Bespoke OLAP: Synthesizing Workload-Specific One-size-fits-one Database Engines

2026-03-02

AI Summary

This work proposes a fully automated, end-to-end approach to synthesizing specialized OLAP engines tailored to specific analytical workloads. Unlike general-purpose OLAP systems, which incur structural overhead that impedes peak performance, the proposed method leverages large language model–driven code generation, iterative performance feedback, automated correctness verification, and co-optimization of storage and query execution architectures to generate complete, custom engines without human intervention. For the first time, this framework enables the automatic synthesis of high-performance OLAP engines within minutes to hours, achieving order-of-magnitude speedups over state-of-the-art general-purpose systems such as DuckDB. By eliminating the traditional trade-off between engineering effort and performance inherent in manual specialization, the approach breaks a key bottleneck in analytical data system design.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Search and Optimization: Distributed Search
• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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.

Problem

• OLAP
• bespoke database
• workload-specific
• code synthesis
• performance overhead

Innovation

• Bespoke OLAP
• LLM-based code synthesis
• workload-specific database
• autonomous system generation
• OLAP engine optimization