我们会定义Job，我们会定义map和reduce程序。那么，这个Job到底是怎么提交的？提交到哪去了？它到底和集群怎么进行交互的呢？
这篇文章将从头讲起。
开发hadoop的程序时，一共有三大块，也就是Driver、map、reduce，在Driver中，我们要定义Configuration，定义Job，在mian方法最后，往往会以这么一段代码结尾：
if (!job.waitForCompletion(true)) return;
而这句的作用，就是提交了我们的Job。进入代码里（其实就是Job类）我们可以看到具体实现：
public boolean waitForCompletion(boolean verbose
) throws IOException, InterruptedException,
ClassNotFoundException {
if (state == JobState.DEFINE) {
//这句是重点，提交。那么从代码里看出这个似乎是异步提交啊，否则后面的监测怎么执行呢？我们拭目以待
submit();
}
if (verbose) {
monitorAndPrintJob();
} else {
// get the completion poll interval from the client.
//从配置里取得轮训的间隔时间，来分析当前job是否执行完毕
int completionPollIntervalMillis =
Job.getCompletionPollInterval(cluster.getConf());
while (!isComplete()) {
try {
Thread.sleep(completionPollIntervalMillis);
} catch (InterruptedException ie) {
}
}
}
return isSuccessful();
}
依然在Job.class里，这个方法主要动作有二，一是找到集群，二是讲Job提交到集群
public void submit()
throws IOException, InterruptedException, ClassNotFoundException {
ensureState(JobState.DEFINE);
setUseNewAPI();
//连接集群/master
connect();
//构造提交器
final JobSubmitter submitter =
getJobSubmitter(cluster.getFileSystem(), cluster.getClient());
status = ugi.doAs(new PrivilegedExceptionAction() {
public JobStatus run() throws IOException, InterruptedException,
ClassNotFoundException {
//提交
return submitter.submitJobInternal(Job.this, cluster);
}
});
state = JobState.RUNNING;
LOG.info("The url to track the job: " + getTrackingURL());
}
我们继续往下看，看下提交的时候都做了什么？
JobStatus submitJobInternal(Job job, Cluster cluster)
throws ClassNotFoundException, InterruptedException, IOException {
// 检查输出目录合法性（已存在？没指定？），这就是为什么每次提交作业，总是这个 错比较靠前的报出来
checkSpecs(job);
Configuration conf = job.getConfiguration();
// 将框架提交到集群缓存（具体左右还未知？）
addMRFrameworkToDistributedCache(conf);
// 获得登录区，用以存放作业执行过程中用到的文件，默认位置/tmp/hadoop-yarn/staging/root/.staging
// ，可通过yarn.app.mapreduce.am.staging-dir修改
Path jobStagingArea = JobSubmissionFiles.getStagingDir(cluster, conf);
// configure the command line options correctly on the submitting dfs
// 这是获取和设置提交job机器的地址和主机名
InetAddress ip = InetAddress.getLocalHost();
if (ip != null) {
submitHostAddress = ip.getHostAddress();
submitHostName = ip.getHostName();
conf.set(MRJobConfig.JOB_SUBMITHOST, submitHostName);
conf.set(MRJobConfig.JOB_SUBMITHOSTADDR, submitHostAddress);
}
// 取得当前Job的ID（后面详细关注此处）
JobID jobId = submitClient.getNewJobID();
job.setJobID(jobId);
// 作业提交目录
Path submitJobDir = new Path(jobStagingArea, jobId.toString());
JobStatus status = null;
try {
conf.set(MRJobConfig.USER_NAME, UserGroupInformation.getCurrentUser().getShortUserName());
conf.set("hadoop.http.filter.initializers",
"org.apache.hadoop.yarn.server.webproxy.amfilter.AmFilterInitializer");
conf.set(MRJobConfig.MAPREDUCE_JOB_DIR, submitJobDir.toString());
LOG.debug("Configuring job " + jobId + " with " + submitJobDir + " as the submit dir");
// get delegation token for the dir
TokenCache.obtainTokensForNamenodes(job.getCredentials(), new Path[] { submitJobDir }, conf);
populateTokenCache(conf, job.getCredentials());
// generate a secret to authenticate shuffle transfers
if (TokenCache.getShuffleSecretKey(job.getCredentials()) == null) {
KeyGenerator keyGen;
try {
int keyLen = CryptoUtils.isShuffleEncrypted(conf)
? conf.getInt(MRJobConfig.MR_ENCRYPTED_INTERMEDIATE_DATA_KEY_SIZE_BITS,
MRJobConfig.DEFAULT_MR_ENCRYPTED_INTERMEDIATE_DATA_KEY_SIZE_BITS)
: SHUFFLE_KEY_LENGTH;
keyGen = KeyGenerator.getInstance(SHUFFLE_KEYGEN_ALGORITHM);
keyGen.init(keyLen);
} catch (NoSuchAlgorithmException e) {
throw new IOException("Error generating shuffle secret key", e);
}
SecretKey shuffleKey = keyGen.generateKey();
TokenCache.setShuffleSecretKey(shuffleKey.getEncoded(), job.getCredentials());
}
// 从本地copy文件到hdfs，比如我们提交的wordcount.jar
copyAndConfigureFiles(job, submitJobDir);
Path submitJobFile = JobSubmissionFiles.getJobConfPath(submitJobDir);
// Create the splits for the job，其实也就是确定了map的数量
LOG.debug("Creating splits at " + jtFs.makeQualified(submitJobDir));
int maps = writeSplits(job, submitJobDir);
conf.setInt(MRJobConfig.NUM_MAPS, maps);
LOG.info("number of splits:" + maps);
// write "queue admins of the queue to which job is being submitted"
// to job file.
String queue = conf.get(MRJobConfig.QUEUE_NAME, JobConf.DEFAULT_QUEUE_NAME);
AccessControlList acl = submitClient.getQueueAdmins(queue);
conf.set(toFullPropertyName(queue, QueueACL.ADMINISTER_JOBS.getAclName()), acl.getAclString());
// removing jobtoken referrals before copying the jobconf to HDFS
// as the tasks don't need this setting, actually they may break
// because of it if present as the referral will point to a
// different job.
TokenCache.cleanUpTokenReferral(conf);
if (conf.getBoolean(MRJobConfig.JOB_TOKEN_TRACKING_IDS_ENABLED,
MRJobConfig.DEFAULT_JOB_TOKEN_TRACKING_IDS_ENABLED)) {
// Add HDFS tracking ids
ArrayList trackingIds = new ArrayList();
for (Token t : job.getCredentials().getAllTokens()) {
trackingIds.add(t.decodeIdentifier().getTrackingId());
}
conf.setStrings(MRJobConfig.JOB_TOKEN_TRACKING_IDS,
trackingIds.toArray(new String[trackingIds.size()]));
}
// Set reservation info if it exists
ReservationId reservationId = job.getReservationId();
if (reservationId != null) {
conf.set(MRJobConfig.RESERVATION_ID, reservationId.toString());
}
// Write job file to submit dir
writeConf(conf, submitJobFile);
//
// Now, actually submit the job (using the submit name)
//
printTokens(jobId, job.getCredentials());
//提交！！！！！！！！
status = submitClient.submitJob(jobId, submitJobDir.toString(), job.getCredentials());
if (status != null) {
return status;
} else {
throw new IOException("Could not launch job");
}
} finally {
if (status == null) {
LOG.info("Cleaning up the staging area " + submitJobDir);
if (jtFs != null && submitJobDir != null)
jtFs.delete(submitJobDir, true);
}
}
}
那么这个最终提交用到的submitClient是哪来的？他是怎么定义的？
它是上文提到的，连接集群的时候创建的。这个集群定义了很多信息：客户端信息、用户组信息、文件系统信息，配置信息，历史job目录，系统目录等。其中客户端信息，提供了初始化方法，如下：
public Cluster(InetSocketAddress jobTrackAddr, Configuration conf)
throws IOException {
this.conf = conf;
this.ugi = UserGroupInformation.getCurrentUser();
//初始化是重点
initialize(jobTrackAddr, conf);
}
具体看下初始化过程：
private void initialize(InetSocketAddress jobTrackAddr, Configuration conf)
throws IOException {
synchronized (frameworkLoader) {
for (ClientProtocolProvider provider : frameworkLoader) {
LOG.debug("Trying ClientProtocolProvider : "
+ provider.getClass().getName());
//根据配置，创建客户端协议提供者
ClientProtocol clientProtocol = null;
try {
if (jobTrackAddr == null) {
//提供者返回的是一个具体的协议
clientProtocol = provider.create(conf);
} else {
clientProtocol = provider.create(jobTrackAddr, conf);
}
if (clientProtocol != null) {
clientProtocolProvider = provider;
//看到没？协议是什么？协议其实就是个类，里面封装了一些约定好的属性，以及操作这些属性的方法。实例化为对象后，就是一个可用于通信的客户端
client = clientProtocol;
LOG.debug("Picked " + provider.getClass().getName()
+ " as the ClientProtocolProvider");
break;
}
else {
LOG.debug("Cannot pick " + provider.getClass().getName()
+ " as the ClientProtocolProvider - returned null protocol");
}
}
catch (Exception e) {
LOG.info("Failed to use " + provider.getClass().getName()
+ " due to error: " + e.getMessage());
}
}
}
if (null == clientProtocolProvider || null == client) {
throw new IOException(
"Cannot initialize Cluster. Please check your configuration for "
+ MRConfig.FRAMEWORK_NAME
+ " and the correspond server addresses.");
}
}
创建客户端协议提供者，用java.util.ServiceLoader,目前包含两个具体实现，LocalClientProtocolProvider（本地作业） YarnClientProtocolProvider（Yarn作业），此处会根据mapreduce.framework.name的配置选择使用哪个创建相应的客户端。
而YarnClientProtocolProvider的本质是创建了一个YarnRunner对象
public ClientProtocol create(Configuration conf) throws IOException {
if (MRConfig.YARN_FRAMEWORK_NAME.equals(conf.get(MRConfig.FRAMEWORK_NAME))) {
return new YARNRunner(conf);
}
return null;
}
YarnRunner对象是干什么的？根据注释解释，是让当前JobClient在yarn上运行的。提供一些提交Job啊，杀死Job之类的方法。它实现了ClientProtocol接口，上面讲的提交的最后一步，其实最终就是调用了YarnRunner的submitJob方法。
它里面封装了ResourceMgrDelegate委托，委托的方法正是YarnClient类里的提交方法submitApplication。这样，当前作业（Application）提交过程，走到了YarnClient阶段。
总结：Job目前提交到了YarnClient实例中。那么YarnClient接下来怎么处理呢？