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Record W6981696425

Eventual Durability of ACID Transactions in Database Systems

2023· dissertation· en· W6981696425 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueUWSpace (University of Waterloo) · 2023
Typedissertation
Languageen
FieldBusiness, Management and Accounting
TopicOrganizational Management and Leadership
Canadian institutionsBlackberry (Canada)
Fundersnot available
KeywordsDurabilityCommitDatabase transactionDistributed transactionTransaction processingCompensating transactionCorrectnessOnline transaction processing
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.469
Threshold uncertainty score0.947

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.021
GPT teacher head0.198
Teacher spread0.177 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it