Leveraging query logs and machine learning for parametric query optimization
Problem
Parametric query optimization must cache a small set of plans for parameterized queries and then quickly pick the best cached plan per query instance.
Why it matters
Database query performance directly affects application speed and infrastructure cost at scale.
What the AI did
Machine learning models trained on query logs select the lowest optimizer-estimated-cost cached plan for each query instance, alongside an efficient plan-caching algorithm.
Results
populateCache achieves geometric mean sub-optimality of 1.2 on complex queries with few plans and scales to large query logs.
Impact
Extensive experiments on benchmark and real workloads demonstrate low sub-optimality and scalable plan selection.
Record
| Venue | Proceedings of the VLDB Endowment |
|---|---|
| Published | 2021 |
| Work type | article |
| Authors | Kapil Vaidya, Anshuman Dutt, Vivek R. Narasayya, Surajit Chaudhuri |
| Citations | 20 (OpenAlex, at import time) |
| References | 21 |
| Model or method | not stated in source |
| Organization | not stated in source |
| Primary source | https://doi.org/10.14778/3494124.3494126 |
| Open access | closed |
| Keywords | parametric query optimization, machine learning, query logs, database systems, plan caching |
Importance rating
real but narrower advance
Faster parametric query optimization improves database speed and cost, a real but narrow technical advance; abstract reports only benchmark results.
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