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Ilya Katsovæ°ã«ãããMapReduce Patterns, Algorithms, and Use Casesãã®ç¿»è¨³ http://highlyscalable.wordpress.com/2012/02/01/mapreduce-patterns/ (ä¸æ¸ãã«å ¥ãã¦æ¨æ²ããã¤ãããããªããå ¬éããã¦ãã¾ã£ã¦ããã®ã§ããã¨ã§ããããä¿®æ£ããã¨æãã¾ã) February 1, 2012 ãã®è¨äºã§ã¯ãWebãç§å¦è«æã§è¦ãããç°ãªããã¯ããã¯ã®ä½ç³»çãªè¦ç¹ãä¸ããããã«ãæ°ã ã®MapReduceãã¿ã¼ã³ã¨ã¢ã«ã´ãªãºã ãã¾ã¨ããã ããã¤ãã®å®ç¨çãªã±ã¼ã¹ã¹ã¿ãã£ãæä¾ãã¦ããã ãã¹ã¦ã®èª¬æã¨ã³ã¼ãã¹ããããã§ã¯ãMapperãReducerãCombinerãPartitionaerãã½ã¼ãã£ã³ã°ã«ããã¦Hadoopã®æ¨æºçãªMapReduceã¢ãã«ãå©ç¨ãã¾ãããã®ãã¬ã¼
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This document discusses using sliding windows to aggregate streaming data in MapReduce. It proposes buffering input tuples in mappers until a window is full, then emitting the aggregate. Combiners and reducers combine partial aggregates across windows. Window ranges are initialized and updated during merging to remove outdated data and handle late arrivals. This approach allows streaming aggregati
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