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Recent work has reemphasized the importance of cardinality estimates for query optimization. While new techniques have continuously improved in accuracy over time, they still generally allow for under-estimates which often lead optimizers to make overly optimistic decisions. This can be very costly for expensive queries. An alternative approach to estimation is cardinality bounding, also called pessimistic cardinality estimation, where the cardinality estimator provides guaranteed upper bounds of the true cardinality. By never underestimating, this approach allows the optimizer to avoid potentially inefficient plans. However, existing pessimistic cardinality estimators are not yet practical: they use very limited statistics on the data, and cannot handle predicates. In this paper, we introduce SafeBound, the first practical system for generating cardinality bounds. SafeBound builds on a recent theoretical work that uses degree sequences on join attributes to compute cardinality bounds, extends this framework with predicates, introduces a practical compression method for the degree sequences, and implements an efficient inference algorithm. Across four workloads, SafeBound achieves up to 80% lower end-to-end runtimes than PostgreSQL, and is on par or better than state of the art ML-based estimators and pessimistic cardinality estimators, by improving the runtime of the expensive queries. It also saves up to 500x in query planning time, and uses up to 6.8x less space compared to state of the art cardinality estimation methods.
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Robust Query Driven Cardinality Estimation under Changing Workloads
Query driven cardinality estimation models learn from a historical log of queries. They are lightweight, having low storage requirements, fast inference and training, and are easily adaptable for any kind of query. Unfortunately, such models can suffer unpredictably bad performance under workload drift, i.e., if the query pattern or data changes. This makes them unreliable and hard to deploy. We analyze the reasons why models become unpredictable due to workload drift, and introduce modifications to the query representation and neural network training techniques to make query-driven models robust to the effects of workload drift. First, we emulate workload drift in queries involving some unseen tables or columns by randomly masking out some table or column features during training. This forces the model to make predictions with missing query information, relying more on robust features based on up-to-date DBMS statistics that are useful even when query or data drift happens. Second, we introduce join bitmaps, which extends sampling-based features to be consistent across joins using ideas from sideways information passing. Finally, we show how both of these ideas can be adapted to handle data updates. We show significantly greater generalization than past works across different workloads and databases. For instance, a model trained with our techniques on a simple workload (JOBLight-train), with 40ksynthetically generated queries of at most 3 tables each, is able to generalize to the much more complex Join Order Benchmark, which include queries with up to 16 tables, and improve query runtimes by 2× over PostgreSQL. We show similar robustness results with data updates, and across other workloads. We discuss the situations where we expect, and see, improvements, as well as more challenging workload drift scenarios where these techniques do not improve much over PostgreSQL. However, even in the most challenging scenarios, our models never perform worse than PostgreSQL, while standard query driven models can get much worse than PostgreSQL.
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- Award ID(s):
- 1751009
- PAR ID:
- 10495282
- Publisher / Repository:
- VLDB Endowment
- Date Published:
- Journal Name:
- Proceedings of the VLDB Endowment
- Volume:
- 16
- Issue:
- 6
- ISSN:
- 2150-8097
- Page Range / eLocation ID:
- 1520 to 1533
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.14778/3583140.3583164
