MapReduce - Anything else except word-counting?

I have been looking at MapReduce and reading through various papers about it and applications of it, but, to me, it seems that MapReduce is only suitable for a very narrow class of scenarios that ultimately result in word-counting.

If you look at the original paper Google's employees provide "various" potential use cases, like "distributed grep", "distributed sort", "reverse web-link graph", "term-vector per host", etc. But if you look closer, all those problems boil down to simply "counting words" - that is counting the number occurrences of something in a chunk of data, then aggregating/filtering and sorting that list of occurrences.

There also are some cases where MapReduce has been used for genetic algorithms or relational databases, but they don't use the "vanilla" MapReduce published by Google. Instead they introduce further steps along the Map-Reduce chain, like Map-Reduce-Merge, etc.

Do you know of any other (documented?) scenarios where "vanilla" MapReduce has been used to perform more than mere word-counting? (Maybe for ray-tracing, video-transcoding, cryptography, etc. - in short anything "computation heavy" that is parallelizable)

Answers


MapReduce is good for problems that can be considered to be embarrassingly parallel. There are a lot of problems that MapReduce is very bad at, such as those that require lots of all-to-all communication between nodes. E.g., fast Fourier transforms and signal correlation.


Atbrox had been maintaining mapreduce hadoop algorithms in academic papers. Here is the link. All of these could be applied for practical purpose.


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