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Posts Tagged ‘Kotlin’

KotlinConf’23 kicked off with an energizing keynote, marking a highly anticipated return to an in-person format in Amsterdam. Hosted by Hadi Hariri from JetBrains, the session brought together key figures from both JetBrains and Google, including Roman Elizarov, Svetlana Isakova, Egor Tolstoy, and Grace Kloba (VP of Engineering for Android Developer Experience at Google), to share exciting updates and future directions for the Kotlin language and its ecosystem. The conference also boasted a global reach with KotlinConf Global events held across 41 countries. For those unable to attend, the key announcements from the keynote are also available in a comprehensive blog post on the official Kotlin blog.

The keynote began by celebrating Kotlin’s impressive growth, with compelling statistics underscoring its widespread adoption, particularly in Android development where it stands as the most popular language, utilized in over 95% of the top 1000 Android applications. A significant emphasis was placed on the forthcoming Kotlin 2.0, which is centered around the revolutionary new K2 compiler. This compiler promises significant performance improvements, enhanced stability, and a robust foundation for the language’s future evolution. The K2 compiler is nearing completion and is slated for release as Kotlin 2.0. Additionally, the IntelliJ IDEA plugin will also adopt the K2 frontend, ensuring alignment with IntelliJ releases and a consistent developer experience.

The Evolution of Kotlin: K2 Compiler and Language Features

The K2 compiler was a central theme of the keynote, signifying a major milestone for Kotlin. This re-architected compiler frontend, which also powers the IDE, is designed to be faster, more stable, and to enable quicker development of new language features and tooling capabilities. Kotlin 2.0, built upon the K2 compiler, is set to bring these profound benefits to all Kotlin developers, improving both compiler performance and IDE responsiveness.

Beyond the immediate horizon of Kotlin 2.0, the speakers provided a glimpse into potential future language features that are currently under consideration. These exciting prospects included:

Prospective Language Enhancements

• Static Extensions: This feature aims to allow static resolution of extension functions, which could potentially improve performance and code clarity.
• Collection Literals: The introduction of a more concise syntax for creating collections, such as using square brackets for lists, with efficient underlying implementations, is on the cards.
• Name-Based Destructuring: Offering a more flexible way to destructure objects based on property names rather than simply their positional order.
• Context Receivers: A powerful capability designed to provide contextual information to functions in a more implicit and structured manner. This feature, however, is being approached with careful consideration to ensure it aligns well with Kotlin’s core principles and doesn’t introduce undue complexity.
• Explicit Fields: This would provide developers with more direct control over the backing fields of properties, offering greater flexibility in certain scenarios.

The JetBrains team underscored a cautious and deliberate approach to language evolution, ensuring that any new features are meticulously designed and maintainable within the Kotlin ecosystem. Compiler plugins were also highlighted as a powerful mechanism for extending Kotlin’s capabilities without altering its core.

Kotlin in the Ecosystem: Google’s Investment and Multiplatform Growth

Grace Kloba from Google took the stage to reiterate Google’s strong and unwavering commitment to Kotlin. She shared insights into Google’s substantial investments in the Kotlin ecosystem, including the development of Kotlin Symbol Processing (KSP) and the continuous emphasis on Kotlin as the default choice for Android development. Google officially championed Kotlin for Android development as early as 2017, a pivotal moment for the language’s widespread adoption. Furthermore, the Kotlin DSL is now the default for Gradle build scripts within Android Studio, significantly enhancing the developer experience with features such as semantic syntax highlighting and advanced code completion. Google also actively contributes to the Kotlin Foundation and encourages community participation through initiatives like the Kotlin Foundation Grants Program, which specifically focuses on supporting multiplatform libraries and frameworks.

Kotlin Multiplatform (KMP) emerged as another major highlight of the keynote, emphasizing its increasing maturity and widespread adoption. The overarching vision for KMP is to empower developers to share code across a diverse range of platforms—Android, iOS, desktop, web, and server-side—while retaining the crucial ability to write platform-specific code when necessary for optimal integration and performance. The keynote celebrated the burgeoning number of multiplatform libraries and tools, including KMM Bridge, which are simplifying KMP development workflows. The future of KMP appears exceptionally promising, with ongoing efforts to further enhance the developer experience and expand its capabilities across even more platforms.

Compose Multiplatform and Emerging Technologies

The keynote also featured significant advancements in Compose Multiplatform, JetBrains’ declarative UI framework for building cross-platform user interfaces. A particularly impactful announcement was the alpha release of Compose Multiplatform for iOS. This groundbreaking development allows developers to write their UI code once in Kotlin and deploy it seamlessly across Android and iOS, and even to desktop and web targets. This opens up entirely new avenues for code sharing and promises accelerated development cycles for mobile applications, breaking down traditional platform barriers.

Finally, the JetBrains team touched upon Kotlin’s expansion into truly emerging technologies, such as WebAssembly (Wasm). JetBrains is actively developing a new compiler backend for Kotlin specifically targeting WebAssembly, coupled with its own garbage collection proposal. This ambitious effort aims to deliver high-performance Kotlin code directly within the browser environment. Experiments involving the execution of Compose applications within the browser using WebAssembly were also mentioned, hinting at a future where Kotlin could offer a unified development experience across an even broader spectrum of platforms. The keynote concluded with an enthusiastic invitation to the community to delve deeper into these subjects during the conference sessions and to continue contributing to Kotlin’s vibrant and ever-expanding ecosystem.

Links:

• Blog Post on KotlinConf’23 Keynote Highlights
• JetBrains Website
• JetBrains on LinkedIn
• Grace Kloba – KotlinConf’23 Speaker Profile

Hashtags: #Keynote #JetBrains #Google #K2Compiler #Kotlin2 #Multiplatform #ComposeMultiplatform #WebAssembly

Navigating Kotlin/Native’s Concurrency Model

At KotlinConf 2019 in Copenhagen, Kevin Galligan, a partner at Touchlab with over 20 years of software development experience, delivered a 39-minute talk on Kotlin/Native’s concurrency model. Kevin Galligan explored the restrictive yet logical rules governing state and concurrency in Kotlin/Native, addressing their controversy among JVM and mobile developers. He explained the model’s mechanics, its rationale, and best practices for multiplatform development. This post covers four key themes: the core rules of Kotlin/Native concurrency, the role of workers, the impact of freezing state, and the introduction of multi-threaded coroutines.

Core Rules of Kotlin/Native Concurrency

Kevin Galligan began by outlining Kotlin/Native’s two fundamental concurrency rules: mutable state is confined to a single thread, and immutable state can be shared across multiple threads. These rules, known as thread confinement, mirror mobile development practices where UI updates are restricted to the main thread. In Kotlin/Native, the runtime enforces these constraints, preventing mutable state changes from background threads to avoid race conditions. Kevin emphasized that while these rules feel restrictive compared to the JVM’s shared-memory model, they align with modern platforms like Go and Rust, which also limit unrestricted shared state.

The rationale behind this model, as Kevin explained, is to reduce concurrency errors by design. Unlike the JVM, which trusts developers to manage synchronization, Kotlin/Native’s runtime verifies state access at runtime, crashing if rules are violated. This strictness, though initially frustrating, encourages intentional state management. Kevin noted that after a year of working with Kotlin/Native, he found the model simple and effective, provided developers embrace its constraints rather than fight them.

Workers as Concurrency Primitives

A central concept in Kevin’s talk was the Worker, a Kotlin/Native concurrency queue similar to Java’s ExecutorService or Android’s Handler and Looper. Workers manage a job queue processed by a private thread, ensuring thread confinement. Kevin illustrated how a Worker executes tasks via the execute function, which takes a producer function to verify state transfer between threads. The execute function supports safe and unsafe transfer modes, with Kevin strongly advising against the unsafe mode due to its bypassing of state checks.

Using a code example, Kevin demonstrated passing a data class to a Worker. The runtime freezes the data—making it immutable—to comply with concurrency rules, preventing illegal state transfers. He highlighted that while Worker is a core primitive, developers rarely use it directly, as higher-level abstractions like coroutines are preferred. However, understanding Worker is crucial for grasping Kotlin/Native’s concurrency mechanics, especially when debugging state-related errors like IllegalStateTransfer.

Freezing State and Its Implications

Kevin Galligan delved into the concept of freezing, a runtime mechanism that designates objects as immutable for safe sharing across threads. Freezing is a one-way operation, recursively applying to an object and its references, with no unfreeze option. This ensures thread safety but introduces challenges, as frozen objects cannot be mutated, leading to InvalidMutabilityException errors if attempted.

In a practical example, Kevin showed how capturing mutable state in a background task can inadvertently freeze an entire object graph, causing runtime failures. He introduced tools like ensureNeverFrozen to debug unintended freezing and stressed intentional mutability—keeping mutable state local to one thread and transforming data into frozen copies for sharing. Kevin also discussed Atomic types, which allow limited mutation of frozen state, but cautioned against overusing them due to performance and memory issues. His experience at Touchlab revealed early missteps with global state and Atomics, leading to a shift toward confined state models.

Multi-Threaded Coroutines and Future Directions

A significant update in Kevin’s talk was the introduction of multi-threaded coroutines, enabled by a draft pull request in 2019. Previously, Kotlin/Native coroutines were single-threaded, limiting concurrency and stunting library development. The new model allows coroutines to switch threads using dispatchers, with data passed between threads frozen to maintain strict mode. Kevin demonstrated replacing a custom background function with a coroutine-based approach, simplifying concurrency while adhering to state rules.

This development clarified the longevity of strict mode, countering speculation about a relaxed mode that would mimic JVM-style shared memory. Kevin noted that multi-threaded coroutines unblocked library development, citing projects like AtomicFu and SQLDelight. He also highlighted Touchlab’s Droidcon app, which adopted multi-threaded coroutines for production, showcasing their practical viability. Looking forward, Kevin anticipated increased community adoption and library growth in 2020, urging developers to explore the model despite its learning curve.

Conclusion

Kevin Galligan’s KotlinConf 2019 talk demystifies Kotlin/Native’s concurrency model, offering a clear path for developers navigating its strict rules. By embracing thread confinement, leveraging workers, managing frozen state, and adopting multi-threaded coroutines, developers can build robust multiplatform applications. This talk is a must for Kotlin/Native enthusiasts seeking to master concurrency in modern mobile development.

Links

• Watch the full talk on YouTube
• Touchlab
• American Express
• KotlinConf
• JetBrains
• Kotlin Website
• Kotlin/Native Repository

Hashtags: #KevinGalligan #KotlinNative #Concurrency #Touchlab #JetBrains #Multiplatform

At Devoxx France 2019, Olivier Revial, a developer at Stackeo in Toulouse, presented Micronaut: The Ultra-Light JVM Framework of the Future. This session introduced Micronaut, a modern JVM framework designed for microservices and serverless applications, offering sub-second startup times and a 10MB memory footprint. Through slides and demos, Revial showcased Micronaut’s cloud-native approach and its potential to redefine JVM development.

Limitations of Existing Frameworks

Revial began by contrasting Micronaut with established frameworks like Spring Boot and Grails. While Spring Boot simplifies development with auto-configuration and standalone applications, it suffers from runtime dependency injection and reflection, leading to slow startup times (20–25 seconds) and high memory usage. As codebases grow, these issues worsen, complicating testing and deployment, especially in serverless environments where rapid startup is critical. Frameworks like Spring create a barrier between unit and integration tests, as long-running servers are often relegated to separate CI processes.

Micronaut addresses these pain points by eliminating reflection and using Ahead-of-Time (AOT) compilation, performing dependency injection and configuration at build time. This reduces startup times and memory usage, making it ideal for containerized and serverless deployments.

Micronaut’s Innovative Approach

Micronaut, created by Grails’ founder Graeme Rocher and Spring contributors, builds on the strengths of existing frameworks—dependency injectiaon, auto-configuration, service discovery, and HTTP client/server simplicity—while introducing innovations. It supports Java, Kotlin, and Groovy, using annotation processors and AST transformations for AOT compilation. This eliminates runtime overhead, enabling sub-second startups and minimal memory footprints.

Micronaut is cloud-native, with built-in support for MongoDB, Kafka, JDBC, and providers like Kubernetes and AWS. It embraces reactive programming via Reactor, supports GraalVM for native compilation, and simplifies testing by allowing integration tests to run alongside unit tests. Security features, including JWT and basic authentication, and metrics for Prometheus, enhance its enterprise readiness. Despite its youth (version 1.0 released in 2018), Micronaut’s ecosystem is rapidly growing.

Demonstration

Revial’s demo showcased Micronaut’s capabilities. He used the Micronaut CLI to create a “hello world” application in Kotlin, adding a controller with REST endpoints, one returning a reactive Flowable. The application started in 1–2 seconds locally (6 seconds in the demo due to environment differences) and handled HTTP requests efficiently. A second demo featured a Twitter crawler storing tweets in MongoDB using a reactive driver. It demonstrated dependency injection, validation, scheduled tasks, and security (basic authentication with role-based access). A GraalVM-compiled version started in 20 milliseconds, with a 70MB Docker image compared to 160MB for a JVM-based image, highlighting Micronaut’s efficiency for serverless use cases.

Links:

• Micronaut Official Website
• Stackeo Official Website
• Olivier Revial’s GitHub

Hashtags: #Micronaut #Microservices #DevoxxFR2019 #OlivierRevial #JVMFramework #CloudNative

At Devoxx France 2019, David Wursteisen and Jérémy Martinez, developers at Margot Bank, delivered a candid talk on their experience with Gradle while building a core banking system from scratch. Their 45-minute session, “Gradle, je t’aime: moi non plus,” explored why they chose Gradle over alternatives, its developer-friendly features, script maintenance strategies, and persistent challenges like memory consumption. This post dives into their insights, offering a comprehensive guide for developers navigating build tools in complex projects.

Choosing Gradle for a Modern Banking System

Margot Bank, a startup redefining corporate banking, embarked on an ambitious project in 2017 to rebuild its IT infrastructure, including a core banking system (CBS) with Kotlin and Java modules. The CBS comprised applications for payments, data management, and a central “core” module, all orchestrated with microservices. Selecting a build tool was critical, given the need for speed, flexibility, and scalability. The team evaluated Maven, SBT, Bazel, and Gradle. Maven, widely used in Java ecosystems, lacked frequent updates, risking obsolescence. SBT’s Scala-based DSL added complexity, unsuitable for a Kotlin-focused stack. Bazel, while powerful for monorepos, didn’t support generic languages well. Gradle emerged as the winner, thanks to its task-based architecture, where tasks like compile, jar, and assemble form a dependency graph, executing only modified components. This incremental build system saved time, crucial for Margot’s rapid iterations. Frequent releases (e.g., Gradle 5.1.1 in 2019) and a dynamic Groovy DSL further cemented its appeal, aligning with Devoxx’s emphasis on modern build tools.

Streamlining Development with Gradle’s Features

Gradle’s developer experience shone at Margot Bank, particularly with IntelliJ IDEA integration. The IDE auto-detected source sets (e.g., main, test, integrationTest) and tasks, enabling seamless task execution. Eclipse support, though less polished, handled basic imports. The Gradle Wrapper, a binary committed to repositories, automated setup by downloading the specified Gradle version (e.g., 5.1.1) from a custom URL, secured with checksums. This ensured consistency across developer machines, a boon for onboarding. Dependency management leveraged dynamic configurations like api and implementation. For example, marking a third-party client like AmazingMail as implementation in a web app module hid its classes from transitive dependencies, reducing coupling. Composite builds, introduced in recent Gradle versions, allowed local projects (e.g., a mailer module) to be linked without publishing to Maven Local, streamlining multi-project workflows. A notable pain point was disk usage: open-source projects’ varying Gradle versions accumulated 4GB on developers’ machines, as IntelliJ redundantly downloaded sources alongside binaries. Addressing an audience question, the team emphasized selective caching (e.g., wrapper binaries) to mitigate overhead, highlighting Gradle’s balance of power and complexity.

Enhancing Builds with Plugins and Kotlin DSL

For script maintainers, standardizing configurations across Margot’s projects was paramount. The team developed an internal Gradle plugin to centralize settings for linting (e.g., Ktlint), Nexus repositories, and releases. Applied via apply plugin: 'com.margotbank.standard', it ensured uniformity, reducing configuration drift. For project-specific logic, buildSrc proved revolutionary. This module housed Kotlin code for tasks like version management, keeping build.gradle files declarative. For instance, a Versions.kt object centralized dependency versions (e.g., junit:5.3.1), with unused ones grayed out in IntelliJ for cleanup. Migrating from Groovy to Kotlin DSL brought static typing benefits: autocompletion, refactoring, and navigation. A sourceSet.create("integrationTest") call, though verbose, clarified intent compared to Groovy’s dynamic integrationTest {}. Migration was iterative, file by file, avoiding disruptions. Challenges included verbose syntax for plugins like JaCoCo, requiring explicit casts. A buildSrc extension for commit message parsing (e.g., extracting Git SHAs) exemplified declarative simplicity. This approach, inspired by Devoxx’s focus on maintainable scripts, empowered developers to contribute to shared tooling, fostering collaboration across teams.

Navigating Performance and Memory Challenges

Gradle’s performance, driven by daemons that keep processes in memory, was a double-edged sword. Daemons reduced startup time, but multiple instances (e.g., 5.1.1 and 5.0.10) occasionally ran concurrently, consuming excessive RAM. On CI servers, Gradle crashed under heavy loads, prompting tweaks: disabling daemons, adjusting Docker memory, and upgrading to Gradle 4.4.5 for better memory optimization. Diagnostics remained elusive, as crashes stemmed from either Gradle or the Kotlin compiler. Configuration tweaks like enabling caching (org.gradle.caching=true) and parallel task execution (org.gradle.parallel=true) improved build times, but required careful tuning. The team allocated maximum heap space (-Xmx4g) upfront to handle large builds, reflecting Margot’s resource-intensive CI pipeline. An audience question on caching underscored selective imports (e.g., excluding redundant sources) to optimize costs. Looking ahead, Margot planned to leverage build caching for granular task reuse and explore tools like Build Queue for cleaner pipelines. Despite frustrations, Gradle’s flexibility and evolving features—showcased at Devoxx—made it indispensable, though memory management demanded ongoing vigilance.

Links :

• Devoxx France 2019 Video
• Gradle Documentation
• Kotlin DSL Guide
• Margot Bank
• Ktlint Documentation

Hashtags: #Gradle #KotlinDSL #BuildTools #DavidWursteisen #JeremyMartinez #DevoxxFrance2019