> It's also a good idea to partition time series data so that the rows do not > grow too big. You can have 2 billion columns in a row, but big rows have > operational down sides.
What are the down sides here? Unfortunately I have an existing system which I modeled with large rows (because I use the sorted nature of columns to get column ranges). After the amount of data grows, I get "mmap failed" exceptions (See my other thread "Cassandra OOM"). I wonder if there is a connection. -- Regards, Ajeet
