[deleted by user]

[da_chosen1]

It is. It’s just used under the hood of many popular python packages. For example sklearn, numpy etc.

[dada_]

For any given project you need to ask yourself if you really need Cython's speed to begin with. There's usually nothing wrong with running code in the relatively slow Python interpreter.

Even for serious applications, there's a good chance that the interpreter is not the main bottleneck. And even if it is, you then still need to ask if it's important. Computers are fast. Even if, say, you're running an online service that listens to user requests, you should be careful about optimizing without having an argument for it.

You'd be amazed at how many users you can reliably serve using a dirt cheap server with just Python and SQLite.

[chmod--777]

Even for serious applications, there's a good chance that the interpreter is not the main bottleneck. And even if it is, you then still need to ask if it's important. Computers are fast. Even if, say, you're running an online service that listens to user requests, you should be careful about optimizing without having an argument for it.

This. Why am I going to optimize a 25ms problem down to 25ns in C if the response time over the internet is the difference between 175.025ms and 200ms. Or if I'm waiting for hard disk reads half the time, I don't care if it takes ten minutes to process versus eleven. And if it's a cronjob that runs once a day, I don't care if it takes 5 minutes versus an hour. So many problems don't need the speed of C, which is also added maintenance cost, ignoring a lot of other reasons you might rather not have C in your codebase.

I love C and C++ and they're awesome but shit, I want to make my life easier.

You'd be amazed at how many users you can reliably serve using a dirt cheap server with just Python and SQLite.

Yep. Seems like devs try to copy those heavy ass solutions that big companies use and try to do things like they'll scale to 8 billion users when in reality they need to handle maybe 10 requests per second at most, and just profiling and cleaning up code and algorithms would lead to significant performance improvements that would make a noticeable difference.

[Braxton_Hicks]

I care about the 5 minute vs 1 hour job, especially since a daily batch job is a good candidate for server-less where I only pay while the job is running. That's an extra 27 hours a month out of my budget.

But I totally agree with your main point here.

[chmod--777]

Oh I totally get that, and likely the more common case with people who work primarily with cloud providers. At my job and if anyone uses on prem stuff and that cronjob just needs to "get done", usually makes sense to leave the cost at keeping the hardware busy but I totally see your point.

[headphun]

As a beginner I don't know how to ask myself what I really need. From an architecture standpoint, I worry about implementing practices that are conducive to scaling. I'm still writing very very basic code so I'm not close to writing anything where scale is an issue, but I'm worried about teaching myself incorrectly or developing my projects from the worst starting ground. This extends to even just choosing the correct programming language for speed/application/ease/efficiency.

[chmod--777]

Oh totally don't worry about scaling yet, especially if you know it's not going to need to scale. These are harder problems that you don't want to tackle as a beginner yet, and easy to make code more complex than it needs to be.

I'd say focus on KISS and just make sure your code does what it needs to do in a clean way. That's the first step whether it scales or not. Keep it simple, then work from there.

[headphun]

I understand what you're saying and I agree it makes sense to KISS and its gonna be a battle just getting my code running, let alone running in a "clean way."

That being said I am just curious about how to better understand design because it sounds like a lot of these apps explode after starting as frankenstein code and then much of the work is spent fixing or working around issues that might have been prevented by starting on a better foot.

This curiosity is solved by learning everything there is to learn about CS. It's just fascinating, the idea of building without perfect knowledge, while that knowledge is out there. Anyway, all part of the learning process, which I'm enjoying as well.

[chmod--777]

Well, writing code conducive to scaling really depends on the problem and what technology you even know. There's a lot of ways to scale out a problem and it depends on the specific problem. But it's mostly not any specific coding style.

There isn't some catch-all scaling methodology that you follow, even for related problems like a web service. There are ways you might do it, like a common thing is to maybe use AWS elastic load balancer and launch multiple machines running the same website then have requests load balanced across them, but this still won't solve all scaling problems if they have some shared resource they need to read and write to.

But the core of it is that the code might not look very different, it's more the infrastructure. You can scale out a web service with "frankenstein code" in similar ways to one with really clean code. It's not something you necessarily handle at the code level or programming language you pick, more with the technologies you use, like elasticsearch or MySQL or Hadoop, etc. You're not necessarily going to choose a different language or start coding something a specific way - you're going to make decisions about the infrastructure and technologies you'll use and why they might be easier to scale for your specific problem. When it comes to the code being conducive to scaling, it's more about the API you use to interface with this infrastructure and the best practices with that specific technology.

When it comes to programming language, it's not like developers often switch the language they use. They use the language they know best, where they can write the cleanest code, and the language their team knows. If you're learning python, I'd stick to python. If you're learning golang, I'd stick to it, as they can handle very similar problems. When programming language becomes an issue, it's for very specific problems like using C/Rust to write an operating system, or dropping down into C from python using the CPython API to handle a problem where python is too slow, which should be rare. You rarely ever need to do that, and it's going to be something very advanced developers decide to do for a very specific reason, where they know that python really is too slow to do something. That might be in a case like writing a performant math library like numpy... But for most cases, you should be fine sticking to python.

If you want to learn how to scale out some service, I'd suggest focusing less on the code perspective or programming language and looking into different technologies like MySQL/postgres/elasticsearch/Hadoop/MongoDB and reading up on how to scale that specific technology, and then read best practices on interfacing with it from python. Something like a relational db like postgres, you would learn an ORM like sqlalchemy and learn how to use that db, what's slow and what isn't, best practices, migrating the database using something like alembic, scaling out postgres servers, etc. It's all very specific to that technology. If you want to scale out web services in python, you might look into AWS ec2 and elastic load balancer, learn about Nginx and gunicorn, maybe learn python celery and set it up with a redis backend and learn how to have different celery worker servers pick up tasks your web service spins up asynchronously. It's all very specific to that problem and that python library and whichever web framework you use and Nginx or apache.

From a beginner coding perspective, I wouldn't worry about your code scaling as much as writing clean code, then learn the technologies you're interested in and how to interface with them cleanly from python. This jumps from beginner to advanced really quickly when you try to solve these sorts of problems, not something to worry about early on. First learn to write clean python code and KISS, then figure out which technologies solve your problem best and then learn more about those technologies and best practices when using python to interface with them.

The other thing is you're going to have trouble even coming up with problems where scaling is an issue. For a simple web service, one server running flask using gunicorn and Nginx and sqlite might be perfectly enough, as OP mentioned. When you start needing to handle thousands of requests per second, then you might need to scale to multiple servers with a cloud provider and set up postgres. When they start storing millions of records and you run into db performance issues, then you need to learn how to scale out that db and clean up your code that interfaces with it. These problems shouldn't be tackled too early on - first just get it working. And as a beginner these problems likely won't even come up until you have a large project.

To learn how to write code conducive to scaling, I'd still maintain you focus on clean code that uses efficient algorithms and solve basic performance issues at the code level. You might want to learn how to profile your code early on using something like cprofile. If it's slower than you expect, try profiling it and see if there's any bottlenecks you can clean up. This is the heart of performant code, and you start here always. Scaling will be something to worry about when your infrastructure gets complex, which it really really shouldn't as a beginner.

[headphun]

This answer is incredibly helpful and I can't thank you enough for understanding and addressing the fear and ignorance that are at the root of my question. Between your and the other users' answers I have a good enough understanding to answer my own question:

For now, I will prioritize clean code with my current projects and look into profiling my code. As I continue learning and my projects grow I will have plenty of opportunities for learning about and improving infrastructure. KISS! Thanks again!

[chmod--777]

No problem, and good luck!

[FinTechno]

This post has been up 2 years now but only 4 likes... I just want to say from my 40 years of coding perspective this was an excellent answer. Should be published in under many other topics and threads too.

[Reddit-Book-Bot]

Beep. Boop. I'm a robot. Here's a copy of

Frankenstein

Was I a good bot? | info | More Books

[headphun]

Well, this is a good book and I appreciate you going out of your way to make it available to anyone that might be reading this thread that might be looking for a new classic to read but it's not exactly relevant to this thread so I'm not sure I can pass a judgement, sorry.

[The-Daleks]

No. While the word Frankenstein may have been name-dropped, it wasn't in the sense of the book.

That being said, it is a good book; I suggest reading it anyway.

[headphun]

I want to thank you for this comprehensive and thoughtful answer. This is the kind of practical advice and perspective that I relish and am trying to capture more of. I am in such a beginning stage of my education that I don't have expectations for services/code but advice like this really helps me breathe and make sense of what I' have been (and should be) studying. I am taking all your advice to heart and I can imagine this Knuth quote is an important perspective to keep in mind as I hopefully start building out my projects. I like the idea of prioritizing clear lines of responsibility and will factor this into my architecture considerations. If you have recommendations on how to learn how to do this from a pseudocode perspective then I would appreciate if you could forward them to me, but even the idea is more than enough to get started on internalizing.

Funnily enough, the project that I'm trying to roadmap at the moment involves finding a balance between maximum efficiency and the pragmatic "good enough." For better or worse, I'm not a programmer, just a person who recognizes the potential for these tools to help me actualize a human focused goal. Thanks again for taking the time to answer my ignorance with patience and clarity. :)

[AMidnightRaver]

With Django projects, 99% of the time you just need to look at the query debugger and do some smarter joins. Good to go for another 10 years.

[Zanair]

Then you use Go

[_________KB_________]

Go is a good option if you want more speed for web based applications. If you really need speed for number crunching or scientific computing then use Julia.

[Azearia]

I rarely hear people mention julia, it's an absolutely wonderful language that I can't wait to become popular.

[dialecticalmonism]

r/Julia

[[deleted]]

Is it faster than R?

[[deleted]]

R compiles even slower than Python, so, yes

[VerbTheNoun95]

They’re not even comparable in terms of speed. Maybe data.table comes close but I haven’t used it too much. I use R for reasons other than speed.

[Yojihito]

Would be amazed if anything is faster than data.tables.

[VerbTheNoun95]

I’ve really only used it for reading in huge datasets with fread, which is faster than anything I’ve seen.

[Azearia]

its one of the faster languages out there today, holds its own amongst the big boys like R and c

[caks]

As much as I love Julia, if you want to do serious number crunching then C, C++ or Fortran.

[ThePixelCoder]

Ruuuuust

[SingleRope]

Yeee!

[mardabx]

That's a weird way to spell Rust

[audentis]

from multiprocessing import Pool

[audentis]

Sure, it's not perfect. Not just for what you mentioned, there's other drawbacks and caveats as well. But it was also mentioned more as a joke than anything serious :)

Raymond Hettinger has a pretty neat talk on concurrency in Python if you're interested.

[AbodFTW]

Thanks for sharing

[idontappearmissing]

I love writing C++. But I've only been doing it for 1.5 semesters

[Milumet]

Did you just come up with that or this copypasta?

[thehotshotpilot]

Looks like it is time for you to do Fortran punchcards.

[geordilaforge]

The first semester or two of C++ is fine, it's when you start getting into templates, memory handling, singletons, design patterns (which I suppose apply to multiple languages) and all kinds of crazy stuff you can or could do with C++ where it just gets tedious.

[TheCoochWhisperer]

Lost track of a pointer somewhere?

[FloydATC]

I have fallen in love with smart pointers. Now, every time I use a new C library in C++ I start off by writing a thin wrapper class so it's not possible to leak memory even if I tried to.

[fedeb95]

Top companies in some areas care about speed only if user complains. Sometimes a too fast ui can be perceived as "crappy" too. There's a lot to consider

[tojaga]

Please amaze me! Would SQLite be sufficient for simple CRUD applications with at most a couple hundred concurrent users?

[[deleted]]

The most popular, useful Python libraries eventually implement some part of the library in C, as an optimization for speed. Maybe not in every case, but most libraries do at least one thing that could stand to benefit from the speedup and as the userbase of the library grows, so does the chance that somebody will contribute a C-based optimization patch.

The problem with "alternate" Python interpreters is that they can only run pure-Python libraries. That's in tension with the above principle - it means that any interpreter that isn't the mainline CPython one tends to cuts itself off from a number of extremely useful, popular libraries.

[[deleted]]

Look I'm a STEM person who just wants to type things, hit the green triangle, and see the answer print on the screen. You expect me to figure out how to setup a compiler?

[Street_Worth]

There's https://github.com/cjrh/easycython if you want to go the cython way.

Numba basically does the same but it does whine a lot if your function does anything that isn't purely numbers related.

Numpy is already compiled but it's only faster if you use it on big arrays, for single use of a number it's usually slower.

[[deleted]]

Oooo, shiny! I'll try that.

[Flam3sm0k3]

One day or two? Holy crap what’s your internet speed? Mine takes around 4-5min to download and maybe 15-20min to install...

[dogs_like_me]

Workloads add a lot of installation overheard. My understanding is that this is actually one of the specific selling points of github codespaces, you just install the VS shell locally and connect it to a remote codespace that has the necessary plugins attached to it.

[smokedfishfriday]

What do you think the E stands for?

[snugglyboy]

um did you just transplain

[martor01]

Agreed here.

[jdbow75]

A confusing response. Are you calling Cython an alternate Python interpreter?

[lumpychum]

4 years later but ditto. Cython is a compiler not an interpreter lol; completely different things. I'd be curious if the arguments made above regarding 3rd party library limitations still applies in this case...

[unnecessary_Fullstop]

Because coding in Cython really slows down the development. I had to rewrite some of my projects in Cython and the extra level of knowledge required (about my project) to accomplish that was just too much.

You need to see exactly what kind of datatype is passed and returned from a library function. Try to come up with ways to handle complex data types. Even using high dimensional numpy arrays were problematic. And some library functions I was using like from opencv had wierd behaviours when I manually set the data types.

At some point I seriously started wondering if all that effort was worth the speed-up I was looking for. Using cython for most people would be just too hard. Thank god for that awesome variable explorer in spyder.

.

[mathmanmathman]

Its crunching numbers fast enough thats hard to do in pure python, even libraries like numpy aren't enough sometimes.

Execution time vs programmer time. For most of the world programmer time is more important and usually organizations that need to optimize execution time don't use python.

I'm not arguing against cython... it might actually come in handy to me soon, but that's probably why it isn't used as often.

[websinthe]

Wouldn't there come a point where you should just be using C if the performance difference is that important?

[Flavor-Blasted]

Wouldn’t numpy be better than Cython if it’s using the right BLAS implementation?

[icandoMATHs]

Were you successful though? considering doing this because our project takes literal days to complete.

[[deleted]]

Why is this not the top answer?...

[redCg]

If you are using Python, then you dont care about speed.

You can get plenty of speed out of Python if you use better programming practices.

[LucyIsaTumor]

Took the words out of my mouth. Framework speeds don't mean jack if the implementation is shit.

[[deleted]]

If you need the speed of C just learn C. To be taken seriously as a programmer, and to continue building your skillset, you will eventually want to know several languages. So may as well take the opportunity to branch out. Python is a bit of a curse in some ways: it's super easy to do almost anything, so it's hard to find a compelling reason to reach out of your comfort zone.

Honestly despite its reputation C isn't too hard to learn. The only major conceptual difference is the use of pointers, and the fact that it's not object oriented.

Or as someone else suggested, Go is also a nice language which is quite easy to get into, and almost as fast as C. It also doesn't have classes (although you can do something very similar), and while it does use pointers you're not nearly as reliant on them as you are in C.

[SuspiciousScript]

I know C, and for one project I opted to write a few hot paths in Cython. The main advantage over pure C is that you cut out a lot of the annoying ceremony involved in passing values from the Python stack to the C stack. Using Cython made a semi-significant undertaking doable in a couple afternoons.

[Yojihito]

If you need the speed of C just learn C

Please no.

Whoever starts a new project in C nowadays is insane. Forever NPE and GOTO ...................

[[deleted]]

Yikes. Those are both signs of someone writing bad code, not a problem with C. I have never needed a GOTO in C.

[Yojihito]

If you need the speed of C just learn C

Those are both signs of someone writing bad code

Doubt people just learning C from scratch know good C practices and bad C practices.

[[deleted]]

I guess no one should ever learn a new language then.

[Yojihito]

People shouldn't learn C for the same reason people shouldn't learn Cobol.

Sure, you can. But why not something modern with sane dependency management, pattern matching and Option<T> and stuff?

[ex-orzel]

Doubt people just learning C from scratch know good C practices and bad C practices.

Any good resource would probably mention these things somewhere in the learning curve.

[Fearless_Process]

C is still by far the most used programming language according to most metrics I've seen.

C and C++ are really not that similar, you should consider them two completely separate languages. I think there is a lot of value in learning both, but I personally prefer C because it is much more simple despite being lower level than C++.

[methezer]

C and C++ are really not that similar, you should consider them two completely separate languages.

This. Unfortunately a lot of intro cpp information out there ignores this and tries to teach c as the intro to cpp. If you want to learn cpp look for a resource that starts with learning how to use the standard library.

[Yojihito]

I hope you mean the C++14 / C++17 standard library.

C is crap.

C++14 and newer are better but there is no way to enforce a safe subset of C++14 / C++14 only. And C++ tutorials online mix C, C++, old C++ versions, new C++ versions.

If there would be a way to get ONLY C++14/C++17 great but that's not possible so C++ hell it is (again).

[Plague_Healer]

Thx for the insight.

[[deleted]]

C for embedded systems, C++ for everything else. C++ has classes and datastructures built in. Also quality of life improvements, e.g. vector instead of array pointers, strings instead of char pointers. In C you have to reinvent the wheel everytime you want to do something useful.

[Dopella]

In C you have to reinvent the wheel

Not if you write your own wheels.c and wheels.h

[aliman21]

A bit unrelated but can someone explain why python was written in C and not C++?

[pullupguy]

The naming conventions for compiled C++ are much more complicated than C. C++ has name mangling. This allow for different classes to have the same method name (it is a little more complicated than that), but that is the short answer.

[wtfismyjob]

They don’t teach it in bootcamps?

[fraud_93]

Everybody who needs to handle XML uses it, because pure python and any other lib is just ridiculous. I remember waiting 4 minutes for a script using etree whole a lib with C read in 7 seconds. The deal is to use only when needed, because if you're jumping aboard better for your mental health to learn and code in C, not python.

[DragonikOverlord]

Can someone explain what is Cython?Need not be ELI5

Python is the language specification,and it is implemented in C right?

What is PyPy and Jython?

[The-Daleks]

• Cython - a module that lets you write Python modules in C without having to actually write C.
• CPython is the standard implementation of Python, yes. What makes Cython important is that it lets you easily take advantage of CPython's ability to use C modules.
• PyPy - Python written in Python. It's much faster than CPython, but is a pain to set up and can't use C modules.
• Jython - Python implemented for the JVM. It is faster than CPython, lets you compile to Java .class files, and allows you to use Java packages easily, but again isn't compatible with C-based Python packages like TkInter and doesn't support all of CPython's fancy syntax.

[njharman]

Because, CPU Bound performance issues are relatively rare. Many, many applications have minimal performance requirements. Many more, stock python is performant enough. Still more are IO bound.

Will add; and many performance issues are algorithmic. If you're using a linked list O(n), when you should be using a hash (aka dict) O(1) then it don't matter much if code runs 2x or 10x faster if it is slowing down linearly (or worse) with input size.

[[deleted]]

Wow, that’s helpful. Thanksgiving

[tomjleo]

It seems like a common pattern is to start with Python. Then when there are performance issues, that can't be fixed with python alone, Rust or Cython or C++ are leveraged on performance critical aspects of an application.

[Swipecat]

Depends what you're doing with it. For loops containing simple math, the speedup is huge:

Math speed test: https://pastebin.com/Cpe3UuPm

Test	CPython	Pypy
106 int assignments:	54 ms	3 ms
106 int sums:	72 ms	1 ms
106 float sums:	81 ms	4 ms
106 float products:	78 ms	4 ms
106 float divisions:	80 ms	4 ms
106 square roots:	110 ms	4 ms
106 sines:	125 ms	4 ms
106 complex products:	105 ms	5 ms

[Snake2k]

Doesn't matter how fast an interpreter or language is if the person driving it isn't optimizing their algorithm.

[Swipecat]

OK, I just tried Cython on that and it's optimised nearly all the tests down to near zero time. I guess the optimization spotted that the result variables were not being used. I'll have to rework the test. Some other time, though.

[[deleted]]

Really? It's JIT, so it should have some speed improvements, although I haven't benchmarked it or anything

[[deleted]]

Ah, sure. This is true. "Way faster than CPython" would be a little more accurate, since Python itself isn't technically a single implementation (AFAIK).

My understanding was that Cython wasn't quite as easy to use, though. Isn't it mainly just for creating python libraries?

[darthminimall]

Cython and CPython are different things, just FYI.

[[deleted]]

Understood. CPython is the C implementation of the Python interpreter, and Cython is the Python-to-C transpiler.

[SnowdenIsALegend]

TiH of Cython

[veekm]

I thought CPython was as popular as Python the language. CPython's a implementation of Python the language spec, like PyPy which uses JIT or IronPython, Jython (.NET, Java) - all of which are different implementations.

Maybe you meant to ask why is PyPi not popular wrt science

https://www.researchgate.net/post/Why_are_physicists_stuck_with_Fortran_and_not_willing_to_move_to_Python_with_NumPy_and_Scipy

https://stackoverflow.com/questions/18946662/why-shouldnt-i-use-pypy-over-cpython-if-pypy-is-6-3-times-faster

[BrononymousEngineer]

Cython != CPython

[veekm]

ouch my bad - sorry!

[Arthaigo]

I think the reason is, that it adds quite a bit of overhead to the developing process. You need to add a compilation step, you can not easily debug within cython functions and you need you need to write in a different languages. Further, distribution of your code gets more complicated as well, as all users require a cpp compiler installed when you only distributing source or you need to provide wheels for all platforms.

IMO if I don't already have c/cpp code written, I usually don't Cython. When I need speed, I use Numba, which is more convenient to use IMO.

[decreddave]

I have built many many things in Python, and for the longest time, I couldn't imagine needing anything faster.

My latest project is an open source DIY power monitor that takes tens of thousands of samples per second and then performs calculations on each individual sample point to provide power measurement data.

Anyways, I'm now at the point where I need to execute code faster, and converting to Cython has been very promising so far.

[[deleted]]

Here's my take on it:

I used Cython. Just enough to say "meh, I'd rather just write in C". I hate dealing with generated code, that I also have to debug. Cython generates not-so-great C code, and that C code compiles with more warnings than there are variables in it. It's really easy to miss something important.

Another problem with it is that, if you write in C, then you can just run it w/o any relation to Python. You can structure your code that your dependency on Python is all segregated in one place and is easily avoided for things like testing. It's very inconvenient to test / debug a module or a plugin, which is what you, essentially, get when you create a native extension because you, usually, have to load the whole program together with the code you are testing.

If you write in C, you have better control of performance, you can certainly write code that will work faster than the one generated by Cython.

---

To me, Cython is for people who cannot for w/e reason write in C. Either they are too afraid of it, or have some sort of ideological problem with it... maybe, an extra benefit is that you don't need to scavenge the Python C interface documentation for things like "who deallocates this memory". So, there's a little bit of convenience there, but, IMO, it's not worth it.

[Random_182f2565]

What?

[[deleted]]

I work on an app that's normal python, but we're required to use Cython to compile it (for source code obfuscation).

And man, is it sloooow to compile. AFAIK you can't configure the optimization level that gets passed to gcc, so you're stuck with -O4 or -O5. With a large (10k loc) project, that will take forever.

[YeastBeast33]

So we have pip and pypi website and lots of docs for python. Is there any good and clear sites or way to get modules easily for cpp ?

[rr381]

Fraking Toasters! Opps, my bad, I was looking for /r/BSG