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:
代码语言:javascript复制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:
代码语言:javascript复制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。
代码语言:javascript复制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:
// 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:
代码语言:javascript复制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:
代码语言:javascript复制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
代码语言:javascript复制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。
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。
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。
代码语言:javascript复制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。
代码语言:javascript复制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,
代码语言:javascript复制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:
代码语言:javascript复制job.logJobHistoryFinishedEvent();
job.finished(JobStateInternal.SUCCEEDED);