What problem does it solve?
Monitoring and observability help you understand what your application is doing, detect failures early, and debug issues quickly instead of guessing when users report problems.
Core Features & Use Cases
- Structured logging: Emit machine-parseable JSON logs with consistent context like timestamps, request IDs, service names, and user IDs for fast search and reliable incident forensics.
- Actionable metrics: Track rates, errors, and durations using RED (Rate, Errors, Duration) and USE (Utilization, Saturation, Errors) patterns to measure system health over time.
- Distributed tracing: Use correlation/trace IDs and OpenTelemetry-style instrumentation to follow a request end-to-end across services and pinpoint latency bottlenecks.
- Alerting best practices: Create symptom-based alerts with meaningful thresholds, runbooks, and escalation policies to reduce noise and improve response quality.
- Health check endpoints: Implement standard endpoints (for example, GET /health) to support load balancers, Kubernetes probes, and deployment verification.
Quick Start
Ask your team to implement structured JSON logs, RED/USE metrics, and an OpenTelemetry-compatible trace correlation strategy, then add a GET /health endpoint and wire alerts with runbooks and escalation.
