> Some light load testing (with (ab -n 1000 -c 1) shows that right now we can serve about 2-3 requests per second (on a ~$10/month VM).
> After turning on template caching, it seems like the site can now pretty easily handle 12 requests per second or so without using all of the CPU. I have not carefully benchmarked the before and after but it seems like it’s made a pretty big difference.
That seems crazy low, I think there has to be something else going on here.
True, but in this case with SQLite, there's unlikely to be much of a difference because there isn't the spare time available when waiting for a separate database server. I don't know what providers are good for a $10/month instance these days.
I have been using Django since 0.95 and I haven't seen anything which is so flexible with amazing DSLs while also making it easy to understand the magic behind it.
For the last 10 years, even in a Golang stack or Java stack, I still use Django for models and migration. I even have generators which generate Gorm (or other framework) DAO or Java hibernate classes using Django models.
With LLMs, it becomes easier since I can now write all the model, custom querysets in Django, ask the LLM to generate Golang DAO, setters and getters... and test the query against the Django generated queries for completeness.
Atlas, sqlx, sqlc and all other ORM like things in golang cannot do migrations the way Django does.
I mostly use Go + SQLite for all the things I used to use Rails, JavaScript, or Python for.
I find python django wastes too much resources, just look at memory usage.
One of my web app backend (go) is serving approx 100 req/s right now and i look at pprof i see it's not bottlenecked by CPU but mostly IO and i love this.
Writing concurrent code in Go is easy, the code i wrote 10yrs ago still compiles with no issue! This is why i am never gonna switch.
My go apps use very little memory, so we can scale to many users for very cheap.
For larger apps i use postgres (why? replication is easy using pgfailover, high demand apps need multiple api servers so it's out of process db like postgres is fine) but most of my web app use HTMX and if we need some reactivity, i use react (simply due to react experience from work)
For our maintenance calorie tracking app, which is free and has no ads, we have to use as few resources as possible as we scale to thousands of users: macrocodex (which figures out maintenance calories from weight and calorie intake). We initially used Haskell.
Later, it became slow and cumbersome to develop in (developing on an Apple Silicon Mac and deploying to x64 is a pain), even though I liked writing Haskell code. I even tried nix and wasted a day on that! I had a choice between OCaml and Rust. I picked Rust and never looked back.
The algorithm serves in 0.1 ms on Rust. In Haskell, it was 0.2 ms, and memory usage was twice that of Rust. There are many optimization possible in Rust which i didn't do (for sake of simplicity) yet i received good performance.
Yeah, I use Docker to compile Rust, but it's pretty fast, much faster than what I had with Haskell, so the developer experience is great.
By switching to Rust, the LOC dropped to half of what we had in Haskell.
project turned out to be successful. It has already produced guaranteed weight loss or weight gain for many people.
So I set out to create an algorithmic workout app, for which I am using Rust and Go. The mobile app is in Flutter.
Good ole Django. Worked with a number of frameworks (tm), but nothing really quite scratches my itch like Django does. I still find the ORM and database migration system unmatched.
I found Django a bit hard to get on with vs. other frameworks and I've used Rails, .NET MVC and Express (and friends). I just found more friction trying to achieve X for any given X for some reason. Not sure why.
The Django filter syntax with the double underscores is like fingernails on a chalkboard to me. I find it insane that they didn't just use operator overloading to create a real query expression language.
Or now that python has ~types, this is really an area where things could be improved. Filtering would just be lambda predicate with fields auto complete as seen in .NET, scala, etc
> After turning on template caching, it seems like the site can now pretty easily handle 12 requests per second or so without using all of the CPU. I have not carefully benchmarked the before and after but it seems like it’s made a pretty big difference.
That seems crazy low, I think there has to be something else going on here.
And if you pay $10/month for a single threaded machine, you’re overpaying by a lot.
For the last 10 years, even in a Golang stack or Java stack, I still use Django for models and migration. I even have generators which generate Gorm (or other framework) DAO or Java hibernate classes using Django models.
With LLMs, it becomes easier since I can now write all the model, custom querysets in Django, ask the LLM to generate Golang DAO, setters and getters... and test the query against the Django generated queries for completeness.
Atlas, sqlx, sqlc and all other ORM like things in golang cannot do migrations the way Django does.
I find python django wastes too much resources, just look at memory usage.
One of my web app backend (go) is serving approx 100 req/s right now and i look at pprof i see it's not bottlenecked by CPU but mostly IO and i love this.
Writing concurrent code in Go is easy, the code i wrote 10yrs ago still compiles with no issue! This is why i am never gonna switch.
My go apps use very little memory, so we can scale to many users for very cheap.
For larger apps i use postgres (why? replication is easy using pgfailover, high demand apps need multiple api servers so it's out of process db like postgres is fine) but most of my web app use HTMX and if we need some reactivity, i use react (simply due to react experience from work)
For our maintenance calorie tracking app, which is free and has no ads, we have to use as few resources as possible as we scale to thousands of users: macrocodex (which figures out maintenance calories from weight and calorie intake). We initially used Haskell.
Later, it became slow and cumbersome to develop in (developing on an Apple Silicon Mac and deploying to x64 is a pain), even though I liked writing Haskell code. I even tried nix and wasted a day on that! I had a choice between OCaml and Rust. I picked Rust and never looked back.
The algorithm serves in 0.1 ms on Rust. In Haskell, it was 0.2 ms, and memory usage was twice that of Rust. There are many optimization possible in Rust which i didn't do (for sake of simplicity) yet i received good performance.
Yeah, I use Docker to compile Rust, but it's pretty fast, much faster than what I had with Haskell, so the developer experience is great.
By switching to Rust, the LOC dropped to half of what we had in Haskell.
project turned out to be successful. It has already produced guaranteed weight loss or weight gain for many people.
So I set out to create an algorithmic workout app, for which I am using Rust and Go. The mobile app is in Flutter.