Style #32
Very similar to style #30, but with an additional twist
Constraints:
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Input data is divided in chunks, similar to what an inverse multiplexer does to input signals
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A map function applies a given worker function to each chunk of data, potentially in parallel
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The results of the many worker functions are reshuffled in a way that allows for the reduce step to be also parallelized
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The reshuffled chunks of data are given as input to a second map function that takes a reducible function as input
Possible names:
- Map-reduce
- Hadoop style
- Double inverse multiplexer