Work

Augur: Predicting View Serializability Violations in Relational Data Store Applications

Year: 2026

Type: article

Abstract: Relational data stores are widely used because they provide persistence, scalability, and fault tolerance with a simple interface. However, most data store applications configure the data store to use weak isolation for scalable performance, permitting sporadic unserializable executions that produce incorrect results or failures. Prior work uses dynamic predictive analysis to infer violations from execution traces, but existing techniques cannot handle relational (i.e., SQL) queries with complex predicates, and they predict executions that do not violate View Serializability, leading to false negatives and false positives. This paper introduces Augur, the first dynamic predictive program analysis that (1) supports data store applications with complex relational queries and (2) reports only executions that violate View Serializability. The evaluation demonstrates that Augur finds feasible, unserializable executions in OLTP-Bench programs and in the widely used e-commerce application Spree.

Source: Proceedings of the ACM on Programming Languages

DOI: 10.1145/3839480

Authors: Chujun Geng, Noah Charlton, Spyros Blanas, Michael D. Bond, Yang Wang

Institution: The Ohio State University

Language: English

Topic: Distributed systems and fault tolerance

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funder: National Science Foundation

Awards: AI Institute for Intelligent CyberInfrastructure with Computational Learning in the Environment (ICICLE), SaTC: CORE: Medium: Practical Fine-Grained Information Flow Control with Rust, Collaborative Research: PPoSS: LARGE: ScaleStuds: Foundations for Correctness Checkability and Performance Predictability of Systems at Scale, CNS Core: Small: Rethinking High-Performance Persistent Transactions