issue_comments: 1684488526

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https://github.com/simonw/datasette/issues/2143#issuecomment-1684488526 https://api.github.com/repos/simonw/datasette/issues/2143 1684488526 IC_kwDOBm6k_c5kZ0FO 9599 2023-08-18T22:18:39Z 2023-08-18T22:18:39Z OWNER

Another option would be, instead of flat datasette.json/datasette.yaml files, we could instead use a Python file, like datasette_config.py. That way one could dynamically generate config (ex dev vs prod, auto-discover credentials, etc.). Kinda like Django settings.

Another option would be, instead of flat datasette.json/datasette.yaml files, we could instead use a Python file, like datasette_config.py. That way one could dynamically generate config (ex dev vs prod, auto-discover credentials, etc.). Kinda like Django settings.

I'm not a fan of that. I feel like software history is full of examples of projects that implemented configuration-as-code and then later regretted it - the most recent example is setup.py in Python turning into pyproject.yaml, but I feel like I've seen that pattern play out elsewhere too.

I don't think having people dynamically generate JSON/YAML for their configuration is a big burden. I'd have to see some very compelling use-cases to convince me otherwise.

That said, I do really like a bias towards settings that can be changed at runtime. Datasette has suffered a bit from some settings that can't be easily changed at runtime already - hence my gnarly https://github.com/simonw/datasette-remote-metadata plugin.

For things like Datasette Cloud for example the more people can configure without rebooting their container the better!

I don't think live reconfiguration at runtime is incompatible with JSON/YAML configuration though. Caddy is one of my favourite examples of software that can be entirely re-configured at runtime by POSTING a big blob of JSON to it: https://caddyserver.com/docs/quick-starts/api

 
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