MapReduce

MapReduce是Google提出的一个软件架构，用于大规模数据集（大于1TB）的并行运算。概念“Map（映射）”和“Reduce（归纳）”，及他们的主要思想，都是从函数式编程语言借来的，还有从矢量编程语言借来的特性。[1] 当前的软件实现是指定一个Map（映射）函数，用来把一组键值对映射成一组新的键值对，指定并发的Reduce（归纳）函数，用来保证所有映射的键值对中的每一个共享相同的键组。

The model is inspired by the map and reduce functions commonly used in functional programming, although their purpose in the MapReduce framework is not the same as in their original forms. The key contributions of the MapReduce framework are not the actual map and reduce functions (which, for example, resemble the 1995 Message Passing Interface standard's reduce and scatter operations), but the scalability and fault-tolerance achieved for a variety of applications by optimizing the execution engine. As such, a single-threaded implementation of MapReduce will usually not be faster than a traditional (non-MapReduce) implementation; any gains are usually only seen with multi-threaded implementations. The use of this model is beneficial only when the optimized distributed shuffle operation (which reduces network communication cost) and fault tolerance features of the MapReduce framework come into play. Optimizing the communication cost is essential to a good MapReduce algorithm.

MapReduce libraries have been written in many programming languages, with different levels of optimization. A popular open-source implementation that has support for distributed shuffles is part of Apache Hadoop. The name MapReduce originally referred to the proprietary Google technology, but has since been genericized. By 2014, Google was no longer using MapReduce as their primary Big Data processing model,[10] and development on Apache Mahout had moved on to more capable and less disk-oriented mechanisms that incorporated full map and reduce capabilities.