Monitoring Node.js: Key Metrics You Should Track
a year ago
- #Performance
- #Node.js
- #Monitoring
- Effective Node.js monitoring requires tracking runtime metrics (memory, CPU), application metrics (request rates, response times), and business metrics (user actions, conversion rates).
- Runtime metrics include memory usage (heap used vs heap total, external memory, garbage collection frequency, RSS), CPU utilization, and event loop metrics (lag, utilization, tick length).
- Application metrics cover HTTP request metrics (request rate, response time, error rate), database and external service performance (query execution time, connection pool utilization).
- Business metrics link technical performance to business outcomes (conversion rate, user engagement, cart abandonment, revenue impact).
- Implement metrics collection using built-in Node.js APIs or observability clients like OpenTelemetry with Last9.
- Custom metrics can track user experience (checkout time) and cache effectiveness (hit/miss ratio).
- Configure effective alerts with multi-threshold levels, correlation-based alerts, and anomaly detection.
- Common mistakes include misinterpreting memory sawtooth patterns, not correlating event loop metrics with performance, and using mean values instead of percentiles.
- Use metrics outside production for benchmarking new features, load testing, and A/B testing performance.
- Start with basic runtime metrics, expand to application and business metrics, and set up smart alerts and dashboards.
- Focus on actionable metrics that help debug faster and make informed decisions.