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: 148

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

Nuxt Introduces Native Request Cancellation and Async Handler Extraction for Performance Gains

Nuxt 4.2 elevates the developer experience with native abort control for data fetching, improved error handling, and experimental TypeScript support. With a 39% reduction in bundle sizes and a streamlined app directory, this release enhances performance and project organization, positioning Nuxt as a leading choice for full-stack web applications built on Vue.js.

By Daniel Curtis

OpenAI and Anthropic Donate AGENTS.md and Model Context Protocol to New Agentic AI Foundation

OpenAI and Anthropic have donated their AGENTS.md and Model Context Protocol projects to the Agentic AI Foundation (AAIF), a new directed fund under the Linux Foundation. Block contributed their agent framework, goose, as another founding project, and several other tech companies have joined as Platinum members.

By Anthony Alford

Pinecone Introduces Dedicated Read Nodes in Public Preview for Predictable Vector Workloads

Pinecone recently announced the public preview of Dedicated Read Nodes (DRN), a new capacity mode for its vector database designed to deliver predictable performance and cost at scale for high-throughput applications such as billion-vector semantic search, recommendation systems, and mission-critical AI services.

By Craig Risi

Article: Building Streaming Infrastructure That Scales: Because Viewers Won't Wait Until Tomorrow

In streaming, the challenge is immediate: customers are watching TV right now, not planning to watch it tomorrow. When systems fail during prime time, there is no recovery window; viewers leave and may not return. One and a half years ago, at ProSiebenSat.1 Media SE, we faced the challenge of scaling streaming applications for international users.

By Daniele Frasca

Target Improves Add to Cart Interactions by 11 Percent with Generative AI Recommendations

Target has deployed GRAM, a GenAI-powered accessory recommendation system for the Home category, using large language models to prioritize product attributes and capture aesthetic cohesion. The system helps shoppers find compatible accessories, integrates human-in-the-loop curation, and achieved measurable improvements in engagement and conversion.

By Leela Kumili

© 2025   Created by Michael Levin.   Powered by

Badges  |  Report an Issue  |  Terms of Service