分布式感知的分布式数据库测试（扩展版）
Distribution-Aware Distributed Database Testing (Extended Version)
- East China Normal University（华东师范大学）
- The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））
- Hunan University（湖南大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对分布式数据库管理系统，提出分布式感知测试方法DAT及工具DistRanger，利用分布信息引导查询变异，在四个生产系统上发现31个新缺陷，优于现有测试工具。
AI中文摘要：
分布式数据库管理系统（DDBMSs）因其影响查询执行和优化的分布特定特性，给评估其可靠性带来了新的挑战。现有的测试方法主要针对集中式数据库管理系统设计，往往无法探索多样化的分布式执行行为，并且生成的测试查询可执行性低，从而限制了其在缺陷检测中的有效性。我们提出了DAT（分布式感知测试），一种新颖的自动化方法，通过在整个测试流程中系统地利用分布式感知信息，来检测DDBMSs中与分布策略和分布式优化相关的查询处理缺陷。DAT基于一组技术，捕获逻辑模式和数据分布策略的多样化组合，并执行引导式查询变异以触发广泛的分布式查询执行行为和优化，同时通过历史反馈提高查询可执行性。我们将我们的方法实现为一个工具DistRanger，并在四个广泛使用的生产级DDBMSs上进行了评估。它发现了31个先前未知的缺陷，其中28个与分布式查询处理和优化相关，并且优于最先进的测试工具。
英文摘要：
Distributed database management systems (DDBMSs) introduce new challenges for assessing their reliability due to distribution-specific characteristics that affect query execution and optimization. Existing testing approaches, largely designed for centralized DBMSs, often fail to explore diverse distributed execution behaviors and suffer from low executability of generated test queries, thereby limiting their effectiveness in bug detection. We propose DAT (Distribution-Aware Testing), a novel automated approach for detecting query-processing bugs related to distribution strategies and distributed optimizations in DDBMSs, by systematically leveraging distribution-aware information throughout the testing pipeline. DAT builds on a set of techniques that capture diverse combinations of logical schemas and data distribution strategies, and performs guided query mutation to trigger a wide range of distributed query execution behaviors and optimizations, while improving query executability via historical feedback. We implement our approach in a tool, DistRanger, and evaluate it on four widely used production DDBMSs. It uncovers 31 previously unknown bugs, including 28 related to distributed query processing and optimization, and outperforms state-of-the-art testers.
