If you're talking about other "Map-Reduce" as in Hadoop MapReduce being "less accessible", it's because Hadoop has a ton of housekeeping infrastructure. Hadoop is aware of the health of other machines and therefore slave jobs can be self-healing, any job errors are properly routed and centralized, etc. Leaving out the housekeeping stuff, the core "map & reduce" part of Hadoop is very simple. This FileMap doesn't seem to have all that infrastructure intelligence, hence it's "simpler".

It's like talking about bwr nuclear reactors. On the one hand, it's just a big glorified pot of boiling water. What's so hard about that?! The complexity isn't boiling the water, it's temperature regulation and redundant failsafes that prevent an explosion of radioactive materials[1] that makes it harder to implement than a kettle of hot water for brewing tea.

[1]http://en.wikipedia.org/wiki/Chernobyl_disaster

You should check out mrjob[1]. It wraps the Hadoop streaming API and makes it super easy to write MapReduce jobs in Python. I find it much easier to understand than this implementation.

[1] https://pythonhosted.org/mrjob/

Yes, FileMap was also extremelly easy to set up.