A Hybrid Cost Model for Evaluating Query Execution Plans
Bibliographic record
Abstract
Query optimization aims to select a query execution plan among all query paths for a given query. The query optimization of traditional relational database management systems (RDBMSs) relies on the estimation of the cost of the alternative query plans in the query plan search space provided by a cost model. The classic cost model (CCM) may lead the optimizer to choose query plans with poor execution time due to inaccurate cardinality estimations and simplifying assumptions [7, 8, 14]. A learned cost model (LCM) based on machine learning does not rely on such estimations and learns the cost from runtime [5, 10, 11]. While learned cost models are shown to improve the average performance, they may not guarantee that optimal performance would be consistently achieved. In addition, the query plans generated using the LCM may not necessarily outperform the query plans generated with the CCM. In this paper, we propose a hybrid approach to solve this problem by striking a balance between the LCM and the CCM. The hybrid model uses the LCM when it is expected to be reliable in selecting a good plan and falls back to the CCM otherwise. The evaluation results of the hybrid model demonstrate promising performance, indicating potential for successful use in future applications.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".