Reactive programming with RxJava

Explore RxJava JavaDocs with marble diagrams to visualize how observables, operators like map, flatMap, zip, and filter transform streams, handle errors, and schedule work on the computation scheduler.

Compare standard Java streams parallel processing with RxJava, showing how flatMap and subscribeOn run per-item work on separate threads and then merge results with blockingSubscribe.

Explore RxJava schedulers that control which threads run your code, using subscribeOn and observeOn, with examples of computation, IO, newThread, trampoline, and a custom executor-based scheduler.

Learn how errors propagate through an RxJava pipeline, triggering onError and skipping downstream steps, and how onErrorResumeNext enables recovery with fallback data.

See how back pressure is handled in RxJava by converting observables to Flowable with toFlowable and applying strategies like buffer, drop, latest, and error to manage unconsumed events.

Learn to test RxJava observables with a test observer, asserting results, no errors, and completion, including multi-threaded scenarios using TestScheduler and interval with zipWith.

Use RxJava's PublishSubject to implement a single event bus that broadcasts type-filtered events to registered observers via onNext, enabling decoupled, asynchronous UI updates and easy disposal.

Implement a backoff strategy for rate-limited APIs using RxJava's retryWhen, with exponential delays and a retry sequence. Explore how range and timer generate delays, and how lastOrError handles exhausted retries.