There are so many footguns[1] in async python but it really has stolen the zeitgeist of modern python web. Synchronous django has a lot to commend it. If you deploy it reasonably well (workers, behind a caching reverse proxy etc) it's easy to operate in production even under duress.<p>It's not going to be the right stack for long lived websocket connections or whatever but for a CRUD-ish or enterprise app, often very productive.<p>[1] buffer bloat is too easy to sleep walk into<p><pre><code> queue = asyncio.Queue() # oops
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unbounded concurrency<p><pre><code> await asyncio.gather(*(fetch(item) for item in items)) # look mum! no outbound sockets left or maybe even no file descriptors
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accidental blocking<p><pre><code> data = requests.get(url).json() # oops! we're blocking the event loop
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and sure you can setup a separate task to measure the event loop latency and alert on that but this is the kinda thing you'll never find in a tutorial, you just need to experience this stuff and figure out a solution you like<p>I can go on and on about async python (cancellation bugs, leaking a task - that has a cataclysmic failure mode where if you forget to hold a reference to your asyncio.create_task() then it's all weak refs in that machinery so your task can get garbage collected before it ran or completed in production! super tricky forensics. Then there's all the obvious stuff like race conditions, new ways of creating deadlocks, blah blah i really can go on for days.
> 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).<p>> 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.<p>That seems crazy low, I think there has to be something else going on here.
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.<p>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.<p>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.<p>Atlas, sqlx, sqlc and all other ORM like things in golang cannot do migrations the way Django does.
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 mostly use Go + SQLite for all the things I used to use Rails, JavaScript, or Python for.<p>I find python django wastes too much resources, just look at memory usage.<p>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.<p>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.<p>My go apps use very little memory, so we can scale to many users for very cheap.<p>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)<p>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.<p>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.<p>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.<p>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.<p>By switching to Rust, the LOC dropped to half of what we had in Haskell.<p>project turned out to be successful. It has already produced guaranteed weight loss or weight gain for many people.<p>So I set out to create an algorithmic workout app, for which I am using Rust and Go. The mobile app is in Flutter.
Appart from the fact that you find that Python wastes too much memory, what is your point ?
I think that any person choosing Django knows that Python by nature will not be the most efficient language.
Apart from that Django is battle-tested and can help bring a stable "product" quite quickly.
if "2-3 requests per second" per author is what you wanna do on $10 server go ahead". My server does 100 RPS on $16 instance<p>neither you are saving any time, nor money.<p>>part from that Django is battle-tested and can help bring a stable "product" quite quickly.<p>this is a myth, you'll not save anytime. Only way you can save time is if you've experience in this but same is true if you write your app from scratch in Go from your learned patterns.
> if "2-3 requests per second" per author is what you wanna do on $10 server go ahead<p>That's not a Django limit and there's something going on with the authors setup. 100 RPS on a $16 instance would be easily doable with Django too.<p>> neither you are saving any time, nor money.<p>How do you know? I'm pretty sure I can set up the same webapp in Django much faster than in go, so I'm saving both.<p>> this is a myth, you'll not save anytime. Only way you can save time is if you've experience in this but same is true if you write your app from scratch in Go from your learned patterns.<p>Why do you think all the built in stuff in Django does not save time? Any argument for that?
> as a meta comment: I’ve been working on talking about my programming opinions by just saying “THING does not feel good to me, I prefer OTHER THING instead”. That post I linked to says that function-based views are the “right way”. I’m not very invested in whether it’s “right”, but it’s validating to know that other people feel similarly to me about inheritance<p>I admire this about Julia a lot. Her texts and zines are exploring software in a way that encourages curiosity rather than promoting a singular point of view.
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
There may be many reason other than rejecting that suggestion leading to what it is know. Your statement somehow suggests that it was deliberately decided against what you propose. I don't think we know that.<p>I can't quite picture how operator overloading would look like, could you give an example?
> I can't quite picture how operator overloading would look like, could you give an example?<p>Instead of this:<p>self.filter(end__gt=self._midnight(today))<p>You could write a "Field" class that implements __getattr__ and __gt__ so you could do<p>self.filter(Field.end > self._midnight(today))<p>The "Field.end > self._midnight(today)" would evaluate to an object that would just store "my field name is end and my value needs to be larger than xyz".<p>filter() can then look into its argument list and construct the filter criteria from the passed Field objects instead of the key value pairs as it does now.