What is MapReduce?

Building efficient data centers that can hold thousands of machines is hard enough. Programming thousands of machines is even harder. One approach pioneered by Google is known as MapReduce. MapReduce provides a programming model that simplifies programming thousands of machines by breaking down distributed programs into two steps: map, and reduce.
Credits: Talking: Geoffrey Challen (Assistant Professor, Computer Science and Engineering, University at Buffalo). Producing: Greg Bunyea (Undergraduate, Computer Science and Engineering, University at Buffalo).
Part of the www.internet-class.org online internet course. A blue Systems Research Group (blue.cse.buffalo.edu) production.

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This video was super helpful for me!! Not only talked about the counting example but also explained that there are some tasks that do not fit in this framework!! Now I think I got the concept!! Thanks!!

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I still refer to this video and I get to understand MR better with every play, if only I could like this video every time I watched it. Thank you for the content.

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MapReduce is distributed sorting Map: splitting data up to multiple computers, and those computers individually conducts the sorting Reduce: combining the sorted results into a lookup table

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The Shuffle step groups data with similar key and sends it for reduce. How does Shuffle work? How does it group the data produced by mappers that is spread across multiple nodes?

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