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Comment by east2west | Hacker News Reader

I have always suspected so since Google still does not open source its MapReduce, but I haven't found any actual evidence directly comparing Hadoop MapReduce and Google's. There could also be technical issues preventing Google from open sourcing its implementation, although I don't know any. Now that we are moving on to more sophisticated models and implementation I am hoping more details can emerge.

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I don't know anything about MR in particular, but the open-sourcing process at Google often gets bogged down by how interconnected the codebase and ecosystem are. Releasing an open source version of a component means reimplementing a lot of stuff that Google isn't ready to release yet but that that component depends on. Look at Bazel/Blaze - it took them a long time to get it out, and the open version doesn't have feature parity. Also take a look at their governance plan going forward, which hints at the difficulties.[1]

That said, I suspect a large part of the reason is that Google's MapReduce is highly optimized for running in a Google datacenter, and they're generally pretty tight-lipped about the real secret sauce in their infrastructure.

[1]http://bazel.io/governance.html

https://github.com/chrisvana/repobuild/wiki/Motivation

(Pre bazel)

> There could also be technical issues preventing Google from open sourcing its implementation, although I don't know any.

My understanding is that sharing details takes time off very valuable employees, away from fixing important internal issues – a lot more than the company being afraid those details might leak. For the handful of companies who can and needs to implement equivalent, poaching said employees is simpler than back-engineering from blogposts and it makes implementation easier.

MapReduce and associated infrastructure at Google is arguably at the center of their massive and proprietary codebase. I suspect opening that would involve opening much of their core code.

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