发布时间:2023-12-24 11:30
点击这里查看 Flink 1.13 源码解析 目录汇总
点击查看相关章节:Flink 1.13 源码解析——JobManager启动流程概览
点击查看相关章节:Flink 1.13 源码解析——JobManager启动流程 WebMonitorEndpoint启动
点击查看相关章节:Flink 1.13 源码解析——JobManager启动流程之Dispatcher启动
目录
一、前言
二、ResourceManager的启动
2.1、触发Onstart回调
2.2、Leader竞选,完成后进行isLeader的回调
2.3、两个心跳以及两个定时任务
2.3.1、两个心跳
2.3.2、两个定时
三、总结:
在开始解析ResourceManager之前,我们先来复习一下Flink主节点中的一些重要概念:
关于Flink的主节点JobManager,他只是一个逻辑上的主节点,针对不同的部署模式,主节点的实现类也不同。
JobManager(逻辑)有三大核心内容,分别为ResourceManager、Dispatcher和WebmonitorEndpoin:
ResourceManager:
Flink集群的资源管理器,只有一个,关于Slot的管理和申请等工作,都有它负责
Dispatcher:
1、负责接收用户提交的JobGraph,然后启动一个JobMaster,类似于Yarn中的AppMaster和Spark中的Driver。
2、内有一个持久服务:JobGraphStore,负责存储JobGraph。当构建执行图或物理执行图时主节点宕机并恢复,则可以从这里重新拉取作业JobGraph
WebMonitorEndpoint:
Rest服务,内部有一个Netty服务,客户端的所有请求都由该组件接收处理
用一个例子来描述这三个组件的功能:
当Client提交一个Job到集群时(Client会把Job构建成一个JobGraph),主节点接收到提交的job的Rest请求后,WebMonitorEndpoint 会通过Router进行解析找到对应的Handler来执行处理,处理完毕后交由Dispatcher,Dispatcher负责大气JobMaster来负责这个Job内部的Task的部署执行,执行Task所需的资源,JobMaster向ResourceManager申请。
ResourceManager在Flink中扮演的角色就是一个资源管理器,负责Slot的管理和申请等工作。
下面我们来看ResourceManager的启动代码。
首先回到dispatcherResourceManagerComponentFactory.create()方法,在完成了WebMonitorEndpoint的创建和启动之后,将进行ResourceManager的启动,我们来看代码:
resourceManager =
resourceManagerFactory.createResourceManager(
configuration,
ResourceID.generate(),
rpcService,
highAvailabilityServices,
heartbeatServices,
fatalErrorHandler,
new ClusterInformation(hostname, blobServer.getPort()),
webMonitorEndpoint.getRestBaseUrl(),
metricRegistry,
hostname,
ioExecutor);
resourceManager.start();
在这里首先初始化了ResourceManager实例,然后调用了start方法启动ResourceManager,我们来看start方法
@Override
public void start() {
// 向自己发送消息,告知已启动
rpcEndpoint.tell(ControlMessages.START, ActorRef.noSender());
}
ResourceManager是一个RpcEndpoint(Actor),在start方法里,知识是向自己发送了一条消息,告知已启动的状态。
那么我们回头去看ResourceManager的构建过程,我们去看resourceManagerFactory.createResourceManager方法。
// TODO 构建ResourceManagerRuntimeServices,加载配置
final ResourceManagerRuntimeServices resourceManagerRuntimeServices =
createResourceManagerRuntimeServices(
effectiveResourceManagerAndRuntimeServicesConfig,
rpcService,
highAvailabilityServices,
slotManagerMetricGroup);
// TODO 构建ResourceManager
return createResourceManager(
getEffectiveConfigurationForResourceManager(
effectiveResourceManagerAndRuntimeServicesConfig),
resourceId,
rpcService,
highAvailabilityServices,
heartbeatServices,
fatalErrorHandler,
clusterInformation,
webInterfaceUrl,
resourceManagerMetricGroup,
resourceManagerRuntimeServices,
ioExecutor);
在方法里, 主要做了两件事:
1、构建ResourceManagerRuntimeServices并加载配置
2、真正构建ResourceManager
我们进入createResourceManager方法里,选择StandaloneResourceManagerFactory的实现,可以看到:
@Override
protected ResourceManager createResourceManager(
Configuration configuration,
ResourceID resourceId,
RpcService rpcService,
HighAvailabilityServices highAvailabilityServices,
HeartbeatServices heartbeatServices,
FatalErrorHandler fatalErrorHandler,
ClusterInformation clusterInformation,
@Nullable String webInterfaceUrl,
ResourceManagerMetricGroup resourceManagerMetricGroup,
ResourceManagerRuntimeServices resourceManagerRuntimeServices,
Executor ioExecutor) {
// TODO ResourceManager启动超时时间: 从启动,到有TaskManager汇报的时间,
// TODO 可以通过resourcemanager.standalone.start-up-time进行设置,如果没有设置则默认等于Slot申请超时时间
final Time standaloneClusterStartupPeriodTime =
ConfigurationUtils.getStandaloneClusterStartupPeriodTime(configuration);
return new StandaloneResourceManager(
rpcService,
resourceId,
highAvailabilityServices,
heartbeatServices,
resourceManagerRuntimeServices.getSlotManager(),
ResourceManagerPartitionTrackerImpl::new,
resourceManagerRuntimeServices.getJobLeaderIdService(),
clusterInformation,
fatalErrorHandler,
resourceManagerMetricGroup,
standaloneClusterStartupPeriodTime,
AkkaUtils.getTimeoutAsTime(configuration),
ioExecutor);
}
在这段代码里,主要完成了两件事:
1、配置ResourceManager启动超时时间,所谓超时时间,是指从ResourceManager启动,一直到有TaskManager向ResourceManager注册的时间长度,当超过配置的时间还没有TaskManager来向当前ResourceManager来注册,则认为当前ResourceManager启动超时,改参数可以通过resourcemanager.standalone.start-up-time进行设置,如果没有设置则默认等于Slot申请超时时间
2、构建了一个StandaloneResourceManager实例
我们继续看StandaloneResourceManager的构建过程,进入StandaloneResourceManager的构造方法,一直追溯到ResourceManager的构造方法,可以看到ResourceManager继承了RpcEndpoint,所以他一定有一个onStart方法,在构建完成之后会被回调,所以我们直接去找ResourceManager的onStart方法:
// ------------------------------------------------------------------------
// RPC lifecycle methods
// ------------------------------------------------------------------------
@Override
public final void onStart() throws Exception {
try {
log.info("Starting the resource manager.");
// TODO 启动ResourceManager 的基础服务
startResourceManagerServices();
} catch (Throwable t) {
final ResourceManagerException exception =
new ResourceManagerException(
String.format("Could not start the ResourceManager %s", getAddress()),
t);
onFatalError(exception);
throw exception;
}
}
可以看到,在方法里调用了startResourceManagerServices()方法来启动ResourceManager的基础服务,我们进入startResourceManagerServices()方法:
private void startResourceManagerServices() throws Exception {
try {
// TODO 获取选举服务
leaderElectionService =
highAvailabilityServices.getResourceManagerLeaderElectionService();
// 在Standalone模式下没有做任何操作
initialize();
// TODO 开始竞选
leaderElectionService.start(this);
jobLeaderIdService.start(new JobLeaderIdActionsImpl());
registerMetrics();
} catch (Exception e) {
handleStartResourceManagerServicesException(e);
}
}
又看到了我们熟悉的操作,Leader竞选。
我们直接来看leaderElectionService.start方法
@Override
public final void start(LeaderContender contender) throws Exception {
checkNotNull(contender, "Contender must not be null.");
Preconditions.checkState(leaderContender == null, "Contender was already set.");
synchronized (lock) {
/*
TODO 在WebMonitorEndpoint中调用时,此contender为DispatcherRestEndPoint
在ResourceManager中调用时,contender为ResourceManager
在DispatcherRunner中调用时,contender为DispatcherRunner
*/
leaderContender = contender;
// TODO 此处创建选举对象 leaderElectionDriver
leaderElectionDriver =
leaderElectionDriverFactory.createLeaderElectionDriver(
this,
new LeaderElectionFatalErrorHandler(),
leaderContender.getDescription());
LOG.info("Starting DefaultLeaderElectionService with {}.", leaderElectionDriver);
running = true;
}
}
哦豁,这不就是上一章WebMonitorEndpoint启动时调用过的方法嘛,只不过由于此处是ResourceManager的选举,当前contender为ResourceManager。老规矩,我们直接去看createLeaderElectionDriver方法。由于是Standalone模式,我们选择ZooKeeperLeaderElectionDriverFactory的实现:
@Override
public ZooKeeperLeaderElectionDriver createLeaderElectionDriver(
LeaderElectionEventHandler leaderEventHandler,
FatalErrorHandler fatalErrorHandler,
String leaderContenderDescription)
throws Exception {
return new ZooKeeperLeaderElectionDriver(
client,
latchPath,
leaderPath,
leaderEventHandler,
fatalErrorHandler,
leaderContenderDescription);
}
可以看到这里返回了一个zk的选举驱动,我们在点进ZooKeeperLeaderElectionDriver类
public ZooKeeperLeaderElectionDriver(
CuratorFramework client,
String latchPath,
String leaderPath,
LeaderElectionEventHandler leaderElectionEventHandler,
FatalErrorHandler fatalErrorHandler,
String leaderContenderDescription)
throws Exception {
this.client = checkNotNull(client);
this.leaderPath = checkNotNull(leaderPath);
this.leaderElectionEventHandler = checkNotNull(leaderElectionEventHandler);
this.fatalErrorHandler = checkNotNull(fatalErrorHandler);
this.leaderContenderDescription = checkNotNull(leaderContenderDescription);
leaderLatch = new LeaderLatch(client, checkNotNull(latchPath));
cache = new NodeCache(client, leaderPath);
client.getUnhandledErrorListenable().addListener(this);
running = true;
// TODO 开始选举
leaderLatch.addListener(this);
leaderLatch.start();
/*
TODO 选举开始后,不就会接收到响应:
1.如果竞选成功,则回调该类的isLeader方法
2.如果竞选失败,则回调该类的notLeader方法
每一个竞选者对应一个竞选Driver
*/
cache.getListenable().addListener(this);
cache.start();
client.getConnectionStateListenable().addListener(listener);
}
又来到了这个选举方法,在上节里我们讲到,在完成选举之后会回调isLeader方法或notLeader方法,我们这里直接去看isLeader方法
/*
选举成功
*/
@Override
public void isLeader() {
leaderElectionEventHandler.onGrantLeadership();
}
在进入leaderElectionEventHandler.onGrantLeadership()方法:
@Override
@GuardedBy("lock")
public void onGrantLeadership() {
synchronized (lock) {
if (running) {
issuedLeaderSessionID = UUID.randomUUID();
clearConfirmedLeaderInformation();
if (LOG.isDebugEnabled()) {
LOG.debug(
"Grant leadership to contender {} with session ID {}.",
leaderContender.getDescription(),
issuedLeaderSessionID);
}
/*
TODO 有4中竞选者类型,LeaderContender有4中情况
1.Dispatcher = DefaultDispatcherRunner
2.JobMaster = JobManagerRunnerImpl
3.ResourceManager = ResourceManager
4.WebMonitorEndpoint = WebMonitorEndpoint
*/
leaderContender.grantLeadership(issuedLeaderSessionID);
} else {
if (LOG.isDebugEnabled()) {
LOG.debug(
"Ignoring the grant leadership notification since the {} has "
+ "already been closed.",
leaderElectionDriver);
}
}
}
}
再进入leaderContender.grantLeadership方法,选择ResourceManager的实现:
// ------------------------------------------------------------------------
// Leader Contender
// ------------------------------------------------------------------------
/**
* Callback method when current resourceManager is granted leadership.
*
* @param newLeaderSessionID unique leadershipID
*/
@Override
public void grantLeadership(final UUID newLeaderSessionID) {
final CompletableFuture acceptLeadershipFuture =
clearStateFuture.thenComposeAsync(
// TODO 选举成功后执行回调函数
(ignored) -> tryAcceptLeadership(newLeaderSessionID),
getUnfencedMainThreadExecutor());
final CompletableFuture confirmationFuture =
acceptLeadershipFuture.thenAcceptAsync(
(acceptLeadership) -> {
if (acceptLeadership) {
// confirming the leader session ID might be blocking,
leaderElectionService.confirmLeadership(
newLeaderSessionID, getAddress());
}
},
ioExecutor);
confirmationFuture.whenComplete(
(Void ignored, Throwable throwable) -> {
if (throwable != null) {
onFatalError(ExceptionUtils.stripCompletionException(throwable));
}
});
}
在这个方法内部,ResourceManager调用了一个tryAcceptLeadership()方法,我们进入这个方法
private CompletableFuture tryAcceptLeadership(final UUID newLeaderSessionID) {
if (leaderElectionService.hasLeadership(newLeaderSessionID)) {
final ResourceManagerId newResourceManagerId =
ResourceManagerId.fromUuid(newLeaderSessionID);
log.info(
"ResourceManager {} was granted leadership with fencing token {}",
getAddress(),
newResourceManagerId);
// clear the state if we've been the leader before
if (getFencingToken() != null) {
clearStateInternal();
}
setFencingToken(newResourceManagerId);
/*
TODO 启动服务
1.启动两个心跳服务
2.启动slotManager服务启动两个定时任务
*/
startServicesOnLeadership();
return prepareLeadershipAsync().thenApply(ignored -> hasLeadership = true);
} else {
return CompletableFuture.completedFuture(false);
}
}
在这里通过调用startServicesOnLeadership方法,启动了两个心跳服务和两个定时任务,我们进入这个方法一探究竟:
private void startServicesOnLeadership() {
// TODO 启动两个心跳服务
startHeartbeatServices();
// TODO 启动两个定时服务
// TODO SlotManager是存在于ResourceManager中用来管理所有TaskManager汇报和注册的Slot的工作的
slotManager.start(getFencingToken(), getMainThreadExecutor(), new ResourceActionsImpl());
onLeadership();
}
我们先来看启动的两个心跳服务,进入startHeartbeatServices()方法:
private void startHeartbeatServices() {
// TODO ResourceManager(主节点)维持和从节点的心跳
// TODO ResourceManager(逻辑JobManager)维持和TaskExecutor(TaskManager)的心跳
taskManagerHeartbeatManager =
heartbeatServices.createHeartbeatManagerSender(
resourceId,
new TaskManagerHeartbeatListener(),
getMainThreadExecutor(),
log);
// TODO ResourceManager维持和JobMaster(主控程序)的心跳
jobManagerHeartbeatManager =
heartbeatServices.createHeartbeatManagerSender(
resourceId,
new JobManagerHeartbeatListener(),
getMainThreadExecutor(),
log);
}
可以看到到这两个心跳分别为:
1、ResourceManager维持和TaskManager的心跳
2、ResourceManager维持和主控程序(JobMaster)的心跳
我们再来看这个心跳是如何实现的,我们进入createHeartbeatManagerSender方法,在进入HeartbeatManagerSenderImpl,再进入this,可以看到:
HeartbeatManagerSenderImpl(
long heartbeatPeriod,
long heartbeatTimeout,
ResourceID ownResourceID,
HeartbeatListener heartbeatListener,
ScheduledExecutor mainThreadExecutor,
Logger log,
HeartbeatMonitor.Factory heartbeatMonitorFactory) {
super(
heartbeatTimeout,
ownResourceID,
heartbeatListener,
mainThreadExecutor,
log,
heartbeatMonitorFactory);
this.heartbeatPeriod = heartbeatPeriod;
// TODO 线程池定时调用this的run方法,由于delay为0L,立即执行
mainThreadExecutor.schedule(this, 0L, TimeUnit.MILLISECONDS);
}
在这里构建了一个延时线程池,不过延迟时间为0,则当代码执行到这里时会立即调用this的run方法,我们去看run方法:
@Override
public void run() {
if (!stopped) {
log.debug("Trigger heartbeat request.");
// 详细说明待后面解析完从节点后在介绍
for (HeartbeatMonitor heartbeatMonitor : getHeartbeatTargets().values()) {
// TODO 发送心跳
requestHeartbeat(heartbeatMonitor);
}
//等heartbeatPeriod=10s之后,再次执行this的run方法,来控制上面的for循环每隔10s执行一次,实现心跳的无限循环
getMainThreadExecutor().schedule(this, heartbeatPeriod, TimeUnit.MILLISECONDS);
}
}
可以看到,当代码第一次执行到这里是,会先调用依次发送心跳的方法,关于这个for循环,我们会在后面的章节里详细解析,这里就先不细说。在发送完心跳之后又出现了一个延时线程池,在heartbeatPeriod(10秒)延时后,会再次触发this的run方法,也就是当前方法,到此就会进入无限的心跳循环,也是在这里构建完成无限心跳。
分析完心跳的实现,我们回去看那两个定时服务是什么:
首先进入slotManager.start方法并选择SlotManagerImpl实现:
@Override
public void start(
ResourceManagerId newResourceManagerId,
Executor newMainThreadExecutor,
ResourceActions newResourceActions) {
LOG.info("Starting the SlotManager.");
this.resourceManagerId = Preconditions.checkNotNull(newResourceManagerId);
mainThreadExecutor = Preconditions.checkNotNull(newMainThreadExecutor);
resourceActions = Preconditions.checkNotNull(newResourceActions);
started = true;
/*
TODO 定时任务checkTaskManagerTimeoutsAndRedundancy
每隔30秒检查一次闲置的TaskManager
*/
taskManagerTimeoutsAndRedundancyCheck =
scheduledExecutor.scheduleWithFixedDelay(
() ->
mainThreadExecutor.execute(
() -> checkTaskManagerTimeoutsAndRedundancy()),
0L,
taskManagerTimeout.toMilliseconds(),
TimeUnit.MILLISECONDS);
/*
TODO 定时任务 checkSlotRequestTimeouts
Slot在申请中是状态为PendingRequest, 这个定时任务就是来检测那些已经超过5分钟的pendingRequest
也就是超时的Slot
*/
slotRequestTimeoutCheck =
scheduledExecutor.scheduleWithFixedDelay(
() -> mainThreadExecutor.execute(() -> checkSlotRequestTimeouts()),
0L,
slotRequestTimeout.toMilliseconds(),
TimeUnit.MILLISECONDS);
registerSlotManagerMetrics();
}
可以看到,这两个定时任务分别为:
1、闲置TaskManager的定时检查,这里当我们是yarn-session模式时,会定时(30秒)检查一次闲置的TaskManager,当有闲置时间超过30秒的Taskmanager是,回去将该从节点回收,并释放资源。
2、定时检查超时的Slot申请,Slot在申请中是状态为PendingRequest, 这个定时任务就是来检测那些已经超过5分钟的pendingRequest 也就是超时的Slot
我们进入超时slot的检查方法checkSlotRequestTimeouts:
private void checkSlotRequestTimeouts() {
if (!pendingSlotRequests.isEmpty()) {
long currentTime = System.currentTimeMillis();
Iterator> slotRequestIterator =
pendingSlotRequests.entrySet().iterator();
// TODO 遍历SlotRequest列表
while (slotRequestIterator.hasNext()) {
PendingSlotRequest slotRequest = slotRequestIterator.next().getValue();
// 判断已超时的slotRequest
if (currentTime - slotRequest.getCreationTimestamp()
>= slotRequestTimeout.toMilliseconds()) {
// 移除掉已超时的slotRequest
slotRequestIterator.remove();
// TODO ResourceManager已经分配给某个Job的Slot,但是该Slot还处于pendingRequest状态
if (slotRequest.isAssigned()) {
// 取消
cancelPendingSlotRequest(slotRequest);
}
// TODO 通知失败
resourceActions.notifyAllocationFailure(
slotRequest.getJobId(),
slotRequest.getAllocationId(),
new TimeoutException("The allocation could not be fulfilled in time."));
}
}
}
}
检查流程为:
1、遍历所有slot请求列表
2、判断已超时的slotRequest
3、移除超时SlotRequest
4、 如果ResourceManager已经分配给某个Job的Slot,但是该Slot还处于pendingRequest状态
5、则先取消当前的slot分配
6、再通知该slot分配失败
到此为止,ResourceManager就已经启动完毕,最后我们总结一下ResourceManager的启动工作:
ResourceManager的启动要点有以下几点:
1、ResourceManager是一个RpcEndpoint(Actor),当构建好对象后启动时会触发onStart(Actor的perStart生命周期方法)方法
2、ResourceManager也是一个LeaderContendr,也会执行竞选, 会执行竞选结果方法
3、ResourceManagerService 具有两个心跳服务和两个定时服务:
a、两个心跳服务:
ⅰ、从节点和主节点之间的心跳
ⅱ、Job的主控程序和主节点之间的心跳
b、两个定时服务:
ⅰ、TaskManager 的超时检查服务
ⅱ、Slot申请的 超时检查服务
在下一章中,我们继续介绍Dispatcher的构建和启动过程!