Analytics Are Heavy. The DBMS Is Busy. When Will My Mission-Critical Transaction Start Running?
Bibliographic record
Abstract
Conventional non-preemptive scheduling strategies struggle to meet the latency requirements of mixed workloads: low-priority, long-running analytics can dominate CPU cores while short, high-priority transactions wait a long time to be scheduled. Although preemptive scheduling appears to be a natural solution, it has long been discouraged in DBMSs by conventional wisdom due to concerns about deadlocks and interrupt-handling overheads. In this demonstration, we highlight that this is no longer the case with PreemptDB, a modern memory-optimized DBMS that we built around (1) optimistic concurrency and (2) userspace interrupts that recently became available in x86 CPUs. PreemptDB proposes user-interruptassisted context switching to renew preemptive scheduling in modern DBMSs. Through a set of demonstration scenarios, we show that preemptive scheduling is practical and prioritizes high-priority transactions while preserving throughput and fairness.
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 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 | 0.001 |
| Open science | 0.001 | 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".