monitoring

Grafana visualizes Prometheus metrics through configurable dashboards. Dashboard JSON models define panels, queries, and layouts programmatically. Panel types include timeseries for graphs, stat for single values, table for tabular data, and gauge for

Performance monitoring identifies slow queries and bottlenecks. I use EXPLAIN ANALYZE to profile query execution. pgstatstatements tracks query statistics over time. Slow query logs capture problematic queries. Query execution time, I/O, and buffer us

Production visibility requires more than basic request logging. I instrument critical code paths using ActiveSupport::Notifications to publish custom metrics that monitoring services consume. Each instrumented block publishes events with timing data,

Spring Boot Actuator provides production-ready features—health checks, metrics, info endpoints. /actuator/health shows application status for load balancers. /actuator/metrics exposes JVM metrics, HTTP stats, custom metrics. /actuator/info displays ap

Observability provides insight into database health and performance. I monitor key metrics—queries per second, connection count, cache hit ratio. Slow query logs identify performance problems. Query latency percentiles show user experience. Lock wait

Prometheus collects and stores time-series metrics via a pull model. It scrapes /metrics endpoints at configured intervals. The prometheus.yml defines scrape_configs with target discovery. static_configs list fixed targets while kubernetes_sd_configs

Load balancers and orchestration platforms like Kubernetes rely on health check endpoints to determine if an application instance is ready to serve traffic. A robust health check doesn't just return 200 OK—it verifies critical dependencies like databa

Time-series data tracks measurements over time—metrics, logs, sensor data. I use TimescaleDB for time-series workloads. Hypertables automatically partition by time. Continuous aggregates precompute rollups. Time-based retention policies auto-delete ol