New Relic

Practical Observability with OpenTelemetry and Prometheus

This technical guide explains how to implement production-grade observability for a Node.js microservice using OpenTelemetry, Prometheus, Grafana, and automated CI/CD validation with GitHub Actions. The article provides a complete, production-ready checkout endpoint example that instruments counters and histograms to capture throughput, status dimensions, and latency distributions with high-cardinality attributes. It advocates writing against the vendor-neutral OpenTelemetry API to avoid vendor lock-in, using the Prometheus exporter to expose metrics (port 9464), and visualizing percentiles (p95/p99) in Grafana. The repo layout includes Prometheus/Grafana docker-compose manifests, unit tests, and a GitHub Actions pipeline (checkout, Node setup, linting, tests) to validate telemetry and deployment. The post emphasizes multidimensional metrics over flat metric names and records best practices for structured logging and CI-driven telemetry validation.

Hands-on Observability Guide with Prometheus and Grafana

This technical guide demonstrates building an observability stack for a Node.js API using Prometheus and Grafana. It explains the three pillars of observability — logs, metrics, and traces — and walks through instrumenting an Express app with the prom-client library to expose counters, histograms, and gauges. The article includes a Prometheus scrape configuration, a docker-compose setup to run Prometheus and Grafana, common PromQL queries (request rate, error rate, p95/p99 latency, active requests, Node.js heap usage), and a traffic-generation example using autocannon. It shows how dashboards and alerts help diagnose issues (example: a spike in error rate on a specific route) and lists practical best practices such as using meaningful labels, avoiding high-cardinality labels, and following the RED method (Rate, Errors, Duration).

SRE Guide: How to Survive Tool Sprawl

An SRE recounts joining a startup that used 14 different monitoring and observability tools, costing $18,000/month and producing zero unified visibility. He outlines a four-phase consolidation framework: inventory tools, map overlaps, define a four-category core stack (metrics/infrastructure, APM, logs, incident management), and execute a 5–12 week migration with a 30-day parallel run. Following the plan the team reduced tooling from 14 to 4, cut monthly costs to $7,200 and reported a 40% MTTR improvement. The author, Samson Tanimawo, is founder and CEO of Nova AI Ops and positions the guide as a practical playbook for reducing cost, complexity and context-switching in SRE operations.