Examples of using Map task in English and their translations into Chinese
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For each input file, a map task is spawned.
每个输入分片交给一个MapTask处理.Map tasks write their output to the local disk, not to HDFS.
Map任务将其输出写入本地硬盘，而非HDFS。This task is always followed by Map Task. .
Reduce任务总是在map任务之后执行。Map tasks write their output to the local disk, not to HDFS.
Map任务将其输出写入到本地磁盘，而非HDFS。The reduce task always comes after map task.
Reduce任务总是在map任务之后执行。Which is specific to a map task or a reduce task. .
In a push model,failure of a reducer would force re-execution of all Map tasks.
在一个推模式中，一个reducer的失败将会导致所有Maptasks的重新执行。That is, reduce tasks can begin as soon as any map task completes.
ReduceTask只能在所有MapTask完成之后才能启动。A worker who is assigned a map task reads the contents of the corresponding input split.
分配了maptask的一个worker读取相应输入划分的内容。None of the Reduce tasks can start before all the Map tasks are done.
ReduceTask只能在所有MapTask完成之后才能启动。The output of the map tasks, called the intermediate keys and values, are sent to the reducers.
Map任务的输出叫中间数据，包括keys和values，发送到reduce端。The master picks idle workers and assigns them either a map task or a reduce task. .
Master选择空闲的worker并且分配这些map任务或者Reduce任务。Obviously, the goal of drawing a map task is to achieve the accuracy of the map as much as possible。
很显然，绘制地图任务的目标是尽可能实现地图的精确性。If the master receives a completion message for an already completed map task, it ignores the message.
如果master接收到了来自一个已完成的maptask的完成消息，它将忽略这条消息。When all map tasks and reduce tasks have been completed, the master wakes up the user program.
当所有的maptasks和reducetasks都完成后，master唤醒用户程序。The master picks idle workers and assigns to each one a map task or a reduce task. .
Master挑选闲置的workers且为每个worker分配一个maptask或reducetask。Each map task in Hadoop is broken into the following phases: record reader, mapper, combiner, and partitioner.
每个map任务被分为以下阶段：recordreader，mapper，combiner，partitioner。The middle-left graph shows therate at which data is sent over the network from the map tasks to the reduce tasks. .
左边中间的图显示了中间数据从Map任务发送到Reduce任务的网络速度。The map task that operates on this input split performs a search for the input split to determine the start of the next record.
对此输入拆分进行操作的map任务将执行对输入拆分的搜索，以确定下一条记录的开始。A reduce task produces one such file, and a map task produces such files(one per reduce task). .
一个reducetask生成一个这样的文件，maptask生成R个这样的文件（每个reducetask一个）。A map task can run on any compute node in the cluster and multiple map tasks can run in parallel across the cluster.
一个Map任务可以在集群的任何计算节点上运行，多个Map任务可以并行地运行在集群上。A reduce task produces one such file, and a map task produces such files(one per reduce task). .
每个Reduce任务生成一个这样的文件，而每个Map任务则生成R个这样的文件（一个Reduce任务对应一个文件）。When a map task completes, the worker sends a message to the master and includes the names of the R temporary files in the message.
当一个Map任务完成时，MapWorker发送一个包含R个临时文件名的完成消息给Master。Apache MapReduce job usually splits the inputdata-set into independent chunks which are processed by the map tasks in a completely parallel manner.
MapReduce作业通常将输入数据集拆分为独立的块，这些任务由地图任务以完全并行的方式进行处理。This is because the sort map tasks spend about half their time and I/O bandwidth writing intermediate output to their local disks.
这是因为排序程序的Map任务花了大约一半的处理时间和I/O带宽把中间输出结果写到本地硬盘。Since word frequencies tend to follow a Zipf distribution, each map task will produce hundreds or thousands of records of the form<the, 1>
由于词频率倾向于一个zipf分布(齐夫分布)，每个Map任务将产生成千上万个这样的记录<the,1>。Similarly, any map task or reduce task in progress on a failed worker is also reset to_idle_ and becomes eligible for rescheduling.
同样，在故障worker上的任何正在进行的maptask和reducetask也被重置为闲置状态以便进行重新调度。Therefore, for each completed map task, the master stores the locations and sizes of the intermediate file regions produced by the map task. .
因此，对于每个已经完成的Map任务，master存储了Map任务产生的R个中间文件存储区域的大小和位置。Therefore, for each completed map task, the master stores the locations and sizes of the intermediate file regions produced by the map task.
因此，对于每个完成了的maptask，master存储由这个maptask生成的R个中间文件区域的位置和大小。
