Right prod architecture (nginx -> gunicorn -> django, with nginx serving static assets), decent PRAGMAs for sqlite3 and caching for generic content should improve the RPS one or two orders of magnitude.
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.
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.
[1] buffer bloat is too easy to sleep walk into
queue = asyncio.Queue() # oops
await asyncio.gather(*(fetch(item) for item in items)) # look mum! no outbound sockets left or maybe even no file descriptors
data = requests.get(url).json() # oops! we're blocking the event loop
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.
> 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.
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.
1. Brackets aren't allowed to help with boolean expressions like {% if a and (b or c) %}
2. You can't do basic arithmetic like {{ x * 2 }}, but you're allowed to do {{ x | add:"2" }}. There's hacks to multiply using {% widthratio a 1 b %} or division with {% widthratio a b 1 %} though (https://stackoverflow.com/questions/18350630/multiplication-...).
3. You can't assign expressions to variables like {% with x = a or b %}, so you have to repeat yourself.
4. You can't capture HTML generated from template code in a variable to pass into a partial template to write slots-style HTML components e.g. {% capture x %}Hello {{ username }}{% endcapture %}{{ include "partials/header.html" with body_html=x }}.
5. You can't pass variables to model methods.
I understand there's a philosophy that templates shouldn't contain complex logic, but I find the above pretty arbitrary and leads to code that's harder to maintain. Addition is okay but not multiplication? Boolean logic is okay but not with brackets? I often have to puzzle out some way to get my code to work that goes against what I'd normally want to do, some I'm forced to duplicate template code because you can't put expressions in variables or move basic one-off logic into views (which has poor locality https://htmx.org/essays/locality-of-behaviour/ and makes it harder to move template snippets between pages).
It's like hiding the kitchen knives because they might be misused.
Is Jinja2 a practical alternative or there's friction to using it?
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.
I had the same issue with incredibly low throughput on beefy machines and it's because the dev server implementation is single threaded and does not do concurrency at all.
Switch to gunicorn.
Using Django is like trying to walk on a highway, with a crutch. Even though it has some convenience features, it's just so impossibly slow you would have to invest a lot of engineering time and resources to mitigate that slowness.
jinja2 is drop in by changing the template backend. You can actually run both at the same time (just can't mix them, ofc).
There are dozens of us! It's a great db management toolkit. I've used it to much success many times for things like managing migrations from mysql to postgres and php to python.
My opinions:
- Django apps are an anitipattern for large internal / single purpose products due to migration overhead as FKs cross application boundaries. I will die on this hill. No team is ever disciplined enough to keep apps as boundaries for relationships and constraints. Strong contrast to the rails crowd that doesn't rely on referential integrity in the db by default where this isn't "a thing".
- Goose[0] migrations in Go are really great but you have to let go of the dsl and the idea that your ORM drives your migrations, as you explicitly called out. Laravel[1] is on par with django IMO and a delight to use when in php-land. I've not tried to repurpose it like I have with Django and sqlalchemy.
- sqlalchemy and alembic is a great toolchain outside of django that get's a bit of a bad wrap / confusion from django devs. it gives you that same ability to drive the changes from the classes / structs without having to drag around all of django. It having more verbose
You also get the Django admin interface for free.
I once tried using SqlAlchemy but I couldn't help asking myself why it felt so complicated compared to Django.
The problem may be the lack of jinja support for 3rd party django apps (mostly legacy). So make sure they support it first.
I don't want to be overly critical, there's languages that people complain about and then there are languages that no one uses... If i compare it to js/ts, some stuff is genuinely better in Python - e.g. if you missed an await. While both ecosystems have lint tools available for this, but the behaviour is just friendlier in python.
Structured concurrency is better in python, but even if TaskGroup is nicer to use than AbortController, it still has its own foibles which means i'll usually advocate for AnyIO.
But the js/ts ecosystem just generally benefits from being async from the get-go where python you're just a time.sleep() away from a bug that will slip through dev envs and ci pipelines undetected and only rear its head under load in prod.
If i have a tip to share its the debug flag for asyncio run:
asyncio.run(f(), debug=True) # find some more issues before prodA few notables differences:
- many controls are now functions/variables (that's good!), and some change name, for example `{% csrf_token %}` -> `{{ csrf_input }}`
- calling methods without parenthesis does not work (that's good!), for example `query.all` -> `query.all()`
- in tests` response.context` is replaced by `response.context_data`, which is not set when calling `render` directly (need to return a proper `TemplateResponse`)
- template folders need to be renamed from `templates` to `jinja2`; there may be a way to change this behavior, but i did not find it, and this change is written so small in the docs i lost an entire day over it
I can't quite picture how operator overloading would look like, could you give an example?
- Django apps are an anitipattern for large internal / single purpose products due to migration overhead as FKs cross application boundaries
For a half-decent VM, I'd be expecting multi-thousand.
Single figures a second, is choked to a single connection at a time.
neither you are saving any time, nor money.
>part from that Django is battle-tested and can help bring a stable "product" quite quickly.
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.
And if you pay $10/month for a single threaded machine, you’re overpaying by a lot.
https://docs.peewee-orm.com/en/latest/peewee/querying.html#f...
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.
> neither you are saving any time, nor money.
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.
> 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.
Why do you think all the built in stuff in Django does not save time? Any argument for that?
Instead of this:
self.filter(end__gt=self._midnight(today))
You could write a "Field" class that implements __getattr__ and __gt__ so you could do
self.filter(Field.end > self._midnight(today))
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".
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.
self.filter(end__gt=self._midnight(today))
self.filter(end__gt=<some_datetime_object>)
self.filter(Field.end > self._midnight(today))
self.filter(<True/False>)> Slicing up your project into apps is something that must be done early, often at the very start of development. This is a simple result of the fact that you need somewhere to put the code you're writing as you go along. In the early days and weeks of work on a new codebase, manage.py startapp gets used a lot, as the high-level structure of the project starts to take shape.
> The issue here is that at this early stage, you often don't really know enough about what the project's final form will look like to correctly draw the boundaries around the apps. Functionality that feels separate at first often becomes deeply entangled, and features that sound similar end up sharing little. Over time, the key concepts and models in your system become clear, and if these are colocated with unrelated or irrelevant code, the waters of the project become muddy and maintenance becomes difficult - before the project is even in production!
> While it is technically possible to migrate models between apps, Django doesn't make it easy, particularly if the models in question have foreign key or many-to-many relationships with other models. And if you think about it, it's not really a technical limitation of the sort that could easily be solved with a PR into Django. It's a conceptual problem: if migrations are a historical record of changes to models, and migrations are encapsulated in the same app as those models, then moving a model to a different app necessarily creates a historical coupling between the two apps that shouldn't really exist.
from datetime import datetime
class Filter():
def __init__(self, name):
self.name = name
def __gt__(self, value):
return {
"field": self.name,
"operator": ">",
"value": value
}
class FieldMeta(type):
def __getattr__(cls, name):
return Filter(name)
class Field(metaclass=FieldMeta):
pass
print(Field.end > datetime(2024, 1, 1))
{'field': 'end', 'operator': '>', 'value': datetime.datetime(2024, 1, 1, 0, 0)}
Edit:
You can even make a little adapter to use this with the current filter system if you really want to:
from datetime import datetime
class Filter():
def __init__(self, name):
self.name = name
def __gt__(self, value):
return {
"field": self.name,
"operator": "gt",
"value": value
}
def __lt__(self, value):
return {
"field": self.name,
"operator": "lt",
"value": value
}
def __eq__(self, value):
return {
"field": self.name,
"operator": "eq",
"value": value
}
class FieldMeta(type):
def __getattr__(cls, name):
return Filter(name)
class Field(metaclass=FieldMeta):
pass
def _(*args):
kwargs = {}
for arg in args:
k = arg["field"] + "__" + arg["operator"]
kwargs[k] = arg["value"]
return kwargs
def filter(**kwargs):
for k, v in kwargs.items():
print(f"{k} = {v}")
filter(**_(Field.end > datetime(2024, 1, 1)))
end__gt = 2024-01-01 00:00:00
Instead of apps you can just split your components inside a single app using regular python modules.
Also, apparently SQLAlchemy does exactly what I proposed so apparently they are erring in their ways too.
I honestly don’t find it that bad.
And despite my misgivings, it’s really ergonomic.
Hello! I’m on a funny journey right now where I’m trying to learn how to make websites in a sort of 2010 style, where I have an SQL database and render some HTML on the backend.
It’s kind of an interesting journey because it doesn’t necessarily feel “easy” to me to make websites in this way: I never learned how to do it in the 2000s or 2010s, and there’s a lot I need to learn.
So here are some Django features that make building this kind of site feel more achievable than when I was trying and failing to use Go’s standard library or Flask. And I’ll talk about a couple of issues with Django I’ve run into.
Previously the toolkit I felt confident with for making websites was:
I really liked this frontend-heavy approach for these super simple applications but when I started thinking about making something with a lot of different pages (instead of literally just one page), I didn’t feel so excited about the options I saw that involved a lot of frontend code. So I figured I’d try the backend.
Writing a backend-focused site that uses as little JS as possible feels the same to me in a way as writing a single-page JS website that does as little on the backend as possible, even though they might seem like opposites. In both cases I’m just trying to keep as much of the logic as possible in one place.
Now for some thoughts about Django!
I learned that I can define a “query set” class in Django with a bunch of methods with different WHERE statements I might want to use while constructing a query:
Here’s how I use it in my view code once I’ve defined what all the methods mean:
Events.objects.approved()
.for_tab(tab)
.with_festivals(tab_params.festival_slugs)
.is_free(tab_params.free)
.is_outdoors(tab_params.outdoors)
and here’s how I define the methods:
class EventQuerySet(SearchableQuerySetMixin, models.QuerySet):
def approved(self):
return self.filter(approved_at__isnull=False)
def future(self):
today = timezone.localdate()
return self.filter(end__gt=self._midnight(today))
def with_tags(self, tags):
if tags:
return self.filter(tags__name__in=tags).distinct()
return self
The syntax for defining the filters isn’t my favourite, but I spend most of my time just using the methods, and it feels super readable and nice to use, and it makes me want to look into other query builder libraries in the future. In the past I thought “I know SQL, who needs a query builder?”, but this kind of structure does make it really nice to read.
I found an example of someone who wrote their own small query builder in Python that I want to read later to think about whether I would enjoy using a more minimal version of this.
There are a bunch of little quality of life filters available in Django templates that are super useful for generating HTML. The ones I’ve used so far are:
<br> ({{ event.description|urlize|linebreaksbr }} ){{ row.date|date:"M j" }})json_script, which takes a Python dictionary and automatically converts it to JSON and inserts it into the HTML as a <script> tag in a safe wayThese are all small things individually but I feel like it makes a big difference somehow to just have them available.
querystring is coolI think my favourite template filter is querystring: in this site sometimes we use filters like ?date=2026-06-01 to decide what’s displayed. querystring that will make a link to the same query string with one change, like this to link to the previous date:
<a href="{% querystring date=nav.prev_date%}">
Or to remove the outdoors parameter:
<a href="{% querystring outdoors=None %}">
I still really love Django’s automatic database system. It’s amazing to be able to just edit a model to add a new field or whatever, and then Django automatically generates the migration.
So far we have done 19 database migrations and I think there will probably be more! It makes a huge difference for me to be able to just easily change the database as my understanding of the problem changes.
Django’s documentation sometimes offers the option of using class-based views and inheritance to organize the code in your views. For example I have four views that share a lot of code, and I could use inheritance to manage that by defining some kind of parent class and then having my other views inherit from it.
I tried it out and I did not enjoy the experience of using inheritance to share code between views. I switched to using functions instead, sort of how this post advocates, and that was a lot more straightforward. I’ve never had a good experience using inheritance in Python and I don’t think I’ll try to use it again.
But I don’t mind using inheritance to use the interfaces Django itself provides: for example if I want to define a query set I need to write something like class EventQuerySet(SearchableQuerySetMixin, models.QuerySet). I don’t think too hard about it and it seems to work.
(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)
At some point the LLM scrapers discovered our site, and started sending us maybe 10 requests per second. I blocked them which is working for now, but it made me think about what the site’s capacity is. I’m used to writing Go backends where the performance situation is pretty straightforward (usually everything is just fast enough), and a Django site is very different.
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).
It’s tempting for me to go down a rabbit hole where I do a bunch of profiling to figure out what’s slow and try to make it faster (there’s py-spy for that, and py-spy is great and super easy to use, and profiling is fun!) But I really don’t understand what I should expect in terms of performance from a Django site and how I should be thinking about at a higher level.
Some things I haven’t figured out yet:
I think one thing I’m learning about Django is that because it’s a Framework (tm), it’s easy to accidentally misconfigure it. For example, when I was thinking about why my site was slow just now, I read the django performance docs and I noticed a comment saying:
Enabling the cached template loader often improves performance drastically, as it avoids compiling each template every time it needs to be rendered.
When I’d done CPU profiling I’d noticed that it was spending a lot of time rendering templates! Maybe this could help me!
Clicking through the link, I saw that the cached template loader was supposed to be on by default, but I’d turned it off by accident while trying to do something else. I think this “I turned off the cached template loader by default” things is an example of how I still find the django settings file to be pretty confusing and difficult. I guess I should just be careful when I go in there.
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.
One thing that’s been surprising to me about Django performance is that I’ve always heard the advice “if you have a performance problem, check your database queries! Maybe add an index!”. But I’ve been running into a variety of performance issues (like this template caching thing) that are not because of slow queries, so instead it’s been more useful for me so far to start by running a CPU profile. And since I’m using SQLite, any slow database query problem will show up on the CPU profile anyway.
Anyway I don’t want to get too far into site performance. Like I said it’s easy for me to get interested in profiling, but actually I know a lot about profiling and it’s not the most important thing for me to learn about.
I might say more about what I’m enjoying (or having a hard time with!) about Django later. Trying to write some shorter blog posts recently.