observability-and-monitoring

Structured logging requires configuring services to emit JSON-formatted logs containing timestamp, level, message, service, and environment fields. This consistent format adds essential context for diagnosing production issues across distributed systems.

The four golden signals for system monitoring are latency, errors, traffic, and saturation. Capturing these essential metrics exposes key performance indicators that help you understand system behavior and address reliability issues in production.

Distributed tracing pinpoints performance bottlenecks by tracing requests across services to visualize their propagation path. This mechanism tracks the exact flow of a request, making it easier to identify slow components in complex microservice architectures.

Defining SLIs and SLOs for reliability engineering involves establishing service level indicators to measure performance and setting targets for system reliability. Tracking these indicators ensures your alerting strategies and dashboards reflect actual system health.

OpenTelemetry is supported for observability instrumentation by integrating it to capture distributed tracing data. When deploying a new microservice, you can use this integration to ensure the system is fully instrumented for comprehensive tracing.

Health check endpoints are needed for production services to provide a standard way to verify system availability. Implementing these endpoints supports incident diagnosis and helps reliability engineering efforts by exposing immediate system state.