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Spark在YARN中有yarn-cluster和yarn-client两种运行模式: I. Yarn Cluster Spark Driver首先作为一个ApplicationMaster在YARN集群中启动，客户端提交给ResourceManager的每一个job都会在集群的worker节点上分配一个唯一的ApplicationMaster，由该Application

Spark在YARN中有yarn-cluster和yarn-client两种运行模式:

I. Yarn Cluster

Spark Driver首先作为一个ApplicationMaster在YARN集群中启动，客户端提交给ResourceManager的每一个job都会在集群的worker节点上分配一个唯一的ApplicationMaster，由该ApplicationMaster管理全生命周期的应用。因为Driver程序在YARN中运行，所以事先不用启动Spark Master/Client，应用的运行结果不能在客户端显示（可以在history server中查看），所以最好将结果保存在HDFS而非stdout输出，客户端的终端显示的是作为YARN的job的简单运行状况。
sparn-yarn1
by @Sandy Ryza
spark-yarn2
by 明风@taobao
从terminal的output中看到任务初始化更详细的四个步骤：

14/09/28 11:24:52 INFO RMProxy: Connecting to ResourceManager at hdp01/172.19.1.231:8032
14/09/28 11:24:52 INFO Client: Got Cluster metric info from ApplicationsManager (ASM), number of NodeManagers: 4
14/09/28 11:24:52 INFO Client: Queue info ... queueName: root.default, queueCurrentCapacity: 0.0, queueMaxCapacity: -1.0,
      queueApplicationCount = 0, queueChildQueueCount = 0
14/09/28 11:24:52 INFO Client: Max mem capabililty of a single resource in this cluster 8192
14/09/28 11:24:53 INFO Client: Uploading file:/usr/lib/spark/examples/lib/spark-examples_2.10-1.0.0-cdh5.1.0.jar to hdfs://hdp01:8020/user/spark/.sparkStaging/application_1411874193696_0003/spark-examples_2.10-1.0.0-cdh5.1.0.jar
14/09/28 11:24:54 INFO Client: Uploading file:/usr/lib/spark/assembly/lib/spark-assembly-1.0.0-cdh5.1.0-hadoop2.3.0-cdh5.1.0.jar to hdfs://hdp01:8020/user/spark/.sparkStaging/application_1411874193696_0003/spark-assembly-1.0.0-cdh5.1.0-hadoop2.3.0-cdh5.1.0.jar
14/09/28 11:24:55 INFO Client: Setting up the launch environment
14/09/28 11:24:55 INFO Client: Setting up container launch context
14/09/28 11:24:55 INFO Client: Command for starting the Spark ApplicationMaster: List($JAVA_HOME/bin/java, -server, -Xmx512m, -Djava.io.tmpdir=$PWD/tmp, -Dspark.master=\"spark://hdp01:7077\", -Dspark.app.name=\"org.apache.spark.examples.SparkPi\", -Dspark.eventLog.enabled=\"true\", -Dspark.eventLog.dir=\"/user/spark/applicationHistory\",  -Dlog4j.configuration=log4j-spark-container.properties, org.apache.spark.deploy.yarn.ApplicationMaster, --class, org.apache.spark.examples.SparkPi, --jar , file:/usr/lib/spark/examples/lib/spark-examples_2.10-1.0.0-cdh5.1.0.jar, , --executor-memory, 1024, --executor-cores, 1, --num-executors , 2, 1>, /stdout, 2>, /stderr)
14/09/28 11:24:55 INFO Client: Submitting application to ASM
14/09/28 11:24:55 INFO YarnClientImpl: Submitted application application_1411874193696_0003
14/09/28 11:24:56 INFO Client: Application report from ASM:
application identifier: application_1411874193696_0003
     appId: 3
     clientToAMToken: null
     appDiagnostics: 
     appMasterHost: N/A
     appQueue: root.spark
     appMasterRpcPort: -1
     appStartTime: 1411874695327
     yarnAppState: ACCEPTED
     distributedFinalState: UNDEFINED
     appTrackingUrl: http://hdp01:8088/proxy/application_1411874193696_0003/
     appUser: spark

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1. 由client向ResourceManager提交请求，并上传jar到HDFS上
这期间包括四个步骤：
a). 连接到RM
b). 从RM ASM（ApplicationsManager ）中获得metric、queue和resource等信息。
c). upload app jar and spark-assembly jar
d). 设置运行环境和container上下文（launch-container.sh等脚本)
2. ResouceManager向NodeManager申请资源，创建Spark ApplicationMaster（每个SparkContext都有一个ApplicationMaster）
3. NodeManager启动Spark App Master，并向ResourceManager AsM注册
4. Spark ApplicationMaster从HDFS中找到jar文件，启动DAGscheduler和YARN Cluster Scheduler
5. ResourceManager向ResourceManager AsM注册申请container资源（INFO YarnClientImpl: Submitted application）
6. ResourceManager通知NodeManager分配Container，这时可以收到来自ASM关于container的报告。（每个container的对应一个executor）
7. Spark ApplicationMaster直接和container（executor）进行交互，完成这个分布式任务。
需要注意的是：
a). Spark中的localdir会被yarn.nodemanager.local-dirs替换
b). 允许失败的节点数(spark.yarn.max.worker.failures)为executor数量的两倍数量，最小为3.
c). SPARK_YARN_USER_ENV传递给spark进程的环境变量
d). 传递给app的参数应该通过–args指定。
部署：
环境介绍：
hdp0[1-4]四台主机
hadoop使用CDH 5.1版本: hadoop-2.3.0+cdh5.1.0+795-1.cdh5.1.0.p0.58.el6.x86_64
直接下载对应2.3.0的pre-build版本http://spark.apache.org/downloads.html
下载完毕后解压，检查spark-assembly目录：
file /home/spark/spark-1.1.0-bin-hadoop2.3/lib/spark-assembly-1.1.0-hadoop2.3.0.jar
/home/spark/spark-1.1.0-bin-hadoop2.3/lib/spark-assembly-1.1.0-hadoop2.3.0.jar: Zip archive data, at least v2.0 to extract
然后输出环境变量HADOOP_CONF_DIR/YARN_CONF_DIR和SPARK_JAR(可以设置到spark-env.sh中）
export HADOOP_CONF_DIR=/etc/hadoop/etc
export SPARK_JAR=/home/spark/spark-1.1.0-bin-hadoop2.3/lib/spark-assembly-1.1.0-hadoop2.3.0.jar
如果使用cloudera manager 5，在Spark Service的操作中可以找到Upload Spark Jar将spark-assembly上传到HDFS上。
spark-yarn3

Spark Jar Location (HDFS)
spark_jar_hdfs_path

/user/spark/share/lib/spark-assembly.jar

默认值

The location of the Spark jar in HDFS

Spark History Location (HDFS)
spark.eventLog.dir

/user/spark/applicationHistory

默认值

The location of Spark application history logs in HDFS. Changing this value will not move existing logs to the new location.

提交任务，此时在YARN的web UI和history Server上就可以看到运行状态信息。

spark-submit --class org.apache.spark.examples.SparkPi --master yarn-cluster /usr/lib/spark/examples/lib/spark-examples_2.10-1.0.0-cdh5.1.0.jar

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II. yarn-client

(YarnClientClusterScheduler)查看对应类的文件
在yarn-client模式下，Driver运行在Client上，通过ApplicationMaster向RM获取资源。本地Driver负责与所有的executor container进行交互，并将最后的结果汇总。结束掉终端，相当于kill掉这个spark应用。一般来说，如果运行的结果仅仅返回到terminal上时需要配置这个。
spark-yarn4
客户端的Driver将应用提交给Yarn后，Yarn会先后启动ApplicationMaster和executor，另外ApplicationMaster和executor都是装载在container里运行，container默认的内存是1G，ApplicationMaster分配的内存是driver- memory，executor分配的内存是executor-memory。同时，因为Driver在客户端，所以程序的运行结果可以在客户端显示，Driver以进程名为SparkSubmit的形式存在。
配置YARN-Client模式同样需要HADOOP_CONF_DIR/YARN_CONF_DIR和SPARK_JAR变量。
提交任务测试：

spark-submit --class org.apache.spark.examples.SparkPi --deploy-mode client /usr/lib/spark/examples/lib/spark-examples_2.10-1.0.0-cdh5.1.0.jar
terminal output：
14/09/28 11:18:34 INFO Client: Command for starting the Spark ApplicationMaster: List($JAVA_HOME/bin/java, -server, -Xmx512m, -Djava.io.tmpdir=$PWD/tmp, -Dspark.tachyonStore.folderName=\"spark-9287f0f2-2e72-4617-a418-e0198626829b\", -Dspark.eventLog.enabled=\"true\", -Dspark.yarn.secondary.jars=\"\", -Dspark.driver.host=\"hdp01\", -Dspark.driver.appUIHistoryAddress=\"\", -Dspark.app.name=\"Spark Pi\", -Dspark.jars=\"file:/usr/lib/spark/examples/lib/spark-examples_2.10-1.0.0-cdh5.1.0.jar\", -Dspark.fileserver.uri=\"http://172.19.17.231:53558\", -Dspark.eventLog.dir=\"/user/spark/applicationHistory\", -Dspark.master=\"yarn-client\", -Dspark.driver.port=\"35938\", -Dspark.httpBroadcast.uri=\"http://172.19.17.231:43804\",  -Dlog4j.configuration=log4j-spark-container.properties, org.apache.spark.deploy.yarn.ExecutorLauncher, --class, notused, --jar , null,  --args  'hdp01:35938' , --executor-memory, 1024, --executor-cores, 1, --num-executors , 2, 1>, /stdout, 2>, /stderr)
14/09/28 11:18:34 INFO Client: Submitting application to ASM
14/09/28 11:18:34 INFO YarnClientSchedulerBackend: Application report from ASM: 
     appMasterRpcPort: -1
     appStartTime: 1411874314198
     yarnAppState: ACCEPTED
......

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##最后将结果输出到terminal中
Pi is roughly 3.14528

原文地址：Spark on YARN, 感谢原作者分享。