MiniCluster的启动流程：

       首先来看最简单的本地模式MiniCluster的启动流程，以此来分析Flink的具体启动流程以及内部各组件之间的交互形式。MiniCluster可以看做是内嵌的Flink运行时环境，所有的组件都在独立的本地线程中运行。MiniCluster的启动入口在LocalStreamEnvironment#execute(jobName)中。其基本的相关的主要类图和actor模型如下：

在MiniCluster#start启动源码中，启动流程大致分为三个阶段：

• 初始化配置信息、创建一些辅助的服务，如RpcService，HighAvailabilityServices，BlobServer，HeartbeatServices等
• 启动ResourceManager、TaskManager等
• 启动Dispatcher等

在MiniCluster中，其集群中的各个角色分配及作用如下：

1. ResouceManager
   1. 负责容器的分配
   2. 使用FencedAkkaRpcActor实现，其rpcEndpoint为org.apache.flink.runtime.resourcemanager.ResourceManager
2. JobMaster
   1. 负责任务执行计划的调度和执行，
   2. 使用FencedAkkaRpcActor实现，其rpcEndpoint为org.apache.flink.runtime.jobmaster.JobMaster
      1. JobMaster持有一个SlotPool的Actor，用来暂存TaskExecutor提供给JobMaster并被接受的slot。JobMaster的Scheduler组件从这个SlotPool中获取资源以调度job的task
3. Dispatcher
   1. 主要职责是接收从Client端提交过来的job并生成一个JobMaster去负责这个job在集群资源管理器上执行。
      1. 不是所有部署方式都需要用到dispatcher，比如yarn-cluster 的部署方式可能就不需要
   2. 使用FencedAkkaRpcActor实现，其rpcEndpoint为 org.apache.flink.runtime.dispatcher.StandaloneDispatcher
4. TaskExecutor
   1. TaskExecutor会与ResouceManager和JobMaster两者进行通信。
      1. 会向ResourceManager报告自身的可用资源；并维护本身slot的状态
      2. 根据slot的分配结果，接收JobMaster的命令在对应的slot上执行指定的task。
      3. TaskExecutor还需要向以上两者定时上报心跳信息。
   2. 使用AkkaRpcActor实现，其rpcEndpoint为org.apache.flink.runtime.taskexecutor.TaskExecutor

/**
 * Starts the mini cluster, based on the configured properties.
 * @throws Exception This method passes on any exception that occurs during the startup of the mini cluster.
 */
public void start() throws Exception {
   synchronized (lock) {
      checkState(!running, "FlinkMiniCluster is already running");

      LOG.info("Starting Flink Mini Cluster");
      LOG.debug("Using configuration {}", miniClusterConfiguration);

      // MiniCluster初始化配置信息
      final Configuration configuration = miniClusterConfiguration.getConfiguration();
      final Time rpcTimeout = miniClusterConfiguration.getRpcTimeout();
      final int numTaskManagers = miniClusterConfiguration.getNumTaskManagers();
      final boolean useSingleRpcService = miniClusterConfiguration.getRpcServiceSharing() == RpcServiceSharing.SHARED;

      try {
         initializeIOFormatClasses(configuration); // 初始化fs write or dir是否允许over_write

         LOG.info("Starting Metrics Registry");
         metricRegistry = createMetricRegistry(configuration); // 创建指标监控服务
         this.jobManagerMetricGroup = MetricUtils.instantiateJobManagerMetricGroup( // 
            metricRegistry,
            "localhost",
            ConfigurationUtils.getSystemResourceMetricsProbingInterval(configuration));

         final RpcService jobManagerRpcService;
         final RpcService resourceManagerRpcService;
         final RpcService[] taskManagerRpcServices = new RpcService[numTaskManagers];

         // bring up all the RPC services // 根据conf信息 构建对应的rpc服务 (本地运行的话 rpc服务是通用的 适用于多个服务)
         LOG.info("Starting RPC Service(s)");

         // we always need the 'commonRpcService' for auxiliary calls
         commonRpcService = createRpcService(configuration, rpcTimeout, false, null);

         // TODO: Temporary hack until the metric query service is ported to the RpcEndpoint
         metricQueryServiceActorSystem = MetricUtils.startMetricsActorSystem(
            configuration,
            commonRpcService.getAddress(),
            LOG);
         metricRegistry.startQueryService(metricQueryServiceActorSystem, null);

         if (useSingleRpcService) { // rpc服务是否可share
            for (int i = 0; i < numTaskManagers; i++) {
               taskManagerRpcServices[i] = commonRpcService;
            }

            jobManagerRpcService = commonRpcService;
            resourceManagerRpcService = commonRpcService;

            this.resourceManagerRpcService = null;
            this.jobManagerRpcService = null;
            this.taskManagerRpcServices = null;
         }
         else {
            // start a new service per component, possibly with custom bind addresses
            final String jobManagerBindAddress = miniClusterConfiguration.getJobManagerBindAddress();
            final String taskManagerBindAddress = miniClusterConfiguration.getTaskManagerBindAddress();
            final String resourceManagerBindAddress = miniClusterConfiguration.getResourceManagerBindAddress();

            jobManagerRpcService = createRpcService(configuration, rpcTimeout, true, jobManagerBindAddress);
            resourceManagerRpcService = createRpcService(configuration, rpcTimeout, true, resourceManagerBindAddress);

            for (int i = 0; i < numTaskManagers; i++) {
               taskManagerRpcServices[i] = createRpcService(
                     configuration, rpcTimeout, true, taskManagerBindAddress);
            }

            this.jobManagerRpcService = jobManagerRpcService;
            this.taskManagerRpcServices = taskManagerRpcServices;
            this.resourceManagerRpcService = resourceManagerRpcService;
         }

         // create the high-availability services // 构建ha高可用服务
         LOG.info("Starting high-availability services");
         haServices = HighAvailabilityServicesUtils.createAvailableOrEmbeddedServices(
            configuration,
            commonRpcService.getExecutor());

         blobServer = new BlobServer(configuration, haServices.createBlobStore()); // 构建分布式文件存储服务
         blobServer.start();

         heartbeatServices = HeartbeatServices.fromConfiguration(configuration); // 心跳服务

         // bring up the ResourceManager(s) 
         // 启动构建ResourceManger服务及其内部组件，主要托管给resourceManagerRunner类，其内部持有ResourceManger实例；
         // 并启动ResourceManager#start()以此来启动资源管理器的服务
         LOG.info("Starting ResourceManger");
         resourceManagerRunner = startResourceManager(
            configuration,
            haServices,
            heartbeatServices,
            metricRegistry,
            resourceManagerRpcService,
            new ClusterInformation("localhost", blobServer.getPort()),
            jobManagerMetricGroup);

         // 构建分布式存储服务
         blobCacheService = new BlobCacheService(
            configuration, haServices.createBlobStore(), new InetSocketAddress(InetAddress.getLocalHost(), blobServer.getPort())
         );

         // bring up the TaskManager(s) for the mini cluster
         LOG.info("Starting {} TaskManger(s)", numTaskManagers);
         taskManagers = startTaskManagers( // 启动对应数量的TaskManager
            configuration,
            haServices,
            heartbeatServices,
            metricRegistry,
            blobCacheService,
            numTaskManagers,
            taskManagerRpcServices);

         // starting the dispatcher rest endpoint
         LOG.info("Starting dispatcher rest endpoint.");

         dispatcherGatewayRetriever = new RpcGatewayRetriever<>(
            jobManagerRpcService,
            DispatcherGateway.class,
            DispatcherId::fromUuid,
            20,
            Time.milliseconds(20L));
         final RpcGatewayRetriever<ResourceManagerId, ResourceManagerGateway> resourceManagerGatewayRetriever = new RpcGatewayRetriever<>(
            jobManagerRpcService,
            ResourceManagerGateway.class,
            ResourceManagerId::fromUuid,
            20,
            Time.milliseconds(20L));

         // 注册开启rest web服务端(主要是RestServerEndpoint)
         // 其会web端的JobSubmitHandler(供web端进行job任务的提交)
         // 之后便会对web端的所有REST接口服务进行handler注册(主要包括web页面展示的job、metric、checkpoint、savepoint等等)
         this.dispatcherRestEndpoint = new DispatcherRestEndpoint(
            RestServerEndpointConfiguration.fromConfiguration(configuration),
            dispatcherGatewayRetriever,
            configuration,
            RestHandlerConfiguration.fromConfiguration(configuration),
            resourceManagerGatewayRetriever,
            blobServer.getTransientBlobService(),
            WebMonitorEndpoint.createExecutorService(
               configuration.getInteger(RestOptions.SERVER_NUM_THREADS, 1),
               configuration.getInteger(RestOptions.SERVER_THREAD_PRIORITY),
               "DispatcherRestEndpoint"),
            new AkkaQueryServiceRetriever(
               metricQueryServiceActorSystem,
               Time.milliseconds(configuration.getLong(WebOptions.TIMEOUT))),
            haServices.getWebMonitorLeaderElectionService(),
            new ShutDownFatalErrorHandler());

         dispatcherRestEndpoint.start(); // 

         restAddressURI = new URI(dispatcherRestEndpoint.getRestBaseUrl());

         // bring up the dispatcher that launches JobManagers when jobs submitted
         LOG.info("Starting job dispatcher(s) for JobManger");

         final HistoryServerArchivist historyServerArchivist = HistoryServerArchivist.createHistoryServerArchivist(configuration, dispatcherRestEndpoint);

         // 
         dispatcher = new StandaloneDispatcher(
            jobManagerRpcService,
            Dispatcher.DISPATCHER_NAME + UUID.randomUUID(),
            configuration,
            haServices,
            resourceManagerRunner.getResourceManageGateway(),
            blobServer,
            heartbeatServices,
            jobManagerMetricGroup,
            metricRegistry.getMetricQueryServicePath(),
            new MemoryArchivedExecutionGraphStore(),
            Dispatcher.DefaultJobManagerRunnerFactory.INSTANCE,
            new ShutDownFatalErrorHandler(),
            dispatcherRestEndpoint.getRestBaseUrl(),
            historyServerArchivist);

         dispatcher.start(); // 启动基本的调度分发服务(其内部持有jobmaster去进行具体的任务执行与分发)

         resourceManagerLeaderRetriever = haServices.getResourceManagerLeaderRetriever();
         dispatcherLeaderRetriever = haServices.getDispatcherLeaderRetriever();

         resourceManagerLeaderRetriever.start(resourceManagerGatewayRetriever);
         dispatcherLeaderRetriever.start(dispatcherGatewayRetriever);
      }
      catch (Exception e) {
         // cleanup everything
         try {
            close();
         } catch (Exception ee) {
            e.addSuppressed(ee);
         }
         throw e;
      }

      // create a new termination future
      terminationFuture = new CompletableFuture<>();

      // now officially mark this as running
      running = true;

      LOG.info("Flink Mini Cluster started successfully");
   }
}

1、HighAvailabilityServices

        HighAvailabilityServicesUtils是创建HighAvailabilityServices的工具类，其主要通过配置config中的high-availability选项来进行具体高可用服务组件的选择及实例化；其通过工厂设计模式来进行具体对象的实例化；主要分为三类：

• NONE：EmbeddedHaServices
• ZOOKEEPER：ZooKeeperHaServices
• FACTORY_CLASS：自定义实现(在配置中指定对应的className)

        在没有配置HA的情况下会创建EmbeddedHaServices。EmbeddedHaServices不具备高可用的特性，适用于ResourceMangaer，TaksManager，JobManager等所有组件都运行在同一个进程的情况。EmbeddedHaService为各组件创建的选举服务为EmbeddedLeaderElectionService，一旦有参与选举的LeaderContender加入，该contender就被选择为leader。

public static HighAvailabilityServices createAvailableOrEmbeddedServices(Configuration config, Executor executor) throws Exception {
   HighAvailabilityMode highAvailabilityMode = LeaderRetrievalUtils.getRecoveryMode(config); // 选择对应的ha模式

   switch (highAvailabilityMode) {
      case NONE:
         return new EmbeddedHaServices(executor);

      case ZOOKEEPER:
         BlobStoreService blobStoreService = BlobUtils.createBlobStoreFromConfig(config);

         return new ZooKeeperHaServices(
            ZooKeeperUtils.startCuratorFramework(config),
            executor,
            config,
            blobStoreService);

      case FACTORY_CLASS:
         return createCustomHAServices(config, executor);

      default:
         throw new Exception("High availability mode " + highAvailabilityMode + " is not supported.");
   }
}

// EmbeddedHaServices高可用服务   其内部主要用于保存对应组件的leaderService服务; 其内部主要保存以下几种组件ha服务(不同EmbeddedLeaderService实例)
private final EmbeddedLeaderService resourceManagerLeaderService; // 用于RM选举相关
private final EmbeddedLeaderService dispatcherLeaderService; // 用于dispatcher选举相关
private final HashMap<JobID, EmbeddedLeaderService> jobManagerLeaderServices; // 用于JobManager选举相关；一个job任务对应一个
private final EmbeddedLeaderService webMonitorLeaderService; // webmonitor

其中ha最主要相关的两个服务：

     1、LeaderElectionService用于组件参与leader选举(其相关的接口如下)；start方法就是将当前的组件加入Leader选举；

        当某个组件被选举为leader时，会回调该组件实现的grantLeadership方法(第一次被选举为leader)，当某个组件不再是leader时，会回调该组件实现的revokeLeadership方法。

public interface LeaderElectionService {
    void start(LeaderContender contender) throws Exception; // 将当前组件加入到leader选举
    void stop() throws Exception;
    void confirmLeaderSessionID(UUID leaderSessionID);
    boolean hasLeadership(@Nonnull UUID leaderSessionId);
}
public interface LeaderContender {
    void grantLeadership(UUID leaderSessionID);
    void revokeLeadership();
    String getAddress();
    void handleError(Exception exception);
}

    2、LeaderRetrievalListener用于leader改变后通知回调，其主要用于获取其他组件Leader的功能

        LeaderRetrievalService非常简洁，提供了start和stop方法，并且start方法只能被调用一次，在ZK模式中因为它只会监听一条ZK上的路径(即一个组件的变化)；

        在启动LeaderRetrievalService的方法中需要接收参数LeaderRetrievalListener，将实现这个接口的类的实例作为参数传入这个方法；

        在相应组件leader发生变化时会回调notifyLeaderAddress方法，在LeaderRetrievalService抛出异常的时候会调用handleError方法；

public interface LeaderRetrievalService {
	void start(LeaderRetrievalListener listener) throws Exception;
	void stop() throws Exception;
}
public interface LeaderRetrievalListener {
	void notifyLeaderAddress(@Nullable String leaderAddress, @Nullable UUID leaderSessionID);
	void handleError(Exception exception);
}

其部分运行debug实例示例如下：

2、ResourceManager

         在创建HighAvailabilityServices之后，就会启动ResourceManager。ResourceManagerRunner#startResourceManager会创建ResourceManager和对应的ResourceManagerRuntimeServices；其中ResourceManagerRuntimeServices主要工作类似于工厂类；其主要负责SlotManager和JobLeaderIdService服务的初始化实例创建，(其中JobLeaderIdService服务主要作用是为每个job任务选择出对应ha可用的JobMaster，并将该job任务分配该JobMatser)

JobLeaderIdService#addJob()
/**
 * Add a job to be monitored to retrieve the job leader id.
 *
 * @param jobId identifying the job to monitor
 * @throws Exception if the job could not be added to the service
 */
public void addJob(JobID jobId) throws Exception {
   Preconditions.checkNotNull(jobLeaderIdActions);
   LOG.debug("Add job {} to job leader id monitoring.", jobId);

   if (!jobLeaderIdListeners.containsKey(jobId)) {
      LeaderRetrievalService leaderRetrievalService = highAvailabilityServices.getJobManagerLeaderRetriever(jobId); // 从ha中选择出合适的leader

      JobLeaderIdListener jobIdListener = new JobLeaderIdListener(jobId, jobLeaderIdActions, leaderRetrievalService);
      jobLeaderIdListeners.put(jobId, jobIdListener);
   }
}

ResourceManager资源管理器其继承了FencedRpcEndpoint实现了RPC服务，其内部组件主要包含

1. 上诉的SlotManager和JobLeaderIdService服务、高可用leader选举服务leaderElectionService等
2. 心跳管理器taskManagerHeartbeatManager、jobManagerHeartbeatManager
3. 指标监控服务MetricRegistry等
4. 所有已注册的TaskExecutors

之后其会调用ResourceManager#start()方法来启动此RM；在ResourceManager启动的回调函数中，会通过HighAvailabilityServices获取到选举服务，从而参与到选举之中。并启动JobLeaderIdService，管理向当前ResourceManager注册的作业的leader id。其主要启动服务内容如下：

ResourceManager#start()方法：
public void start() throws Exception {
   // start a leader
   super.start(); // 调用父类RPC接口；以此启动RPC服务

   leaderElectionService = highAvailabilityServices.getResourceManagerLeaderElectionService(); // 可用的ha leader选举服务

   initialize(); // 初始化 do nothing；不做任何操作

   try {
      // 将该RM加入并选举作为leader; 
      // 并在里面启动SlotManager相关的周期性超时检测线程(主要包括checkTaskManagerTimeouts、checkSlotRequestTimeouts)
      leaderElectionService.start(this); 
   } catch (Exception e) {
      throw new ResourceManagerException("Could not start the leader election service.", e);
   }

   try {
      jobLeaderIdService.start(new JobLeaderIdActionsImpl()); // 
   } catch (Exception e) {
      throw new ResourceManagerException("Could not start the job leader id service.", e);
   }

   registerSlotAndTaskExecutorMetrics(); // 添加slots指标监控，taskSlotsAvailable、taskSlotsTotal、numRegisteredTaskManagers
}

3、TaskExecutor

        之后便是启动对应的TaskManager，TaskManager的启动流程类似于ResourceManager，也是委托给TaskManagerRunner#startTaskManager()；TaskManagerRunner内部的主要工作包括参数校验和初始化组件如下：

1. 先初始化config参数配置，主要包括network、memory、JVM内存或堆外内存等等
2. 其次启动对应的network、I/O manager、memory manager、BroadcastVariableManager、TaskSlotTable、JobManagerTable、TaskExecutorLocalStateStoresManager等等
3. 最终根据上诉的内部组件启动TaskExecutor实例

TaskExecutor任务管理器也继承了RpcEndpoint实现了RPC服务；其主要的内部组件包含(其实也就是TaskManagerRunner初始化的组件)：

1. 高可用服务HighAvailabilityServices
2. 心跳管理器jobManagerHeartbeatManager、resourceManagerHeartbeatManager
3. 上诉对应的network、I/O manager、memory manager、BroadcastVariableManager、TaskSlotTable、JobManagerTable、TaskExecutorLocalStateStoresManager等等组件
4. 其次会和ResourceManager建立连接EstablishedResourceManagerConnection、TaskExecutorToResourceManagerConnection；(在TM#start()的时候建立)

      以及和对应JobManager建立的连接jobManagerConnections；

之后其会调用TaskManager#start()方法来启动此TM；在TaskManager启动的回调函数中，会通过HighAvailabilityServices获取到选举服务，从而参与到选举之中。并启动JobLeaderIdService，管理向当前ResourceManager注册的作业的leader id。其主要启动服务内容如下：

TaskExecutor#start()方法：
public void start() throws Exception {
   super.start(); // 调用父类RPC接口；以此启动RPC服务

   // start by connecting to the ResourceManager
   try {
      // 采用LeaderRetrievalService进行选举出leader后的回调通知；其会调用listener中的notifyLeaderAddress()方法；
      // 进行RM的连接; 向RM注册自己以及建立相关的心跳连接；建立连接(resourceManagerConnection = new TaskExecutorToResourceManagerConnection(......., ResourceManagerRegistrationListener))
      // 其在连接建立成功之后；会通过ResourceManagerRegistrationListener监听RM连接成功回调函数进行 将slot资源通过RPC向RM进行上报
      resourceManagerLeaderRetriever.start(new ResourceManagerLeaderListener());
   } catch (Exception e) {
      onFatalError(e);
   }

   // tell the task slot table who's responsible for the task slot actions // 
   taskSlotTable.start(new SlotActionsImpl());

   // start the job leader service
   // 开启jobLeaderService服务并添加回调监听；等待jobmanager参与leader选举，监听回调JobLeaderListenerImpl进行establishJobManagerConnection
   // 也向其jobmanager提供其管理的job任务jobId对应申请的slot资源状态；offerSlotsToJobManager(jobId);
   jobLeaderService.start(getAddress(), getRpcService(), haServices, new JobLeaderListenerImpl()); // 

   fileCache = new FileCache(taskManagerConfiguration.getTmpDirectories(), blobCacheService.getPermanentBlobService());

   startRegistrationTimeout();
}

其连接主要是通过LeaderRetrievalListener来进行的，首先其会注册自己的监听器ResourceManagerLeaderListener，之后便等待leader选举并发送执行NotifyOfLeaderCall通知；其会调用对应注册监听器ResourceManagerLeaderListener的listener.notifyLeaderAddress(); 方法产生回调；一旦获取ResourceManager的leader被确定以及Call通知之后，就可以获取到ResourceManager对应的RpcGateway，之后便会异步的与对应的RM建立连接reconnectToResourceManager；在成功建立连接到RM之后，便会通过RPC异步的调用resourceManager.registerTaskExecutor()；向RM注册自己以及建立相关的心跳连接；在注册成功之后，其注册成功信息会通过ResourceManagerRegistrationListener监听函数异步回调；向RM上报自己的slot资源；

/**
 * The listener for leader changes of the resource manager.
 */
private final class ResourceManagerLeaderListener implements LeaderRetrievalListener {
   @Override
   public void notifyLeaderAddress(final String leaderAddress, final UUID leaderSessionID) {
      // 获得ResourceManager的地址, 和ResourceManager建立连接(主要是TaskExecutorToResourceManagerConnection和EstablishedResourceManagerConnection)
      runAsync(
         () -> notifyOfNewResourceManagerLeader(
            leaderAddress,
            ResourceManagerId.fromUuidOrNull(leaderSessionID)));
   }

   @Override
   public void handleError(Exception exception) {
      onFatalError(exception);
   }
}

private RetryingRegistration<F, G, S> createNewRegistration() {
   // 创建生成对应的注册连接；该处为TaskExecutorToResourceManagerConnection.ResourceManagerRegistration
   RetryingRegistration<F, G, S> newRegistration = checkNotNull(generateRegistration());

   CompletableFuture<Tuple2<G, S>> future = newRegistration.getFuture();

   future.whenCompleteAsync(
      (Tuple2<G, S> result, Throwable failure) -> {
         if (failure != null) {
            if (failure instanceof CancellationException) {
               // we ignore cancellation exceptions because they originate from cancelling
               // the RetryingRegistration
               log.debug("Retrying registration towards {} was cancelled.", targetAddress);
            } else {
               // this future should only ever fail if there is a bug, not if the registration is declined
               onRegistrationFailure(failure);
            }
         } else {
            targetGateway = result.f0;
            // 成功后异步回调执行connection连接确认及TM slot资源上报
            // 其异步注册的监听器为ResourceManagerRegistrationListener
            onRegistrationSuccess(result.f1);
         }
      }, executor);

   return newRegistration;
}

public void startRegistration() { // 向RM的注册
   ......
   try {
      // trigger resolution of the resource manager address to a callable gateway
      final CompletableFuture<G> resourceManagerFuture;

      if (FencedRpcGateway.class.isAssignableFrom(targetType)) {
         resourceManagerFuture = (CompletableFuture<G>) rpcService.connect( // rpc连接
            targetAddress,
            fencingToken,
            targetType.asSubclass(FencedRpcGateway.class));
      } else {
         resourceManagerFuture = rpcService.connect(targetAddress, targetType);
      }

      // upon success, start the registration attempts
      CompletableFuture<Void> resourceManagerAcceptFuture = resourceManagerFuture.thenAcceptAsync(
         (G result) -> {
            log.info("Resolved {} address, beginning registration", targetName);
            register(result, 1, initialRegistrationTimeout); 
            //注册主体  // 其会调用ResourceManagerRegistration#invokeRegistration()来进行实际的注册
         },
         rpcService.getExecutor());

      // upon failure, retry, unless this is cancelled
     ......  // 注册失败会重试
}

4、DispatcherRestEndpoint、WebMonitorEndpoint

        web界面展示接口相关，其主要初始化web相关的服务及接口；主要包括jobmanager、taskmanager信息及日志监控、web job submit初始化、metric监控指标、checkpoint检查点、savepoint保存点等等web界面可查询到的指标；并且将服务以netty 其handler以及接口初始化log日志信息如下(部分)：

DEBUG: org.apache.flink.runtime.dispatcher.DispatcherRestEndpoint - Register handler org.apache.flink.runtime.rest.handler.cluster.ClusterConfigHandler@48f4713c under GET@/jobmanager/config.
DEBUG: org.apache.flink.runtime.dispatcher.DispatcherRestEndpoint - Register handler org.apache.flink.runtime.rest.handler.job.metrics.JobManagerMetricsHandler@4ba6ec50 under GET@/jobmanager/metrics.
DEBUG: org.apache.flink.runtime.dispatcher.DispatcherRestEndpoint - Register handler org.apache.flink.runtime.rest.handler.legacy.ConstantTextHandler@642413d4 under GET@/v1/jobmanager/stdout.
DEBUG: org.apache.flink.runtime.dispatcher.DispatcherRestEndpoint - Register handler org.apache.flink.runtime.rest.handler.job.JobIdsHandler@fb2e3fd under GET@/v1/jobs.
DEBUG: org.apache.flink.runtime.dispatcher.DispatcherRestEndpoint - Register handler org.apache.flink.runtime.rest.handler.job.JobSubmitHandler@43a09ce2 under POST@/jobs.
DEBUG: org.apache.flink.runtime.dispatcher.DispatcherRestEndpoint - Register handler org.apache.flink.runtime.rest.handler.job.metrics.AggregatingJobsMetricsHandler@3f183caa under GET@/v1/jobs/metrics.
DEBUG: org.apache.flink.runtime.dispatcher.DispatcherRestEndpoint - Register handler org.apache.flink.runtime.rest.handler.job.JobsOverviewHandler@7b66322e under GET@/v1/jobs/overview.
DEBUG: org.apache.flink.runtime.dispatcher.DispatcherRestEndpoint - Register handler org.apache.flink.runtime.rest.handler.job.JobDetailsHandler@63538bb4 under GET@/v1/jobs/:jobid.
DEBUG: org.apache.flink.runtime.dispatcher.DispatcherRestEndpoint - Register handler org.apache.flink.runtime.rest.handler.job.JobAccumulatorsHandler@5a50d9fc under GET@/v1/jobs/:jobid/accumulators.
DEBUG: org.apache.flink.runtime.dispatcher.DispatcherRestEndpoint - Register handler org.apache.flink.runtime.rest.handler.job.checkpoints.CheckpointingStatisticsHandler@106d77da under GET@/v1/jobs/:jobid/checkpoints.
DEBUG: org.apache.flink.runtime.dispatcher.DispatcherRestEndpoint - Register handler org.apache.flink.runtime.rest.handler.job.JobConfigHandler@767f6ee7 under GET@/v1/jobs/:jobid/config.
DEBUG: org.apache.flink.runtime.dispatcher.DispatcherRestEndpoint - Register handler org.apache.flink.runtime.rest.handler.job.JobExceptionsHandler@7b6c6e70 under GET@/jobs/:jobid/exceptions.
DEBUG: org.apache.flink.runtime.dispatcher.DispatcherRestEndpoint - Register handler org.apache.flink.runtime.rest.handler.job.metrics.JobMetricsHandler@3a894088 under GET@/jobs/:jobid/metrics.
DEBUG: org.apache.flink.runtime.dispatcher.DispatcherRestEndpoint - Register handler org.apache.flink.runtime.rest.handler.job.JobPlanHandler@370c1968 under GET@/jobs/:jobid/plan.
DEBUG: org.apache.flink.runtime.dispatcher.DispatcherRestEndpoint - Register handler org.apache.flink.runtime.rest.handler.job.savepoints.SavepointHandlers$SavepointStatusHandler@15eb0ae9 under GET@/jobs/:jobid/savepoints/:triggerid.
DEBUG: org.apache.flink.runtime.dispatcher.DispatcherRestEndpoint - Register handler org.apache.flink.runtime.rest.handler.job.JobVertexDetailsHandler@65e0b505 under GET@/jobs/:jobid/vertices/:vertexid.
DEBUG: org.apache.flink.runtime.dispatcher.DispatcherRestEndpoint - Register handler org.apache.flink.runtime.rest.handler.job.JobVertexAccumulatorsHandler@67de7a99 under GET@/jobs/:jobid/vertices/:vertexid/accumulators.
DEBUG: org.apache.flink.runtime.dispatcher.DispatcherRestEndpoint - Register handler org.apache.flink.runtime.rest.handler.job.JobVertexBackPressureHandler@795f5d51 under GET@/jobs/:jobid/vertices/:vertexid/backpressure.
DEBUG: org.apache.flink.runtime.dispatcher.DispatcherRestEndpoint - Register handler org.apache.flink.runtime.rest.handler.job.metrics.JobVertexMetricsHandler@34aeacd1 under GET@/jobs/:jobid/vertices/:vertexid/metrics.
DEBUG: org.apache.flink.runtime.dispatcher.DispatcherRestEndpoint - Register handler org.apache.flink.runtime.rest.handler.job.metrics.AggregatingSubtasksMetricsHandler@4098dd77 under GET@/jobs/:jobid/vertices/:vertexid/subtasks/metrics.
DEBUG: org.apache.flink.runtime.dispatcher.DispatcherRestEndpoint - Register handler org.apache.flink.runtime.rest.handler.job.SubtaskCurrentAttemptDetailsHandler@43aeb5e0 under GET@/jobs/:jobid/vertices/:vertexid/subtasks/:subtaskindex.
DEBUG: org.apache.flink.runtime.dispatcher.DispatcherRestEndpoint - Register handler org.apache.flink.runtime.rest.handler.job.SubtaskExecutionAttemptDetailsHandler@2274160 under GET@/jobs/:jobid/vertices/:vertexid/subtasks/:subtaskindex/attempts/:attempt.
DEBUG: org.apache.flink.runtime.dispatcher.DispatcherRestEndpoint - Register handler org.apache.flink.runtime.rest.handler.job.SubtaskExecutionAttemptAccumulatorsHandler@65383667 under GET@/jobs/:jobid/vertices/:vertexid/subtasks/:subtaskindex/attempts/:attempt/accumulators.
DEBUG: org.apache.flink.runtime.dispatcher.DispatcherRestEndpoint - Register handler org.apache.flink.runtime.rest.handler.job.metrics.SubtaskMetricsHandler@63cd2cd2 under GET@/jobs/:jobid/vertices/:vertexid/subtasks/:subtaskindex/metrics.
DEBUG: org.apache.flink.runtime.dispatcher.DispatcherRestEndpoint - Register handler org.apache.flink.runtime.rest.handler.job.SubtasksTimesHandler@557a84fe under GET@/jobs/:jobid/vertices/:vertexid/subtasktimes.
DEBUG: org.apache.flink.runtime.dispatcher.DispatcherRestEndpoint - Register handler org.apache.flink.runtime.rest.handler.job.JobVertexTaskManagersHandler@6deee370 under GET@/jobs/:jobid/vertices/:vertexid/taskmanagers.
DEBUG: org.apache.flink.runtime.dispatcher.DispatcherRestEndpoint - Register handler org.apache.flink.runtime.rest.handler.job.savepoints.SavepointDisposalHandlers$SavepointDisposalTriggerHandler@423c5404 under POST@/savepoint-disposal.
DEBUG: org.apache.flink.runtime.dispatcher.DispatcherRestEndpoint - Register handler org.apache.flink.runtime.rest.handler.job.metrics.AggregatingTaskManagersMetricsHandler@5a02bfe3 under GET@/taskmanagers/metrics.
DEBUG: org.apache.flink.runtime.dispatcher.DispatcherRestEndpoint - Register handler org.apache.flink.runtime.rest.handler.taskmanager.TaskManagerLogFileHandler@3c79088e under GET@/taskmanagers/:taskmanagerid/log.
DEBUG: org.apache.flink.runtime.dispatcher.DispatcherRestEndpoint - Register handler org.apache.flink.runtime.rest.handler.job.metrics.TaskManagerMetricsHandler@4a37191a under GET@/taskmanagers/:taskmanagerid/metrics.
DEBUG: org.apache.flink.runtime.dispatcher.DispatcherRestEndpoint - Register handler org.apache.flink.runtime.rest.handler.taskmanager.TaskManagerStdoutFileHandler@5854a18 under GET@/taskmanagers/:taskmanagerid/stdout.

5、StandaloneDispatcher

        在 MiniCluster 模式下，其会创建一个StandaloneDispatcher，该类继承自Dispatcher；其是任务调度执行分发的基类，主要作用是提交任务到JobManager，持久化存储，异常恢复任务等等；其主要的组件为：

• RPC服务、blobServer、heartbeatServices等；
• archivedExecutionGraphStore(用于ExecutionGraph的存储)
• jobManagerRunnerFactory工厂类(该工厂类主要用于实例化JobManagerRunner，该类内部持有具体需要执行的jobGraph、jobMaster等组件)

在实例化JobManagerRunner过程中；其会生成对应的jobMaster；其jobmaster对应的作用是(核心主体)：

1. 工作图的调度执行和管理
2. 资源管理（Leader、Gateway、心跳等）
3. 任务管理
4. 调度分配
5. BackPressure控制

在构建完成之后；会调用其dispatcher.start()方法；启动并注册自己的回调函数，其当前的Dispatcher也会通过LeaderElectionService参与选举。

Dispatcher#start()
public void start() throws Exception {
   super.start();  // 启动rpc服务

   submittedJobGraphStore.start(this);
   leaderElectionService.start(this); // 当前组件参与leader选举

   registerDispatcherMetrics(jobManagerMetricGroup);
}

6、提交JobGraph

        在MiniCluster构建完成之后，其会通过MiniCluster#executeJobBlocking来提交JobGraph并等待运行完成，提交JobGraph和请求运行结果的逻辑如下，都是通过RPC调用来实现，其会先通过RPC调用向Dispatcher提交JobGraph，之后便异步等待任务执行结果：

MiniCluster#executeJobBlocking
public JobExecutionResult executeJobBlocking(JobGraph job) throws JobExecutionException, InterruptedException {
   checkNotNull(job, "job is null");

   final CompletableFuture<JobSubmissionResult> submissionFuture = submitJob(job); // 提交任务

   final CompletableFuture<JobResult> jobResultFuture = submissionFuture.thenCompose(
      (JobSubmissionResult ignored) -> requestJobResult(job.getJobID())); // 请求job任务状态

   final JobResult jobResult;

   try {
      jobResult = jobResultFuture.get(); // 获取异步结果并返回
   } catch (ExecutionException e) {
      throw new JobExecutionException(job.getJobID(), "Could not retrieve JobResult.", ExceptionUtils.stripExecutionException(e));
   }

   try {
      return jobResult.toJobExecutionResult(Thread.currentThread().getContextClassLoader());
   } catch (IOException | ClassNotFoundException e) {
      throw new JobExecutionException(job.getJobID(), e);
   }
}

其主要的submitJob执行流程如下(通过RPC调用dispatcherGateway.submitJob(jobGraph, rpcTimeout)向Dispatcher提交JobGraph)：

public CompletableFuture<JobSubmissionResult> submitJob(JobGraph jobGraph) {
   final DispatcherGateway dispatcherGateway;
   try {
      dispatcherGateway = getDispatcherGateway(); // 通过Dispatcher的gateway retriever获取DispatcherGateway
   } catch (LeaderRetrievalException | InterruptedException e) {
      ExceptionUtils.checkInterrupted(e);
      return FutureUtils.completedExceptionally(e);
   }

   // we have to allow queued scheduling in Flip-6 mode because we need to request slots
   // from the ResourceManager
   jobGraph.setAllowQueuedScheduling(true);

   final CompletableFuture<InetSocketAddress> blobServerAddressFuture = createBlobServerAddress(dispatcherGateway);

   final CompletableFuture<Void> jarUploadFuture = uploadAndSetJobFiles(blobServerAddressFuture, jobGraph);
   
   // 通过RPC调用向Dispatcher提交JobGraph
   final CompletableFuture<Acknowledge> acknowledgeCompletableFuture = jarUploadFuture.thenCompose(
      (Void ack) -> dispatcherGateway.submitJob(jobGraph, rpcTimeout));

   return acknowledgeCompletableFuture.thenApply(
      (Acknowledge ignored) -> new JobSubmissionResult(jobGraph.getJobID()));
}

Dispatcher在接收到提交JobGraph的请求后，会将提交的JobGraph保存在SubmittedJobGraphStore中(用于故障恢复)，并为提交的JobGraph启动JobManager：

1. Dispatcher接手job之后，会实例化一个JobManagerRunner，然后用这个runner启动job；
2. JobManagerRunner接下来把job交给了JobMaster去处理；
3. JobMaster使用ExecutionGraph的方法启动了整个执行图；整个任务就启动起来了。

Dispatcher#createJobManagerRunner(jobGraph) // 创建JobManagerRunner(实例化JobMaster); 并启动该JobManagerRunner
private CompletableFuture<JobManagerRunner> createJobManagerRunner(JobGraph jobGraph) {
   final RpcService rpcService = getRpcService();

   final CompletableFuture<JobManagerRunner> jobManagerRunnerFuture = CompletableFuture.supplyAsync(
      CheckedSupplier.unchecked(() ->
         jobManagerRunnerFactory.createJobManagerRunner(
            ResourceID.generate(),
            jobGraph,
            configuration,
            rpcService,
            highAvailabilityServices,
            heartbeatServices,
            blobServer,
            jobManagerSharedServices,
            new DefaultJobManagerJobMetricGroupFactory(jobManagerMetricGroup),
            fatalErrorHandler)),
      rpcService.getExecutor());
   
   // 启动的JobManagerRunner会竞争leader，一旦被选举为leader，其就会调用verifyJobSchedulingStatusAndStartJobManager；
   // 就会启动执行jobMaster.start(); 在jobMaster内部分别启动slotPool超时监控线程、建立和ResourceManager的ResourceManagerConnection连接；
   // 一旦连接建立之后, JobMaster就可以通过RPC调用和ResourceManager进行通信了; 在此之后就进入了任务调度执行的流程
   return jobManagerRunnerFuture.thenApply(FunctionUtils.uncheckedFunction(this::startJobManagerRunner));
}

JobManagerRunner#start()
public void start() throws Exception {
   try {
      leaderElectionService.start(this);
   } catch (Exception e) {
      log.error("Could not start the JobManager because the leader election service did not start.", e);
      throw new Exception("Could not start the leader election service.", e);
   }
}

JobMaster#startJobExecution#startJobMasterServices
private void startJobMasterServices() throws Exception {
   // start the slot pool make sure the slot pool now accepts messages for this leader
   slotPool.start(getFencingToken(), getAddress());

   //TODO: Remove once the ZooKeeperLeaderRetrieval returns the stored address upon start
   // try to reconnect to previously known leader
   reconnectToResourceManager(new FlinkException("Starting JobMaster component."));

   // job is ready to go, try to establish connection with resource manager
   //   - activate leader retrieval for the resource manager
   //   - on notification of the leader, the connection will be established and
   //     the slot pool will start requesting slots
   resourceManagerLeaderRetriever.start(new ResourceManagerLeaderListener());
}

Standalone Cluster模式的启动流程：

       在windows或者linux环境下通过 /flink/bin/start-cluster.bat 或者 start-cluster.sh来启动对应的flink集群Standalone Cluster模式；

       在启动脚本中可以看到主要的组件实例化入口类为：

# 简化shell启动脚本后，其组件实例化入口类主要为下：
case $DAEMON in
    (taskexecutor)  CLASS_TO_RUN=org.apache.flink.runtime.taskexecutor.TaskManagerRunner
    (zookeeper)  CLASS_TO_RUN=org.apache.flink.runtime.zookeeper.FlinkZooKeeperQuorumPeer
    (historyserver)  CLASS_TO_RUN=org.apache.flink.runtime.webmonitor.history.HistoryServer
    (standalonesession)  CLASS_TO_RUN=org.apache.flink.runtime.entrypoint.StandaloneSessionClusterEntrypoint
    (standalonejob)  CLASS_TO_RUN=org.apache.flink.container.entrypoint.StandaloneJobClusterEntryPoint
esac

        可以看到在Standalone模式下；Standalone Cluster有两种启动方式，即standalonesession模式和standalonejob方式，它们主要区别在于Dispatcher的实现方式不同。standalonesession模式的入口类是StandaloneSessionClusterEntrypoint，继承自SessionClusterEntrypoint；与此对应的是以standalonejob方式启动JobManager的入口类是StandaloneJobClusterEntryPoint，继承自JobClusterEntrypoint。它们都由公共父类ClusterEntrypoint派生而来，区别在于生成的DispatcherResourceManagerComponent不同。(此处主要讨论standalonesession)模式；

       首先通过集群启动的入口类StandaloneSessionClusterEntrypoint#main()函数可以看到，首先会先解析flink当前java进程的启动命令，加载配置文件flink-conf.yaml并将其转化为flink环境内可识别的configuration信息，之后便调用flink集群base class去启动当前的集群环境：

// StandaloneSessionClusterEntrypoint#main()函数
public static void main(String[] args) {
   // startup checks and logging (启动log输出当前配置环境java及启动的jvm参数等基本信息)
   EnvironmentInformation.logEnvironmentInfo(LOG, StandaloneSessionClusterEntrypoint.class.getSimpleName(), args);
   SignalHandler.register(LOG);
   JvmShutdownSafeguard.installAsShutdownHook(LOG);

   EntrypointClusterConfiguration entrypointClusterConfiguration = null;
   final CommandLineParser<EntrypointClusterConfiguration> commandLineParser = new CommandLineParser<>(new EntrypointClusterConfigurationParserFactory());

   try {
      entrypointClusterConfiguration = commandLineParser.parse(args);
   } catch (FlinkParseException e) {
      LOG.error("Could not parse command line arguments {}.", args, e);
      commandLineParser.printHelp(StandaloneSessionClusterEntrypoint.class.getSimpleName());
      System.exit(1);
   }
   // 配置加载当前 flink-conf.yaml 中的配置设置信息
   Configuration configuration = loadConfiguration(entrypointClusterConfiguration);
   StandaloneSessionClusterEntrypoint entrypoint = new StandaloneSessionClusterEntrypoint(configuration);
   // Standalone Cluster模式启动
   ClusterEntrypoint.runClusterEntrypoint(entrypoint);
}

在ClusterEntrypoint.runClusterEntrypoint(entrypoint)集群启动JobManager过程中；其主要的源代码如下：

private void runCluster(Configuration configuration) throws Exception {
   synchronized (lock) {
      // 初始化基础的服务类，主要包括创建commonRpcService(基础rpc服务提供基础的ip:port), haServices, blobServer, heartbeatServices, metricRegistry监控服务等等
      // 这里生成的HighAvailabilityServices区别于MiniCluster模式; 由于Standalone模式下各组件不在同一个进程中, 因而需要从配置中加载配置:
      // 1、如果采用基于Zookeeper的HA模式，则创建ZooKeeperHaServices，基于zookeeper获取leader通信地址
      // 2、如果没有配置HA, 则创建StandaloneHaServices, 并从配置文件中获取各组件的RPC地址信息(resourceManagerRpcUrl、dispatcherRpcUrl、jobManagerRpcUrl)。
      initializeServices(configuration);

      // write host information into configuration
      configuration.setString(JobManagerOptions.ADDRESS, commonRpcService.getAddress());
      configuration.setInteger(JobManagerOptions.PORT, commonRpcService.getPort());

       // 生成DispatcherResourceManagerComponentFactory, 由具体子类实现
      final DispatcherResourceManagerComponentFactory<?> dispatcherResourceManagerComponentFactory = createDispatcherResourceManagerComponentFactory(configuration);

      // 创建DispatcherResourceManagerComponent, 其内部持有并启动ResourceManager, Dispatcher
      clusterComponent = dispatcherResourceManagerComponentFactory.create(
         configuration,
         commonRpcService,
         haServices,
         blobServer,
         heartbeatServices,
         metricRegistry,
         archivedExecutionGraphStore,
         new AkkaQueryServiceRetriever(
            metricQueryServiceActorSystem,
            Time.milliseconds(configuration.getLong(WebOptions.TIMEOUT))),
         this);

      clusterComponent.getShutDownFuture().whenComplete(...); // 执行完成, 关闭各项服务
   }
}

       在生成具体的DispatcherResourceManagerComponentFactory类的过程中，其针对不同的Standalone模式交由其具体子类去实现；

1. standalonesession模式的入口类StandaloneSessionClusterEntrypoint；其具体生成的工厂类委托给SessionDispatcherResourceManagerComponentFactory，该工厂类内部持有对应的负责创建相应组件的细化工厂类；其对应的组件实例化工厂类为：SessionDispatcherFactory-->StandaloneDispatcher；
   StandaloneResourceManagerFactory-->StandaloneResourceManager；
   SessionRestEndpointFactory-->DispatcherRestEndpoint；
2. standalonejob方式的入口类StandaloneJobClusterEntryPoint；其具体生成的工厂类委托给在JobDispatcherResourceManagerComponentFactory，该工厂类内部持有对应的负责创建相应组件的细化工厂类；其对应的组件实例化工厂类为：JobDispatcherFactory-->MiniDispatcher；
   StandaloneResourceManagerFactory-->StandaloneResourceManager；
   JobRestEndpointFactory-->MiniDispatcherRestEndpoint；

      在standalonejob方式中；一个MiniDispatcher和一个JobGraph绑定，一旦绑定的JobGraph执行结束，则关闭MiniDispatcher，进而停止JobManager进程

其后续的具体组件实例化创建以及组件服务启动都在clusterComponent=dispatcherResourceManagerComponentFactory.create()中；主要创建以及启动的组件(主要是resourceManager、dispatcher、webMonitorEndpoint组件)如下，其各个组件具体启动方式、服务内部的启动流程以及作用和MiniCluster中的一致，这里不再赘述。

@Override
public DispatcherResourceManagerComponent<T> create(...) throws Exception {
   ......
   LeaderRetrievalService dispatcherLeaderRetrievalService = null;
   LeaderRetrievalService resourceManagerRetrievalService = null;
   WebMonitorEndpoint<U> webMonitorEndpoint = null;
   ResourceManager<?> resourceManager = null;
   JobManagerMetricGroup jobManagerMetricGroup = null;
   T dispatcher = null;

   try {
      dispatcherLeaderRetrievalService = highAvailabilityServices.getDispatcherLeaderRetriever();
      resourceManagerRetrievalService = highAvailabilityServices.getResourceManagerLeaderRetriever();

      final LeaderGatewayRetriever<DispatcherGateway> dispatcherGatewayRetriever = new RpcGatewayRetriever<>(
         rpcService,
         DispatcherGateway.class,
         DispatcherId::fromUuid,
         10,
         Time.milliseconds(50L));

      final LeaderGatewayRetriever<ResourceManagerGateway> resourceManagerGatewayRetriever = new RpcGatewayRetriever<>(
         rpcService,
         ResourceManagerGateway.class,
         ResourceManagerId::fromUuid,
         10,
         Time.milliseconds(50L));

      webMonitorEndpoint = restEndpointFactory.createRestEndpoint();

      log.debug("Starting Dispatcher REST endpoint.");
      webMonitorEndpoint.start();

      jobManagerMetricGroup = MetricUtils.instantiateJobManagerMetricGroup(
         metricRegistry,
         rpcService.getAddress(),
         ConfigurationUtils.getSystemResourceMetricsProbingInterval(configuration));

      resourceManager = resourceManagerFactory.createResourceManager(
         configuration,
         ResourceID.generate(),
         rpcService,
         highAvailabilityServices,
         heartbeatServices,
         metricRegistry,
         fatalErrorHandler,
         new ClusterInformation(rpcService.getAddress(), blobServer.getPort()),
         webMonitorEndpoint.getRestBaseUrl(),
         jobManagerMetricGroup);

      final HistoryServerArchivist historyServerArchivist = HistoryServerArchivist.createHistoryServerArchivist(configuration, webMonitorEndpoint);

      dispatcher = dispatcherFactory.createDispatcher(
         configuration,
         rpcService,
         highAvailabilityServices,
         resourceManager.getSelfGateway(ResourceManagerGateway.class),
         blobServer,
         heartbeatServices,
         jobManagerMetricGroup,
         metricRegistry.getMetricQueryServicePath(),
         archivedExecutionGraphStore,
         fatalErrorHandler,
         webMonitorEndpoint.getRestBaseUrl(),
         historyServerArchivist);

      log.debug("Starting ResourceManager.");
      resourceManager.start();
      resourceManagerRetrievalService.start(resourceManagerGatewayRetriever);

      log.debug("Starting Dispatcher.");
      dispatcher.start();
      dispatcherLeaderRetrievalService.start(dispatcherGatewayRetriever);

      return createDispatcherResourceManagerComponent(
         dispatcher,
         resourceManager,
         dispatcherLeaderRetrievalService,
         resourceManagerRetrievalService,
         webMonitorEndpoint,
         jobManagerMetricGroup);
   } catch (Exception exception) {
      // clean up all started components
      ......
   }
}

TaskManager的启动

       TaskManager的启动入口在CLASS_TO_RUN=org.apache.flink.runtime.taskexecutor.TaskManagerRunner中，它的启动流程和MiniCluster模式下基本一致，从flink-conf.yaml文件中加载对应的config文件配置(jobmanager地址等等)，启动对应的TaskExecutor，并向ResourceManager注册自己；其和MiniCluster模式下的区别在于：

1. 运行在独立的进程中；
2. HighAvailabilityServices的创建要依赖配置文件获取；
3. TaskManagerRunner会创建TaskExecutor，TaskExecutor通过HighAvailabilityServices获取ResourceManager的通信地址，并和ResourceManager建立连接；

Yarn Cluster模式的启动流程：

        Yarn Cluster模式的启动入口在FlinkYarnSessionCli中；首先根据命令行参数(--ship、jar、jobManagerMemory、container、slots等等)解析并创建对应的YarnConfiguration；之后便创建YarnClusterDescriptor(其内部持有YarnClient客户端、yarnConfiguration、JarPath等等)，接着调用YarnClusterDescriptor#deploySessionCluster来触发Yarn Cluster集群的部署；其实际上会调用启动AbstractYarnClusterDescriptor#deployInternal方法，主要就是通过YarnClient向yarn集群提交AppMaster应用，启动对应的ApplicationMaster。

// AbstractYarnClusterDescriptor#deployInternal
protected ClusterClient<ApplicationId> deployInternal(
      ClusterSpecification clusterSpecification,
      String applicationName,
      String yarnClusterEntrypoint,
      @Nullable JobGraph jobGraph,
      boolean detached) throws Exception {

   // ------------------ Check if configuration is valid --------------------
   validateClusterSpecification(clusterSpecification);

   if (UserGroupInformation.isSecurityEnabled()) {......}  // hasKerberos鉴权

   isReadyForDeployment(clusterSpecification); // 配置及请求yarn最大资源检查

   // ------------------ Check if the specified queue exists --------------------
   checkYarnQueues(yarnClient); // yarn queue检查

   // ------------------ Add dynamic properties to local flinkConfiguraton ------
   Map<String, String> dynProperties = getDynamicProperties(dynamicPropertiesEncoded);
   for (Map.Entry<String, String> dynProperty : dynProperties.entrySet()) {
      flinkConfiguration.setString(dynProperty.getKey(), dynProperty.getValue());
   }

   // ------------------ Check if the YARN ClusterClient has the requested resources --------------

   // Create application via yarnClient
   final YarnClientApplication yarnApplication = yarnClient.createApplication(); // 创建申请Yarn AppMaster
   final GetNewApplicationResponse appResponse = yarnApplication.getNewApplicationResponse();

   ......... // cluster资源检查

   LOG.info("Cluster specification: {}", validClusterSpecification);

   final ClusterEntrypoint.ExecutionMode executionMode = detached ? // 执行模式选择(是否长连接输出应用信息：提交后本地断开)
      ClusterEntrypoint.ExecutionMode.DETACHED
      : ClusterEntrypoint.ExecutionMode.NORMAL;

   flinkConfiguration.setString(ClusterEntrypoint.EXECUTION_MODE, executionMode.toString());

   // 启动Yarn AppMaster应用；在启动AppMaster过程中，主要是构建提交的应用上下文ApplicationSubmissionContext appContext = yarnApplication.getApplicationSubmissionContext();
   // appContext里面定义了Yarn container启动的资源、jar、config等等信息；其中比较重要的是：
   // ContainerLaunchContext amContainer = setupApplicationMasterContainer(); 里面设置了 启动Application Master进程的java cmd指令
   // ContainerLaunchContext amContainer中指定了 启动的入口class类；该处分别指向YarnSessionClusterEntrypoint或者YarnJobClusterEntrypoint;
   // 之后便调用yarnClient.submitApplication(appContext); 向Yarn提交该应用
   ApplicationReport report = startAppMaster(
      flinkConfiguration,
      applicationName,
      yarnClusterEntrypoint,
      jobGraph,
      yarnClient,
      yarnApplication,
      validClusterSpecification);

   // Correctly initialize the Flink config
   .........
   
   // the Flink cluster is deployed in YARN. Represent cluster
   return createYarnClusterClient(
      this,
      validClusterSpecification.getNumberTaskManagers(),
      validClusterSpecification.getSlotsPerTaskManager(),
      report,
      flinkConfiguration,
      true);
}

        在YarnCluster模式下；根据sessioncluster和jobcluster者两种启动的区别，提交到Yarn中ApplicationMatser的入口类分别为YarnSessionClusterEntrypoint和YarnJobClusterEntrypoint，其最终的父类都是继承自ClusterEntrypoint，最终集群启动过程都是依托给ClusterEntrypoint.runClusterEntrypoint(entrypoint)中；其启动过程与上述Standalone Cluster过程一致。主要的区别点在于Dispatcher分别为StandaloneDispatcher和MiniDispatcher。ResourceManager的具体实现类为YarnResourceManager。

        和Standalone Cluster不同的是，Yarn Cluster模式下启动的Flink集群，其TaskManager是由YarnResourceManager根据JobMaster的请求动态向Yarn的ResourceManager进行申请的。在JobMaster向flink其启动的内部组件的ResourceManager申请资源时，如果当前没有足够的资源分配，则YarnResourceManager会向Yarn集群的ResourceManager申请新的container，并启动TaskManager。

/**
 * The yarn implementation of the resource manager. Used when the system is started
 * via the resource framework YARN.
 */
 // 其内部主要持有的组件 以及 向yarn申请新container的方法
public class YarnResourceManager extends ResourceManager<YarnWorkerNode> implements AMRMClientAsync.CallbackHandler {
   /** Default heartbeat interval between this resource manager and the YARN ResourceManager. */
   private final int yarnHeartbeatIntervalMillis;
   private final Configuration flinkConfig;
   private final YarnConfiguration yarnConfig;
 
   private final int numberOfTaskSlots;
   private final int defaultTaskManagerMemoryMB;
   private final int defaultCpus;

   /** The heartbeat interval while the resource master is waiting for containers. */
   private final int containerRequestHeartbeatIntervalMillis;

   /** Client to communicate with the Resource Manager (YARN's master). */
   private AMRMClientAsync<AMRMClient.ContainerRequest> resourceManagerClient; // Yarn ResourceManager Client

   /** Client to communicate with the Node manager and launch TaskExecutor processes. */
   private NMClient nodeManagerClient; //

   /** The number of containers requested, but not yet granted. */
   private int numPendingContainerRequests;

   private final Map<ResourceProfile, Integer> resourcePriorities = new HashMap<>();
   private final Collection<ResourceProfile> slotsPerWorker;
   private final Resource resource;
   ......
}

申请到对应的Container资源后，通过YarnRM的回调函数，构建TaskManager启动命令以及ContainerLaunchContext，以此来启动对应的TaskExecutor：

// YarnResourceManager
// 向Yarn申请container
private void requestYarnContainer() {
	resourceManagerClient.addContainerRequest(getContainerRequest());

	// make sure we transmit the request fast and receive fast news of granted allocations
	resourceManagerClient.setHeartbeatInterval(containerRequestHeartbeatIntervalMillis);
	numPendingContainerRequests++;

	log.info("Requesting new TaskExecutor container with resources {}. Number pending requests {}.",
        resource,
        numPendingContainerRequests);
}

// 申请到container资源后，异步回调
@Override
public void onContainersAllocated(List<Container> containers) {
	runAsync(() -> {
		final Collection<AMRMClient.ContainerRequest> pendingRequests = getPendingRequests();
		final Iterator<AMRMClient.ContainerRequest> pendingRequestsIterator = pendingRequests.iterator();

		for (Container container : containers) {
			log.info(
				"Received new container: {} - Remaining pending container requests: {}",
				container.getId(),
				numPendingContainerRequests);

			if (numPendingContainerRequests > 0) {
				removeContainerRequest(pendingRequestsIterator.next());

				final String containerIdStr = container.getId().toString();
				final ResourceID resourceId = new ResourceID(containerIdStr);

				workerNodeMap.put(resourceId, new YarnWorkerNode(container));

				try {
					// Context information used to start a TaskExecutor Java process
                                   // 构建TaskManager启动的ContainerLaunchContext信息
					ContainerLaunchContext taskExecutorLaunchContext = createTaskExecutorLaunchContext(
						container.getResource(),
						containerIdStr,
						container.getNodeId().getHost());
                                   // 远程提交执行 启动TaskManager
					nodeManagerClient.startContainer(container, taskExecutorLaunchContext);
				} catch (Throwable t) {
					log.error("Could not start TaskManager in container {}.", container.getId(), t);

					// release the failed container
					workerNodeMap.remove(resourceId);
					resourceManagerClient.releaseAssignedContainer(container.getId());
					// and ask for a new one
					requestYarnContainerIfRequired();
				}
			} else {
				// return the excessive containers
				log.info("Returning excess container {}.", container.getId());
				resourceManagerClient.releaseAssignedContainer(container.getId());
			}
		}

		// if we are waiting for no further containers, we can go to the
		// regular heartbeat interval
		if (numPendingContainerRequests <= 0) {
			resourceManagerClient.setHeartbeatInterval(yarnHeartbeatIntervalMillis);
		}
	});
}

// 构建TaskManager启动的ContainerLaunchContext信息(并指定container启动的类为YarnTaskExecutorRunner)
private ContainerLaunchContext createTaskExecutorLaunchContext(Resource resource, String containerId, String host)
		throws Exception {
	// init the ContainerLaunchContext
	final String currDir = env.get(ApplicationConstants.Environment.PWD.key());

	final ContaineredTaskManagerParameters taskManagerParameters =
			ContaineredTaskManagerParameters.create(flinkConfig, resource.getMemory(), numberOfTaskSlots);

	log.debug("TaskExecutor {} will be started with container size {} MB, JVM heap size {} MB, " +
			"JVM direct memory limit {} MB",
			containerId,
			taskManagerParameters.taskManagerTotalMemoryMB(),
			taskManagerParameters.taskManagerHeapSizeMB(),
			taskManagerParameters.taskManagerDirectMemoryLimitMB());

	Configuration taskManagerConfig = BootstrapTools.cloneConfiguration(flinkConfig);

	log.debug("TaskManager configuration: {}", taskManagerConfig);
   // 构建TaskManager的启动container上下文并指定启动类为:YarnTaskExecutorRunner
   // 构建TaskManager进程启动的java cmd指令 java ${jvmmem} ${javaOps} -Dxxx class xxx 1> xxx/taskmanager.out 2> xxx/taskmanager.err
	ContainerLaunchContext taskExecutorLaunchContext = Utils.createTaskExecutorContext(
		flinkConfig,
		yarnConfig,
		env,
		taskManagerParameters,
		taskManagerConfig,
		currDir,
		YarnTaskExecutorRunner.class, // 入口类
		log);

	// set a special environment variable to uniquely identify this container
	taskExecutorLaunchContext.getEnvironment()
			.put(ENV_FLINK_CONTAINER_ID, containerId);
	taskExecutorLaunchContext.getEnvironment()
			.put(ENV_FLINK_NODE_ID, host);
	return taskExecutorLaunchContext;
}