ASM: Harmonizing Autoregressive model, Sampling, and Multi-dimensional Statistics Merging for Cardinality Estimation
- Title
- ASM: Harmonizing Autoregressive model, Sampling, and Multi-dimensional Statistics Merging for Cardinality Estimation
- Authors
- HAN, WOOK SHIN; KIM, KYOUNG MIN; LEE, SANGOH; KIM, INJUNG
- Date Issued
- 2024-06-09
- Publisher
- ACM SIGMOD
- Abstract
- Recent efforts in learned cardinality estimation (CE) have substantially improved estimation accuracy and query plans inside query optimizers. However, achieving decent efficiency, scalability, and the support of a wide range of queries at the same time, has remained questionable. Rather than falling back to traditional approaches to trade off one criterion with another, we present a new learned approach that achieves all these. Our method, called ASM, harmonizes autoregressive models for per-table statistics estimation, sampling for merging these
statistics for join queries, and multi-dimensional statistics merging that extends the sampling for estimating thousands of sub-queries, without assuming independence between join keys. Extensive experiments show that ASM significantly improves query plans under a similar or smaller overhead than the previous learned methods and supports a wider range of queries.
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/121775
- Article Type
- Conference
- Citation
- The 50th Int’l Conf. on Management of Data (ACM SIGMOD), 2024-06-09
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