It all started with a desire: I wanted some basic analytics for this blog to know how it's doing, which articles are popular and which are the main referrers. But therein lied the conundrum: I am a privacy zealot, very much against extensive online tracking (just look at the browser extensions I use), so using a third-party service that collected all my visitors' data was against the question.

I knew there were open-source web analytics projects out there, like Piwik which has been around for a long time, but never had the time or need to look into them seriously. Until now.

Please note that this article is by no means an extensive list. There are a lot of other options out there, just check out this big list. Each software has its own pros and cons, but I hope this article can help you choose and make the jump towards self-hosted analytics, or at least to abandon Google Analytics in favor of an open-source-based SAAS option.

My requirements

I researched options for my specific use case:

1. Must be free software (free as in freedom, not as in free beer) and self-hosted.

2. Simple deployment, the fewer moving parts the better. SQLite if possible so no DB server will be needed. No Redis/Memcached if it can be avoided. Option to deploy without Docker would be nice.

3. Reasonable resource usage. My sites don't usually get a lot of traffic, so I would like to reuse the 2 GB VPS that hosts this blog.

4. Technology that I can easily hack is a plus. I will favor Python projects, but YMMV.

5. Lightweight and not too intrusive. I basically just want to know which articles are seen when, plus referrers, no need for advanced analytics.

6. Actively maintained.

Matomo (formerly Piwik)

Home page

1. GPLv3. Paid SAAS cloud service with free self-hosting option.

2. Installation docs.

   • Requires a webserver (Apache, Nginx, etc.)
   • Requires MySQL.
   • Configuration of Matomo is all done in-app.

3. Matomo self-hosted requirements:

   • <100k page views/month: 1 server for both app and DB. 2 CPU, 2 GB RAM, 50GB SSD disk.
   • ~1M page views/month: 1 server for both app and DB. 4 CPU, 8 GB RAM, 250GB SSD disk.
   • ~10M page views/month: 2 servers for app and DB. 8 CPUs, 16 GB RAM, 400GB SSD disk
   • ~100M page views/month: 3 or more servers for app, DB and load balancer. 16 CPUs, 32 GB RAM, 1 TB SSD disk.

4. PHP for backend. VueJS and TypeScript for frontend. Github repository and developer guide.

5. Very comprehensive set of collected analytics. Wordpress plugin available.

6. About 10 commits per week, multiple contributors.

Plausible

Home page

1. AGPLv3. Paid SAAS cloud service with free self-hosting option, but the latter lags behind in features (2 releases per year).

2. Official installation documentation. tldr; needs docker-compose, PostgreSQL and Clickhouse.

3. From Plausible's requirements documentation:

   The server must have a CPU with x86_64 architecture and support for SSE 4.2 instructions. We recommend using a minimum of 4GB of RAM but the requirements will depend on your site traffic.

4. Elixir/Phoenix backend. React and Tailwind frontend. Github repository and developer guide.

5. <1 KiB tracking script. No cookies. I couldn't find a concrete list of tracked metrics, but it seems less extensive than Matomo or OWA.

6. Weekly activity, usually condensed in long-form pull requests.

Shynet

Home page

1. Apache License Version 2.0. Self-hosted only.

2. Easy deployment with Docker or docker-compose, otherwise undocumented manual deployment. Supports any DB that Django supports, including SQLite. Cache server optional.

3. Low resource consumption, basically just a Python process running Django, ~100 MiB of RAM when using SQLite.

4. Python/Django backend. Uses its own TailwindCSS plugin for frontend. Easy to build from source (Docker image). Github repository and developer guide

5. <1 KiB tracking script. No cookies. 1x1 pixel tracking fallback. Well-defined list of captured metrics, pretty basic.

6. Not a lot of new feature development, but dependencies are kept up to date and the main developer is very responsive to issues and pull requests (see conclusions).

Open Web Analytics (OWA)

Home page

1. GPLv2. Self-hosted only.

2. From OWA's installation documentation and technical requirements:

   • Only PHP projects supported.
   • In-app install wizard.
   • Only Apache 2.x officially supported. Nginx works but needs manual configuration.
   • MySQL with strict mode off.

3. Unclear as to the exact requirements.

4. PHP backend. Webpack for frontend assets. Multiple repositories: base repo, Wordpress plugin repo, SDK for PHP repo.

5. Very detailed tracking, including heatmaps and "Domstream session recordings", but only for PHP apps. Dedicated integration plugins for WordPress and MediaWiki.

6. Actively maintained, but apparently by a single developer.

Conclusions and my winner

Looking at the highlighted requirements my choice for this humble blog was easy: Shynet won hands down, it checked every box. For bigger sites Plausible and Matomo both seem to be good options. I would say Plausible has a better foundational technology (it launched in 2019 after all) but Matomo has been around for longer (2007), it's more battle-tested and seems to collect more data. Plausible has a comparison page between Plausible and Matomo. OWA might be a good option for PHP and Wordpress sites.

Shynet's hackability, point 4 in my requirements list, quickly came into the spotlight because SQLite support was broken. Fortunately I was able to fix it and deploy it into the very server where you have been reading this article. This is why I love free software.

Another issue with the deployment was that the server didn't have Docker installed and I wanted to keep it that way, so I used the opportunity to learn about Podman. I instantly fell in love with it and its ability to run rootless containers, which is a boon to security. Only issue I encountered was having to change the permissions of the mounted data volume, otherwise Shynet's Django process was unable to write the SQLite DB as files were owned by root inside the container. This article explains it well, tldr; need to use podman unshare.

To end this article, here is a screenshot of Shynet's data for this blog (isn't it great to be able to share knowing that nobody's privacy is at stake?):