So, what you’re essentially advocating for is to use some kind of Spark/compute framework (I was going to use AWS Glue) job to write the ‘materialized views’ as separate tables (maybe tied together with some kind of a naming convention?)
In this case, we’d end up with some sticky data consistency issues if the write job failed halfway through (some ‘materialized view’ tables would be updated, and some wouldn’t). Can I use Phoenix transactions to wrap the write jobs together, to make sure either all the data is updated, or none?
For your use case, i would suggest to create another table that stores the matrix. Since this data doesnt change that often, maybe you can write a nightly spark/MR job to update/rebuild the matrix table.(If you want near real time that is also possible with any streaming system) Have you looked into bloom filters? It might help if you have sparse dataset and you are using Phoenix dynamic columns.
We use dynamic columns for a table that has columns upto 40k. Here is the presentation and optimizations we made for that use case: https://www.slideshare.net/anilgupta84/phoenix-con2017-truecarfinal
IMO, Hive integration with HBase is not fully baked and it has a lot of rough edges. So, it better to stick with native Phoenix/HBase if you care about performance and ease of operations.
On Wed, Sep 25, 2019 at 10:01 AM Gautham Acharya <firstname.lastname@example.org> wrote:
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 (a few times a week at most), 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?
Thanks & Regards,