Guides to logs, metrics, traces and monitoring architecture. Build useful dashboards, investigate application behavior and manage telemetry collection, retention and cost.
observability
A common starting point looks like this: Prometheus scraping cluster metrics, Grafana holding the dashboards everyone trusts, a simple uptime checker watching the public status page, and logs split across a cloud provider console and a separate search tool. That setup works until an incident spans all of them. Then
observability
Production logging usually gets attention only after something breaks. A service starts throwing exceptions, a background worker stalls, or a customer reports a failed request, and the only evidence is a stream of mixed print() output with no timestamps, no severity, and no way to connect one line to the
observability
Your container monitoring stack worked perfectly for months. Prometheus scraped metrics reliably, Grafana dashboards loaded instantly, alerts fired when they should. Then one morning you wake up to a flood of "monitoring down" notifications, and suddenly you're flying blind through a production incident with no visibility
observability
Logrotate manages Linux log files by rotating them, compressing older copies, and removing archives according to your configuration. This guide covers /etc/logrotate.conf, application rules in /etc/logrotate.d/, daily and size-based rotation, and the checks to run when logs are not rotating. Logrotate runs when a scheduler invokes
observability
Managing multiple containers manually becomes a nightmare as your infrastructure grows. When you need Prometheus collecting metrics, Grafana visualizing data, databases storing information, and various exporters gathering system stats, coordinating these services individually leads to configuration drift, networking issues, and deployment inconsistencies. Before diving into Docker Compose, make sure you
observability
When your Prometheus server needs 64GB RAM just to track which user clicked which button, you've got a cardinality problem. Someone adds a user_id label to track user behavior, suddenly metrics storage balloons from gigabytes to hundreds of gigabytes, queries start timing out, monitoring bills triple overnight,
observability
When Prometheus Runs Out of Room You've got Prometheus running, it's scraping your endpoints every 15 seconds, Grafana dashboards look great, and alerting is working. Life is good. Then someone asks "can we look at metrics from six months ago?" and you realize your