What is Swift?
Who uses Swift?
Swift Integrations
Why developers like Swift?
Here are some stack decisions, common use cases and reviews by companies and developers who chose Swift in their tech stack.
I use Visual Studio Code because at this time is a mature software and I can do practically everything using it.
It's free and open source: The project is hosted on GitHub and it’s free to download, fork, modify and contribute to the project.
Multi-platform: You can download binaries for different platforms, included Windows (x64), MacOS and Linux (
.rpm
and.deb
packages)LightWeight: It runs smoothly in different devices. It has an average memory and CPU usage. Starts almost immediately and it’s very stable.
Extended language support: Supports by default the majority of the most used languages and syntax like JavaScript, HTML, C#, Swift, Java, PHP, Python and others. Also, VS Code supports different file types associated to projects like
.ini
,.properties
, XML and JSON files.Integrated tools: Includes an integrated terminal, debugger, problem list and console output inspector. The project navigator sidebar is simple and powerful: you can manage your files and folders with ease. The command palette helps you find commands by text. The search widget has a powerful auto-complete feature to search and find your files.
Extensible and configurable: There are many extensions available for every language supported, including syntax highlighters, IntelliSense and code completion, and debuggers. There are also extension to manage application configuration and architecture like Docker and Jenkins.
Integrated with Git: You can visually manage your project repositories, pull, commit and push your changes, and easy conflict resolution.( there is support for SVN (Subversion) users by plugin)
Excerpts from how we developed (and subsequently open sourced) Uber's cross-platform mobile architecture framework, RIBs , going from Objective-C to Swift in the process for iOS: https://github.com/uber/RIBs
Uber’s new application architecture (RIBs) extensively uses protocols to keep its various components decoupled and testable. We used this architecture for the first time in our new rider application and moved our primary language from Objective-C to Swift. Since Swift is a very static language, unit testing became problematic. Dynamic languages have good frameworks to build test mocks, stubs, or stand-ins by dynamically creating or modifying existing concrete classes.
Needless to say, we were not very excited about the additional complexity of manually writing and maintaining mock implementations for each of our thousands of protocols.
The information required to generate mock classes already exists in the Swift protocol. For Uber’s use case, we set out to create tooling that would let engineers automatically generate test mocks for any protocol they wanted by simply annotating them.
The iOS codebase for our rider application alone incorporates around 1,500 of these generated mocks. Without our code generation tool, all of these would have to be written and maintained by hand, which would have made testing much more time-intensive. Auto-generated mocks have contributed a lot to the unit test coverage that we have today.
We built these code generation tools ourselves for a number of reasons, including that there weren’t many open source tools available at the time we started our effort. Today, there are some great open source tools to generate resource accessors, like SwiftGen. And Sourcery can help you with generic code generation needs:
https://eng.uber.com/code-generation/ https://eng.uber.com/driver-app-ribs-architecture/
(GitHub : https://github.com/uber/RIBs )
By mid-2015, around the time of the Series E, the Digital department at WeWork had grown to more than 40 people to support the company’s growing product needs.
By then, they’d migrated the main website off of WordPress to Ruby on Rails, and a combination React, Angular, and jQuery, though there were efforts to move entirely to React for the front-end.
The backend was structured around a microservices architecture built partially in Node.js, along with a combination of Ruby, Python, Bash, and Go. Swift/Objective-C and Java powered the mobile apps.
These technologies power the listings on the website, as well as various internal tools, like community manager dashboards as well as RFID hardware for access management.
At the heart of Uber’s mobile app development are four primary apps: Android rider, Android driver, iOS rider, and iOS driver. Android developers build in Java, iOS in Objective C and Swift. Engineers across both platforms land code into a monolithic code base that ships each week.
They use some third-party libraries, but often build their own, since “Many open source libraries available are general-purpose, which can create binary bloat. For mobile engineering, every kilobyte matters.”
On Android, the build system is Gradle. For the UI, Butter Knife binds views and callbacks to fields and methods via annotation processing, and Picasso provides image loading.
As for iOS, all of the code lives in a monorepo built with Buck. For crash detection, KSCrash reports crashes to the internal reporting framework.
Since the beginning, Cal Henderson has been the CTO of Slack. Earlier this year, he commented on a Quora question summarizing their current stack.
Apps- Web: a mix of JavaScript/ES6 and React.
- Desktop: And Electron to ship it as a desktop application.
- Android: a mix of Java and Kotlin.
- iOS: written in a mix of Objective C and Swift.
- The core application and the API written in PHP/Hack that runs on HHVM.
- The data is stored in MySQL using Vitess.
- Caching is done using Memcached and MCRouter.
- The search service takes help from SolrCloud, with various Java services.
- The messaging system uses WebSockets with many services in Java and Go.
- Load balancing is done using HAproxy with Consul for configuration.
- Most services talk to each other over gRPC,
- Some Thrift and JSON-over-HTTP
- Voice and video calling service was built in Elixir.
- Built using open source tools including Presto, Spark, Airflow, Hadoop and Kafka.
- For server configuration and management we use Terraform, Chef and Kubernetes.
- We use Prometheus for time series metrics and ELK for logging.