OptiQL: Robust Optimistic Locking for Memory-Optimized Indexes
Bibliographic record
Abstract
Modern memory-optimized indexes often use optimistic locks for concurrent accesses. Read operations can proceed optimistically without taking the lock, greatly improving performance on multicore CPUs. But this is at the cost of robustness against contention where many threads contend on a small set of locks, causing excessive cacheline invalidation, interconnect traffic and eventually performance collapse. Yet existing solutions often sacrifice desired properties such as compact 8-byte lock size and fairness among lock requesters. This paper presents optimistic queuing lock (OptiQL), a new optimistic lock for database indexing to solve this problem. OptiQL extends the classic MCS lock---a fair, compact and robust mutual exclusion lock---with optimistic read capabilities for index workloads to achieve both robustness and high performance while maintaining various desirable properties. Evaluation using memory-optimized B+-trees on a 40-core, dual-socket server shows that OptiQL matches existing optimistic locks for read operations, while avoiding performance collapse under high contention.
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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 | 0.006 |
| 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 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".