arXiv subjects

Timo Eckmann

Publications and source records attributed to Timo Eckmann.

3 recordsLinked to original sources

arXiv2026

Bespoke-Card: Why Tune When You Can Generate? Synthesizing Workload-Specific Cardinality Estimators

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 estimators' strategies, a coding agent implements them, and a validator scores the estimates against true cardinalities and PostgreSQL estimates, forming a robust and deterministic harness. Going beyond naive prompting, Bespoke-Card uses structured q-error feedback, regression analysis, concrete outlier subplans, a curriculum isolating 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 from 190.5 to 11.5 (-94%), 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.

cs.DB↗

arXiv2026

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

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.

cs.DB↗

arXiv2025

JOB-Complex: A Challenging Benchmark for Traditional & Learned Query Optimization

Query optimization is a fundamental task in database systems that is crucial to providing high performance. To evaluate learned and traditional optimizer's performance, several benchmarks, such as the widely used JOB benchmark, are used. However, in this paper, we argue that existing benchmarks are inherently limited, as they do not reflect many real-world properties of query optimization, thus overstating the performance of both traditional and learned optimizers. In fact, simple but realistic properties, such as joins over string columns or complex filter predicates, can drastically reduce the performance of existing query optimizers. Thus, we introduce JOB-Complex, a new benchmark designed to challenge traditional and learned query optimizers by reflecting real-world complexity. Overall, JOB-Complex contains 30 SQL queries and comes together with a plan-selection benchmark containing nearly 6000 execution plans, making it a valuable resource to evaluate the performance of query optimizers and cost models in real-world scenarios. In our evaluation, we show that traditional and learned cost models struggle to achieve high performance on JOB-Complex, providing a runtime of up to 11x slower compared to the optimal plans.

cs.DB↗