Home Android & Kotlin Books Data Structures & Algorithms in Kotlin

Kotlin & Kotlin Standard Library Written by Matei Suica, Kelvin Lau and Vincent Ngo

Kotlin is a modern, multi-paradigm, programming language developed by JetBrains. It first appeared in 2011 and slowly evolved into one of the coolest languages available today.

One of the reasons developers love Kotlin so much is because it makes app development easier by providing a significant amount of out-of-the-box classes and utilities. Kotlin’s classes and utilities are wrapped inside the Kotlin Standard Library, which contains the core components of the Kotlin language. Inside this library, you’ll find a variety of tools and data types to help build your apps.

Before you start building your own custom data structures, it’s essential to know about the primary data structures that the Kotlin Standard Library already provides.

In this chapter, you’ll start by learning a few things about Kotlin like variables, data types, optionals, conditionals, loops and functions. You’ll then focus on two specific data structures, List and Map, both of which are included with the Kotlin Standard Library. You’ll end this chapter with a discussion about mutability in the context of these two data structures.

Introduction to Kotlin

To understand data structures and algorithms in Kotlin, you first need to understand the main features of the language. But don’t worry: There’s nothing overly complicated about Kotlin, especially if you have experience with other modern programming languages. However, regardless of your experience, there are a few things you need to know before diving deep into the details of data structures:

  • How to declare variables and functions.
  • How to create custom data types.
  • How to manipulate data in loops and decision-making structures.

Once you get comfortable with the basics, you can move on to something a little more complicated: Generics. You can find most of the code of this chapter in the provided projects.

Ready to get started?

Variables and data types

A variable is a way to store information. Typically, a variable has a name and a data type. Variables can also have modifiers that add extra options or restrictions to it.

In Kotlin, there are two types of variables, val and var:

val name = "Bill Clinton"
var country = "Romania"

The difference between val and var is that variables declared with val cannot be reassigned:

name = "Matei Suica" // compile error
country = "Pakistan" // Ok

Since name was defined with val its value cannot be changed from “Bill Clinton”, but since country was defined with var, its value can be updated.

With regard to data types: The Kotlin compiler can sometimes determine the data type of the variable. When it does, it’s referred to as type inference, which most modern programming languages have.

The variables in the previous example are of type String. This is clear to the compiler because they were initialized when they were declared. Since nothing was misleading about them, you did not have to declare the data type. However, this may not always be the case. For example, you can declare a variable that references a number but initializes it later:

var score: Int

In this example, since no initial value is set, its type cannot be inferred and so you must explicitly set the data type for score.

There are several data types in Kotlin that are already defined for you. The Kotlin Standard Library includes more than are covered here, but the basic types are:

  • Numbers: Double, Float, Long, Int, Short, Byte.
  • Characters: Char, String.
  • Other: Boolean, Array.

As you work through this book, you’ll encounter most of these data types. However, at this point, you don’t need to study their specifics, only acknowledge their existence. Later, you’ll create complex structures to store these kinds of data types.

Optionals and null-safety

Many programming languages, including Kotlin, have the concept of a null value. You can assign null to a variable whenever you want to signal that an object has no value.

For example, if you have a variable that can hold a Car but you’ve not yet created a Car object, the variable can hold a null:

var car: Car? = null

Upon object creation, you could easily reassign the variable:

car = Car("Mercedes-Benz")

The problem with the presence of null is that you might try to use it. Assuming that Car contains a drive() method, you might decide to try something like this:


When you have a value assigned, like car = Car("Mercedes-Benz"), you won’t have an issue; however, if you try to do this with a null value, the program will crash. This is where the infamous NPE, Null-Pointer Exception, was born.

To prevent an NPE, Kotlin has a neat system baked into the language. Noticed the ? after the Car data type in the first declaration? That question mark changes the variable type to an optional. An optional tells the compiler that your object could contain a null value. This small detail triggers a chain reaction in the code.

For example, with an Optional, you cannot use:


Instead, you need to use the safe-call operator ?.:


Using a safe-call operator means that this function will only execute if the object is not null.

To assign values to variables that hold null values, you can use ?:, which is also known as the Elvis operator:

val immutableCar: Car = car ?: Car("Porche")

This code does a lot in a single line:

  1. Creates a variable immutableCar that cannot be reassigned.
  2. The immutableCar is not an Optional now. You can be sure that there’s a real car that you can drive() in that variable.
  3. The immutableCar can be either the same as the car or a Porche if the car is null (not a real car).

These language features are nice, but there are cases where you don’t want to play by the rules.

You know that your variable is not a null — even though it’s an Optional — and you need to tell the compiler to use the value that it holds. For this, Kotlin has the not-null assertion operator !!. You can use it instead of the safe-call operator:


This calls drive() on the non-null value that the car holds; if, however, it holds a null, it’ll throw an NPE. Therefore, you should think twice before using it. That could be why the JetBrains team made this operator a double-bang: Think! Twice!

Conditional statements

Programs in Kotlin execute linearly; in other words, one line at a time. While this is easy to follow and clean, it’s not very useful. There are a lot of situations where making a decision or repeating a step can come in handy. Kotlin has structures that resolve both of these problems in a concise fashion.

For decision making, Kotlin has two constructs, if-else and when. Unlike other languages, there’s no ternary operator in Kotlin; you have to use if-else to get the same result. Here’s an example:

val a = 5
val b = 12
var max = -1

if (a > b) {
  max = a
} else {
  max = b

println(max) // prints 12

The code above makes a decision based on the condition inside the brackets.

If a > b is true, the first block of code is executed, and max takes the value of a. If the condition is false, then max takes the value of b.

In the structure, the else part is optional. You might want only to do something if the condition is true but not when it’s false. Instead of leaving the else block empty, you can omit it.

when is much like a series of if-else that can handle many cases:

val groupSize = 3

when (groupSize) {
  1 -> println("Single")
  2 -> println("Pair")
  3 -> { // Note the block
  else -> println("This is either nobody or a big crowd")

In this example, the when structure makes a decision based on the value of groupSize. It has some particular cases like 1, 2 or 3, and then an else clause that handles everything that isn’t specified above.

For the when structure, the else can be optional if the compiler determines that you already handled all of the possible values. You won’t dig into those right now because you need to learn more language features first.


There are two types of loops in Kotlin, for and while. Although you can do just fine only using while, in some situations, using for is easier and more elegant.

Let’s start with the elegant one:

for (i in 1..3) {

for can iterate over any iterable collection of data. In this example, 1..3 creates an IntRange that holds the numbers from 1 to 3. The i variable takes each value, one at a time, and goes into the code block with it. In the block, println() is executed and the value of i goes into the standard output.

Here’s a more generic example:

for (item in collection) println(item)

This prints all of the items in the collection.

The second type of loop is the while loop, which executes the same block of code as long as its condition remains true:

var x = 10
while (x > 0) {

This code starts with x having the value of 10 and since 10 is greater than 0, it executes the code inside the block. There, x decreases by 1, becoming 9. Then, the loop goes back to the condition: “Is 9 greater than 0?”. This continues until eventually x gets to 0 and the condition becomes false.

There is a variation of while known as do-while. The do-while loop first executes the code and then checks for the condition to continue. This ensures that the block of code is executed at least once:

var x = 10
do {
} while (x > 0)

One thing to notice with while loops is that you can easily create an infinite loop. If you do, your program will get stuck and eventually die in a pitiful StackOverflowException or something similar:

var x = 10
while (x > 0) {
println("The light at the end of the tunnel!")

This time, x gets incremented instead of decremented: 10, 11, 12, 13, and so on. It never gets less than or equal to 0, so the while loop has no reason to stop. In other words, you’ll never get to see the light at the end of the tunnel.


Functions are an important part of any programming language, especially in Kotlin as it’s a multi-paradigm programming language. Kotlin has a lot of functional programming features, so it treats functions with the respect they deserve!

In general, programming is based on small units of code that can be abstracted and reused. Functions are the smallest units of code that you can easily reuse. Here’s an example of a function:

fun max(a: Int, b: Int): Int {
  return if (a > b) a else b

This is a simple function that compares two numbers and determines which is higher.

Functions are declared using the fun keyword, followed by the name of the function. By naming functions, you can then call them using their name, as you’ll see momentarily.

Functions can also have a list of parameters. Each parameter has a name that you can use in the code block; parameters also include their data type. This function has two parameters, a and b; however, functions can also have more parameters or no parameters at all.

After a colon :, there’s another data type; this is the function’s return value data type. In other words, the result of this function will have that data type. A function cannot have more than one return type; however, it’s possible for it to have no return type at all. In that case, the function executes its block, usually handling some smaller or commonly shared operation.

Lastly, there’s the function’s code block. Because this function has a return type, it also needs to return a value. In this case, it returns either a or b, and since both are Ints, the return type is also Int.

Here’s an example of a function that has no return type:

fun printMax(c: Int, d: Int) {
  val maxValue = max(c, d)

Again, because this function does not declare a return type, there’s no need for a return keyword. So what’s the point of this function if it contains no return value?

Well, if you look closely, you’ll see that this function calls max by its name, and passes in two parameters, c and d (which max renames to a and b). From there, printMax takes the result of max, stores it into a variable named maxValue and prints it the console.

Note: This chapter does not cover higher-order functions and lambdas as these concepts are more complex. You will, however, touch on them in later chapters.


Generics are a great way to abstract your code whenever you can manipulate multiple data types in the same way.

Consider a class that emulates a box. A class is simply a collection of data and functions that are logically grouped to perform a set of specific tasks. When creating a class, think about how you might use it. In this case, with a box, you:

  • Put something in it.
  • Grab something out of it.
  • Check if the box is empty.

Here’s some code that can perform these tasks:

class Box {
  var content: Any? = null

  fun put(content: Any?) {
    this.content = content

  fun retrieve(): Any? {
    return content

  fun isEmpty(): Boolean {
    return content == null

This is a simple class that can store a value via put(), can retrieve a value via retrieve(), and can check if the box is empty via the isEmpty() method.

Since you want the box to store different kids of objects, the type is set to Any since the Any class is the superclass of all objects in Kotlin.

This could work, but there’s one drawback: Once you put something into the box, you lose the knowledge of the object’s type since you had to use the Any type to story any kind of object.

To get a more specialized box, you could replace Any with the data type you need; for example, a Cat or a Radio. But you’d need to create a different type of Box for every type of object you’d want to store, i.e. you’d have to create CatBox and RadioBox separately.

Generics are an excellent way to keep the code abstract and let the objects specialize once instantiated. To abstract Box, you can write it like this:

class Box<T> {
  var content: T? = null

  fun put(content: T?) {
    this.content = content

  fun retrieve(): T? {
    return content

  fun isEmpty(): Boolean {
    return content == null

Now, to benefit from a specialized box for this generic, you need to instantiate it:

val box = Box<Int>()

val boolBox = Box<Boolean>()

Your box can handle any data type you want, and you’ll be sure that whatever you put in it, has the same data type when you remove it from the box.

You can also apply generics at a function level, and there can be restrictions applied to the kind of data types the generic will accept. In Kotlin, there’s a way to say “I want all functions to return this generic data type” or “I want only the input parameters to be this generic type”.

There’s a lot to learn about Generics, and you’ll need to research it as you progress with your data structures and algorithms. But for now, you’ll start with two of the most common generic data structures that are already provided by the Kotlin Standard Library.

The Kotlin Standard Library

With the Kotlin Standard Library you can get away without using any third-party libraries for most things. It contains useful classes and functions for text manipulation, math, streams, multithreading, annotations, collections and more.

There are many things to mention, but this book can’t cover everything now, so keep your focus on the parts of the library that will help you with the algorithms.

Here are a few things to consider:

Package kotlin

This package contains many helpful higher-order functions. It also contains the definition of the most basic classes, exceptions and annotations. In this package, you’ll find Any, Array, Int, ClassCastException and Deprecated to name a few. The most interesting things are the scoping functions defined in this package.


The let function helps you with null-checks and creates a new local scope to safely perform operations. Here’s an example:

fun printCar(car: Car?) {
  val isCoupe = car?.let {
    (it.doors <= 2)

  if (isCoupe == true) {
    println("Coupes are awesome")

Inside let, it is the unwrapped value of car. Since you’re using the safe-call operator ?., the code block won’t run if car is null. That’s how the compiler can unwrap it without complaining. As you might notice, let can return anything. In this case, it returns a Boolean telling you if the printed car was a coupé.

let uses the instance of the class as this inside the block, and the target object as it. This is helpful in a lot of situations. There are other functions that have a different approach.


run is similar to let, but it’s more focused on the target object — the one you’re using to call the function. Inside the block, run passes the target object as this and isolates the block from the outer scope.

fun printCar2(car: Car?) {
  val isCoupe = car?.run {
    (this.doors <= 2)

  if (isCoupe == true) {
    println("Coupes are awesome")

This is the same example, but now you’re isolated inside run. The return value can still be anything.

These two functions are “transformational” functions. They’re called “transformational” because the object they return can be different from the object you call the function on. This is not the case with the following “mutating” functions.


If you try to replace run with also, you’ll get compile errors. Unlike with let or run which return a transformation, the also function returns the original object.

Now, don’t get tricked into thinking that original means that it’s unmodified. It’s just the same object. also uses it to refer to the object inside of the block and has access to the outer scope using this.

fun printCar3(car: Car?) {
  car?.also {
    it.doors = 4
  }.let {
    if (it?.doors != null && it.doors <= 2) {
      println("Coupes are awesome")

Since also returns the same car object, you can use it to mutate the object and then chain other calls to it. In this example, the check to see if the car is a coupe is within a let block, but since it was modified to have 4 doors within also, it won’t print “Coupes are awesome”


By now, you should be able to guess how apply works. It’s an also that is isolated like a run. It returns the same object as the target, and it uses this inside the block:

fun printCar4(car: Car?) {
  car?.apply {
    doors = 4
  }.let {
    if (it?.doors != null && it.doors <= 2) {
      println("Coupes are awesome")

Again, the car has been updated to have 4 doors so it also won’t print “Coupes are awesome”

These functions will come in handy from time to time, especially if you want to write clean and concise code.

There’s one more function defined in Standard.kt that you’ll see a lot. It’s not the most useful one, but it’s very common.


The JetBrains team decided to define TODO inside the Standard Kotlin Library to prevent one of the centuries-old habit of software developers: forgetting about TODOs.

Have a look at the definition of TODO:

public inline fun TODO(): Nothing = throw NotImplementedError()

TODO() throws an error when the code reaches one of these TODOs. This is a clever trick to prevent forgetting that you still have to write something. You’ll see this every time IntelliJ generates a piece of code for you to implement. Just don’t forget about it!


The second important package in the Kotlin Standard Library is kotlin.collections. You’ll use it a lot in the following chapters and even more in real-life programming. For this introduction, you’ll focus only on two basic collections, List and Map.

A List is a general-purpose, generic container for storing an ordered collection of elements; it’s used commonly in many types of Kotlin programs.

You can create a List by using a helper function from the Kotlin Standard Library named listOf(). For example:

val places = listOf("Paris", "London", "Bucharest")

Note: Kotlin defines lists using interfaces. Each of these interface layers more capabilities on the list. For example, a List is an Iterable, which means that you can iterate through it at least once.

It’s also a Collection, which means it can be traversed multiple times, non-destructively, and it can be accessed using a subscript operator [].

For List, the positional access function get() guarantees access efficiency and it’s the same as using the subscript operator.

Because Kotlin differentiates between mutable and immutable data structures, you’ll want to create a MutableList to talk about all the operations lists have.

You’ll learn more about mutability in Kotlin later in this chapter, but for now, just add another layer on top of your list, and create it like this:

val mutablePlaces = mutableListOf("Paris", "London", "Bucharest")

The Kotlin List is known as a generic collection because it can work with any type. In fact, most of the Kotlin standard library is built with generic code. Unlike the Java Collections that lose information about the type of collection, Kotlin’s List is invariant. This means you cannot assign a List<String> to a List<Any>. Kotlin knows that these are different types of lists.

As with any data structure, there are certain notable traits of which you should be aware. The first of these is the notion of order.


Elements in a list are explicitly ordered. Using the above places list as an example, Paris appears before London.

All of the elements in a list have a corresponding zero-based, integer index. For example, places from the above example has three indices, one corresponding to each element, starting with 0.

You can retrieve the value of an element in the list by writing the following:

places[0] // Paris
places[1] // London
places[2] // Bucharest

The order should not be taken for granted. Some data structures, such as Map, have a weaker concept of the order or no order at all. You can end up with a different order when you try to access elements out of different collections.


Random-access is a trait that data structures can claim if they can handle element retrieval in a constant amount of time.

For example, getting "London" from places takes constant time. This means that there’s no performance difference in accessing the first element, the 3rd element or any other element of the list. Again, this performance should not be taken for granted. Other data structures such as Linked Lists and Trees do not have constant time access.

For linked lists, the further the element is, the longer it takes to access it. You’ll learn more about the complexity of the operations in the next chapter.

List performance

Aside from being a random-access collection, there are other areas of performance that are of interest on how well or poorly does the data structure fare when the amount of data it contains needs to grow. For lists, this varies on two factors.

Insertion location

The first factor is one in which you choose to insert the new element inside the list. The most efficient scenario for adding an element to a list is to append it at the end of the list:

println(mutablePlaces) // prints [Paris, London, Bucharest, Budapest]

Inserting "Budapest" using add() places the string at the end of the list. This is a constant-time operation, meaning the time it takes to perform this operation stays the same no matter how large the list becomes.

However, there may come a time that you need to insert an element in a particular location, such as in the middle of the list. To help illustrate, consider the following analogy. You’re standing in line for the movies. Someone new comes along to join the lineup. If they just go to the end of the line, nobody will even notice the newcomer. But, if the newcomer tried to insert himself into the middle of the line, he would have to convince half the lineup to shuffle back to make room. And if he were terribly rude, he may try to insert himself at the head of the line. This is the worst-case scenario because every single person in the lineup would need to shuffle back to make room for this new person in front!

This is exactly how lists work. Inserting new elements from anywhere aside from the end will force elements to shift back to make room for the new element:

mutablePlaces.add(0, "Kiev")
// [Kiev, Paris, London, Bucharest, Budapest]

To be precise, every element must shift back by one index. If we consider the number of items in the list to be n, this would take n steps. The time for this operation grows as the number of elements in the list grows. If the number of elements in the list doubles, the time required for this add operation will also double.

If inserting elements in front of a collection is a common operation for your program, you may want to consider a different data structure to hold your data.


The second factor that determines the speed of insertion is the list’s capacity.

Underneath the hood, Kotlin lists are allocated with a predetermined amount of space for its elements. If you try to add new elements to a list that is already at maximum capacity, the List must restructure itself to make more room for more elements.

This is done by copying all the current elements of the list in a new and bigger container in memory. However, this comes at a cost. Each element of the list has to be accessed and copied. This means that any insertion, even at the end, could take n steps to complete if a copy is made.

Note: Standard Library employs a strategy that minimizes the times this copying needs to occur. Each time it runs out of storage and needs to copy, it doubles the capacity.


A Map is another generic collection that holds key-value pairs. For example, here’s a map containing a user’s name and a score:

val scores = mutableMapOf("Eric" to 9, "Mark" to 12, "Wayne" to 1)

There’s no restriction on what type of object the Key is, but you should know that a Map uses the hashCode() function to store the data. Usually, the Key is one of the standard library data types which have the hashCode() function implemented. But if you want to use your own data type, you need to implement the function yourself.

It’s not difficult to override hashCode(), you just have to investigate a little bit what are the most common strategies to get the best result out of the Map.

You can add a new entry to the map with the following syntax:

scores["Andrew"] = 0

This creates a new key-value pair in the map:

{Eric=9, Mark=12, Wayne=1, Andrew=0}

Maps are unordered, so you can’t guarantee where new entries will be put. This is because maps put data into different buckets, depending on the result that the hashCode() function returns. The data in each bucket is ordered, but the general order of the data in the map is unpredictable.

It is possible to traverse through the key-values of a map multiple times as the Collection protocol affords. This order, while not defined, will be the same until the collection is changed.

The lack of explicit ordering disadvantage comes with some redeeming traits.

Unlike the list, maps don’t need to worry about elements shifting around. Inserting into a map always takes a constant amount of time.

Note: Lookup operations also take a constant amount of time, which is significantly faster than finding a particular element in a list which requires a walk from the beginning of the list to the insertion point.

Mutable vs. immutable

As you’ve seen throughout the chapter, there’s a distinction between mutable and immutable data structures in Kotlin.

When referring to the concept of a List, it’s usually referring to the Kotlin’s MutableList. Unlike List, MutableList also has functions for adding and removing elements. Kotlin doesn’t allow a List to be changed in any way.

To change a data structure, you must express this intent by using the Mutable version of that data structure. These data structures have functions for adding and removing elements.

So why would you ever use the immutable version? For safety.

Whenever you need to pass your data structure as a parameter, and you want to be sure that the function doesn’t produce a side effect, you should use an immutable collection as the parameter.

Consider this code:

fun noSideEffectList(names: List<String>) {

fun sideEffectList(names: MutableList<String>) {

fun mutableVsImmutable() {
  val people = mutableListOf("Brian", "Stanley", "Ringo")
  noSideEffectList(people) // [Brian, Stanley, Ringo]
  sideEffectList(people)   // Adds a Joker to the list
  noSideEffectList(people) // [Brian, Stanley, Ringo, Joker]

The sideEffectList function adds a Joker to it. These kind of side-effects are usually the ones generating bugs. Avoiding them by using a List instead of a MutableList is preferred.

Key points

  • Every data structure has advantages and disadvantages. Knowing them is key to writing performant software.
  • Functions such as add(Int, Any) for List have performance characteristics that can cripple performance when used haphazardly. If you find yourself needing to use add(Int, Any) frequently with indices near the beginning of the list, you may want to consider using a different data structure such as the Linked List.
  • Map trades the ability to maintain the order of its elements for fast insertion and searching.

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