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本文以 MapReduce 为基础，实现了一套基于浏览器实现的分布式系统。加之如今 Chrome 对各个平台近乎完美的兼容性，实现了一次编写，处处运行的目标。同时得力于个人移动设备的普及，手机，平板，甚至是家用游戏机，智能电视。如果急需性能，还可以通过朋友圈的方式，号召朋友们使用自己的设备，在后台开启几个标签的方式，成为计算节点，加快整体计算速度。
一、摘要
以 2003 年，Google 发表的三篇论文为标志的大数据时代，至今已过去近二十年时间，MapReduce 那篇论文虽然只有理论，并为公开底层软件实现。但这么多年过去，Hadoop，Spark 等框架早已实现论文中所描述的功能，甚至还有所改进。
本文以 MapReduce 为基础，实现了一套基于浏览器实现的分布式系统。加之如今 Chrome 对各个平台近乎完美的兼容性，实现了一次编写，处处运行的目标。同时得力于个人移动设备的普及，手机，平板，甚至是家用游戏机，智能电视。如果急需性能，还可以通过朋友圈的方式，号召朋友们使用自己的设备，在后台开启几个标签的方式，成为计算节点，加快整体计算速度。
在 BMR 系统下，用户甚至不需要学习 C++，Java 等传统分布式计算用到的语言；只需要会简单的 JS，即可完成分布式计算任务的开发，开发成本极低。本文对 BMR 系统的设计，以及实现时做的取舍做了详细说明，对分布式计算平台的研究具有一定的指导意义。
关键词：MapReduce、分布式计算、高性能
MapReduce-based Distributed Computing System
二、Abstract
Intelligent In 2003, Google published three papers as a sign of the era of big data, nearly two decades have passed, MapReduce that paper, although only the theory, and for the public underlying software implementation. However, after so many years, Hadoop, Spark and other frameworks have already achieved the functions described in the paper, and even improved them.
This paper implements a browser-based distributed system based on MapReduce. With the near-perfect compatibility of Chrome with all platforms today, the goal of writing once and running everywhere is achieved. And thanks to the popularity of personal mobile devices, phones, tablets, and even home consoles and smart TVs. If performance is desperately needed, it is also possible to call on friends to use their own devices by opening several tabs in the background to become computing nodes and speed up the overall computing speed.
With the BMR system, users do not even need to learn C++, JAVA, and other languages traditionally used in distributed computing;
they only need to know simple JS to develop distributed computing tasks, and the development cost is extremely low. This paper provides a detailed description of the design of the BMR system and the trade-offs made in its implementation, which is a guideline for the study of distributed computing platforms.
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