是否有任何其他规范马云preduce（谷歌，Hadoop的）？有没有其他合理的方式如何分割和放大器;合并大的问题？

Are there any alternative paradigms to MapReduce (Google, Hadoop)? Is there any other reasonable way how to split & merge big problems?

推荐答案

明确。退房，例如，散装同步并行。的Map / Reduce其实是在降低的问题非常有限的方式，但这种限制使得它像Hadoop的框架管理。现在的问题是，如果它是麻烦少了preSS您的问题到的Map / Reduce设置，或者如果它更容易地创建一个特定领域的并行方式，和考虑到所有的执行细节照顾自己。猪，其实是在Hadoop之上，它能够自动很多标准的问题转变，从没有-的map-reduce-Y向的map-reduce兼容只是一个抽象层。

Definitively. Check out, for example, Bulk Synchronous Parallel. Map/Reduce is in fact a very restricted way of reducing problems, however that restriction makes it manageable in a framework like Hadoop. The question is if it is less trouble to press your problem into a Map/Reduce setting, or if its easier to create a domain-specific parallelization scheme and having to take care of all the implementation details yourself. Pig, in fact, is only an abstraction layer on top of Hadoop which automates many standard problem transformations from not-Map-Reduce-y to Map-Reduce-compatible.

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