Demonstrating GenDB: Instance-Optimized and Customized Query Processing Code Generation via LLM Agents
Traditional query processing engines require continuous development and extensions to support new techniques and user requirements, and in some cases, entirely new systems must be built from scratch. However, these engines are difficult to extend due to their internal complexity, and building new systems demands significant engineering effort and cost. To address this, we demonstrate GenDB, a generative query engine that shifts query processing from manually engineered systems to query processing code generation driven by Large Language Models (LLMs). An early prototype of GenDB uses LLM agents to generate instance-optimized query execution code tailored to specific data, workloads, and h...
| Authors | Jiale Lao, Immanuel Trummer |
|---|---|
| Primary category | cs.DB |
| Published | 22 July 2026 |
| Last updated at source | 22 July 2026 |
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