duffn/gunicorn-statsd-prometheus-demo — explained in plain English
Analysis updated 2026-07-18 · repo last pushed 2019-08-30
Spin up a Gunicorn app with Prometheus monitoring already wired together using Docker Compose.
Investigate why a web app is slow by checking request counts, latency, and memory metrics.
View real-time server metrics in the Prometheus graph interface.
Use the project as a template for wiring Gunicorn, statsd, and Prometheus in your own app.
| duffn/gunicorn-statsd-prometheus-demo | 0verflowme/alarm-clock | 0verflowme/seclists | |
|---|---|---|---|
| Language | — | CSS | — |
| Last pushed | 2019-08-30 | 2022-10-03 | 2020-05-03 |
| Maintenance | Dormant | Dormant | Dormant |
| Setup difficulty | moderate | easy | easy |
| Complexity | 3/5 | 2/5 | 1/5 |
| Audience | ops devops | vibe coder | ops devops |
Figures from each repo's GitHub metadata at analysis time.
Requires Docker Compose, exact ports differ between Docker Machine and Docker on Mac setups.
This repository is a demonstration project that shows how to monitor a Python web application using Gunicorn (a web server) and track its performance metrics in Prometheus (a monitoring system). When you run the project using Docker Compose, it sets up a complete monitoring stack. The application collects statistics about how the web server is performing, things like how many requests it's handling, how long they take, and how much memory it's using, and sends this data to Prometheus. You can then view these metrics in a visual dashboard. After starting the project, you can visit two main interfaces. One shows you the Prometheus graph interface where you can explore and visualize the collected metrics over time. The other displays the raw statistics being gathered from Gunicorn in a metrics format. The specific ports and addresses are provided in the documentation, with different instructions depending on whether you're using Docker Machine or Docker on Mac. This type of setup is useful for developers or system administrators who want to understand how their web applications are performing in production. Instead of guessing why an application might be slow or using vague logs, you get concrete, real-time data about server behavior. A concrete example: if you notice your application is running slowly during certain hours, you could look at these metrics to see whether the problem is too many concurrent requests, memory usage, or something else entirely. The project essentially acts as a working template, rather than explaining how to wire these tools together in documentation, it shows you exactly how Gunicorn, statsd (a metrics collection protocol), and Prometheus fit together in practice.
A Docker Compose demo showing how to monitor a Gunicorn Python web server's performance metrics using statsd and Prometheus dashboards.
Dormant — no commits in 2+ years (last push 2019-08-30).
Setup difficulty is rated moderate, with roughly 30min to a first successful run.
Mainly ops devops.
This repo across BitVibe Labs
double-check against the repo, no cap.