忘了网站链接怎么做企业网站营销的实现方式

由于系统原因导致Flink作业无法正常运行的情况非常多，且很多时候都是无法避免的。对于Flink集群来讲，能够快速从异常状态中恢复，同时保证处理数据的正确性和一致性非常重要。Flink主要借助Checkpoint的方式保障整个系统状态数据的一致性，也就是基于ABS算法实现轻量级快照服务。

本节我们详细了解Checkpoint的设计与实现。

 

1. Checkpoint的整体设计

Checkpoint的执行过程分为三个阶段：启动、执行以及确认完成。其中Checkpoint的启动过程由JobManager管理节点中的CheckpointCoordinator组件控制，该组件会周期性地向数据源节点发送执行Checkpoint的请求，执行频率取决于用户配置的CheckpointInterval参数。

执行过程：

1. 在JobManager管理节点通过CheckpointCoordinator组件向每个数据源节点发送Checkpoint执行请求，此时数据源节点中的算子会将消费数据对应的Position发送到JobManager管理节点中。
2. JobManager节点会存储Checkpoint元数据，用于记录每次执行Checkpoint操作过程中算子的元数据信息，例如在FlinkKafkaConsumer中会记录消费Kafka主题的偏移量，用于确认从Kafka主题中读取数据的位置。
3. 在数据源节点执行完Checkpoint操作后，继续向下游节点发送CheckpointBarrier事件，下游算子通过对齐Barrier事件，触发该算子的Checkpoint操作。
   当下游的map算子接收到数据源节点的Checkpoint
   Barrier事件后，首先对当前算子的数据进行处理，并等待其他上游数据源节点的Barrier事件到达。该过程就是Checkpoint
   Barrier对齐，目的是确保属于同一Checkpoint的数据能够全部到达当前节点。

Barrier事件的作用就是切分不同Checkpoint批次的数据。

• 当map算子接收到所有上游的Barrier事件后，就会触发当前算子的Checkpoint操作，并将状态数据快照到指定的外部持久化介质中，该操作主要借助状态后端存储实现。

• 当状态数据执行完毕后，继续将Barrier事件发送至下游的算子，进行后续算子的Checkpoint操作。

• 另外，在map算子中执行完Checkpoint操作后，也会向JobManager管理节点发送Ack消息，确认当前算子的Checkpoint操作正常执行。此时Checkpoint数据会存储该算子对应的状态数据，如果StateBackend为MemoryStateBackend，则主要会将状态数据存储在JobManager的堆内存中。

sink节点的ack

像map算子节点一样，当Barrier事件到达sink类型的节点后，sink节点也会进行Barrier对齐操作，确认上游节点的数据全部接入。然后对接入的数据进行处理，将结果输出到外部系统中。完成以上步骤后，sink节点会向JobManager管理节点发送Ack确认消息，确认当前Checkpoint中的状态数据都正常进行了持久化操作。（之后呢？当任务结束之后，cp会消失还是？）

 

2. Checkpoint创建源码解析

通过调用StreamExecutionEnvironment.enableCheckpointing()，开启Checkpoint。
此时Checkpoint的配置会被存储在StreamGraph中，然后将StreamGraph中的CheckpointConfig转换为JobCheckpointingSettings数据结构存储在JobGraph对象中，并伴随JobGraph提交到集群运行。启动JobMaster服务后，JobMaster调度和执行Checkpoint操作。

2.1. DefaultExecutionGraphBuilder.buildGraph

如下代码，通过JobGraph构建ExecutionGraph的过程中，获取JobGraph中存储的JobCheckpointingSettings配置，然后创建ExecutionGraph。

1）根据snapshotSettings配置获取triggerVertices、ackVertices以及confirmVertices节点集合，并转换为对应的ExecutionJobVertex集合。

• 其中triggerVertices集合存储了所有SourceOperator节点，这些节点通过CheckpointCoordinator主动触发Checkpoint操作。
• ackVertices和confirmVertices集合存储了StreamGraph中的全部节点，代表所有节点都需要返回Ack确认信息并确认Checkpoint执行成功。

2）创建CompletedCheckpointStore组件，用于存储Checkpoint过程中的元数据。

• 当对作业进行恢复操作时会在CompletedCheckpointStore中检索最新完成的Checkpoint元数据信息，然后基于元数据信息恢复Checkpoint中存储的状态数据。CompletedCheckpointStore有两种实现，分别为StandaloneCompletedCheckpointStore和ZooKeeperCompletedCheckpointStore。
• 在CompletedCheckpointStore中通过maxNumberOfCheckpointsToRetain参数配置以及结合checkpointIdCounter计数器保证只会存储固定数量的CompletedCheckpoint。

3）创建CheckpointStatsTracker实例
用于监控和追踪Checkpoint执行和更新的情况，包括Checkpoint执行的统计信息以及执行状况，WebUI中显示的Checkpoint监控数据主要来自CheckpointStatsTracker。

4）创建StateBackend，从UserClassLoader中反序列化出应用指定的StateBackend并设定为applicationConfiguredBackend。

5）初始化用户自定义的Checkpoint Hook函数

6）最终调用executionGraph.enableCheckpointing()方法，在作业的执行和调度过程中开启Checkpoint。

// 配置状态数据checkpointing
// 从jobGraph中获取JobCheckpointingSettings
JobCheckpointingSettings snapshotSettings = jobGraph.getCheckpointingSettings();
//如果snapshotSettings不为空，则开启checkpoint功能
if (snapshotSettings != null) {List<ExecutionJobVertex> triggerVertices =idToVertex(snapshotSettings.getVerticesToTrigger(), executionGraph);List<ExecutionJobVertex> ackVertices =idToVertex(snapshotSettings.getVerticesToAcknowledge(), executionGraph);List<ExecutionJobVertex> confirmVertices =idToVertex(snapshotSettings.getVerticesToConfirm(), executionGraph);//创建CompletedCheckpointStoreCompletedCheckpointStore completedCheckpoints;CheckpointIDCounter checkpointIdCounter;try {int maxNumberOfCheckpointsToRetain = jobManagerConfig.getInteger(CheckpointingOptions.MAX_RETAINED_CHECKPOINTS);if (maxNumberOfCheckpointsToRetain <= 0) {maxNumberOfCheckpointsToRetain = CheckpointingOptions.MAX_RETAINED_CHECKPOINTS.defaultValue();}// 通过recoveryFactory创建CheckpointStorecompletedCheckpoints = recoveryFactory.createCheckpointStore(jobId, maxNumberOfCheckpointsToRetain, classLoader);   // 通过recoveryFactory创建CheckpointIDCountercheckpointIdCounter = recoveryFactory.createCheckpointIDCounter(jobId);}catch (Exception e) {throw new JobExecutionException(jobId, "Failed to initialize high-availability checkpoint handler", e);}// 获取checkpoints最长的记录次数int historySize = jobManagerConfig.getInteger(WebOptions.CHECKPOINTS_HISTORY_SIZE);// 创建CheckpointStatsTracker实例CheckpointStatsTracker checkpointStatsTracker = new CheckpointStatsTracker(historySize,ackVertices,snapshotSettings.getCheckpointCoordinatorConfiguration(),metrics);// 从application中获取StateBackendfinal StateBackend applicationConfiguredBackend;final SerializedValue<StateBackend> serializedAppConfigured = snapshotSettings.getDefaultStateBackend();if (serializedAppConfigured == null) {applicationConfiguredBackend = null;}else {try {applicationConfiguredBackend = serializedAppConfigured.deserializeValue(classLoader);} catch (IOException | ClassNotFoundException e) {throw new JobExecutionException(jobId,"Could not deserialize application-defined state backend.", e);}}// 获取最终的rootBackendfinal StateBackend rootBackend;try {rootBackend = StateBackendLoader.fromApplicationOrConfigOrDefault(applicationConfiguredBackend, jobManagerConfig, classLoader, log);}catch (IllegalConfigurationException | IOException | DynamicCodeLoadingException e) {throw new JobExecutionException(jobId, "Could not instantiate configured state backend", e);}// 初始化用户自定义的checkpoint Hooks函数final SerializedValue<MasterTriggerRestoreHook.Factory[]> serializedHooks = snapshotSettings.getMasterHooks();final List<MasterTriggerRestoreHook<?>> hooks;// 如果serializedHooks为空，则hooks为空if (serializedHooks == null) {hooks = Collections.emptyList();}else {// 加载MasterTriggerRestoreHookfinal MasterTriggerRestoreHook.Factory[] hookFactories;try {hookFactories = serializedHooks.deserializeValue(classLoader);}catch (IOException | ClassNotFoundException e) {throw new JobExecutionException(jobId, "Could not instantiate user-defined checkpoint hooks", e);}// 设定ClassLoader为UserClassLoaderfinal Thread thread = Thread.currentThread();final ClassLoader originalClassLoader = thread.getContextClassLoader();thread.setContextClassLoader(classLoader);// 创建hooks函数try {hooks = new ArrayList<>(hookFactories.length);for (MasterTriggerRestoreHook.Factory factory : hookFactories) {hooks.add(MasterHooks.wrapHook(factory.create(), classLoader));}}// 将thread的ContextClassLoader设定为originalClassLoaderfinally {thread.setContextClassLoader(originalClassLoader);}}// 获取CheckpointCoordinatorConfigurationfinal CheckpointCoordinatorConfiguration chkConfig = snapshotSettings.getCheckpointCoordinatorConfiguration();// 开启executionGraph中的Checkpoint功能executionGraph.enableCheckpointing(chkConfig,triggerVertices,ackVertices,confirmVertices,hooks,checkpointIdCounter,completedCheckpoints,rootBackend,checkpointStatsTracker);
}

 

2.2. ExecutionGraph.enableCheckpointing

继续看ExecutionGraph.enableCheckpointing()方法的实现，包含如下逻辑。

1. 将tasksToTrigger、tasksToWaitFor以及tasksToCommitTo三个ExecutionJobVertex集合转换为ExecutionVertex[]数组，每个ExecutionVertex代表ExecutionJobVertex中的一个SubTask节点。
2. 容错管理：创建CheckpointFailureManager，用于Checkpoint执行过程中的容错管理，包含failJob和failJobDueToTaskFailure两个处理方法。
3. 定时调度和执行：创建checkpointCoordinatorTimer，用于Checkpoint异步线程的定时调度和执行。
4. 协调和管理作业中的Checkpoint：创建CheckpointCoordinator组件，通过CheckpointCoordinator协调和管理作业中的Checkpoint，同时收集各Task节点中Checkpoint的执行状况等信息。
5. Hook：将Master Hook注册到CheckpointCoordinator中，实现用户自定义Hook代码的调用。
6. 控制CheckpointCoordinator的启停：将JobStatusListener的实现类CheckpointCoordinatorDeActivator注册到JobManager中，此时系统会根据作业的运行状态控制CheckpointCoordinator的启停，当作业的状态为Running时会触发启动CheckpointCoordinator组件。

public void enableCheckpointing(CheckpointCoordinatorConfiguration chkConfig,List<ExecutionJobVertex> verticesToTrigger,List<ExecutionJobVertex> verticesToWaitFor,List<ExecutionJobVertex> verticesToCommitTo,List<MasterTriggerRestoreHook<?>> masterHooks,CheckpointIDCounter checkpointIDCounter,CompletedCheckpointStore checkpointStore,StateBackend checkpointStateBackend,CheckpointStatsTracker statsTracker) {checkState(state == JobStatus.CREATED, "Job must be in CREATED state");checkState(checkpointCoordinator == null, "checkpointing already enabled");ExecutionVertex[] tasksToTrigger = collectExecutionVertices(verticesToTrigger);ExecutionVertex[] tasksToWaitFor = collectExecutionVertices(verticesToWaitFor);ExecutionVertex[] tasksToCommitTo = collectExecutionVertices(verticesToCommitTo);checkpointStatsTracker = checkNotNull(statsTracker, "CheckpointStatsTracker");// 创建CheckpointFailureManagerCheckpointFailureManager failureManager = new CheckpointFailureManager(chkConfig.getTolerableCheckpointFailureNumber(),new CheckpointFailureManager.FailJobCallback() {@Overridepublic void failJob(Throwable cause) {getJobMasterMainThreadExecutor().execute(() -> failGlobal(cause));}@Overridepublic void failJobDueToTaskFailure(Throwable cause, ExecutionAttemptID failingTask) {getJobMasterMainThreadExecutor().execute(()  -> failGlobalIfExecutionIsStillRunning(cause, failingTask));}});// 创建checkpointCoordinatorTimercheckState(checkpointCoordinatorTimer == null);checkpointCoordinatorTimer = Executors.newSingleThreadScheduledExecutor(new DispatcherThreadFactory(Thread.currentThread().getThreadGroup(), "Checkpoint Timer"));// 创建checkpointCoordinatorcheckpointCoordinator = new CheckpointCoordinator(jobInformation.getJobId(),chkConfig,tasksToTrigger,tasksToWaitFor,tasksToCommitTo,checkpointIDCounter,checkpointStore,checkpointStateBackend,ioExecutor,new ScheduledExecutorServiceAdapter(checkpointCoordinatorTimer),SharedStateRegistry.DEFAULT_FACTORY,failureManager);// 向checkpoint Coordinator中注册master Hooksfor (MasterTriggerRestoreHook<?> hook : masterHooks) {if (!checkpointCoordinator.addMasterHook(hook)) {LOG.warn("Trying to register multiple checkpoint hooks with the name: {}",hook.getIdentifier());}}//向checkpointCoordinator中设定checkpointStatsTrackercheckpointCoordinator.setCheckpointStatsTracker(checkpointStatsTracker);// 注册JobStatusListener，用于自动启动CheckpointCoordinatorif (chkConfig.getCheckpointInterval() != Long.MAX_VALUE) {registerJobStatusListener(checkpointCoordinator.createActivatorDeactivator());}this.stateBackendName = checkpointStateBackend.getClass().getSimpleName();
}

 

参考：《Flink设计与实现：核心原理与源码解析》–张利兵