An array is like a box with compartments, where you can store a set number of items of the same kind. Arrays play a crucial role in Kotlin, helping us hold many items together. They allow us to send multiple values to a function easily, or make various changes to the data. There are various different forms of arrays in Kotlin, including the object-type array, represented by something called the array class.

In Swift there are 3 primary types of collections to store your data in a structured way, namely: In this article we aim to give you an overview of each. Specifically we want to show how they’re declared, illustrate the most common operations of each, provide comparisons between them where applicable and highlight the various performance considerations.

Closures provide a powerful, flexible way for iOS developers to define and use functions in Swift, replacing the blocks used in its predecessor Objective-C. They provide self-contained modules of functionality that you can move around in your code, similar to the lambdas found in other programming languages. Crucially, closures can capture and store references to any constants and variables from the context in which they’re defined.

If you’ve been writing code for any reasonable amount of time, then it’s virtually impossible that you haven’t handled logging in any way, since it’s one of the most essential parts of modern, “real life” app development. If you’re a.NET developer, then you’ve probably used some of the many famous logging frameworks available for use on this platform. Today’s post will cover one of these frameworks: log4net.

GraphQL is a query language and runtime for APIs, developed by Facebook in 2012 and later open-sourced in 2015. And it has changed the way we fetch data from our server. Typically, most front-end clients – like React, Angular, Vue, or mobile apps like iOS and Android – use REST APIs to fetch data from the server. REST APIs require more HTTP calls than GraphQL, which leads to over and underfetching.

We use them to manage users’ log-in sessions, impose time-outs, display dates when content was posted and show the most recent publications to users. This is crucial to a variety of apps, from digital diaries to diet and exercise planners, and onto travel-booking resources. As our user bases become more geographically diverse, so time management gets even more important.

Kotlin has gradually replaced Java as the lingua franca of Android programming. It’s a more concise language than Java, meaning your code works harder and you can build leaner applications. And Kotlin Collections are fundamental. These collections play a fundamental role in our work as programmers by simplifying the organization and management of data. Whether it’s a list, set, map or other data structure, they allow us to categorize and store data logically.

In the 21st century, practically all technological innovation on the planet has been channeled into the mobile phone. The first generation of mobile phones simply allowed you to make calls, store numbers and play rudimentary games (some of which, like Snake, didn’t even have an end sequence because the designers didn’t think anyone would complete them). Today mobile phones are computers in our pocket, allowing us to shop, date, stream videos, buy food, order cabs and find our way around.

The software industry has evolved more quickly in 10 years than most industries do in 1,000, yet we continue to grapple with the basic elements of date and time. Practically all our apps rely on time in some way, and when our users are spread all over the world it can be fiendishly difficult to get the timestamps right. Kotlin language, the dominant Android programming language, provides native classes (or blueprints) to help us deal with date and time.

We are excited to announce the general availability of Snowflake Event Tables for logging and tracing, an essential feature to boost application observability and supportability for Snowflake developers. In our conversations with developers over the last year, we’ve heard that monitoring and observability are paramount to effectively develop and monitor applications. But previously, developers didn’t have a centralized, straightforward way to capture application logs and traces.