JobClient

/**
 * <code>JobClient</code> is the primary interface for the user-job to interact
 * with the {@link JobTracker}.
 * 翻译：JobClient是用户的作业与JobTracker进行交互的最基本接口
 * <code>JobClient</code> provides facilities to submit jobs, track their
 * progress, access component-tasks' reports/logs, get the Map-Reduce cluster
 * status information etc.
 * 翻译：JobClient提供提交作业的工具，追踪作业的进度，获取component-tasks（合成任务）的日志，获取Map-Reduce集群状态信息等等。
 * <p>The job submission process involves:翻译：作业提交过程包括如下
 * <ol>
 *   <li>
 *   Checking the input and output specifications of the job.翻译：检测作业的输入和输入描述
 *   </li>
 *   <li>
 *   Computing the {@link InputSplit}s for the job.翻译：计算作业的InputSplit
 *   </li>
 *   <li>
 *   Setup the requisite accounting information for the {@link DistributedCache}
 *   of the job, if necessary.

*   翻译：如果有必要的话，为作业的DistributedCache设置必要的accounting information
 *   </li>
 *   <li>
 *   Copying the job's jar and configuration to the map-reduce system directory
 *   on the distributed file-system.

*   翻译：拷贝作业的jar文件和配置文件到分布式文件系统里的map-reduce系统目录
 *   </li>
 *   <li>
 *   Submitting the job to the <code>JobTracker</code> and optionally monitoring
 *   it's status.

*  翻译：提交作业到JobTracker，并选择性的监控它的状态
 *   </li>
 * </ol></p>
 *  
 * Normally the user creates the application, describes various facets of the
 * job via {@link JobConf} and then uses the <code>JobClient</code> to submit
 * the job and monitor its progress.
 * 翻译：通常用户创建应用程序，通过JobConf来描述作业的各个方面，并且用JobClient来提交作业，并监视它的进度
 * <p>Here is an example on how to use <code>JobClient</code>:</p>翻译：这里有一个例子，教你如何使用JobClient
 * <p><blockquote><pre>
 *     // Create a new JobConf 翻译：创建一个JobConf对象
 *     JobConf job = new JobConf(new Configuration(), MyJob.class);
 *     
 *     // Specify various job-specific parameters   翻译：指定各种各样的和作业有关的具体参数
 *     job.setJobName("myjob");
 *     
 *     job.setInputPath(new Path("in"));
 *     job.setOutputPath(new Path("out"));
 *     
 *     job.setMapperClass(MyJob.MyMapper.class);
 *     job.setReducerClass(MyJob.MyReducer.class);
 *
 *     // Submit the job, then poll for progress until the job is complete翻译：提交作业，不停的询问进度，知道作业完成
 *     JobClient.runJob(job);
 * </pre></blockquote></p>
 *
 * <h4 id="JobControl">Job Control</h4>
 *
 * <p>At times clients would chain map-reduce jobs to accomplish complex tasks
 * which cannot be done via a single map-reduce job. This is fairly easy since
 * the output of the job, typically, goes to distributed file-system and that
 * can be used as the input for the next job.</p>
 * 翻译：有时，clients会把许多的map-reduce作业“链”在一起，取完成一些复杂的任务，这些作业是不能通过一个单一的map-reduce作业来完成的。

　　　　这是非常容易实现的，因为作业的输出通常是在分布式文件系统，所以这些在分布式文件系统的输出可以用作下一个作业的输入。
 * <p>However, this also means that the onus on ensuring jobs are complete
 * (success/failure) lies squarely on the clients. In such situations the
 * various job-control options are:

* 然而，这也意味着，确保作业成功或者失败的重任直接就落在了clients上。在这种情况下，job-control选项如下：
 * <ol>
 *   <li>
 *   {@link #runJob(JobConf)} : submits the job and returns only after
 *   the job has completed.翻译：提交作业，并且只有在作业完成之后返回。
 *   </li>
 *   <li>
 *   {@link #submitJob(JobConf)} : only submits the job, then poll the
 *   returned handle to the {@link RunningJob} to query status and make
 *   scheduling decisions.

*   翻译：仅提交作业，此时，通过RunningJob（<p>Clients can get hold of <code>RunningJob</code> via the {@link JobClient}
 * and then query the running-job for details such as name, configuration,
 * progress etc.</p> ）不停的请求句柄，来查询状态和调度决策
 *   </li>
 *   <li>
 *   {@link JobConf#setJobEndNotificationURI(String)} : setup a notification
 *   on job-completion, thus avoiding polling.

*   翻译：设置一个作业完成通知，因此就可以避免不停的询问进度
 *   </li>
 * </ol></p>
 *
 * @see JobConf
 * @see ClusterStatus
 * @see Tool
 * @see DistributedCache
 */