Map reduce opens up a number of interesting possibilities in data processing, especially in the commonly talked about areas of big data, but there are number of other less explored applications of map reduce. One such possibility is pipelined map reduce operations gradually funneling from the database down to the browser. This allows for device manageable levels of data, while providing different levels of filtering.
On the database level, CouchDB’s incremental map reduce will be used to turn large volumes of data into an efficiently indexed structure allowing a variety of dynamic queries. These queries be aggregated on different levels, providing efficient queries in either raw form, or aggregated down to a desired data size.
The final map reduce level will happen client side in the browser. This is where low latency filtering on top of funneled data will provide fast interactive feedback for user queries and visualizations.
This talk will take a deep dive into exploring the “map reduce all the way down” paradigm, and examine how to progress through the different levels of the pipeline.
I’m Russell Branca, you may know me as Chewbranca. I’m a programmer at Cloudant, CouchDB aficionado, enjoyer of food, and father to a red headed tornado of a toddler.
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