Implementing the PostgreSQL query optimizer within the OPT++ framework
Why this work is in the frame
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Bibliographic record
Abstract
Our work studies the application of an existing framework, called OPT++, for query optimization for relational databases. The initial application was a simple bottom-up optimizer, while the second application was to implement the query optimization strategies of PostgreSQL with the framework. Our experience illustrates the power and the pitfalls of reusing frameworks. During the course of the two applications we found substantial need to improve the design at the detailed level though the main abstractions of OPT++ did not change. Our second application raised many issues with the performance of OPT++, which is surprising since its fundamental purpose was a major study of relative performance of query optimization strategies by its author. We have addressed many performance issues, but some with broad impact on the framework's code are still being addressed.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 it