Does Python still have performance issues? That's always been my concern when considering its adoption.
You're not going to get maximum performance out of a python program, generally. But unless your python program is using 100% cpu that's not really an issue, and there are very few cases where I'm using python and it's CPU that's slowing down the program. Mostly it ends up being some kind of IO.
If you're looking to do complicated mathematical operations, you can call out to an external library like `numpy` of tensorflow to take better advantage of cpu/gpu compute.
You can also use a faster python interpreter, like `pypy`, although then you start getting slower startup times and more memory usage. The performance is closer to something like java but drawbacks are generally enough that they don't get used, since it's very rare that you need that extra performance.
There's also micropython, which lets you run python on a microcontroller and makes it pretty easy to optimize things for your hardware, although you lose some useful debugging and introspection features.
But for the most part, I haven't run into anything I'd call "performance issues", just the knowledge that python is a scripting language and it's never going to be the fastest thing around. Python's performance just isn't an issue very often.
You have to work pretty hard to hit a CPU bottleneck, and I can't imagine how you'd do that building a simple CRUD website.
Can you explain a bit more about how the people you know are hitting that bottleneck? I mean I've literally built CRUD apps on an esp32 microcontroller using python, and they work performantly. If real python with a real web-framework can't do the same then something is going horrible wrong.
But then maybe your problems are deeper that choice of language.
So many people immediately dismiss relational databases, then re-implement all that functionality in their apps with various bugs and performance bottlenecks.
ORM is another bad technique. It supposedly promises you that you don't need to know SQL to use, and that is true for the simplest examples, but you absolutely have to know it for anything less trivial, then you have to figure out how to write query in ORM to get a desired SQL statement (it makes it very difficult to use advanced SQL functionality), and that's not the end. ORM constantly will make unnecessary SQL queries and by default request all fields, even if you don't use them, adding additional performance bottleneck.
In my current workplace thanks to ORM we make on average 10 queries per request and at peak generate 1Gbps throughput from/to database because of those inefficiencies.
I think the way to go is SQL support in IDE, I recently saw PyCharm with DataGrip where if you configure it to connect to a database it will start recognizing SQL in string statements and start treating it like code (so you now have autocomplete, refactoring etc). I think this is probably the proper way to do it and I wish that other IDEs would have similar features.
please support this assertion with an ORM whose documentation promises this.
> ORM constantly will make unnecessary SQL queries and by default request all fields,
as does "SELECT * FROM table" if you don't write out the fields and use a buffering database adapter (which is the case for nearly all Python database adapters), so, when using an ORM, you need to give it instructions over what columns you need to fetch. This is not unusual nor even anything a library could possibly guess for you if you do not give it this intent.
I don't think any ORM would officially claim that, but many people decide to go that way to avoid SQL. For many simple examples it looks simpler than SQL.
I also experimented myself and wrote my code using SQLAlchemy's ORM. Then edited my code to use psycopg2 natively. I realized that SQLAlchemy didn't save any code for me, in fact it was more verbose. It also heavily encouraged me to get the raw data from database and do processing of it in the application. It also did unnecessary extra queries.
So with it now I need to:
1. still understand SQL
2. trying to figure how to write the SQL statement using ORM
3. figure out how to make ORM not to do extra queries
> as does "SELECT * FROM table" if you don't write out the fields and use a buffering database adapter (which is the case for nearly all Python database adapters), so, when using an ORM, you need to give it instructions over what columns you need to fetch. This is not unusual nor even anything a library could possibly guess for you if you do not give it this intent.
My problem is that in addition to knowing SQL, I also need to know how to do in that specific ORM that's for the specific language I'm using.
ORMs that are more honest about this, such as SQLAlchemy, are generally better than those that try to pretend that you really are dealing with entities.
But anyway even query builder is not that great. Recently used PyCharm's integration with DataGrip. Basically the way it works is that if you configured a database in your project and let PyCharm fetch its schema, suddenly the IDE started recognizing the SQL statements providing autocomplete not only the statements but also table names etc, it also offered refactoring which created migration scripts.
After using that I think that's the proper way of solving the impedance mismatch and at that point you no longer need ORM or query builders. I hope other IDEs will start doing the same thing.
Good point: I'm no longer doing object mapping in that case. :) Though I think the most complicated part of the process that the Django "ORM" does for you is build the query. Maybe it should be called Django query generator.
Some things:
- to make my application scalable I want two endpoints one that is read only, that goes to replicas and one that goes to master for modification. That way in my application I have full control which queries go to replica and which need master
- use datatypes that are recommended by the database
- do upsert
- do select and only return fields that I need
- perform insert and return only fields that I want (my ORM was performing another select)
- ORM also does bunch of queries that I don't need, I don't want it to add extra load to database if I never use the results of it
Those are very simple things, but I'm also curious how you can do some advanced operations like group by having, window functions, CTEs, aggregate functions that return JSONB structure (so you don't have to send duplicate data and avoid N+1 queries). How do you use PostGIS?
You don't! That is not what an ORM is for. Use SQL. Any decent ORM with allow you to integrate SQL.
Check out the Fluent (query builder) and Eloquent (ORM) from Laravel. They allow all the features you want.
It’s possible, of course, but I’ve usually seen it as a symptom of not having a good culture around monitoring and troubleshooting — e.g. I remember someone porting an entire site to Jinja2 alleging performance wins, and it’s true that template rendering got (IIRC) 10-15% faster but they’d missed that 99.999% of the total runtime was being taken up by unoptimized database access generating many thousands of queries.
What I mean is: when you start optimising, no matter how well you do, you will eventually hit a CPU performance wall. Then you will realise that only a rewrite will get you out of that hole, and by then it will be late.
That said the Python VM is a well optimized beast, it's very dynamic, you can monkey patch anything and everything at runtime on-the-fly. This is of course what makes it hard to speed up, too many things to "check for overloadedness" when doing anything. A JIT could help, but that's a lot of work. (Hence why PyPy took a decade, and the whole Python C API compatibility problem is still there - PyPy can only JIT the Py parts.)
Anyway. With that much dynamic stuff and with the VM and the language already well tuned for concurrent memory safety its 'async' story is great and performant. (Like NodeJS'.)
So Python is great for orchestrating whatever you need to handle requests. And if you want to scale up, just spin up more instances. (Just like with Java, but you don't have to think much about the overhead garbage collection, because CPython uses simple reference counting.)
Finally, my concern with Python was the lack of static typing, which made any large project hard to manage. (And resulted in a lot of boilerplate tests and excessive defensive coding.) But mypy is great, and with that it's a joy to work in Python. (Just like TypeScript made the JS world exponentially much more saner.)
Nowadays it works correctly with almost all C modules. Mostly you just need to use `pypy3 -m pip install foo` instead of `python3 -m pip install foo`.
It seems that any new/greenfield Python project should target PyPy though, just in case, and the performance never hurts.
You can use Python (or Ruby, etc) to build fast websites that scale well if you are smart about how you use it and how you design your application, but it still isn't a particularly fast language compared to others.
That's how asyncpg can outperform[1] some libraries that you normally would think was not possible.
Instagram can afford it so it works fine for them. Reddit can't, so their site is always slow or down.