Comment by barrell

3 days ago

Maybe hyperbole on the frequency, but not the condensation. I meant more along the lines of “most of the complicated code I write in Clojure is an order of magnitude more dense.” _Most_ of the code I write would be 1:1 or 1:2 with other languages, it I don’t think it’s the type of code OP was referring to.

The 1:20+ is definitely not hyperbole though. Using transducers to stream lazy reductions of nested sequences; using case, cond-> and condp->; anywhere where you can lean on the clojure.core library. I don’t know how to give specific examples without giving a whole blog post of context, but 4 or 5 examples from the past year spring to mind.

It’s also often the case that optimizing my clojure code results in a significant reduction of lines of code, whereas optimizing Python code always resulted in an explosion of LoC

Personally I find Python particularly egregious. No map/filter/reduce, black formatting, no safe nested property access. File length was genuinely one of the reasons I stopped using it. The ratio would not be so high with some languages, ie JavaScript

Even with Elixir though, many solutions require 5-10 times the amount of lines for the same thing thing in Clojure. I just converted two functions yesterday that were 6 & 12 lines respectively in Clojure, and they are both 2 pages in Elixir (and would have been much longer in Python)

15 comments

barrell

geokon 3 days ago

I find 95% Clojure has the right tools to write very terse code. But in some cases the functional transducer/piped paradigm can't be contorted to the problem.

Usually these are problems where you need to run along a list and check neighboring elements. You can use amap or map-indexed but it's just not ergonomic or Clojure-y (vs for instance the imperative C++ iterator model)

The best short example I can think of is Fibbonacci

https://4clojure.oxal.org/#/problem/26/solutions

I find all the solutions hard to read. They're all ugly. Their performance characteristics are hard to know at a glance

barrell 3 days ago
Personally, I would normally reach for loop to check neighboring elements very ergonomically.
(loop [[a b c & more] coll] (recur (apply list b c more)))
There’s also partition if you're working with transducers/threads/list comprehension
(partition 3 1 coll)
Or if you need to apply more complicated transformations to the neighbors/cycle the neighbors
(->> coll cycle rest (map xform) (map f coll))
Using map-indexed to look up related indices is something I don’t think I do anywhere in my codebase. Agreed that it’s not ergonomic
EDIT: those Fibonacci functions are insane, even I don’t understand most of them. They’re far from the Clojure I would advocate for, most likely written for funsies with a very specific technical constraint in mind
- geokon 2 days ago
  
  Yeah, I guess partition to me always looks like a dangerous tool - for instance I have a sequence of numbers and I want to do a 5 point rolling average
  You could do `(partition 5 1 coll)` and then average each element in the resulting seq.. It's very easy to reason about. But I'm guessing the performance will be abysmal? You're getting a lazy list and each time you access a 5 neighbor set.. you're rerunning down you coll building the 5 unit subsets? Maybe if you start with an Array type it'll be okay, but you're always coercing to seq and to me it's hard
  Taking the first 5 elements, recurring on a list with the top element dropped is probably better, but I find the code hard to read. Maybe it's a familiarity issue..
  
  7 replies →
- drob518 2 days ago
  
  Exactly. I would use loop or partition+map/reduce for that case. I almost never use map-indexed. In fact I almost never use indexing at all. Mostly, when I have a sequential collection (vector, list, or generic seq), I need to iterate over all the elements, and I’m doing that with map or reduce. IMO, map-indexed has a code-smell that indicates that you’re reaching for an imperative algorithm when perhaps a functional algorithm would be better. Surely, there are times when map-indexed is just what you need, which is why it’s there, but typically not in my experience.
  
  1 reply →

NeutralForest 3 days ago

Maybe I'm just too used to Python (and I only know some Clojure) but I don't have the same experience. Usually using generators and itertools will really help you shorten your code. I'm working in a data science adjacent field so a lot of code is just frameworks anyways but I don't feel limited in pure Python either.

If you come across a post or an example that shows those differences, I would be very interested!

cess11 3 days ago

Could you show an example or two between Elixir and Clojure?

barrell 2 days ago

This is not the best example, it's just the most recent example (what I was doing last night) that can fit in one screen:

  (defn report [date]
    (let [[d w m q y] (-> (comp tier* recall* (partial c/shift date :day)) 
                          (map [1 7 30 90 365]))]
      (reduce (fn [memo {:keys [card code]}]
                (cond-> memo 
                  true (update code (fnil update [0 0 0 0 0 0 0 0 0 0]) (q card) inc)
                  (<= 4 (d card)) (update-in [code 6] inc)
                  (<= 4 (w card)) (update-in [code 7] inc)
                  (<= 4 (m card)) (update-in [code 8] inc)
                  (<= 4 (y card)) (update-in [code 9] inc)))
              {} 
              (k/index :intels)))))

The elixir code I was able to condense down into:

  def report(facets, intels, day) do
    [d, w, m, q, y] = for x <- [1, 7, 30, 90, 365], do: Date.shift(day, day: x)

    Enum.reduce(intels, %{}, fn intel, acc ->
      facet = Map.get(facets, intel.uuid, :zero)

      [q0, q1, q2, q3, q4, q5, d4, w4, m4, y4] =
        acc[intel.code] || [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]

      quarterly_tier = tier(facet, q)

      Map.put(acc, intel.code, [
        if(quarterly_tier == 0, do: q0 + 1, else: q0),
        if(quarterly_tier == 1, do: q1 + 1, else: q1),
        if(quarterly_tier == 2, do: q2 + 1, else: q2),
        if(quarterly_tier == 3, do: q3 + 1, else: q3),
        if(quarterly_tier == 4, do: q4 + 1, else: q4),
        if(quarterly_tier == 5, do: q5 + 1, else: q5),
        if(tier(facet, d) >= 4, do: d4 + 1, else: d4),
        if(tier(facet, w) >= 4, do: w4 + 1, else: w4),
        if(tier(facet, m) >= 4, do: m4 + 1, else: m4),
        if(tier(facet, y) >= 4, do: y4 + 1, else: y4),
      ])
    end)
  end

It was much longer prior to writing this comment (I originally used multiple arity helper functions), but it was only fair I tried my best to get the elixir version as concise as possible before sharing. Still 2x the lines of effective code, substantially more verbose imho, and required dedicated (minor) golfing to get it this far.

Replacing this report function (12 lines) + one other function (6 lines) + execution code (18 lines) is now spread across 3 modules in Elixir, each over 100 lines. It's not entirely apples to oranges, but trying to provide as much context as possible.

This is all just to say that the high effort in reading it is normally a result of information density, not complexity or syntax. There are real advantages to being able to see your entire problem space on a single page.