Eventual Durability of ACID Transactions in Database Systems
Why this work is in the frame
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Bibliographic record
Abstract
Modern database systems that support ACID transactions, and applications built around \nthese databases, may choose to sacrifice transaction durability for performance when they \ndeem it necessary. While this approach may yield good performance, it has three major \ndownsides. Firstly, users are often not provided information about when and if the issued \ntransactions become durable. Secondly, users cannot know if durable and non-durable \ntransactions see each other’s effects. Finally, this approach pushes durability handling \noutside the scope of the transactional model, making it difficult for applications to reason \nabout correctness and data consistency. \n \nTo address these issues, we present the idea of “Eventual Durability” (ED) to provide a \nprincipled way for applications to manage transaction durability trade-offs. The ED model \nextends the traditional transaction model by decoupling a transaction’s commit point from \nits durability point – therefore, allowing applications to control which transactions should \nbe acknowledged at commit point and which ones at their durability point. Furthermore, \nwe redefine serialisability and recoverability under ED to allow applications to ascertain \nif fast transactions became durable and how they might have interacted with safe ones. \nWith ED, users and applications can know what to expect to lose when there is a failure \n– thus, bringing back managing durability inside the transaction model. \n \nWe implement the ED model in PostgreSQL and evaluate it to understand the model’s \neffect on transaction latency, abort rates and throughput. We show that ED Postgres \nachieves significant latency improvements even while ensuring the guarantees provided by \nthe model. Since a transaction’s resources are released earlier in ED Postgres, we expected \nto see lower abort rates and higher throughput. Consequently, we observed that ED \nPostgres provides an average of 91.25% – 93% reduction in abort rates under a contentious \nworkload and an average of 75% increase in throughput compared to baseline Postgres. \nWe also run the TPC-C benchmark against ED Postgres and discuss the findings. Lastly, \nwe discuss how ED Postgres can be used in realistic settings to obtain latency benefits, \nthroughput improvements, reduced abort rates, and fresher reads.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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 it