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DolphinScheduler容错Worker
引言
上一篇文章介绍了DolphinScheduler中Master的容错机制，作为去中心化的多Master和多Worker服务对等架构，Worker的容错机制也是我们需要关注的。
和Master一样源码的版本基于3.1.3
Worker容错源码分析
worker启动注册
首先Worker的启动入口是在WorkerServer中，在Worker启动后就会执行其run方法
@PostConstruct
public void run() {
this.workerRpcServer.start();
this.workerRpcClient.start();
this.taskPluginManager.loadPlugin();
this.workerRegistryClient.setRegistryStoppable(this);
this.workerRegistryClient.start();
this.workerManagerThread.start();
this.messageRetryRunner.start();
/*
* registry hooks, which are called before the process exits
*/
Runtime.getRuntime().addShutdownHook(new Thread(() -> {
if (!ServerLifeCycleManager.isStopped()) {
close("WorkerServer shutdown hook");
}
}));
}
这里我们只关心this.workerRegistryClient.start();方法所做的事情：注册当前worker信息到Zookeeper，并且启动了一个心跳任务定时更新worker的信息到Zookeeper。
/**
* registry
*/
private void registry() {
WorkerHeartBeat workerHeartBeat = workerHeartBeatTask.getHeartBeat();
String workerZKPath = workerConfig.getWorkerRegistryPath();
// remove before persist
registryClient.remove(workerZKPath);
registryClient.persistEphemeral(workerZKPath, JSONUtils.toJsonString(workerHeartBeat));
log.info("Worker node: {} registry to ZK {} successfully", workerConfig.getWorkerAddress(), workerZKPath);
while (!registryClient.checkNodeExists(workerConfig.getWorkerAddress(), NodeType.WORKER)) {
ThreadUtils.sleep(SLEEP_TIME_MILLIS);
}
// sleep 1s, waiting master failover remove
ThreadUtils.sleep(Constants.SLEEP_TIME_MILLIS);
workerHeartBeatTask.start();
log.info("Worker node: {} registry finished", workerConfig.getWorkerAddress());
}
这里和master的注册流程基本一致，来看看worker注册的目录：
worker注册到zk的路径如下，并且和master都有相同的父级目录名称是/node：
// /nodes/worker/+ip:listenPortworkerConfig.setWorkerRegistryPath(REGISTRY_DOLPHINSCHEDULER_WORKERS + "/" + workerConfig.getWorkerAddress());
注册的内容就是当前worker节点的健康状况，包含了cpu，内存，负载，磁盘等信息，通过这些信息就可以标识当前worker是否健康，可以接收任务的分配并且去执行。
@Override
public WorkerHeartBeat getHeartBeat() {
double loadAverage = OSUtils.loadAverage();
double cpuUsage = OSUtils.cpuUsage();
int maxCpuLoadAvg = workerConfig.getMaxCpuLoadAvg();
double reservedMemory = workerConfig.getReservedMemory();
double availablePhysicalMemorySize = OSUtils.availablePhysicalMemorySize();
int execThreads = workerConfig.getExecThreads();
int workerWaitingTaskCount = this.workerWaitingTaskCount.get();
int serverStatus = getServerStatus(loadAverage, maxCpuLoadAvg, availablePhysicalMemorySize, reservedMemory,
execThreads, workerWaitingTaskCount);
return WorkerHeartBeat.builder()
.startupTime(ServerLifeCycleManager.getServerStartupTime())
.reportTime(System.currentTimeMillis())
.cpuUsage(cpuUsage)
.loadAverage(loadAverage)
.availablePhysicalMemorySize(availablePhysicalMemorySize)
.maxCpuloadAvg(maxCpuLoadAvg)
.memoryUsage(OSUtils.memoryUsage())
.reservedMemory(reservedMemory)
.diskAvailable(OSUtils.diskAvailable())
.processId(processId)
.workerHostWeight(workerConfig.getHostWeight())
.workerWaitingTaskCount(this.workerWaitingTaskCount.get())
.workerExecThreadCount(workerConfig.getExecThreads())
.serverStatus(serverStatus)
.build();
}
Master监听worker在zk节点的状态
接下来，master就会对注册的worker节点进行监控，在上一篇的介绍中，master启动注册后对node节点已经进行了监听，大家可以进行回顾一下，这里监听了/node/节点，当其下面的子路径/master或者/worker有变动就会触发回调 ：
//node registryClient.subscribe(REGISTRY_DOLPHINSCHEDULER_NODE, new MasterRegistryDataListener());
因此当worker临时节点异常后，master就会感知到其变化。最终会回调MasterRegistryDataListener中的notify方法，并根据变动的路径来判断是master还是worker：
@Override
public void notify(Event event) {
final String path = event.path();
if (Strings.isNullOrEmpty(path)) {
return;
}
//monitor master
if (path.startsWith(REGISTRY_DOLPHINSCHEDULER_MASTERS + Constants.SINGLE_SLASH)) {
handleMasterEvent(event);
} else if (path.startsWith(REGISTRY_DOLPHINSCHEDULER_WORKERS + Constants.SINGLE_SLASH)) {
//monitor worker
handleWorkerEvent(event);
}
}
这段代码在之前master的容错中也见到过。这里是对于worker的容错，就会触发handleWorkerEvent方法。
private void handleWorkerEvent(Event event) {
final String path = event.path();
switch (event.type()) {
case ADD:
logger.info("worker node added : {}", path);
break;
case REMOVE:
logger.info("worker node deleted : {}", path);
masterRegistryClient.removeWorkerNodePath(path, NodeType.WORKER, true);
break;
default:
break;
}
}
接下来就是获取到下线worker节点的host信息进行进一步的容错处理了：
public void removeWorkerNodePath(String path, NodeType nodeType, boolean failover) {
logger.info("{} node deleted : {}", nodeType, path);
try {
//获取节点信息
String serverHost = null;
if (!StringUtils.isEmpty(path)) {
serverHost = registryClient.getHostByEventDataPath(path);
if (StringUtils.isEmpty(serverHost)) {
logger.error("server down error: unknown path: {}", path);
return;
}
if (!registryClient.exists(path)) {
logger.info("path: {} not exists", path);
}
}
// failover server
if (failover) {
failoverService.failoverServerWhenDown(serverHost, nodeType);
}
} catch (Exception e) {
logger.error("{} server failover failed", nodeType, e);
}
}
整个worker容错的大致过程如下：
1-获取需要容错worker节点的启动时间，用于后续判断worker节点是否还在下线状态，或者是否已经重新启动
2-根据异常的worker的信息查询需要容错的任务实例，获取只属于当前master节点需要容错的任务实例信息，这里也是和master不同的，并且容错没加锁的原因。
3-遍历所有要容错的任务实例进行容错 这里注意的是需要容错的任务是在worker重新启动之前的任务，之后worker异常重启后分配的新任务不要容错
/**
* Do the worker failover. Will find the SUBMITTED_SUCCESS/DISPATCH/RUNNING_EXECUTION/DELAY_EXECUTION/READY_PAUSE/READY_STOP tasks belong the given worker,
* and failover these tasks.
* <p>
* Note: When we do worker failover, the master will only failover the processInstance belongs to the current master.
*
* @param workerHost worker host
*/
public void failoverWorker(@NonNull String workerHost) {
LOGGER.info("Worker[{}] failover starting", workerHost);
final StopWatch failoverTimeCost = StopWatch.createStarted();
//获取需要容错worker节点的启动时间，用于后续判断worker节点是否还在下线状态，或者是否已经重新启动
// we query the task instance from cache, so that we can directly update the cache
final Optional<Date> needFailoverWorkerStartTime =
getServerStartupTime(registryClient.getServerList(NodeType.WORKER), workerHost);
//根据异常的worker的信息查询需要容错的任务实例，获取只属于当前master节点需要容错的任务实例信息，这里也是和master不同的，并且容错没加锁的原因。
final List<TaskInstance> needFailoverTaskInstanceList = getNeedFailoverTaskInstance(workerHost);
if (CollectionUtils.isEmpty(needFailoverTaskInstanceList)) {
LOGGER.info("Worker[{}] failover finished there are no taskInstance need to failover", workerHost);
return;
}
LOGGER.info(
"Worker[{}] failover there are {} taskInstance may need to failover, will do a deep check, taskInstanceIds: {}",
workerHost,
needFailoverTaskInstanceList.size(),
needFailoverTaskInstanceList.stream().map(TaskInstance::getId).collect(Collectors.toList()));
final Map<Integer, ProcessInstance> processInstanceCacheMap = new HashMap<>();
for (TaskInstance taskInstance : needFailoverTaskInstanceList) {
LoggerUtils.setWorkflowAndTaskInstanceIDMDC(taskInstance.getProcessInstanceId(), taskInstance.getId());
try {
ProcessInstance processInstance = processInstanceCacheMap.computeIfAbsent(
taskInstance.getProcessInstanceId(), k -> {
WorkflowExecuteRunnable workflowExecuteRunnable = cacheManager.getByProcessInstanceId(
taskInstance.getProcessInstanceId());
if (workflowExecuteRunnable == null) {
return null;
}
return workflowExecuteRunnable.getProcessInstance();
});
//这里注意的是需要容错的任务是在worker重新启动之前的任务，之后worker异常重启后分配的新任务不要容错
if (!checkTaskInstanceNeedFailover(needFailoverWorkerStartTime, processInstance, taskInstance)) {
LOGGER.info("Worker[{}] the current taskInstance doesn't need to failover", workerHost);
continue;
}
LOGGER.info(
"Worker[{}] failover: begin to failover taskInstance, will set the status to NEED_FAULT_TOLERANCE",
workerHost);
failoverTaskInstance(processInstance, taskInstance);
LOGGER.info("Worker[{}] failover: Finish failover taskInstance", workerHost);
} catch (Exception ex) {
LOGGER.info("Worker[{}] failover taskInstance occur exception", workerHost, ex);
} finally {
LoggerUtils.removeWorkflowAndTaskInstanceIdMDC();
}
}
failoverTimeCost.stop();
LOGGER.info("Worker[{}] failover finished, useTime:{}ms",
workerHost,
failoverTimeCost.getTime(TimeUnit.MILLISECONDS));
}
4-更新taskInstance的状态为TaskExecutionStatus.NEED_FAULT_TOLERANCE。并且构造TaskStateEvent事件，设置其状态为需要容TaskExecutionStatus.NEED_FAULT_TOLERANCE的，其类型是TASK_STATE_CHANGE。最后提交需要容错的event。
private void failoverTaskInstance(@NonNull ProcessInstance processInstance, @NonNull TaskInstance taskInstance) {
TaskMetrics.incTaskInstanceByState("failover");
boolean isMasterTask = TaskProcessorFactory.isMasterTask(taskInstance.getTaskType());
taskInstance.setProcessInstance(processInstance);
if (!isMasterTask) {
LOGGER.info("The failover taskInstance is not master task");
TaskExecutionContext taskExecutionContext = TaskExecutionContextBuilder.get()
.buildTaskInstanceRelatedInfo(taskInstance)
.buildProcessInstanceRelatedInfo(processInstance)
.buildProcessDefinitionRelatedInfo(processInstance.getProcessDefinition())
.create();
if (masterConfig.isKillYarnJobWhenTaskFailover()) {
// only kill yarn job if exists , the local thread has exited
LOGGER.info("TaskInstance failover begin kill the task related yarn job");
ProcessUtils.killYarnJob(logClient, taskExecutionContext);
}
} else {
LOGGER.info("The failover taskInstance is a master task");
}
taskInstance.setState(TaskExecutionStatus.NEED_FAULT_TOLERANCE);
taskInstance.setFlag(Flag.NO);
processService.saveTaskInstance(taskInstance);
//提交event
TaskStateEvent stateEvent = TaskStateEvent.builder()
.processInstanceId(processInstance.getId())
.taskInstanceId(taskInstance.getId())
.status(TaskExecutionStatus.NEED_FAULT_TOLERANCE)
.type(StateEventType.TASK_STATE_CHANGE)
.build();
workflowExecuteThreadPool.submitStateEvent(stateEvent);
}
event的提交会去根据其所属的工作流实例来选择其对应的WorkflowExecuteRunnable进行提交容错：
public void submitStateEvent(StateEvent stateEvent) {
WorkflowExecuteRunnable workflowExecuteThread =
processInstanceExecCacheManager.getByProcessInstanceId(stateEvent.getProcessInstanceId());
if (workflowExecuteThread == null) {
logger.warn("Submit state event error, cannot from workflowExecuteThread from cache manager, stateEvent:{}",
stateEvent);
return;
}
workflowExecuteThread.addStateEvent(stateEvent);
logger.info("Submit state event success, stateEvent: {}", stateEvent);
}
处理容错event事件
在上面的代码中已经对需要容错的任务提交了一个event事件，那么肯定会有线程对这个event进行具体的处理。我们来看WorkflowExecuteRunnable类，submitStateEvent就是将event提交到了这个类中的stateEvents队列中：
private final ConcurrentLinkedQueue<StateEvent> stateEvents = new ConcurrentLinkedQueue<>();
WorkflowExecuteRunnable在master启动的时候就已经启动了，并且会不停的从stateEvents中获取event进行处理：
/**
* handle event
*/
public void handleEvents() {
if (!isStart()) {
logger.info(
"The workflow instance is not started, will not handle its state event, current state event size: {}",
stateEvents);
return;
}
StateEvent stateEvent = null;
while (!this.stateEvents.isEmpty()) {
try {
stateEvent = this.stateEvents.peek();
LoggerUtils.setWorkflowAndTaskInstanceIDMDC(stateEvent.getProcessInstanceId(),
stateEvent.getTaskInstanceId());
// if state handle success then will remove this state, otherwise will retry this state next time.
// The state should always handle success except database error.
checkProcessInstance(stateEvent);
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);
if (stateEventHandler.handleStateEvent(this, stateEvent)) {
this.stateEvents.remove(stateEvent);
}
} catch (StateEventHandleError stateEventHandleError) {
logger.error("State event handle error, will remove this event: {}", stateEvent, stateEventHandleError);
this.stateEvents.remove(stateEvent);
ThreadUtils.sleep(Constants.SLEEP_TIME_MILLIS);
} catch (StateEventHandleException stateEventHandleException) {
logger.error("State event handle error, will retry this event: {}",
stateEvent,
stateEventHandleException);
ThreadUtils.sleep(Constants.SLEEP_TIME_MILLIS);
} catch (Exception e) {
// we catch the exception here, since if the state event handle failed, the state event will still keep
// in the stateEvents queue.
logger.error("State event handle error, get a unknown exception, will retry this event: {}",
stateEvent,
e);
ThreadUtils.sleep(Constants.SLEEP_TIME_MILLIS);
} finally {
LoggerUtils.removeWorkflowAndTaskInstanceIdMDC();
}
}
}
根据提交事件的类型StateEventType.TASK_STATE_CHANGE 可以获取到具体的StateEventHandler实现是TaskStateEventHandler。在TaskStateEventHandler的handleStateEvent方法中主要对需要容错的任务做了如下处理：
if (task.getState().isFinished()) {
if (completeTaskMap.containsKey(task.getTaskCode())
&& completeTaskMap.get(task.getTaskCode()) == task.getId()) {
logger.warn("The task instance is already complete, stateEvent: {}", stateEvent);
return true;
}
workflowExecuteRunnable.taskFinished(task);
if (task.getTaskGroupId() > 0) {
logger.info("The task instance need to release task Group: {}", task.getTaskGroupId());
workflowExecuteRunnable.releaseTaskGroup(task);
}
return true;
}
其中判断是否完成的具体实现中就包含了是否是容错的状态。
public boolean isFinished() {
return isSuccess() || isKill() || isFailure() || isPause();
}
public boolean isFailure() {
return this == TaskExecutionStatus.FAILURE || this == NEED_FAULT_TOLERANCE;
}
接着就会调用workflowExecuteRunnable.taskFinished(task);方法去处理各种任务实例状态变化后的事件。这里我们只关注容错相关的代码分支：
} else if (taskInstance.taskCanRetry() && !processInstance.getState().isReadyStop()) {
// retry task
logger.info("Retry taskInstance taskInstance state: {}", taskInstance.getState());
retryTaskInstance(taskInstance);
}
//判断了是否容错的状态，前面对其已经进行了更新
public boolean taskCanRetry() {
if (this.isSubProcess()) {
return false;
}
if (this.getState() == TaskExecutionStatus.NEED_FAULT_TOLERANCE) {
return true;
}
return this.getState() == TaskExecutionStatus.FAILURE && (this.getRetryTimes() < this.getMaxRetryTimes());
}
/**
* crate new task instance to retry, different objects from the original
*
*/
private void retryTaskInstance(TaskInstance taskInstance) throws StateEventHandleException {
if (!taskInstance.taskCanRetry()) {
return;
}
TaskInstance newTaskInstance = cloneRetryTaskInstance(taskInstance);
if (newTaskInstance == null) {
logger.error("Retry task fail because new taskInstance is null, task code:{}, task id:{}",
taskInstance.getTaskCode(),
taskInstance.getId());
return;
}
waitToRetryTaskInstanceMap.put(newTaskInstance.getTaskCode(), newTaskInstance);
if (!taskInstance.retryTaskIntervalOverTime()) {
logger.info(
"Failure task will be submitted, process id: {}, task instance code: {}, state: {}, retry times: {} / {}, interval: {}",
processInstance.getId(), newTaskInstance.getTaskCode(),
newTaskInstance.getState(), newTaskInstance.getRetryTimes(), newTaskInstance.getMaxRetryTimes(),
newTaskInstance.getRetryInterval());
stateWheelExecuteThread.addTask4TimeoutCheck(processInstance, newTaskInstance);
stateWheelExecuteThread.addTask4RetryCheck(processInstance, newTaskInstance);
} else {
addTaskToStandByList(newTaskInstance);
submitStandByTask();
waitToRetryTaskInstanceMap.remove(newTaskInstance.getTaskCode());
}
}
最终将需要容错的任务实例重新加入到了readyToSubmitTaskQueue队列中，重新进行submit：
addTaskToStandByList(newTaskInstance); submitStandByTask();
后面就是和正常任务一样处理了通过submitTaskExec方法提交任务到具体的worker执行。
总结
对于Worker的容错流程大致如下：
1-Master基于ZK的监听来感知需要容错的Worker节点信息
2-每个Master只负责容错属于自己调度的工作流实例，在容错前会比较实例的开始时间和服务节点的启动时间，在服务启动时间之后的则跳过容错；
3-需要容错的任务实例会重新加入到readyToSubmitTaskQueue，并提交运行。
到此，对于Worker的容错，就到这里了，更多关于DolphinScheduler容错Worker的资料请关注码农之家其它相关文章！
