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源码解析Flink源节点数据读取是如何与checkpoint串行执行

Flink版本：1.13.6

前置知识：源节点的Checkpoint是由Checkpointcoordinate触发，具体是通过RPC调用TaskManager中对应的Task的StreamTask类的performChecpoint方法执行Checkpoint。

本文思路：本文先分析checkpoint阶段，然后再分析数据读取阶段，最后得出结论：源节点Checkpoint时和源节点读取数据时，都需要抢SourceStreamTask类中lock变量的锁，最终实现串行执行checkpoint与写数据

Checkpoint阶段

Checkpoint在StreamTask的performCheckpoint方法中执行，该方法调用过程如下

// 在StreamTask类中 执行checkpoint操作
private boolean performCheckpoint(CheckpointMetaData checkpointMetaData,CheckpointOptions checkpointOptions,CheckpointMetricsBuilder checkpointMetrics )throws Exception {if (isRunning) {//使用actionExecutor 同步触发checkpointactionExecutor.runThrowing(() -> {....//经过一系列检查subtaskCheckpointCoordinator.checkpointState(checkpointMetaData,checkpointOptions,checkpointMetrics,operatorChain,this::isRunning);});return true;} else {....}}

从上述代码可以看出，Checkpoint执行是由actionExecutor执行器执行

StreamTask类变量actionExecutor的实现和初始化

StreamTask类变量actionExecution的实现

通过代码注释可以知道该执行器的实现是StreamTaskActionExecutor.SynchronizedStreamTaskActionExecutor；从SynchronizedStreamTaskActionExecutor源代码可知，该执行器每次执行都需要获得mutex对象锁

  /*** All actions outside of the task {@link #mailboxProcessor mailbox} (i.e. performed by another* thread) must be executed through this executor to ensure that we don't have concurrent method* calls that void consistent checkpoints.** <p>CheckpointLock is superseded by {@link MailboxExecutor}, with {@link* StreamTaskActionExecutor.SynchronizedStreamTaskActionExecutor* SynchronizedStreamTaskActionExecutor} to provide lock to {@link SourceStreamTask}.*/
private final StreamTaskActionExecutor actionExecutor;class SynchronizedStreamTaskActionExecutor implements StreamTaskActionExecutor {private final Object mutex;public SynchronizedStreamTaskActionExecutor(Object mutex) {this.mutex = mutex;}@Overridepublic void run(RunnableWithException runnable) throws Exception {synchronized (mutex) {runnable.run();}}
}

StreamTask变量actionExecution初始化

actionExecutor变量在StreamTask中定义，在构造方法中初始化；该构造方法由SourceStreamTask调用，并传入SynchronizedStreamTaskActionExecutor对象，代码如下所示

//   SourceStreamTask的方法
private SourceStreamTask(Environment env, Object lock) throws Exception {//调用的StreamTask构造函数，传入SynchronizedStreamTaskActionExecutor对象super(env,null,FatalExitExceptionHandler.INSTANCE,//初始化actionExecutorStreamTaskActionExecutor.synchronizedExecutor(lock));//将lock对象赋值给类变量lockthis.lock = Preconditions.checkNotNull(lock);this.sourceThread = new LegacySourceFunctionThread();getEnvironment().getMetricGroup().getIOMetricGroup().setEnableBusyTime(false);
}//  StreamTask的方法
protected StreamTask(Environment environment,@Nullable TimerService timerService,Thread.UncaughtExceptionHandler uncaughtExceptionHandler,//初始化actionExecutorStreamTaskActionExecutor actionExecutor)throws Exception {this(environment,timerService,uncaughtExceptionHandler,actionExecutor,new TaskMailboxImpl(Thread.currentThread()));
}protected StreamTask(Environment environment,@Nullable TimerService timerService,Thread.UncaughtExceptionHandler uncaughtExceptionHandler,StreamTaskActionExecutor actionExecutor,TaskMailbox mailbox)throws Exception {super(environment);this.configuration = new StreamConfig(getTaskConfiguration());this.recordWriter = createRecordWriterDelegate(configuration, environment);//初始化actionExecutorthis.actionExecutor = Preconditions.checkNotNull(actionExecutor);this.mailboxProcessor = new MailboxProcessor(this::processInput, mailbox, actionExecutor);.......}

小结

actionExecutor执行器每次执行都需要获得mutex对象，mutex对象就是SourceStreamTask类中的lock对象；即算子每次执行Checkpoint时都需要获得SourceStreamTask类中lock对象锁才能进行

数据读取阶段

在执行Checkpoint时控制读取源端，则控制点必定是在调用SourceContext的collect方法时

@Override
public void run(SourceContext<String> ctx) throws Exception {int i = 0;while (true) {//在这个方法里处理ctx.collect(String.valueOf(i));}
}

点击collection查看实现，选择NonTimestampContext查看代码，collect()实现如下

@Override
public void collect(T element) {synchronized (lock) {output.collect(reuse.replace(element));}
}

所以这里控制数据读取发送是通过lock来控制，lock是如何初始化的？

通过NonTimestampContext构造方法可以定位到StreamSourceContexts->getSourceContext方法；

public static <OUT> SourceFunction.SourceContext<OUT> getSourceContext(TimeCharacteristic timeCharacteristic,ProcessingTimeService processingTimeService,Object checkpointLock,StreamStatusMaintainer streamStatusMaintainer,Output<StreamRecord<OUT>> output,long watermarkInterval,long idleTimeout) {final SourceFunction.SourceContext<OUT> ctx;switch (timeCharacteristic) {....case ProcessingTime://初始化NonTimestampContextctx = new NonTimestampContext<>(checkpointLock, output);break;default:throw new IllegalArgumentException(String.valueOf(timeCharacteristic));}return ctx;
}

向上追踪，在StreamSource类中调用getSourceContext：

public void run(final Object lockingObject,final StreamStatusMaintainer streamStatusMaintainer,final Output<StreamRecord<OUT>> collector,final OperatorChain<?, ?> operatorChain)throws Exception {....this.ctx =StreamSourceContexts.getSourceContext(timeCharacteristic,getProcessingTimeService(),lockingObject,streamStatusMaintainer,collector,watermarkInterval,-1);....}
// 再向上最终run方法的调用点->是由内部方法run调用
public void run(final Object lockingObject,final StreamStatusMaintainer streamStatusMaintainer,final OperatorChain<?, ?> operatorChain)throws Exception {run(lockingObject, streamStatusMaintainer, output, operatorChain);
}//再向上最终run方法的调用点->SourceStreamTask 调用run 然后再代用mainOpterator run方法
@Override
public void run() {try {// 使用的是类变量lockmainOperator.run(lock, getStreamStatusMaintainer(), operatorChain);if (!wasStoppedExternally && !isCanceled()) {synchronized (lock) {operatorChain.setIgnoreEndOfInput(false);}}completionFuture.complete(null);} catch (Throwable t) {// Note, t can be also an InterruptedExceptioncompletionFuture.completeExceptionally(t);}
}

小结

所以在源端写数据时，必须获得SourceStreamTask中的类变量lock的锁才能进行写数据；类变量lock刚好和执行器时同一个对象

总结

flink的source算子在Checkpoint时，是通过锁对象SourceStreamTask.lock，来控制源端数据产生和Checkpoint的有序进行