R3, a map-reduce engine with Python and Redis

Do the map-reduce results get put back into redis? I always worry about OOM problems when I'm putting a somewhat unbounded set of things into redis.

Neat; but seems to missing copyright notices and an explicit license, which means no one can actually use it or redistribute it with their application.

Can you horizontaly scale the redis backend or it supports only one instance?<p>Why restrain to sequential reducers when you can parallelize with partitions and sorting?

Anyone have some insight into situations where running map reduce on redis makes more sense than other software like the traditional hadoop?

I can think of one case where a redis dictionary is used to represent a tree, and reductions are needed over a subtree. Calculations on river networks are like this. You might want to use redis instead of a cPickled dictionary, and you might not want the overhead of a full Hadoop.

"Getting one up in your system is beyond the scope of this document."<p>- 67 characters<p>brew install redis<p>redis-server<p>- 31 characters

This is pretty interesting, I have a related project (plug, hadoopy.com). The way I went about this (in an experimental branch) is to use Celery running on Redis.

Can multiple users run tasks simultaneously? Can they set task priorities?

This looks like an interesting project.<p>Is there something like this for php?