Hadoop之JobTrack分析（转）
1.client端指定Job的各种参数配置之后调用job.waitForCompletion(true) 方法提交Job给JobTracker，等待Job 完成。view plaincopyprint?
[*]public void submit() throws IOException, InterruptedException,
[*] ClassNotFoundException {
[*] ensureState(JobState.DEFINE);//检查JobState状态
[*] setUseNewAPI();//检查及设置是否使用新的MapReduce API
[*]
[*] // Connect to the JobTracker and submit the job
[*] connect();//链接JobTracker
[*] info = jobClient.submitJobInternal(conf);//将job信息提交
[*] super.setJobID(info.getID());
[*] state = JobState.RUNNING;//更改job状态
[*] }
以上代码主要有两步骤,连接JobTracker并提交Job信息。connect方法主要是实例化JobClient对象，包括设置JobConf和init工作：
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[*]public void init(JobConf conf) throws IOException {
[*] String tracker = conf.get("mapred.job.tracker", "local");//读取配置文件信息用于判断该Job是运行于本地单机模式还是分布式模式
[*] tasklogtimeout = conf.getInt(
[*] TASKLOG_PULL_TIMEOUT_KEY, DEFAULT_TASKLOG_TIMEOUT);
[*] this.ugi = UserGroupInformation.getCurrentUser();
[*] if ("local".equals(tracker)) {//如果是单机模式，new LocalJobRunner
[*] conf.setNumMapTasks(1);
[*] this.jobSubmitClient = new LocalJobRunner(conf);
[*] } else {
[*] this.jobSubmitClient = createRPCProxy(JobTracker.getAddress(conf), conf);
[*] }
[*] }
分布式模式下就会创建一个RPC代理链接：
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[*]public static VersionedProtocol getProxy(
[*] Class<? extends VersionedProtocol> protocol,
[*] long clientVersion, InetSocketAddress addr, UserGroupInformation ticket,
[*] Configuration conf, SocketFactory factory, int rpcTimeout) throws IOException {
[*]
[*] if (UserGroupInformation.isSecurityEnabled()) {
[*] SaslRpcServer.init(conf);
[*] }
[*] VersionedProtocol proxy =
[*] (VersionedProtocol) Proxy.newProxyInstance(
[*] protocol.getClassLoader(), new Class[] { protocol },
[*] new Invoker(protocol, addr, ticket, conf, factory, rpcTimeout));
[*] long serverVersion = proxy.getProtocolVersion(protocol.getName(),
[*] clientVersion);
[*] if (serverVersion == clientVersion) {
[*] return proxy;
[*] } else {
[*] throw new VersionMismatch(protocol.getName(), clientVersion,
[*] serverVersion);
[*] }
[*] }
从上述代码可以看出hadoop实际上使用了Java自带的Proxy API来实现Remote Procedure Call
初始完之后，需要提交job
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[*]info = jobClient.submitJobInternal(conf);//将job信息提交
submit方法做以下几件事情：
1.将conf中目录名字替换成hdfs代理的名字
2.检查output是否合法：比如路径是否已经存在，是否是明确的
3.将数据分成多个split并放到hdfs上面，写入job.xml文件
4.调用JobTracker的submitJob方法
该方法主要新建JobInProgress对象，然后检查访问权限和系统参数是否满足job，最后addJob：
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[*]private synchronized JobStatus addJob(JobID jobId, JobInProgress job)
[*] throws IOException {
[*] totalSubmissions++;
[*]
[*] synchronized (jobs) {
[*] synchronized (taskScheduler) {
[*] jobs.put(job.getProfile().getJobID(), job);
[*] for (JobInProgressListener listener : jobInProgressListeners) {
[*] listener.jobAdded(job);
[*] }
[*] }
[*] }
[*] myInstrumentation.submitJob(job.getJobConf(), jobId);
[*] job.getQueueMetrics().submitJob(job.getJobConf(), jobId);
[*]
[*] LOG.info("Job " + jobId + " added successfully for user '"
[*] + job.getJobConf().getUser() + "' to queue '"
[*] + job.getJobConf().getQueueName() + "'");
[*] AuditLogger.logSuccess(job.getUser(),
[*] Operation.SUBMIT_JOB.name(), jobId.toString());
[*] return job.getStatus();
[*] }
totalSubmissions记录client端提交job到JobTracker的次数。而jobs则是JobTracker所有可以管理的job的映射表
Map<JobID, JobInProgress> jobs = Collections.synchronizedMap(new TreeMap<JobID, JobInProgress>());
taskScheduler是用于调度job先后执行策略的，其类图如下所示：
hadoop job调度机制；
public enum SchedulingMode {
FAIR, FIFO
}
1.公平调度FairScheduler
对于每个用户而言，分布式资源是公平分配的，每个用户都有一个job池，假若某个用户目前所占有的资源很多，对于其他用户而言是不公平的，那么调度器就会杀掉占有资源多的用户的一些task，释放资源供他人使用
2.容量调度JobQueueTaskScheduler
在分布式系统上维护多个队列，每个队列都有一定的容量，每个队列中的job按照FIFO的策略进行调度。队列中可以包含队列。
两个Scheduler都要实现TaskScheduler的public synchronized List<Task> assignTasks(TaskTracker tracker)方法，该方法通过具体的计算生成可以分配的task
接下来看看JobTracker的工作：
记录更新JobTracker重试的次数：
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[*]while (true) {
[*] try {
[*] recoveryManager.updateRestartCount();
[*] break;
[*] } catch (IOException ioe) {
[*] LOG.warn("Failed to initialize recovery manager. ", ioe);
[*] // wait for some time
[*] Thread.sleep(FS_ACCESS_RETRY_PERIOD);
[*] LOG.warn("Retrying...");
[*] }
[*] }
启动Job调度器,默认是FairScheduler:
taskScheduler.start();主要是初始化一些管理对象，比如job pool管理池
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[*]// Initialize other pieces of the scheduler
[*] jobInitializer = new JobInitializer(conf, taskTrackerManager);
[*] taskTrackerManager.addJobInProgressListener(jobListener);
[*] poolMgr = new PoolManager(this);
[*] poolMgr.initialize();
[*] loadMgr = (LoadManager) ReflectionUtils.newInstance(
[*] conf.getClass("mapred.fairscheduler.loadmanager",
[*] CapBasedLoadManager.class, LoadManager.class), conf);
[*] loadMgr.setTaskTrackerManager(taskTrackerManager);
[*] loadMgr.setEventLog(eventLog);
[*] loadMgr.start();
[*] taskSelector = (TaskSelector) ReflectionUtils.newInstance(
[*] conf.getClass("mapred.fairscheduler.taskselector",
[*] DefaultTaskSelector.class, TaskSelector.class), conf);
[*] taskSelector.setTaskTrackerManager(taskTrackerManager);
[*] taskSelector.start();
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[*]JobInitializer有一个确定大小的ExecutorService threadPool，每个thread用于初始化job
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[*]try {
[*] JobStatus prevStatus = (JobStatus)job.getStatus().clone();
[*] LOG.info("Initializing " + job.getJobID());
[*] job.initTasks();
[*] // Inform the listeners if the job state has changed
[*] // Note : that the job will be in PREP state.
[*] JobStatus newStatus = (JobStatus)job.getStatus().clone();
[*] if (prevStatus.getRunState() != newStatus.getRunState()) {
[*] JobStatusChangeEvent event =
[*] new JobStatusChangeEvent(job, EventType.RUN_STATE_CHANGED, prevStatus,
[*] newStatus);
[*] synchronized (JobTracker.this) {
[*] updateJobInProgressListeners(event);
[*] }
[*] }
[*] }
初始化操作主要用于初始化生成tasks然后通知其他的监听者执行其他操作。initTasks主要处理以下工作：
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[*]// 记录用户提交的运行的job信息
[*] try {
[*] userUGI.doAs(new PrivilegedExceptionAction<Object>() {
[*] @Override
[*] public Object run() throws Exception {
[*] JobHistory.JobInfo.logSubmitted(getJobID(), conf, jobFile,
[*] startTimeFinal, hasRestarted());
[*] return null;
[*] }
[*] });
[*] } catch(InterruptedException ie) {
[*] throw new IOException(ie);
[*] }
[*]
[*] // 设置并记录job的优先级
[*] setPriority(this.priority);
[*]
[*] //
[*] //生成每个Task需要的密钥
[*] //
[*] generateAndStoreTokens();
[*]
然后读取JobTracker split的数据的元信息，元信息包括以下属性信息：
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[*]private TaskSplitIndex splitIndex;//洗牌后的索引位置
[*] private long inputDataLength;//洗牌后数据长度
[*] private String[] locations;//数据存储位置
然后根据元信息的长度来计算numMapTasks并校验数据存储地址是否可以连接
接下来生成map tasks和reducer tasks：
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[*]maps = new TaskInProgress;
[*] for(int i=0; i < numMapTasks; ++i) {
[*] inputLength += splits.getInputDataLength();
[*] maps = new TaskInProgress(jobId, jobFile,
[*] splits,
[*] jobtracker, conf, this, i, numSlotsPerMap);
[*] }
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[*]this.jobFile = jobFile;
[*] this.splitInfo = split;
[*] this.jobtracker = jobtracker;
[*] this.job = job;
[*] this.conf = conf;
[*] this.partition = partition;
[*] this.maxSkipRecords = SkipBadRecords.getMapperMaxSkipRecords(conf);
[*] this.numSlotsRequired = numSlotsRequired;
[*] setMaxTaskAttempts();
[*] init(jobid);
以上除了task对应的jobTracker，split信息和job信息外，还设置了
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[*]maxSkipRecords ---记录task执行的时候最大可以跳过的错误记录数；
[*]<pre name="code" class="java">setMaxTaskAttempts--设置task最多可以执行的次数。当一个task执行两次都失败了之后，会以skip mode模式再重新执行一次，记录那些bad record，
[*]然后第四次再执行的时候，跳过这些bad records</pre><p></p>
[*]<pre></pre>
[*]新建reducer task的过程也很类似。
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