Aggregate-based Training Phase for ML-based Cardinality Estimation
Lucas Woltmann, Claudio Hartmann, Dirk Habich, Wolfgang Lehner · Datenbank-Spektrum · 2022
Abstract Cardinality estimation is a fundamental task in database query processing and optimization. As shown in recent papers, machine learning (ML)-based approaches may deliver more accurate cardinality estimations than traditional approaches. However, a lot of training queries have to be executed during themodel training phaseto learn a data-dependent ML model making it very time-consuming. Many of those training or example queries use the same base data, have the same query structure, and only differ in their selective predicates. To speed up the model training phase, our core idea is to determine a predicate-independent pre-aggregationof the base data and to execute the example queries over this pre-aggregated data. Based on this idea, we present a specificaggregate-based training phasefor ML-based cardinality estimation approaches in this paper. As we are going to show with different workloads in our evaluation, we are able to achieve an average speedup of 90 with ouraggregate-based training phaseand thus outperform indexes.