Posts Tagged ‘Netflix’
[KotlinConf2025] Closing Panel
The concluding panel of KotlinConf2025 offered a vibrant and candid discussion, serving as the capstone to the conference. The diverse group of experts from JetBrains, Netflix, and Google engaged in a wide-ranging dialogue, reflecting on the state of Kotlin, its evolution, and the path forward. They provided a unique blend of perspectives, from language design and backend development to mobile application architecture and developer experience. The conversation was an unfiltered look into the challenges and opportunities facing the Kotlin community, touching on everything from compiler performance to the future of multiplatform development.
The Language and its Future
A central theme of the discussion was the ongoing development of the Kotlin language itself. The panel members, including Simon from the K2 compiler team and Michael from language design, shared insights into the rigorous process of evolving Kotlin. They addressed questions about new language features and the careful balance between adding functionality and maintaining simplicity. A notable point of contention and discussion was the topic of coroutines and the broader asynchronous programming landscape. The experts debated the best practices for managing concurrency and how Kotlin’s native features are designed to simplify these complex tasks. There was a consensus that while new features are exciting, the primary focus remains on stability, performance, and enhancing the developer experience.
The State of Multiplatform Development
The conversation naturally shifted to Kotlin Multiplatform (KMP), which has become a cornerstone of the Kotlin ecosystem. The panelists explored the challenges and successes of building applications that run seamlessly across different platforms. Representatives from companies like Netflix and AWS, who are using KMP for large-scale projects, shared their experiences. They discussed the complexities of managing shared codebases, ensuring consistent performance, and maintaining a robust build system. The experts emphasized that while KMP offers immense benefits in terms of code reuse, it also requires a thoughtful approach to architecture and toolchain management. The panel concluded that KMP is a powerful tool, but its success depends on careful planning and a deep understanding of the underlying platforms.
Community and Ecosystem
Beyond the technical discussions, the panel also reflected on the health and vibrancy of the Kotlin community. A developer advocate, SA, and others spoke about the importance of fostering an inclusive environment and the role of the community in shaping the language. They highlighted the value of feedback from developers and the critical role it plays in guiding the direction of the language and its tooling. The discussion also touched upon the broader ecosystem, including the various libraries and frameworks that have emerged to support Kotlin development. The panel’s enthusiasm for the community was palpable, and they expressed optimism about Kotlin’s continued growth and adoption in the years to come.
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[DevoxxFR2014] Reactive Programming with RxJava: Building Responsive Applications
Lecturer
Ben Christensen works as a software engineer at Netflix. He leads the development of reactive libraries for the JVM. Ben serves as a core contributor to RxJava. He possesses extensive experience in constructing resilient, low-latency systems for streaming platforms. His expertise centers on applying functional reactive programming principles to microservices architectures.
Abstract
This article provides an in-depth exploration of RxJava, Netflix’s implementation of Reactive Extensions for the JVM. It analyzes the Observable pattern as a foundation for composing asynchronous and event-driven programs. The discussion covers essential operators for data transformation and composition, schedulers for concurrency management, and advanced error handling strategies. Through concrete Netflix use cases, the article demonstrates how RxJava enables non-blocking, resilient applications and contrasts this approach with traditional callback-based paradigms.
The Observable Pattern and Push vs. Pull Models
RxJava revolves around the Observable, which functions as a push-based, composable iterator. Unlike the traditional pull-based Iterable, Observables emit items asynchronously to subscribers. This fundamental duality enables uniform treatment of synchronous and asynchronous data sources:
Observable<String> greeting = Observable.just("Hello", "RxJava");
greeting.subscribe(System.out::println);
The Observer interface defines three callbacks: onNext for data emission, onError for exceptions, and onCompleted for stream termination. RxJava enforces strict contracts for backpressure—ensuring producers respect consumer consumption rates—and cancellation through unsubscribe operations.
Operator Composition and Declarative Programming
RxJava provides over 100 operators that transform, filter, and combine Observables in a declarative manner. These operators form a functional composition pipeline:
Observable.range(1, 10)
.filter(n -> n % 2 == 0)
.map(n -> n * n)
.subscribe(square -> System.out.println("Square: " + square));
The flatMap operator proves particularly powerful for concurrent operations, such as parallel API calls:
Observable<User> users = getUserIds();
users.flatMap(userId -> userService.getDetails(userId), 5)
.subscribe(user -> process(user));
This approach eliminates callback nesting (callback hell) while maintaining readability and composability. Marble diagrams visually represent operator behavior, illustrating timing, concurrency, and error propagation.
Concurrency Control with Schedulers
RxJava decouples computation from threading through Schedulers, which abstract thread pools:
Observable.just(1, 2, 3)
.subscribeOn(Schedulers.io())
.observeOn(Schedulers.computation())
.map(this::cpuIntensiveTask)
.subscribe(result -> display(result));
Common schedulers include:
– Schedulers.io() for I/O-bound operations (network, disk).
– Schedulers.computation() for CPU-bound tasks.
– Schedulers.newThread() for fire-and-forget operations.
This abstraction enables non-blocking I/O without manual thread management or blocking queues.
Error Handling and Resilience Patterns
RxJava treats errors as first-class citizens in the data stream:
Observable risky = Observable.create(subscriber -> {
subscriber.onNext(computeRiskyValue());
subscriber.onError(new RuntimeException("Failed"));
});
risky.onErrorResumeNext(throwable -> Observable.just("Default"))
.subscribe(value -> System.out.println(value));
Operators like retry, retryWhen, and onErrorReturn implement resilience patterns such as exponential backoff and circuit breakers—critical for microservices in failure-prone networks.
Netflix Production Use Cases
Netflix employs RxJava across its entire stack. The UI layer composes multiple backend API calls for personalized homepages:
Observable<Recommendation> recs = userIdObservable
.flatMap(this::fetchUserProfile)
.flatMap(profile -> Observable.zip(
fetchTopMovies(profile),
fetchSimilarUsers(profile),
this::combineRecommendations));
The API gateway uses RxJava for timeout handling, fallbacks, and request collapsing. Backend services leverage it for event processing and data aggregation.
Broader Impact on Software Architecture
RxJava embodies the Reactive Manifesto principles: responsive, resilient, elastic, and message-driven. It eliminates common concurrency bugs like race conditions and deadlocks. For JVM developers, RxJava offers a functional, declarative alternative to imperative threading models, enabling cleaner, more maintainable asynchronous code.