Sampling-based Predictive Database Buffer Management
Bibliographic record
Abstract
This thesis presents a database buffer caching policy that uses information about long- \nrunning scans to estimate future accesses. These estimates are used to approximate the \noptimal caching policy, which requires knowledge about future accesses. The buffer caching \npolicy must be efficient with low CPU overhead, which is achieved with sampling: buffer \neviction considers only a small random sample of buffers and access time estimates are \nused to select among the sample. This design is easily tuned by adjusting the sample size, \nand easily modified to improve the access time estimates and expand the set of workload \ntypes that can be predicted effectively. \n \nThis approach is implemented in PostgreSQL and evaluated on a series of experiments \nbased on TPC-H. Based on the experiments, this approach works very well for workloads \nwith mainly sequential scans, reducing I/O volume by up to 38% over PostgreSQL’s Clock- \nsweep implementation, and is competitive with standard approaches for workloads using a \nmix of sequential scans and index accesses.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".