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| In ] '''MapReduce''' programming model, parallel computations over large data sets are implemented by specifying a ''Map'' function that maps key-value pairs to new key-value pairs and a subsequent ''Reduce'' function that consolidates all mapped key-value pairs sharing the same keys to single key-value pairs. | In ] '''MapReduce''' programming model, parallel computations over large data sets are implemented by specifying a ''Map'' function that maps key-value pairs to new key-value pairs and a subsequent ''Reduce'' function that consolidates all mapped key-value pairs sharing the same keys to single key-value pairs. | ||
| MapReduce is often used in conjunction with ], for greater parallelization. | |||
| ==References== | ==References== | ||
| * |
* Dean, Jeffrey & Ghemawat, Sanjay (2004). . Retrieved Apr. 6, 2005. | ||
| ] | ] | ||
Revision as of 15:24, 7 September 2005
In Google's MapReduce programming model, parallel computations over large data sets are implemented by specifying a Map function that maps key-value pairs to new key-value pairs and a subsequent Reduce function that consolidates all mapped key-value pairs sharing the same keys to single key-value pairs.
MapReduce is often used in conjunction with Google File System, for greater parallelization.
References
- Dean, Jeffrey & Ghemawat, Sanjay (2004). "MapReduce: Simplified Data Processing on Large Clusters". Retrieved Apr. 6, 2005.
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