定制化OLAP：合成面向特定工作负载的“一型一机”数据库引擎
Bespoke OLAP: Synthesizing Workload-Specific One-size-fits-one Database Engines
Johannes Wehrstein, Timo Eckmann, Matthias Jasny, Carsten Binnig
AI 速览：提出全自动合成流水线 Bespoke OLAP，用 LLM 代码合成加迭代性能反馈，为固定分析负载从零生成专用数据库引擎。
Modern OLAP engines are designed to support arbitrary analytical workloads, but this generality incurs structural overhead, including runtime schema interpretation, indirection layers, and abstraction boundaries, even in highly optimized systems. An engine specialized to a fixed workload can eliminate these costs and exploit workload-specific data structures and execution algorithms for substantially higher performance. Historically, constructing such bespoke engines has been economically impractical due to the high manual engineering effort. Recent advances in LLM-based code synthesis challenge this tradeoff by enabling automated system generation. However, naively prompting an LLM to produce a database engine does not yield a correct or efficient design, as effective synthesis requires systematic performance feedback, structured refinement, and careful management of deep architectural interdependencies. We present Bespoke OLAP, a fully autonomous synthesis pipeline for constructing high-performance database engines tightly tailored to a given workload. Our approach integrates iterative performance evaluation and automated validation to guide synthesis from storage to query execution. We demonstrate that Bespoke OLAP can generate a workload-specific engine from scratch within minutes to hours, achieving order-of-magnitude speedups over modern general-purpose systems such as DuckDB.