Is something that you probably wouldn’t even notice, to be perfectly honest. We are going to work on our map/reduce implementation.
This is freakishly complex, because we need to do updatable, persistent map/reduce. It got so complex that I decided that I can’t really implement this on my own in the RavenDB solution and moved to spiking the solution in isolation.
If you care, you can look at this here. There would be additional optimizations to worry about in RavenDB, but it is pretty much all there, in less than 400 lines of code.
I couldn’t find anything out there which was nearly as useful. Most of the map/reduce implementations are about distributing the work load and scheduling it. None of them really deal with the notion of updatable map/reduce results.
Note that the Storage layer there is both only there for the sole purpose of actually showing we can persist and restart from any point and also has critical behavior in its behavior (for example, scheduling).
I’ll probably do a set of posts about this, but for now, here is the source, have fun poking at it: https://github.com/ayende/updatable-persistent-map-reduce