Kotlin Thursdays - Introduction to Functional Programming in Kotlin Part 1


Resources

Introduction

Welcome to Kotlin Thursdays! This week I’m starting a series on functional programming with Kotlin. Functional programming is something I’m passionate about, and Kotlin has some great functional programming support! Functional programming can lead to code that is easier to think about, has fewer bugs and is easier to test.

What better way to dive into functional programming than to learn about functions! It might help to think of functions as “mini-programs”. Functions allow us to write some code once and then use that code multiple times throughout the rest of our program.



The Main Function

To make a Kotlin program, we need to make a function to “kickstart” our program. This function is called the main function. To create the mainfunction, let’s make a file called FunWithFunctions.kt and write the following code in it:

fun main() {
println(“Hello World!”)
}

We can compile this into a jar file that we can run with the following command on our terminal:

kotlinc-jvm FunWithFunctions.kt -include-runtime -d functions.jar

Then we can run the program with the following command:

java -jar functions.jar

For those not familiar, a jar file is how Java Virtual Machine programs are packaged into programs that we can easily run. Kotlin, like Java, uses the Java Virtual Machine to run.

Kotlin knows that when we run our code, it should start by running the mainfunction. But we can make our own custom functions as well! Let’s write our first custom function in my FunWithFunctions.kt file:

fun myFirstFunction(): String {
return "I made my first Kotlin function!"
}

We’re using the same fun keyword as before, and we’re giving this function the name of myFirstFunction. Notice the : String that comes after the function name; this tells Kotlin that when this function finishes running, it is going to return a String object. If we don’t specify that, Kotlin assumes that our function is returning Unit, which is the same thing as void in Java. Finally, we use the return keyword to return our string.

This new function we’ve created can now be used in the main function of our program. Here’s the final content of the FunWithFunctions.kt file:

fun myFirstFunction(): String {
return "I made my first Kotlin function!"
}
fun main() {
val result = myFirstFunction()
println(result)
}

Now we can compile and run it:

kotlinc-jvm FunWithFunctions.kt -include-runtime -d functions.jar
java -jar functions.jar

Higher Order Functions

Kotlin has support for higher order functions. A function is a higher order function if it can do at least one of the following things:

  • Accept another function as an argument.
  • Return a function.

Passing Functions to Other Functions

Let’s examine passing functions into other functions. First, let’s define a function that returns a String:

fun a(): String {
return "I can haz functionz!"
}

Next, we’ll create a second function that can accept this function as a parameter:

fun b(parameter: () -> String): String {
return parameter()
}

We can call function b, passing in function a in our main function:

fun main() {
println(b(::a))
}

When function b runs, it will take function a as the parameter and execute it, returning the value:

I can haz functionz!

Functions That Return Functions

Let’s talk about the second capability that higher order functions possess: returning other functions. Let’s start off again with our function a from before:

fun a(): String {
return “I can haz functionz!”
}

Next we define a new function c that will return the function a we defined earlier:

fun c(): () -> String {
return ::a
}

Note that function c has a return type of () -> String. This is because c is returning the function a which returns a String. Kotlin, being strongly typed, requires us to match our return types correctly. This is different from dynamically typed languages like Ruby, which do not require types to be specified. This might seem like a nuisance at first, but in future episodes we’ll examine how using a strongly typed language like Kotlin actually helps us prevent bugs in our code by using types.

When the function c executes, it returns the function a, but it does not execute function a. Let’s see what happens when we call this in the main function:

fun main() {
println(c())
}

This will print:

function a (Kotlin reflection is not available)

This is because function a is being directly returned as a function, instead of executing and returning a String type. To make it execute, we would have to add two sets of parentheses in the println statement:

fun main() {
println(c()())
}

Now the function a, which is returned by the function c, will execute and return the String value:

I can haz functionz!

This prints out correctly because now we’re calling c which returns a function, then calling that returned function (c is returning a).

To help clarify what’s going on here, let’s write our main function a different way:

fun main() {
val functionA = c()
println(functionA)
}

Note the lack of parentheses around functionA in the println statement. Kotlin will print this out again:

function a (Kotlin reflection is not available)

We can add parentheses to functionA in the println statement to make functionA execute:

fun main() {
val functionA = c()
println(functionA())
}

Now we get the result we wanted:

I can haz functionz!

Higher order functions are a key component of functional programming, as it allows us to build up our program by connecting functions to functions. In future episodes we’ll start to examine how this becomes useful especially as we later learn about function composition, which allows us to write software that is easier to comprehend.

Join me next week on Kotlin Thursdays when I talk about anonymous functions and lambdas!

Views: 94

Happy 10th year, JCertif!

Notes

Welcome to Codetown!

Codetown is a social network. It's got blogs, forums, groups, personal pages and more! You might think of Codetown as a funky camper van with lots of compartments for your stuff and a great multimedia system, too! Best of all, Codetown has room for all of your friends.

When you create a profile for yourself you get a personal page automatically. That's where you can be creative and do your own thing. People who want to get to know you will click on your name or picture and…
Continue

Created by Michael Levin Dec 18, 2008 at 6:56pm. Last updated by Michael Levin May 4, 2018.

Looking for Jobs or Staff?

Check out the Codetown Jobs group.

 

Enjoy the site? Support Codetown with your donation.



InfoQ Reading List

Presentation: Sleeping at Scale - Delivering 10k Timers per Second per Node with Rust, Tokio, Kafka, and Scylla

Lily Mara, Hunter Laine walk through the design of a system, its performance characteristics, and how they scaled it.

By Lily Mara, Hunter Laine

Article: Is Your Test Suite Brittle? Maybe It’s Too DRY

One important design principle in software development is DRY – Don’t Repeat Yourself. However, when DRY is applied to test code, it can cause the test suite to become brittle — difficult to understand, maintain, and change. In this article, I will present some indications that a test suite is brittle, guidelines to follow when reducing duplication in tests, and better ways to DRY up tests.

By Kimberly Hendrick

Allegro Reduces Kafka Producer Latency Outliers by 82% After Switching to XFS

Allegro experimented with different performance optimization options to improve Apache Kafka producer tail latency and eventually switched all its clusters to the XFS filesystem. The company used Kafka protocol sniffing, JVM profiling, and eBPF, which proved instrumental in identifying and eliminating performance bottlenecks.

By Rafal Gancarz

Presentation: Several Components are Rendering: Client Performance at Slack-Scale

Jenna Zeigen discusses front-end performance issues encountered by Slack as they continue to grow and evolve the desktop app.

By Jenna Zeigen

SSH Backdoor from Compromised XZ Utils Library

When Microsoft Engineer Andres Freund noticed SSH was taking longer than usual he discovered a backdoor in xz utils, one of the underlying libraries for systemd, that had taken years to be put in place. The backdoor had found its way into testing releases of Linux distributions like Debian Sid, Fedora 41 and Fedora Rawhide but was caught before propagating into more highly used stable releases.

By Chris Swan

© 2024   Created by Michael Levin.   Powered by

Badges  |  Report an Issue  |  Terms of Service