An algorithm is an integral part of programming that is usually complimented with a data structure. Together, they form what is known as data structure and algorithm (DSA), which is key for higher code efficiency. In programming, algorithms generally refer to the pattern you choose to follow when solving a particular problem, while a data structure is how you choose to structure your data.
One popular type of algorithm is the sorting algorithm. We can use a sorting algorithm to rearrange sets of arrays or elements using a comparison operator, which determines the new order of elements using the respective data structures.
In this article, we’ll explore some popular sorting algorithms in Kotlin, including bubble sort, merge sort, radix sort, and heap sort. Then, we’ll dive deep into the bubble sort algorithm, considering some of its benefits.
To follow along with this tutorial, you’ll need basic knowledge of Kotlin and either Android Studio or IntelliJ IDE installed. Let’s get started!
The Replay is a weekly newsletter for dev and engineering leaders.
Delivered once a week, it's your curated guide to the most important conversations around frontend dev, emerging AI tools, and the state of modern software.
An in-place sorting algorithm sorts the list by modifying the arrangement of elements within the list, using constant space to produce an output. A good example of this includes the insertion and selection sorts, which don’t require any additional space for sorting the list.
Sorting algorithms can be categorized in various ways, for example, either stable or unstable or internal or external, depending on the scenario of the data being used.
Internal sorting occurs when all the data is placed inside the internal or main memory. Some examples include heap, bubble, selection, quick, and insertion sorts. Note that in internal sorting, the number of inputs that can be accommodated is tied to the size of the memory.
External sorting occurs when all the data that needs to be sorted cannot be placed in memory at one time, making it very suitable for large amounts of data. You can achieve external sorting by using external storage devices like hard disks, flash drives, and CDs. Some examples include merge with all its variations, tag, and external radix sorts.
Stable sorting occurs when the same two data points appear in the same order, even after sorting the data. Some examples include merge, insertion, and bubble sorts. Unstable sorting is the opposite of stable sorting. It occurs when the same two data points appear in a different order after data sorting, resulting in a change in position. Some examples include heap and quick sorts.
One of the simplest sorting algorithms, the bubble sort compares adjacent values. To achieve this sorting, it swaps these adjacent values whenever it’s required. This sorting entails placing the larger values at the top, that is, bubbled up. However, it is not suitable for massive data sets because the time complexity is high.
The merge sorting algorithm follows a divide and conquer paradigm. It entails two arrays being divided into two equal halves and then combined together through sorting. This combination results in a merge, which will in turn be combined together with another array. This operation will only come to an end if the array is empty or has only one element left with no other to merge with. The merge operation typically entails taking two smaller arrays to form a single larger one.
The Radix sorting algorithm uses digit-by-digit sorting, starting from the least significant digit to the most significant digit. It is a non-comparative algorithm, and it achieves this by creating and distributing elements into buckets based on their radix. For this reason, is also referred to as a bucket or digital sort.
Heap sort is a comparison-based sorting technique that is based on the binary heap data structure. It is both an in-place and unstable algorithm, but it can be made stable. With no advanced computer science concepts like recursion and minimal memory usage, it is very simple to understand. It is mainly used in hybrid algorithms, like IntroSort.
The quick sorting algorithm is similar to the merge sort because they are both involved in the divide-and-conquer concept. However, the quick sort algorithm is also an in-place algorithm. It operates by picking a pivot element from the array and partitioning the other elements into two sub-arrays depending on whether they are greater or less than the pivot. It is also referred to as partition-exchange sort.
The bubble sort algorithm is simple to write, easy to understand, and requires only a few lines of code. This straightforwardness in its implementation helps Android developers reduce error rates in their application design and also improve application efficiency. It can detect tiny errors in an array, which can be fixed using linear complexity, 2n.
Implementing the bubble sorting algorithm basically involves comparing two values and swapping them if required, as shown in the code snippet below:
class BubbleSortingAlgorithm {
static void bubbleSorting(int arrayNumber[])
{
int n = arrayNumber.length;
int temp=0;
for (int i = 0; i < n; i++){
for (int j = 1; j < (n - i); j++){
if (arrayNumber[j-1] > arrayNumber[j]) {
// the element will be swapped using the swapping method below
temp = arrayNumber[j-1];
arrayNumber[j-1] = arrayNumber[j];
arrayNumber[j] = temp;
}
}
}
}
// This is the main method to test run the bubbleSorting implementation logic
public static void main(String args[])
{
int arrayVariable[] = {10, 50, 110, 90, 1, 9, 200, 4, 2000};
System.out.println("This is the Array values before Sorting")
for(int i=0; i < arrayVariable.length; i++){
System.out.print(arrayVariable[i] + " ")
}
System.out.print();
// Sorting the elements using Bubble Sorting Algorithm
bubbleSorting(arrayVariable);
System.out.println("This is the value of the Array after Sorting");
for(int i=0; i < arrayVariable.length; i++){
System.out.print(arrayVariable[i] + " ")
}
}
}
The output from the code above is below:
This is the Array values before Sorting 10, 50, 110, 90, 1, 9, 200, 4, 2000 This is the value of the Array after Sorting 1, 4, 9, 10, 50, 90, 110, 200, 2000
The major challenge of using the bubble sort algorithm is the time complexity. It is not an efficient approach for large data sets because it takes a longer time to complete its sorting, resulting in a run time of O(n2). Therefore, a bubble sort will take a longer time to complete all its required swaps. However, there is an improved version of bubble sort known as modified bubble sort, which is more efficient and can be used for such use cases.
The chart below compares the bubble, insertion, and selection sort algorithms in terms of time and space complexities:
| Worst case space complexity | Average case space complexity | Best case time complexity | |
|---|---|---|---|
| Bubble sort algorithm | O(n^2) and O(1) | O(n^2) | O(n) |
| Selection sort algorithm | O(n^2) and O(1) | O(n^2) | O(n^2) |
| Insertion sort algorithm | O(n^2) and O(1) | O(n^2) | O(n) |
From the comparison above, we observe that there seems to be a tie between the bubble and insertion sort algorithms. However, the insertion sort algorithm requires fewer swaps than bubble sort to complete its operation.
The bubble sort algorithm’s performance clearly shows that it only works for small data sets; it has a worst case time complexity of O(n2) and a space complexity of O(n).
The number of swaps occurring in a bubble sort equals the number of pairs to be inverted in the given array. Therefore, a higher number of swaps results in a higher time for the bubble sort algorithm.
In this tutorial, we reviewed a few sorting algorithms in Kotlin from a holistic point of view. We focused on the bubble sorting algorithm, its benefits, implementation, challenges, and performance. The bubble algorithm stands out as the simplest and easiest way to understand the sorting algorithm type, however, we determined that it is not ideal for large and complex datasets.
I hope you enjoyed this article. If you adopt this algorithm when developing your Android application, be sure to leave a comment. Happy coding!
LogRocket is an Android monitoring solution that helps you reproduce issues instantly, prioritize bugs, and understand performance in your Android apps.
LogRocket's Galileo AI watches sessions for you, instantly identifying and explaining user struggles with automated monitoring of your entire product experience.
LogRocket also helps you increase conversion rates and product usage by showing you exactly how users are interacting with your app. LogRocket's product analytics features surface the reasons why users don't complete a particular flow or don't adopt a new feature.
Start proactively monitoring your Android apps — try LogRocket for free.
Learn more →
@container scroll-state: Replace JS scroll listeners nowCSS @container scroll-state lets you build sticky headers, snapping carousels, and scroll indicators without JavaScript. Here’s how to replace scroll listeners with clean, declarative state queries.
Russ Miles, a software development expert and educator, joins the show to unpack why “developer productivity” platforms so often disappoint.
Discover what’s new in The Replay, LogRocket’s newsletter for dev and engineering leaders, in the February 18th issue.
Learn how to recreate Claude Skills–style workflows in GitHub Copilot using custom instruction files and smarter context management.
Would you be interested in joining LogRocket's developer community?
Join LogRocket’s Content Advisory Board. You’ll help inform the type of content we create and get access to exclusive meetups, social accreditation, and swag.
Sign up now
One Reply to "Kotlin sorting algorithms for Android development"
Thank you for providing such an easy-to-understand explanation. It was extremely helpful