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MapReduce is an algorithm design pattern that originated in the functional programming world. In this design, a mapper function or script scans the input data and outputs a series of keys and values to use in calculating the results. The keys are used to cluster together bits of data that will be needed to calculate a single output result. The unordered list of keys and values is then put through a sort step that ensures that all the fragments that have the same key are next to one another in the file. The reducer stage then goes through the sorted output and receives all of the values that have the same key in a contiguous block.

Although it may seems a roundabout way of processing the data, its prime advantage is that it removes unplanned random accesses, with all scattering and gathering handled in the sorting phase. Even on single machines, this boosts performance, thanks to the increased locality of memory accesses, but it also allows the process to be split across a large number of machines easily, by dealing with the input in many independent chunks and partitioning the data based on the key.

Hadoop is the best-known public system for running MapReduce algorithms, but many modern databases, such as MongoDB, also support it as an option.

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