Scalable database replication through dynamic multiversioning
Bibliographic record
Abstract
We scale the database back-end in dynamic content clus-ter servers by distributing read-only transactions on a set of lightweight database replicas while maintaining 1-copy-serializability. This is contrary to conventional wis-dom in replicated databases which says that one could have either 1-copy serializability or scalability, but not both. The key to scaling is a novel integrated fine-grained concurrency control and data replication algorithm called Dynamic Multiversioning that provides fine-grained dis-tributed concurrency control at the level of a memory page across a database cluster. We exploit the differ-ent distributed data versions that naturally come about as a result of asynchronous data replication in order to increase concurrency by running conflicting transactions in parallel on different replicas. At the same time, the serialization order is deter-mined using fine-grained concurrency control at a master database and enforced through a version-aware schedul-ing technique. Our technique does not put any crucial data in the scheduler, which permits easy reconfigura-tion, without loss of data, in the case of single-node fail-ures of any node in the system. Our measurements show near-linear scaling up to 8 databases for the browsing, shopping and even for the write-heavy ordering workload of the industry-standard e-commerce TPC-W benchmark. 1
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.004 | 0.003 |
| 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".