Supporting multi-row distributed transactions with global snapshot isolation using bare-bones HBase
Bibliographic record
Abstract
Snapshot isolation (SI) is an important database transactional isolation level adopted by major database management systems (DBMS). Until now, there is no solution for any traditional DBMS to be easily replicated with global SI for distributed transactions in cloud computing environments. HBase is a column-oriented data store for Hadoop that has been proven to scale and perform well on clouds. HBase features random access performance on par with open source DBMS such as MySQL. However, HBase only provides single atomic row writes based on row locks and very limited transactional support. In this paper, we show how multi-row distributed transactions with global SI guarantee can be easily supported by using bare-bones HBase with its default configuration so that the high throughput, scalability, fault tolerance, access transparency and easy deployability properties of HBase can be inherited. Through performance studies, we quantify the cost of adopting our technique. The contribution of this paper is that we provide a novel approach to use HBase as a cloud database solution with global SI at low added cost. Our approach can be easily extended to other column-oriented data stores.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| 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".