Bibliographic record
Abstract
Distributed transactions, which access data items at multiple sites atomically, face well-known scalability challenges. To avoid the high overhead, in prior work Fan et al. proposed Epoch-based Concurrency Control (ECC), which makes transactions visible at epoch boundaries, and presented a system that supports high performance read-only and write-only transactions. However, this idea has a clear difficulty to overcome: the common case of a single transaction that does both reading and writing. This paper proposes ALOHA-DB, a scalable distributed transaction processing system. ALOHA-DB uses a novel paradigm of serializable transaction processing using functors, which conceptually resemble futures in modern programming languages. A functor is a placeholder for the value of a key, which can be computed asynchronously in the future in parallel with other functor computations of the same or other transactions. With multi-versioning in ECC, the functor computations only rely on accessing historical versions, and so the traditional locking mechanism is not needed for concurrency control. Functors elevate ECC to a new level: supporting serializable distributed read-write transactions. This combination of techniques never aborts transactions due to read-write or write-write conflicts, but allows transactions to fail due to logic errors or constraint violations. We used functor-enabled ECC to implement ALOHA-DB and evaluated it using TPC-C and YCSB read-write distributed transactions. Experimental results demonstrate that our system's performance on the TPC-C benchmark is nearly 2 million transactions per second over 20 eight-core virtual machines, which outperforms Calvin, a state-of-the-art transaction processing and replication layer, by one to two orders of magnitude.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.003 | 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".