Demo of LearnedWMP: Workload Memory Prediction Using Deep Query Template Representations
Bibliographic record
Abstract
The database management system (DBMS) relies on a limited amount of working memory for in-memory operations during query execution. Inaccurate estimations can result in suboptimal query performance and potential failures. Achieving precise working memory estimations is therefore crucial to enhance the DBMS efficiency. We present LearnedWMP, a novel approach for simultaneously estimating working memory for a set of queries (i.e., a workload), as opposed to the conventional practice of estimating resources separately for each query. In a series of comprehensive experiments, the LearnedWMP model demonstrates improved accuracy in memory estimation and faster performance during both training and inference when compared to standard methods. The findings highlight the effectiveness and potential impact of the LearnedWMP approach on optimizing query performance. The audience can explore LearnedWMP through various scenarios showcasing its estimation accuracy and internal model workings.
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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 | 0.000 | 0.000 |
| 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".