Coming from someone who started selfhosting on a pi 2B (similar-ish specs), you’d be surprised. If you don’t need anything fast or fancy, that 1GB will go a long way, and plenty of selfhosted apps require very little CPU. The only real problem I faced was that all HTTPS-related network tasks were limited at ~3MB/s, as that is how fast my pi could encrypt the data (presumably, I just saw my webserver utilising the entire CPU and figured this was the most likely explanation)
It depends what you’re optimising for. If you want a single (relatively small) download to be available on your HDD as fast as possible, then your current setup might be better (optimising for lower latency). However, if you want to be maxing out your internet speeds at all time and increase your HDD speeds by making the copy sequential (optimising for throughput), then the setup with the catch drive will be better. Keep in mind that a HDD’s sequential write performance is significantly higher than its random write performance, so copying a large file in one go will be faster than copying a whole bunch of random chunks in a random order (like torrents do). You can check the difference for yourself by doing a disk benchmark and comparing the sequential vs random writes of your drive.
qBittorrent has exactly the option you’re looking for, I believe it’s called “incomplete download path” in the settings, letting you store incomplete downloads at a temporary path and moving them to their regular location when the download finishes. Aside from the download speed improvement, this will also lead to less fragmentation on your HDD (which might be part of the reason why it is so slow when downloading directly to it). Pre-allocating space could have the same effect, but I would recommend only using one of these two solutions at once (pre-allocating space on your SSD would only waste space)
It’s possible for a certain hardware/software setup not to support a certain codec. For example, my jellyfin client (Finamp) uses the iOS native decoders (afaik), which means opus files are practically broken. My music library (8000+ songs) contained exactly 1 lossy file, which just so happened to be an opus file. I decided to spend the extra ~20MB to standardise my entire library to flac files, ensuring I could play every song on all my devices.
Edit cause I posted too soon: you are generally correct; only in very specific circumstances will you encounter compatibility issues like this one in the modern world. This is 100% apple being apple, and you can expect pretty much every other (reasonably modern) device to support all codecs you might encounter in the wild.
To add to the audio compression: it isn’t possible to further compress an mp3 file without losing any quality. You can either:
If you’re willing to spend some extra time learning about audio compression, you can download lossless files and compress those directly to whatever format and bitrate you want. The quality will be better than option 1 above, as the audio is only lossely compressed once instead of twice.
Which compression level are you using? My old server is able to compress flac’s at the highest (and therefore “slowest”) compression level at >50x speed, so bumping the level up shouldn’t be too hard on your CPU.
I’ve been running some external drives on my server for about a year now. In my experience, hard drives with an external power supply suffer less from random disconnects. The specific PC also makes quite a large difference in reliability. My server is just a regular desktop and has very little problem staying connected and powering my 3 external drives. My seedbox is an old laptop, and has been having almost constant problems with random disconnects and power issues. Maybe test how well your framework does with some external drives before committing to the plan?
Have you tried the official guide from the jellyfin website?
As for the guide this AI generated: it bothers me that they instruct you to use chocolatey for the *arrs, but still advice you to install docker, qbittorrent and jellyfin manually (all of which have chocolatey packages). I disagree with the comment that external storage would be recommended, as internal storage is generally more reliable (depending on a lot of factors of course). Also, I believe the “adding a library”-section of the jellyfin setup is a bit too short to be of any use, and would recommend referring to the jellyfin docs instead.
This guide also doesn’t explain how to make jellyfin accessible outside of your LAN. Once again, I’d recommend referring to the jellyfin docs if you want to do this.
I personally have only set up qbittorrent, jellyfin and docker (not the *arr suite), so I can’t comment on the completeness of the guide, but I wouldn’t trust it too much (seeing the previous oversights).
And finally, as someone who started their selfhosted server journey on windows: don’t. There is a reason why almost all guides are written for linux, as it is (in my humble opinion) vastly superior for server usage once you get used to it.
Luxury! My homeserver has an i5 3470 with 6GB or RAM (yes, it’s a cursed 4+2 setup)! </badMontyPythonReference>
Interesting, I also run Nextcloud and pihole, and vaultwarden, jellyfin, paperless-ngx, gitea, vscode-server and a minecraft server (every now and then).
You’re right that such a system really does show its age, but only when doing multiple intensive tasks at the same time. I try not to backup my photos to Nextcloud while running minecraft, for example, as the imagine identification task pins my CPU at 100%. So yes, I agree, you’re probably not doing anything out of the ordinary on your setup.
The point I was trying to make still stands though, as that pi 2B could run more than I would’ve expected beforehand. I believe it once even ran jellyfin, a simple file server, samba, and a webserver with a simple HTML website. Jellyfin worked just fine, as long as the pi didn’t have to transcode (never got hardware transcoding to work).
It is funny that you should run out of memory, seeing as everything fits (albeit, just barely) on my machine in 1/5 the memory. Would de overhead of running VM’s account for such a large difference?