> Standard OLAP solutions right now are really good at "What's the X day sum of this column as of this timestamp", but when every row of your training data has a precise intra-day timestamp that you need windowed aggregations to be accurate as-of, this is a different challenge.
As long as your OLAP table/projection/materialized view is sorted/clustered by that timestamp, it will be able to efficiently pick only the data in that interval for your query, regardless of the precision you need.
> And when you have many people sharing these aggregations, but with potentially different timestamps/timelines, you also want them sharing partial aggregations where possibly for efficiency.
> All of this is well beyond the scope that is addressed by standard OLAP data solutions.
I think the StarRocks open-source OLAP DB supports this as a query rewrite mechanism that optimizes performance by using data from materialized views. It can build UNION queries to handle date ranges [1]
As long as your OLAP table/projection/materialized view is sorted/clustered by that timestamp, it will be able to efficiently pick only the data in that interval for your query, regardless of the precision you need.
> And when you have many people sharing these aggregations, but with potentially different timestamps/timelines, you also want them sharing partial aggregations where possibly for efficiency.
> All of this is well beyond the scope that is addressed by standard OLAP data solutions.
I think the StarRocks open-source OLAP DB supports this as a query rewrite mechanism that optimizes performance by using data from materialized views. It can build UNION queries to handle date ranges [1]
[1] https://docs.starrocks.io/docs/using_starrocks/query_rewrite...