服务器和电脑的区别是什么

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2024-02-23

flinkjob 提交流程


在这里插入图片描述

任务启动流程图

请添加图片描述
请添加图片描述
可以先简单看下流程图，对比下面详细说明，再回来看会更加清晰

1客户端的工作内容

1.1解析命令

  • 第一步是命令行的解析，根据用户提交的 flink run 命令，在客户端类cliFronted中进行解析
  • 通过空格与-c -m 等参数指令，提取出，用户提交的参数详情
  • 获取flink的conf目录的路径
  • 根据conf目录的路径，加载配置
  • 加载命令行的输入 command line 封装命令行接口:按顺序 generic yarn default
  • 获取run动作 默认的配置项
  • 根据用户指定的配置项 进行解析

1.2 执行用户代码

用户代码会从env.execute开始执行

  • 从StreamExecutionEnvironment开始 execute
  • 根据用户代码 ，调用的算子，生成streamgraph图
  • streamgraph 转化 jobgraph
  • yarnjobClusterExcutor 创建启动 yarnclient 包含了一些yarn flink的配置和环境信息，并构造了一个yarnClusterDescriptor
  • yarnClusterDescriptor 获取集群特有配置 ： jobmanager 内存 每个taskmanager 内存 每个 slot槽数
  • 部署前检查： jar包路径 conf路径 yarn最大核数 检查置顶的yarn队列是否存在， 检查yarn有足够的资源
  • yarn启动appmaster AM 通过startAppMaster
YarnClusterDescriptor.java

private ClusterClientProvider<ApplicationId> deployInternal(
		ClusterSpecification clusterSpecification,
		String applicationName,
		String yarnClusterEntrypoint,
		@Nullable JobGraph jobGraph,
		boolean detached) throws Exception {
... ...
// 创建应用
	final YarnClientApplication yarnApplication = yarnClient.createApplication();
... ...
	ApplicationReport report = startAppMaster(
			flinkConfiguration,
			applicationName,
			yarnClusterEntrypoint,
			jobGraph,
			yarnClient,
			yarnApplication,
			validClusterSpecification);
... ...
}


private ApplicationReport startAppMaster(
		Configuration configuration,
		String applicationName,
		String yarnClusterEntrypoint,
		JobGraph jobGraph,
		YarnClient yarnClient,
		YarnClientApplication yarnApplication,
		ClusterSpecification clusterSpecification) throws Exception {
... ...
	// 初始化文件系统（HDFS）
	final FileSystem fs = FileSystem.get(yarnConfiguration);
... ...
ApplicationSubmissionContext appContext = yarnApplication.getApplicationSubmissionContext();

final List<Path> providedLibDirs = getRemoteSharedPaths(configuration);
// 上传文件的工具类
final YarnApplicationFileUploader fileUploader = YarnApplicationFileUploader.from(
	fs,
	fs.getHomeDirectory(),
	providedLibDirs,
	appContext.getApplicationId(),
	getFileReplication());
... ...
	final ApplicationId appId = appContext.getApplicationId();
... ...
	if (HighAvailabilityMode.isHighAvailabilityModeActivated(configuration)) {
		// yarn重试次数，默认2
		appContext.setMaxAppAttempts(
						configuration.getInteger(
						YarnConfigOptions.APPLICATION_ATTEMPTS.key(),
						YarnConfiguration.DEFAULT_RM_AM_MAX_ATTEMPTS));

		activateHighAvailabilitySupport(appContext);
	} else {
		//不是高可用重试次数为1
		appContext.setMaxAppAttempts(
				configuration.getInteger(
						YarnConfigOptions.APPLICATION_ATTEMPTS.key(),
						1));
	}
... ...
	
	// 多次调用上传HDFS的方法，分别是：
	//  => systemShipFiles：日志的配置文件、lib/目录下除了dist的jar包
	//  => shipOnlyFiles：plugins/目录下的文件
	//  => userJarFiles：用户代码的jar包
fileUploader.registerMultipleLocalResources (... ...);
... ...
	// 上传和配置ApplicationMaster的jar包：flink-dist*.jar
	final YarnLocalResourceDescriptor localResourceDescFlinkJar = fileUploader.uploadFlinkDist(flinkJarPath);
... ...
//
fileUploader.registerSingleLocalResource(
					jobGraphFilename,
					new Path(tmpJobGraphFile.toURI()),
					"",
					true,
					false);
... ...
	// 上传flink配置文件
	String flinkConfigKey = "flink-conf.yaml";
	Path remotePathConf = setupSingleLocalResource(
		flinkConfigKey,
		fs,
		appId,
		new Path(tmpConfigurationFile.getAbsolutePath()),
		localResources,
		homeDir,
		"");
... ...
	// 将JobGraph写入tmp文件并添加到本地资源，并上传到HDFS
	fileUploader.registerSingleLocalResource(
		jobGraphFilename,
		new Path(tmpJobGraphFile.toURI()),
		"",
		true,
		false);
... ...
// 上传flink配置文件
String flinkConfigKey = "flink-conf.yaml";
fileUploader.registerSingleLocalResource(
	flinkConfigKey,
	new Path(tmpConfigurationFile.getAbsolutePath()),
	"",
	true,
	true);
... ...
final JobManagerProcessSpec processSpec = JobManagerProcessUtils.processSpecFromConfigWithNewOptionToInterpretLegacyHeap(
flinkConfiguration,
			JobManagerOptions.TOTAL_PROCESS_MEMORY);
	//封装启动AM container的Java命令
	final ContainerLaunchContext amContainer = setupApplicationMasterContainer(
			yarnClusterEntrypoint,
			hasKrb5,
			processSpec);
... ...	
appContext.setApplicationName(customApplicationName);
appContext.setApplicationType(applicationType != null ? applicationType : "Apache Flink");
appContext.setAMContainerSpec(amContainer);
appContext.setResource(capability);
... ...
	yarnClient.submitApplication(appContext);
... ...	
}

2集群工作内容

2.1 启动AM

  • 初始化文件系统
  • 封装appcontext 一些环境信息 appid
  • yarn应用的文件上传器初始化： fs hdfs路径
  • 配置appid zk的namespace，高可用重试次数 id

2.2启动JobManager和 ResourceManager

  • ApplicationMaster启动dispatch和ResourceManager
  • ResourceManager 中的 slotmanager 组件负责真正像yarn申请资源
  • dispatch 启动 jobManager
  • jobManage中的slotpool 负责真正发送请求

这么看来，在JobManager启动的过程中 主要发生了4个动作：

  • 初始化各种服务initializeservices(…) 7个服务
  • webMonitorEndpoint启动
  • ResourceManager启动
  • Dispatcher启动
    以上四个动作全部完成，才算JobManager完成启动：下面是关键点的代码

其中 webMonitorEndpoint又启动了netty的服务端，一个leadership服务，这个服务里注册了几十个handler， 来接收客户端提交的命令，例如savepoint或停止指令等，他本身

其中ResourceManager 启动了两个心跳管理器，一个jobManagerHeartbeatManager，一个taskManagerHeartbeatManager
jobManagerHeartbeatManager -> jobmaster-ResourceManager
taskManagerHeartbeatManager -> taskManager-taskexecutor

每个job 都有一个jobmaster，jobmaster会被注册到ResourceManager，这些jobmaster会被维持与ResourceManager的心跳

ResourceManager还启动了两个定时服务，与taskmanager和slot相关，如果 taskmanager掉线或者slot分配超时，会参与一些处理。

//yarn启动入口  YarnApplicationClusterEntryPoint.java

    public static void main(final String[] args) {
    ...
            ClusterEntrypoint.runClusterEntrypoint(yarnApplicationClusterEntrypoint);
	...
}

//ClusterEntrypoint.java
import org.apache.flink.runtime.webmonitor.retriever.impl.RpcMetricQueryServiceRetriever;
    public static void runClusterEntrypoint(ClusterEntrypoint clusterEntrypoint) {
    ...
                clusterEntrypoint.startCluster();
	...
	}
    public void startCluster() throws ClusterEntrypointException {
    ...
    runCluster(configuration, pluginManager);
    ...
	}

private void runCluster(Configuration configuration, PluginManager pluginManager)
            throws Exception {
        synchronized (lock) {
            initializeServices(configuration, pluginManager);

            // write host information into configuration
            configuration.setString(JobManagerOptions.ADDRESS, commonRpcService.getAddress());
            configuration.setInteger(JobManagerOptions.PORT, commonRpcService.getPort());
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            final DispatcherResourceManagerComponentFactory
                    dispatcherResourceManagerComponentFactory =
                            createDispatcherResourceManagerComponentFactory(configuration);

            clusterComponent =
                    dispatcherResourceManagerComponentFactory.create(
                            configuration,
                            resourceId.unwrap(),
                            ioExecutor,
                            commonRpcService,
                            haServices,
                            blobServer,
                            heartbeatServices,
                            metricRegistry,
                            executionGraphInfoStore,
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                            new RpcMetricQueryServiceRetriever(
                                    metricRegistry.getMetricQueryServiceRpcService()),
                            this);

            clusterComponent
                    .getShutDownFuture()
                    .whenComplete(
                            (ApplicationStatus applicationStatus, Throwable throwable) -> {
                                if (throwable != null) {
                                    shutDownAsync(
                                            ApplicationStatus.UNKNOWN,
                                            ShutdownBehaviour.GRACEFUL_SHUTDOWN,
                                            ExceptionUtils.stringifyException(throwable),
                                            false);
                                } else {
                                    // This is the general shutdown path. If a separate more
                                    // specific shutdown was
                                    // already triggered, this will do nothing
                                    shutDownAsync(
                                            applicationStatus,
                                            ShutdownBehaviour.GRACEFUL_SHUTDOWN,
                                            null,
                                            true);
                                }
                            });
        }
    }
    
各种初始化

    protected void initializeServices(Configuration configuration, PluginManager pluginManager)
            throws Exception {

        LOG.info("Initializing cluster services.");

        synchronized (lock) {
            resourceId =
                    configuration
                            .getOptional(JobManagerOptions.JOB_MANAGER_RESOURCE_ID)
                            .map(
                                    value ->
                                            DeterminismEnvelope.deterministicValue(
                                                    new ResourceID(value)))
                            .orElseGet(
                                    () ->
                                            DeterminismEnvelope.nondeterministicValue(
                                                    ResourceID.generate()));

            LOG.debug(
                    "Initialize cluster entrypoint {} with resource id {}.",
                    getClass().getSimpleName(),
                    resourceId);

            workingDirectory =
                    ClusterEntrypointUtils.createJobManagerWorkingDirectory(
                            configuration, resourceId);

            LOG.info("Using working directory: {}.", workingDirectory);

            rpcSystem = RpcSystem.load(configuration);

            commonRpcService =
                    RpcUtils.createRemoteRpcService(
                            rpcSystem,
                            configuration,
                            configuration.getString(JobManagerOptions.ADDRESS),
                            getRPCPortRange(configuration),
                            configuration.getString(JobManagerOptions.BIND_HOST),
                            configuration.getOptional(JobManagerOptions.RPC_BIND_PORT));

            JMXService.startInstance(configuration.getString(JMXServerOptions.JMX_SERVER_PORT));

            // update the configuration used to create the high availability services
            configuration.setString(JobManagerOptions.ADDRESS, commonRpcService.getAddress());
            configuration.setInteger(JobManagerOptions.PORT, commonRpcService.getPort());

            ioExecutor =
                    Executors.newFixedThreadPool(
                            ClusterEntrypointUtils.getPoolSize(configuration),
                            new ExecutorThreadFactory("cluster-io"));
            haServices = createHaServices(configuration, ioExecutor, rpcSystem);
            blobServer =
                    BlobUtils.createBlobServer(
                            configuration,
                            Reference.borrowed(workingDirectory.unwrap().getBlobStorageDirectory()),
                            haServices.createBlobStore());
            blobServer.start();
            configuration.setString(BlobServerOptions.PORT, String.valueOf(blobServer.getPort()));
            heartbeatServices = createHeartbeatServices(configuration);
            metricRegistry = createMetricRegistry(configuration, pluginManager, rpcSystem);

            final RpcService metricQueryServiceRpcService =
                    MetricUtils.startRemoteMetricsRpcService(
                            configuration,
                            commonRpcService.getAddress(),
                            configuration.getString(JobManagerOptions.BIND_HOST),
                            rpcSystem);
            metricRegistry.startQueryService(metricQueryServiceRpcService, null);

            final String hostname = RpcUtils.getHostname(commonRpcService);

            processMetricGroup =
                    MetricUtils.instantiateProcessMetricGroup(
                            metricRegistry,
                            hostname,
                            ConfigurationUtils.getSystemResourceMetricsProbingInterval(
                                    configuration));

            executionGraphInfoStore =
                    createSerializableExecutionGraphStore(
                            configuration, commonRpcService.getScheduledExecutor());
        }
    }

2.3 申请资源 启动 taskmanager

  • JobManager 将JobGraph 转换 ExcuetionGraph
  • jobManager 中的slotpool 想 ResourceManager发出申请资源的请求
  • jobManager 收到 ResourceManager中来自 yarn 的许可的请求回复后，会启动TaskManager

3分配任务

3.1 资源计算

其实这是2.3中的工作，在申请资源时，flink在生成StreamGraph时，会根据用户代码，来计算任务的并行度，并计算出所需多少个slot，根据系统配置的slot大小，来计算所需任务的内容大小

3.2 分发任务

  • TaskManager启动后，TaskManager中 TaskExecutor 会像 resourceManager 注册slot
  • TaskExecutor 收到 resourceManager回复的肯定分配指令后，会把resourceManager给过来的offset，给到JobMaster
  • JobMaster 会提交具体的task到TaskExecutor
  • 任务就启动了

4 Task 任务调度执行图

请添加图片描述

5 任务提交过程

在那么client的东西是如何提交到JobManager的呢？

  • JobManager:
    • WebMonitorEndpoint:维护了一个netty服务端，client通过RestClient 提交job（JobSubmitHandler）
    • ResourceManager:资源集群的主节点
    • Dispatcher：job的调度执行
  • TaskManager:
    • TaskExecutor:提供计算资源，注册给ResourceManager，维持心跳，执行JobMaster发送给他要执行的task

在JobSubmitHandler.java 是真正处理提交过来的东西的类。

JobSubmitHandler.java  集群入口
    protected CompletableFuture<JobSubmitResponseBody> handleRequest(
            @Nonnull HandlerRequest<JobSubmitRequestBody> request,
            @Nonnull DispatcherGateway gateway){
    		//获取jobGraph  requestBody 就是客户端通过RestClient 发来的。
            CompletableFuture<JobGraph> jobGraphFuture = loadJobGraph(requestBody, nameToFile);

			//通过dispatcher提交jobgraph
			CompletableFuture<Acknowledge> jobSubmissionFuture =
                finalizedJobGraphFuture.thenCompose(
                        jobGraph -> gateway.submitJob(jobGraph, timeout));

	}

Dispatcher.java
//提交jobGraph 去执行

//重点1 创建Jobmaster jobGraph-> ExecutionGraph  
   private JobManagerRunner createJobMasterRunner(JobGraph jobGraph) throws Exception {
       Preconditions.checkState(!jobManagerRunnerRegistry.isRegistered(jobGraph.getJobID()));
       return jobManagerRunnerFactory.createJobManagerRunner(
               jobGraph,
               configuration,
               getRpcService(),
               highAvailabilityServices,
               heartbeatServices,
               jobManagerSharedServices,
               new DefaultJobManagerJobMetricGroupFactory(jobManagerMetricGroup),
               fatalErrorHandler,
               System.currentTimeMillis());
   }
   
//重点2 启动Jobmaster
private void runJob(JobManagerRunner jobManagerRunner, ExecutionType executionType)
           throws Exception {
       jobManagerRunner.start();
}
跑起来jobmaster相关服务，主要是注册和星跳
开始申请slot,并部署task

总结

  1. flink cliFronted类， 解析参数，封装commandLine， 执行用户代码生成streamGraph 并将streamGraph转化成JobGraph
  2. yarnjobClusterExcutor 初始化一个yarnclient对象,构造yarnClusterDescriptor
  3. yarnClusterDescriptor 将 依赖,jar，及其集群配置上传到Yarn Resource manager上
  4. yarn检查集群配置
  5. yarn的ResourceManager分配Container资源并通知对应的NodeManager启动ApplicationMaster。
  6. ApplicationMaster启动后加载Flink的Jar包和配置构建环境
  7. ApplicationMaster 启动 dispatch 和 ResourceManager（里面有slotmanager 真正管理资源向yarn申请资源的）
  8. dispatch 启动 JobMaster （里面有slotpool 真正发送请求的）
  9. JobMaster 将 JobGraph 转换 ExcuetionGraph
  10. JobMaster 向 Resourcemanager申请资源
  11. 启动JobManager之后ApplicationMaster向ResourceManager申请资源启动TaskManager。
  12. ResourceManager分配Container资源后，由ApplicationMaster通知资源所在节点
  13. NodeManager加载Flink的Jar包和配置构建环境并启动TaskManager
  14. TaskManager启动后 TaskExecutor 向 resourceManager 注册slot
  15. TaskExecutor 接收到分配的指令，提供offset给JobMaster
  16. JobMaster 提交具体的task 到 TaskExecutor
  17. JobManager 的 职 责 主 要 是 接 收 Flink 作 业 ， 调 度 Task ， 收 集 作 业 状 态 和 管 理TaskManager。
  18. TaskManager 相当于整个集群的 Slave 节点，负责具体的任务执行和对应任务在每个节点上的资源申请和管理。

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