Hi, Currently I'm using Hbase to store large, sparse matrices of 50,000 columns 10+ million rows of integers.
This matrix is used for fast, random access - we need to be able to fetch random row/column subsets, as well as entire columns. We also want to very quickly fetch aggregates (Mean, median, etc) on this matrix. The data does not change very often for these matrices, so pre-computing is very feasible here. What I would like to do is maintain a column store (store the column names as row keys, and a compressed list of all the row values) for the use case where we select an entire column. Additionally, I would like to maintain a separate table for each precomputed aggregate (median table, mean table, etc). The query time for all these use cases needs to be low latency - under 100ms. When the data does change for a certain matrix, it would be nice to easily update the optimized table. Ideally, I would like the column store/aggregation tables to just be materialized views of the original matrix. It doesn't look like Apache Phoenix supports materialized views. It looks like Hive does, but unfortunately Hive doesn't normally offer low latency queries. Maybe Hive can create the materialized view, and we can just query the underlying Hbase store for lower latency responses? What would be a good solution for this? --gautham
