编写yarn应用

目的：

本文在high level中描述了在YARN中实现应用的方法。

理念和流程

大概的理念是，应用提交client提交一项应用给yarn的Resource Manager。这可以通过建立一个yarn client来实现。yarnclient启动之后，client会建立应用，准备包含application  master的container的应用，然后提交应用。你需要提供给本地文件/jar包的信息细节，以便让你的应用来运行，实际上需要运行的命令（命令行+变量），还有OS环境变量等等。

大致上，你需要给你的application master提供linux进程的信息。

YARN的resource manager会发布application master在分配的container中。application master和YARN cluster交互，处理应用的执行。她执行同步的特性，应用发布的时候，application master的主要认为是：和RM通信，分配未来container的资源，和NM交互来发布应用的container。任务可以同时通过AMRMclientAsync来同步，事件处理方法特定一种事件处理器-AMRMClientAsync.CallbackHandler。事件处理器需要明确地设定给client。task bcontainer分配需要发布一个可运行的对象，发布container。在发布container的时候，AM需要指定containerlaunchcontext，来说明发布的信息比如命令行，环境变量等等。

在应用的执行过程中，AM会通过NMClientAsync和NM通信。所有的container时间都有AMCLIENT来处理。传统的回收处理机制，处理client的启动，停止，状态更新和运行错误。AM也会向RM提交执行状况，通过getProgress方法。

我们现在讨论的是异步client，她有更简单使用率的优势，我们本文主要介绍异步client。

除了异步client，特定的workflow也有同步的版本（ARMClient和NMClient)。异步client具有更简单的用法，所以被推荐使用。这篇文章主要介绍异步client。

YARN应用的三个主要协议依然是适用的：application client protocol,application master protocol,container management protocol。这三个client共同为yarn应用提供简单地编程模型。

在很罕有的环境下，码农可能想要直接使用这三个协议来实现一个应用。然而这样的举动不再被鼓励。

编写一个简单的yarn应用：

client需要做的第一步是初始化并且启动一个yarn client

代码如下：

  YarnClient yarnClient = YarnClient.createYarnClient();  yarnClient.init(conf);  yarnClient.start();

一旦client建立之后，client需要创建一个应用，得到应用的ID

代码：

 YarnClientApplication app = yarnClient.createApplication();  GetNewApplicationResponse appResponse = app.getNewApplicationResponse();

yarn client application对于新建一个应用的回应还包括集群最多/最少容量的资源信息。这是为了确保在application master发布的时候，你可以特殊设定container的信息。了解更多信息，请查看GetNewApplicationResponse。

client的关键任务在于建立application submission context，定义了RM发布AM需要了解的信息。client需要设定如下信息：应用的id，名字，队列，优先级，应用提交队列，提交的优先级，提交应用的用户。

containerLaunchContext：信息定义了AM发布和运行的container，定义了要运行比如本地资源，环境设定和命令的应用所需要的所有信息。

代码如下：

// set the application submission contextApplicationSubmissionContext appContext = app.getApplicationSubmissionContext();ApplicationId appId = appContext.getApplicationId();appContext.setKeepContainersAcrossApplicationAttempts(keepContainers);appContext.setApplicationName(appName);// set local resources for the application master// local files or archives as needed// In this scenario, the jar file for the application master is part of the local resourcesMap<String, LocalResource> localResources = new HashMap<String, LocalResource>();LOG.info("Copy App Master jar from local filesystem and add to local environment");// Copy the application master jar to the filesystem// Create a local resource to point to the destination jar pathFileSystem fs = FileSystem.get(conf);addToLocalResources(fs, appMasterJar, appMasterJarPath, appId.toString(),    localResources, null);// Set the log4j properties if neededif (!log4jPropFile.isEmpty()) {  addToLocalResources(fs, log4jPropFile, log4jPath, appId.toString(),      localResources, null);}// The shell script has to be made available on the final container(s)// where it will be executed.// To do this, we need to first copy into the filesystem that is visible// to the yarn framework.// We do not need to set this as a local resource for the application// master as the application master does not need it.String hdfsShellScriptLocation = "";long hdfsShellScriptLen = 0;long hdfsShellScriptTimestamp = 0;if (!shellScriptPath.isEmpty()) {  Path shellSrc = new Path(shellScriptPath);  String shellPathSuffix =      appName + "/" + appId.toString() + "/" + SCRIPT_PATH;  Path shellDst =      new Path(fs.getHomeDirectory(), shellPathSuffix);  fs.copyFromLocalFile(false, true, shellSrc, shellDst);  hdfsShellScriptLocation = shellDst.toUri().toString();  FileStatus shellFileStatus = fs.getFileStatus(shellDst);  hdfsShellScriptLen = shellFileStatus.getLen();  hdfsShellScriptTimestamp = shellFileStatus.getModificationTime();}if (!shellCommand.isEmpty()) {  addToLocalResources(fs, null, shellCommandPath, appId.toString(),      localResources, shellCommand);}if (shellArgs.length > 0) {  addToLocalResources(fs, null, shellArgsPath, appId.toString(),      localResources, StringUtils.join(shellArgs, " "));}// Set the env variables to be setup in the env where the application master will be runLOG.info("Set the environment for the application master");Map<String, String> env = new HashMap<String, String>();// put location of shell script into env// using the env info, the application master will create the correct local resource for the// eventual containers that will be launched to execute the shell scriptsenv.put(DSConstants.DISTRIBUTEDSHELLSCRIPTLOCATION, hdfsShellScriptLocation);env.put(DSConstants.DISTRIBUTEDSHELLSCRIPTTIMESTAMP, Long.toString(hdfsShellScriptTimestamp));env.put(DSConstants.DISTRIBUTEDSHELLSCRIPTLEN, Long.toString(hdfsShellScriptLen));// Add AppMaster.jar location to classpath// At some point we should not be required to add// the hadoop specific classpaths to the env.// It should be provided out of the box.// For now setting all required classpaths including// the classpath to "." for the application jarStringBuilder classPathEnv = new StringBuilder(Environment.CLASSPATH.$$())  .append(ApplicationConstants.CLASS_PATH_SEPARATOR).append("./*");for (String c : conf.getStrings(    YarnConfiguration.YARN_APPLICATION_CLASSPATH,    YarnConfiguration.DEFAULT_YARN_CROSS_PLATFORM_APPLICATION_CLASSPATH)) {  classPathEnv.append(ApplicationConstants.CLASS_PATH_SEPARATOR);  classPathEnv.append(c.trim());}classPathEnv.append(ApplicationConstants.CLASS_PATH_SEPARATOR).append(  "./log4j.properties");// Set the necessary command to execute the application masterVector<CharSequence> vargs = new Vector<CharSequence>(30);// Set java executable commandLOG.info("Setting up app master command");vargs.add(Environment.JAVA_HOME.$$() + "/bin/java");// Set Xmx based on am memory sizevargs.add("-Xmx" + amMemory + "m");// Set class namevargs.add(appMasterMainClass);// Set params for Application Mastervargs.add("--container_memory " + String.valueOf(containerMemory));vargs.add("--container_vcores " + String.valueOf(containerVirtualCores));vargs.add("--num_containers " + String.valueOf(numContainers));vargs.add("--priority " + String.valueOf(shellCmdPriority));for (Map.Entry<String, String> entry : shellEnv.entrySet()) {  vargs.add("--shell_env " + entry.getKey() + "=" + entry.getValue());}if (debugFlag) {  vargs.add("--debug");}vargs.add("1>" + ApplicationConstants.LOG_DIR_EXPANSION_VAR + "/AppMaster.stdout");vargs.add("2>" + ApplicationConstants.LOG_DIR_EXPANSION_VAR + "/AppMaster.stderr");// Get final commmandStringBuilder command = new StringBuilder();for (CharSequence str : vargs) {  command.append(str).append(" ");}LOG.info("Completed setting up app master command " + command.toString());List<String> commands = new ArrayList<String>();commands.add(command.toString());// Set up the container launch context for the application masterContainerLaunchContext amContainer = ContainerLaunchContext.newInstance(  localResources, env, commands, null, null, null);// Set up resource type requirements// For now, both memory and vcores are supported, so we set memory and// vcores requirementsResource capability = Resource.newInstance(amMemory, amVCores);appContext.setResource(capability);// Service data is a binary blob that can be passed to the application// Not needed in this scenario// amContainer.setServiceData(serviceData);// Setup security tokensif (UserGroupInformation.isSecurityEnabled()) {  // Note: Credentials class is marked as LimitedPrivate for HDFS and MapReduce  Credentials credentials = new Credentials();  String tokenRenewer = conf.get(YarnConfiguration.RM_PRINCIPAL);  if (tokenRenewer == null | | tokenRenewer.length() == 0) {    throw new IOException(      "Can't get Master Kerberos principal for the RM to use as renewer");  }  // For now, only getting tokens for the default file-system.  final Token<?> tokens[] =      fs.addDelegationTokens(tokenRenewer, credentials);  if (tokens != null) {    for (Token<?> token : tokens) {      LOG.info("Got dt for " + fs.getUri() + "; " + token);    }  }  DataOutputBuffer dob = new DataOutputBuffer();  credentials.writeTokenStorageToStream(dob);  ByteBuffer fsTokens = ByteBuffer.wrap(dob.getData(), 0, dob.getLength());  amContainer.setTokens(fsTokens);}appContext.setAMContainerSpec(amContainer);

一旦建立过程完成之后，client就可以提交应用（其中包括队列和优先级）

// Set the priority for the application masterPriority pri = Priority.newInstance(amPriority);appContext.setPriority(pri);// Set the queue to which this application is to be submitted in the RMappContext.setQueue(amQueue);// Submit the application to the applications manager// SubmitApplicationResponse submitResp = applicationsManager.submitApplication(appRequest);yarnClient.submitApplication(appContext);

在这种程度上，RM会接受应用，并且在后台中进行分配特殊需求的container，最终在分配的container中建立和发布Application master

client监控任务有很多方法

她可以喝RM进行通信，通过getApplicationReport方法来请求应用的报告。

代码：

// Get application report for the appId we are interested inApplicationReport report = yarnClient.getApplicationReport(appId);

RM收到的应用报告包括以下内容：

(我怎么感觉这根linux下的进程差不多啊，大概都一样）

1：大概的应用信息：id，队列，用户，启动时间

2：application master的信息：AM运行的host，client监听请求的端口，client和application master通信使用的token

3：应用跟踪信息：如果应用支持某种形式上的进度跟踪，他可以设定一个追中的URL，通过ApplicationReport's的getTrackingUrl方法。

4：应用状态：RM可以通过application Report的getYarnApplicationState方法来查看应用状态。如果yarnapplicationstate被设定中介，client回想getFinalApplicationStatus来检查应用实际的状态。

5：如果AM支持，client可以直接查询AM来获得app进度。也可以通过URL状态来获得。

在某种特定的情况下，应用占用太多资源，client会想要停止应用。yarnclient支持killapplication的call，来让client发送停止信号给AM通过RM。

 yarnClient.killApplication(appId);

编写一个ApplicationMaster

AM是任务实际的拥有者，RM发布AM，clien提供需要的信息和资源。

AM发布的时候，基于多租户特性，她的container会和其他的container共享一个物理机器，所以没有单独监听的端口

AM启动的时候，很多参数都是可用的，包括containerID,应用提交时间和NM宿主机器的信息。（application  constants的参数）

AM和RM交互需要通过一个applicationattemptid，她在container的ID中报关。

代码如下：

Map<String, String> envs = System.getenv();String containerIdString =    envs.get(ApplicationConstants.AM_CONTAINER_ID_ENV);if (containerIdString == null) {  // container id should always be set in the env by the framework  throw new IllegalArgumentException(      "ContainerId not set in the environment");}ContainerId containerId = ConverterUtils.toContainerId(containerIdString);ApplicationAttemptId appAttemptID = containerId.getApplicationAttemptId();

一旦AM初始化成功之后，我们可以启动两个client，一个负责RM，一个负责NM，我们使用传统的事件处理器来建立他们，后文细讲。

代码如下：

  AMRMClientAsync.CallbackHandler allocListener = new RMCallbackHandler();  amRMClient = AMRMClientAsync.createAMRMClientAsync(1000, allocListener);  amRMClient.init(conf);  amRMClient.start();  containerListener = createNMCallbackHandler();  nmClientAsync = new NMClientAsyncImpl(containerListener);  nmClientAsync.init(conf);  nmClientAsync.start();

AM会想RM发送心跳来证明正常工作。过期的时间参数在 YarnConfiguration.RM_AM_EXPIRY_INTERVAL_MS中来设定。AM需要向RM注册自己来启动心跳。

代码如下：

// Register self with ResourceManager// This will start heartbeating to the RMappMasterHostname = NetUtils.getHostname();RegisterApplicationMasterResponse response = amRMClient    .registerApplicationMaster(appMasterHostname, appMasterRpcPort,        appMasterTrackingUrl);

注册之后，如果包含最大资源容量，可以检查一下应用的请求

// Dump out information about cluster capability as seen by the// resource managerint maxMem = response.getMaximumResourceCapability().getMemory();LOG.info("Max mem capabililty of resources in this cluster " + maxMem);int maxVCores = response.getMaximumResourceCapability().getVirtualCores();LOG.info("Max vcores capabililty of resources in this cluster " + maxVCores);// A resource ask cannot exceed the max.if (containerMemory > maxMem) {  LOG.info("Container memory specified above max threshold of cluster."      + " Using max value." + ", specified=" + containerMemory + ", max="      + maxMem);  containerMemory = maxMem;}if (containerVirtualCores > maxVCores) {  LOG.info("Container virtual cores specified above max threshold of  cluster."    + " Using max value." + ", specified=" + containerVirtualCores + ", max="    + maxVCores);  containerVirtualCores = maxVCores;}List<Container> previousAMRunningContainers =    response.getContainersFromPreviousAttempts();LOG.info("Received " + previousAMRunningContainers.size()        + " previous AM's running containers on AM registration.");

基于任务需要，AM可以申请一系列的container来处理任务，我们可以计算container数量，然后请求这些container

List<Container> previousAMRunningContainers =    response.getContainersFromPreviousAttempts();List<Container> previousAMRunningContainers =    response.getContainersFromPreviousAttempts();LOG.info("Received " + previousAMRunningContainers.size()    + " previous AM's running containers on AM registration.");int numTotalContainersToRequest =    numTotalContainers - previousAMRunningContainers.size();// Setup ask for containers from RM// Send request for containers to RM// Until we get our fully allocated quota, we keep on polling RM for// containers// Keep looping until all the containers are launched and shell script// executed on them ( regardless of success/failure).for (int i = 0; i < numTotalContainersToRequest; ++i) {  ContainerRequest containerAsk = setupContainerAskForRM();  amRMClient.addContainerRequest(containerAsk);}

在setupContainerAskForm()中，一下的东西需要设置：

1：资源容量，YARN现在的资源需求需要制定内存，要小于最大内存，

2：优先级，一当申请很多container时，AM需要定义优先级。比如说：mapreduce AM会对map任务设置一个较高的优先级，对reduce设定一个较低的优先级

代码如下：

private ContainerRequest setupContainerAskForRM() {  // setup requirements for hosts  // using * as any host will do for the distributed shell app  // set the priority for the request  Priority pri = Priority.newInstance(requestPriority);  // Set up resource type requirements  // For now, memory and CPU are supported so we set memory and cpu requirements  Resource capability = Resource.newInstance(containerMemory,    containerVirtualCores);  ContainerRequest request = new ContainerRequest(capability, null, null,      pri);  LOG.info("Requested container ask: " + request.toString());  return request;}

application manager发送了container分配请求之后，container会异步发布，通过AMRMClientAsync client。处理器会实现AMRMClientAsync.CallbackHandler接口。

当container分配之后，处理器会设置一个线程，启动代码来发布container，我们使用LaunchContainerRunnable 来证明。

代码如下：

@Overridepublic void onContainersAllocated(List<Container> allocatedContainers) {  LOG.info("Got response from RM for container ask, allocatedCnt="      + allocatedContainers.size());  numAllocatedContainers.addAndGet(allocatedContainers.size());  for (Container allocatedContainer : allocatedContainers) {    LaunchContainerRunnable runnableLaunchContainer =        new LaunchContainerRunnable(allocatedContainer, containerListener);    Thread launchThread = new Thread(runnableLaunchContainer);    // launch and start the container on a separate thread to keep    // the main thread unblocked    // as all containers may not be allocated at one go.    launchThreads.add(launchThread);    launchThread.start();  }}

心跳方面，时间处理器会报告应用的进度

代码：

@Overridepublic float getProgress() {  // set progress to deliver to RM on next heartbeat  float progress = (float) numCompletedContainers.get()      / numTotalContainers;  return progress;}

container发布线程会在NM上发布container，之后，她需要执行一个类似的过程，设定containerLaunchContext，来处理在分配的container要运行的最终任务。一旦ContainerLaunchContext定义之后，AM会通过NMClientAsync来启动她。

代码如下：

// Set the necessary command to execute on the allocated containerVector<CharSequence> vargs = new Vector<CharSequence>(5);// Set executable commandvargs.add(shellCommand);// Set shell script pathif (!scriptPath.isEmpty()) {  vargs.add(Shell.WINDOWS ? ExecBatScripStringtPath    : ExecShellStringPath);}// Set args for the shell command if anyvargs.add(shellArgs);// Add log redirect paramsvargs.add("1>" + ApplicationConstants.LOG_DIR_EXPANSION_VAR + "/stdout");vargs.add("2>" + ApplicationConstants.LOG_DIR_EXPANSION_VAR + "/stderr");// Get final commmandStringBuilder command = new StringBuilder();for (CharSequence str : vargs) {  command.append(str).append(" ");}List<String> commands = new ArrayList<String>();commands.add(command.toString());// Set up ContainerLaunchContext, setting local resource, environment,// command and token for constructor.// Note for tokens: Set up tokens for the container too. Today, for normal// shell commands, the container in distribute-shell doesn't need any// tokens. We are populating them mainly for NodeManagers to be able to// download anyfiles in the distributed file-system. The tokens are// otherwise also useful in cases, for e.g., when one is running a// "hadoop dfs" command inside the distributed shell.ContainerLaunchContext ctx = ContainerLaunchContext.newInstance(  localResources, shellEnv, commands, null, allTokens.duplicate(), null);containerListener.addContainer(container.getId(), container);nmClientAsync.startContainerAsync(container, ctx);

NMclientAsync对象，和事件处理器共同处理container的事件，包括container启动停止，更新。

AM决定工作完成之后，需要和AM-RM client 解除注册关系，然后停止client。

代码如下：

try {  amRMClient.unregisterApplicationMaster(appStatus, appMessage, null);} catch (YarnException ex) {  LOG.error("Failed to unregister application", ex);} catch (IOException e) {  LOG.error("Failed to unregister application", e);}amRMClient.stop();