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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花瞭大概3-4個小時快速看完,溫習瞭一下Input/OutputFormat, RecordReader/Writer, InputSplit,基本沒收獲,比較適閤剛會寫MapReduce的碼農們快速瀏覽一遍
评分大概13年的時候讀過這本書,當時覺得覺得收獲非常大,基本覆蓋瞭用mr處理數據的常用方法,不過現在看開用hive就夠瞭。
评分就告訴你如何用MR實現SQL中的JOIN、聚閤函數等
评分一般吧,有些可以藉鑒的東西,看分析算法這東西和係統設計不一樣,不太好有個design pattern,所以也隻是參考
评分入門瞭,略拖遝。
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