Publication
Bespoke-Card: Why Tune When You Can Generate? Synthesizing Workload-Specific Cardinality Estimators
Johannes Wehrstein; Anton Winter; Timo Eckmann; Carsten Binnig
In: Computing Research Repository eprint Journal (CoRR), Vol. abs/2606.09361, Pages 1-10, arXiv, 2026.
Abstract
Cardinality estimators are built to support arbitrary schemas and
workloads, forcing them to rely on generic statistics even when
the schema and workload is known in advance, leaving optimizers
prone to large errors and poor plans. We present Bespoke-Card, an
agent-driven system that synthesizes workload-specific cardinality
estimators as executable code: a planning agent designs the estima-
tors strategies, a coding agent implements them, and a validator
scores the estimates against true cardinalities and PostgreSQL esti-
mates, forming a robust and deterministic harness. Going beyond
naive prompting, Bespoke-Card uses structured q-error feedback,
regression analysis, concrete outlier subplans, a curriculum iso-
lating join-only, filter-only, and full-subplan errors, and archival
selection of the best implementation. Injecting its estimates into the
optimizer cuts total PostgreSQL runtime on JOB by 33% and reduces
median q-error over all JOB subplans by 41%, while synthesizing a
strong estimator in under one hour for less than $10. Bespoke-Card
is opening a new avenue for cardinality estimation next to classical
generic estimators and learned estimator architectures.