Merge branch 'master' into upload_file
This commit is contained in:
commit
f08af56544
373
yarn/job_start.md
Normal file
373
yarn/job_start.md
Normal file
@ -0,0 +1,373 @@
|
||||
# 作业启动
|
||||
|
||||
作业提交的客户端比较核心的类是Job.java，看作业启动的源码需要从这个类开始看。
|
||||
|
||||
## Job.java
|
||||
|
||||
作业启动的入口函数为waitForCompletion函数。当前函数的核心函数为submit()，主要如下：
|
||||
|
||||
```java
|
||||
public void submit()
|
||||
throws IOException, InterruptedException, ClassNotFoundException {
|
||||
ensureState(JobState.DEFINE);
|
||||
setUseNewAPI();
|
||||
connect();
|
||||
final JobSubmitter submitter =
|
||||
getJobSubmitter(cluster.getFileSystem(), cluster.getClient());
|
||||
status = ugi.doAs(new PrivilegedExceptionAction<JobStatus>() {
|
||||
public JobStatus run() throws IOException, InterruptedException,
|
||||
ClassNotFoundException {
|
||||
return submitter.submitJobInternal(Job.this, cluster);
|
||||
}
|
||||
});
|
||||
state = JobState.RUNNING;
|
||||
LOG.info("The url to track the job: " + getTrackingURL());
|
||||
}
|
||||
```
|
||||
|
||||
其中，connect主要为连接ResourceManager。核心提交类为submitJobInternal，在submitJobInternal中主要包含：
|
||||
|
||||
- 检查是否开启分布式缓存，核心函数为：`addMRFrameworkToDistributedCache(conf);`
|
||||
- 从yarn上面获取Yarn ApplicationId。
|
||||
- 将需要上传的文件拷贝到submitJobDir下面，将上传的结果添加到指定的配置中。主要实现在函数`copyAndConfigureFiles(job, submitJobDir);`里面，主要上传当前作业需要的jar包等信息到staging目录。当上传Jar包比较频繁的时候可以考虑开启分布式缓存。
|
||||
- 初始化核心配置，主要实现在函数：`writeConf(conf, submitJobFile);`里面。
|
||||
- 最后才是真正提交作业的部分：`status = submitClient.submitJob(jobId, submitJobDir.toString(), job.getCredentials());`通过submitClient.submitJob之后是远程调用到ResourceManager的类：YARNRunner.java，开始作业提交。
|
||||
|
||||
|
||||
|
||||
## YARNRunner.java
|
||||
|
||||
在当前类中，处理逻辑主要包含下面几步：
|
||||
|
||||
- 创建上下问信息：ApplicationSubmissionContext，当前这一步当中主要是构造AM相关参数，比如AM的启动命令等。在AM的启动命令中会设置AM的启动主函数MRAppMaster，在资源调度到当前作业时，会先启动AM的主函数MRAppMaster
|
||||
- 提交作业。最后会调用到`rmClient.submitApplication(request);`发送启动作业的请求，在发送请求之后会一直等到作业启动完成。启动成功之后会返回appilicationId
|
||||
|
||||
## 资源调度
|
||||
|
||||
Yarn资源调度过程待完善，后面会单独章节学习。
|
||||
|
||||
## MRAppMaster.java
|
||||
|
||||
当前类是启动AM的入口函数，所以要从main函数开始读代码。main函数里面主要做了下面几件事：
|
||||
|
||||
- 初始化MRAppMaster实例。
|
||||
- 加载job.xml信息。
|
||||
- 初始化web信息。主要包含： MR history server、MR Server。
|
||||
- 启动APPMaster。
|
||||
|
||||
|
||||
|
||||
### initAndStartAppMaster：启动AppMaster
|
||||
|
||||
MRAppMaster在yarn内部是一个服务，最终启动的时候会调用到serviceStart函数里面，所以我们主要看这个函数里面做了什么。
|
||||
|
||||
|
||||
|
||||
#### 1、创建并且初始化Job
|
||||
|
||||
创建Job对象并且将其初始化掉。但是不会启动当前作业。
|
||||
|
||||
- 初始化JobImpl对象。在JobImpl初始化的时候做了下面几件事：
|
||||
|
||||
- 初始化线程池。
|
||||
|
||||
- 初始化作业状态机的核心代码如下：
|
||||
|
||||
```java
|
||||
protected static final
|
||||
StateMachineFactory<JobImpl, JobStateInternal, JobEventType, JobEvent>
|
||||
stateMachineFactory
|
||||
= new StateMachineFactory<JobImpl, JobStateInternal, JobEventType, JobEvent>
|
||||
(JobStateInternal.NEW)
|
||||
// Transitions from NEW state
|
||||
.addTransition(JobStateInternal.NEW, JobStateInternal.NEW,
|
||||
JobEventType.JOB_DIAGNOSTIC_UPDATE,
|
||||
DIAGNOSTIC_UPDATE_TRANSITION)
|
||||
.addTransition(JobStateInternal.NEW, JobStateInternal.NEW,
|
||||
JobEventType.JOB_COUNTER_UPDATE, COUNTER_UPDATE_TRANSITION)
|
||||
// ....省略...
|
||||
.addTransition(JobStateInternal.REBOOT, JobStateInternal.REBOOT,
|
||||
JobEventType.JOB_COUNTER_UPDATE, COUNTER_UPDATE_TRANSITION)
|
||||
// create the topology tables
|
||||
.installTopology();
|
||||
|
||||
- 初始化其他配置。
|
||||
|
||||
- 在中央处理器里面注册JobFinishEvent类型事件以及事件处理的handler。
|
||||
|
||||
```java
|
||||
protected Job createJob(Configuration conf, JobStateInternal forcedState,
|
||||
String diagnostic) {
|
||||
// create single job
|
||||
Job newJob =
|
||||
new JobImpl(jobId, appAttemptID, conf, dispatcher.getEventHandler(),
|
||||
taskAttemptListener, jobTokenSecretManager, jobCredentials, clock,
|
||||
completedTasksFromPreviousRun, metrics,
|
||||
committer, newApiCommitter,
|
||||
currentUser.getUserName(), appSubmitTime, amInfos, context,
|
||||
forcedState, diagnostic);
|
||||
((RunningAppContext) context).jobs.put(newJob.getID(), newJob);
|
||||
dispatcher.register(JobFinishEvent.Type.class,
|
||||
createJobFinishEventHandler());
|
||||
return newJob;
|
||||
}
|
||||
```
|
||||
|
||||
|
||||
|
||||
#### 2、发送inited事件
|
||||
|
||||
发送inited事件的对象主要是下面两个：
|
||||
|
||||
- 通过dispatcher给历史AM发送。
|
||||
- 当前AM。代码如下：
|
||||
|
||||
```java
|
||||
// Send out an MR AM inited event for this AM.
|
||||
dispatcher.getEventHandler().handle(
|
||||
new JobHistoryEvent(job.getID(), new AMStartedEvent(amInfo
|
||||
.getAppAttemptId(), amInfo.getStartTime(), amInfo.getContainerId(),
|
||||
amInfo.getNodeManagerHost(), amInfo.getNodeManagerPort(), amInfo
|
||||
.getNodeManagerHttpPort(), this.forcedState == null ? null
|
||||
: this.forcedState.toString(), appSubmitTime)));
|
||||
```
|
||||
|
||||
|
||||
|
||||
#### 3、创建job init事件，并且处理
|
||||
|
||||
创建init事件，核心代码如下：
|
||||
|
||||
```java
|
||||
JobEvent initJobEvent = new JobEvent(job.getID(), JobEventType.JOB_INIT);
|
||||
jobEventDispatcher.handle(initJobEvent);
|
||||
```
|
||||
|
||||
事件处理的核心类为InitTransition，核心代码如下：
|
||||
|
||||
```java
|
||||
public JobStateInternal transition(JobImpl job, JobEvent event) {
|
||||
job.metrics.submittedJob(job);
|
||||
job.metrics.preparingJob(job);
|
||||
// 初始化上下文。
|
||||
if (job.newApiCommitter) {
|
||||
job.jobContext = new JobContextImpl(job.conf,
|
||||
job.oldJobId);
|
||||
} else {
|
||||
job.jobContext = new org.apache.hadoop.mapred.JobContextImpl(
|
||||
job.conf, job.oldJobId);
|
||||
}
|
||||
|
||||
try {
|
||||
// 初始化token等信息。
|
||||
setup(job);
|
||||
job.fs = job.getFileSystem(job.conf);
|
||||
|
||||
//log to job history
|
||||
JobSubmittedEvent jse = new JobSubmittedEvent(job.oldJobId,
|
||||
job.conf.get(MRJobConfig.JOB_NAME, "test"),
|
||||
job.conf.get(MRJobConfig.USER_NAME, "mapred"),
|
||||
job.appSubmitTime,
|
||||
job.remoteJobConfFile.toString(),
|
||||
job.jobACLs, job.queueName,
|
||||
job.conf.get(MRJobConfig.WORKFLOW_ID, ""),
|
||||
job.conf.get(MRJobConfig.WORKFLOW_NAME, ""),
|
||||
job.conf.get(MRJobConfig.WORKFLOW_NODE_NAME, ""),
|
||||
getWorkflowAdjacencies(job.conf),
|
||||
job.conf.get(MRJobConfig.WORKFLOW_TAGS, ""), job.conf);
|
||||
job.eventHandler.handle(new JobHistoryEvent(job.jobId, jse));
|
||||
//TODO JH Verify jobACLs, UserName via UGI?
|
||||
// 初始化并行度等信息。
|
||||
TaskSplitMetaInfo[] taskSplitMetaInfo = createSplits(job, job.jobId);
|
||||
job.numMapTasks = taskSplitMetaInfo.length;
|
||||
job.numReduceTasks = job.conf.getInt(MRJobConfig.NUM_REDUCES, 0);
|
||||
|
||||
if (job.numMapTasks == 0 && job.numReduceTasks == 0) {
|
||||
job.addDiagnostic("No of maps and reduces are 0 " + job.jobId);
|
||||
} else if (job.numMapTasks == 0) {
|
||||
job.reduceWeight = 0.9f;
|
||||
} else if (job.numReduceTasks == 0) {
|
||||
job.mapWeight = 0.9f;
|
||||
} else {
|
||||
job.mapWeight = job.reduceWeight = 0.45f;
|
||||
}
|
||||
|
||||
checkTaskLimits();
|
||||
|
||||
// 加载其他参数，具体代码省略。。
|
||||
|
||||
cleanupSharedCacheUploadPolicies(job.conf);
|
||||
|
||||
// create the Tasks but don't start them yet，， 创建map task
|
||||
createMapTasks(job, inputLength, taskSplitMetaInfo);
|
||||
// 创建reduce tasks
|
||||
createReduceTasks(job);
|
||||
|
||||
job.metrics.endPreparingJob(job);
|
||||
return JobStateInternal.INITED;
|
||||
} catch (Exception e) {
|
||||
LOG.warn("Job init failed", e);
|
||||
job.metrics.endPreparingJob(job);
|
||||
job.addDiagnostic("Job init failed : "
|
||||
+ StringUtils.stringifyException(e));
|
||||
// Leave job in the NEW state. The MR AM will detect that the state is
|
||||
// not INITED and send a JOB_INIT_FAILED event.
|
||||
return JobStateInternal.NEW;
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
|
||||
|
||||
#### 4、检查初始化结果并且启动作业
|
||||
|
||||
当init成功时，handler返回的结果是JobStateInternal.INITED；如果是失败了则返回的结果是JobStateInternal.NEW。
|
||||
|
||||
对于初始化失败的作业会触发JobEventType.JOB_INIT_FAILED事件。
|
||||
|
||||
对于初始化成功的作业会调用函数startJobs，继续启动作业。触发
|
||||
|
||||
```java
|
||||
protected void startJobs() {
|
||||
/** create a job-start event to get this ball rolling */
|
||||
JobEvent startJobEvent = new JobStartEvent(job.getID(),
|
||||
recoveredJobStartTime);
|
||||
/** send the job-start event. this triggers the job execution. */
|
||||
dispatcher.getEventHandler().handle(startJobEvent);
|
||||
}
|
||||
```
|
||||
|
||||
核心处理逻辑如下，主要是触发了几个事件：
|
||||
|
||||
- JobHistoryEvent：事件处理的handler为JobHistoryEventHandler。
|
||||
- JobInfoChangeEvent：
|
||||
- CommitterJobSetupEvent：作业启动的事件，核心处理逻辑在EventProcessor中的函数handleJobSetup中。
|
||||
|
||||
```java
|
||||
public void transition(JobImpl job, JobEvent event) {
|
||||
JobStartEvent jse = (JobStartEvent) event;
|
||||
if (jse.getRecoveredJobStartTime() != -1L) {
|
||||
job.startTime = jse.getRecoveredJobStartTime();
|
||||
} else {
|
||||
job.startTime = job.clock.getTime();
|
||||
}
|
||||
JobInitedEvent jie =
|
||||
new JobInitedEvent(job.oldJobId,
|
||||
job.startTime,
|
||||
job.numMapTasks, job.numReduceTasks,
|
||||
job.getState().toString(),
|
||||
job.isUber());
|
||||
job.eventHandler.handle(new JobHistoryEvent(job.jobId, jie));
|
||||
JobInfoChangeEvent jice = new JobInfoChangeEvent(job.oldJobId,
|
||||
job.appSubmitTime, job.startTime);
|
||||
job.eventHandler.handle(new JobHistoryEvent(job.jobId, jice));
|
||||
job.metrics.runningJob(job);
|
||||
|
||||
job.eventHandler.handle(new CommitterJobSetupEvent(
|
||||
job.jobId, job.jobContext));
|
||||
}
|
||||
```
|
||||
|
||||
handleJobSetup的核心处理逻辑：
|
||||
|
||||
- 创建attempt路径。
|
||||
- 触发JobSetupCompletedEvent事件。从事件实现来看会触发JobImpl里面的JOB_SETUP_COMPLETED事件类型，由SetupCompletedTransition来处理当前事件。在当前函数里面会触发JOB_COMPLETED事件。最终会走到JobImpl的checkReadyForCommit函数里面。
|
||||
|
||||
```java
|
||||
protected void handleJobSetup(CommitterJobSetupEvent event) {
|
||||
try {
|
||||
// 主要是创建attempt路径
|
||||
committer.setupJob(event.getJobContext());
|
||||
context.getEventHandler().handle(
|
||||
new JobSetupCompletedEvent(event.getJobID()));
|
||||
} catch (Exception e) {
|
||||
LOG.warn("Job setup failed", e);
|
||||
context.getEventHandler().handle(new JobSetupFailedEvent(
|
||||
event.getJobID(), StringUtils.stringifyException(e)));
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
SetupCompletedTransition的处理逻辑如下，可以看到会定时启动MapTask和ReduceTask。
|
||||
|
||||
```java
|
||||
public void transition(JobImpl job, JobEvent event) {
|
||||
job.setupProgress = 1.0f;
|
||||
job.scheduleTasks(job.mapTasks, job.numReduceTasks == 0);
|
||||
job.scheduleTasks(job.reduceTasks, true);
|
||||
|
||||
// If we have no tasks, just transition to job completed
|
||||
if (job.numReduceTasks == 0 && job.numMapTasks == 0) {
|
||||
job.eventHandler.handle(new JobEvent(job.jobId,
|
||||
JobEventType.JOB_COMPLETED));
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
checkReadyForCommit函数的实现如下，可以看到在触发了CommitterJobCommitEvent事件,在CommitterJobCommitEvent里面会触发JOB_COMMIT事件。主要处理逻辑在handleJobCommit里面。
|
||||
|
||||
```java
|
||||
protected JobStateInternal checkReadyForCommit() {
|
||||
JobStateInternal currentState = getInternalState();
|
||||
if (completedTaskCount == tasks.size()
|
||||
&& currentState == JobStateInternal.RUNNING) {
|
||||
eventHandler.handle(new CommitterJobCommitEvent(jobId, getJobContext()));
|
||||
return JobStateInternal.COMMITTING;
|
||||
}
|
||||
// return the current state as job not ready to commit yet
|
||||
return getInternalState();
|
||||
}
|
||||
```
|
||||
|
||||
handleJobCommit处理逻辑如下，
|
||||
|
||||
```java
|
||||
protected void handleJobCommit(CommitterJobCommitEvent event) {
|
||||
boolean commitJobIsRepeatable = false;
|
||||
try {
|
||||
// 检查作业是否重复。
|
||||
commitJobIsRepeatable = committer.isCommitJobRepeatable(
|
||||
event.getJobContext());
|
||||
} catch (IOException e) {
|
||||
LOG.warn("Exception in committer.isCommitJobRepeatable():", e);
|
||||
}
|
||||
|
||||
try {
|
||||
// 创建文件：/tmp/hadoop-yarn/staging//user/.staging/{jobid}/COMMIT_STARTED
|
||||
touchz(startCommitFile, commitJobIsRepeatable);
|
||||
jobCommitStarted();
|
||||
// 检查和RM的心跳。
|
||||
waitForValidCommitWindow();
|
||||
// 提交作业，核心处理函数在commitJobInternal里面
|
||||
committer.commitJob(event.getJobContext());
|
||||
// 创建文件：/tmp/hadoop-yarn/staging//user/.staging/{jobid}/COMMIT_SUCCESS
|
||||
touchz(endCommitSuccessFile, commitJobIsRepeatable);
|
||||
context.getEventHandler().handle(
|
||||
new JobCommitCompletedEvent(event.getJobID()));
|
||||
} catch (Exception e) {
|
||||
LOG.error("Could not commit job", e);
|
||||
try {
|
||||
// 失败之后创建：/tmp/hadoop-yarn/staging//user/.staging/{jobid}/COMMIT_FAIL
|
||||
touchz(endCommitFailureFile, commitJobIsRepeatable);
|
||||
} catch (Exception e2) {
|
||||
LOG.error("could not create failure file.", e2);
|
||||
}
|
||||
context.getEventHandler().handle(
|
||||
new JobCommitFailedEvent(event.getJobID(),
|
||||
StringUtils.stringifyException(e)));
|
||||
} finally {
|
||||
jobCommitEnded();
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
|
||||
|
||||
##### CommitSucceededTransition
|
||||
|
||||
提交成功的事件处理handler为CommitSucceededTransition，核心处理逻辑如下：
|
||||
|
||||
```java
|
||||
job.logJobHistoryFinishedEvent();
|
||||
job.finished(JobStateInternal.SUCCEEDED);
|
||||
```
|
||||
124
yarn/resourcemanager.md
Normal file
124
yarn/resourcemanager.md
Normal file
@ -0,0 +1,124 @@
|
||||
|
||||
# 简介
|
||||
|
||||
ResourceManager(RM)，RM是全局的资源管理器，负责整个系统的资源管理和分配。主要由以下两部分组成：
|
||||
|
||||
- 调度器：根据容量、队列限制条件将系统资源分配给各个应用。
|
||||
- 资源分配的单位是container，container是一个动态资源单位，它将内存、CPU、磁盘、网络等资源封装在一起，从而限定了资源使用量。
|
||||
- 调度器是一个可插拔的组件，用户可以自己定制，也可以选择Fair或Capacity调度器.
|
||||
- 应用程序管理器：负责管理所有应用程序的以下内容：
|
||||
- 应用提交
|
||||
- 与调度器协商资源以启动AM.
|
||||
- 监控AM运行状态并在失败时重启它
|
||||
|
||||
|
||||
# RM内部架构
|
||||
|
||||
- 交互模块：RM对普通用户、管理员、Web提供了三种对外服务：
|
||||
- ClientRMService:为普通用户提供服务，它处理来自客户端的各种RPC，比如:
|
||||
- 应用提交
|
||||
- 终止应用
|
||||
- 获取应用状态等
|
||||
- AdminService:为管理员提供的独立接口，主要目的是为了防止大量普通用户请求阻塞管理员通道，提供如下功能：
|
||||
- 动态更新节点列表
|
||||
- 更新ACL列表
|
||||
- 更新队列信息等
|
||||
- WebApp:提供一个Web界面来让用户更友好的获知集群和应用的状态
|
||||
- NM管理模块：用来管理NM的模块，主要包含以下三个组件：
|
||||
- ResourceTrackerService:处理来自NodeManager的请求，主要包括：
|
||||
- 注册：注册是NM启动时发生的行为，NM提供的信息包括：
|
||||
- 节点ID、可用资源上限信息等.
|
||||
- 心跳：心跳是周期行为
|
||||
- NM提供的信息包括：
|
||||
- 各个Container运行状态、运行的Application列表、节点健康状态等.
|
||||
- RM返回的信息包括：
|
||||
- 等待释放的Container列表、Application列表等.
|
||||
- NMLivelinessMonitor:监控NM是否活着，如果NM在一定时间(默认10m)内未上报心跳，则认为它死掉，需要移除.
|
||||
- NodesListManager:维护正常节点和异常节点列表，管理exclude(类似黑名单)和include(类似白名单)节点列表，
|
||||
这两个列表均是在配置文件中设置的，可以动态加载。
|
||||
- AM管理模块：主要是用来管理所有AM，主要包括：
|
||||
- ApplicationMasterService(AMS):处理来自AM的请求，包括：
|
||||
- 注册：是AM启动时发生的行为，信息包括：
|
||||
- AM的启动节点、对外RPC端口、tracking URL等.
|
||||
- 心跳：是周期行为
|
||||
- AM提供的信息包括：所需资源的描述、待释放Container列表、黑名单列表等.
|
||||
- AMS返回的信息包括：新分配的Container、失败的Container、待抢占的Container列表等
|
||||
- AMLivelinessMonitor:监控AM是否活着，如果AM在一定时间(默认10m)内未上报心路，
|
||||
则认为它死掉，它上面正在运行的Container将会被置为失败状态，而AM本身会被分配到另一个节点上(用户可以指定重试次数，默认5)
|
||||
- ApplicationMasterLauncher：与某个NM通信，要求它为某个应用程序启动AM.
|
||||
- 应用管理模块：主要是各个应用外围的管理，并不涉及到应用内部
|
||||
- ApplicationACLsManager:管理应用程序访问权限，包含两部分：
|
||||
- 查看权限：主要用于查看应用程序基本信息
|
||||
- 修改权限：主要用于修改应用程序优先级、杀死应用程序等
|
||||
- RMAppManager:管理应用程序的启动和关闭.
|
||||
- ContainerAllocationExpirer:当AM收到RM新分配的Container后，必须在一定时间(默认10m)内在对应的NM上启动该Container，
|
||||
否则RM将强制回收该Container，而一个已经分配的Container是否该被回收则是由ContainerAllocationExpirer决定和执行的
|
||||
- 状态机管理模块：RM使用有限状态机维护有状态对象的生命周期，状态机的引入使得Yarn的架构设计清晰，RM内部的状态机有：
|
||||
- RMApp:维护一个应用程序的整个运行周期，包括从启动到运行结束的整个过程
|
||||
- 由于一个APP的生命周期可能会启动多个运行实例(Attempt)，RMApp维护的是所有的这些Attempt
|
||||
- RMAppAttempt:一次应用程序的运行实例的整个生命周期，可以理解为APP的一次尝试运行
|
||||
- RMContainer:一个Container的运行周期，包括从创建到运行结束的整个过程。
|
||||
- RM将资源封装成Container发送给应用程序的AM，AM在Container描述的运行环境中启动任务
|
||||
- Yarn不支持Container重用，一个Container用完后会立刻释放
|
||||
- RMNode:维护了一个NM的生命周期，包括从启动到运行结束的整个过程
|
||||
- 安全模块：RM自带了非常全面的权限管理机制，主要包括：
|
||||
- ClientToAMSecretManager
|
||||
- ContainerTokenSecretManager
|
||||
- ApplicationTokenSecretManager
|
||||
- 调度模块：主要包含一个组件ResourceScheduler。
|
||||
- 资源调度器，它按照一定的约束条件(比如队列容量限制等)将集群中的资源分配给各个应用程序，目前主要考虑内存和CPU。
|
||||
- ResourceScheduler是一个可插拔式的模块，自带三个调度器，用户可以自己定制。
|
||||
- FIFO：先进先出，单用户。
|
||||
- Fair Scheduler:公平调度器(FairScheduler基本上具备其它两种的所有功能)
|
||||
- Capacity Scheduler:容量调度器
|
||||
|
||||
# RM事件与事件处理器
|
||||
|
||||
Yarn采用了事件驱动机制，而RM是的实现则是最好的例证。所有服务和组件均是通过中央异步调度器组织在一起的，
|
||||
不同组件之间通过事件交互，从而实现了一个异步并行的高效系统。
|
||||
|
||||
## 服务
|
||||
|
||||
|组件名称 | 输出事件类型| 用途 |
|
||||
|-----|------|-------|
|
||||
| ClientRMService | RMAppAttemptEvent <br> RMAppEvent <br> RMNodeEvent | |
|
||||
| NMLivelinessMonitor | RMNodeEvent | |
|
||||
| ResourceTrackerService | RMNodeEvent <br> RMAppAttemptEvent | |
|
||||
| AMLivelinessMonitor | RMAppAttemptEvent | |
|
||||
| ContainerAllocationExpirer | SchedulerEvent | |
|
||||
|
||||
|
||||
## 事件处理器
|
||||
|
||||
|组件名称 | 处理的事件类型 | 输出事件类型 | 用途 |
|
||||
|-----|------|-------|-------|
|
||||
| ApplicationMasterLauncher | AMLauncherEvent | - | |
|
||||
| RMAppManager | RMAppManagerEvent | RMAppEvent | |
|
||||
| NodesListManager | NodesListManagerEvent | RMNodeEvent <br> RMAppEvent | |
|
||||
| RMApp | RMAppEvent | RMAppAttemptEvent <br> RMNodeEvent <br> SchedulerEvent <br> RMAppManagerEvent | |
|
||||
| RMAppAttempt | RMAppAttemptEvent | SchedulerEvent <br> RMAppAttemptEvent <br> RMAppEvent <br> AMLauncherEvent <br> RMNodeEvent | |
|
||||
| RMNode | RMNodeEvent | RMAppEvent <br> SchedulerEvent <br> NodesListManagerEvent <br> RMNodeEvent | |
|
||||
| ResourceScheduler | SchedulerEvent | RMAppEvent <br> RMAppAttemptEvent | |
|
||||
| RMContainer | RMContainerEvent | RMAppEvent <br> RMAppAttemptEvent <br> RMNodeEvent | |
|
||||
|
||||
### 事件处理器实现类
|
||||
|
||||
- RMApp 实现类：
|
||||
- ApplicationEventDispatcher
|
||||
- RMAppImpl
|
||||
|
||||
- RMAppAttempt 实现类
|
||||
- ApplicationAttemptEventDispatcher
|
||||
- RMAppAttemptImpl
|
||||
|
||||
- RMNode实现类
|
||||
- NodeEventDispatcher
|
||||
- RMNodeImpl
|
||||
|
||||
- ResourceScheduler实现类
|
||||
- EventDispatcher
|
||||
- FairScheduler
|
||||
|
||||
- RMContainer实现类
|
||||
- RMContainerImpl
|
||||
|
||||
304
yarn/yarn_event.md
Normal file
304
yarn/yarn_event.md
Normal file
@ -0,0 +1,304 @@
|
||||
|
||||
# 简介
|
||||
|
||||
Yarn采用了基于事件驱动的并发模型：
|
||||
|
||||
- 所有状态机都实现了EventHandler接口，很多服务（类名通常带有Service后缀）也实现了该接口，它们都是事件处理器。
|
||||
- 需要异步处理的事件由中央异步调度器（类名通常带有Dispatcher后缀）统一接收/派发，需要同步处理的事件直接交给相应的事件处理器。
|
||||
|
||||
|
||||
|
||||
某些事件处理器不仅处理事件，也会向中央异步调度器发送事件。
|
||||
|
||||
|
||||
# 事件处理器定义
|
||||
|
||||
事件处理器定义如下：
|
||||
|
||||
```java
|
||||
@SuppressWarnings("rawtypes")
|
||||
@Public
|
||||
@Evolving
|
||||
public interface EventHandler<T extends Event> {
|
||||
|
||||
void handle(T event);
|
||||
|
||||
}
|
||||
```
|
||||
|
||||
只有一个handler函数，如参是事件：
|
||||
|
||||
|
||||
# 中央处理器AsyncDispatcher
|
||||
|
||||
AsyncDispatcher 实现了接口Dispatcher，Dispatcher中定义了事件Dispatcher的接口。主要提供两个功能：
|
||||
- 注册不同类型的事件，主要包含事件类型和事件处理器。
|
||||
- 获取事件处理器，用来派发事件，等待异步执行真正的EventHandler。
|
||||
|
||||
```java
|
||||
@Public
|
||||
@Evolving
|
||||
public interface Dispatcher {
|
||||
|
||||
EventHandler<Event> getEventHandler();
|
||||
|
||||
void register(Class<? extends Enum> eventType, EventHandler handler);
|
||||
|
||||
}
|
||||
```
|
||||
|
||||
AsyncDispatcher实现了Dispatcher接口，也扩展了AbstractService，表明AsyncDispatcher也是一个服务，
|
||||
是一个典型的生产者消费这模型。
|
||||
|
||||
```java
|
||||
public class AsyncDispatcher extends AbstractService implements Dispatcher {
|
||||
...
|
||||
}
|
||||
```
|
||||
|
||||
# 事件处理器的注册
|
||||
|
||||
事件注册就是将事件写入到eventDispatchers里面，eventDispatchers的定义：`Map<Class<? extends Enum>, EventHandler> eventDispatchers`，键是事件类型，value是事件的处理器。
|
||||
|
||||
对于同一事件类型注册多次handler处理函数时，将使用MultiListenerHandler代替，MultiListenerHandler里面保存了多个handler，调用handler函数时，会依次调用每个handler。
|
||||
|
||||
```java
|
||||
public void register(Class<? extends Enum> eventType,
|
||||
EventHandler handler) {
|
||||
/* check to see if we have a listener registered */
|
||||
EventHandler<Event> registeredHandler = (EventHandler<Event>) eventDispatchers.get(eventType);
|
||||
LOG.info("Registering " + eventType + " for " + handler.getClass());
|
||||
if (registeredHandler == null) {
|
||||
eventDispatchers.put(eventType, handler);
|
||||
} else if (!(registeredHandler instanceof MultiListenerHandler)){
|
||||
/* for multiple listeners of an event add the multiple listener handler */
|
||||
MultiListenerHandler multiHandler = new MultiListenerHandler();
|
||||
multiHandler.addHandler(registeredHandler);
|
||||
multiHandler.addHandler(handler);
|
||||
eventDispatchers.put(eventType, multiHandler);
|
||||
} else {
|
||||
/* already a multilistener, just add to it */
|
||||
MultiListenerHandler multiHandler
|
||||
= (MultiListenerHandler) registeredHandler;
|
||||
multiHandler.addHandler(handler);
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
|
||||
# 事件处理
|
||||
|
||||
AsyncDispatcher#getEventHandler()是异步派发的关键：
|
||||
|
||||
```java
|
||||
private final EventHandler<Event> handlerInstance = new GenericEventHandler();
|
||||
|
||||
// 省略.....
|
||||
|
||||
@Override
|
||||
public EventHandler<Event> getEventHandler() {
|
||||
return handlerInstance;
|
||||
}
|
||||
```
|
||||
|
||||
## GenericEventHandler：一个特殊的事件处理器
|
||||
|
||||
GenericEventHandler是一个特殊的事件处理器，用于接受各种事件。由指定线程处理接收到的事件。
|
||||
|
||||
```java
|
||||
public void handle(Event event) {
|
||||
if (blockNewEvents) {
|
||||
return;
|
||||
}
|
||||
drained = false;
|
||||
/* all this method does is enqueue all the events onto the queue */
|
||||
int qSize = eventQueue.size();
|
||||
if (qSize != 0 && qSize % 1000 == 0
|
||||
&& lastEventQueueSizeLogged != qSize) {
|
||||
lastEventQueueSizeLogged = qSize;
|
||||
LOG.info("Size of event-queue is " + qSize);
|
||||
}
|
||||
if (qSize != 0 && qSize % detailsInterval == 0
|
||||
&& lastEventDetailsQueueSizeLogged != qSize) {
|
||||
lastEventDetailsQueueSizeLogged = qSize;
|
||||
printEventQueueDetails();
|
||||
printTrigger = true;
|
||||
}
|
||||
int remCapacity = eventQueue.remainingCapacity();
|
||||
if (remCapacity < 1000) {
|
||||
LOG.warn("Very low remaining capacity in the event-queue: "
|
||||
+ remCapacity);
|
||||
}
|
||||
try {
|
||||
eventQueue.put(event);
|
||||
} catch (InterruptedException e) {
|
||||
if (!stopped) {
|
||||
LOG.warn("AsyncDispatcher thread interrupted", e);
|
||||
}
|
||||
// Need to reset drained flag to true if event queue is empty,
|
||||
// otherwise dispatcher will hang on stop.
|
||||
drained = eventQueue.isEmpty();
|
||||
throw new YarnRuntimeException(e);
|
||||
}
|
||||
};
|
||||
```
|
||||
|
||||
|
||||
|
||||
- blockNewEvents: 是否阻塞事件处理，只有当中央处理器停止之后才会停止接受事件。
|
||||
|
||||
- eventQueue：将接收到的请求放置到当前阻塞队列里面。方便指定线程及时处理。
|
||||
|
||||
|
||||
|
||||
## 事件处理线程
|
||||
|
||||
在服务启动时（serviceStart函数）创建一个线程，会循环处理接受到的事件。核心处理逻辑在函数dispatch里面。
|
||||
|
||||
```java
|
||||
Runnable createThread() {
|
||||
return new Runnable() {
|
||||
@Override
|
||||
public void run() {
|
||||
while (!stopped && !Thread.currentThread().isInterrupted()) {
|
||||
drained = eventQueue.isEmpty();
|
||||
// 省略。。。
|
||||
Event event;
|
||||
try {
|
||||
event = eventQueue.take();
|
||||
} catch(InterruptedException ie) {
|
||||
if (!stopped) {
|
||||
LOG.warn("AsyncDispatcher thread interrupted", ie);
|
||||
}
|
||||
return;
|
||||
}
|
||||
if (event != null) {
|
||||
// 省略。。。
|
||||
dispatch(event);
|
||||
// 省略。。。
|
||||
}
|
||||
}
|
||||
}
|
||||
};
|
||||
}
|
||||
```
|
||||
|
||||
|
||||
|
||||
### dispatch详解
|
||||
|
||||
- 从已经注册的eventDispatchers列表里面查找当前事件对应的处理器，调用当前处理器的handler函数。
|
||||
- 如果当前handler处理出现异常时，默认会退出RM。
|
||||
|
||||
```java
|
||||
protected void dispatch(Event event) {
|
||||
//all events go thru this loop
|
||||
LOG.debug("Dispatching the event {}.{}", event.getClass().getName(),
|
||||
event);
|
||||
|
||||
Class<? extends Enum> type = event.getType().getDeclaringClass();
|
||||
|
||||
try{
|
||||
EventHandler handler = eventDispatchers.get(type);
|
||||
if(handler != null) {
|
||||
handler.handle(event);
|
||||
} else {
|
||||
throw new Exception("No handler for registered for " + type);
|
||||
}
|
||||
} catch (Throwable t) {
|
||||
//TODO Maybe log the state of the queue
|
||||
LOG.error(FATAL, "Error in dispatcher thread", t);
|
||||
// If serviceStop is called, we should exit this thread gracefully.
|
||||
if (exitOnDispatchException
|
||||
&& (ShutdownHookManager.get().isShutdownInProgress()) == false
|
||||
&& stopped == false) {
|
||||
stopped = true;
|
||||
Thread shutDownThread = new Thread(createShutDownThread());
|
||||
shutDownThread.setName("AsyncDispatcher ShutDown handler");
|
||||
shutDownThread.start();
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
# 状态机
|
||||
|
||||
状态转换由成员变量StateMachine管理，所有的StateMachine都由StateMachineFactory进行管理。由addTransition函数实现状态机。
|
||||
|
||||
```java
|
||||
private static final StateMachineFactory<RMAppImpl,
|
||||
RMAppState,
|
||||
RMAppEventType,
|
||||
RMAppEvent> stateMachineFactory
|
||||
= new StateMachineFactory<RMAppImpl,
|
||||
RMAppState,
|
||||
RMAppEventType,
|
||||
RMAppEvent>(RMAppState.NEW)
|
||||
|
||||
|
||||
// Transitions from NEW state
|
||||
.addTransition(RMAppState.NEW, RMAppState.NEW,
|
||||
RMAppEventType.NODE_UPDATE, new RMAppNodeUpdateTransition())
|
||||
.addTransition(RMAppState.NEW, RMAppState.NEW_SAVING,
|
||||
RMAppEventType.START, new RMAppNewlySavingTransition())
|
||||
.addTransition(RMAppState.NEW, EnumSet.of(RMAppState.SUBMITTED,
|
||||
RMAppState.ACCEPTED, RMAppState.FINISHED, RMAppState.FAILED,
|
||||
RMAppState.KILLED, RMAppState.FINAL_SAVING),
|
||||
RMAppEventType.RECOVER, new RMAppRecoveredTransition())
|
||||
.addTransition(RMAppState.NEW, RMAppState.KILLED, RMAppEventType.KILL,
|
||||
new AppKilledTransition())
|
||||
.addTransition(RMAppState.NEW, RMAppState.FINAL_SAVING,
|
||||
RMAppEventType.APP_REJECTED,
|
||||
new FinalSavingTransition(new AppRejectedTransition(),
|
||||
RMAppState.FAILED))
|
||||
|
||||
.addTransition(
|
||||
RMAppState.KILLED,
|
||||
RMAppState.KILLED,
|
||||
EnumSet.of(RMAppEventType.APP_ACCEPTED,
|
||||
RMAppEventType.APP_REJECTED, RMAppEventType.KILL,
|
||||
RMAppEventType.ATTEMPT_FINISHED, RMAppEventType.ATTEMPT_FAILED,
|
||||
RMAppEventType.NODE_UPDATE, RMAppEventType.START))
|
||||
|
||||
.installTopology();
|
||||
```
|
||||
|
||||
|
||||
|
||||
Transition定义了“从一个状态转换到另一个状态”的行为，由转换操作、开始状态、事件类型、事件组成：
|
||||
|
||||
```java
|
||||
public interface StateMachine
|
||||
<STATE extends Enum<STATE>,
|
||||
EVENTTYPE extends Enum<EVENTTYPE>, EVENT> {
|
||||
public STATE getCurrentState();
|
||||
public STATE getPreviousState();
|
||||
public STATE doTransition(EVENTTYPE eventType, EVENT event)
|
||||
throws InvalidStateTransitionException;
|
||||
}
|
||||
```
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
## ResourceManager中状态机
|
||||
|
||||
- RMApp：用于维护一个Application的生命周期，实现类 - RMAppImpl
|
||||
- RMAppAttempt：用于维护一次试探运行的生命周期，实现类 - RMAppAttemptImpl
|
||||
- RMContainer：用于维护一个已分配的资源最小单位Container的生命周期，实现类 - RMContainerImpl
|
||||
- RMNode：用于维护一个NodeManager的生命周期，实现类 - RMNodeImpl
|
||||
|
||||
NodeManager中状态机：
|
||||
|
||||
- Application：用于维护节点上一个Application的生命周期，实现类 - ApplicationImpl
|
||||
- Container：用于维护节点上一个容器的生命周期，实现类 - ContainerImpl
|
||||
- LocalizedResource：用于维护节点上资源本地化的生命周期，没有使用接口即实现类 - LocalizedResource
|
||||
|
||||
|
||||
|
||||
|
||||
29
yarn/yarn_event_detail.md
Normal file
29
yarn/yarn_event_detail.md
Normal file
@ -0,0 +1,29 @@
|
||||
# 简介
|
||||
|
||||
Yarn状态机的基础部分参见：[Yarn 状态机以及事件机制](./yarn_event.md)
|
||||
|
||||
本章主要将Yarn当中详细的事件以及处理过程。
|
||||
|
||||
AsyncDispatcher是中央处理器的核心线程。通过使用AsyncDispatcher的对象可以分析Yarn里面有多少个中央处理器，每个处理器都由什么用途。
|
||||
|
||||
|
||||
|
||||
## Component dispatcher
|
||||
|
||||
当前的处理器注册了下面几个事件：
|
||||
|
||||
- ServiceEventHandler:
|
||||
- ComponentEventHandler:
|
||||
- ComponentInstanceEventHandler:
|
||||
|
||||
```java
|
||||
dispatcher.register(ServiceEventType.class, new ServiceEventHandler());
|
||||
dispatcher.register(ComponentEventType.class, new ComponentEventHandler());
|
||||
dispatcher.register(ComponentInstanceEventType.class,
|
||||
new ComponentInstanceEventHandler());
|
||||
```
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user