Benchmarking the Full Pipeline of Materialized-View-Based Query Rewriting

2026-07-21

AI Summary

This work addresses the limitation of existing research, which often focuses narrowly on a single stage of materialized view acceleration, lacking holistic evaluation of the end-to-end pipeline encompassing candidate generation, view selection, and query rewriting. The paper introduces the first comprehensive benchmarking framework that systematically assesses the interplay among these three stages under storage constraints through modular design and controlled ablation studies. It further incorporates a cross-engine protocol to compare native optimizer-level rewriting against portable SQL-based rewriting baselines. The study reveals strong interdependencies among pipeline stages, significant performance disparities across systems, and identifies representative failure patterns that lead to performance degradation after rewriting.

Abstract

Materialized views (MVs) accelerate OLAP and data-warehouse workloads by precomputing reusable subexpressions, but practical MV-based query acceleration is a multi-stage pipeline: candidate enumeration, view selection under storage budgets, and query rewriting inside the optimizer. Existing evaluations typically study only parts of this pipeline and within a single system, leaving end-to-end trade-offs and cross-system behavior unclear. In this paper, we benchmark MV-based query rewriting by jointly evaluating enumeration, selection, and rewriting with a modular evaluation framework and by using controlled ablations. We also introduce a cross-engine protocol allowing us to compare systems that expose only execution plans by contrasting native optimizer-level rewriting with portable SQL rewriting baselines when available. Across representative academic methods and modern open-source and commercial systems, we find strong interaction effects across stages and large variability in MV usage and realized savings. We identify recurring failure modes that explain performance regressions after rewriting. Our results highlight which pipeline stages most often limit performance and provide evidence to guide future MV enumeration, selection, and rewriting designs.

Problem

• materialized views
• query rewriting
• benchmarking
• OLAP
• data warehouse

Innovation

• materialized views
• query rewriting
• benchmarking
• cross-engine evaluation
• optimization pipeline