mapreduce

For this article , the MapReduce algorithm was implemented on a system using

在本文中，要实现MapReduce算法，系统应装有以下软件

In this example , you see the MapReduce process on a small set of data .

在这个示例中，我们使用MapReduce处理一个小数据集。

We 're providing three distinct access routes to Elastic MapReduce .

我们提供了三种不同的访问ElaastcMapReduce的途径。

Processing in Elastic MapReduce is centered around the concept of a Job Flow .

ElasticMapReduce的处理是围绕着任务流这一概念为中心来开展的。

A MapReduce application or a web crawler application fits perfectly with this model .

典型的如MapReduce框架，或者一个webcrawler应用都很适合这个模型。

The MapReduce programming mode was developed at Google .

MapReduce编程模式是在Google开发出来的。

Next , I make the mapReduce method call on the retweets collection .

下一步，我在retweets集合上制定mapReduce方法调用。

Main function configures the MapReduce job and runs it .

Main函数配置并运行MapReduce作业。

The third parameter is the name of the object that holds the results of MapReduce .

第三个参数是持有MapReduce结果的对象的名称。

The combination of cloud computing and MapReduce seems tailored for big data jobs .

云计算和MapReduce的组合看起来非常适合处理BigData作业。

In a cloud environment , the MapReduce structure increases the efficiency of throughput for large data sets .

在云环境中，MapReduce结构提高了大型数据集的吞吐效率。

MapReduce programs are designed to compute large volumes of data in a parallel fashion .

MapReduce程序用于以并行方式计算大量数据。

Our experiment with Hadoop MapReduce and load balancing lead to two inescapable conclusions

我们的HadoopMapReduce和负载平衡的实验可以得到两个必然结论

Google introduced the idea of MapReduce as a programming model for processing or generating large sets of data .

Google引用MapReduce的概念作为处理或生成大型数据集的编程模型。

Remember , views are just MapReduce functions in action ; thus , you must define them .

记住，视图就是实际的MapReduce函数；因此，您必须定义它们。

Check out this list of Mapreduce and Hadoop algorithms in academic papers .

检查学术文章中的Mapreduce和Hadoop算法的列表。

Next in the MapReduce life cycle : All of the intermediate results get boiled down and summarized .

在MapReduce生命周期中，下一步是浓缩和汇总所有中间结果。

MapReduce is about the simplest way you can break down a big job into little pieces .

MapReduce是把大任务分解为小任务的最简单的方法。

MapReduce is intended for large clusters of systems that can work in parallel on a large dataset .

MapReduce（）的目的是为了大型集群的系统能在大数据集上进行并行工作。

Hadoop is an open source distributed computing platform that includes implementations of MapReduce and a distributed file system .

Hadoop是一个开源的分布式计算平台，它主要由MapReduce的算法执行和一个分布式的文件系统等两部分组成。

Instead of traditional file processing , HBase makes database tables the input and output form for MapReduce processing .

代替传统的文件处理，HBase使数据库将MapReduce处理的输入和输出格式列表。

Next , request the MapReduce job for grep .

接下来，请求用于执行grep的MapReduce作业。

The scenario is one where a startup company wishes to offer a MapReduce service to their clients .

这个情景是关于一个新起步的公司，他们希望为他们的客户提供MapReduce服务。

Many NoSQL stores also provide MapReduce capabilities , allowing for efficient access to predefined queries .

许多NoSQL存储还提供MapReduce功能，以便允许有效的访问预定义的查询。

The advantage of MapReduce is that it allows for the distributed processing of the map and reduction operations .

MapReduce的优点是它允许对映射和缩减操作进行分布式处理。

It 's an interesting read to explore how the MapReduce model can apply to a variety of computational algorithms .

这是一个有趣的阅读用来探索MapReduce模型如何应用到各种不同的可计算算法。

Real world implementations of MapReduce would normally assign controllers , map (), and reduce () tasks to a single system .

显示情况下MapReduce的实现可能通常分配给控制者，map（）和reduce（）任务分配给单独的系统。

There is no shortage of cloud-based MapReduce options available both as open source and commercial offerings .

基于云的MapReduce系统既有开放源码的，也有商用产品。

The Google operational model is based on deploying MapReduce applications across large clusters of commodity systems , or white boxes .

Google操作模型是基于跨越大量的廉价硬件设备上组成的集群或者白盒子上面部署MapReduce应用。

The processing API lets the developer quickly assemble complex distributed processes without having to " think " in MapReduce .

该处理API使开发者可以快速装配复杂的分布式流程，而无需“考虑”MapReduce。