/**
* <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
*/