这篇文章主要介绍“Hadoo是怎么将作业提交给集群的”，在日常操作中，相信很多人在Hadoo是怎么将作业提交给集群的问题上存在疑惑，小编查阅了各式资料，整理出简单好用的操作方法，希望对大家解答”Hadoo是怎么将作业提交给集群的”的疑惑有所帮助！接下来，请跟着小编一起来学习吧！
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通过图可知主要有三个部分，即： 1) JobClient:作业客户端。 2) JobTracker:作业的跟踪器。 3) TaskTracker:任务的跟踪器。
MapReduce将作业提交给JobClient，然后JobClient与JobTracker交互，JobTracker再去监控与分配TaskTracker，完成具体作业的处理。
以下分析的是Hadoop2.6.4的源码。请注意： 源码与之前Hadoop版本的略有差别，所以有些概念还是与上图有点差别。
**job.waitForCompletion(true)**
跟踪waitForCompletion， 注意其中的submit()，如下：
/**
* Submit the job to the cluster and wait for it to finish.
*/
public boolean waitForCompletion(boolean verbose
) throws IOException, InterruptedException,
ClassNotFoundException {
if (state == JobState.DEFINE) {
submit();
}
if (verbose) {
monitorAndPrintJob();
} else {
// get the completion poll interval from the client.
int completionPollIntervalMillis =
Job.getCompletionPollInterval(cluster.getConf());
while (!isComplete()) {
try {
Thread.sleep(completionPollIntervalMillis);
} catch (InterruptedException ie) {
}
}
}
return isSuccessful();
}参数 verbose ，如果想在控制台打印当前的任务执行进度，则设为true
**
** 在submit 方法中会把Job提交给对应的Cluster，然后不等待Job执行结束就立刻返回
同时会把Job实例的状态设置为JobState.RUNNING，从而来表示Job正在进行中
然后在Job运行过程中，可以调用getJobState()来获取Job的运行状态
/**
* Submit the job to the cluster and return immediately.
*/
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() {
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());
} 而在任务提交前，会先通过connect()方法链接集群(Cluster):
private synchronized void connect()
throws IOException, InterruptedException, ClassNotFoundException {
if (cluster == null) {
cluster =
ugi.doAs(new PrivilegedExceptionAction() {
public Cluster run()
throws IOException, InterruptedException,
ClassNotFoundException {
return new Cluster(getConfiguration());
}
});
}
} 这是一个线程保护方法。这个方法中根据配置信息初始化了一个Cluster对象，即代表集群
public Cluster(Configuration conf) throws IOException {
this(null, conf);
}
public Cluster(InetSocketAddress jobTrackAddr, Configuration conf)
throws IOException {
this.conf = conf;
this.ugi = UserGroupInformation.getCurrentUser();
initialize(jobTrackAddr, conf);
}
private void initialize(InetSocketAddress jobTrackAddr, Configuration conf)
throws IOException {
synchronized (frameworkLoader) {
for (ClientProtocolProvider provider : frameworkLoader) {
LOG.debug("Trying ClientProtocolProvider : "
+ provider.getClass().getName());
ClientProtocol clientProtocol = null;
try {
if (jobTrackAddr == null) {
clientProtocol = provider.create(conf);
} else {
clientProtocol = provider.create(jobTrackAddr, conf);
}
if (clientProtocol != null) {
clientProtocolProvider = provider;
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.");
}
}而在上段代码之前，
private static ServiceLoaderframeworkLoader = ServiceLoader.load(ClientProtocolProvider.class);
可以看出创建客户端代理阶段使用了java.util.ServiceLoader，包含LocalClientProtocolProvider（本地作业）和YarnClientProtocolProvider（yarn作业）(hadoop有一个Yarn参数mapreduce.framework.name用来控制你选择的应用框架。在MRv2里，mapreduce.framework.name有两个值：local和yarn)，此处会根据mapreduce.framework.name的配置创建相应的客户端
mapred-site.xml：
mapreduce.framework.name yarn
submitter.submitJobInternal(Job.this, cluster);
JobStatus submitJobInternal(Job job, Cluster cluster)
throws ClassNotFoundException, InterruptedException, IOException {
//validate the jobs output specs
checkSpecs(job);
Configuration conf = job.getConfiguration();
addMRFrameworkToDistributedCache(conf);
Path jobStagingArea = JobSubmissionFiles.getStagingDir(cluster, conf);
//configure the command line options correctly on the submitting dfs
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);
}
JobID jobId = submitClient.getNewJobID();
job.setJobID(jobId);
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());
}
copyAndConfigureFiles(job, submitJobDir);
Path submitJobFile = JobSubmissionFiles.getJobConfPath(submitJobDir);
// Create the splits for the job
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());
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);
}
}
} 通过如下代码正式提交Job到Yarn：
status = submitClient.submitJob( jobId, submitJobDir.toString(), job.getCredentials());
到最后，通过RPC的调用，最终会返回一个JobStatus对象，它的toString方法可以在JobClient端打印运行的相关日志信息。
if (status != null) {
return status;
}public String toString() {
StringBuffer buffer = new StringBuffer();
buffer.append("job-id : " + jobid);
buffer.append("uber-mode : " + isUber);
buffer.append("map-progress : " + mapProgress);
buffer.append("reduce-progress : " + reduceProgress);
buffer.append("cleanup-progress : " + cleanupProgress);
buffer.append("setup-progress : " + setupProgress);
buffer.append("runstate : " + runState);
buffer.append("start-time : " + startTime);
buffer.append("user-name : " + user);
buffer.append("priority : " + priority);
buffer.append("scheduling-info : " + schedulingInfo);
buffer.append("num-used-slots" + numUsedSlots);
buffer.append("num-reserved-slots" + numReservedSlots);
buffer.append("used-mem" + usedMem);
buffer.append("reserved-mem" + reservedMem);
buffer.append("needed-mem" + neededMem);
return buffer.toString();
}（到这里任务都给yarn了，这里就只剩下监控（如果设置为true的话）），即：
if (verbose) {
monitorAndPrintJob();
}这只是完成了作业Job的提交。
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