客户端MapReduce提交到YARN过程(一)

时间:2022-09-17 08:34:20
在Mapreduce v1中是使用JobClient来和JobTracker交互完成Job的提交,用户先创建一个Job,通过JobConf设置好参数,通过JobClient提交并监控Job的进展,在JobClient中有一个内部成员变量JobSubmissionProtocol,JobTracker实现了该接口,通过该协议客户端和JobTracker通信完成作业的提交
  public void init(JobConf conf) throws IOException {
String tracker = conf.get("mapred.job.tracker", "local");
tasklogtimeout = conf.getInt(
TASKLOG_PULL_TIMEOUT_KEY, DEFAULT_TASKLOG_TIMEOUT);
this.ugi = UserGroupInformation.getCurrentUser();
//如果mapred.job.tracker设置成local,则创建本地LocalJobRunner,否则创建RPC代理
if ("local".equals(tracker)) {
conf.setNumMapTasks(1);
this.jobSubmitClient = new LocalJobRunner(conf);
} else {
this.jobSubmitClient = createRPCProxy(JobTracker.getAddress(conf), conf);
}
}
按顺序调用: 
Job.waitForCompletion()
Job.submit()
jobClient.submitJobInternal()
jobSubmitClient.submitJob(jobId, submitJobDir.toString(), jobCopy.getCredentials()) 
完成作业提交

而YARN的作业提交procotol是ClientRMProtocol 提交MRv2作业时,首先会生成集群信息类cluster,里面有一个frameworkLoader内部变量会从配置文件中加载ClientProtocolProvider的实现类,这里​分别是LocalClientProtocolProvider和​YarnClientProtocolProvider​。Cluster类在initialize中,会遍历frameworkLoader,由ClientProtocolProvider来生成具体的ClientProtocol​,比如在YarnClientProtocolProvider中就会判断JobConf中的​mapreduce.framework.name是否为​yarn,如果是的话则会生成YARNRunner
YarnClientProtocolProvider的create方法:
  @Override
public ClientProtocol create(Configuration conf) throws IOException {
if (MRConfig.YARN_FRAMEWORK_NAME.equals(conf.get(MRConfig.FRAMEWORK_NAME))) {
return new YARNRunner(conf);
}
return null;
}

ClientProtocol目前有两个实现​YARNRunner​和LocalJobRunner,LocalJobRunner(mapreduce.framework.name为local​)主要是在本地执行mapreduce,可以方便对程序进行调试。YARNRunner是将作业提交到YARN上​​。
YARNRunner初始化会和ResourceManager建立RPC链接(默认是8032端口 ),真正和RM通信的协议是 ClientRMProtocol ​,客户端和RM交互的所有操作都会通过YARNRunner的成员变量 rmClient( ​ClientRMProtocol ​)提交出去,比如killApplication, getNodeReports, getJobCounters等等
  public synchronized void start() {
YarnRPC rpc = YarnRPC.create(getConfig());
this.rmClient = (ClientRMProtocol) rpc.getProxy(
ClientRMProtocol.class, rmAddress, getConfig());
if (LOG.isDebugEnabled()) {
LOG.debug("Connecting to ResourceManager at " + rmAddress);
}
super.start();
}

Cluster类初始化完成后,就要生成Application了,先和RM通信申请一个Application(getNewApplication​),得到一个GetNewApplicationResponse,里面封装了ApplicationID,和RM能提供的最小、最大Resource Capacity
public interface GetNewApplicationResponse {
public abstract ApplicationId getApplicationId();
public Resource getMinimumResourceCapability();
public Resource getMaximumResourceCapability();
public void setMaximumResourceCapability(Resource capability);
}
Resource定义了一组集群计算资源,目前只把memory和cpu纳入进来,这边的cpu指virtual core,也就是一个物理core可以被认为抽象成多个virtual core,而非一对一对应关系
public abstract class Resource implements Comparable<Resource> {
public abstract int getMemory();
public abstract void setMemory(int memory);
public abstract int getVirtualCores();
public abstract void setVirtualCores(int vCores);
}

然后需要构造ApplicationSubmissionContext,其中包含了启动MR AM的信息,​比如提交的job在HDFS的staging目录路径(job.xml, ​job.split, job.splitmetainfo, libjars, files, archives等​),用户ugi信息,Secure Tokens。完成context构造后,调用resMgrDelegate.submitApplication(appContext)​
YARNRunner的submitJob方法:
  @Override
public JobStatus submitJob(JobID jobId, String jobSubmitDir, Credentials ts)
throws IOException, InterruptedException {
// Construct necessary information to start the MR AM
ApplicationSubmissionContext appContext =
createApplicationSubmissionContext(conf, jobSubmitDir, ts);

// Submit to ResourceManager
ApplicationId applicationId = resMgrDelegate.submitApplication(appContext);

ApplicationReport appMaster = resMgrDelegate.getApplicationReport(applicationId);
String diagnostics = (appMaster == null ?
"application report is null" : appMaster.getDiagnostics());
if (appMaster == null || appMaster.getYarnApplicationState() == YarnApplicationState.FAILED
|| appMaster.getYarnApplicationState() == YarnApplicationState.KILLED) {
throw new IOException("Failed to run job : " +
diagnostics);
}
return clientCache.getClient(jobId).getJobStatus(jobId);
}

最后通过getJobStatus方法获得Job状态信息
    org.apache.hadoop.mapreduce.v2.api.records.JobId jobId =
TypeConverter.toYarn(oldJobID);
GetJobReportRequest request =
recordFactory.newRecordInstance(GetJobReportRequest.class);
request.setJobId(jobId);
JobReport report = ((GetJobReportResponse) invoke("getJobReport",
GetJobReportRequest.class, request)).getJobReport();