YHK - yet another hacker news clone

I think this is because in an imperative language, `reduce` does not actually give you much over a `for item in collection` loop. With `map` and `filter`, you immediately learn something about the result (it's a list of the same length as the original, with each item only depending on the corresponding original item; it's a list containing some of the original elements unchanged and nothing else). This is useful, so `map` and `filter` are good.

With `reduce`, the result could be anything, and in an imperative language, side effects are also possible. So it's just a loop with worse syntax.

(Admittedly, in an imperative language, `map` and `filter` could also have side effects, though I think most people would consider this bad style.)

    .fold(init, move |acc, x| {…})  // or
    .fold((state, init), |(state, acc), x| {…}).1

  Items.BEGIN
    min = ∞
    max = -∞
    sum = 0
    n = 0
  ITER
    min = Min(min,_)
    max = Max(max,_)
    n += 1
    sum += _
  RETURN
    average = sum / n
    (min, max, average)

  sum = items.reduce(0,+)

I was going to write a question asking if reduce is the thing I know as accumulate (I think I picked this up from SICP). But then I went to wikipedia, and it seems that an even more common name is fold.

Here's a hypothesis: The fact that the same operation has half a dozen different names makes it sound like there is a lot to learn. If I am totally familiar with fold, and i come upon a reduce, I may need to think more about what's going on, which is distracting.

I don't think map and filter have so many synonyms? I know select for filter, but it seems to me less common.

For can have another set of variables in the header too. You can simulate it more readably even if you need to call the lambda.

Ive only used reduce at work half a dozen times and it does raise an eyebrow each time.

But for unioning a bunch of spark dataframes together i think

    df = reduce(DataFrame.union, list_of_dfs) 

is much nicer than

    df, *rest = list_of_dfs

    for other in rest:
        df = df.union(other)

People just get a bit funny, especially now you have to import it from functools

Reduce requires knowing that the sum of zero entities is zero but the multiply of zero entities is one. They forget to throw the correct number and think that reduce() just do not work for them.

At least in Python, I've found that "reduce" is very rarely needed. Most of the times, "sum" is enough, sometimes with "start" values customized (set it to [] to flatten an array for example). It is both easier to read, faster, and needs no imports. It also works great with list comprehensions - "sum(foo(x) for x in input if x > 5)" is much easier to read than reduce equivalent.

If you are multiplying, you are likely doing heavy math, and you'll be using numpy - which does not need reduce either.

If you are going to return a list of dict, then it's much faster to mutate the results, so using "reduce" will have significant performance implications (unless you want to return input argument, mis-using it as a glorified "for" loop)

And if returning not a list/dict, if you can use "min" or "max" or "any" or "all" or "next" (take the first element), then you should use it - it will be easier to read and faster too.

So what does this leave us for "reduce"? Frankly, not much. I've only seen it in merging immutable status codes, and that was pretty niche usecase to begin with.

(this was all for Python. In other languages without nice list of built-ins reduce might make more sense)

    Python 3.13.5 (main, Jul 15 2026, 20:25:40) [GCC 14.2.0] on linux
    >>> x = [[n]*1000 for n in range(1000)]; import timeit, itertools, functools, operator
    >>> timeit.timeit("len(list(sum(x, [])))", number=10, globals=globals())
    13.009033881127834
    >>> timeit.timeit("len(list(list(functools.reduce(operator.add, x, []))))", number=10, globals=globals())
    12.941937348805368
    >>> timeit.timeit("len(list(itertools.chain.from_iterable(x)))", number=10, globals=globals())
    0.0706032607704401
    >>> timeit.timeit("out=[]; [out.extend(i) for i in x]; len(out)", number=10, globals=globals())
    0.06334403157234192
    >>> timeit.timeit("len([i for a in x for i in a])", number=10, globals=globals())
    0.1232151910662651

I agree, but I think a lot of it is variable name abuse on the accumulator, making it unclear. I've seen a lot of single letter or worse, a coworker who named it "cum" for short which is super not okay

The name itself is confusing to begin with.

I come across reduce once in a few months, then I think it's a neat trick and a nice to have function.

then I forget it's even available and don't ever use unless these days LLM brings it up again.

It's part of the functional trio: map, filter, reduce--and half of MapReduce.

Well yeah, it's the lowest-level array function. All of the others can be written with reduce, but not vice-versa. Of course it's going to be less friendly.

I like reduce in principle since it generalizes a simple concept pretty nicely. I don't use it that much in practice since its alternatives just require less brainpower. It competes against using local mutable state with a loop or iterator combinator which I would argue are easier to wrap your head around (i.e. loop with variable/map with closure). I would argue its one of those cases where something is just harder to do/understand in functional vs imperative programming.

I find `reduce` useful for operations where:

- arg1, arg2 and return value are all of the same type e.g `ADD`, `MAX`, `CONCAT` etc

- and there is an identity value e.g zero for `ADD`, -math.inf for `MAX`

I recommend checking this article[1] on how monoids play nicely with reduce.

[1] https://fsharpforfunandprofit.com/posts/monoids-without-tear...

I've always like reduce myself, didn't realize others had a negative attitude towards it.

I assume the author is talking about `fold`, as in `[A] -> B -> ((B,A) -> B) -> B`, and not what I often think of as reduce as `[A] -> ((A,A) -> A) -> A`.

`fold` is awesome and super useful. It's the easiest and most convenient way to turn a collection into a single value. Put me anecdotally in the opposite bucket.

Map and filter usually have only one arg and if they have 2, the 2nd is almost always a 0-based index. They look identical in most languages, even when Microsoft chooses to call them Select and Where.

Reduce has an accumulator and a 2-arg function and languages are not very consistent amongst each other as to whether it's reduce(initial_acc, callback(acc, elem)) or reduce(callback(acc, elem), initial_acc) or reduce(callback(elem, acc), initial_acc) or what.

Hard to remember. Also some languages have a version of reduce that doesn't take an initial accumulator at all, which is just a footgun waiting for you to hit an empty collection. Also ALSO, the accumulator can easily become awkward in languages that don't support anonymous types or don't support easy mutation of an anonymous type record. Which is most of them!

Even in the world of functional programming, there's an argument to be made that `fold` is a bit of a code smell, in a similar vein as `while` being slightly smelly in an imperative code base. There's good reasons for each to be used, but they are such low level iteration primitives that you might be better off with a higher one (e.g. for loops or iterators in imperative programs; in FP you might reach for monoidic reduces (as opposed to folds where the accumulator is a different type from the list element), monadic traverses, or recursion schemes). Even though you can implement iterators or for loops in terms of while loops, you probably shouldn't, and similar for functional traversals.

In languages like python or Java though, you don't really have access to many of the higher power functional traversals however. So that puts you into a similar kind of bind as working in a language with only while loops

     i = 0
     while i != len(todo):
         process(todo[i])
         i = i + 1

     for value in todo:
         process(value)

Map and Filter are nice because they let you reason locally about a single element in isolation. Reduce(Fold) forces you to reason globally about intermediate results. Reduce also forces you to conjure up a "zero" value of the relevant type, which isn't usually difficult but it does constitute some extra mental overhead.

Related to the point about worse performance, I'm pretty sure I was there when reduce was "banished" from Python 3 -- demoted to functools.reduce(), instead of the builtin reduce() in Python 2

The story is that sometime in 2006 or 2007, Guido van Rossum was debugging why a web page in Google's internal code review tool (which he wrote) was taking 30+ seconds to render.

This is basically a "production" incident, since thousands of Google engineers relied on the tool. Requests like this were probably tying up threads and exhausting thread pools, perhaps

Eventually it was tracked down to a line wrapping algorithm written with reduce(). I don't think he wrote it -- it may have come in through a dependency. As many know, reduce() is basically:

     s1 + s2
     s1 + s2 + s3
     s1 + s2 + s3 + s4 
     ...

And that's O(n^2) when s_i are strings. And I think it showed up if you viewed a 5000+ line diff, or a 5000+ line file. (Newer programs like Github also suffer here)

I believe, in Python at that time, += was already optimized to avoid this (just like essentially all JS VMs are). Or you can use the idiom of append() to list and join() after.

But reduce() basically forces the inefficient implementation, and I'm sure this is still true in Python 3.

---

So basically Guido spent a long time debugging a performance problem related to reduce(), and made the decision to eject it, to help users avoid "footguns". I was his officemate at the time, so I recall this, but I wasn't involved directly

Also, somebody contributed reduce() to Python way back in the 90's, as well as other functional idioms. He wouldn't have added that himself -- it was never his preferred style.

He preferred a more imperative style. But he allowed those contributions, and then slightly regretted it later.

https://docs.python.org/3/library/functools.html#functools.r...

you guys still reading and review code with ur eyes and brain?

I like it, but I don't use it anywhere near as much as other built-in closures.

I find the two ways that you call it to be a bit annoying (not a showstopper). It just seems a bit "kludgy" to me.

I wanted to add that from personal experience tastes can change! I didn't like reduce when I was first exposed to functional programming, but have come to prefer it.

Might be nonsensical, but one thing I sometimes wonder is why I reach for reducing a list to a value more often than I need to generate a list from a starting value. I guess the asymmetry has something to do with the kinds of applications I work on.

Anywhere that I could use reduce, I instead write a tail recursive function. This is also why I do not and will not ever choose python or javascript voluntarily.

I like it conceptually, but the main issue for me with reduce is that it's hard to know exactly how the reduction will actually be executed.

The FUBAR potential with map and filter is much smaller, with reduce it depends on deep knowledge of the internals of the reduction itself, which makes it not as useful as a safe abstraction.

I like neither of the three and prefer for loops and if statements instead. Yay for shallower stacks!

It's on my list of things that are awkwardly named because there's not a great name to choose, particularly given how wide the different use cases are.

At least in TypeScript, it's a bit clunky to type, and I usually forget the order of the reduce function's arguments (accumulator, current item). Maybe it's just me, but it's especially easy to forget the order when the position of the accumulator is the 1st argument to the callback but the 2nd argument of the reduce function:

    array.reduce(
      (accumulator, currentItem) => {...},
      initialValue,
    )

In .filter(), The current item is the 1st argument and the intermediate/accumulated value comes later: filter((currentItem, index, intermediateArray)) => ...)

I use .filter() more often, so that argument ordering where currentItem is right next to the array is more intuitive for me

I've worked with developers that were reduce maximalist. During PR reviews, anything that could be rewritten with reduce was flagged. One of the benefits of AI is not having to care as much about things like that.

I like it, but don't care for the name. I find it easier to think about in terms of an accumulator.

Speaking as someone who often tries to reduce my use of reduce by replacing it with map and filter where possible, for me, falling back to reduce is analogous to falling back to a while loop or a for loop: I avoid it if I can.

The problem with reduce is that it can do so much, and therefore it is less clear when reading it quickly what it might be doing.

It's simple really: looping is something we've all done a ton. Map is just a specialized version of something you do all the time, made better/simpler: what's not to like (and learn quickly)?

Reduces are used much, much less often. Most devs don't get familiar with them as a result, so every time they have to read a `reduce` they have to re-learn it. And of course, it's a much more involved/complex function, so that exacerbates it.

One of the books that most affected my understanding, ability, and joy of programming was Mark Jason Dominus' "Higher Order Perl."

So I love reduce, and have for many years.

I dislike reduce because people sometimes do wild things in the callback that take a lot of mental effort to understand.

Sometimes people abuse .map as well to do things that are not obvious (i.e. instead of mapping elements of an array to another array, they modify global variables in a for-loop fashion, and discard the result).

But reduce is abused more often and you always need to think really hard if e.g. the initial accumulator is passed or not (it's optional in some languages!), if a correct one is passed (when a compound type is used) and so on.

I've seen a lot of technical points about reduce, all of which are true.

But I think the real reason might be even simpler: you can't tell what it does just from the name. What `map` does is consistent with well-known programming jargon. What `filter` does is consistent with the word's everyday meaning. But if you don't already know what `reduce` does, it's name isn't even enough to hazard an educated guess.

That's not true in Clojure because for lisp programmers for two reasons. First, `reduce` is a ubiquitous and well-known concept in lisp.

Second, in most lisps manually doing the same task with imperative code is an ugly verbose eyesore. But in algol-style languages, the imperative alternative is only 1-2 extra lines of very simple code, so using `reduce` is arguably just code golf.

It would be clearer if the operation were part of the name. The most common operations have good names, like sum(), product(), concat(), and so on.

If there's no standard function for it, it's trivial to write a utility function.

And as part of writing the function, give it a good name and think a bit about the order of operations?

So I think reduce() is just unnecessarily generic, unless it's part of a more complicated system like running a map-reduce.

I like it, but it is by far the most ungainly of the three with the most footguns in it's usage.

While not as functionally pure, I always appreciate the Ruby each_with_object https://ruby-doc.org/3.4.1/Enumerable.html#method-i-each_wit... as a more pleasant interface for it.

In my experience, it depends a lot on the language and the folks you work with. I’ve gotten an eyebrow and a stern talking to for using ‘map’ in JavaScript once. Some people are die-hard about statements and keywords and imperative programming and their world view and be myopic.

“We can’t have map in our codebase, we need to be able to hire anyone off the street and have them comfortable in our codebase.”

Well… since when did we hire random people off the street?

I’m used to functional programming. For me, reduce is perfectly normal. Fewer intermediate variables. No pesky statements, just a nice expression. Great.

Buuuut… some languages think implementing tail call optimization is too hard or bad or for ivory tower academics. Or they’re dynamically typed. And then reduce does become difficult to special case and make performant. So even if you like the juice it’s probably not worth the squeeze.

It was a great time working with Haskell professionally. I didn’t have to constantly defend my style of programming! But in “everything” languages… well you do. Everyone has to agree on which subset to use. And programmers are like cats. Good luck getting them to agree on anything. Even once you agree there will always be that one challenging the decree.

Reduce introduces state (accumulator), unlike map/filter which normally are used for immutability.

I use both, but do not like reduce at all. It's harder to read, yes. But I see the point of using them all.

(JS/TS is my main language) I love reduce()! It's a hammer/nail method for me. Everything looks like a problem solvable by reduce. (I'm often wrong on that, but I quite enjoy learning why by trying).

I really like taking the implementation away from the call site, so that the call site reads

    const myNewValue = data.reduce(doSomethingMagic); 

(and then `doSomethingMagic` is defined somewhere else). So simple.

I failed a job interview once by using reduce() in a coding test. The reviewer didn't understand why I hadn't used a loop. Loops are easier, for sure, but they sprawl and are open to hacking. They can bring in state from outside the loop. They make the call site long (you always have to read the implementation to learn that you don't need to read it). The same interviewer actively liked to have loop bodies modify the loop conditions (e.g. by taking items out of the source array and decrementing the end condition, so the loop would end earlier). That's the kind of "clever" I find unpredictable and hard to think about. Probably a good thing he rejected me.

For me it's the name[0]. map puts out an array that has been mapped from another array. filter puts out an array that is a filter of the input array. both of those are always true. reduce, on the other hand, may put out a reduction of the input array (probably most of the time), but the fact that it may not means that what is happening is not actually a reduction. In languages like js/ts, you don't even have to return anything of the same type as the input array's elements. You could literally "reduce" and array of integers to a cancellation token, or a state object, or anything else.

I realize it's not the most efficient way to work, but I like my code to read like instructions. There's nothing reduce will do that a for loop won't accomplish and the for loop (+ an accumulator, of course) is more clearly "readable" than reduce. If I read map, I know what's going on. If I read filter, I know what's going on. If I read reduce, I have to figure out what's going on, even if I'm pretty sure what is going on. If I could rely on reduce to always give me back an element of the input array, I would use it more. But since it can give back anything, I prefer the simplicity of a for loop.

[0] I don't have any suggestions for "better" names because the whole operation is hard to sum up in a word? "dispatch" makes sense, as a function dispatching a function over each element in an array, but it masks the concept of accumulation from return values. "transform" is accurate, but hardly descriptive at all. the list goes on. It's an undeniably useful little function, it's just hard to make it easy to understand and therefore debug.

Alternative theory - reduce is badly named.

combine, accumulate it aggregate would have way more use.