Django aside, I think this is a really important point:
Being able to abandon a project for months or years and then come back
to it is really important to me (that’s how all my projects work!) ...
It's perhaps especially true for a hobbyist situation, but even in a bigger environment, there is a cost to keeping people on hand who understand how XYZ works, getting new people up to speed, etc.
I, too, have found found that my interactions with past versions of myself across decades has been a nice way to learn good habits that also benefit me professionally.
This is the main reason I'm extremely disciplined about making sure all of my personal projects have automated tests (configure to run in CI) and decent documentation.
It makes it so much easier to pick them up again in the future when enough time has passed that I've forgotten almost everything about them.
I'm finding that in this build fast and break things culture, it is hard to revisit a project that is more than 3 years old.
I have a couple of android projects that are four years old. I have the architecture documented, my notes (to self) about some important details that I thought I was liable to forget, a raft of tests. Now I can't even get it to load inside the new version of Android Studio or to build it. There's a ton of indirection between different components spread over properties, xml, kotlin but what makes it worse is that any attempt to upgrade is a delicate dance between different versions and working one's ways around deprecated APIs. It isn't just the mobile ecosystem.
Build fast and break things works great if you're the consumer, not the dev polishing the dark side of the monolith (helps if you're getting paid well though)
As a consumer, I can not remember any feature that I was so enamored about having a week earlier than I otherwise would have, at the expense of breaking things.
If you know what you are doing, you can hibernate other kinds of tortoises by placing them in a fridge (as opposed to a freezer). One of my
friends does this with their Russian tortoise.
If you need to travel, make sure you have someone reliable who can check on them, in case of a power outage.
Django is objectively the most productive "boring technology" I've ever worked with for developing web applications. They don't regularly add too many bells and whistles on every release, but they keep it stable and reasonably backwards compatible.
The Django ORM / migrations are still basically unmatched in happiness factor.
Its crazy to me after all these years that django-like migrations aren't in every language. On the one hand they seem so straightforward and powerful, but there must be some underlying complexities of having it autogenerate migrations.
Its always a surprise when i went to Elixir or Rust and the migration story was more complicated and manual compared to just changing a model, generating a migration and committing.
In the pre-LLM world, I was writing ecto files, and it was super repetitive to define make large database strucutres compared to Django.
Going from Django to Phoenix I prefer manual migrations. Despite being a bit tedious and repetitive, by doing a "double pass" on the schema I often catch bugs, typos, missing indexes, etc. that I would have missed with Django. You waste a bit of time on the simple schemas, but you save a ton of time when you are defining more complex ones. I lost count on how many bugs were introduced because someone was careless with Django migrations, and it is also surprising that some Django devs don't know how to translate the migrations to the SQL equivalent.
At least you can opt-in to automated migrations in Elixir if you use Ash.
There are some subtle edge cases in the django migrations where doing all the migrations at once is not the same as doing migrations one by one. This has bitten me on multiple django projects.
Can you give an example how this would happen?
Ok, from memory --
There's a pre, do and post phase for the migrations. When you run a single migration, it's: pre, do, post. When you run 2 migrations, it's: pre [1,2], do: [1,2], post: [1,2].
So, if you have a migration that depends on a previous migration's post phase, then it will fail if it is run in a batch with the previous migration.
When I've run into this is with data migrations, or if you're adding/assigining permissions to groups.
There’s like an atomic flag you can pull it out of the transaction . Solves a lot of these issues.
well in elixir you can have two schemas for the same table, which could represent different views, for example, an admin view and a user view. this is not (necessarily) for security but it reduces the number of columns fetched in the query to only what you need for the purpose.
[deleted]
There is no way to autogenerate migrations that work in all cases. There are lots of things out there that can generate migrations that work for most simple cases.
I end up needing to write a manual migration maybe once every other year in real world use.
Django manages to autogenerate migrations that work in the VAST majority of cases.
That's why you can do your own migrations in Django for those edge cases.
I found it very lacking in how to do CD with no downtime.
It requires a particular dance if you ever want to add/delete a field and make sure both new-code and old-code work with both new-schema and old-schema.
The workaround I found was to run tests with new-schema+old-code in CI when I have schema changes, and then `makemigrations` before deploying new-code.
Are there better patterns beyond "oh you can just be careful"?
Kubernetes and Helm cover a lot of these cases: Pods of the new version are started one by one, while the pods of the new version are shutdown. For a short period, you have pods of both versions running.
When you add new stuff or make benign modifications to the schema (e.g. add an index somewhere), you won't notice a thing.
If the introduced schema changes are not compatible with the old code, you may get a few ProgramingErrors raised from the old pods, before they are replaced. Which is usually acceptable.
There are still some changes that may require planning for downtime, or some other sort of special handling. E.g. upgrading a SmallIntegerField to an IntegerField in a frequently written table with millions of rows.
Without care new-schema will make old-code fail user requests, that is not zero downtime.
One option is to do multi-stage rollout of your database schema and code, over some time windows. I recall a blog post here (I think) lately from some Big Company (tm) that would run one step from the below plan every week:
1. Create new fields in the DB.
2. Make the code fill in the old fields and the new fields.
3. Make the code read from new fields.
4. Stop the code from filling old fields.
5. Remove the old fields.
Personally, I wouldn't use it until I really need it. But a simpler form is good: do the required schema changes (additive) iteratively, 1 iteration earlier than code changes. Do the destructive changes 1 iteration after your code stops using parts of the schema. There's opposite handling of things like "make non-nullable field nullable" and "make nullable field non-nullable", but that's part of the price of smooth operations.
I simplify it this way. I don't delete fields or tables in migrations once an app is in production. Only manually clean them up after they are impossible to be used by any production version. I treat the database schema as-if it were "append only" - Only add new fields. This means you always "roll-forward", a database. Rollback migrations are 'not a thing' to me. I don't rename physical columns in production. If you need an old field and a new field to be running simultaneously that represent the same datum, a trigger keeps them in sync.
You can do three stage:
1. Make a schema migration that will work both with old and new code
2. Make a code change
3. Clean up schema migration
Example: deleting a field:
1. Schema migration to make the column optional
2. Remove the field in the code
3. Schema migration to remove the column
Yes, it's more complex than creating one schema migration, but that's the price you pay for zero-downtime. If you can relax that to "1s downtime midnight on sunday", you can keep things simpler. And if you do so many schema migrations you need such things often ... I would submit you're holding it wrong :)
I'm doing all of these and None of it works out of the box.
Adding a field needs a default_db, otherwise old-code fails to `INSERT`. You need to audit all the `create`-like calls otherwise.
Deleting similarly will make old-code fail all `SELECT`s.
For deletion I need a special 3-step dance with managed=False for one deploy. And for all of these I need to run old-tests on new-schema to see if there's some usage any member of our team missed.
I was just in the middle of writing something similar above, thanks!
The general approach is to do multiple migrations (add first and make new-code work with both, deploy, remove old-code, then delete old-schema) and this is not specific to Django's ORM in any way, the same goes for any database schema deployment. Take a peek at https://medium.com/@pranavdixit20/zero-downtime-migrations-i... for some ideas.
oh the automatic migrations scare the bejesus out of me. i really prefer writing out schemas and migrations like in elixir/ecto. plus i like the option of having two different schemas for the same table (even if i never use it)
You can ask Django to show you what exact SQL will run for a migration using `manage.py sqlmigrate`.
You can run raw SQL in a Django migration. You can even substitute your SQL for otherwise autogenerated operations using `SeparateDatabaseAndState`.
You have a ton of control while not having to deal with boilerplate. Things usually can just happen automatically, and it's easy to find out and intervene when they can't.
The nice thing in this case is that Django will meet you where you are with your preferences. Want to go the manual route? Sure. Want it to take a shot at auto-generation and then you customize? Very doable and. Want to let Django take the wheel fully the majority of the time? Sure.
I have never done it, but I believe you could setup multiple schemas under the same database -by faking it as different databases and then use a custom router to flip between them as you like.
That sounds like the path to madness, but I do believe it would work out of the box.
sounds inconvenient and error-prone
It is not much code to setup the router. Now, why you would want to bounce between schemas, I do not have a good rationale, but whatever floats your boat.
Have you ever tried Rails? I think that Django's approach on those is an adaptation from it.
Of course, ActiveRecord back in 2005.
In hindsight, maybe I should've tried to use Django for my previous project instead of build a lot of custom stuff in Go and React. It was basically an admin interface, but with dozens of models and hundreds if not thousands of individual fields, each with their own validation / constraints. But it was for internal users, so visually it mainly needed to be clear.
I always return to Django for any project. It's fantastic. Enough batteries are included with it that it is very powerful.
> I love being able to backup by just doing a VACUUM INTO and then copying the resulting single file.
Naively, I would probably just copy the sqlite file. Is that a bad idea?
That's fine if SQLite isn't running, but you risk corruption if you copy a file while it is being actively written to.
VACUUM INTO eliminates that risk.
Thank you for mentioning this. I was planning to do exactly this soon and had no idea.
After working with Django for 8 years, I find it hard to move on to anything else. It's just the right amount of magic, and just the right amount of flexibility, and it's just such a joy to work with.
Re: Django is OK for simple CRUD, but falls apart on anything complex - this is just untrue. I have worked in a company with a $500M valuation that is backed by a Django monolith. Reporting, recommender systems, file ingestion pipelines, automatic file tagging with LLM agents -- everything lives inside Django apps and interconnects beautifully. Just because it's a Django app doesn't mean you cannot use other libraries and do other stuff besides basic HTTP request processing.
Recently I had the misfortune of doing a contract on a classic SPA project with Flask and sqlalchemy on the backend and React on the frontend, and the amount of code necessary to add a couple of fields to a form is boggling.
> Recently I had the misfortune of doing a contract on a classic SPA project with Flask and sqlalchemy on the backend and React on the frontend, and the amount of code necessary to add a couple of fields to a form is boggling.
Same here, and the reason to do all the Flask + SQLAlchemy + React was to keep things simple, as they are simple tools but Django is a complex tool. In particular the Flask part was juggling plugins for admin, forms and templates that Django already has included. But yeah, I am sure it is easier to code and to mantain because Flask is made for simple sites :/.
Thanks for this! I wish there were more cross-comparisons like this out there of what it is actually like to use some of these frameworks, the note on Django being a little less magic than Rails makes me genuinely interested in it.
if you want "less magic than rails" check out ecto, i would say it has less magic than django
After spending a lot of my time on Django, it's fine for simple to moderately complex things. The ORM mostly good. DRF is fine for APIs. And the admin is super nice as well.
But once something gets significantly complex, the ORM starts to fall down, and DRF becomes more of a hindrance.
But if you're just doing simple CRUD apps, Django is perfectly serviceable.
What does significantly complex mean though? You have to make sure you understand the queries made by the ORM, avoid pitfalls like SELECT N+1 queries and so on. If you don't do this, it'll be slow but it's not the ORM's fault - it's that of the programmer.
Significantly complex means when ORM starts to become bigger and bigger and you need multiple threads and more complex processes that run in workers. When you start to run into scaling problems, your solution is within that framework and that becomes a limiting factor from my experience.
Then as a programmer, you have to find workarounds in Django instead of workarounds with programming.
PS: Dealing with a lot of scaling issues right now with a Django app.
You can absolutely scale Django.
The framework itself is not the limiting factor. The main constraint of performance usually comes from Python itself (really slow). And possibly I/O.
There are well established ways to work around that. In practice, lots of heavy lifting happens in the DB, can you can offload workloads to separate processes as well (whether those are Python, Go, Rust, Java etc).
You need to identify the hotspots, and blindly trusting a framework to "do the job for you" (or for that matter, trusting an LLM to write the code for you without understanding the underlying queries) is not a good idea.
I'm not saying you are doing that, but how often do you use the query planner? Whenever I've heard someone saying Django can't scale, it's not Django's fault.
> When you start to run into scaling problems, your solution is within that framework and that becomes a limiting factor from my experience.
Using Django doesn't mean that everything needs to run inside of it. I am working on an API that needs async perf, and I run separate FastAPI containers will still using Django to maintain the data model + migrations.
Occasionally I will drop down to raw SQL, or materialized views (if you are not using them with Django, you are missing out). And the obvious for any Django dev; select_related, prefetch_related, annotate, etc etc.
> And the obvious for any Django dev; select_related, prefetch_related, annotate
And sometimes not so obvious, I have been bitten by forgetting one select_related while inadvertedly joining 5 tables but using only 4 select_related: the tests work OK, but the real data has a number of records that cause a N+1. A request that used to take 100ms now issues "30 seconds timeout" from time to time.
Once we added the missing select_related we went back to sub-second request, but it was very easy to start blaming Django itself because the number of records to join was getting high.
The cases that we usually walk out of the Django path is for serializations and representations, trying to avoid the creation of intermediate objects when we only need the "values()" return.
Yeah, I don’t get the issues here. I’ve led projects that served millions of requests a day, had dozens of apps and while there are always going to be pain points and bottlenecks, nothing about the framework itself is a hinderance to refactoring. If anything, Django plus good tests made me much braver about what I would try.
> Then as a programmer, you have to find workarounds in Django instead of workarounds with programming.
The mental unlock here is: Django is only a convention, not strictly enforced. It’s just Python. You can change how it works.
See the Instagram playbook. They didn’t reach a point where Django stopped scaling and move away from Django. They started modifying Django because it’s pluggable.
As an example, if you’re dealing with complex background tasks, at some point you need something more architecturally robust, like a message bus feeding a pool of workers. One simple example could be, Django gets a request, you stick a message on Azure Service Bus (or AWS SQS, GCP PubSub, etc), and return HTTP 202 Accepted to the client with a URL they can poll for the result. Then you have a pool of workers in Azure Container Apps (or AWS/GCP thing that runs containers) that can scale to zero, and gets woken up when there’s a message on the service bus. Usually I’d implement the worker as a Django management command, so it can write back results to Django models.
Or if your background tasks have complex workflow dependencies then you need an orchestrator that can run DAGs (directed acyclic graph) like Airflow or Dagster or similar.
These are patterns you’d need to reach for regardless of tech stack, but Django makes it sane to do the plumbing.
The lesson from Instagram is that you don’t have to hit a wall and do a rewrite. You can just keep modifying Django until it’s almost unrecognizable as a Django project. Django just starts you with a good convention that (mostly) prevents you from doing things that you’ll regret later (except for untangling cross-app foreign keys, this part requires curse words and throwing things).
In my experience, django-ninja is a great alternative to DRF, precisely because it's so thin, unlike DRF.
If you're doing simple CRUD apps, try https://iommi.rocks/ which we built because imo it's way way too slow and produces too much code to use standard Django to make CRUD stuff.
Claude Code is also very good at building basic CRUD apps with Django.
No kidding, it is really good especially with htmx which helps you get some of the advantages of a full SPA without the complexity of a separate frontend.
Been building a project in the side to help my studies and it usually implement new complete apps from one prompt, working on the first try
Yeah, I've noticed it regularly suggests htmx (and perhaps something light like alpinejs or some vanilla JS glue logic) to build powerful yet simple interfaces in Django. And it seems to get them right - saving you a lot of time.
The author makes a great last point about Settings and it’s something I’ve not considered… ever! I wonder if there’s a feature request for this because having a pre-configured object would be nice for the ability to verify correctness on startup.
In TypeScript, I use the same validation library (Zod) anywhere I need to validate data. So, I validate my config / environment variables on startup using a Zod schema, I validate my RPC endpoint arguments the same way, etc.
I presume you could do the same thing with Django— use Django’s validation feature to validate everything including your config. It’s a nice pattern that gives uniformity and predictability to all of your validation logic.
I use a project generator tool for a Django project. One of the things it does is generate setting file using string manipulation. I have been trying to think of a more sane way to do this. leverage something like dataclass or Pydantic models to have the typing information available and render a typed and validated Python object. If Django ever made that possible, it would be amazing for dev ex.
I much prefer Python but am not really seeing any point to doing anything other than JavaScript for web projects at this point.
I also do not see much reason to do more than emit JSON on the server side.
You still need clear separation between frontend and backend (react server components notwithstanding), so nothing's stopping you from using Python on the backend if you prefer it.
Django with DRF or django-ninja works really nice for that use case.
> I much prefer Python
Well... that's a valid reason. Why should I work with tool B when I prefer tool A ?
> I also do not see much reason to do more than emit JSON on the server side.
That's the "SPA over API" mindset we need to reconsider. A lot (and I mean A LOT) of projects are way easier to produce and maintain with server-side rendered views.
HTMX with Django backend really excels in this regard.
I'd prefer Unpoly over HTMX, but yes, it excels.
Figuring out which "json" to "emit" is the hard part.
Django aside, I think this is a really important point:
It's perhaps especially true for a hobbyist situation, but even in a bigger environment, there is a cost to keeping people on hand who understand how XYZ works, getting new people up to speed, etc.I, too, have found found that my interactions with past versions of myself across decades has been a nice way to learn good habits that also benefit me professionally.
This is the main reason I'm extremely disciplined about making sure all of my personal projects have automated tests (configure to run in CI) and decent documentation.
It makes it so much easier to pick them up again in the future when enough time has passed that I've forgotten almost everything about them.
I'm finding that in this build fast and break things culture, it is hard to revisit a project that is more than 3 years old.
I have a couple of android projects that are four years old. I have the architecture documented, my notes (to self) about some important details that I thought I was liable to forget, a raft of tests. Now I can't even get it to load inside the new version of Android Studio or to build it. There's a ton of indirection between different components spread over properties, xml, kotlin but what makes it worse is that any attempt to upgrade is a delicate dance between different versions and working one's ways around deprecated APIs. It isn't just the mobile ecosystem.
Build fast and break things works great if you're the consumer, not the dev polishing the dark side of the monolith (helps if you're getting paid well though)
As a consumer, I can not remember any feature that I was so enamored about having a week earlier than I otherwise would have, at the expense of breaking things.
"Cold-blooded software" is a great term for that https://dubroy.com/blog/cold-blooded-software/ (HN: https://news.ycombinator.com/item?id=46488261 )
If you know what you are doing, you can hibernate other kinds of tortoises by placing them in a fridge (as opposed to a freezer). One of my friends does this with their Russian tortoise.
If you need to travel, make sure you have someone reliable who can check on them, in case of a power outage.
Django is objectively the most productive "boring technology" I've ever worked with for developing web applications. They don't regularly add too many bells and whistles on every release, but they keep it stable and reasonably backwards compatible.
The Django ORM / migrations are still basically unmatched in happiness factor.
Its crazy to me after all these years that django-like migrations aren't in every language. On the one hand they seem so straightforward and powerful, but there must be some underlying complexities of having it autogenerate migrations.
Its always a surprise when i went to Elixir or Rust and the migration story was more complicated and manual compared to just changing a model, generating a migration and committing.
In the pre-LLM world, I was writing ecto files, and it was super repetitive to define make large database strucutres compared to Django.
Going from Django to Phoenix I prefer manual migrations. Despite being a bit tedious and repetitive, by doing a "double pass" on the schema I often catch bugs, typos, missing indexes, etc. that I would have missed with Django. You waste a bit of time on the simple schemas, but you save a ton of time when you are defining more complex ones. I lost count on how many bugs were introduced because someone was careless with Django migrations, and it is also surprising that some Django devs don't know how to translate the migrations to the SQL equivalent.
At least you can opt-in to automated migrations in Elixir if you use Ash.
There are some subtle edge cases in the django migrations where doing all the migrations at once is not the same as doing migrations one by one. This has bitten me on multiple django projects.
Can you give an example how this would happen?
Ok, from memory --
There's a pre, do and post phase for the migrations. When you run a single migration, it's: pre, do, post. When you run 2 migrations, it's: pre [1,2], do: [1,2], post: [1,2].
So, if you have a migration that depends on a previous migration's post phase, then it will fail if it is run in a batch with the previous migration.
When I've run into this is with data migrations, or if you're adding/assigining permissions to groups.
There’s like an atomic flag you can pull it out of the transaction . Solves a lot of these issues.
well in elixir you can have two schemas for the same table, which could represent different views, for example, an admin view and a user view. this is not (necessarily) for security but it reduces the number of columns fetched in the query to only what you need for the purpose.
There is no way to autogenerate migrations that work in all cases. There are lots of things out there that can generate migrations that work for most simple cases.
I end up needing to write a manual migration maybe once every other year in real world use.
Django manages to autogenerate migrations that work in the VAST majority of cases.
That's why you can do your own migrations in Django for those edge cases.
I found it very lacking in how to do CD with no downtime.
It requires a particular dance if you ever want to add/delete a field and make sure both new-code and old-code work with both new-schema and old-schema.
The workaround I found was to run tests with new-schema+old-code in CI when I have schema changes, and then `makemigrations` before deploying new-code.
Are there better patterns beyond "oh you can just be careful"?
Kubernetes and Helm cover a lot of these cases: Pods of the new version are started one by one, while the pods of the new version are shutdown. For a short period, you have pods of both versions running.
When you add new stuff or make benign modifications to the schema (e.g. add an index somewhere), you won't notice a thing.
If the introduced schema changes are not compatible with the old code, you may get a few ProgramingErrors raised from the old pods, before they are replaced. Which is usually acceptable.
There are still some changes that may require planning for downtime, or some other sort of special handling. E.g. upgrading a SmallIntegerField to an IntegerField in a frequently written table with millions of rows.
Without care new-schema will make old-code fail user requests, that is not zero downtime.
One option is to do multi-stage rollout of your database schema and code, over some time windows. I recall a blog post here (I think) lately from some Big Company (tm) that would run one step from the below plan every week:
1. Create new fields in the DB.
2. Make the code fill in the old fields and the new fields.
3. Make the code read from new fields.
4. Stop the code from filling old fields.
5. Remove the old fields.
Personally, I wouldn't use it until I really need it. But a simpler form is good: do the required schema changes (additive) iteratively, 1 iteration earlier than code changes. Do the destructive changes 1 iteration after your code stops using parts of the schema. There's opposite handling of things like "make non-nullable field nullable" and "make nullable field non-nullable", but that's part of the price of smooth operations.
I simplify it this way. I don't delete fields or tables in migrations once an app is in production. Only manually clean them up after they are impossible to be used by any production version. I treat the database schema as-if it were "append only" - Only add new fields. This means you always "roll-forward", a database. Rollback migrations are 'not a thing' to me. I don't rename physical columns in production. If you need an old field and a new field to be running simultaneously that represent the same datum, a trigger keeps them in sync.
You can do three stage:
1. Make a schema migration that will work both with old and new code
2. Make a code change
3. Clean up schema migration
Example: deleting a field:
1. Schema migration to make the column optional
2. Remove the field in the code
3. Schema migration to remove the column
Yes, it's more complex than creating one schema migration, but that's the price you pay for zero-downtime. If you can relax that to "1s downtime midnight on sunday", you can keep things simpler. And if you do so many schema migrations you need such things often ... I would submit you're holding it wrong :)
I'm doing all of these and None of it works out of the box.
Adding a field needs a default_db, otherwise old-code fails to `INSERT`. You need to audit all the `create`-like calls otherwise.
Deleting similarly will make old-code fail all `SELECT`s.
For deletion I need a special 3-step dance with managed=False for one deploy. And for all of these I need to run old-tests on new-schema to see if there's some usage any member of our team missed.
I was just in the middle of writing something similar above, thanks!
The general approach is to do multiple migrations (add first and make new-code work with both, deploy, remove old-code, then delete old-schema) and this is not specific to Django's ORM in any way, the same goes for any database schema deployment. Take a peek at https://medium.com/@pranavdixit20/zero-downtime-migrations-i... for some ideas.
oh the automatic migrations scare the bejesus out of me. i really prefer writing out schemas and migrations like in elixir/ecto. plus i like the option of having two different schemas for the same table (even if i never use it)
You can ask Django to show you what exact SQL will run for a migration using `manage.py sqlmigrate`.
You can run raw SQL in a Django migration. You can even substitute your SQL for otherwise autogenerated operations using `SeparateDatabaseAndState`.
You have a ton of control while not having to deal with boilerplate. Things usually can just happen automatically, and it's easy to find out and intervene when they can't.
https://docs.djangoproject.com/en/6.0/ref/django-admin/#djan...
https://docs.djangoproject.com/en/6.0/ref/migration-operatio...
The nice thing in this case is that Django will meet you where you are with your preferences. Want to go the manual route? Sure. Want it to take a shot at auto-generation and then you customize? Very doable and. Want to let Django take the wheel fully the majority of the time? Sure.
I have never done it, but I believe you could setup multiple schemas under the same database -by faking it as different databases and then use a custom router to flip between them as you like.
That sounds like the path to madness, but I do believe it would work out of the box.
sounds inconvenient and error-prone
It is not much code to setup the router. Now, why you would want to bounce between schemas, I do not have a good rationale, but whatever floats your boat.
Have you ever tried Rails? I think that Django's approach on those is an adaptation from it.
Of course, ActiveRecord back in 2005.
In hindsight, maybe I should've tried to use Django for my previous project instead of build a lot of custom stuff in Go and React. It was basically an admin interface, but with dozens of models and hundreds if not thousands of individual fields, each with their own validation / constraints. But it was for internal users, so visually it mainly needed to be clear.
I always return to Django for any project. It's fantastic. Enough batteries are included with it that it is very powerful.
> I love being able to backup by just doing a VACUUM INTO and then copying the resulting single file.
Naively, I would probably just copy the sqlite file. Is that a bad idea?
That's fine if SQLite isn't running, but you risk corruption if you copy a file while it is being actively written to.
VACUUM INTO eliminates that risk.
Thank you for mentioning this. I was planning to do exactly this soon and had no idea.
I learned about the VACUUM INTO command through the following guide on how to backup your sqlite database: https://litestream.io/alternatives/cron/
After working with Django for 8 years, I find it hard to move on to anything else. It's just the right amount of magic, and just the right amount of flexibility, and it's just such a joy to work with.
Re: Django is OK for simple CRUD, but falls apart on anything complex - this is just untrue. I have worked in a company with a $500M valuation that is backed by a Django monolith. Reporting, recommender systems, file ingestion pipelines, automatic file tagging with LLM agents -- everything lives inside Django apps and interconnects beautifully. Just because it's a Django app doesn't mean you cannot use other libraries and do other stuff besides basic HTTP request processing.
Recently I had the misfortune of doing a contract on a classic SPA project with Flask and sqlalchemy on the backend and React on the frontend, and the amount of code necessary to add a couple of fields to a form is boggling.
> Recently I had the misfortune of doing a contract on a classic SPA project with Flask and sqlalchemy on the backend and React on the frontend, and the amount of code necessary to add a couple of fields to a form is boggling.
Same here, and the reason to do all the Flask + SQLAlchemy + React was to keep things simple, as they are simple tools but Django is a complex tool. In particular the Flask part was juggling plugins for admin, forms and templates that Django already has included. But yeah, I am sure it is easier to code and to mantain because Flask is made for simple sites :/.
Thanks for this! I wish there were more cross-comparisons like this out there of what it is actually like to use some of these frameworks, the note on Django being a little less magic than Rails makes me genuinely interested in it.
if you want "less magic than rails" check out ecto, i would say it has less magic than django
After spending a lot of my time on Django, it's fine for simple to moderately complex things. The ORM mostly good. DRF is fine for APIs. And the admin is super nice as well.
But once something gets significantly complex, the ORM starts to fall down, and DRF becomes more of a hindrance.
But if you're just doing simple CRUD apps, Django is perfectly serviceable.
What does significantly complex mean though? You have to make sure you understand the queries made by the ORM, avoid pitfalls like SELECT N+1 queries and so on. If you don't do this, it'll be slow but it's not the ORM's fault - it's that of the programmer.
Significantly complex means when ORM starts to become bigger and bigger and you need multiple threads and more complex processes that run in workers. When you start to run into scaling problems, your solution is within that framework and that becomes a limiting factor from my experience.
Then as a programmer, you have to find workarounds in Django instead of workarounds with programming.
PS: Dealing with a lot of scaling issues right now with a Django app.
You can absolutely scale Django.
The framework itself is not the limiting factor. The main constraint of performance usually comes from Python itself (really slow). And possibly I/O.
There are well established ways to work around that. In practice, lots of heavy lifting happens in the DB, can you can offload workloads to separate processes as well (whether those are Python, Go, Rust, Java etc).
You need to identify the hotspots, and blindly trusting a framework to "do the job for you" (or for that matter, trusting an LLM to write the code for you without understanding the underlying queries) is not a good idea.
I'm not saying you are doing that, but how often do you use the query planner? Whenever I've heard someone saying Django can't scale, it's not Django's fault.
> When you start to run into scaling problems, your solution is within that framework and that becomes a limiting factor from my experience.
Using Django doesn't mean that everything needs to run inside of it. I am working on an API that needs async perf, and I run separate FastAPI containers will still using Django to maintain the data model + migrations.
Occasionally I will drop down to raw SQL, or materialized views (if you are not using them with Django, you are missing out). And the obvious for any Django dev; select_related, prefetch_related, annotate, etc etc.
> And the obvious for any Django dev; select_related, prefetch_related, annotate
And sometimes not so obvious, I have been bitten by forgetting one select_related while inadvertedly joining 5 tables but using only 4 select_related: the tests work OK, but the real data has a number of records that cause a N+1. A request that used to take 100ms now issues "30 seconds timeout" from time to time.
Once we added the missing select_related we went back to sub-second request, but it was very easy to start blaming Django itself because the number of records to join was getting high.
The cases that we usually walk out of the Django path is for serializations and representations, trying to avoid the creation of intermediate objects when we only need the "values()" return.
Yeah, I don’t get the issues here. I’ve led projects that served millions of requests a day, had dozens of apps and while there are always going to be pain points and bottlenecks, nothing about the framework itself is a hinderance to refactoring. If anything, Django plus good tests made me much braver about what I would try.
> Then as a programmer, you have to find workarounds in Django instead of workarounds with programming.
The mental unlock here is: Django is only a convention, not strictly enforced. It’s just Python. You can change how it works.
See the Instagram playbook. They didn’t reach a point where Django stopped scaling and move away from Django. They started modifying Django because it’s pluggable.
As an example, if you’re dealing with complex background tasks, at some point you need something more architecturally robust, like a message bus feeding a pool of workers. One simple example could be, Django gets a request, you stick a message on Azure Service Bus (or AWS SQS, GCP PubSub, etc), and return HTTP 202 Accepted to the client with a URL they can poll for the result. Then you have a pool of workers in Azure Container Apps (or AWS/GCP thing that runs containers) that can scale to zero, and gets woken up when there’s a message on the service bus. Usually I’d implement the worker as a Django management command, so it can write back results to Django models.
Or if your background tasks have complex workflow dependencies then you need an orchestrator that can run DAGs (directed acyclic graph) like Airflow or Dagster or similar.
These are patterns you’d need to reach for regardless of tech stack, but Django makes it sane to do the plumbing.
The lesson from Instagram is that you don’t have to hit a wall and do a rewrite. You can just keep modifying Django until it’s almost unrecognizable as a Django project. Django just starts you with a good convention that (mostly) prevents you from doing things that you’ll regret later (except for untangling cross-app foreign keys, this part requires curse words and throwing things).
In my experience, django-ninja is a great alternative to DRF, precisely because it's so thin, unlike DRF.
If you're doing simple CRUD apps, try https://iommi.rocks/ which we built because imo it's way way too slow and produces too much code to use standard Django to make CRUD stuff.
Claude Code is also very good at building basic CRUD apps with Django.
No kidding, it is really good especially with htmx which helps you get some of the advantages of a full SPA without the complexity of a separate frontend.
Been building a project in the side to help my studies and it usually implement new complete apps from one prompt, working on the first try
Yeah, I've noticed it regularly suggests htmx (and perhaps something light like alpinejs or some vanilla JS glue logic) to build powerful yet simple interfaces in Django. And it seems to get them right - saving you a lot of time.
The author makes a great last point about Settings and it’s something I’ve not considered… ever! I wonder if there’s a feature request for this because having a pre-configured object would be nice for the ability to verify correctness on startup.
In TypeScript, I use the same validation library (Zod) anywhere I need to validate data. So, I validate my config / environment variables on startup using a Zod schema, I validate my RPC endpoint arguments the same way, etc.
I presume you could do the same thing with Django— use Django’s validation feature to validate everything including your config. It’s a nice pattern that gives uniformity and predictability to all of your validation logic.
I use a project generator tool for a Django project. One of the things it does is generate setting file using string manipulation. I have been trying to think of a more sane way to do this. leverage something like dataclass or Pydantic models to have the typing information available and render a typed and validated Python object. If Django ever made that possible, it would be amazing for dev ex.
https://docs.pydantic.dev/latest/concepts/pydantic_settings/
Alternatively look at https://sqlite.org/backup.html for backing up the sqlite db, instead of using VACUUM INTO
I much prefer Python but am not really seeing any point to doing anything other than JavaScript for web projects at this point.
I also do not see much reason to do more than emit JSON on the server side.
You still need clear separation between frontend and backend (react server components notwithstanding), so nothing's stopping you from using Python on the backend if you prefer it.
Django with DRF or django-ninja works really nice for that use case.
> I much prefer Python
Well... that's a valid reason. Why should I work with tool B when I prefer tool A ?
> I also do not see much reason to do more than emit JSON on the server side.
That's the "SPA over API" mindset we need to reconsider. A lot (and I mean A LOT) of projects are way easier to produce and maintain with server-side rendered views.
HTMX with Django backend really excels in this regard.
I'd prefer Unpoly over HTMX, but yes, it excels.
Figuring out which "json" to "emit" is the hard part.