Resources

Introduction

Last week, we went over higher order functions in Kotlin. We learned how higher order functions can accept functions as parameters and are also able to return functions. This week, we will take a look at lambdas. Lambdas are another type of function and they are very popular in the functional programming world.



Logic & Data

Computer programs are made up of two parts: logic and data. Usually, logic is described in functions and data is passed to those functions. The functions do things with the data, and return a result. When we write a function we would typically create a named function. As we saw last week, this is a typical named function:

fun hello(name: String): String {
return "Hello, $name"
}

Then you can call this function:

fun main() {
println(hello("Matt"))
}

Which gives us the result:

Hello, Matt

Functions as Data

There is a concept in the functional programming world where functions are treated as data. Lambdas (functions as data) can do the same thing as named functions, but with lambdas, the content of a given function can be passed directly into other functions. A lambda can also be assigned to a variable as though it were just a value.

Lambda Syntax

Lambdas are similar to named functions but lambdas do not have a name and the lambda syntax looks a little different. Whereas a function in Kotlin would look like this:

fun hello() {
return "Hello World"
}

The lambda expression would look like this:

{ "Hello World" }

Here is an example with a parameter:

fun(name: String) {
return "Hello, ${name}"
}

The lambda version:

{ name: String -> "Hello, $name" }

You can call the lambda by passing the parameter to it in parentheses after the last curly brace:

{ name: String -> "Hello, $name" }("Matt")

It’s also possible to assign a lambda to a variable:

val hello = { name: String -> "Hello, $name" }

You can then call the variable the lambda has been assigned to, just as if it was a named function:

hello("Matt")

Lambdas provide us with a convenient way to pass logic into other functions without having to define that logic in a named function. This is very useful when processing lists or arrays of data. We’ll take a look at processing lists with lambdas in the next post!

Views: 146

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: From Dashboard Soup to Observability Lasagna: Building Better Layers

Martha Lambert introduces the "Observability Lasagna" - a four-layer framework (Overview, System, Logs, Traces) focused on connecting layers for an optimized debugging UX. Learn practical tips for instrumentation, visualizing limits, and using event logs/exemplars to shift from general metrics to user-impact focused triaging. Essential for engineering leaders aiming for system reliability.

By Martha Lambert

AWS Disruption Exposes Fragility in Critical Cloud Infrastructure

On October 20, 2025, Amazon Web Services (AWS) experienced a major outage that disrupted global internet services, affecting millions of users and thousands of companies across more than 60 countries. The incident originated in the US-EAST-1 region and was traced to a DNS resolution failure affecting the DynamoDB endpoint, which cascaded into outages across multiple dependent services.

By Craig Risi

The Decisions You Don't Know You're Making: QCon Keynote Explores Hidden Choices in Engineering

Engineering teams make their most consequential decisions not in architecture reviews or sprint planning, but through invisible choices embedded in metrics, defaults, and everyday behaviors. In their QCon San Francisco 2025 keynote, Shawna Martell and Dan Fike challenged the industry's focus on documented decision-making while the decisions that truly shape systems and culture go unrecognized.

By Eran Stiller

Parting the Clouds: The Rise of Disaggregated Systems by Murat Demirbas at QCon SF 2025

Cloud computing is evolving through disaggregation, addressing inefficiencies of traditional architectures by decoupling compute and storage. This shift enhances scalability, fault isolation, and operational simplicity, driven by advancements in networking. As seen in cloud databases such as Amazon Aurora, embracing these principles enables true economic optimization and innovative design.

By Steef-Jan Wiggers

Cloudflare Workflows Adds Python Support for Durable AI Pipelines

Innovative Cloudflare Workflows now supports both TypeScript and Python, enabling developers to orchestrate complex applications seamlessly. With durable execution and state persistence, it simplifies the development of robust data pipelines and AI/ML models. Experience enhanced concurrency and intuitive design, making orchestration effortless for Python enthusiasts.

By Steef-Jan Wiggers

© 2025   Created by Michael Levin.   Powered by

Badges  |  Report an Issue  |  Terms of Service