标签：exce orm hdfs name pst dal 文件中 相同 指定
个人感觉学习Flink其实最不应该错过的博文是Flink社区的博文系列，里面的文章是不会让人失望的。强烈安利：https://ververica.cn/developers-resources/。
本文是自己第一次尝试写源码阅读的文章，会努力将原理和源码实现流程结合起来。文中有几个点目前也是没有弄清楚，若是写在一篇博客里，时间跨度太大，但又怕后期遗忘，所以先记下来，后期进一步阅读源码后再添上，若是看到不完整版博文的看官，对不住！
文中若是写的不准确的地方欢迎留言指出。
源码系列基于Flink 1.9
Flink on Yarn模式下提交任务整体流程图如下（图源自Flink社区，链接见Ref [1]）
图1 Flink Runtime层架构图
Flink采取的是经典的master-salve模式，图中的AM（ApplicationMater）为master，TaskManager是salve。
AM中的Dispatcher用于接收client提交的任务和启动相应的JobManager ；JobManager用于任务的接收，task的分配、管理task manager等；ResourceManager主要用于资源的申请和分配。
这里有点需要注意：Flink本身也是具有ResourceManager和TaskManager的，这里虽然是on Yarn模式，但Flink本身也是拥有一套资源管理架构，虽然各个组件的名字一样，但这里yarn只是一个资源的提供者，若是standalone模式，资源的提供者就是物理机或者虚拟机了。
1）执行Flink程序，就类似client，主要是将代码进行优化形成JobGraph，向yarn的ResourceManager中的ApplicationManager申请资源启动AM（ApplicationMater）,AM所在节点是Yarn上的NodeManager上；
2）当AM起来之后会启动Dispatcher、ResourceManager，其中Dispatcher会启动JobManager，ResourceManager会启动slotManager用于slot的管理和分配；
3）JobManager向ResourceManager（RM）申请资源用于任务的执行，最初TaskManager还没有启动，此时，RM会向yarn去申请资源，获得资源后，会在资源中启动TaskManager，相应启动的slot会向slotManager中注册，然后slotManager会将slot分配给只需资源的task，即向JobManager注册信息，然后JobManager就会将任务提交到对应的slot中执行。其实Flink on yarn的session模式和Per-job模式最大的区别是，提交任务时RM已向Yarn申请了固定大小的资源，其TaskManager是已经启动的。
资源分配如详细过程图下：
图2 slot管理图，源自Ref[1]
更详细的过程解析，强烈推荐Ref [2]，是阿里Flink大牛写的，本博客在后期的源码分析过程也多依据此博客。
提交任务语句
./flink run -m yarn-cluster ./flinkExample.jar
flink脚本的入口类是org.apache.flink.client.cli.CliFrontend。
1）在CliFronted类的main()方法中，会加载flnk以及一些全局的配置项之后，根据命令行参数run，调用run()->runProgram()->deployJobCluster()，具体的代码如下：
private <T> void runProgram( CustomCommandLine<T> customCommandLine, CommandLine commandLine, RunOptions runOptions, PackagedProgram program) throws ProgramInvocationException, FlinkException { final ClusterDescriptor<T> clusterDescriptor = customCommandLine.createClusterDescriptor(commandLine); try { final T clusterId = customCommandLine.getClusterId(commandLine); final ClusterClient<T> client; // directly deploy the job if the cluster is started in job mode and detached if (clusterId == null && runOptions.getDetachedMode()) { int parallelism = runOptions.getParallelism() == -1 ? defaultParallelism : runOptions.getParallelism(); //构建JobGraph final JobGraph jobGraph = PackagedProgramUtils.createJobGraph(program, configuration, parallelism); final ClusterSpecification clusterSpecification = customCommandLine.getClusterSpecification(commandLine);
//将任务提交到yarn上 client = clusterDescriptor.deployJobCluster( clusterSpecification, jobGraph, runOptions.getDetachedMode()); logAndSysout("Job has been submitted with JobID " + jobGraph.getJobID()); ...................... } else{........}
2）提交任务会调用YarnClusterDescriptor 类中deployJobCluster()->AbstractYarnClusterDescriptor类中deployInteral()，该方法会一直阻塞直到ApplicationMaster/JobManager在yarn上部署成功，其中最关键的调用是对startAppMaster()方法的调用，代码如下：
1 protected ClusterClient<ApplicationId> deployInternal( 2 ClusterSpecification clusterSpecification, 3 String applicationName, 4 String yarnClusterEntrypoint, 5 @Nullable JobGraph jobGraph, 6 boolean detached) throws Exception { 7 8 //1、验证集群是否可以访问 9 //2、若用户组是否开启安全认证 10 //3、检查配置以及vcore是否满足flink集群申请的需求 11 //4、指定的对列是否存在 12 //5、检查内存是否满足flink JobManager、NodeManager所需 13 //.................................... 14 15 //Entry 16 ApplicationReport report = startAppMaster( 17 flinkConfiguration, 18 applicationName, 19 yarnClusterEntrypoint, 20 jobGraph, 21 yarnClient, 22 yarnApplication, 23 validClusterSpecification); 24 25 //6、获取flink集群端口、地址信息 26 //.......................................... 27 }
3）startAppMaster()方法的所在类是AbstractYarnClutserDescriptor，该方法主要是将配置文件和相关文件上传至分布式存储如HDFS，以及向Yarn上提交任务等，源码分析如下：
1 public ApplicationReport startAppMaster( 2 Configuration configuration, 3 String applicationName, 4 String yarnClusterEntrypoint, 5 JobGraph jobGraph, 6 YarnClient yarnClient, 7 YarnClientApplication yarnApplication, 8 ClusterSpecification clusterSpecification) throws Exception { 9 10 // ....................... 11 12 //1、上传conf目录下logback.xml、log4j.properties 13 14 //2、上传环境变量中FLINK_PLUGINS_DIR ,FLINK_LIB_DIR包含的jar 15 addEnvironmentFoldersToShipFiles(systemShipFiles); 16 //........... 17 //3、设置applications的高可用的方案，通过设置AM重启次数，默认为1 18 //4、上传ship files、user jars、 19 //5、为TaskManager设置slots、heap memory 20 //6、上传flink-conf.yaml 21 //7、序列化JobGraph后上传 22 //8、登录权限检查 23 24 //................. 25 26 //获得启动AM container的Java命令 27 final ContainerLaunchContext amContainer = setupApplicationMasterContainer( 28 yarnClusterEntrypoint, 29 hasLogback, 30 hasLog4j, 31 hasKrb5, 32 clusterSpecification.getMasterMemoryMB()); 33 34 //9、为aAM启动绑定环境参数以及classpath和环境变量 35 36 //.......................... 37 38 final String customApplicationName = customName != null ? customName : applicationName; 39 //10、应用名称、应用类型、用户提交的应用ContainerLaunchContext 40 appContext.setApplicationName(customApplicationName); 41 appContext.setApplicationType(applicationType != null ? applicationType : "Apache Flink"); 42 appContext.setAMContainerSpec(amContainer); 43 appContext.setResource(capability); 44 45 if (yarnQueue != null) { 46 appContext.setQueue(yarnQueue); 47 } 48 49 setApplicationNodeLabel(appContext); 50 51 setApplicationTags(appContext); 52 53 //11、部署失败删除yarnFilesDir 54 // add a hook to clean up in case deployment fails 55 Thread deploymentFailureHook = new DeploymentFailureHook(yarnClient, yarnApplication, yarnFilesDir); 56 Runtime.getRuntime().addShutdownHook(deploymentFailureHook); 57 58 LOG.info("Submitting application master " + appId); 59 60 //Entry 61 yarnClient.submitApplication(appContext); 62 63 LOG.info("Waiting for the cluster to be allocated"); 64 final long startTime = System.currentTimeMillis(); 65 ApplicationReport report; 66 YarnApplicationState lastAppState = YarnApplicationState.NEW; 67 //12、阻塞等待直到running 68 loop: while (true) { 69 //................... 70 //每隔250ms通过YarnClient获取应用报告 71 Thread.sleep(250); 72 } 73 //........................... 74 //13、部署成功删除shutdown回调 75 // since deployment was successful, remove the hook 76 ShutdownHookUtil.removeShutdownHook(deploymentFailureHook, getClass().getSimpleName(), LOG); 77 return report; 78 }
4）应用提交的Entry是YarnClientImpl中submitApplication()方法，主要是通过ClientRMService的实例调用方法submitApplication将应用请求提交到Yarn上，之后，程序会循环等待应用任务提交成功（不是NEW和NEW_SAVING即认为成功）。该方法代码量较少，就不详细展开了。
至此，client端的流程就走完了，应用请求已提交到Yarn的ResourceManager上了，下面着重分析Flink Cluster启动流程。
1）在ClientRMService类的submitApplication()方法中，会先检查任务是否已经提交（通过applicationID）、Yarn的queue是否为空等，然后将请求提交到RMAppManager（ARN RM内部管理应用生命周期的组件），若提交成功会输出Application with id {applicationId.getId()} submitted by user {user}的信息，具体分析如下：
1 public SubmitApplicationResponse submitApplication( 2 SubmitApplicationRequest request) throws YarnException { 3 ApplicationSubmissionContext submissionContext = request 4 .getApplicationSubmissionContext(); 5 ApplicationId applicationId = submissionContext.getApplicationId(); 6 7 // ApplicationSubmissionContext needs to be validated for safety - only 8 // those fields that are independent of the RM‘s configuration will be 9 // checked here, those that are dependent on RM configuration are validated 10 // in RMAppManager. 11 //这里仅验证不属于RM的配置，属于RM的配置将在RMAppManager验证 12 13 //1、检查application是否已提交 14 //2、检查提交的queue是否为null，是，则设置为默认queue（default） 15 //3、检查是否设置application名，否，则为默认（N/A） 16 //4、检查是否设置application类型，否，则为默认（YARN）；是，若名字长度大于给定的长度（20），则会截断 17 //............................. 18 19 try { 20 // call RMAppManager to submit application directly 21 //直接submit任务 22 rmAppManager.submitApplication(submissionContext, 23 System.currentTimeMillis(), user); 24 25 //submit成功 26 LOG.info("Application with id " + applicationId.getId() + 27 " submitted by user " + user); 28 RMAuditLogger.logSuccess(user, AuditConstants.SUBMIT_APP_REQUEST, 29 "ClientRMService", applicationId); 30 } catch (YarnException e) { 31 //失败会抛出异常 32 } 33 //.................. 34 }
2）RMAppManager类的submitApplication()方法主要是创建RMApp和向ResourceScheduler申请AM container，该部分直到在NodeManager上启动AM container都是Yarn本身所为，其中具体过程在这里不详细分析，详细过程后期会分析，这里仅给出入口，代码如下：
1 protected void submitApplication( 2 ApplicationSubmissionContext submissionContext, long submitTime, 3 String user) throws YarnException { 4 ApplicationId applicationId = submissionContext.getApplicationId(); 5 6 //1、创建RMApp，若具有相同的applicationId会抛出异常 7 RMAppImpl application = 8 createAndPopulateNewRMApp(submissionContext, submitTime, user); 9 ApplicationId appId = submissionContext.getApplicationId(); 10 11 //security模式有simple和kerberos，在配置文件中配置 12 //开始kerberos 13 if (UserGroupInformation.isSecurityEnabled()) { 14 //.................. 15 } else { 16 //simple模式 17 // Dispatcher is not yet started at this time, so these START events 18 // enqueued should be guaranteed to be first processed when dispatcher 19 // gets started. 20 //2、向ResourceScheduler（可插拔的资源调度器）提交任务？？？？？？？？？？ 21 this.rmContext.getDispatcher().getEventHandler() 22 .handle(new RMAppEvent(applicationId, RMAppEventType.START)); 23 } 24 }
3）Flink在Per-job模式下，AM container加载运行的入口是YarnJobClusterEntryPoint中的main()方法，源码分析如下：
1 public static void main(String[] args) { 2 // startup checks and logging 3 //1、输出环境信息如用户、环境变量、Java版本等，以及JVM参数 4 EnvironmentInformation.logEnvironmentInfo(LOG, YarnJobClusterEntrypoint.class.getSimpleName(), args); 5 //2、注册处理各种SIGNAL的handler:记录到日志 6 SignalHandler.register(LOG); 7 //3、注册JVM关闭保障的shutdown hook：避免JVM退出时被其他shutdown hook阻塞 8 JvmShutdownSafeguard.installAsShutdownHook(LOG); 9 10 Map<String, String> env = System.getenv(); 11 12 final String workingDirectory = env.get(ApplicationConstants.Environment.PWD.key()); 13 Preconditions.checkArgument( 14 workingDirectory != null, 15 "Working directory variable (%s) not set", 16 ApplicationConstants.Environment.PWD.key()); 17 18 try { 19 //4、输出Yarn运行的用户信息 20 YarnEntrypointUtils.logYarnEnvironmentInformation(env, LOG); 21 } catch (IOException e) { 22 LOG.warn("Could not log YARN environment information.", e); 23 } 24 //5、加载flink的配置 25 Configuration configuration = YarnEntrypointUtils.loadConfiguration(workingDirectory, env, LOG); 26 27 YarnJobClusterEntrypoint yarnJobClusterEntrypoint = new YarnJobClusterEntrypoint( 28 configuration, 29 workingDirectory); 30 //6、Entry 创建并启动各类内部服务 31 ClusterEntrypoint.runClusterEntrypoint(yarnJobClusterEntrypoint); 32 }
4）后续的调用过程：ClusterEntrypoint类中runClusterEntrypoint()->startCluster()->runCluster()，该过程比较简单，这里着实分析runCluster()方法，如下：
1 //#ClusterEntrypint.java 2 private void runCluster(Configuration configuration) throws Exception { 3 synchronized (lock) { 4 initializeServices(configuration); 5 6 // write host information into configuration 7 configuration.setString(JobManagerOptions.ADDRESS, commonRpcService.getAddress()); 8 configuration.setInteger(JobManagerOptions.PORT, commonRpcService.getPort()); 9 //1、创建dispatcherResour、esourceManager对象，其中有从本地重新创建JobGraph的过程 10 final DispatcherResourceManagerComponentFactory<?> dispatcherResourceManagerComponentFactory = createDispatcherResourceManagerComponentFactory(configuration); 11 //2、Entry 启动RpcService、HAService、BlobServer、HeartbeatServices、MetricRegistry、ExecutionGraphStore等 12 clusterComponent = dispatcherResourceManagerComponentFactory.create( 13 configuration, 14 commonRpcService, 15 haServices, 16 blobServer, 17 heartbeatServices, 18 metricRegistry, 19 archivedExecutionGraphStore, 20 new RpcMetricQueryServiceRetriever(metricRegistry.getMetricQueryServiceRpcService()), 21 this); 22 23 //............ 24 } 25 }
4）在create()方法中，会启动Flink的诸多组件，其中与提交任务强相关的是Dispatcher、ResourceManager，具体代码如下：
1 public DispatcherResourceManagerComponent<T> create( 2 Configuration configuration, 3 RpcService rpcService, 4 HighAvailabilityServices highAvailabilityServices, 5 BlobServer blobServer, 6 HeartbeatServices heartbeatServices, 7 MetricRegistry metricRegistry, 8 ArchivedExecutionGraphStore archivedExecutionGraphStore, 9 MetricQueryServiceRetriever metricQueryServiceRetriever, 10 FatalErrorHandler fatalErrorHandler) throws Exception { 11 12 LeaderRetrievalService dispatcherLeaderRetrievalService = null; 13 LeaderRetrievalService resourceManagerRetrievalService = null; 14 WebMonitorEndpoint<U> webMonitorEndpoint = null; 15 ResourceManager<?> resourceManager = null; 16 JobManagerMetricGroup jobManagerMetricGroup = null; 17 T dispatcher = null; 18 19 try { 20 dispatcherLeaderRetrievalService = highAvailabilityServices.getDispatcherLeaderRetriever(); 21 22 resourceManagerRetrievalService = highAvailabilityServices.getResourceManagerLeaderRetriever(); 23 24 final LeaderGatewayRetriever<DispatcherGateway> dispatcherGatewayRetriever = new RpcGatewayRetriever<>( 25 rpcService, 26 DispatcherGateway.class, 27 DispatcherId::fromUuid, 28 10, 29 Time.milliseconds(50L)); 30 31 final LeaderGatewayRetriever<ResourceManagerGateway> resourceManagerGatewayRetriever = new RpcGatewayRetriever<>( 32 rpcService, 33 ResourceManagerGateway.class, 34 ResourceManagerId::fromUuid, 35 10, 36 Time.milliseconds(50L)); 37 38 final ExecutorService executor = WebMonitorEndpoint.createExecutorService( 39 configuration.getInteger(RestOptions.SERVER_NUM_THREADS), 40 configuration.getInteger(RestOptions.SERVER_THREAD_PRIORITY), 41 "DispatcherRestEndpoint"); 42 43 final long updateInterval = configuration.getLong(MetricOptions.METRIC_FETCHER_UPDATE_INTERVAL); 44 final MetricFetcher metricFetcher = updateInterval == 0 45 ? VoidMetricFetcher.INSTANCE 46 : MetricFetcherImpl.fromConfiguration( 47 configuration, 48 metricQueryServiceRetriever, 49 dispatcherGatewayRetriever, 50 executor); 51 52 webMonitorEndpoint = restEndpointFactory.createRestEndpoint( 53 configuration, 54 dispatcherGatewayRetriever, 55 resourceManagerGatewayRetriever, 56 blobServer, 57 executor, 58 metricFetcher, 59 highAvailabilityServices.getWebMonitorLeaderElectionService(), 60 fatalErrorHandler); 61 62 log.debug("Starting Dispatcher REST endpoint."); 63 webMonitorEndpoint.start(); 64 65 final String hostname = getHostname(rpcService); 66 67 jobManagerMetricGroup = MetricUtils.instantiateJobManagerMetricGroup( 68 metricRegistry, 69 hostname, 70 ConfigurationUtils.getSystemResourceMetricsProbingInterval(configuration)); 71 //1、返回的是new YarnResourceManager 72 /*调度过程：AbstractDispatcherResourceManagerComponentFactory 73 ->ActiveResourceManagerFactory 74 ->YarnResourceManagerFactory 75 */ 76 ResourceManager<?> resourceManager1 = resourceManagerFactory.createResourceManager( 77 configuration, 78 ResourceID.generate(), 79 rpcService, 80 highAvailabilityServices, 81 heartbeatServices, 82 metricRegistry, 83 fatalErrorHandler, 84 new ClusterInformation(hostname, blobServer.getPort()), 85 webMonitorEndpoint.getRestBaseUrl(), 86 jobManagerMetricGroup); 87 resourceManager = resourceManager1; 88 89 final HistoryServerArchivist historyServerArchivist = HistoryServerArchivist.createHistoryServerArchivist(configuration, webMonitorEndpoint); 90 //2、在此反序列化获取JobGraph实例；返回new MiniDispatcher 91 dispatcher = dispatcherFactory.createDispatcher( 92 configuration, 93 rpcService, 94 highAvailabilityServices, 95 resourceManagerGatewayRetriever, 96 blobServer, 97 heartbeatServices, 98 jobManagerMetricGroup, 99 metricRegistry.getMetricQueryServiceGatewayRpcAddress(), 100 archivedExecutionGraphStore, 101 fatalErrorHandler, 102 historyServerArchivist); 103 104 log.debug("Starting ResourceManager."); 105 //启动resourceManager，此过程中会经历以下阶段 106 //leader选举->(ResourceManager.java中) 107 // ->grantLeadership(...) 108 // ->tryAcceptLeadership(...) 109 // ->slotManager的启动 110 resourceManager.start(); 111 resourceManagerRetrievalService.start(resourceManagerGatewayRetriever); 112 113 log.debug("Starting Dispatcher."); 114 115 //启动Dispatcher，经历以下阶段： 116 //leader选举->(Dispatcher.java中)grantLeadership->tryAcceptLeadershipAndRunJobs 117 // ->createJobManagerRunner->startJobManagerRunner->jobManagerRunner.start() 118 // 119 //->(JobManagerRunner.java中)start()->leaderElectionService.start(...) 120 //->grantLeadership(...)->verifyJobSchedulingStatusAndStartJobManager(...) 121 //->startJobMaster(leaderSessionId)这里的startJobmaster应该是启动的JobManager 122 // 123 //->(JobManagerRunner.java中)jobMasterService.start(...) 124 //->(JobMaster.java)startJobExecution(...) 125 // ->{startJobMasterServices()在该方法中会启动slotPool->resourceManagerLeaderRetriever.start(...)} 126 //->startJobExecution(...)-> 127 dispatcher.start(); 128 dispatcherLeaderRetrievalService.start(dispatcherGatewayRetriever); 129 130 return createDispatcherResourceManagerComponent( 131 dispatcher, 132 resourceManager, 133 dispatcherLeaderRetrievalService, 134 resourceManagerRetrievalService, 135 webMonitorEndpoint, 136 jobManagerMetricGroup); 137 138 } catch (Exception exception) { 139 // clean up all started components 140 //失败会清除已启动的组件 141 //.............. 142 } 143 }
5）此后，JobManager中的slotPool会向SlotManager申请资源，而SlotManager则向Yarn的ResourceManager申请，申请到后会启动TaskManager，然后将slot信息注册到slotManager和slotPool中，详细过程在此就不展开分析了，留作后面分析。
该博客中还有诸多不完善的地方，需要自己后进一步的阅读源码、弄清设计架构后等一系列之后才能有更好的完善，此外，后期也会对照着Flink 的Per-job模式下任务提交的详细日志进一步验证。
若是文中有描述不清的，非常建议参考以下博文；若是存在不对的地方，非常欢迎大伙留言指出，谢谢了！
Ref
[1]https://files.alicdn.com/tpsservice/7bb8f513c765b97ab65401a1b78c8cb8.pdf
[2]https://yq.aliyun.com/articles/719262?spm=a2c4e.11153940.0.0.3ea9469ei7H3Wx#
[3]https://www.jianshu.com/p/52da8b2e4ccc
Flink源码阅读（一）——Flink on Yarn的Per-job模式源码简析
标签：exce orm hdfs name pst dal 文件中 相同 指定
原文地址：https://www.cnblogs.com/love-yh/p/11657791.html
