Design patterns for the MapReduce framework, until now, have been scattered among various research papers, blogs, and books. This handy guide brings together a unique collection of valuable MapReduce patterns that will save you time and effort regardless of the domain, language, or development framework you're using. Each pattern is explained in context, with pitfalls and caveats clearly identified - so you can avoid some of the common design mistakes when modeling your Big Data architecture. This book also provides a complete overview of MapReduce that explains its origins and implementations, and why design patterns are so important. Hadoop MapReduce code is provided to help you learn how to apply the design patterns by example. Topics include: Basic patterns, including map-only filter, group by, aggregation, distinct, and limit Joins: traditional reduce-side join, reduce-side join with Bloom filter, replicated join with distributed cache, merge join, Cartesian products, and intersections Binning, sharding for other systems, sorting, sampling, unions, and other patterns for organizing data Job optimization patterns, including multi-job map-only job folding, and overloading the key grouping to perform two jobs at once
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作者也是屌。几道MR例题也能出本书。
评分入门了，略拖沓。
评分太浅
评分如何编写一个真正的不依赖于in-memory sort的find median算法？单机版本的max-N都已经是O(N)的了，Hadoop版本的（作者这里描述的）就有点弱了
评分慢慢思索，仍需品味…
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