Thanks for all the responses. My leading questions then are ->
- Should I go with the OrderPreservingPartitioner based on timestamps so I can do time range queries - is this recommended ? any special cases regarding load balancing I need to keep in mind ? I have read buzz over blogs/forums on how RandomPartitioner yields better load balancing, and it is discouraged to use OrderPreservingPartitioner. Can someone expand/comment on this ? - Also, lets say I query all partitioned data between timestampuuid1 and timestampuuid2 (over several weeks) .. this would potentially , in my case, return anywhere to 20 - 30 million records. How would I go about aggregating this data "by hand" ? Will this perform ? Since I am only interested in aggregating over a finite set of 10-20 attributes. Does it make more sense to have a column family per finite attribute ? In this case, I do not need to do any aggregation, since all the data for that attribute resides in one column family. Is there an upper bound to the number of column families Cassandra currently supports ? On Fri, Mar 25, 2011 at 7:31 AM, buddhasystem <potek...@bnl.gov> wrote: > Hello Saurabh, > > I have a similar situation, with a more complex data model, and I do an > equivalent of map-reduce "by hand". The redeeming value is that you have > complete freedom in how you hash, and you design the way you store indexes > and similar structures. If there is a pattern in data store, you use it to > your advantage. In the end, you get good performance. > > -- > View this message in context: > http://cassandra-user-incubator-apache-org.3065146.n2.nabble.com/data-aggregation-in-Cassandra-tp6206994p6207879.html > Sent from the cassandra-u...@incubator.apache.org mailing list archive at > Nabble.com.
