3.1.9 生产“稳”担当：Master 服务启动源码全方位解析

杠氯 · 2025-9-24 11:04:39

作者 | 李杰移动云，Apache DolphinScheduler贡献者

在现代数据驱动的企业中，工作流调度系统是数据管道（Data Pipeline）的“中枢神经”。从 ETL 任务到机器学习训练，从报表生成到实时监控，几乎所有关键业务都依赖于一个稳定、高效、易扩展的调度引擎。
笔者认为Apache DolphinScheduler 3.1.9是稳定且广泛使用的版本，故本文将聚焦于这一版本，解析 Master 服务启动时相关流程，深入其源码核心，剖析其架构设计、模块划分与关键实现机制，帮助开发者理解 Master “如何工作”，并为进一步二次开发或性能优化打下基础。
本系列文章分为 3 个部分，分别为 Master Server 启动流程、Worker server 启动流程，以及相关流程图，本文为第一部分。
1. Master Server启动核心概览

代码入口：org.apache.dolphinscheduler.server.master.MasterServer#run

public void run() throws SchedulerException {
// 1、init rpc server
this.masterRPCServer.start();
// 2、install task plugin
this.taskPluginManager.loadPlugin();
// 3、self tolerant
this.masterRegistryClient.start();
this.masterRegistryClient.setRegistryStoppable(this);
// 4、master 调度
this.masterSchedulerBootstrap.init();
this.masterSchedulerBootstrap.start();
// 5、事件执行服务
this.eventExecuteService.start();
// 6、容错机制
this.failoverExecuteThread.start();
// 7、Quartz调度
this.schedulerApi.start();
...
}

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1.1 rpc启动:

描述：注册相关命令的process处理器，如task执行中、task执行结果、终止task等。
代码入口：org.apache.dolphinscheduler.server.master.rpc.MasterRPCServer#start

public void start() {
...
// 任务执行中的请求处理器
this.nettyRemotingServer.registerProcessor(CommandType.TASK_EXECUTE_RUNNING, taskExecuteRunningProcessor);
// 任务执行结果的请求处理器
this.nettyRemotingServer.registerProcessor(CommandType.TASK_EXECUTE_RESULT, taskExecuteResponseProcessor);
// 任务终止的请求处理器
this.nettyRemotingServer.registerProcessor(CommandType.TASK_KILL_RESPONSE, taskKillResponseProcessor);
this.nettyRemotingServer.registerProcessor(CommandType.STATE_EVENT_REQUEST, stateEventProcessor);
this.nettyRemotingServer.registerProcessor(CommandType.TASK_FORCE_STATE_EVENT_REQUEST, taskEventProcessor);
this.nettyRemotingServer.registerProcessor(CommandType.TASK_WAKEUP_EVENT_REQUEST, taskEventProcessor);
this.nettyRemotingServer.registerProcessor(CommandType.CACHE_EXPIRE, cacheProcessor);
this.nettyRemotingServer.registerProcessor(CommandType.TASK_REJECT, taskRecallProcessor);
this.nettyRemotingServer.registerProcessor(CommandType.WORKFLOW_EXECUTING_DATA_REQUEST,
workflowExecutingDataRequestProcessor);
// 流式任务启动请求处理器
this.nettyRemotingServer.registerProcessor(CommandType.TASK_EXECUTE_START, taskExecuteStartProcessor);
// logger server
// log相关，查看或者获取日志等操作的处理器
this.nettyRemotingServer.registerProcessor(CommandType.GET_LOG_BYTES_REQUEST, loggerRequestProcessor);
this.nettyRemotingServer.registerProcessor(CommandType.ROLL_VIEW_LOG_REQUEST, loggerRequestProcessor);
this.nettyRemotingServer.registerProcessor(CommandType.VIEW_WHOLE_LOG_REQUEST, loggerRequestProcessor);
this.nettyRemotingServer.registerProcessor(CommandType.REMOVE_TAK_LOG_REQUEST, loggerRequestProcessor);
this.nettyRemotingServer.start();
logger.info("Started Master RPC Server...");
}

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1.2 任务插件初始化：

描述：task的相关模板操作，如创建task、解析task参数、获取task资源信息等。对于该插件，api、master、worker都需要进行注册，在master的作用是设置数据源和UDF信息等。

1.3 Self Tolerant(Master注册):

描述：将自身信息注册至注册中心（本文以zookeeper为例），同时监听自身、其他master和所有worker节点的注册情况变化，从而做相应的容错处理。
代码入口：org.apache.dolphinscheduler.server.master.registry.MasterRegistryClient#start

public void start() {
try {
this.masterHeartBeatTask = new MasterHeartBeatTask(masterConfig, registryClient);
// 1、将自身信息注册至注册中心；
registry();
// 2、监听自身与注册中心的连接情况；
registryClient.addConnectionStateListener(
new MasterConnectionStateListener(masterConfig, registryClient, masterConnectStrategy));
// 3、监听其他master与所有worker在注册中心的活跃情况，做相应的容错工作处理
// 如对灭亡的master上面的任务进行容错，同时将在worker节点上kill任务
registryClient.subscribe(REGISTRY_DOLPHINSCHEDULER_NODE, new MasterRegistryDataListener());
} catch (Exception e) {
throw new RegistryException("Master registry client start up error", e);
}
}

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1.4 Master 调度

描述：一个扫描线程，定时扫描数据库中的 command 表，根据不同的命令类型进行不同的业务操作，是工作流启动、实例容错等处理的核心逻辑。
代码入口：org.apache.dolphinscheduler.server.master.runner.MasterSchedulerBootstrap#run

public void run() {
while (!ServerLifeCycleManager.isStopped()) {
try {
if (!ServerLifeCycleManager.isRunning()) {
// the current server is not at running status, cannot consume command.
logger.warn("The current server {} is not at running status, cannot consumes commands.", this.masterAddress);
Thread.sleep(Constants.SLEEP_TIME_MILLIS);
}
// todo: if the workflow event queue is much, we need to handle the back pressure
boolean isOverload =
OSUtils.isOverload(masterConfig.getMaxCpuLoadAvg(), masterConfig.getReservedMemory());
// 如果cpu以及memory负载过高，那么就暂时不处理命令
if (isOverload) {
logger.warn("The current server {} is overload, cannot consumes commands.", this.masterAddress);
MasterServerMetrics.incMasterOverload();
Thread.sleep(Constants.SLEEP_TIME_MILLIS);
continue;
}
// 从数据库中获取commands执行命令，如启动工作流，容错工作流实例等
List<Command> commands = findCommands();
if (CollectionUtils.isEmpty(commands)) {
// indicate that no command ,sleep for 1s
Thread.sleep(Constants.SLEEP_TIME_MILLIS);
continue;
}
// 将相应的commands转为工作流实例,转换成功后删除相应的commands
List<ProcessInstance> processInstances = command2ProcessInstance(commands);
if (CollectionUtils.isEmpty(processInstances)) {
// indicate that the command transform to processInstance error, sleep for 1s
Thread.sleep(Constants.SLEEP_TIME_MILLIS);
continue;
}
MasterServerMetrics.incMasterConsumeCommand(commands.size());
processInstances.forEach(processInstance -> {
try {
LoggerUtils.setWorkflowInstanceIdMDC(processInstance.getId());
if (processInstanceExecCacheManager.contains(processInstance.getId())) {
logger.error(
"The workflow instance is already been cached, this case shouldn't be happened");
}
// 创建工作流执行线程，负责DAG任务切分、任务提交监控、各种不同事件类型的逻辑处理
WorkflowExecuteRunnable workflowRunnable = new WorkflowExecuteRunnable(processInstance,
processService,
processInstanceDao,
nettyExecutorManager,
processAlertManager,
masterConfig,
stateWheelExecuteThread,
curingGlobalParamsService);
// 此处将每个工作流执行线程进行缓存，后续从缓存中获取该线程进行执行
processInstanceExecCacheManager.cache(processInstance.getId(), workflowRunnable);
// 将启动工作流事件放入工作流事件队列中，然后workflowEventLooper不断从队列中获取事件进行处理
workflowEventQueue.addEvent(new WorkflowEvent(WorkflowEventType.START_WORKFLOW,
processInstance.getId()));
} finally {
LoggerUtils.removeWorkflowInstanceIdMDC();
}
});
} catch (InterruptedException interruptedException) {
logger.warn("Master schedule bootstrap interrupted, close the loop", interruptedException);
Thread.currentThread().interrupt();
break;
} catch (Exception e) {
logger.error("Master schedule workflow error", e);
// sleep for 1s here to avoid the database down cause the exception boom
ThreadUtils.sleep(Constants.SLEEP_TIME_MILLIS);
}
}
}

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上述步骤产生工作流事件后，WorkflowEventLooper不断地消费处理：

public void run() {
WorkflowEvent workflowEvent = null;
while (!ServerLifeCycleManager.isStopped()) {
...
workflowEvent = workflowEventQueue.poolEvent();
LoggerUtils.setWorkflowInstanceIdMDC(workflowEvent.getWorkflowInstanceId());
logger.info("Workflow event looper receive a workflow event: {}, will handle this", workflowEvent);
WorkflowEventHandler workflowEventHandler =
workflowEventHandlerMap.get(workflowEvent.getWorkflowEventType());
// 相应的事件处理器来处理工作流事件，主要功能是执行上述中缓存的工作流执行线程WorkflowExecuteRunnable
workflowEventHandler.handleWorkflowEvent(workflowEvent);
...
}
}

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启动WorkflowExecuteRunnable时，主要功能是初始化DAG、提交且分发task等：

public WorkflowSubmitStatue call() {
...
LoggerUtils.setWorkflowInstanceIdMDC(processInstance.getId());
if (workflowRunnableStatus == WorkflowRunnableStatus.CREATED) {
// 构建工作流的DAG
buildFlowDag();
workflowRunnableStatus = WorkflowRunnableStatus.INITIALIZE_DAG;
logger.info("workflowStatue changed to :{}", workflowRunnableStatus);
}
if (workflowRunnableStatus == WorkflowRunnableStatus.INITIALIZE_DAG) {
// 初始化相关队列，将相关队列都清空
initTaskQueue();
workflowRunnableStatus = WorkflowRunnableStatus.INITIALIZE_QUEUE;
logger.info("workflowStatue changed to :{}", workflowRunnableStatus);
}
if (workflowRunnableStatus == WorkflowRunnableStatus.INITIALIZE_QUEUE) {
// 从起始节点开始执行，提交所有节点任务
submitPostNode(null);
workflowRunnableStatus = WorkflowRunnableStatus.STARTED;
logger.info("workflowStatue changed to :{}", workflowRunnableStatus);
}
return WorkflowSubmitStatue.SUCCESS;
...
}

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此时parentNodeCode为null，表示从根节点开始启动所有node：

private void submitPostNode(String parentNodeCode) throws StateEventHandleException {
...
// 根据起点节点parentNodeCode获取其后续待执行的task
List<TaskInstance> taskInstances=...
for (TaskInstance task : taskInstances) {
...
// 将task放到 “预提交”队列 readyToSubmitTaskQueue
addTaskToStandByList(task);
}
// 处理“预提交”队列readyToSubmitTaskQueue，提交task
submitStandByTask();
...
}

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public void submitStandByTask() throws StateEventHandleException {
int length = readyToSubmitTaskQueue.size();
for (int i = 0; i < length; i++) {
TaskInstance task = readyToSubmitTaskQueue.peek();
...
// 检测task的依赖关系是否构建成功，如果成功，则进行提交操作
DependResult dependResult = getDependResultForTask(task);
if (DependResult.SUCCESS == dependResult) {
logger.info("The dependResult of task {} is success, so ready to submit to execute", task.getName());
// 提交task
Optional<TaskInstance> taskInstanceOptional = submitTaskExec(task);
// 提交失败
if (!taskInstanceOptional.isPresent()) {
...
} else {
// 提交成功，从“预提交”队里中清除该task
removeTaskFromStandbyList(task);
}
}
...
}
}

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private Optional<TaskInstance> submitTaskExec(TaskInstance taskInstance) {
...
// 根据master侧任务类型（不是shell、spark那种，此处是例如Common、Condition、SubTask、SwitchTask等），做相应的初始化操作，为了便于理解，本文采用通用task来处理
ITaskProcessor taskProcessor = TaskProcessorFactory.getTaskProcessor(taskInstance.getTaskType());
taskProcessor.init(taskInstance, processInstance);
...
// 补充taskInstance参数，且提交保存至db
boolean submit = taskProcessor.action(TaskAction.SUBMIT);
...
// 若为通用task类型，则将任务提交到一个待dispatch的task队列taskPriorityQueue中，有消费者TaskPriorityQueueConsumer专门消费该队列
boolean dispatchSuccess = taskProcessor.action(TaskAction.DISPATCH);
...
// 若为通用task类型，则不做任何处理
taskProcessor.action(TaskAction.RUN);
// 增加超时检测，若是超时，会发生告警
stateWheelExecuteThread.addTask4TimeoutCheck(processInstance, taskInstance);
// 增加状态检查，当成功或者其他状态时，会进行相应的处理
stateWheelExecuteThread.addTask4StateCheck(processInstance, taskInstance);
...
return Optional.of(taskInstance);
...
}

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TaskPriorityQueueConsumer是一个专门消费上述taskPriorityQueue队列的线程，在程序启动时开始监听taskPriorityQueue队列：

public void run() {
int fetchTaskNum = masterConfig.getDispatchTaskNumber();
while (!ServerLifeCycleManager.isStopped()) {
try {
// 消费需要dispatch的task
// 为task挑选可用worker节点，然后将task分配至该worker节点
List<TaskPriority> failedDispatchTasks = this.batchDispatch(fetchTaskNum);
...
} catch (Exception e) {
TaskMetrics.incTaskDispatchError();
logger.error("dispatcher task error", e);
}
}
}

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public List<TaskPriority> batchDispatch(int fetchTaskNum) throws TaskPriorityQueueException, InterruptedException {
...
// 利用多线程并发消费task
CountDownLatch latch = new CountDownLatch(fetchTaskNum);
for (int i = 0; i < fetchTaskNum; i++) {
TaskPriority taskPriority = taskPriorityQueue.poll(Constants.SLEEP_TIME_MILLIS, TimeUnit.MILLISECONDS);
...
consumerThreadPoolExecutor.submit(() -> {
try {
// 为task进行分发操作
boolean dispatchResult = this.dispatchTask(taskPriority);
...
} finally {
// make sure the latch countDown
latch.countDown();
}
});
}
latch.await();
...
}

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protected boolean dispatchTask(TaskPriority taskPriority) {
...
try {
WorkflowExecuteRunnable workflowExecuteRunnable =
processInstanceExecCacheManager.getByProcessInstanceId(taskPriority.getProcessInstanceId());
...
Optional<TaskInstance> taskInstanceOptional =
workflowExecuteRunnable.getTaskInstance(taskPriority.getTaskId());
...
TaskInstance taskInstance = taskInstanceOptional.get();
TaskExecutionContext context = taskPriority.getTaskExecutionContext();
ExecutionContext executionContext =
new ExecutionContext(toCommand(context), ExecutorType.WORKER, context.getWorkerGroup(),
taskInstance);
...
// 挑选可用worker节点，然后将task分配至该worker节点
result = dispatcher.dispatch(executionContext);
...
} catch (RuntimeException | ExecuteException e) {
logger.error("Master dispatch task to worker error, taskPriority: {}", taskPriority, e);
}
return result;
}

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具体的分发操作：

public Boolean dispatch(final ExecutionContext context) throws ExecuteException {
...
// host select
// 根据配置的选择器，筛选符合要求的worker节点信息
Host host = hostManager.select(context);
...
context.setHost(host);
...
// 将task信息通过RPC发送给挑选的worker节点，要是发送失败，则往其他可用的worker节点发送
return executorManager.execute(context);
...
}

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1.5 事件执行服务:

描述：主要负责工作流实例的事件队列的轮询，因为工作流在执行过程中会不断产生事件，如工作流提交失败、任务状态变更等，下面方法就是处理产生的的相关事件。
代码入口：org.apache.dolphinscheduler.server.master.runner.EventExecuteService#run

public void run() {
while (!ServerLifeCycleManager.isStopped()) {
try {
// 处理工作流执行线程的相关事件，最终会触发WorkflowExecuteRunnable#handleEvents方法
workflowEventHandler();
// 处理流式任务执行线程的相关事件
streamTaskEventHandler();
TimeUnit.MILLISECONDS.sleep(Constants.SLEEP_TIME_MILLIS_SHORT);
} ...
}
}

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工作流和实时任务的事件处理逻辑基本一致，下述只描述工作流的处理过程：

public void handleEvents() {
...
StateEvent stateEvent = null;
while (!this.stateEvents.isEmpty()) {
try {
stateEvent = this.stateEvents.peek();
...
StateEventHandler stateEventHandler =
StateEventHandlerManager.getStateEventHandler(stateEvent.getType())
.orElseThrow(() -> new StateEventHandleError(
"Cannot find handler for the given state event"));
logger.info("Begin to handle state event, {}", stateEvent);
// 根据不同事件处理器做不同的处理逻辑
if (stateEventHandler.handleStateEvent(this, stateEvent)) {
this.stateEvents.remove(stateEvent);
}
} ...
}
}

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下面以工作流提交失败为例：

public boolean handleStateEvent(WorkflowExecuteRunnable workflowExecuteRunnable,
StateEvent stateEvent) throws StateEventHandleException {
WorkflowStateEvent workflowStateEvent = (WorkflowStateEvent) stateEvent;
ProcessInstance processInstance = workflowExecuteRunnable.getProcessInstance();
measureProcessState(workflowStateEvent);
log.info(
"Handle workflow instance submit fail state event, the current workflow instance state {} will be changed to {}",
processInstance.getState(), workflowStateEvent.getStatus());
// 将实例状态改为FAILURE后入库
workflowExecuteRunnable.updateProcessInstanceState(workflowStateEvent);
workflowExecuteRunnable.endProcess();
return true;
}

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1.6 容错机制：

描述：主要负责Master容错和Worker容错的相关逻辑。
代码入口：org.apache.dolphinscheduler.server.master.service.MasterFailoverService#checkMasterFailover

public void checkMasterFailover() {
// 获取需要容错的master节点
List<String> needFailoverMasterHosts = processService.queryNeedFailoverProcessInstanceHost()
.stream()
// failover myself || dead server
// 自身或者发生已经灭亡的master
.filter(host -> localAddress.equals(host) || !registryClient.checkNodeExists(host, NodeType.MASTER))
.distinct()
.collect(Collectors.toList());
if (CollectionUtils.isEmpty(needFailoverMasterHosts)) {
return;
}
...
for (String needFailoverMasterHost : needFailoverMasterHosts) {
failoverMaster(needFailoverMasterHost);
}
}

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private void doFailoverMaster(@NonNull String masterHost) {
...
// 从注册中心获取master的启动时间
Optional<Date> masterStartupTimeOptional = getServerStartupTime(registryClient.getServerList(NodeType.MASTER),
masterHost);
// 从获取与当前master的需要容错的工作路实例(主要根据需要容错的状态去筛选，如：SUBMITTED_SUCCESS、RUNNING_EXECUTION)
List<ProcessInstance> needFailoverProcessInstanceList = processService.queryNeedFailoverProcessInstances(
masterHost);
...
for (ProcessInstance processInstance : needFailoverProcessInstanceList) {
...
// 判断该实例是否需要容错处理，判断逻辑例如：
// 1、其他已经灭亡的master还未重新启动，此时需要进行容错
// 2、若工作流实例的启动时间比master的启动时间早，说明master重启过,此时需要容错
// ...
if (!checkProcessInstanceNeedFailover(masterStartupTimeOptional, processInstance)) {
LOGGER.info("WorkflowInstance doesn't need to failover");
continue;
}
List<TaskInstance> taskInstanceList =...
for (TaskInstance taskInstance : taskInstanceList) {
...
if (!checkTaskInstanceNeedFailover(taskInstance)) {
LOGGER.info("The taskInstance doesn't need to failover");
continue;
}
// 对于worker侧的任务，需要进行kill处理，同时将任务实例状态标记为NEED_FAULT_TOLERANCE
failoverTaskInstance(processInstance, taskInstance);
...
}
ProcessInstanceMetrics.incProcessInstanceByState("failover");
// updateProcessInstance host is null to mark this processInstance has been failover
// and insert a failover command
processInstance.setHost(Constants.NULL);
// 生成需要容错的command入库，待master调度进行扫描
processService.processNeedFailoverProcessInstances(processInstance);
...
}
}

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结语

以上是笔者对 Apache DolphinScheduler 3.1.9 版本特性与架构的初步理解，基于个人学习与实践整理而成，后续还会输出 Worker 启动流程以及 Master 与 Worker 的交互流程相关文章。由于水平有限，文中难免存在理解偏差或疏漏之处，恳请各位读者不吝指正。如有不同见解，欢迎交流讨论，共同进步。

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