Set up Prometheus monitoring for applications with custom metrics, scraping configurations, and service discovery. Use when implementing time-series metrics collection, monitoring applications, or building observability infrastructure.

Prometheus Monitoring Table of Contents [Overview]( overview) [When to Use]( when to use) [Quick Start]( quick start) [Reference Guides]( reference guides) [Best Practices]( best practices) Overview Implement comprehensive Prometheus monitoring infrastructure for collecting, storing, and querying time series metrics from applications and infrastructure. When to Use Setting up metrics collection Creating custom application metrics Configuring scraping targets Implementing service discovery Building monitoring infrastructure Quick Start Minimal working example: Reference Guides Detailed implementations in the references/ directory: Guide Contents [Prometheus Configuration](references/prometheus configuration.md) Prometheus Configuration [Node.js Metrics Implementation](references/nodejs metrics implementation.md) Node.js Metrics Implementation [Python Prometheus Integration](references/python prometheus integration.md) Python Prometheus Integration [Alert Rules](references/alert rules.md) Alert Rules [Docker Compose Setup](references/docker compose setup.md) Docker Compose Setup Best Practices ✅ DO Use consistent metric naming conventions Add comprehensive labels for filtering Set appropriate scrape intervals (10 60s) Implement retention policies Monitor Prometheus itself Test alert rules before deployment Document metric meanings ❌ DON'T Add unbounded cardinality labels Scrape too frequently (< 10s) Ignore metric naming conventions Create alerts without runbooks Store raw event data in Prometheus Use counters for gauge like values

prometheus-monitoring