JetStream: Generating Query Accelerators for Existing Database Systems

Abstract

Recent work has shown that LLMs can synthesize highly specialized database systems for fixed workloads, but existing approaches typically assume static data and replace the database's native storage with generated representations. We present JetStream, a system for generating query-specific accelerators that instead extend an existing DBMS. JetStream consists of three parts. First, a staged, measurement-driven agentic workflow generates and optimizes query-specific accelerators, including persistent auxiliary state when beneficial. Second, a fixed, engine-neutral substrate provides the common interfaces for execution, transaction coordination, state management, and maintenance. Third, a separate synthesis workflow generates engine-specific backend adapters that connect the substrate to the underlying engine. For stateful accelerators, JetStream also generates maintenance logic and uses a runtime policy to choose among incremental maintenance, rebuilds, and lazy repair as the database changes. On TPC-H at SF=20, stateful accelerators generated by JetStream achieve an 833x geomean read-only speedup over DuckDB, compared with 34.07x for GenDB and 12.35x for Bespoke OLAP. Under TPC-H refreshes every 60 seconds, JetStream maintains a 375x geomean workload speedup. We also show that JetStream generalizes to new, unseen workloads, achieving geomean read-only speedups of 102x over DuckDB and 486x over PostgreSQL. These results show that aggressive generated specialization can be integrated seamlessly with existing DBMSes and support dynamic workloads.