arXiv:2602.10387v2 Announce Type: replace-cross Abstract: Traditional query optimization relies on cost-based optimizers that estimate execution cost (e.g., runtime, memory, and I/O) using predefined heuristics and statistical models. Improving these requires substantial engineering effort, yet they often cannot exploit semantic correlations in queries and schemas that could enable better physical plans. Large language models (LLMs), however, can reason about column semantics, value distributions, and broader domain context that classical statistics miss. We introduce DBPlanBench, a harness fo
The rapid advancement and increased capabilities of large language models have positioned them as viable tools for complex reasoning tasks, including database optimization, which was previously dominated by heuristics.
This development indicates a potential paradigm shift in how database systems are designed and optimized, moving from rigid, cost-based models to more dynamic, semantics-aware LLM-driven approaches, enhancing efficiency and reducing engineering overhead.
Traditional query optimization, which relies on pre-defined heuristics and statistical models, may be augmented or replaced by LLM-driven test-time optimization that leverages semantic understanding of queries and data.
- · Database vendors and developers
- · Companies with complex data infrastructure
- · LLM developers and researchers
- · Cloud infrastructure providers
- · Traditional cost-based optimizer specialists
- · Companies unable to integrate new AI optimization techniques
Database systems become significantly more efficient and adaptive to varying workloads and data characteristics.
The demand for specialized database performance engineers may pivot towards those skilled in integrating and fine-tuning AI optimizers.
This could accelerate the trend towards fully autonomous data infrastructure layers, further collapsing traditional IT operational roles.
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Read at arXiv cs.AI