MAPREDUCE in Chinese translation

• mapreduce

Examples of using Mapreduce in English and their translations into Chinese

Bottom line: Spark is the Swiss army knife of data processing, while Hadoop MapReduce is the commando knife of batch processing.
小结：Spark是数据处理的瑞士军刀;HadoopMapReduce是批处理的突击刀。

Each reduce instance can write records to an output file, which forms part of the“answer” to a MapReduce computation.
每一个reduce实例可以将records写到输出文件中，组成MapReduce计算的"answer"的一部分。

This is an area of ongoing research; algorithms that combine and extend MapReduce and Hadoop have been proposed and studied.
这是一个正在进行的研究领域；结合和扩展MapReduce和Hadoop的算法已经被提出和研究。

Seeing that MapReduce is about permanent storage, it stores data on disk, which means it can handle large datasets.
因为MapReduce是关于永久存储的，它将数据存储在磁盘上，这意味着它能解决大型数据集。

Like MapReduce, the River system tries to provide good average case performance even in the presence of non-uniformities introduced by heterogeneous hardware or system perturbations.
像MapReduce一样，River尽量提高系统的平均性能，即使是由于硬件异构或者系统扰动出现了非对称的情况。

As you can see, using XML and JSON in MapReduce is very bad and has strict requirements on how to lay out data.
正如你所看到的，在MapReduce中使用XML和JSON是非常糟糕的，并且对如何布局数据有严格要求。

MapReduce is a programming model and the best way to understand this is to note that Map and Reduce are two separate items.
MapReduceMapReduce是一个编程模型，最好的理解就是要注意到Map和Reduce是两个不同的过程。

MapReduce cannot use indices and implies a full scan of all input data;
LMapReduce不能使用索引，同时意味着对所有输入数据的完全扫描.

In the case of MapReduce, you would need to store the results of each of these three phases on disk(HDFS).
在MapReduce的情况下，您需要将这三个阶段的每个阶段的结果存储在磁盘（HDFS）上。

MapReduce: A distributed data processing model and execution environment that runs on large clusters of commodity machines.
MapReduce:分布式数据处理模型和执行环境，运行于大型商用机集群。

Processing a single XML file in parallel in MapReduce is tricky because XML does not contain synchronization tokens for its data format.
在MapReduce中并行处理单个XML文件很棘手，因为XML不包含其数据格式的同步标记。

The framework is designed in a way that a MapReduce cluster can scale to thousands of nodes in a faulttolerant manner.
该框架被设计为：一个MapReduce集群可以通过容错的方式扩展到上千个节点。

Just as the enterprise is locking into MapReduce, Google seems to be moving past it.
正如企业正在锁定MapReduce一样，谷歌似乎正在舍弃它而大步向前。

There is MapReduce, which is where the actual processing of data takes place.
最后一个方法是GetCell，这里就是数据绑定实际发生的地方。

Naturally, we can write a program in MapReduce to compute this output.
当然没问题，我们可以写一个MapReduce程序来计算得到这个输出。

This allows the built-in mapreduce text-based input format(for example, Textinputformat), which treats each row as a record and split.
这允许使用内置的MapReduce基于文本的输入格式(例如TextInputFormat)，它将每一行视为记录并拆分。

MapReduce: Simplified Data Processing on Large Clusters MapReduce is a programming model and an associated implementation for processing and generating large data sets.
MapReduce:SimplifiedDataProcessingonLargeClustersMapReduce既是一种编程模型，也是一种与之关联的、用于处理和产生大数据集的实现。

I think the computational model can be as general as MapReduce or other distributed processing frameworks, but with the ability to produce low-latency results.
我认为流计算处理的计算模型可以是如同MapReduce那样的分布式处理框架一样的通用，只要它能提供低延迟的结果就可以。

MapReduce, and Hadoop in particular, offers a powerful means of distributing computation among commodity servers.
MapReduce尤其是Hadoop实现提供了在普通服务器上进行分布式计算的强有力的方式。

And the use of MapReduce or the more recent Spark is almost a given since they bring speed and agility to the Hadoop platform.
并且使用Map/Reduce或更新近的Spark几乎是给定的，因为它们为Hadoop平台带来了速度和灵活性。