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LeapDB includes "star schema optimization" which is a technique for improving query performance on star schema queries that filter out rows based on filters in dimension tables. This reduces IO for star schema queries and provides opportunities for query parallelization.
I use my benchmark queries on the BTS Ontime data set in a materialized view context and compare performance of the queries when using the materialized view against querying the base table.
A comparison between InnoDB and WARP on two decades of the BTS ontime flight data, a 109 column wide table.
The WARP storage engine provides columnar storage, automatic bitmap indexing and parallel query with star schema optimization
An introduction to materialized views
An overview of change data capture with LeapDB
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