Cassandra multiget performance

I've got a cassandra cluster with a fairly small number of rows (2 million or so, which I would hope is "small" for cassandra). Each row is keyed on a unique UUID, and each row has about 200 columns (give or take a few). All in all these are pretty small rows, no binary data or large amounts of text. Just short strings.

I've just finished the initial import into the cassandra cluster from our old database. I've tuned the hell out of cassandra on each machine. There were hundreds of millions of writes, but no reads. Now that it's time to USE this thing, I'm finding that read speeds are absolutely dismal. I'm 开发者_JAVA百科doing a multiget using pycassa on anywhere from 500 to 10000 rows at a time. Even at 500 rows, the performance is awful sometimes taking 30+ seconds.

What would cause this type of behavior? What sort of things would you recommend after a large import like this? Thanks.

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Sounds like you are io-bottlenecked. Cassandra does about 4000 reads/s per core, IF your data fits in ram. Otherwise you will be seek-bound just like anything else.

I note that normally "tuning the hell" out of a system is reserved for AFTER you start putting load on it. :)

See:

• http://spyced.blogspot.com/2010/01/linux-performance-basics.html
• http://www.datastax.com/docs/0.7/operations/cache_tuning

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Is it an option to split up the multi-get into smaller chunks? By doing this you would be able to spread your get across multiple nodes, and potentially increase your performance, both by spreading the load across nodes and having smaller packets to deserialize.

That brings me to the next question, what is your read consistency set to? In addition to an IO bottleneck as @jbellis mentioned, you could also have a network traffic issue if you are requiring a particularly high level of consistency.