Bibliographic record
Abstract
Automating index design has been an active area of research for decades due to the significant impact that indexes have on query performance and database efficiency. Existing approaches range from brute-force search to cost-based optimizations and, more recently, machine learning techniques. However, many suffer from high computational costs, reliance on inaccurate cost models, or the need for deep integration with database internals. We introduce SQL:Trek, a time-efficient tool for automated index design that operates entirely as an external utility. SQL:Trek leverages query compiler cost models to identify effective indexes while mitigating false positives through execution on a lightweight simulation database. This approach enables fast, iterative index selection without modifying database internals, making it broadly applicable across relational databases, including most MySQL ® and PostgreSQL ® derivative databases. Our evaluation demonstrates that SQL:Trek delivers significant query performance improvements while keeping index selection computationally efficient, with most workloads analyzed in under five minutes. Unlike many cost-based what-if analysis methods, SQL:Trek significantly improved performance of many production workloads while avoiding the majority of detrimental index recommendations caused by optimizer misestimates. These results highlight SQL:Trek as a practical, scalable solution for automated index tuning in modern database environments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".