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This includes server, client, web, and Android development. Structs/exceptions/unions are generated as standard value types (POJOs). The Mobius 2018 conference held in Saint Petersburg earlier this year featured a talk by the guys from Revolut – Roman Yatsina and Ivan Vazhnov, called Multiplatform architecture with Kotlin for iOS and Android. Overall, Kotlin resulted in 40 percent fewer lines of source code than Java, Kotlin is well-known for being able to achieve more with less code, and our experiments were a testament to this common knowledge. A platform is an ideal option for developing Android, Desktop, Web, and iOS applications. It would be easy for iOS apps to reach the Android platform. . The goal was simple: measure Kotlin build performance at scale and understand the tradeoffs of different project structures. To declare or not declare the types might be a question that comes up while writing code. Afterwards, another part of the script was responsible for synchronizing the results repository and shipping the data to our in-house databases, where it could be analyzed. To declare or not declare the types might be a question that comes up while writing code. Ionic: It tends to support Android 4.4+ versions, iOS 8+ and Windows 10. Other than that, they need a good bit of boilerplate to communicate with the platform code such as React Native. Because of this, Java is the favorite of many Android app developers. Our build performance data relates to compilation time rather than build time. In total, we successfully ran 129 experiments. We recommend you to try out XCode first before checking out the others. We needed a large enough amount of data in order to eliminate any outliers that could harm the data analysis. It’s developed by JetBrains who are currently working to bring Kotlin to embedded systems and iOS as well, potentially making it a one-stop language for all application areas. Gradle's unmatched versatility can be relied on to build it all. To support the generation of mixed source sets, we added support for specifying exactly which Thrift elements should be generated in each language. Moreover, since it is still incubating, we were not surprised to see that it runs slower than the other type inference systems we tested. Capturing this data requires much more effort than the ones presented in this document and would be worth its own article. While it is important to keep the project size small so no single project blocks a thread by taking too long to finish and the system can continue to be performant, it is not a concern when it comes to causing exponential growth for individual build times. . (CLoC) CLI and regular expressions, an analysis that looks into the generated project source files and not its compiled bytecode. To most accurately execute our tests, we needed to leverage non-trivial code we would use in production environments. Unlike Java, Kotlin has a more expressive syntax like python. The syntax of Swift doesn’t just resemble that of Kotlin: in small chunks of code there can be up to 77% string similarity. Tho Nguyen is a senior software engineer on Uber's Amsterdam Mobile Developer Experience team. Currently this is only implemented in Gradle, but it may be a possible area to improve compilation speeds on Buck projects as well with using Kapt. Buck’s multi-thread build was turned off. Therefore, the curve seems to be more biased towards the size of the project than by the amount of Java in the project. Build times are tightly coupled to the build system in use, e.g., Gradle Incremental Builds or Buck Parallel building. Weak iOS features support – Most of the SDK functions work effectively on Android but there are some issues with iOS. Kotlin multi-platform compiles the code exactly in the same format as the platform on which you wish to deploy. On average, there are 27 files per project (i.e., the average total files across all 354 projects in the 13 build performance types). The debate of whether to choose Java or Kotlin has been very common in the Android community. The most interesting insights are described below: Throughout the 129 experiments, both javac and kotlinc reported consistent times. React Native: It supports Android 4.0.3+ versions and iOS 8+. ), and fine-grained arguments and language controls to the code generation. It permits building mobile applications for Android and iOS in JavaScript. As a first step, download the materials for this tutorial by clicking the Download materials button at the top or bottom of the page. One theory we developed for why Apt-powered builds perform so well is because the software is much older and is kept under constant development, which allows for performance optimizations over time, something that has yet to happen with Kapt, since it is a much younger solution. To facilitate the success of this adoption, we launched an initiative, in collaboration with JetBrains, to measure Kotlin build performance at scale across different project structures, a process that informed our decisions for best practices for Android development. Developers who love Java can continue using it, and also add Kotlin code incrementally and make use of Kotlin libraries. Since we cannot infer that the work performed by each thread throughout a build execution is deterministic, we do not want multi-thread mode to interfere with the times from the compiler thread. Java projects that import Kotlin dependencies, or vice versa, do not fall under this bucket. Kotlin is known for generating byte-code which is identical to byte-code of Java. What’s more, the striking resemblance of the two languages can help in native development of one app for both Android and iOS. The apps enjoy the intuitive and robust features of native apps without compromising on the performance, all in a reduced timelines, cost, and resources. Kotlin language also has … . At the code generation level, we implemented support for generating Java and Kotlin code using JavaPoet and KotlinPoet. Help is never far away – consult extensive community resources or ask the Kotlin team directly. Swift and Kotlin are two great languages for iOS and Android development respectively. A coroutine is a concurrency design pattern that you can use on Android to simplify code that executes asynchronously.Coroutines were added to Kotlin in version 1.3 and are based on established concepts from other languages.. On Android, coroutines help to manage long-running tasks that might otherwise block the main thread and cause your app to become unresponsive. 4) Adaptability and Flexibility Kotlin allows designers to scale up the developed app without any virtual machine. In this table, the displayed compilation time for line one represents the average compilation time across all 354 projects for all experiment runs. One of its main features is to let users extend its analysis power by adding custom checkers. With that in mind, we did not want to rely on results that would be improved only by using those features. TypeScript definition generation is only available in the new Kotlin … We already implemented a flexible plugin system in the code generation to support custom post-processing, so adding the necessary controls to facilitate these new variants was easy. Then, all that the analysis script had to do was run a command, such as: Given our stack’s usage of Buck, we leverage OkBuck to wrap the Buck usage. We decided to execute our experiments in our CI machines because these experiments ran so slowly, and our CI boxes were much more powerful than personal machines. The BuildPerfType enum member contains all the required information to generate a project for that specification, including potential custom arguments to kotlinc, dependencies (including, Kotlin stdlib, and Kapt, etc. Who uses Kotlin Multiplatform Mobile. Structs/exceptions/unions are generated as standard value types (POJOs). While performing this sort of analysis, it is very difficult to cover all the different permutations in which the subject languages could be used. This can involve inferred return types, lambdas, member references, and generics. Figure 2, below, shows the distribution of the generated projects based on their size, as measured by the number of files. Additionally,  since code is generated, we can control the morphology of these projects; for instance, we can generate them with only Java code, or only with Kotlin code, a mix between the both of them, and enable or disable annotation processors, among other combinations. This section presents some of the metrics and analysis that we wanted to perform, but for various reasons, were not able to; we encourage others to explore these possibilities on their own. A project is always compiled from a clean state, which means that any type of compilation avoidance or incremental compilation is neither possible nor wanted (more on why this happens in the next item). Isolating them from analysis might lead to greater insights. Not anymore. A few other IDEs that you can try are AppCode, Code Runner, and Swifty. Removing the checkers that are not triggered as often and start sharing their classloaders are the immediate actionable items we can take in order to speed up this scenario. Also, annotation processing was designed for Java and can run in-process with the javac compiler, as both share the same AST. Study feasibility of incremental Kapt on Buck. I tried sorting an array of size 100000000 in Swift and Kotlin and i can see a huge performance gap between them. Compiler avoidance/caching mechanisms can vary significantly between build systems, so we decided not to index on it for this project. See the original article here. Many Android developers agree that Kotlin makes development quicker and more fun. is still experimental, it adds a number of new improvements. Heavy dependencies might be playing a bigger role in the measured scenarios. These projects, in turn, contain a Buck file with a custom genrule that invokes the code generator to generate the appropriate source files for the project. . In a recent internal survey at Uber, we asked nearly 100 mobile engineers if they were willing to accept slower build times in order to be able to use Kotlin. This is a good indicator that the experiment environment was well-controlled. I recorded some results and i found that swift is faster when the size is around 10000 or less but once the number goes up, Swift becomes significantly slow as compare to Kotlin. Below, we highlight some additional design considerations and knowledge we had in mind before embarking on this project: Based on these considerations, we created a project generation workflow that enabled us to develop hundreds of models with which to compare build performance for our new Kotlin-based applications. specifications. This design is faulty and creates a quirky and confusing system, exposing system-level complexity to the developer. Our standard model generation pipeline is a simple command line interface around a project generator. Kotlin shares many similarities with Swift, which is the programming language for iOS. Because of this, Kotlin is useful for not only new apps, but also expanding existing Java apps. 8. This article was written in collaboration with the Kotlin team at JetBrains. We analyzed the data using ElasticSearch and Kibana. iven our stack’s usage of Buck, we leverage. It reads from a directory of Thrift specs, infers project dependencies, and then generates a flat set of projects that reflect those specs. Uber already used Buck as our Android/Java build system, so we did not test using tools like the Kotlin Gradle daemon and incremental Kapt. The way this data is indexed would directly impact our ability to build visualizations in Kibana, our front-end system. In iOS, generally, the hooks provided match the lifecycle of the view, creating an intuitive and easy system, and successfully hiding system-level concerns from the developer. Other than that, Apt usage was not as heavy as Kotlin, since the focus of our experiments was Kotlin. A natural progression in the Android space was to start adopting Kotlin, a modern multi-platform programming language and an increasingly popular alternative for Android development that fully interoperates with Java. This may have implications on performance in the sense that Buck’s implementation may not be as heavily optimized as first-party tools. To support Kapt-less generation, we implemented support for optional direct generation of classes that would otherwise be generated during annotation processing. Apple has come down hard on the side of creating something optimized and free of most of the chains C/Objective-C. Google (JetBrains, really) has come down equally hard on the side of integrating seamlessly with existing code. In terms of typing, both Kotlin and Swift are strong and static and they both allow work with dynamic types. Due to the functional and syntactical similarities between Kotlin and Swift, the approaches to application functionality can be very identical. By sharing our results and lessons learned, we hope that others can use it to inform their own decisions. Kotlin syntax is also easy to learn for iOS developers because it is based on the same modern concepts they are used to, so your whole team will be able to write cross-platform code efficiently. To achieve this, we established the following conditions for our model structures: We were in a unique position to perform such a measurement because we generate our network API models and services for Android from Apache Thrift specifications. This is harder to optimize with Kotlin. Figure 8: We measured project performance between Pure Kotlin with Kapt (pink) and pure Kotlin (blue) setups, on the left, and pure Java with Apt (brown) and pure Java (turquoise), on the right. Namely, support for generating. Java vs Kotlin – In Terms of Performance. An optimized implementation would have all Kapt stages and compilation to run in single `kotlinc` call. Kotlin developers can efficiently work on the local environmental factors such as JS for the internet, Swift, and XCode for iOS, Android Studio. At the code generation level, we implemented support for generating Java and Kotlin code using. Kotlin JS gets the new Kotlin JS IR compiler, which is still in alpha today. Marketing Blog, It is fast and able to complete tasks promptly. At least from a build performance perspective, it doesn’t matter whether or not implicit or explicit types are used in the code. There are many things that can be done in order to improve Kotlin build performance analysis. The following features make Gradle easy to use: Gradle supports many major IDEs (Integrated Development Environments), including Visual Studio 2017, Android Studio, IntelliJ IDEA, Eclipse, and XCode. Having an open source data set of projects only increases the range of analysis and unveils better insights. The increasing number of custom checkers, along with the fact that we do not share their classloaders for different javac invocations, are likely responsible for the 70 percent overhead observed in this benchmark. In 2019, more enterprise leaders are migrating to Kotlin or planning to do so. On top of these developer considerations, we had to ensure that this decision didn’t impact the Uber user experience on our Android apps. Android was written in Java. number of files, the number of lines that are blank, comments, or code and the number of generated classes and interfaces, we used a mix of the Count Lines of Code (CLoC) CLI and regular expressions, an analysis that looks into the generated project source files and not its compiled bytecode. Afterwards, another part of the script was responsible for synchronizing the results repository and shipping the data to our in-house databases, where it could be analyzed. Moreover, since it is still incubating, we were not surprised to see that it runs slower than the other type inference systems we tested. It is expressive and concise and works with Java. That gives Kotlin hope for a broader future use apart from Android development. In the end, determining whether or not to adopt a programming language—or a combination of them—requires that you assess their tradeoffs. We had to agree on a format for the data before shipping it to the database. The average number of lines per file is 200 (i.e., the average number of files divided per average number of lines, which is the sum of the average number of lines of code, comments, and blank lines for Java and Kotlin). Kotlin and Java files can exist together in Android apps. Running the experiment in a development environment (on a laptop) was not an option. Native: View (Swift and Kotlin) Up until now all of the code has been written in Kotlin common module shared across both iOS and Android. Edgar Fernandes is a senior software engineer on Uber's Amsterdam Mobile Developer Experience team. This conflates the life cycle of the view with the state of the controller, breaking the fundamental abstraction of MVC. In total, we successfully ran 129 experiments. The wide set of features that Kotlin offers leaves us wanting to improve this analysis even further in order to account for situations such as: Smart casting: We don’t need smart casting in our generated model projects and so it was not covered in our analysis. These are pure JVM projects. Buck support for Kotlin was added by the open source community and it is not being actively maintained. In the case of Android, hooks follow the state of the controller. Learn more. ), and fine-grained arguments and language controls to the code generation. Buck’s implementation of Kapt is not optimal, as it calls `kotlinc` twice to run annotation processing (once for generating stubs and again for the real annotation processing) and once more for the actual compilation, totaling three calls to `kotlinc`. Teams are smaller in the size and thus easier to manage. We believe that this happens due to the following reasons: Even with these reasons, it is odd to see that pure Java plus Apt is much faster when compared to pure Java with no Apt (only a ~5 percent overhead). To simplify this setup, we created a `BuildPerfType` enum with the aforementioned matrices, and added a `–build-perf` option to the project generation CLI. This can involve inferred return types, lambdas, member references, and generics. It’s a workbench that is based upon GUI, created to assist developers in building software applications with an integrated environment containing all the required tools at hand. 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