Si Liu

TBA

Modern databases embrace weak isolation levels to cater for highly available transactions. However, weak isolation bugs have recently manifested in many production databases. This raises the concern of whether database implementations actually deliver their promised isolation guarantees in practice. In this paper we present Plume, the first efficient, complete, black-box checker for weak isolation levels. Plume builds on modular, fine-grained transactional anomalous patterns, with which we establish sound and complete characterizations of representative weak isolation levels, including read committed, read atomicity, and transactional causal consistency. Plume leverages a novel combination of two techniques, vectors and tree clocks, to accelerate isolation checking. Our extensive assessment shows that Plume can reproduce all known violations in a large collection of anomalous database execution histories, detect new isolation bugs in three production databases along with informative counterexamples, find more weak isolation anomalies than the state-of-the-art checkers, and efficiently validate isolation guarantees under a wide variety of workloads.

Substantial research efforts have been devoted to studying the performance optimality problem for distributed database transactions. However, they focus just on optimizing transactional reads, and thus overlook crucial factors, such as the efficiency of writes, which also impact the overall system performance. Motivated by a recent study on Twitter’s workloads showing the prominence of write-heavy workloads in practice, we make a substantial step towards performance-optimal distributed transactions by also aiming to optimize writes, a fundamentally new dimension to this problem. We propose a new design objective and establish impossibility results with respect to the achievable isolation levels. Guided by these results, we present two new transaction algorithms with different isolation guarantees that fulfill this design objective. Our evaluation demonstrates that these algorithms outperform the state of the art.

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 efciently 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-specifc 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, identifes their causes, outperforms the state-of-the-art black-box checkers under a wide range of workloads, and can scale up to large workloads.

Transactions are an important feature of database management systems (DBMSs), as they provide the ACID guarantees for a sequence of database operations. Consequently, approaches have been proposed to automatically find transactional bugs in DBMSs. However, they cannot handle complex operations and predicates common in real-world database queries, and thus miss bugs. This paper introduces a general, effective technique for finding transactional bugs in DBMSs that supports complex SQL queries and predicates. At the conceptual level, we address the test-oracle problem by constructing semantically-equivalent test cases based on fine-grained statement-level dependencies in transactions. At the technical level, we introduce (1) statement-dependency graphs to describe dependencies among SQL statements in transactions, (2) SQL-level instrumentation to capture possible statement-level dependencies, and (3) transactional oracle construction to generate semantically-equivalent test cases using statement-dependency graphs. We also establish the correctness of our approach in generating semantically-equivalent test cases. We have realized our technique as a tool, TxCheck, and evaluated it on three widely-used and well-tested DBMSs, namely TiDB, MySQL, and MariaDB. In total, TxCheck found 56 unique bugs, 52 of which have been confirmed and 18 already fixed. We believe that TxCheck can help solidify DBMSs’ support for transactions thanks to its generality and effectiveness.

Despite the central importance of DNS, numerous attacks and vulnerabilities are regularly discovered. The root of the problem is the ambiguity and tremendous complexity of DNS protocol specifications, amid a rapidly evolving Internet infrastructure. To counteract the vicious break-and-fix cycle for improving DNS infrastructure, we instigate a foundational approach: we construct the first formal semantics of end-to-end name resolution, a collection of components for the formal analyses of both qualitative and quantitative properties, and an automated tool for discovering DoS attacks. Our formal framework represents an important step towards a substantially more secure and reliable DNS infrastructure.

Today’s distributed systems must satisfy both qualitative and quantitative properties. These properties are analyzed using very different formal frameworks: expressive untimed and non-probabilistic frameworks, such as TLA+ and Hoare/separation logics, for qualitative properties; and timed/probabilistic-automaton-based ones, such as Uppaal and Prism, for quantitative ones. This requires developing two quite different models of the same system, without guarantees of semantic consistency between them. Furthermore, it is very hard or impossible to represent intrinsic features of distributed object systems—such as unbounded data structures, dynamic object creation, and an unbounded number of messages—using finite automata. In this paper we bridge this semantic gap, overcome the problem of manually having to develop two different models of a system, and solve the representation problem by: (i) defining a transformation from a very general class of distributed systems (a generalization of Agha’s actor model) that maps an untimed non-probabilistic distributed system model suitable for qualitative analysis to a probabilistic timed model suitable for quantitative analysis; and (ii) proving the two models semantically consistent. We formalize our models in rewriting logic, and can therefore use the Maude tool to analyze qualitative properties, and statistical model checking with PVeStA to analyze quantitative properties. We have automated this transformation and integrated it, together with the PVeStA statistical model checker, into the Actors2PMaude tool. We illustrate the expressiveness of our framework and our tool’s ease of use by automatically transforming untimed, qualitative models of numerous distributed system designs—including an industrial data store and a state-of-the-art transaction system—into quantitative models to analyze and compare the performance of different designs.

Distributed read atomic transactions are important building blocks of modern cloud databases that magnificently bridge the gap between data availability and strong data consistency. The performance of their transactional reads is particularly critical to the overall system performance, as many real-world database workloads are dominated by reads. Following the SNOW design principle for optimal reads, we develop LORA, a novel SNOW-optimal algorithm for distributed read atomic transactions. LORA completes its reads in exactly one round trip, even in the presence of conflicting writes, without imposing additional overhead to the communication, and it outperforms the state-of-the-art read atomic algorithms.To guide LORA’s development, we present a rewriting-logic-based framework and toolkit for design, verification, implementation, and evaluation of distributed databases. Within the framework, we formalize LORA and mathematically prove its data consistency guarantees. We also apply automatic model checking and statistical verification to validate our proofs and to estimate LORA’s performance. We additionally generate from the formal model a correct-by-construction distributed implementation for testing and performance evaluation under realistic deployments. Our design-level and implementation-based experimental results are consistent, which together demonstrate LORA’s promising data consistency and performance achievement.