I know people joke around about this a lot. But I was blown away by a recent switch to I did from using cv2 with python to using cv2 with C++.
I had literally hundreds of thousands of images to analyze for a dataset. My python script would have taken 12 hours.
I ported it to C++ and it literally destroyed it in 20 minutes.
I’m sure I was doing something that really wasn’t optimized well for python. I know somewhere in the backend it probably was using a completely different library with multi thread optimization. Or maybe turbojpg is just garbage in python. I’m still not even sure what the bottleneck was. I don’t know enough to really explain why.
But holy shit. I never had that much of a performance difference in such a simple task.
Was very impressed.
If you had let me write the C++ code, I could have literally destroyed your dataset in a couple of seconds.
I don’t even need C++ for this just give me a rm with a side dish of -rf
Exactly what this comic is saying. C++ can handle in 20 minutes what takes python 12 hours, but something gets destroyed.
But make sure to bill for the full 12 hours.
Was a personal project. But absolutely. Half my job is trying to explain why something is taking so long when in reality I actually it’s already done I just don’t want to do nothing for the next few days.
Managers never really know. And other engineers don’t care. It’s all about balancing expectations.
I had a similar experience processing PDFs of building plans. 4-8k PDFs, took 5-10minutes in Python.
I ended up switching to node.js and it processed the same PDFs in 120 seconds.
Over the years I only really find Python useful for interviewing and occasionally in ci pipelines.
It’s a great tinkering language. Which is a lot of what I do for personal projects no one else will ever see. I find it’s biggest strength is also it’s biggest weakness. It’s really easy to write that you assume you don’t have to care about under the hood stuff.
But something as simple as using a list instead of a set can turn a 2 minute script into a 2 hour script pretty quickly.
I remember when I first started using it I was working with building a list and then comparing elements of another list to see if it was contained.
My list was static in the comparison so I just did a “x in Y”.
Y was massive though.
If I was using any other language I would have thought about the data type more and obviously use a set/hash for O(1) lookup. But since I was new to python I didn’t even think about it because it didn’t seem to give a fuck about data types.
A simple
set_a = set(list_a)was all I needed. I think python is so easy to pick up that no one even bothers to optimize what they are writing. So you get even worse performance than it should have.
I ran into a similar situation many years ago, when I was trying to write a software synthesizer using Visual Basic (version 4 at the time). The big problem is that if you’re doing sample-by-sample processing of audio data in a loop (like doing pixel-by-pixel processing of images) and your chosen language’s compiler can’t compile to a native EXE or inline calls, then you end up suffering the performance hit of function calls that have to be made for each sample (or pixel). In many applications you’re not making a lot of function calls and the overall performance hit is negligible, but when you’re doing something where you’re making hundreds of thousands or even millions of calls per second, you’re screwed by the overhead of the function calls themselves - without there being any other sort of inefficiency going on.
In my case, I eventually offloaded the heavy sample processing to a compiled DLL I wrote in C, and I was able to keep using Visual Basic for what it did really well, which was quickly building a reliable Windows GUI.
Might be latency? Python is probably slower to respond to things like when one job is done and the next job can start, since it needs time to interpret the code and call the relevant C libraries.
alternative joke
C++: “Hey, what’s your name?”
Python: *grabs C++* “what is my name?”please explain I want to laugh
Pythons weak typing means it never knows what is what. It’s up to the programmer to not screw it up.
I think it’s more that a ton of popular python libraries (things like pandas, tensor, etc) are actually built in C, so python is just C wearing a mask
Not just libraries, the entire language itself is actually C under the hood
Python is strongly typed. I get your point since it is dynamically typed, but still strongly.
not really what i was going for
one of python’s strong suits is it’s foreign function interface. it’s very easy to call code in other languages from python, which can speed execution up significantly
oh alright. Cheers, heheh. I laughed
One of the main implementations of Python is CPython, which is written in C or C++
It’s now cannon in my head that Python talks like Parappa the rapper
These comics are always fun to read because as an R user, I only ever hear about how fast python is.
Not having true vectorization and having to regularly code that into Python helps even the odds.
you mean __name__?
__main__
This has got to be the best, most legitimately funny programmer/computer joke I’ve seen in years.
I thought that too! :D
Python’s core implementation is in C though. It’s just a fancy way to call C libraries.
You could say this about a lot of things…
Pizza’s core implementation is in C though. It’s just a fancy way to call C libraries.
Today I learned pepperoni is a C library.
I code in JS, so I’m something of a C dev myself!
Beginning your sentence with “I code in JS” really makes any follow-up statement sound sane in comparison.
Aschhually the most used JS engine is made in C++ (v8)
Python is actually pretty baller with string operations too since so much of the C has been heavily optimized.
so?
It might not be a language but a skill issue if your python code performs significantly worse than your C code
That completely depends on what you’re doing. If you’re doing tasks that python can completely offload to some highly optimised library written in C/C++/Fortran, then yes. However at that point you’re not really comparing Python to C anymore, but rather your C implementation to whatever library you used.
A fair comparison is to compare pure python to pure C, in which case you need to mess up the C-code pretty bad if Python is to stand a chance.
I do think, it’s fair to use Python code which uses C under the hood in benchmarks, because it does often match reality. But then it also has to be realistic code. If it’s just a single line of Python code, which calls into C and then everything happens there, then there is really no point for you to use Python.
Python only makes sense to use, if you do write some amount of glue code with it. And then it does require significantly more skill to write performant code than it does with many other languages, as lots of costly abstractions are hidden from you. And it often also requires more effort, since you might not find performant libraries, since so much of the ecosystem only has performance as an afterthought. Reality is messy, which is why realistic Python code still tends to do terribly in benchmarks, whether it calls into C or not.
100 % agree here. If you’re testing an actual use-case, it’s fair to compare realistic python to realistic C. However, I would argue that at that point you’re no longer benchmarking Python vs. C as languages, but Python vs. C for that particular use-case.
JavaScript fits too
Javascript’s still arriving to the meeting
It had to download 300 of it’s buddies first
nodejs hasnt started yet
It’s waiting for the “light weight” node development server to start as well.
After that it’s waiting for the call to open a browser to go through.
Then it’s waiting for Google telemetry to finish phoning home before the page can load.
Javascript took the wrong turn and ended up in [object Object]
NaN
It’s interesting that, with Python, the reference implementation is the implementation — yeah there’s Jython but really, Python means both the language and a particular interpreter.
Many compiled languages aren’t this way at all — C compilers come from Intel, Microsoft, GNU, LLVM, among others. And even some scripting languages have this diversity — there are multiple JavaScript implementations, for example, and JS is…weird, yes, but afaik can be faster than Python in many cases.
I don’t know what my point is exactly, but Python a) is sloooow, and b) doesn’t really have competition of interpreters. Which is interesting, at least, to me.
Pypy is often considered the “best” alternative Python implementation. In some cases it can be much faster. But it’s often one or two versions behind, and not 100% compatible, and of course it doesn’t work with native Cpython extensions.
Ah, Python. It might not be fast, but it gets it right in the end. :)
$ python3 -c 'f = (lambda x: x + 0.5 - 0.5); print(f(2**52))' 4503599627370495.5Ahh, contrived examples of floating point errors. I haven’t seen those since I asked Javascript to add 0.1 and 0.2 :)
deleted by creator
Just wait till they get rid of GIL!
And get a whole bunch of multi threading horrors…
Well… I’m sorry to tell you… But they’re already working on it.
my dudes i had the idea for the stupidest little app that i could charge 50 cents for that maybe a million people would download because like 50 cents but i have no idea how to code beyond “hello world” and I AM NOT VIBE CODING AAAA
it’s a map you put pins with notes in it. you will get nothing more than that out of me because it’s very cute and then you will make it
Ideas are cheap
very cheap
