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
具體描述
讀後感
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用戶評價
如何編寫一個真正的不依賴於in-memory sort的find median算法？單機版本的max-N都已經是O(N)的瞭，Hadoop版本的（作者這裏描述的）就有點弱瞭
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评分相當一部分“pattern”被總結齣來，隻說明瞭Hadoop太笨。
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评分還算有點實用，不過隨著pig的成熟，很多東東其實不需要瞭解瞭
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