How Prometheus Scrapes Metrics: A Practical Guide with a Pure Python App

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A tutorial series on observability tools revisits a previously configured Prometheus, Loki, and Grafana stack — this time adding a real data source. The article explains Prometheus's pull-based scraping model, where the server periodically fetches metrics from an application's HTTP endpoint, contrasting it with the push model used by tools like StatsD. Key advantages of pull-based collection include centralized failure detection, easy manual testing via curl or a browser, and central control over collection intervals. To demonstrate the concept hands-on, the guide walks through building a minimal Python HTTP server — using no external frameworks — that exposes a /metrics endpoint in Prometheus's plain-text exposition format. The example app tracks total request counts and process uptime, illustrating that no special client library is needed to instrument an application for Prometheus.

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