Insertion Sort in Clojure
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# Insertion Sort in Clojure

Insertion sort in Clojure can be implemented in different ways. In this post we compare implementations with and without Clojure transients.

# Unit Test

Sometimes, after writing a test, it turns out that everything works and there’s nothing to implement. So, let’s start with a test and see if that’s the case here:

```1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 (ns poligon.algorithms.sorting-test (:require [poligon.algorithms.sorting :refer :all] [clojure.test :refer :all])) ;; random 10.000 numbers: (def unsorted-data (vec (doall (repeatedly 10000 #(rand-int 10000))))) ;; expected result: (def sorted-data (sort unsorted-data)) (deftest insertion-sort-test (is (= [] (insertion-sort []))) (is (= [1 2 3] (insertion-sort [1 2 3]))) (is (= [1 2 3 4 5] (insertion-sort [5 2 3 4 1]))) ;; transients version: (is (= sorted-data (time (insertion-sort unsorted-data)))) ;; persistent vector version: (is (= sorted-data (time (insertion-sort-simple unsorted-data))))) ```

# Insertion sort

First let’s implement the algorithm. The flow is simple: take next element and insert into correct position in already sorted vector. In this version we just reorder elements of persistent vector as we usually do in Clojure:

```1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 (defn- insert-simple [tv idx] (let [current-value (get tv idx)] (loop [i idx v tv] (let [left-value (get v (dec i))] (if (and (pos? i) (> left-value current-value)) (recur (dec i) (assoc v i left-value)) (assoc v i current-value)))))) (defn insertion-sort-simple [v] (let [size (-> v count dec)] (loop [i 1 tv v] (if (<= i size) (recur (inc i) (insert-simple tv i)) tv)))) ```

After running the tests we can see that this version works. But here we have overhead of handling of persistent vector. If we would like to change only a few values it wouldn’t matter much, but we cannot assume that. Insertion sort, as many other algorithms, may reorder many elements. In such cases we can resort to transients and mutate local data structure.

# Transients for performance

A nice thing about transients is that the code structure is almost the same as we normally write in Clojure. So we can develop an algorithm and then introduce transients. The only differences are:

1. Conversion of persistent data structure to transient using `transient` function.
2. Mutation using bang versions of the functions (`assoc!`, `dissoc!`, etc.)
3. Turning transient back into persistent structure using `persistent!`.

In case of insertion sort we only use `transient`, `assoc!`, and `persistent!`. The rest of the code stays the same:

```1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 (ns poligon.algorithms.sorting) (defn- insert "Insert element from `idx` into correct position in transient vector `tv`." [tv idx] (let [current-value (get tv idx)] (loop [i idx v tv] (let [left-value (get v (dec i))] (if (and (pos? i) (> left-value current-value)) (recur (dec i) (assoc! v i left-value)) (assoc! v i current-value)))))) (defn insertion-sort "Insertion sort using transients." [v] (let [size (-> v count dec)] (loop [i 1 tv (transient v)] (if (<= i size) (recur (inc i) (insert tv i)) (persistent! tv))))) ```

# Difference in performance

After running the tests a couple of times, the run-times were pretty much the same as here. The transients version is about 3 times faster:

```1 2 3 4 5 6 7 8 lein test :only poligon.algorithms.sorting-test lein test poligon.algorithms.sorting-test "Elapsed time: 8855.431509 msecs" "Elapsed time: 24010.397826 msecs" Ran 2 tests containing 6 assertions. 0 failures, 0 errors. ```

Of course the numbers way be different for different data. But it’s pretty clear that transients increase performance without complicating code much.