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I agree on your take, but I don't think that "future scaling" is a concern for the most home users.
It may not affect this current use case for a home media server, but people should still be aware of it so as they learn and grow, they don’t paint themselves in a corner by knowing only the anti patterns as the path forward.
as someone who does stuff in my lab that can translate to a work context, i absolutely second this opinion.
if i am labbing to learn, then learning the best way to do it is always be the main focus, even if it means restarting what I was doing to change how some prerequisite is setup or functions.
today, OP is working with jellyfin, but as an example, what happens if later they get security cameras and want to use some sort of local ML to analyze events, and don't want to put a lot cpu utilization to that task during lulls in activity? a solution might be to dynamically create and destroy containers for the analysis tasks, and the background on a network setup in an unrelated container stack that would allow scaling that means one less problem to solve later.
I'm glad you commented as I didn't know I can define 2 networks in Docker. At the moment I'm trying to get Arr working in docker and it was going well until I realised my containers can't communicate with Plex. I believe it's because I'm using Gluetun and I haven't enabled LAN networking on my VPN. but theoretically the apps that need to see Plex don't need to be behind the VPN, but they didn't work when they weren't because they couldn't talk to Prowlarr.
So theoretically I could just slap "bridge" in my network as well, and then they'll be in Gluetun and out of it at the same time.
I may try it tomorrow. Thanks for your comment