本节描述了如何使用高阶API开发YARN应用。
一般概念是，应用程序提交客户端将应用程序提交到YARN ResourceManager(RM)。可以通过创建YarnClient对象来完成，启动YarnClient之后，客户端可以设置应用程序上下文，准备包含ApplicationMaster(AM)的应用程序的第一个容器，然后提交应用程序。需要提供的信息：例如关于运行应用所需的本地文件/Jar的详细信息，需要执行的实际命令(带有必要的命令行参数)，任何OS环境设置(可选)等等，实际上，需要描述为ApplicationMaster启动的Unix进程。
然后，YARN ResourceManager将在分配的容器上启动ApplicationMaster(如指定)。ApplicationMaster与YARN集群通信，并处理应用程序执行。它以异步方式执行操作。在应用程序启动期间，ApplicationMaster的主要任务是：
a）与ResourceManager进行通信以协商和分配将来的容器资源；
b）容器分配后，与YARN NodeManager(NM)进行通信启动NM上的应用程序容器。
任务a）可以通过AMRMClientAsync对象异步执行，使用事件处理程序的AMRMClientAsync.CallbackHandler类型中指定的事件处理方法。需要将事件处理程序显式设置为客户端。任务b）可以通过启动可运行对象来执行，然后在分配了容器后启动容器。作为启动此容器的一部分，AM必须指定具有启动信息(例如命令行规范，环境等)的ContainerLaunchContext。
在应用程序执行期间，ApplicationMaster通过NMClientAsync对象与NodeManager通信。所有容器事件均由与NMClientAsync关联的NMClientAsync.CallbackHandler处理，典型的回调处理程序处理客户端的启动，停止，状态更新和错误。ApplicationMaster还通过处理AMRMClientAsync.CallbackHandler的getProgress()方法向ResourceManager报告Job执行进度。
除了异步客户端，某些工作流(AMRMClient与NMClient)还有同步执行的版本。 但是推荐使用异步客户端是因为更简单的用法，本文将主要介绍异步客户端。 有关同步客户端的更多信息，请参考AMRMClient和NMClient。
以下是重要的接口：
注意：
YarnClient yarnClient = YarnClient.createYarnClient();
yarnClient.init(conf);
yarnClient.start();
YarnClientApplication app = yarnClient.createApplication();
GetNewApplicationResponse appResponse = app.getNewApplicationResponse();
YarnClientApplication对新应用程序的响应还包含有关群集的信息，例如群集的最小/最大资源能力。这是必需的，以便确保可以正确设置将在其中启动ApplicationMaster的容器的规格。 有关更多详细信息，请参考GetNewApplicationResponse。
客户端的主要关键在于设置ApplicationSubmissionContext，它定义RM启动AM所需的所有信息。 客户需要在上下文中设置以下内容：
1）应用信息，例如：id，name
2）队列，优先级消息：提交申请的队列，为申请分配的优先级
3）用户：提交应用的用户
4）ContainerLaunchContext：定义将在其中启动和运行AM的容器的信息。 如前所述，ContainerLaunchContext定义了运行应用程序所需的所有必需信息，例如本地资源：二进制文件，jar，文件等，环境设置(CLASSPATH等)，要执行的命令和安全令牌(RECT)。
// set the application submission context
ApplicationSubmissionContext 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 resources
Map<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 path
FileSystem fs = FileSystem.get(conf);
addToLocalResources(fs, appMasterJar, appMasterJarPath, appId.toString(),
localResources, null);
// Set the log4j properties if needed
if (!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 run
LOG.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 scripts
env.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 jar
StringBuilder classPathEnv = new StringBuilder(Environment.CLASSPATH.$$())
.append(ApplicationConstants.CLASS_PATH_SEPARATOR).append("./*");
for (String c : conf.getStrings(
YarnConfiguration.YARN_APPLICATION_CLASSPATH,
YarnConfiguration.DEFAULT_YARN_CROSS_PLATFORM_APPLICATION_CLASSPATH)) {
classPathEnv.append(ApplicationConstants.CLASS_PATH_SEPARATOR);
classPathEnv.append(c.trim());
}
classPathEnv.append(ApplicationConstants.CLASS_PATH_SEPARATOR).append(
"./log4j.properties");
// Set the necessary command to execute the application master
Vector<CharSequence> vargs = new Vector<CharSequence>(30);
// Set java executable command
LOG.info("Setting up app master command");
vargs.add(Environment.JAVA_HOME.$$() + "/bin/java");
// Set Xmx based on am memory size
vargs.add("-Xmx" + amMemory + "m");
// Set class name
vargs.add(appMasterMainClass);
// Set params for Application Master
vargs.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 command
StringBuilder 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 master
ContainerLaunchContext 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 requirements
Resource 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 tokens
if (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);
// Set the priority for the application master
Priority pri = Priority.newInstance(amPriority);
appContext.setPriority(pri);
// Set the queue to which this application is to be submitted in the RM
appContext.setQueue(amQueue);
// Submit the application to the applications manager
// SubmitApplicationResponse submitResp = applicationsManager.submitApplication(appRequest);
yarnClient.submitApplication(appContext);
至此，RM将在后台接受该应用程序，并将分配具有所需规范的容器，然后最终在所分配的容器上设置并启动AM的过程。
客户可以通过多种方式跟踪实际任务的进度:
// Get application report for the appId we are interested in
ApplicationReport report = yarnClient.getApplicationReport(appId);
从RM收到的ApplicationReport包括以下内容：
- *General application information*：应用程序ID，提交应用程序的队列，提交应用程序的用户以及应用程序的开始时间
- *ApplicationMaster details*：运行AM的主机，正在监听来自客户端的请求的rpc端口以及客户端需要与AM通信的令牌
- *Application tracking information*：如果应用程序支持某种形式的进度跟踪，则可以设置一个跟踪URL，该URL可通过ApplicationReport的getTrackingUrl方法获得，客户端可以查看该URL来监视进度
- *Application status*：ResourceManager看到的应用程序状态可通过ApplicationReport.getYarnApplicationState获得
- 如果ApplicationMaster支持，则客户端可以通过从应用程序报告中获取的host:rpcport信息直接向AM本身查询进度更新。 如果可用，它也可以使用从报告中获得的跟踪URL。 yarnClient.killApplication(appId);
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();
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();
// Register self with ResourceManager
// This will start heartbeating to the RM
appMasterHostname = NetUtils.getHostname();
RegisterApplicationMasterResponse response = amRMClient
.registerApplicationMaster(appMasterHostname, appMasterRpcPort,
appMasterTrackingUrl);
// Dump out information about cluster capability as seen by the
// resource manager
int maxMem = response.getMaximumResourceCapability().getMemory();
LOG.info("Max mem capability of resources in this cluster " + maxMem);
int maxVCores = response.getMaximumResourceCapability().getVirtualCores();
LOG.info("Max vcores capability 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.");
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setupContainerAskForRM()中，需要设置以下两件事：
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;
}
在ApplicationMaster发送了容器分配请求之后，会通过AMRMClientAsync客户端的事件处理程序异步启动容器。 处理程序应实现AMRMClientAsync.CallbackHandler接口。
@Override
public 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();
}
}
- 心跳时，事件处理程序将报告应用程序的进度 @Override
public float getProgress() {
// set progress to deliver to RM on next heartbeat
float progress = (float) numCompletedContainers.get()
/ numTotalContainers;
return progress;
}
// Set the necessary command to execute on the allocated container
Vector<CharSequence> vargs = new Vector<CharSequence>(5);
// Set executable command
vargs.add(shellCommand);
// Set shell script path
if (!scriptPath.isEmpty()) {
vargs.add(Shell.WINDOWS ? ExecBatScripStringtPath
: ExecShellStringPath);
}
// Set args for the shell command if any
vargs.add(shellArgs);
// Add log redirect params
vargs.add("1>" + ApplicationConstants.LOG_DIR_EXPANSION_VAR + "/stdout");
vargs.add("2>" + ApplicationConstants.LOG_DIR_EXPANSION_VAR + "/stderr");
// Get final command
StringBuilder 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对象及其事件处理程序一起处理容器事件。 包括容器的启动，停止，状态更新以及发生错误
ApplicationMaster确定工作完成后，需要通过AM-RM客户端注销自身，然后停止该客户端
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();
可以使用LocalResource将资源添加到应用程序请求中。 这将导致YARN将资源分发到ApplicationMaster节点。 如果资源是tgz，zip或jar可以让YARN解压缩。 然后，要做的就是将解压缩的文件夹添加到类路径中。 例如，在创建应用程序请求时：
File packageFile = new File(packagePath);
URL packageUrl = ConverterUtils.getYarnUrlFromPath(
FileContext.getFileContext().makeQualified(new Path(packagePath)));
packageResource.setResource(packageUrl);
packageResource.setSize(packageFile.length());
packageResource.setTimestamp(packageFile.lastModified());
packageResource.setType(LocalResourceType.ARCHIVE);
packageResource.setVisibility(LocalResourceVisibility.APPLICATION);
resource.setMemory(memory);
z.setResource(resource);
containerCtx.setCommands(ImmutableList.of(
"java -cp './package/*' some.class.to.Run "
+ "1>" + ApplicationConstants.LOG_DIR_EXPANSION_VAR + "/stdout "
+ "2>" + ApplicationConstants.LOG_DIR_EXPANSION_VAR + "/stderr"));
containerCtx.setLocalResources(
Collections.singletonMap("package", packageResource));
appCtx.setApplicationId(appId);
appCtx.setUser(user.getShortUserName);
appCtx.setAMContainerSpec(containerCtx);
yarnClient.submitApplication(appCtx);
setLocalResources命令采用名称到资源的映射。 该名称将成为应用程序cwd中的符号链接，因此只需使用./package/*即可引用其中的工件。
注意：Java的classpath参数非常敏感。 确保语法完全正确。
将包裹分发到AM后，只要AM启动新的容器(假设希望将资源发送到容器)，就需要遵循相同的过程。 此代码是相同的。 只需要确保为AM提供了包路径(HDFS或本地)，以便它可以将资源URL与容器ctx一起发送。
ApplicationAttemptId?ApplicationAttemptId将通过环境传递到AM，并且可以通过ConverterUtils帮助函数将来自环境的值转换为ApplicationAttemptId对象。
这可能是由于高内存使用量超出了您请求的容器内存大小。 有许多原因可以导致此。 首先，查看NodeManager杀死容器时转储的进程树。 两个配置是关键：物理内存和虚拟内存。 如果超出了物理内存限制，则应用程序将使用过多的物理内存。 如果正在运行Java应用程序，则可以使用-hprof查看堆中正在占用空间的内容。 如果超出了虚拟内存，则可能需要增加群集范围的配置变量yarn.nodemanager.vmem-pmem-ratio的值(该值设置虚拟内存是物理内存的倍数)。
启动容器时在命令行上设置-Djava.library.path可能导致Hadoop使用的本地库无法正确加载，并可能导致错误。 改用LD_LIBRARY_PATH更干净。
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