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 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".