Sampling-Based Predictive Database Buffer Management
Bibliographic record
Abstract
Systems often need to support analytical (OLAP) workloads that perform concurrent scans of data on secondary storage. The buffer manager is tasked with fetching data into the database system's buffer pool and caching it there so as to increase the hit rate on these data, thereby lowering query latencies. This paper presents a database buffer caching policy that uses information about long-running scans to estimate future accesses. These estimates are used to approximate an optimal buffer caching policy that would otherwise infeasibly require knowledge about future accesses. Since a buffer caching policy must be efficient with low overhead, we present sampling-based predictive buffer management techniques where buffer eviction considers only a small random sample of buffers and access time estimates are used to select from the sample. This design is advantageous as it is easily tuned by adjusting the sample size, and easily modified to improve access time estimates and to expand the set of workload types that can be predicted effectively. We evaluate our techniques through both simulation studies on real Amazon Redshift workload traces and through implementation into the well-known open-source PostgreSQL database system on the popular TPC-H and YCSB benchmarks. We show that our approach delivers substantial performance improvements for workloads with scans, reducing I/O volume significantly by up to 40% over PostgreSQL's Clock-sweep policy and over prior predictive approaches for workloads using sequential scans and index accesses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".