DBPecker: A Graph-Based Compound Anomaly Diagnosis System for Distributed RDBMSs

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Abstract

This demonstration introduces DBPecker, an integrated diagnostic platform tailored for distributed relational database systems. DBPecker leverages a graph-based anomaly modeling approach to capture inter-node dependencies and effectively localize compound anomalies, while a causality-aware metric prioritization module automatically isolates critical performance indicators. By unifying anomaly detection with a comprehensive root cause analysis pipeline, the system facilitates rapid and precise diagnosis in distributed database environments. Evaluated on a multinode OceanBase cluster, DBPecker not only accelerates the identification of underlying anomalies but also substantially improves operational reliability, offering practical insights and actionable recommendations for real-world distributed database management.