Flink推测机制
1、配置
execution.batch.speculative.enabled：false，推测机制开关，必须在AdaptiveBatchScheduler模式下使用
execution.batch.speculative.max-concurrent-executions：2，同时最多几次执行
execution.batch.speculative.block-slow-node-duration：1分钟，慢速节点会如黑名单，控制在黑名单中的时长
slow-task-detector.check-interval：1秒，慢任务检查间隔
slow-task-detector.execution-time.baseline-lower-bound：1分钟，慢任务检测基线的下限
slow-task-detector.execution-time.baseline-ratio：0.75，开始检测慢任务基线的任务完成率，即有75%任务完成后，开始计算剩下的任务是否为慢任务
slow-task-detector.execution-time.baseline-multiplier：1.5，慢任务基线乘数
2、SpeculativeScheduler
推测机制在AdaptiveBatchScheduler模式下使用，在AdaptiveBatchSchedulerFactory当中，创建调度器时，如果开启了推测机制，会创建SpeculativeScheduler
if (enableSpeculativeExecution) {
return new SpeculativeScheduler(
log,
jobGraph,
ioExecutor,
jobMasterConfiguration,2.1、启动
调度器启动时有三个操作：1、注册指标；2、父类通用的启动流程，会有算子的一些初始化；3、启动慢任务检测任务
protected void startSchedulingInternal() {
registerMetrics(jobManagerJobMetricGroup);
super.startSchedulingInternal();
slowTaskDetector.start(getExecutionGraph(), this, getMainThreadExecutor());
}2.2、SlowTaskDetector
SlowTaskDetector负责检测慢任务，实现类是ExecutionTimeBasedSlowTaskDetector，基于schedule进行检测
this.scheduledDetectionFuture =
mainThreadExecutor.schedule(
() -> {
listener.notifySlowTasks(findSlowTasks(executionGraph));
scheduleTask(executionGraph, listener, mainThreadExecutor);
},
checkIntervalMillis,
TimeUnit.MILLISECONDS);核心是findSlowTasks，首先是获取需要校验的拓扑集
private List<ExecutionJobVertex> getJobVerticesToCheck(final ExecutionGraph executionGraph) {
return IterableUtils.toStream(executionGraph.getVerticesTopologically())
.filter(ExecutionJobVertex::isInitialized)
.filter(ejv -> ejv.getAggregateState() != ExecutionState.FINISHED)
.filter(ejv -> getFinishedRatio(ejv) >= baselineRatio)
.collect(Collectors.toList());
}getFinishedRatio就是获取完成任务数超过基线比率的，就是拓扑集中完成任务数和总任务数的比值
private double getFinishedRatio(final ExecutionJobVertex executionJobVertex) {
checkState(executionJobVertex.getTaskVertices().length > 0);
long finishedCount =
Arrays.stream(executionJobVertex.getTaskVertices())
.filter(ev -> ev.getExecutionState() == ExecutionState.FINISHED)
.count();
return (double) finishedCount / executionJobVertex.getTaskVertices().length;
}接下来是获取基线和在基线基础上计算慢速任务的，接口是getBaseline和findExecutionsExceedingBaseline，本质就是执行时间和基线的对比，注意这里不仅用到了时间，还用到了输入字节数，所以慢任务的检测可能是基于吞吐来的
private ExecutionTimeWithInputBytes getBaseline(
final ExecutionJobVertex executionJobVertex, final long currentTimeMillis) {
final ExecutionTimeWithInputBytes weightedExecutionTimeMedian =
calculateFinishedTaskExecutionTimeMedian(executionJobVertex, currentTimeMillis);
long multipliedBaseline =
(long) (weightedExecutionTimeMedian.getExecutionTime() * baselineMultiplier);
return new ExecutionTimeWithInputBytes(
multipliedBaseline, weightedExecutionTimeMedian.getInputBytes());
}
return Double.compare(
(double) executionTime / Math.max(inputBytes, Double.MIN_VALUE),
(double) other.getExecutionTime()
/ Math.max(other.getInputBytes(), Double.MIN_VALUE));2.3、notifySlowTasks
获取慢速任务以后，SlowTaskDetector会触发监听器，监听器的处理实现在SpeculativeScheduler的notifySlowTasks接口
首先把节点加入黑名单
// add slow nodes to blocklist before scheduling new speculative executions
blockSlowNodes(slowTasks, currentTimestamp);这边会检测任务是否支持推测，默认是支持
if (!executionVertex.isSupportsConcurrentExecutionAttempts()) {
continue;
}基于时间戳，对慢任务新建Execution
final Collection<Execution> attempts =
IntStream.range(0, newSpeculativeExecutionsToDeploy)
.mapToObj(
i ->
executionVertex.createNewSpeculativeExecution(
currentTimestamp))
.collect(Collectors.toList());之后会进行一系列的配置，加入监控
setupSubtaskGatewayForAttempts(executionVertex, attempts);
verticesToDeploy.add(executionVertexId);
newSpeculativeExecutions.addAll(attempts);最后发起调度
executionDeployer.allocateSlotsAndDeploy(
newSpeculativeExecutions,
executionVertexVersioner.getExecutionVertexVersions(verticesToDeploy));3、任务结束
任务结束主要核心在DefaultExecutionGraph的jobFinished，判断在上层ExecutionJobVertex.executionVertexFinished，这里是通过任务并行度来判断的，所有子任务完成则认为job完成
void executionVertexFinished() {
checkState(isInitialized());
numExecutionVertexFinished++;
if (numExecutionVertexFinished == parallelismInfo.getParallelism()) {
getGraph().jobVertexFinished();
}
}这个的调用是由Execution触发的，也就是每个子任务完成会去调用一次
if (transitionState(current, FINISHED)) {
try {
finishPartitionsAndUpdateConsumers();
updateAccumulatorsAndMetrics(userAccumulators, metrics);
releaseAssignedResource(null);
vertex.getExecutionGraphAccessor().deregisterExecution(this);
} finally {
vertex.executionFinished(this);
}
return;
}最终一个jobVertex（对应Job的一个任务，任务根据并行度有子任务）完成的时候会通知所有子任务完成
public void jobVertexFinished() {
assertRunningInJobMasterMainThread();
final int numFinished = ++numFinishedJobVertices;
if (numFinished == numJobVerticesTotal) {
FutureUtils.assertNoException(
waitForAllExecutionsTermination().thenAccept(ignored -> jobFinished()));
}
}原文地址：https://blog.csdn.net/blackjjcat/article/details/138605327
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