ASM in Action: Fast and Practical Learned Cardinality Estimation

Sangoh Lee, Kyoungmin Kim, Wook-Shin Han

Abstract: Learned cardinality estimators have shown remarkable improvements in estimation accuracy by exploiting machine learning techniques, yet suffer from inefficiency or sub-optimal query plans when deployed in query optimizers. ASM is a new learned cardinality estimator that significantly outperformed previous approaches in terms of end-to-end execution times. This demonstration illustrates the internal estimation process of that utilizes autoregressive models, sampling, and multi-dimensional statistics merging, and compares its performance with other alternatives. To do so, we visualize the detailed plan space exploration utilizing the estimation results.