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分布式任务调度Schedulerx2.0工作原理

news 来源：原创 2024/5/3 1:45:16

一、前言

Schedulerx2.0是阿里巴巴开发的一个基于akka的分布式任务调度框架，提供分布式执行、多种任务类型、统一日志等功能，用户只要依赖schedulerx-worker这个jar包，通过schedulerx2.0提供的编程模型，简单几行代码就能实现一套高可靠可运维的分布式执行引擎。本文主要讲解schedulerx-worker的工作原理

二、整体架构

Schedulerx2.0是中心化的调度框架，包括Server和Worker。Server负责任务的触发和调度，通过分发引擎提交任务给Worker。Worker负责任务的执行。

Worker分为TaskMaster, Container, Processor三层：

分为TaskMaster, Container, Processor三层：

TaskMaster：类似于yarn的AppMaster，支持可扩展的分布式执行框架，进行整个jobInstance的生命周期管理、container的资源管理，同时还有failover等能力。默认实现StandaloneTaskMaster（单机执行），BroadcastTaskMaster（广播执行），MapTaskMaster（并行计算、内存网格、网格计算），MapReduceTaskMaster（并行计算、内存网格、网格计算）。
Container：执行业务逻辑的容器框架，支持线程/进程/docker/actor等。
Processor：业务逻辑框架，不同的processor表示不同的任务类型。

三、任务执行流程-worker部分

Server触发任务调度执行，提交任务给Worker，Worker通过JobInstanceActor接收Server提交的任务：

public class JobInstanceActor extends UntypedActor {
    private TaskMasterPool masterPool;
    private LogCollector logCollector;
    private static final Logger LOGGER = LogFactory.getLogger(JobInstanceActor.class);

    public JobInstanceActor() {
        this.masterPool = TaskMasterPool.INSTANCE;
        this.logCollector = LogCollectorFactory.get();
    }

    public void onReceive(Object obj) throws Throwable {
        if (obj instanceof ServerSubmitJobInstanceRequest) {
            this.handleSubmitJobInstance((ServerSubmitJobInstanceRequest)obj);
        } else if (obj instanceof ServerKillJobInstanceRequest) {
            this.handleKillJobInstance((ServerKillJobInstanceRequest)obj);
        } else if (obj instanceof ServerRetryTasksRequest) {
            this.handleRetryTasks((ServerRetryTasksRequest)obj);
        } else if (obj instanceof ServerKillTaskRequest) {
            this.handleKillTask((ServerKillTaskRequest)obj);
        } else if (obj instanceof ServerCheckTaskMasterRequest) {
            this.handCheckTaskMaster((ServerCheckTaskMasterRequest)obj);
        } else if (obj instanceof MasterNotifyWorkerPullRequest) {
            this.handleInitPull((MasterNotifyWorkerPullRequest)obj);
        } else if (obj instanceof ServerThreadDumpRequest) {
            this.handleThreadDump((ServerThreadDumpRequest)obj);
        } else if (obj instanceof ServerPushLogConfigRequest) {
            this.handlePushLogConfig((ServerPushLogConfigRequest)obj);
        }

    }

其中，任务执行的消息类是ServerSubmitJobInstanceRequest，Worker在接收到消息时，会交给函数handleSubmitJobInstance来处理：

private void handleSubmitJobInstance(ServerSubmitJobInstanceRequest request) {
       
        ServerSubmitJobInstanceResponse response = null;
        //任务正在执行中，直接返回
        if (this.masterPool.contains(request.getJobInstanceId())) {
            
            this.logCollector.collect(IdUtil.getUniqueIdWithoutTask(request.getJobId(), request.getJobInstanceId()), ClientLoggerMessage.appendMessage("server trigger client fail.", new String[]{errMsg}));
            response = ServerSubmitJobInstanceResponse.newBuilder().setSuccess(false).setMessage(errMsg).build();
            this.getSender().tell(response, this.getSelf());
        } else {
            response = ServerSubmitJobInstanceResponse.newBuilder().setSuccess(true).build();
            this.getSender().tell(response, this.getSelf());

            try {
                JobInstanceInfo jobInstanceInfo = this.convet2JobInstanceInfo(request);
                //根据任务的类型，创建TaskMaster对象
                TaskMaster taskMaster = TaskMasterFactory.create(jobInstanceInfo, this.getContext());
              
                this.masterPool.put(jobInstanceInfo.getJobInstanceId(), taskMaster);
                //提交任务执行
                taskMaster.submitInstance(jobInstanceInfo);
               
                ......
            } catch (Throwable var5) {
               ......
            }
        }

    }

handleSubmitJobInstance中会创建一个TaskMaster对象来执行任务，TaskMaster是一个抽象类，它有几个继承类，对应几个分布式任务的编程模型，本文以Map类型的任务为例子，说明任务执行流程。

map模型作业提供了并行计算、内存网格、网格计算三种执行方式：

并行计算：子任务300以下，有子任务列表。
内存网格：子任务5W以下，无子任务列表，速度快。
网格计算：子任务100W以下，无子任务列表。

3.1 TaskMaster

MapTaskMaster作为TaskMaster的继承类，定义如下：

public abstract class MapTaskMaster extends TaskMaster {
   
    protected volatile int pageSize = ConfigUtil.getWorkerConfig().getInt("map.master.page.size", 100);
    protected volatile int queueSize = ConfigUtil.getWorkerConfig().getInt("map.master.queue.size", 10000);
    private volatile int dispatcherSize = ConfigUtil.getWorkerConfig().getInt("map.master.dispatcher.size", 5);
    protected ReqQueue<ContainerReportTaskStatusRequest> taskStatusReqQueue;
    protected TMStatusReqHandler<ContainerReportTaskStatusRequest> taskStatusReqBatchHandler;
    //存放map生成的任务
    protected ReqQueue<MasterStartContainerRequest> taskBlockingQueue;
   //存放即将转发的任务，并将任务转发给各个worker执行
    protected TaskDispatchReqHandler<MasterStartContainerRequest> taskDispatchReqHandler;
    private volatile String rootTaskResult;
    protected TaskPersistence taskPersistence;
    protected Map<String, TaskProgressCounter> taskProgressMap = Maps.newConcurrentMap();
    protected Map<String, WorkerProgressCounter> workerProgressMap = Maps.newConcurrentMap();
    private Map<Long, String> taskResultMap = Maps.newHashMap();
    private Map<Long, TaskStatus> taskStatusMap = Maps.newHashMap();
    ......
}

MapTaskMaster的submitInstance函数如下:

public void submitInstance(JobInstanceInfo jobInstanceInfo) throws Exception {
        try {
           ......
            //各个对象的初始化，启动线程池中的任务
            this.startBatchHandler();
            this.createRootTask();
            this.init();
        } catch (Throwable var4) {
           ......
        }

    }

1、startBatchHandler主要是做一些初始化，启动线程池中的任务定时执行。

2、createRootTask主要是创建一个根任务，即由这个任务来map出来多个子任务。

3、init主要是启动线程池中的任务定时执行。

createRootTask的代码如下：

protected void createRootTask() throws Exception {
        String taskName = "MAP_TASK_ROOT";
        ByteString taskBody = ByteString.copyFrom(HessianUtil.toBytes("MAP_TASK_ROOT"));
        //初始化任务计数器，当前job有一个task正在执行
        this.initTaskProgress(taskName, 1);
        //参数转换为MasterStartContainerRequest 
        MasterStartContainerRequest startContainerRequest = this.convert2StartContainerRequest(this.jobInstanceInfo, this.aquireTaskId(), taskName, taskBody);
        //将root任务分发给本地Worker执行
        this.batchDispatchTasks(Lists.newArrayList(new MasterStartContainerRequest[]{startContainerRequest}), this.getLocalWorkerIdAddr());
    }

函数batchDispatchTasks将任务分配给各个Worker执行，将root类型的任务分配给本地的Worker执行：

public void batchDispatchTasks(List<MasterStartContainerRequest> masterStartContainerRequests, String remoteWorker) {
        Map<String, List<MasterStartContainerRequest>> worker2ReqsWithNormal = Maps.newHashMap();
        Map<String, List<MasterStartContainerRequest>> worker2ReqsWithFailover = Maps.newHashMap();
        this.batchHandlePulledProgress(masterStartContainerRequests, worker2ReqsWithNormal, worker2ReqsWithFailover, remoteWorker);
        Iterator var5 = worker2ReqsWithNormal.entrySet().iterator();

        Entry entry;
        while(var5.hasNext()) {
            entry = (Entry)var5.next();
            this.batchHandleContainers((String)entry.getKey(), (List)entry.getValue(), false, TaskDispatchMode.PUSH);
        }

        var5 = worker2ReqsWithFailover.entrySet().iterator();

        while(var5.hasNext()) {
            entry = (Entry)var5.next();
            this.batchHandleContainers((String)entry.getKey(), (List)entry.getValue(), true, TaskDispatchMode.PUSH);
        }

    }

函数batchHandleContainers将任务写入本地数据库H2，同时将任务转发给对应的worker。

private void batchHandleContainers(final String workerIdAddr, final List<MasterStartContainerRequest> reqs, boolean isFailover, TaskDispatchMode dispatchMode) {
         ......

        try {
            //将task写入本地数据库H2,task的状态为running
            this.batchHandlePersistence(workerId, workerAddr, reqs, isFailover);
            if (dispatchMode.equals(TaskDispatchMode.PUSH)) {
                //将task发送给对应的worker
                final long startTime = System.currentTimeMillis();
                ActorSelection selection = this.getActorContext().actorSelection(ActorPathUtil.getContainerRouterPath(workerIdAddr));
                MasterBatchStartContainersRequest request = MasterBatchStartContainersRequest.newBuilder().setJobInstanceId(this.jobInstanceInfo.getJobInstanceId()).setJobId(this.jobInstanceInfo.getJobId()).addAllStartReqs(reqs).build();
                Timeout timeout = new Timeout(Duration.create(15L, TimeUnit.SECONDS));
                Future<Object> future = Patterns.ask(selection, request, timeout);
                
        } catch (Throwable var13) {
           ......
        }

    }

3.2 Container

Container是执行业务逻辑的容器框架，TaskMaster转发给worker的任务会Containe模块执行。其中ContainerRoutingActor是一个路由Actor，里面包含多个ContainerActor，ContainerRoutingActor将接收到的消息转发给其中的一个ContainerActor。ContainerActor的定义如下：

public class ContainerActor extends UntypedActor {
    public void onReceive(Object obj) throws Throwable {
        if (obj instanceof MasterStartContainerRequest) {
            this.handleStartContainer((MasterStartContainerRequest)obj);
        } else if (obj instanceof MasterBatchStartContainersRequest) {
            this.handleBatchStartContainers((MasterBatchStartContainersRequest)obj);
        } else if (obj instanceof MasterKillContainerRequest) {
            this.handleKillContainer((MasterKillContainerRequest)obj);
        } else if (obj instanceof MasterDestroyContainerPoolRequest) {
            this.handleDestroyContainerPool((MasterDestroyContainerPoolRequest)obj);
        }

    }
}

当接收到MasterBatchStartContainersRequest类型的消息时，会调用函数startContainer来执行

private String startContainer(MasterStartContainerRequest request) throws Exception {
        String uniqueId = IdUtil.getUniqueId(request.getJobId(), request.getJobInstanceId(), request.getTaskId());
      
        JobContext context = ContanerUtil.convert2JobContext(request);
        Container container = ContainerFactory.create(context);
        if (container != null) {
                this.containerPool.submit(context.getJobId(), context.getJobInstanceId(), context.getTaskId(), container, consumerNum);
            
        } 
        ......
    }

最后通过submit提交任务、执行任务。