I’ve recently started a Clojure / MongoDB project at work to help us with our proteogenomic annotation work. Naturally, I’m using Congomongo to interact with the database. It’s a great wrapper for the MongoDB Java driver, written in a very nice functional style.

Lately I’ve been looking into the map-reduce capabilities of MongoDB and have been trying to figure out how to make it work from Clojure. Looking at the Congomongo API, I came across the server-eval function, which looked like a promising place to start.

I decided to kick the tires a bit:

user> (use 'somnium.congomongo)
nil
user> (server-eval "function(){return 3+3}")
6.0

So far, so good. server-eval takes a string of JavaScript code defining a function with no arguments. This gets sent over to MongoDB, where it gets evaluated and run.

Under the hood, Congomongo is passing off to the MongoDB Java driver’s com.mongodb.DB.doEval method, which effectively runs this command (as typed into the MongoDB JavaScript console):

> db.$cmd.findOne({$eval:"function(){return 3+3}"})
{ "retval" : 6, "ok" : 1 }

It’s calling the special eval command in MongoDB and passing the result back. Check out the MongoDB Command Documentation as well as the List of Database Commands for more on how this stuff works out.

That’s all well and good, but it doesn’t actually help for kicking off a map-reduce job from Clojure. As the MongoDB documentation says:

Use map/reduce instead of db.eval() for long running jobs. db.eval blocks other operations!

That’s a bummer. The only facility Congomongo currently provides for executing code server-side is the aforementioned server-eval function, which only uses the MongoDB eval command; mapReduce is a separate command. It’s actually pretty straightforward to add support for map-reduce in Congomongo, though. Though we could easily use com.mongodb.DB.doEval to perform our map-reduce job, the Java driver helpfully provides com.mongodb.DBCollection.mapReduce, which provides a little bit of sugar for such things. Studying the code for some other Congomongo functions leads to this solution:

My Congomongo fork, now with map-reduce!

The nice thing about this function is that it fully exposes all the capabilities of the native MongoDB map-reduce framework. Want to add a finalize function? No problem! Want sorted or limited query results? Done! Want results or just the collection? You got it. There’s lots of documentation for how it all works; the test cases will help, too.