How to Implement Logging and Monitoring
Set up structured logging, metrics collection, and alerting for production application observability.
What You'll Learn
This intermediate-level guide walks you through how to implement logging and monitoring step by step. Estimated time: 12 min.
Step 1: Implement structured logging
Configure Pino or Winston for JSON-structured logging with correlation IDs, log levels, and contextual metadata.
Step 2: Set up log aggregation
Send logs to a centralized platform like Datadog, Grafana Loki, or AWS CloudWatch for search, analysis, and retention.
Step 3: Add application metrics
Instrument your application with custom metrics for request counts, latency histograms, error rates, and business KPIs.
Step 4: Configure alerting
Set up alerts on error rate thresholds, latency spikes, and availability drops with escalation policies and on-call rotation.
Step 5: Build operational dashboards
Create Grafana or Datadog dashboards showing system health, deployment markers, and key performance indicators at a glance.
Frequently Asked Questions
What should I log?▾
Log request/response metadata, errors with stack traces, business events, and security events. Avoid logging PII, passwords, or sensitive data.
How long should I retain logs?▾
Keep detailed logs for 30 days, aggregated metrics for 1 year, and security audit logs as required by compliance. Balance cost with debugging needs.
Which observability stack should I use?▾
Datadog for all-in-one enterprise observability. Grafana plus Prometheus plus Loki for open-source flexibility. Vercel and Sentry for simpler applications.