Wii: Dynamic Budget Reallocation In Index Tuning
Bibliographic record
Abstract
Index tuning aims to find the optimal index configuration for an input workload. It is often a time-consuming and resource-intensive process, largely attributed to the huge amount of "what-if" calls made to the query optimizer during configuration enumeration. Therefore, in practice it is desirable to set a budget constraint that limits the number of what-if calls allowed. This yields a new problem of budget allocation, namely, deciding on which query-configuration pairs (QCP's) to issue what-if calls. Unfortunately, optimal budget allocation is NP-hard, and budget allocation decisions made by existing solutions can be inferior. In particular, many of the what-if calls allocated by using existing solutions are devoted to QCP's whose what-if costs can be approximated by using cost derivation, a well-known technique that is computationally much more efficient and has been adopted by commercial index tuning software. This results in considerable waste of the budget, as these what-if calls are unnecessary. In this paper, we propose "Wii," a lightweight mechanism that aims to avoid such spurious what-if calls. It can be seamlessly integrated with existing configuration enumeration algorithms. Experimental evaluation on top of both standard industrial benchmarks and real workloads demonstrates that Wii can eliminate significant number of spurious what-if calls. Moreover, by reallocating the saved budget to QCP's where cost derivation is less accurate, existing algorithms can be significantly improved in terms of the final configuration found.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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 source (direct Gemma or distilled Codex), 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".