Perfce: Performance Debugging on Databases with Chaos Engineering-Enhanced Causality Analysis

Debugging performance anomalies in databases is challenging. Causal inference techniques enable qualitative and quantitative root cause analysis of performance downgrades. Nevertheless, causality analysis is challenging in practice, particularly due to limited observability. Recently, chaos engineer...

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Veröffentlicht in:IEEE/ACM International Conference on Automated Software Engineering : [proceedings] S. 1454 - 1466
Hauptverfasser: Ji, Zhenlan, Ma, Pingchuan, Wang, Shuai
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 11.09.2023
Schlagworte:
Causality Analysis
Chaos
Chaos Engineering
Debugging
Distributed databases
Observability
Performance Debugging
Root cause analysis
Software systems
Software testing
ISSN:2643-1572
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Zusammenfassung:Debugging performance anomalies in databases is challenging. Causal inference techniques enable qualitative and quantitative root cause analysis of performance downgrades. Nevertheless, causality analysis is challenging in practice, particularly due to limited observability. Recently, chaos engineering (CE) has been applied to test complex software systems. CE frameworks mutate chaos variables to inject catastrophic events (e.g., network slowdowns) to stress-test these software systems. The systems under chaos stress are then tested (e.g., via differential testing) to check if they retain normal functionality, such as returning correct SQL query outputs even under stress. To date, CE is mainly employed to aid software testing. This paper identifies the novel usage of CE in diagnosing performance anomalies in databases. Our framework, PERFCE, has two phases - offline and online. The offline phase learns statistical models of a database using both passive observations and proactive chaos experiments. The online phase diagnoses the root cause of performance anomalies from both qualitative and quantitative aspects on-the-fly. In evaluation, Perfce outperformed previous works on synthetic datasets and is highly accurate and moderately expensive when analyzing real-world (distributed) databases like MySQL and TiDB.
ISSN:2643-1572
DOI:10.1109/ASE56229.2023.00106