5 research outputs found
Efficient Black-box Checking of Snapshot Isolation in Databases
Snapshot isolation (SI) is a prevalent weak isolation level that avoids the
performance penalty imposed by serializability and simultaneously prevents
various undesired data anomalies. Nevertheless, SI anomalies have recently been
found in production cloud databases that claim to provide the SI guarantee.
Given the complex and often unavailable internals of such databases, a
black-box SI checker is highly desirable.
In this paper we present PolySI, a novel black-box checker that efficiently
checks SI and provides understandable counterexamples upon detecting
violations. PolySI builds on a novel characterization of SI using generalized
polygraphs (GPs), for which we establish its soundness and completeness. PolySI
employs an SMT solver and also accelerates SMT solving by utilizing the compact
constraint encoding of GPs and domain-specific optimizations for pruning
constraints. As demonstrated by our extensive assessment, PolySI successfully
reproduces all of 2477 known SI anomalies, detects novel SI violations in three
production cloud databases, identifies their causes, outperforms the
state-of-the-art black-box checkers under a wide range of workloads, and can
scale up to large-sized workloads.Comment: 20 pages, 15 figures, accepted by PVLD
SALI: A Scalable Adaptive Learned Index Framework based on Probability Models
The growth in data storage capacity and the increasing demands for high
performance have created several challenges for concurrent indexing structures.
One promising solution is learned indexes, which use a learning-based approach
to fit the distribution of stored data and predictively locate target keys,
significantly improving lookup performance. Despite their advantages,
prevailing learned indexes exhibit constraints and encounter issues of
scalability on multi-core data storage.
This paper introduces SALI, the Scalable Adaptive Learned Index framework,
which incorporates two strategies aimed at achieving high scalability,
improving efficiency, and enhancing the robustness of the learned index.
Firstly, a set of node-evolving strategies is defined to enable the learned
index to adapt to various workload skews and enhance its concurrency
performance in such scenarios. Secondly, a lightweight strategy is proposed to
maintain statistical information within the learned index, with the goal of
further improving the scalability of the index. Furthermore, to validate their
effectiveness, SALI applied the two strategies mentioned above to the learned
index structure that utilizes fine-grained write locks, known as LIPP. The
experimental results have demonstrated that SALI significantly enhances the
insertion throughput with 64 threads by an average of 2.04x compared to the
second-best learned index. Furthermore, SALI accomplishes a lookup throughput
similar to that of LIPP+.Comment: Accepted by Conference SIGMOD 24, June 09-15, 2024, Santiago, Chil
Efficient Black-box Checking of Snapshot Isolation in Databases
Snapshot isolation (SI) is a prevalent weak isolation level that avoids the performance penalty imposed by serializability and simultaneously prevents various undesired data anomalies. Nevertheless, SI anomalies have recently been found in production cloud databases that claim to provide the SI guarantee. Given the complex and often unavailable internals of such databases, a black-box SI checker is highly desirable.
In this paper we present PolySI, a black-box checker that efficiently checks SI and provides understandable counterexamples upon detecting violations. PolySI builds on a characterization of SI using generalized polygraphs (GPs), for which we establish its soundness and completeness. PolySI employs an SMT solver and also accelerates SMT solving by utilizing a compact constraint encoding of GPs and domain-specific optimizations for pruning constraints. As our extensive assessment demonstrates, PolySI successfully reproduces all of 2477 known SI anomalies, detects novel SI violations in three production cloud databases, identifies their causes, outperforms the state-of-the-art black-box checkers under a wide range of workloads, and can scale up to large workloads.ISSN:2150-809