Publication
Bespoke OLAP: Synthesizing Workload-Specific One-size-fits-one Database Engines
Johannes Wehrstein; Timo Eckmann; Matthias Jasny; Carsten Binnig
In: Computing Research Repository eprint Journal (CoRR), Vol. abs/2603.02001, Pages 1-13, arXiv, 2026.
Abstract
Modern OLAP engines are designed to support arbitrary analyti-
cal workloads, but this generality incurs structural overhead, in-
cluding runtime schema interpretation, indirection layers, and ab-
straction boundaries, even in highly optimized systems. An engine
specialized to a fixed workload can eliminate these costs and ex-
ploit 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 sys-
tem 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, struc-
tured refinement, and careful management of deep architectural
interdependencies. We present Bespoke OLAP, a fully autonomous
synthesis pipeline for constructing high-performance database en-
gines 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 Be-
spoke 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.