[BULLETIN: Quicklisp now has the latest version of FSet.]

Sycamore, primarily by Neil Dantam, is a functional collections library that is built around the same weight-balanced binary tree data structure (with leaf vectors) that FSet uses.  While the README on that page comments briefly on the differences between Sycamore and FSet, I don't feel that it does FSet justice.  Here is my analysis.

Dantam claims that his library is 30% to 50% faster than FSet on common operations.  While I haven't done comprehensive micro-benchmarking, a couple of quick tests indicates that this claim is plausible.  A look through the internals of the implementation confirms that it is clean and tight, and I must commend him.  There may be some techniques in here that I could usefully borrow.

Most of the performance difference is necessitated by two design choices that were made differently in the two libraries.  One of these Dantam mentions in his comparison: FSet's use of a single, global ordering relation implemented as a CLOS generic function, vs. Sycamore's more standard choice of requiring a comparison function to be supplied when a collection is created.  The other one he doesn't mention: the fact that FSet supports a notion of equivalent-but-unequal values, which are values that are incomparable — there's no way, or at least no obvious way, to say which is less than the other, and yet we want to treat them as unequal.  The simplest example is the integer 1 and the single-float 1.0, which have equal numerical values (and cl:= returns true on them), but which are nonetheless not eql.  (I have a previous blog post that goes into a lot more detail about equality and comparison.)  Since Sycamore expects the user-supplied comparison function to return an integer that is negative, zero, or positive to indicate the ordering of its arguments, there's no encoding for the equivalent-but-unequal case, nor is there any of the code that would be required to handle that case.

Both of these decisions were driven by my goal for the FSet project.  I didn't just want to provide a functional collections library that could be called occasionally when one had a specific need for such a data structure.  My ambition was much grander: to make functional collections into a reasonable default choice for the vast majority of programming situations.  I wanted FSet users (including, of course, myself) to be able to use functional collections freely, with very little extra effort or thought.  While Lisp by itself reaches a little bit in this direction — lists can certainly be used functionally — lists used as functional collections run into severe time complexity problems as those collections get large.  I wanted the FSet collections to be as convenient and well-supported as lists, but without the time complexity issues.

— Or rather, I wanted them to be even more convenient than lists.  Before writing FSet, I had spent years working in a little-known proprietary language called Refine, which happened to be implemented on top of Common Lisp, so it was not unusual to switch between the two languages.  And I had noticed something.  In contrast to CL, with its several different predefined equality predicates and with its functions that take :test arguments to specify which one to use, Refine has a single notiion of equality.  The value space is cleanly divided between immutable types, which are compared by value — along with numbers, these include strings, sets, maps, and seqs — and mutable objects, which are always compared by identity.  And it worked!  I found I did not miss the ability to specify an equality predicate when performing an operation such as "union".  It was just never needed.  Get equality right at the language level, and the problem goes away.

Although FSet's compare generic function isn't just for equality — it also defines an ordering that is used by the binary trees — I thought it would probably turn out to be the case that a single global ordering, implemented as a generic function and therefore extensible, would be fine the vast majority of the time.  I think experience has borne this out.  And just as you can mix types in Lisp lists — say, numbers and symbols — without further thought, so you can have any combination of types in an FSet set, effortlessly.  (A project I'm currently working on actually takes considerable advantage of this capability.)

As for supporting equivalent-but-unequal values, this desideratum flows directly from the principle of least astonishment.  While it might not be too surprising for a set or map implementation to fail distinguish the integer 1 from the float 1.0, it certainly would be very surprising, and almost certainly a source of bugs in a compiler that used it, for it to fail to distinguish two uninterned symbols with the same name.  (I saw a macro expansion recently that contained two distinct symbols that both printed as #:NEW.  It happens.)  A compiler using Sycamore for a map on symbols would have to supply a comparison function that accounted for this; it couldn't just compare the package name and symbol name.  (You'd have to do something like keep a weak hash table mapping symbols to integers, assigned in the order in which the comparison function encountered them.  It's doable, but FSet protects you from this madness.)

Along with those deep semantic design choices, I've spent a lot of time on developing a wide and featureful API for FSet (an effort that's ongoing).  FSet has many features that Sycamore lacks, including:

• seqs, a binary-tree sequence implementation that holds arbitrary Lisp objects (Sycamore ropes hold only characters, which is certainly an important special case, but why restrict ourselves?)
• default values for maps and seqs (the value to return when the key is outside the domain is associated with the collection, not supplied at the call site; this turns out to be a significant convenience)
• generic functions that operate on both lists and FSet collections, to shadow the CL builtins
  
• the powerful map-union and map-intersection operations (I'll blog about these in the future)
• more ways to iterate over the collections (the FSet tutorial has a good summary, about 3/4 of the way down)
• speaking of the tutorial, FSet has lots more documentation

Let me digress slightly to give an example of how FSet makes programming more elegant and convenient.  Joe Marshall just put up a blog post comparing Go(lang) with Common Lisp, which is worth a read on its own; I'm just going to grab a code snippet from there to show a little bit of what programming with FSet is like.  Here's Joe's code:

 (defun collate (items &key (key #'identity) (test #'eql) (merger (merge-adjoin #'eql)) (default nil))
   (let ((table (make-hash-table :test test)))
     (dolist (item items table)
       (let ((k (funcall key item)))
         (setf (gethash k table) (funcall merger (gethash k table default) item))))))

 (defun merge-adjoin (test)
   (lambda (collection item)
     (adjoin item collection :test test)))

And here's what I would write using FSet:

(Well, I would probably move result outside the dolist form to make it clearer what the return value is, but let's go with Joe's stylistic choice here.)

For those who haven't used FSet: the form (map :default (set)) creates a map whose default is the empty set, meaning that lookups on that map will return the empty set if the key is not in the map.  This saves the includef form from having to handle that possibility.

My version makes assumptions, it's true, about how you want to collect the items with a given key; it doesn't give you other choices.  It could, but what would be the point?  It's already using a general set with better time complexity than lists, and saving you from having to write anything like merge-adjoin.  The extensible global equivalence relation means you're not going to need to supply a :test either.

I think the FSet-enhanced code is cleaner, more elegant, and therefore clearer than the plain-CL version.  Don't you agree?  Maybe you wouldn't say it's a huge improvement, okay, but it's a small example; in a larger codebase, I would argue, these small improvements add up.

* * * * *

To summarize: if you just want a library you can call in a few places for specific purposes, Sycamore might work better for you (but think hard if you're writing a comparator for symbols).  FSet can certainly be used that way, but it can be much more.  If you want to see one way in which Common Lisp can be made into a better language, without giving up anything that we love about it, I urge you to give FSet a try.

FSet has changed the way I write Lisp programs.  — an FSet user

(UPDATE: the magnitude of the performance difference between FSet and Sycamore surprised me, and inspired me to do some profiling of FSet.  It turned out that I could get a 20% speedup on one micro-benchmark simply by adding some inline declarations.  Mea culpa, mea culpa, mea maxima culpa; I should have done this years ago.   With that change, the generic function overhead appears to be the only significant cause of the remaining ~20% performance difference.  I tried creating a Sycamore set using a thin wrapper around fset:compare, and the resulting performance was very similar to that of FSet with its new inlines.)

Last week I finished a new service written in Common Lisp. It now runs in production© every mornings, and it expands the set of services I offer to clients.

It’s the 4th service of this kind that I developed: - they are not big - but have to be done nonetheless, and the quicker the better (they each amount to 1k to 2k lines of Lisp code), - they are not part of a super advanced domain that requires Common Lisp superpowers - I am the one who benefits from CL during development, - I could have written them in Python - and conversely nothing prevented me from writing them in Common Lisp.

So here lies the goal of this post: illustrate that you don’t need to need a super difficult problem to use Common Lisp. This has been asked many times, directly to me or on social media :)

At the same time, I want to encourage you to write a little something about how you use Common Lisp in the real world. Sharing creates emulation. Do it! If you don’t have a blog you can simply write in a new GitHub repository or in a Gist and come share on /r/lisp. We don’t care. Thanks <3

We’ll briefly see what my scripts do, what libraries I use, how I deploy them, what I did along the way.

Needless to say that I dogfooded my CIEL (beta) meta-library and scripting tool for all those projects.

Table of Contents

• Scripts n°4 and 2 - shaping and sending data - when you can write Lisp on the side
  • SFTP
  • Deploying
  • Script n°2 and simple FTP
• Scripts n°3 and 1 - complementary web apps
• Lasting words
• Links

Scripts n°4 and 2 - shaping and sending data - when you can write Lisp on the side

My latest script needs to read data from a DB, format what’s necessary according to specifications, and send the result by SFTP.

In this case I read a DB that I own, created by a software that I develop and host. So I could have developed this script in the software itself, right? I could have, but I would have been tied to the main project’s versioning scheme, quirks, and deployment. I rather had to write this script on the side. And since it can be done on the side, it can be done in Common Lisp.

I have to extract products and their data (price, VAT...), aggregate the numbers for each day, write this to a file, according to a specification.

To read the DB, I used cl-dbi. I didn’t format the SQL with SxQL this time like in my web apps (where I use the Mito light ORM), but I wrote SQL directly. I’m spoiled by the Django ORM (which has its idiosyncrasies and shortcomings), so I double checked the different kinds of JOINs and all went well.

I had to group rows by some properties, so it was a great time to use serapeum:assort. I left you an example here: https://dev.to/vindarel/common-lisps-group-by-is-serapeumassort-32ma

Dates have to be handled in different formats. I used local-time of course, and I still greatly appreciate its lispy formatter syntax:

(defun date-yymmddhhnnss (&optional date stream)
  (local-time:format-timestring stream
                                (or date (local-time:now))
                                :format
                                '((:year 4)
                                  (:month 2)
                                  (:day 2)
                                  (:hour 2)
                                  (:min 2)
                                  (:sec 2)
                                  )))

the 2 in (:month 2) is to ensure the month is written with 2 digits.

Once the file is written, I have to send it to a SFTP server, with the client’s codes.

I wrote a profile class to encapsulate the client’s data as well as some functions to read the credentials from either environment variables, the file system, or a lisp variable. I had a top-level profile object for ease of testing, but I made sure that my functions formatting or sending data required a profile parameter.

(defun send-stock (profile &key date) ...)
(defun write-stock (profile filename) ...)

Still nothing surprising, but it’s tempting to only use global parameters for a one-off script. Except the program grows and you pay the mess later.

SFTP

To send the result through SFTP, I had to make a choice. The SFTP command line doesn’t make it possible to give a password as argument (or via an environment variable, etc). So I use lftp (in Debian repositories) that allows to do that. In the end, we format a command like this:

lftp sftp://user:****@host  -e "CD I/; put local-file.name; bye"

You can format the command string and run it with uiop:run-program: no problem, but I took the opportunity to release another utility:

• https://github.com/vindarel/lftp-wrapper

First, you create a profile object. This one-liner reads the credentials from a lispy file:

(defvar profile (make-profile-from-plist (uiop:read-file-form "CREDS.lisp-expr"))

then you define the commands you’ll want to run:

(defvar command (put :cd "I/" :local-filename "data.csv"))
;; #<PUT cd: "I/", filename: "data.csv" {1007153883}>

and finally you call the run method on a profile and a command. Tada.

Deploying

Build a binary the classic way (it’s all on the Cookbook), send it to your server, run it.

(during a testing phase I have deployed “as a script”, from sources, which is a bit quicker to pull changes and try again on the server)

Set up a CRON job.

No Python virtual env to activate in the CRON environment...

Add command line arguments the easy way or with the library of your choice (I like Clingon).

Script n°2 and simple FTP

My script #2 at the time was similar and simpler. I extract the same products but only take their quantities, and I assemble lines like

EXTRACTION STOCK DU 11/04/2008
....978202019116600010000001387
....978270730656200040000000991

For this service, we have to send the file to a simple FTP server.

We have a pure Lisp library for FTP (and not SFTP) which works very well, cl-ftp.

It’s a typical example of an old library that didn’t receive any update in years and so that looks abandoned, that has seldom documentation but whose usage is easy to infer, and that does its job as requested.

For example we do this to send a file:

(ftp:with-ftp-connection (conn :hostname hostname
                                   :username username
                                   :password password
                                   :passive-ftp-p t)
      (ftp:store-file conn local-filename filename))

I left you notes about cl-ftp and my SFTP wrapper here:

• https://dev.to/vindarel/ftp-and-sftp-clients-for-common-lisp-1c3b

Scripts n°3 and n°1 - specialized web apps

A recent web app that I’m testing with a couple clients extends an existing stock management system.

This one also was done in order to avoid a Python monolith. I still needed additions in the Python main software, but this little app can be independent and grow on its own. The app maintains its state and communicates it with a REST API.

It gives a web interface to their clients (so my clients’ clients, but not all of them, only the institutional) so that they can:

• search for products
• add them in shopping carts
• validate the cart, which sends the data to the main software and notifies the owner, who will work on them.

The peculiarities of this app are that:

• there is no user login, we use unique URLs with UUIDs in the form: http://command.client.com/admin-E9DFOO82-R2D2-007/list?id=1
• I need a bit of file persistence but I didn’t want the rigidity of a database so I am using the clache library. Here also, not a great activity, but it works©. I persist lists and hash-tables. Now that the needs grow and the original scope doesn’t cut it any more, I wonder how long I’ll survive without a DB. Only for its short SQL queries VS lisp code to filter data.

I deploy a self-contained binary: code + html templates in the same binary (+ the implementation, the web server, the debugger...), with Systemd.

I wrote more on how to ship a standalone binary with templates and static assets with Djula templates here:

• https://lisp-journey.gitlab.io/blog/lisp-for-the-web-build-standalone-binaries-foreign-libraries-templates-static-assets/

I can connect to the running app with a Swank server to check and set parameters, which is super helpful and harmless.

It is possible to reload the whole app from within itself and I did it with no hiccups for a couple years, but it isn’t necessary the most reliable, easiest to set up and fastest method. You can do it, but nobody forces you to do this because you are running CL in production. You can use the industry’s boring and best practices too. Common Lisp doesn’t inforce a “big ball of mud” approach. Develop locally, use Git, use a CI, deploy a binary...

Every thing that I learned I documented it along the way in the Cookbook ;)

Another app that I’ll mention but about which I also wrote earlier is my first web app. This one is open-source. It still runs :)

In this project I had my friend and colleague contribute five lines of Lisp code to add a theme switcher in the backend that would help him do the frontend. He had never written a line of Lisp before. Of course, he did so by looking at my existing code to learn the existing functions at hand, and he could do it because the project was easy to install and run.

(defun get-template(template &optional (theme *theme*))
  "Loads template from the base templates directory or from the given theme templates directory if it exists."
  (if (and (str:non-blank-string-p theme)
           (probe-file (asdf:system-relative-pathname "abstock" (str:concat "src/templates/themes/" theme "/" template))))
      ;; then
      (str:concat "themes/" theme "/" template)
      ;; else :D
      template))

He had to annotate the if branches :] This passed the code review.

Lasting words

The 5th script/app is already on the way, and the next ones are awaiting that I open their .docx specification files. This one was a bit harder but the Lisp side was done sucessfully with the efficient collaboration of another freelance lisper (Kevin to not name him).

All those tasks (read a DB, transform data...) are very mundane.

They are everywhere. They don’t always need supercharged web framework or integrations.

You have plenty of opportunities to make yourself a favor, and use Common Lisp in the wild. Not counting the super-advanced domains where Lisp excels at ;)

Links

• https://lispcookbook.github.io/cl-cookbook/
• awesome-cl
• companies using Common Lisp in production (at least the ones we know)
• Common Lisp course in videos – it helps me, and you ;) I added 9 videos about CLOS last month, and more are coming. It’s 86 minutes of an efficient code-first approach, out of 7+ hours of total content in the course. After this chapter you know enough to read the sources of the Hunchentoot web server or of the Kandria game.

I have done some preliminary Common Lisp exploration prior to this course but had a lot of questions regarding practical use and development workflows. This course was amazing for this! I learned a lot of useful techniques for actually writing the code in Emacs, as well as conversational explanations of concepts that had previously confused me in text-heavy resources. Please keep up the good work and continue with this line of topics, it is well worth the price! [Preston, October of 2024]

A comment on my previous post said,

The most difficult thing when coming to a different language is to leave the other language behind. The kind of friction experienced here is common when transliterating ideas from one language to another. Go (in this case) is telling you it just doesn't like to work like this.

Try writing simple Go, instead of reaching for Lisp idioms. Then find the ways that work for Go to express the concepts you find.

That's not at all how I approach programming.

A friend of mine once paid me a high compliment. He said, “Even your C code looks like Lisp.”

When I write code, I don't think in terms of the language I'm using, I think in terms of the problem I'm solving. I'm a mostly functional programmer, so I like to think in terms of functions and abstractions. I mostly reason about my code informally, but I draw upon the formal framework of Lambda Calculus. Lambda Calculus is a simple, but powerful (and universal) model of computation.

Programming therefore becomes a matter of expressing the solution to a problem with the syntax and idioms of the language I'm using. Lisp was inspired by Lambda Calculus, so there is little friction in expressing computations in Lisp. Lisp is extensible and customizable, so I can add new syntax and idioms as desired.

Other languages are less accommodating. Some computations are not easily expressable in the syntax of the language, or the semantics of the language are quirky and inconsistent. Essentially, every general purpose fourth generation programming language can be viewed as a poorly-specified, half-assed, incomplete, bug-ridden implementation of half of Common Lisp. The friction comes from working around the limitations of the language.

 .

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