Improved Cardinality Estimation by Learning Queries Containment Rates
This work addresses query optimization for database systems, offering incremental improvements in cardinality estimation.
The paper tackles the problem of cardinality estimation in query optimization by introducing a novel approach that uses estimated containment rates between SQL queries, achieving significant improvements over state-of-the-art methods on a real-world database.
The containment rate of query Q1 in query Q2 over database D is the percentage of Q1's result tuples over D that are also in Q2's result over D. We directly estimate containment rates between pairs of queries over a specific database. For this, we use a specialized deep learning scheme, CRN, which is tailored to representing pairs of SQL queries. Result-cardinality estimation is a core component of query optimization. We describe a novel approach for estimating queries result-cardinalities using estimated containment rates among queries. This containment rate estimation may rely on CRN or embed, unchanged, known cardinality estimation methods. Experimentally, our novel approach for estimating cardinalities, using containment rates between queries, on a challenging real-world database, realizes significant improvements to state of the art cardinality estimation methods.