo: Added broadcast_0_piece0 in memory on worker2:44472 (size: 19.6 KB, free: 517.4 MB)
16/01/27 07:24:00 INFO scheduler.TaskSetManager: Finished task 0.0 in stage 0.0 (TID 0) in 37460 ms on worker3 (1/2)
16/01/27 07:24:09 INFO scheduler.DAGScheduler: ShuffleMapStage 0 (map at WordCount_Cluster.scala:47) finished in 75.810 s
16/01/27 07:24:09 INFO scheduler.TaskSetManager: Finished task 1.0 in stage 0.0 (TID 1) in 29446 ms on worker2 (2/2)
16/01/27 07:24:09 INFO scheduler.DAGScheduler: looking for newly runnable stages
16/01/27 07:24:09 INFO scheduler.DAGScheduler: running: Set()
16/01/27 07:24:09 INFO scheduler.DAGScheduler: waiting: Set(ResultStage 1)
16/01/27 07:24:09 INFO scheduler.DAGScheduler: failed: Set()
16/01/27 07:24:09 INFO cluster.YarnScheduler: Removed TaskSet 0.0, whose tasks have all completed, from pool 
16/01/27 07:24:09 INFO scheduler.DAGScheduler: Submitting ResultStage 1 (ShuffledRDD[4] at reduceByKey at WordCount_Cluster.scala:53), which has no missing parents
16/01/27 07:24:09 INFO storage.MemoryStore: Block broadcast_2 stored as values in memory (estimated size 2.6 KB, free 241.4 KB)
16/01/27 07:24:09 INFO storage.MemoryStore: Block broadcast_2_piece0 stored as bytes in memory (estimated size 1595.0 B, free 242.9 KB)
16/01/27 07:24:09 INFO storage.BlockManagerInfo: Added broadcast_2_piece0 in memory on 192.168.112.130:33873 (size: 1595.0 B, free: 517.4 MB)
16/01/27 07:24:09 INFO spark.SparkContext: Created broadcast 2 from broadcast at DAGScheduler.scala:1006
16/01/27 07:24:09 INFO scheduler.DAGScheduler: Submitting 2 missing tasks from ResultStage 1 (ShuffledRDD[4] at reduceByKey at WordCount_Cluster.scala:53)
16/01/27 07:24:09 INFO cluster.YarnScheduler: Adding task set 1.0 with 2 tasks
16/01/27 07:24:09 INFO scheduler.TaskSetManager: Starting task 0.0 in stage 1.0 (TID 2, worker2, partition 0,NODE_LOCAL, 1951 bytes)
16/01/27 07:24:09 INFO scheduler.TaskSetManager: Starting task 1.0 in stage 1.0 (TID 3, worker3, partition 1,NODE_LOCAL, 1951 bytes)
16/01/27 07:24:09 INFO storage.BlockManagerInfo: Added broadcast_2_piece0 in memory on worker2:44472 (size: 1595.0 B, free: 517.4 MB)
16/01/27 07:24:09 INFO storage.BlockManagerInfo: Added broadcast_2_piece0 in memory on worker3:41970 (size: 1595.0 B, free: 517.4 MB)
16/01/27 07:24:10 INFO spark.MapOutputTrackerMasterEndpoint: Asked to send map output locations for shuffle 0 to worker2:42174
16/01/27 07:24:10 INFO spark.MapOutputTrackerMaster: Size of output statuses for shuffle 0 is 158 bytes
16/01/27 07:24:10 INFO spark.MapOutputTrackerMasterEndpoint: Asked to send map output locations for shuffle 0 to worker3:56136
16/01/27 07:24:13 INFO scheduler.TaskSetManager: Finished task 1.0 in stage 1.0 (TID 3) in 3634 ms on worker3 (1/2)
16/01/27 07:24:13 INFO scheduler.DAGScheduler: ResultStage 1 (collect at WordCount_Cluster.scala:55) finished in 3.691 s
16/01/27 07:24:13 INFO scheduler.TaskSetManager: Finished task 0.0 in stage 1.0 (TID 2) in 3696 ms on worker2 (2/2)
16/01/27 07:24:13 INFO cluster.YarnScheduler: Removed TaskSet 1.0, whose tasks have all completed, from pool 
16/01/27 07:24:13 INFO scheduler.DAGScheduler: Job 0 finished: collect at WordCount_Cluster.scala:55, took 82.513256 s
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./bin/pyspark:1
A:1
through:1
#:1
library:1
following:2
More:1
which:2
also:4
storage:1
should:2
To:2
for:11
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setup:1
mesos://:1
Maven](http://maven.apache.org/).:1
latest:1
processing,:1
the:21
your:1
not:1
different:1
distributions.:1
given.:1
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if:4
instructions.:1
be:2
do:2
Tests:1
no:1
./bin/run-example:2
programs,:1
including:3
`./bin/run-example:1
Spark.:1
Versions:1
HDFS:1
individual:1
spark://:1
It:2
an:3
programming:1
machine:1
run::1
environment:1
clean:1
1000::2
And:1
run:7
./bin/spark-shell:1
URL,:1
"local":1
MASTER=spark://host:7077:1
on:5
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threads.:1
against:1
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help:1
print:1
tests:2
examples:2
at:2
in:5
-DskipTests:1
optimized:1
downloaded:1
versions:1
graphs:1
Guide](http://spark.apache.org/docs/latest/configuration.html):1
online:1
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abbreviated:1
comes:1
directory.:1
overview:1
[building:1
`examples`:2
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Running:1
way:1
use:3
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find:1
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contains:1
project:1
you:4
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name:1
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to:14
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programs:2
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["Building:1
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Hadoop:3
16/01/27 07:24:13 INFO handler.ContextHandler: stopped o.s.j.s.ServletContextHandler{/metrics/json,null}
16/01/27 07:24:13 INFO handler.ContextHandler: stopped o.s.j.s.ServletContextHandler{/stages/stage/kill,null}
16/01/27 07:24:13 INFO handler.ContextHandler: stopped o.s.j.s.ServletContextHandler{/api,null}
16/01/27 07:24:13 INFO handler.ContextHandler: stopped o.s.j.s.ServletContextHandler{/,null}
16/01/27 07:24:13 INFO handler.ContextHandler: stopped o.s.j.s.ServletContextHandler{/static,null}
16/01/27 07:24:13 INFO handler.ContextHandler: stopped o.s.j.s.ServletContextHandler{/executors/threadDump/json,null}
16/01/27 07:24:13 INFO handler.ContextHandler: stopped o.s.j.s.ServletContextHandler{/executors/threadDump,null}
16/01/27 07:24:13 INFO handler.ContextHandler: stopped o.s.j.s.ServletContextHandler{/executors/json,null}
16/01/27 07:24:13 INFO handler.ContextHandler: stopped o.s.j.s.ServletContextHandler{/executors,null}
16/01/27 07:24:13 INFO handler.ContextHandler: stopped o.s.j.s.ServletContextHandler{/environment/json,null}
16/01/27 07:24:13 INFO handler.ContextHandler: stopped o.s.j.s.ServletContextHandler{/environment,null}
16/01/27 07:24:13 INFO handler.ContextHandler: stopped o.s.j.s.ServletContextHandler{/storage/rdd/json,null}
16/01/27 07:24:13 INFO handler.ContextHandler: stopped o.s.j.s.ServletContextHandler{/storage/rdd,null}
16/01/27 07:24:13 INFO handler.ContextHandler: stopped o.s.j.s.ServletContextHandler{/storage/json,null}
16/01/27 07:24:13 INFO handler.ContextHandler: stopped o.s.j.s.ServletContextHandler{/storage,null}
16/01/27 07:24:13 INFO handler.ContextHandler: stopped o.s.j.s.ServletContextHandler{/stages/pool/json,null}
16/01/27 07:24:13 INFO handler.ContextHandler: stopped o.s.j.s.ServletContextHandler{/stages/pool,null}
16/01/27 07:24:13 INFO handler.ContextHandler: stopped o.s.j.s.ServletContextHandler{/stages/stage/json,null}
16/01/27 07:24:13 INFO handler.ContextHandler: stopped o.s.j.s.ServletContextHandler{/stages/stage,null}
16/01/27 07:24:13 INFO handler.ContextHandler: stopped o.s.j.s.ServletContextHandler{/stages/json,null}
16/01/27 07:24:13 INFO handler.ContextHandler: stopped o.s.j.s.ServletContextHandler{/stages,null}
16/01/27 07:24:13 INFO handler.ContextHandler: stopped o.s.j.s.ServletContextHandler{/jobs/job/json,null}
16/01/27 07:24:13 INFO handler.ContextHandler: stopped o.s.j.s.ServletContextHandler{/jobs/job,null}
16/01/27 07:24:13 INFO handler.ContextHandler: stopped o.s.j.s.ServletContextHandler{/jobs/json,null}
16/01/27 07:24:13 INFO handler.ContextHandler: stopped o.s.j.s.ServletContextHandler{/jobs,null}
16/01/27 07:24:13 INFO ui.SparkUI: Stopped Spark web UI at http://192.168.112.130:4040
16/01/27 07:24:14 INFO cluster.YarnClientSchedulerBackend: Interrupting monitor thread
16/01/27 07:24:14 INFO cluster.YarnClientSchedulerBackend: Shutting down all executors
16/01/27 07:24:14 INFO cluster.YarnClientSchedulerBackend: Asking each executor to shut down
16/01/27 07:24:14 INFO cluster.YarnClientSchedulerBackend: Stopped
16/01/27 07:24:14 INFO spark.MapOutputTrackerMasterEndpoint: MapOutputTrackerMasterEndpoint stopped!
16/01/27 07:24:14 INFO storage.MemoryStore: MemoryStore cleared
16/01/27 07:24:14 INFO storage.BlockManager: BlockManager stopped
16/01/27 07:24:14 INFO storage.BlockManagerMaster: BlockManagerMaster stopped
16/01/27 07:24:14 INFO scheduler.OutputCommitCoordinator$OutputCommitCoordinatorEndpoint: OutputCommitCoordinator stopped!
16/01/27 07:24:15 INFO remote.RemoteActorRefProvider$RemotingTerminator: Shutting down remote daemon.
16/01/27 07:24:15 INFO remote.RemoteActorRefProvider$RemotingTerminator: Remote daemon shut down; proceeding with flushing remote transports.
16/01/27 07:24:15 INFO spark.SparkContext: Successfully stopped SparkContext
16/01/27 07:24:15 INFO util.ShutdownHookManager: Shutdown hook called
16/01/27 07:24:16 INFO util.ShutdownHookManager: Deleting directory /tmp/spark-1273b284-86a3-45a5-ab5f-7996b7f78a5f
16/01/27 07:24:16 INFO util.ShutdownHookManager: Deleting directory /tmp/spark-1273b284-86a3-45a5-ab5f-7996b7f78a5f/httpd-c0ad1a3a-48fc-4aef-8ac1-ddc28159fe2c
root@master1:/usr/local/spark/spark-1.6.0-bin-hadoop2.6/bin# 

运行结果和本地的一样。

[b]4.通过写一个wordcount.sh，实现自动化。[/b]
创建一个wordcount.sh文件，内容如下，保存并退出。然后chmod +x wordcount.sh给shell文件执行权限。运行下面的命令，执行即可，执行结果和上面的一样。

root@master1:/usr/local/sparkApps# ./wordcount.sh

6.通过浏览器查看程序运行结果：


[img]http://dl2.iteye.com/upload/attachment/0114/8238/f61a8013-ee02-3000-b1b0-225c30848aee.png[/img]

[img]http://dl2.iteye.com/upload/attachment/0114/8240/b6adadae-def6-3775-8bbe-96786936c9aa.png[/img]