Making Databases Faster with LLM Evolutionary Sampling

2026-02-11

AI Summary

Traditional query optimizers rely on handcrafted heuristics and statistical models, which struggle to capture the semantic relationships between queries and schemas, thereby limiting the effectiveness of physical execution plan optimization. This work proposes a novel approach that serializes execution plans into compact representations and leverages the semantic understanding capabilities of large language models (LLMs) to generate localized editing suggestions. These suggestions are integrated within an evolutionary search framework to iteratively refine candidate plans. By uniquely combining LLMs with evolutionary sampling, the method enables automatic, implicit optimization of execution plans, transcending the constraints of conventional cost models. Experiments on the DBPlanBench framework with the DataFusion engine demonstrate up to a 4.78× speedup on certain queries, and show that optimization strategies discovered in small-scale settings effectively transfer to large-scale databases.

Technology Category

• Search and Optimization: Learning to Search
• Planning, Routing, and Scheduling: Planning with Language Models
• Machine Learning: Optimization

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Large language models for search
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

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 heuristics requires substantial engineering effort, and even when implemented, these heuristics often cannot take into account semantic correlations in queries and schemas that could enable better physical plans. Using our DBPlanBench harness for the DataFusion engine, we expose the physical plan through a compact serialized representation and let the LLM propose localized edits that can be applied and executed. We then apply an evolutionary search over these edits to refine candidates across iterations. Our key insight is that LLMs can leverage semantic knowledge to identify and apply non-obvious optimizations, such as join orderings that minimize intermediate cardinalities. We obtain up to 4.78$\times$ speedups on some queries and we demonstrate a small-to-large workflow in which optimizations found on small databases transfer effectively to larger databases.

Problem

• query optimization
• cost-based optimizer
• semantic correlations
• physical plan
• database performance

Innovation

• LLM-based query optimization
• evolutionary sampling
• physical plan editing
• semantic-aware optimization
• query performance acceleration