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arXiv · 2003.10554

Elle: Inferring Isolation Anomalies from Experimental Observations

Kyle Kingsbury·Peter Alvaro
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Abstract

Users who care about their data store it in databases, which (at least in principle) guarantee some form of transactional isolation. However, experience shows [Kleppmann 2019, Kingsbury and Patella 2019a] that many databases do not provide the isolation guarantees they claim. With the recent proliferation of new distributed databases, demand has grown for checkers that can, by generating client workloads and injecting faults, produce anomalies that witness a violation of a stated guarantee. An ideal checker would be sound (no false positives), efficient (polynomial in history length and concurrency), effective (finding violations in real databases), general (analyzing many patterns of transactions), and informative (justifying the presence of an anomaly with understandable counterexamples). Sadly, we are aware of no checkers that satisfy these goals. We present Elle: a novel checker which infers an Adya-style dependency graph between client-observed transactions. It does so by carefully selecting database objects and operations when generating histories, so as to ensure that the results of database reads reveal information about their version history. Elle can detect every anomaly in Adya et al's formalism [Adya et al. 2000] (except for predicates), discriminate between them, and provide concise explanations of each. This paper makes the following contributions: we present Elle, demonstrate its soundness, measure its efficiency against the current state of the art, and give evidence of its effectiveness via a case study of four real databases.

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Kyle Kingsbury, Peter Alvaro. 2020-03-23. Elle: Inferring Isolation Anomalies from Experimental Observations. https://arxiv.org/abs/2003.10554

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