DolphinDB is a high-performance time-series database increasingly used in financial and IoT applications. Operating it at scale requires more than just basic health checks. This article walks through a real-world monitoring platform built for DolphinDB 2.x, covering how to collect key metrics, set up meaningful alerts, and implement self-healing actions to reduce manual intervention. The approach emphasizes a closed-loop system where anomalies trigger automated responses, improving cluster reliability and reducing downtime. For teams running DolphinDB or similar distributed databases, the patterns described offer a solid foundation for building their own operational tooling. The focus on practical implementation rather than theory makes it a valuable reference for DevOps and database engineers.
A practical guide to monitoring DolphinDB 2.x clusters with metrics collection, alerting, and automated recovery.