Distribution-Aware Distributed Database Testing (Extended Version)

2026-09-16

AI Summary

针对分布式数据库管理系统的可靠性评估难题，提出了一种新的自动化方法DAT，通过利用分布感知信息生成测试查询来检测与分布策略和优化相关的错误。

Abstract

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.

Problem

• Distributed Database
• Query Execution
• Distribution Characteristics
• Reliability Assessment
• Bug Detection

Innovation

• Distribution-Aware Testing
• Distributed Database Management Systems
• Query Processing Bugs
• Distributed Optimizations
• Guided Query Mutation