A feedback-driven lifecycle for SQL optimization, based on empirically validated query rewrites, which supports a source-neutral lifecycle in which alternative SQL formulations are admitted, measured, retained, activated, or rejected according to accumulated evidence rather than the generating mechanism.
Abstract
Cost-based query optimizers are essential for relational database systems, but SQL formulation can still affect selected execution plans and runtime, especially in recurring analytical workloads and machine-generated queries. This paper proposes a feedback-driven lifecycle for SQL optimization, based on empirically validated query rewrites. The contribution of the presented approach is a persistent candidate-management process that covers the path from candidate intake to provenance recording, structural admissibility checks, empirical result-equivalence validation, paired runtime evidence, guarded activation, retention, rejection, and later deactivation. Candidate rewrites may come from deterministic rules, local large language models, manual alternatives, or external rewrite systems; the source is recorded but does not determine acceptance. The evaluation uses a controlled research implementation with deterministic-rule cases, repeated TPC-H SF1 runs, a real-world-style anti-pattern corpus, and JOB/IMDB. The results show conservative behavior on mature analytical templates, including mostly withheld TPC-H candidates with one held-out positive case, stable evidence for selected anti-patterns, and comparable but non-identical JOB/IMDB positives across runs. The findings support a source-neutral lifecycle in which alternative SQL formulations are admitted, measured, retained, activated, or rejected according to accumulated evidence rather than the generating mechanism.
Statement-level SQL rewriting can improve query performance and maintainability without changing the DBMS kernel, but existing benchmarks do not evaluate rewrite methods as deployable systems. They typically focus on DBMS performance, rule regression, query equivalence, or dialect translation, while missing the full path from accepting an input query to producing an executable, result-consistent, and operationally useful rewrite. We present SQL-RewriteBench, a benchmark for statement-level SQL rewriting that applies correctness gating and full-denominator accounting. Its metric suite explicitly separates Source Acceptance, Generation Rate, Execution Coverage, Result Consistency, UnsafeRewrite Rate, and speedup distribution. It also defines SCS, a deterministic index of static SQL structure, and CGOQ, a correctness-gated optimization-quality score that gives optimization credit only after the case-specific Checker Contract is satisfied. CGOQ combines runtime improvement with structural simplification through a continuous scoring function, making it suitable for deployment-oriented rewrite assessment. As an artifact, SQL-RewriteBench provides 180 executable Benchmark Instances organized into EQUIV, PERF, ROBUST, and DIALECT pools, each packaged with SQL, schema metadata, provenance, evidence, and rewrite-opportunity documentation. Across seven representative academic and LLM-based methods, every full-benchmark CGOQ is negative. Existing methods often fail before rewriting, fail result checks, or return correct rewrites that are slower or no better than the input. These results show that deployable SQL rewrite requires broader input handling, result validation, and benefit-aware rewrite decisions.
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