4 years ago by heisig
Speaking of Common Lisp - the European Lisp Symposium starts tomorrow (May 3 and May 4, https://european-lisp-symposium.org/2021/index.html). The entire conference will be broadcast on Twitch. Python programmers are invited, too :)
4 years ago by ollran
Thanks for sharing this. Call-site optimization for Common Lisp seems quite interesting after looking at the article.
4 years ago by jes
This is exciting. Thanks for the reference.
4 years ago by mark_l_watson
Nice article.
One thing, re âIn Python we typically restart everything at each code changeâ: I sometimes run Python in Emacs with a REPL. I evaluate region to pick up edits. Not bad.
The big win for the Common Lisp REPL is being able to modify data, do restarts, etc. I usually use Common Lisp, but for right now I am heavily using Clojure to write examples for a new Clojure AI book that I am writing. I miss the Common Lisp REPL!
4 years ago by lukashrb
The first time I encountered "interactive development" was when I had to use python with jupiter notebook. I really liked this style of development. The tdd approach I practiced before was somewhat similar but by far not as visual.
Clojure was an eye opener for me and I think it offers a great developer experience (e.g. I'm addicted to C-c C-p)
It seems my journey hasn't ended and I definitely have to check out CL!
But atm it's hard for me to give up the things clojure offers to me: persistent datastructures, access to a great ecosystem and a very good designed standard library.
4 years ago by agumonkey
Not to start a flamewar, every time I see a python talk (pycon or else), with fancy tricks like metaclasses.. all I can think is that, well, CLOS would have been perfectly fit for this too.
I know people are tired of the "lisp/smalltalk did it better" but what features of python are not possible (or hard) in CL[OS] ?
ps: how many CL shops are out there ? I'd work near free just to try a CL team once.
4 years ago by dasyatidprime
CLOS (as you presumably know) models method application as calls to generic functions, instead of the now-more-mainstream Smalltalk-like message-dispatch approach which Python uses. The latter allows for things like overriding __getattr__ to intercept âallâ method calls and property accesses, for which I don't think there's any equivalent in CLOS.
The way methods are âattachedâ to classes gives you a natural form of type-directed name lookup. CL generics have the advantage that you can define your own methods on existing classes while naming the methods in your own package so they don't conflict, but also have a curse of inconvenience along the way where importing a class doesn't naturally pull in everything associated with it, and you wind up writing the class name again and again when dealing with fields of an object. with-slots et al. are poor substitutes. (In an experimental sublanguage at one point I actually had local variables with object type declarations implicitly look up the class using the MOP and symbol-macrolet every available var.slot combination within the scope as a brute hack around the most common desirable case.)
Python's short infix/prefix operators are naturally generic, since they're implemented as method calls. In CL there's the generic-cl extension, but I haven't seen it have that much uptake⌠in particular, any library code that isn't explicitly aware of it won't use it ânaturallyâ on foreign objects, which could be good or bad.
That shades into the very-concrete type system that CL starts out with, where any attempt at ad-hoc polymorphic interop is a disaster unless everyone already agrees on what methods to use. I can't make a thing that acts like a hash table but uses a different implementation underneath, then pass it to something that expects to be able to gethash on it. I especially seem to get bitten by this in cases where alists are the expected way of representing key-value maps: there's no way to extricate yourself from the linear search without rewriting every piece of code that touches it, there's often an implicit contract that you don't want duplicate keys but it's easy to violate by accident and create bad behavior down the line, and so on. By comparison, Java collections in particular got this very right in terms of decoupling intention from implementation, and Python does basically the same thing but with a looser set of âexpectedâ methods.
By default, Python objects have a âpurelyâ dynamic set of properties, rather than the fixed slots CLOS imputes on an object via its class. Indeed the class-level property one can set in Python to constrain this for possible performance gains is called __slots__.
4 years ago by pjmlp
> The latter allows for things like overriding __getattr__ to intercept âallâ method calls and property accesses, for which I don't think there's any equivalent in CLOS.
CLOS already offered AOP, which you can use to control such calls.
https://lispcookbook.github.io/cl-cookbook/clos.html#dispatc...
4 years ago by dasyatidprime
I'm not sure what you're pointing at there. I'm aware of method combinations and method qualifiersâare you referring to being able to add a general :before/:after/:around on a single generic? If so, that's not what I mean; what I mean is vaguely similar but on the first-arg dispatch axis. Here's a toy Python example. Given:
class KnowItAll(object):
def __getattr__(self, attr):
return lambda: "yes, I know how to " + attr
>>> k = KnowItAll()
>>> k.reveal()
'yes, I know how to reveal'
>>> k.transfigure()
'yes, I know how to transfigure'
>>> k.make_sandwiches()
'yes, I know how to make_sandwiches'
If we were in CLOS, and started with:
;;; widget.lisp
(defclass widget () ((radius :initarg :radius)))
(defmethod grow ((w widget)) (incf (slot-value w 'radius)))
;;; tracer.lisp
(declaim (ftype (function (t) t) make-tracer))
(defun make-tracer (object) ...)
;; ... further code goes here ...
;;; fiddle-with-widgets.lisp
(let* ((w (make-instance 'widget :radius 3))
(w* (make-tracer w)))
;; ???
(grow w*))
In CLOS, the class doesn't âownâ the method, it provides a type for dispatching on, so there's no way to do âgive me some control over every generic function so long as the first arg is of âmyâ typeâ. Which is a reasonable model, but means you can't do the same thing. Indeed the flip side is that in the Smalltalk-like model, generics are not reified, and methods that are specializations of the âsameâ thing have no ârealâ identity to them, so you can't do a type-ignoring :around method for an âentire genericâ. (Often there will be a superclass to attach to instead, but it's considered dangerous âmonkey patchingâ to mess around with someone else's class hierarchy like that, and in the case of more abstract interfaces there's nothing.)
Does that make sense?
4 years ago by agumonkey
thanks for your points, it's true that python dynamic operator genericity is very handy
4 years ago by eikenberry
> every time I see a python talk (pycon or else), with fancy tricks like metaclasses..
All I can think of is what a mistake those features were in python. "Fancy tricks" are generally the author trying to be clever (in the Kernighan sense) and ends up obfuscating the result. Not saying it works this way in CL, I don't have the experience to make the call, but in Python it was (and probably still is) prevalent.
4 years ago by erik_seaberg
The goal is for a little bit of clever work to make the computer do a lot more stupid work. People get bored and make mistakes, and our brains are not getting any faster, so this kind of leverage is the only game in town.
4 years ago by fzil
I am sorry but what does this mean:
> the author trying to be clever (in the Kernighan sense)
I know that Kernighan is the author of the C book, but its been a while since I skimmed it.
4 years ago by jmercouris
Please email hello@atlas.engineer with your details. We are a CL shop responsible for Nyxt browser. Thanks for your time!
4 years ago by kbutler
Beginner approachability seems to be the key feature. Possibly also integration with external libraries.
4 years ago by mumblemumble
This is my impression, too. When I was TAing an intro CS course back in university, I saw students new to programming struggling with Lisp (specifically Scheme in this case) much earlier in the learning process than even C.
This manifested in a few spots. The first is, yes, s-expressions. Yes, yes, I know, s-expressions are integral to the power of lisp, and they're really not hard to read once you get used to them. All of that is beside the point. The reality I saw on the ground, when teaching people to program, is that even people who have no prior programming experience whatsoever, and therefore no preconceptions to get over, have a harder time grasping s-expressions than they do algol-style syntax. I don't know why. I didn't have the same problem myself. But it's a real phenomenon that I struggled to help people through on a regular basis, and the lisp community's defensiveness about it is not going to make it go away.
Arbitrary-seeming names with zero mnemonic value is another problem. Car, cdr, progn, etc. - it takes a special kind of personality to not be put off by this sort of stuff. Not everyone has that kind of personality. Not everyone should have that kind of personality.
Finally, all the hair-splitty (at least to a newcomer) distinctions to contend with. =, eq, eql, equal, and equalp, or let, let* and letrec. Sure, there are reasons for these distinctions. But a language that can get by without quite so many of them is going to be a lot more attractive to newcomers. Even if it comes at the cost of footguns, if they're unlikey to be discovered until later.
4 years ago by agumonkey
That's my take on it, but I've ran into people praising it like it was alien theory of everything dropped by gods as a gift.
I like python but I'm a bit fed up with the mobthink (how surprising).
4 years ago by tzs
Another factor, I think, is that a heck of a lot of people don't think they will still be doing this for a long time.
Programming is something interesting and fun they are going to do for a few years while young, until (pick one) {their band takes off, someone funds their startup idea and they hire others to do the programming while they generate genius ideas and run the business, they get promoted to a high paying executive position that involved management and architecture and others do the coding, their podcast becomes a hit and they can live off that, the small company they work at IPOs or gets bought and they make enough to retire at 30, etc).
So they learn a fairly easy language that has lots of libraries that cover most things you do in a routine developer job.
...and before they know it they are 50-60 and still writing a lot of code, and realizing that if they had known they would still be doing this 30-40 years later they would have been better off if they had learned and used and gotten good with some of the languages that have a reputation of being very productive but hard to learn.
I'd also add spreadsheets and database to that. At one point I was the database guy at work, because no one else was available. I learned enough SQL to get by, but was in no way a database expert. Heck, we had to pick job titles at one point that described what we did to have on the business cards the company was giving us, and I put down "Database Roustabout" [1], which should give you an idea of where I stood. That was 20 years and I'm still the database guy at work. It would have been a lot better if sometime early on I had said "I'm going to become really good at SQL even though I'm sure someone else will become database guy in a year or so".
[1] Roustabout. NOUN. An unskilled or casual laborer. (North American) A circus laborer.
4 years ago by PeterStuer
I like Lisp, and I'm not a fan of e.g. Python's whitespace sensitivity. That said, for niches such as ML and data science, I find you just can't beat the Python ecosystem.
4 years ago by chalst
> for niches such as ML and data science, I find you just can't beat the Python ecosystem
There definitely are many areas where Python is best, but for ML and data science, Julia is (i) very competitive in library coverage, (ii) more performant and flexible, and (iii) has a very good Python bridge if it's needed.
I can imagine there are niches within ML and data science where what you need are Python-only libraries, you don't miss anything restricting yourself to the numpy type hierarchy and there's no advantage to calling the libraries from Julia, but I'm curious to check if that is what you actually meant and if so, what you are doing.
4 years ago by SatvikBeri
After using python for data science for 3 years (since founding the current startup), I mostly switched to Julia about 4 months ago. So far the only Python libraries I've really missed are boto3 and sqlalchemy for sql generation â both of which can easily be called in Julia using PyCall.
I think people often underestimate just how much faster Julia is than numpy, I've consistently seen performance improvements on the order of 10x-30x when porting code.
4 years ago by pjmlp
Many of those libraries are implemented in C++, any language can call them.
4 years ago by rhlap
Are people actually doing science with Python or are they talking about doing science?
There's so much buggy low quality stuff in that space that I'd write a serious application in C or C++ from scratch.
It would be a custom application, sure, but not everything needs to be general.
Also, I find Lisp much more natural for mathematical reasoning.
4 years ago by gustavo-fring
Yeah, they're very much doing it.
Pandas is huge, libraries like Spacy, NetworkX, etc exist. It's a massive and good ecosystem. Python is the goto for scientific computing in most of the sciences for newer students I'd hazard a guess over the older R and Julia.
This will be blindingly obvious if you work in that area. Yes, you can do it in another language, but you're missing out on a lot of stuff that is just done and is state of the art and is fast because the speedy parts aren't in Python. The complaints about parens for lisp are superficial, but it's my experience the same same goes for whitespace in Python. They just don't matter.
4 years ago by tluyben2
> and is fast because the speedy parts aren't in Python.
Having worked months with a slew of senior data scientists, this was a bit painful. Python is so slow and those data scientists were very good at coming up with solutions for the issues of the company, but the implementations (using Spacy, Pandas and other libs) had enough Python in them to make them not practical for the company use case. Nice prototypes which I then had to fix them or even rewrite to C/C++(we worked Rust as well to try it out) to make them usable in the company data pipeline.
I think companies are burning millions (billions in total?) on depressingly slow solutions in this space by throwing massive power at it all to make them complete their computations before the sun dies out.
Example: we needed a specific keyword extraction algorithm for multiple languages; my colleague used Spacy and Python to create it. It took a couple of seconds per page of text; we needed max a few ms on modern hardware. He spent quite a lot of time rewriting and changing it, but never got it under 1s per page on xlarge aws instances. My version takes a few ms on average executing the same algorithm but in optimised c/c++.
Sure we could've spun up a lot more instances, but my rewrite was far cheaper than that, even in the first month.
4 years ago by beforeolives
> Are people actually doing science with Python or are they talking about doing science?
People are actually doing it. And a lot of it too. Both in terms of data science (as a broad term that can mean a bunch of different things) and in terms of computation for specific scientific fields like physics or biology.
4 years ago by 7thaccount
Very much actively doing so on my end where nearly all work in the industry is in Python, with some Matlab, C, C++, and Julia sprinkled in.
Python is a great high level language for basically everything, but hardcore low latency apps. I can parse text, connect to databases, do sparse matrix computations on massive matrices, calculate network flows, generate large node-graph diagrams, use a Python based API to connect to any vendor software I've seen, do any kind of statistical analysis thing I need with pandas, amazing and free IDE allows me to use a REPL, code editor, and data structure viewer with ease, Python notebooks for education...etc etc. I've frequently found that I can rewrite a vendor's 10k line C++ program in a few pages of Python as the built-in Python data structures make text parsing extremely flexible and simple.
4 years ago by enriquto
> do sparse matrix computations on massive matrices
This is completely impossible to do in the Python language, unless you resort to external tooling written in C or Fortran. Sure, you can call these codes from Python, as you can call them from any other language.
4 years ago by golergka
Yes. Python is pretty much the main tool in biology, for example. C or C++ would be abysmal for similar exploratory scientific takes.
4 years ago by whalesalad
I use hot reloading with an iPython repl. I write my code in such a way that I can interact with any individual part of the system via a REPL. Lisp excels here, but you can have a decent approximation of a real-time evaluation loop going.
4 years ago by gustavo-fring
This might seem slightly unrelated, but I was reading Elixir in Action and one of the statements is along the lines of a debugger being difficult to use in its naturally concurrent environment. The Elixir strategy is to kill erroring processes, capturing their exit signals with supervisor processes and then possibly recreating a replacement process.
Can the common lisp condition system be adapted to Elixir? Is there an advantage to doing so? Is there some obvious tradeoff between the two I'm not expressing?
Thanks.
see this thread form HN for more about adapting the condition system elsewhere.
4 years ago by alpaca128
Elixir is based on the BEAM virtal machine developed for Erlang. Restarting crashed (very lightweight) processes to handle errors is the normal way of doing things in that system.
LFE(Lisp-flavored Erlang) is an existing language that combines Lisp syntax with Erlang's backend, though I haven't used it myself yet.
4 years ago by BiteCode_dev
He forgot: in python you read the code of somebody else and it's familiar.
In lisp, you are learning a new language with every lib because each author think they are a god language designer and that their macro rock. Also they don't need a good doc cause they are obvious. Or good error message cause they never break.
Also the way the author dismiss the number gap of packages available is ignoring the elephant in the room.
4 years ago by diggan
> In lisp, you are learning a new language with every lib because each author think they are a god language designer and that their macro rock. Also they don't need a good doc cause they are obvious. Or good error message cause they never break.
You have any specific examples of Common Lisp libraries you found hard to understand? I had a hard time when first learning the language, but once I got proficient to write my own code, I found it easy to understand most libraries I ended up using myself, the same as any language really. That the REPL makes it so easy to explore them with your own context, helped a lot as well.
> Also the way the author dismiss the number gap of packages available is ignoring the elephant in the room.
In the very same section, the author describes why the number of package don't matter as much as you think it does. Curation VS free-for-all-publishing (like APT vs NPM). Add together that Common Lisp "the language" has been stable for decades, makes it much more possible to be able to use any of the libraries you find as well, where in the Python world, we both know this not to be true (just Python2 VS Python3 makes this a whole other world of messes).
4 years ago by p_l
There are some famous examples[1], but to be honest, they are rare outliers for libraries published. And even for internal use, the amount of "local custom style" seems pretty minimal in my experience.
[1] Off the top of my head, I can recall Cells (early reactive/dataflow system - weirdness included symbol names made to sort first in Allegro CL IDE) and hu.dwim.* stuff which had its own wrapper around CL:DEFUN and CL:DEFMETHOD, iirc. But I successfully used their stuff without caring about that.
4 years ago by diggan
> There are some famous examples
As someone new to the ecosystem of Common Lisp and in general a bit sadist (ref https://www.youtube.com/watch?v=mZyvIHYn2zk), could you share which ones these are so I can enjoy not understanding them at all?
Edit: I see now after I made my comment you added examples, thanks :)
4 years ago by Hammershaft
> In lisp, you are learning a new language with every lib because each author think they are a god language designer and that their macro rock. Also they don't need a good doc cause they are obvious. Or good error message cause they never break.
I want to say this is an over generalization but I am living this in clojure right now.
4 years ago by wexq
I'm not a particularly experienced or good Lisp programmer, I can customize Emacs, but that's pretty much it.
However, I think this article is a bit skewed and not highlighting things Python has.
For instance, the standard library means that Python is more usable out of the box.
Also when you've got things like iPython or Jupyter it means you can get off the ground easily.
So, in the end, they're two different languages, and I do not think either is better of the two. Right tool for the job and all that.
4 years ago by mplanchard
This point seems to be addressed in the âState of the Librariesâ section[0]
[0]: https://lisp-journey.gitlab.io/pythonvslisp/#state-of-the-li...
4 years ago by wexq
Not really, the basic Python install has a lot of things ready to use without any external libraries.
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