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After immersing yourself into the field of distributed computing and large data sets you inevitably come to appreciate the elegance of Google’s Map-Reduce framework. Both the generality and the simplicity of its map, emit, and reduce phases is what makes it such a powerful tool. However, while Google has made the theory public, the underlying software implementation remains closed source and is arguably one of their biggest competitive advantages (GFS, BigTable, etc). Of course, there is a multitude of the open source variants (Apache Hadoop, Disco, Skynet, amongst many others), but one can’t help but to notice the disconnect between the elegance and simplicity of the theory and the painful implementation: custom protocols, custom servers, file systems, redundancy, and the list goes on! Which begs the question, how do we lower the barrier?
Massively Collaborative Computation
After several iterations, false starts, [From Collaborative Map-Reduce in the Browser – igvita.com]
Collaborative Map-Reduce in the Browser – igvita.com