Join Cardinality Estimation with OmniSketches
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866909752239325184 |
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| author | Justen, David Boehm, Matthias |
| author_facet | Justen, David Boehm, Matthias |
| contents | Join ordering is a key factor in query performance, yet traditional cost-based optimizers often produce sub-optimal plans due to inaccurate cardinality estimates in multi-predicate, multi-join queries. Existing alternatives such as learning-based optimizers and adaptive query processing improve accuracy but can suffer from high training costs, poor generalization, or integration challenges. We present an extension of OmniSketch - a probabilistic data structure combining count-min sketches and K-minwise hashing - to enable multi-join cardinality estimation without assuming uniformity and independence. Our approach introduces the OmniSketch join estimator, ensures sketch interoperability across tables, and provides an algorithm to process alpha-acyclic join graphs. Our experiments on SSB-skew and JOB-light show that OmniSketch-enhanced cost-based optimization can improve estimation accuracy and plan quality compared to DuckDB. For SSB-skew, we show intermediate result decreases up to 1,077x and execution time decreases up to 3.19x. For JOB-light, OmniSketch join cardinality estimation shows occasional individual improvements but largely suffers from a loss of witnesses due to unfavorable join graph shapes and large numbers of unique values in foreign key columns. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_17931 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Join Cardinality Estimation with OmniSketches Justen, David Boehm, Matthias Databases Join ordering is a key factor in query performance, yet traditional cost-based optimizers often produce sub-optimal plans due to inaccurate cardinality estimates in multi-predicate, multi-join queries. Existing alternatives such as learning-based optimizers and adaptive query processing improve accuracy but can suffer from high training costs, poor generalization, or integration challenges. We present an extension of OmniSketch - a probabilistic data structure combining count-min sketches and K-minwise hashing - to enable multi-join cardinality estimation without assuming uniformity and independence. Our approach introduces the OmniSketch join estimator, ensures sketch interoperability across tables, and provides an algorithm to process alpha-acyclic join graphs. Our experiments on SSB-skew and JOB-light show that OmniSketch-enhanced cost-based optimization can improve estimation accuracy and plan quality compared to DuckDB. For SSB-skew, we show intermediate result decreases up to 1,077x and execution time decreases up to 3.19x. For JOB-light, OmniSketch join cardinality estimation shows occasional individual improvements but largely suffers from a loss of witnesses due to unfavorable join graph shapes and large numbers of unique values in foreign key columns. |
| title | Join Cardinality Estimation with OmniSketches |
| topic | Databases |
| url | https://arxiv.org/abs/2508.17931 |