Processing data with Clojure and Go

I recently wrote about a better playlist shuffle with Go, where I presented my ideal implementation of a "shuffle" that isn't very random at all. In that post I outlined the algorithm and showed off my implementation in Go (one of my earliest uses of said language). In this post I'll present the same algorithm in Clojure, compare parts to the Go implementation, and muse a little about the difference between these two languages when it comes to data processing (performance, typing, etc).

First index

Unlike Go, Clojure has indexOf for collections by calling out to the Java method:

(.indexOf [:a :b :c :d :e] :c) ;;=> 2

However, for the distribute algorithm we really need first-index-of that starts at a given index, and wraps around if necessary. Like so:

(defn first-index-of [coll item start]
  (let [idx (.indexOf (drop start coll) item)]
    (if (< idx 0)
      (.indexOf coll item)
      (+ idx start))))

Distribute Items

The first integral part of the algorithm is the distribute function, which takes a collection, a label (e.g. genre name, artist name, etc), and a list of items (e.g. tracks) to distribute evenly across the available positions in the collection. nil marks available positions.

You might remember the Go version, that used for without any loop conditions:

func Distribute(distribution []string, artist string, tracks int) []string {
  stepSize := float64(len(distribution)) / float64(tracks)
  index := 0
  remainder := stepSize

  for {
    if tracks == 0 {
      return distribution

    if remainder >= stepSize {
      index = IndexOf(distribution, "", index)
      distribution[index] = artist
      remainder -= stepSize


The Clojure version is essentially the same, except it uses loop in place of for, which is a similar but not the same construct. loop works more like a recursive function. Instead of modifying local variables, which is something you practically don't do in Clojure, it creates new values and passes them to the next iteration:

(defn distribute [dist [bucket items]]
  (let [step-size (/ (count dist) (count items))]
    (loop [idx 0
           n (count items)
           remainder step-size
           dist (into [] dist)]
        (= n 0) dist

        (<= step-size remainder)
        (let [idx (first-index-of dist nil idx)]
          (recur (inc idx) (dec n) (inc (- remainder step-size)) (assoc dist idx bucket)))

       :default (recur (inc idx) n (inc remainder) dist)))))

A few notes:

Distributing By Artist

Next up, we'll distribute tracks by artist. In Go this looked like:

func DistributeByArtist(items []Track) []Track {
  grouped := GroupBy(items, func (track Track))
  buckets := bucketsByOccurrences(grouped)
  distribution := make([]string, len(items))

  for _, bucket := range buckets {
    Distribute(distribution, bucket[0], len(bucket[1]))

  return ReifyDistribution(distribution, grouped)

The Clojure version is conceptually very similar:

(defn distribute-by-artist [tracks]
  (let [grouped (group-by :artist tracks)
        distribution (->> grouped
                          (sort-by #(count (second %)))
                          reverse ;; Most frequently recurring artists first
                          (reduce distribute (map (constantly nil) tracks)))]
    (reify-distribution distribution grouped)))

There are a few very important differences:

Distribute By Anything

I closed off the previous article wanting for a solution that would distribute my list by any number of factors: genres, artists, albums, and so on. In Clojure, such an extension to distribute-by looks like:

(defn distribute-by [items fns]
  (if-not (first fns)
    (let [grouped (group-by (first fns) items)
          distribution (->> grouped
                            (sort-by #(count (second %)))
                            (reduce distribute (map (constantly nil) items)))
          groups (->> grouped
                      (map #(vector (first %) (distribute-by (second %) (rest fns))))
                      (into {}))]
      (reify-distribution distribution groups))))

Because keywords implement IFn, it can be called as such:

(distribute-by playlist [:genre :artist :album])

It should be noted that this solution uses a relatively inefficient form of recursion, both in the above Clojure implementation, and my Go implementation. In practice it will only recur a handful of times, so it's not really worth worrying about.

Reify Distribution

The final step is to reify the distribution: replace the bucket markers in the distribution with actual items. This was straight-forward in Go:

func ReifyDistribution(distribution []string, groups GroupedItems) []Item {
  items := make([]Item, len(distribution))

  for i, key := range distribution {
    items[i], groups[key] = groups[key][0], groups[key][1:]

  return items

This is the only function that is longer in my Clojure version, as it cannot mutate its operands as it goes:

(defn reify-distribution [positions grouped]
  (loop [positions positions
         data grouped
         res []]
    (if (= (count positions) 0)
      (let [bucket (first positions)]
        (recur (drop 1 positions)
               (update-in data [bucket] #(drop 1 %))
               (conj res (first (get data bucket))))))))

I'm sure smarter people than me could write it more compactly.


So, what are the main differences? The way I see it, there are three interesing aspects to consider:

  1. Expressiveness/generality
  2. Performance
  3. Static vs dynamic types


There is no doubt that overall, the Clojure implementation is much more terse, and relies more heavily on existing funcitonality from its standard library. Whether you consider an advantage will likely depend on prior experience: people unfamiliar with Clojure will probably find this version somewhat cryptic and maybe too terse. Someone on the other side might consider the Go implementation somewhat inelegant and "manual".

Some qualities can be assessed objectively:

Personally, I find it much more natural to process data with Clojure, but I am heavily biased towards its functional and dynamically typed nature.


There is no doubt that the Go version is faster. I wrote a simple benchmark using Go's built-in benchmark tool like so:

var benchPlaylist []Item = []Item{
  // ...

var gaa []func(Item) string = AttributeAccessors([]string{"genre", "artist", "album"})

func BenchmarkShuffleBy(b *testing.B) {
  for i := 0; i < b.N; i++ {
    ShuffleBy(benchPlaylist, gaa)

This shuffles a 70 track playlist by genre, artist, and album in about 40.000 nanoseconds. I'm not really sure what a good way to benchmark Clojure code is, what with JVM warm up and all, but some tinkering with the REPL and time yields a result an order of magnitude slower. However, we're still talking less than a millisecond to shuffle what I consider a realistic playlist.

While Go is clearly faster, performance is good enough in both implementations for the target problem domain to be nearly irrelevant.

Static vs Dynamic Types

I'm not going to attempt reiterating this debate. During my career, I have mostly used dynamic languages, know how to use them well, and have a clear (if somewhat unfounded) bias towards them. I don't really have enough static typing experience to present an informed argument.

Whenever I work in a statically typed language like Go, I always appreciate the documentation effect of typed function signatures. Coming back to a piece of Clojure code way after it was written, it can often be hard to know exactly what a certain function expects to find in a generic map. Luckily, Clojure now has clojure.spec, which can partially bridge the gap.

Unfortunately, the kind of code demonstrated in this and the previous post don't benefit hugely from typing in my opinion. Had Go supported generics, we could've forgone the Item type, and written a fully generic solution as such:

func ShuffleBy(items []T, fns []func(T) string) []T

Clojure Spec has little to offer in such generic cases. We could have done this:

(require '[clojure.spec.alpha :as s])

(s/def ::items coll?)
(s/def ::fns (s/coll-of ifn?))

(defn shuffle-by [items fns]
  (start-randomly (distribute-by (shuffle items) fns)))

(s/fdef shuffle-by
        :args (s/cat :items ::items :fns ::fns)
        :ret ::items)

Using instrumentation this could provide some very basic error-handling development time, but I don't think this is the most efficient use of spec.

Spec really shines for code that has more specific expectations of our data. Imagine we'd used our generic constructs to create a highly specific interface:

(defn shuffle-playlist [playlist]
  (shuffly-by playlist [:genre :artist :album]))

This is a function that would benefit more from some structural hinting:

(s/def ::genre string?)
(s/def ::artist string?)
(s/def ::album string?)
(s/def ::track string?)
(s/def ::track (s/keys :req-un [::genre ::artist ::album ::track]))
(s/def ::playlist (s/coll-of ::track))

(defn shuffle-playlist [playlist]
  (shuffly-by playlist [:genre :artist :album]))

(s/fdef shuffle-playlist
        :args (s/cat :playlist ::playlist)
        :ret ::playlist)

When the function has more specific requirements about the shape of data passed to it, spec provides lots of useful documentation, and can even provide some additional direction during development and tests. With expound and Orchestra it can even provide full instrumentation with nice human-readable error messages.

Dynamic Go Code

In the same way that Clojure can gradually add type hints and optionally enforce invariants on data, Go could be bent to be more dynamic. I've said several times that the Clojure implementation is more generic than the Go one, and it's true. It became this way as I tried to leverage each language's strengths, and write somewhat idiomatic code. However, we could make everything an interface{} in Go, and write a version that would be as dynamic as the Clojure one. I'm pretty sure it would still match up performance-wise, but it still feels... wrong.

Parting Thoughts

This has been an interesting experience. I've gotten to know Go a lot better. I have a pretty good grasp on it's strength and weaknesses, particularly when it comes to data modelling and processing. In this specific example, the single biggest differentiator is the standard library. By extension this includes either dynamic vs static typing or the lack of generics in Go. Clojure also lacks generics, but it isn't as much of a problem as it's dynamically typed. In either case, the Clojure standard library includes many more data processing building blocks, which ultimately allows you to perform such tasks in a more expressive and terse manner.