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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.16455 |
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| _version_ | 1866912721090379776 |
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| author | Mu, Pei Carniel, Anderson Chaves Barbalace, Antonio Shaikhha, Amir |
| author_facet | Mu, Pei Carniel, Anderson Chaves Barbalace, Antonio Shaikhha, Amir |
| contents | Unreliable cardinality estimation remains a critical performance bottleneck in database management systems (DBMSs). Adaptive Query Processing (AQP) strategies address this limitation by providing a more robust query execution mechanism. Specifically, plan-based AQP achieves this by incrementally refining cardinality using feedback from the execution of sub-plans. However, the actual reason behind the improvements of plan-based AQP, especially across different storage architectures (on-disk vs. in-memory DBMSs), remains unexplored.
This paper presents the first comprehensive analysis of state-of-the-art plan-based AQP. We implement and evaluate this strategy on both on-disk and in-memory DBMSs across two benchmarks. Our key findings reveal that while plan-based AQP provides overall speedups in both environments, the sources of improvement differ significantly. In the on-disk DBMS, PostgreSQL, performance gains primarily come from the query plan reorderings, but not the cardinality updating mechanism; in fact, updating cardinalities introduces measurable overhead. Conversely, in the in-memory DBMS, DuckDB, cardinality refinement drives significant performance improvements for most queries. We also observe significant performance benefits of the plan-based AQP compared to a state-of-the-art related-based AQP method. These observations provide crucial insights for researchers on when and why plan-based AQP is effective, and ultimately guide database system developers on the tradeoffs between the implementation effort and performance improvements. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_16455 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | [Experiment, Analysis, and Benchmark] Systematic Evaluation of Plan-based Adaptive Query Processing Mu, Pei Carniel, Anderson Chaves Barbalace, Antonio Shaikhha, Amir Databases Unreliable cardinality estimation remains a critical performance bottleneck in database management systems (DBMSs). Adaptive Query Processing (AQP) strategies address this limitation by providing a more robust query execution mechanism. Specifically, plan-based AQP achieves this by incrementally refining cardinality using feedback from the execution of sub-plans. However, the actual reason behind the improvements of plan-based AQP, especially across different storage architectures (on-disk vs. in-memory DBMSs), remains unexplored. This paper presents the first comprehensive analysis of state-of-the-art plan-based AQP. We implement and evaluate this strategy on both on-disk and in-memory DBMSs across two benchmarks. Our key findings reveal that while plan-based AQP provides overall speedups in both environments, the sources of improvement differ significantly. In the on-disk DBMS, PostgreSQL, performance gains primarily come from the query plan reorderings, but not the cardinality updating mechanism; in fact, updating cardinalities introduces measurable overhead. Conversely, in the in-memory DBMS, DuckDB, cardinality refinement drives significant performance improvements for most queries. We also observe significant performance benefits of the plan-based AQP compared to a state-of-the-art related-based AQP method. These observations provide crucial insights for researchers on when and why plan-based AQP is effective, and ultimately guide database system developers on the tradeoffs between the implementation effort and performance improvements. |
| title | [Experiment, Analysis, and Benchmark] Systematic Evaluation of Plan-based Adaptive Query Processing |
| topic | Databases |
| url | https://arxiv.org/abs/2511.16455 |