CouchDB is a non-relational database which uses MapReduce inspired views to query data. There are lots of cool things to tell about its design, but I rather want to talk about its performance.
Today I’ve been busy hacking together a little script to import all e-mails of a long e-mail thread into a couchdb database to write views to extract all kinds of statistics. I already imported these e-mails into a MySQL database a few months ago, but was quite disappointed by the (performance) limitations of SQL. The e-mail thread contains over 20,000 messages which weren’t a real problem for MySQL. When importing, however, couchdb was adding them at a rate of only a few dozen per second with a lot of (seek)noise of my HDD.
So I decided to do a simple benchmark. First of, a simple script (ser.py) that adds empty documents sequentially. It’s averaging 16 per second. It occurred to me that couchdb waits for a fsync before sending a response and that asynchronously the performance would be way better. A simple modification to the script later (par.py) it still averaged 16 creations per second.
I guess, for I haven’t yet figured out how to let straces tell me, that it’s the fsync after each object creation which causes the mess. couchdb itself doesn’t write or seek a lot, but my journaling filesystem (XFS) does on a fsync.
Can anyone test it on a different filesystem?
Update Around 17/sec with reiserfs.
Update I had some trouble with the bulk update feature. I switched from svn to the 0.7.2 release. I got about 600/sec, which dropped to a steady-ish 350/sec when using sequential bulkupdates of 100 docs. Two bulk updates in parallel yield about 950/sec initially, dropping to 550/sec after a while. Three parallel updates yield similar performance.
Look at the CouchDB bulk save featues. With that you should be able create 1000s per second.