For what its worth, we have been using Cassandra for storing time series for about 2 years now at ~2k writes a sec. I would say every issue was self-induced and Cassandra has been amazingly patient with us. It works amazing with this scenario. We experimented with MongoDB a lot initially (along with riak, hbase, etc) and found about the same thing. Turns out using a database in a way its designed to work turns out in your favor. That said hbase did well too, but it scared our ops team.
All the new changes in 1.2 and 2.0 with cql really make it seem like datastax is focused on being mysql and ignoring the time series use case though which makes me nervous.
I sympathize with that perspective. I was a huge Thrift fan five years ago. But it became clear early on that, despite your experience and mine, most people had a really hard time wrapping their heads around Cassandra's Thrift API. Teaching CQL for the last 8 months has been a night and day difference. Ease of use does matter.
It doesn't hurt either that CQL is substantially more performant [1]. Perhaps that will sweeten the pill for you. :)
That said, while CQL may get the most publicity, we certainly haven't been neglecting the rest of the stack, e.g. [2], [3], [4], ...
I understand the nervousness, but I was able to convert a thrift/hector time series model to exactly equivalent CQL3 without too much trouble. The (perhaps non-obvious) options involved were "WITH COMPACT STORAGE" for wide rows and "WITH CLUSTERING ORDER BY (blah DESC)" for a reversed comparator.
Thanks for the pointers. We are also looking at that as a future plan. We are currently using Thrift which has been rightly pointed out and which I should have mentioned.
All the new changes in 1.2 and 2.0 with cql really make it seem like datastax is focused on being mysql and ignoring the time series use case though which makes me nervous.