Flink原理与实现：如何生成ExecutionGraph及物理执行图

在这里插入图片描述

ExecutionGraph生成过程

StreamGraph和JobGraph都是在client生成的，这篇文章将描述如何生成ExecutionGraph以及物理执行图。同时会讲解一个作业提交后如何被调度和执行。

client生成JobGraph之后，就通过submitJob提交至JobMaster。
在其构造函数中，会生成ExecutionGraph：

    this.executionGraph = ExecutionGraphBuilder.buildGraph(...)

看下这个方法，比较长，略过了一些次要的代码片断：


     // 流式作业中，schedule mode固定是EAGER的
        executionGraph.setScheduleMode(jobGraph.getScheduleMode());
        executionGraph.setQueuedSchedulingallowed(jobGraph.getAllowQueuedScheduling());
 <span class="hljs-comment">// 设置json plan</span>
 <span class="hljs-comment">// ...</span>

 <span class="hljs-comment">// 检查executableClass(即operator类)，设置最大并发</span>
 <span class="hljs-comment">// ...</span>
 
    <span class="hljs-comment">// 按拓扑顺序，获取所有的JobVertex列表</span>
    List&lt;JobVertex&gt; sortedTopology = jobGraph.getVerticesSortedTopologicallyFromSources();
    
    <span class="hljs-comment">// 根据JobVertex列表，生成execution graph</span>
    executionGraph.attachJobGraph(sortedTopology);
    
    <span class="hljs-comment">// checkpoint检查</span></code></pre>

可以看到，生成execution graph的代码，主要是在最后一行，即ExecutionGraph.attachJobGraph方法：

    public void attachJobGraph(List<JobVertex> topologiallySorted) throws JobException, IOException {
       // 遍历job vertex
        for (JobVertex jobVertex : topologiallySorted) {
            // 根据每一个job vertex，创建对应的ExecutionVertex
            ExecutionJobVertex ejv = new ExecutionJobVertex(this, jobVertex, 1, rpcCallTimeout, createTimestamp);
            // 将创建的ExecutionJobVertex与前置的IntermediateResult连接起来
            ejv.connectToPredecessors(this.intermediateResults);
        ExecutionJobVertex prevIoUsTask = <span class="hljs-keyword">this</span>.tasks.putIfAbsent(jobVertex.getID(), ejv);

    <span class="hljs-comment">// sanity check</span>
    <span class="hljs-comment">// ...</span>
    
        <span class="hljs-keyword">this</span>.verticesInCreationorder.add(ejv);
    }
}</code></pre>

可以看到，创建ExecutionJobVertex的重点就在它的构造函数中：

     // 上面是并行度相关的设置
 <span class="hljs-comment">// 序列化后的Task@R_966_4045@ion，这个信息很重要</span>
 <span class="hljs-comment">// 后面deploy的时候会将Task@R_966_4045@ion分发到具体的Task中。</span>
    <span class="hljs-keyword">this</span>.serializedTask@R_966_4045@ion = <span class="hljs-keyword">new</span> SerializedValue&lt;&gt;(<span class="hljs-keyword">new</span> Task@R_966_4045@ion(
        jobVertex.getID(),
        jobVertex.getName(),
        parallelism,
        maxParallelism,
        <span class="hljs-comment">// 这个就是Task将要执行的Operator的类名</span>
        jobVertex.getInvokableClassName(),
        jobVertex.getConfiguration()));
 
 <span class="hljs-comment">// ExecutionVertex列表，按照JobVertex并行度设置      </span>
    <span class="hljs-keyword">this</span>.taskVertices = <span class="hljs-keyword">new</span> ExecutionVertex[numTaskVertices];
    
    <span class="hljs-keyword">this</span>.inputs = <span class="hljs-keyword">new</span> ArrayList&lt;&gt;(jobVertex.getInputs().size());
    
    <span class="hljs-comment">// slot sharing和coLocation相关代码</span>
    <span class="hljs-comment">// ...</span>
    
    <span class="hljs-comment">// 创建intermediate results，这是由当前operator的出度确定的，如果当前operator只向下游一个operator输出，则为1</span>
    <span class="hljs-comment">// 注意一个IntermediateResult包含多个IntermediateResultPartition</span>
    <span class="hljs-keyword">this</span>.producedDataSets = <span class="hljs-keyword">new</span> IntermediateResult[jobVertex.getNumberOfProducedIntermediateDataSets()];

    <span class="hljs-keyword">for</span> (<span class="hljs-keyword">int</span> i = <span class="hljs-number">0</span>; i &lt; jobVertex.getProducedDataSets().size(); i++) {
        <span class="hljs-keyword">final</span> IntermediateDataSet result = jobVertex.getProducedDataSets().get(i);

        <span class="hljs-keyword">this</span>.producedDataSets[i] = <span class="hljs-keyword">new</span> IntermediateResult(
                result.getId(),
                <span class="hljs-keyword">this</span>,
                numTaskVertices,
                result.getResultType());
    }

    <span class="hljs-comment">// 根据job vertex的并行度，创建对应的ExecutionVertex列表。</span>
    <span class="hljs-comment">// 即，一个JobVertex/ExecutionJobVertex代表的是一个operator，而</span>
    <span class="hljs-comment">// 具体的ExecutionVertex则代表了每一个Task</span>
    <span class="hljs-keyword">for</span> (<span class="hljs-keyword">int</span> i = <span class="hljs-number">0</span>; i &lt; numTaskVertices; i++) {
        ExecutionVertex vertex = <span class="hljs-keyword">new</span> ExecutionVertex(
                <span class="hljs-keyword">this</span>, i, <span class="hljs-keyword">this</span>.producedDataSets, timeout, createTimestamp, maxPriorAttemptsHistoryLength);

        <span class="hljs-keyword">this</span>.taskVertices[i] = vertex;
    }
    
    <span class="hljs-comment">// sanity check</span>
    <span class="hljs-comment">// ...</span>
    
    <span class="hljs-comment">// set up the input splits, if the vertex has any</span>
    <span class="hljs-comment">// 这是batch相关的代码</span>
    <span class="hljs-comment">// ...</span>
         
    finishedSubtasks = <span class="hljs-keyword">new</span> <span class="hljs-keyword">boolean</span>[parallelism];</code></pre>

ExecutionJobVertex和ExecutionVertex是创建完了，但是ExecutionEdge还没有创建呢，接下来看一下attachJobGraph方法中这一行代码：

    ejv.connectToPredecessors(this.intermediateResults);

这个方法代码如下：

     // 获取输入的Jobedge列表
        List<Jobedge> inputs = jobVertex.getInputs();
    <span class="hljs-comment">// 遍历每条Jobedge        </span>
    <span class="hljs-keyword">for</span> (<span class="hljs-keyword">int</span> num = <span class="hljs-number">0</span>; num &lt; inputs.size(); num++) {
        Jobedge edge = inputs.get(num);
        
        <span class="hljs-comment">// 获取当前Jobedge的输入所对应的IntermediateResult</span>
        IntermediateResult ires = intermediateDataSets.get(edge.getSourceId());
        <span class="hljs-keyword">if</span> (ires == <span class="hljs-keyword">null</span>) {
            <span class="hljs-keyword">throw</span> <span class="hljs-keyword">new</span> JobException(<span class="hljs-string">"Cannot connect this job graph to the prevIoUs graph. No prevIoUs intermediate result found for ID "</span>
                    + edge.getSourceId());
        }
        
        <span class="hljs-comment">// 将IntermediateResult加入到当前ExecutionJobVertex的输入中。</span>
        <span class="hljs-keyword">this</span>.inputs.add(ires);
        
        <span class="hljs-comment">// 为IntermediateResult注册consumer</span>
        <span class="hljs-comment">// consumerIndex跟IntermediateResult的出度相关</span>
        <span class="hljs-keyword">int</span> consumerIndex = ires.registerConsumer();
        
        <span class="hljs-keyword">for</span> (<span class="hljs-keyword">int</span> i = <span class="hljs-number">0</span>; i &lt; parallelism; i++) {
            ExecutionVertex ev = taskVertices[i];
            <span class="hljs-comment">// 将ExecutionVertex与IntermediateResult关联起来</span>
            ev.connectSource(num, ires, edge, consumerIndex);
        }
    }</code></pre>

看下ExecutionVertex.connectSource方法代码：

    public void connectSource(int inputNumber, IntermediateResult source, Jobedge edge, int consumerNumber) {
 <span class="hljs-comment">// 只有forward的方式的情况下，pattern才是POINTWISE的，否则均为ALL_TO_ALL</span>
    <span class="hljs-keyword">final</span> distributionPattern pattern = edge.getdistributionPattern();
    <span class="hljs-keyword">final</span> IntermediateResultPartition[] sourcePartitions = source.getPartitions();

    ExecutionEdge[] edges;

    <span class="hljs-keyword">switch</span> (pattern) {
        <span class="hljs-keyword">case</span> POINTWISE:
            edges = connectPointwise(sourcePartitions, inputNumber);
            <span class="hljs-keyword">break</span>;

        <span class="hljs-keyword">case</span> ALL_TO_ALL:
            edges = connectAllToAll(sourcePartitions, inputNumber);
            <span class="hljs-keyword">break</span>;

        <span class="hljs-keyword">default</span>:
            <span class="hljs-keyword">throw</span> <span class="hljs-keyword">new</span> RuntimeException(<span class="hljs-string">"Unrecognized distribution pattern."</span>);

    }

    <span class="hljs-keyword">this</span>.inputEdges[inputNumber] = edges;

    <span class="hljs-comment">// 之前已经为IntermediateResult添加了consumer，这里为IntermediateResultPartition添加consumer，即关联到ExecutionEdge上</span>
    <span class="hljs-keyword">for</span> (ExecutionEdge ee : edges) {
        ee.getSource().addConsumer(ee, consumerNumber);
    }
}

connectAllToAll方法：

        ExecutionEdge[] edges = new ExecutionEdge[sourcePartitions.length];
    <span class="hljs-keyword">for</span> (<span class="hljs-keyword">int</span> i = <span class="hljs-number">0</span>; i &lt; sourcePartitions.length; i++) {
        IntermediateResultPartition irp = sourcePartitions[i];
        edges[i] = <span class="hljs-keyword">new</span> ExecutionEdge(irp, <span class="hljs-keyword">this</span>, inputNumber);
    }

    <span class="hljs-keyword">return</span> edges;</code></pre>

看这个方法之前，需要知道，ExecutionVertex的inputEdges变量，是一个二维数据。它表示了这个ExecutionVertex上每一个input所包含的ExecutionEdge列表。

即，如果ExecutionVertex有两个不同的输入：输入A和B。其中输入A的partition=1，输入B的partition=8，那么这个二维数组inputEdges如下(为简短，以irp代替IntermediateResultPartition)

[ ExecutionEdge[ A.irp[0]] ]
[ ExecutionEdge[ B.irp[0], B.irp[1], ..., B.irp[7] ]

所以上面的代码就很容易理解了。

到这里为止，ExecutionJobGraph就创建完成了。接下来看下这个ExecutionGraph是如何转化成Task并开始执行的。

Task调度和执行

接下来我们以最简单的mini cluster为例讲解一下Task如何被调度和执行。

简单略过client端job的提交和StreamGraph到JobGraph的翻译，以及上面ExecutionGraph的翻译。

提交后的job的流通过程大致如下：

env.execute('<job name>')
  --> MiniCluster.runJobBlocking(jobGraph)
  --> MiniClusterdispatcher.runJobBlocking(jobGraph)
  --> MiniClusterdispatcher.startJobRunners
    --> JobManagerRunner.start
    --> JobMaster.<init> (build ExecutionGraph)

创建完JobMaster之后，JobMaster就会进行leader election，得到leader之后，会回调grantleadership方法，从而调用jobManager.start(leaderSessionID);开始运行job。

JobMaster.start 
    --> JobMaster.startJobExecution（这里还没开始执行呢..）
    --> resourceManagerleaderRetriever.start(new ResourceManagerleaderListener());

重点是在下面这行，获取到resource manage之后，就会回调ResourceManagerleaderListener.notifyleaderAddress，整个调用流如下：

ResourceManagerleaderListener.notifyleaderAddress
    --> JobMaster.notifyOfNewResourceManagerleader
    --> ResourceManagerConnection.start
    --> ResourceManagerConnection.onRegistrationSuccess(callback,由flink rpc框架发送并回调)
    --> JobMaster.onResourceManagerRegistrationSuccess

然后终于来到了最核心的调度代码，在JobMaster.onResourceManagerRegistrationSuccess方法中：

    executionContext.execute(new Runnable() {
        @Override
        public void run() {
            try {
                executionGraph.restoreExternalCheckpointedStore();
                executionGraph.setQueuedSchedulingallowed(true);
                executionGraph.scheduleForExecution(slotPool.getSlotProvider());
            }
            catch (Throwable t) {
                executionGraph.fail(t);
            }
        }
    });

ExecutionGraph.scheduleForExecution --> ExecutionGraph.scheduleEager

这个方法会计算所有的ExecutionVertex总数，并为每个ExecutionVertex分配一个SimpleSlot（暂时不考虑slot sharing的情况），然后封装成ExecutionAndSlot，顾名思义，即ExecutionVertex + Slot（更为贴切地说，应该是ExecutionAttempt + Slot）。

然后调用execAndSlot.executionAttempt.deployToslot(slot);进行deploy，即Execution.deployToslot。

这个方法先会进行一系列状态迁移和检查，然后进行deploy，比较核心的代码如下：

        final TaskDeploymentDescriptor deployment = vertex.createDeploymentDescriptor(
            attemptId,
            slot,
            taskState,
            attemptNumber);
    <span class="hljs-comment">// register this execution at the execution graph, to receive call backs</span>
    vertex.getExecutionGraph().registerExecution(<span class="hljs-keyword">this</span>);
        
    <span class="hljs-keyword">final</span> TaskManagerGateway taskManagerGateway = slot.getTaskManagerGateway();        <span class="hljs-keyword">final</span> Future&lt;AckNowledge&gt; submitResultFuture = taskManagerGateway.submitTask(deployment, timeout);

ExecutionVertex.createDeploymentDescriptor方法中，包含了从Execution Graph到真正物理执行图的转换。如将IntermediateResultPartition转化成ResultPartition，ExecutionEdge转成InputChannelDeploymentDescriptor（最终会在执行时转化成InputGate）。

最后通过RPC方法提交task，实际会调用到TaskExecutor.submitTask方法中。
这个方法会创建真正的Task，然后调用task.startTaskThread();开始task的执行。

在Task构造函数中，会根据输入的参数，创建InputGate, ResultPartition, ResultPartitionWriter等。

而startTaskThread方法，则会执行executingThread.start，从而调用Task.run方法。
它的最核心的代码如下：

     // ...
    <span class="hljs-comment">// Now load the task's invokable code</span>
    invokable = loadAndInstantiateInvokable(userCodeClassLoader, nameOfInvokableClass);

  <span class="hljs-comment">// ...</span>
  invokable.setEnvironment(env);
  
  <span class="hljs-comment">// ...</span>
  <span class="hljs-keyword">this</span>.invokable = invokable;
  invokable.invoke();
  
  <span class="hljs-comment">// task finishes or fails, do cleanup</span>
  <span class="hljs-comment">// ...</span></code></pre>

这里的invokable即为operator对象实例，通过反射创建。具体地，即为OneInputStreamTask，或者SourceStreamTask等。这个nameOfInvokableClass是哪里生成的呢？其实早在生成StreamGraph的时候，这就已经确定了，见StreamGraph.addOperator方法：

        if (operatorObject instanceof StoppableStreamSource) {
            addNode(vertexID, slotSharingGroup, StoppableSourceStreamTask.class, operatorObject, operatorName);
        } else if (operatorObject instanceof StreamSource) {
            addNode(vertexID, slotSharingGroup, SourceStreamTask.class, operatorObject, operatorName);
        } else {
            addNode(vertexID, slotSharingGroup, OneInputStreamTask.class, operatorObject, operatorName);
        }

这里的OneInputStreamTask.class即为生成的StreamNode的vertexClass。这个值会一直传递，当StreamGraph被转化成JobGraph的时候，这个值会被传递到JobVertex的invokableClass。然后当JobGraph被转成ExecutionGraph的时候，这个值被传入到ExecutionJobVertex.Task@R_966_404 [email protected]中，一直传到Task中。

那么用户真正写的逻辑代码在哪里呢？比如word count中的Tokenizer，去了哪里呢？
OneInputStreamTask的基类StreamTask，包含了headOperator和operatorChain。当我们调用dataStream.flatMap(new Tokenizer())的时候，会生成一个StreamFlatMap的operator，这个operator是一个AbstractUdfStreamOperator，而用户的代码new Tokenizer，即为它的userFunction。

所以再串回来，以OneInputStreamTask为例，Task的核心执行代码即为OneInputStreamTask.invoke方法，它会调用StreamTask.run方法，这是个抽象方法，最终会调用其派生类的run方法，即OneInputStreamTask, SourceStreamTask等。

OneInputStreamTask的run方法代码如下：

    final OneInputStreamOperator<IN, OUT> operator = this.headOperator;
    final StreamInputProcessor<IN> inputProcessor = this.inputProcessor;
    final Object lock = getCheckpointLock();
<span class="hljs-keyword">while</span> (running &amp;&amp; inputProcessor.processInput(operator, lock)) {
    <span class="hljs-comment">// all the work happens in the "processInput" method</span>
}</code></pre>

就是一直不停地循环调用inputProcessor.processInput(operator, lock)方法，即StreamInputProcessor.processInput方法：

    public boolean processInput(OneInputStreamOperator<IN, ?> streamOperator, final Object lock) throws Exception {
     // ...
    <span class="hljs-keyword">while</span> (<span class="hljs-keyword">true</span>) {
        <span class="hljs-keyword">if</span> (currentRecordDeserializer != <span class="hljs-keyword">null</span>) {
       <span class="hljs-comment">// ...</span>
       
            <span class="hljs-keyword">if</span> (result.isFullRecord()) {
                StreamElement recordOrMark = deserializationDelegate.getInstance();
                
          <span class="hljs-comment">// 处理watermark，则框架处理</span>
                <span class="hljs-keyword">if</span> (recordOrMark.isWatermark()) {
                   <span class="hljs-comment">// watermark处理逻辑</span>
                   <span class="hljs-comment">// ...</span>
                    <span class="hljs-keyword">continue</span>;
                } <span class="hljs-keyword">else</span> <span class="hljs-keyword">if</span>(recordOrMark.isLatencyMarker()) {
                    <span class="hljs-comment">// 处理latency mark，也是由框架处理</span>
                    <span class="hljs-keyword">synchronized</span> (lock) {
                        streamOperator.processLatencyMarker(recordOrMark.asLatencyMarker());
                    }
                    <span class="hljs-keyword">continue</span>;
                } <span class="hljs-keyword">else</span> {
                    <span class="hljs-comment">// ***** 这里是真正的用户逻辑代码 *****</span>
                    StreamRecord&lt;IN&gt; record = recordOrMark.asRecord();
                    <span class="hljs-keyword">synchronized</span> (lock) {
                        numRecordsIn.inc();
                        streamOperator.setKeyContextElement1(record);
                        streamOperator.processElement(record);
                    }
                    <span class="hljs-keyword">return</span> <span class="hljs-keyword">true</span>;
                }
            }
        }

    <span class="hljs-comment">// 其他处理逻辑</span>
    <span class="hljs-comment">// ...</span>
    }
}</code></pre>

上面的代码中，streamOperator.processElement(record);才是真正处理用户逻辑的代码，以StreamFlatMap为例，即为它的processElement方法：

    public void processElement(StreamRecord<IN> element) throws Exception {
        collector.setTimestamp(element);
        userFunction.flatMap(element.getValue(), collector);
    }

这样，整个调度和执行逻辑就全部串起来啦。

Flink原理与实现：如何生成ExecutionGraph及物理执行图

ExecutionGraph生成过程

Task调度和执行

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