一、前言
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
二、Per-job提交任务原理
Flink on Yarn模式下提交任务整体流程图如下
图1 Flink Runtime层架构图
2.1. Runtime层架构简介
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。
2.2. Flink on Yarn 的Per-job模式提交任务的整体流程：
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。
三、源码简析
提交任务语句
./flink run -m yarn-cluster ./flinkExample.jar1、Client端提交任务阶段分析
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）方法AbstractYarnClutserDescriptor.startAppMaster()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.submitApplication()，该方法在于调用了ApplicationClientProtocolPBClientImpl.submitApplication()，其具体代码如下：
1 public SubmitApplicationResponse submitApplication(SubmitApplicationRequest request) throws YarnException, IOException {
2 //取出报文
3 SubmitApplicationRequestProto requestProto = ((SubmitApplicationRequestPBImpl)request).getProto();
4
5 try {
6 //将报文发送发送到服务端，并将返回结果构成response
7 return new SubmitApplicationResponsePBImpl(this.proxy.submitApplication((RpcController)null, requestProto));
8 } catch (ServiceException var4) {
9 RPCUtil.unwrapAndThrowException(var4);
10 return null;
11 }
12 }View Code
报文就会通过RPC到达服务端，服务端处理报文的方法是ApplicationClientProtocolPBServiceImpl.submitApplication()，方法中会重新构建报文，然后通过ClientRMService.submitApplication()将应用请求提交到Yarn上的RMAppManager去提交任务（在Yarn的分配过后面会专门写一系列的博客去说明）。
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。
2、Flink Cluster启动流程分析
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。
四、总结
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。
本文章为转载内容，我们尊重原作者对文章享有的著作权。如有内容错误或侵权问题，欢迎原作者联系我们进行内容更正或删除文章。
