Backpressure Explained with a Story | Reactive Programming in Java скачать в хорошем качестве

Backpressure Explained with a Story | Reactive Programming in Java

Are your systems crashing, applications slowing down, or memory overflowing because too much data is coming in too fast? This video tackles that exact problem by explaining Backpressure, one of the most important concepts in Reactive Programming. We use a simple water tank story to illustrate the issue: a large water tank (the Publisher) releases data much faster than a thin pipe (the Subscriber) can handle, leading to overflows and system breaks. This mismatch causes system crashes, slow responses, and memory issues. Learn how the implementation of a controlled valve—called Backpressure—solves this problem. In Reactive Streams, the Subscriber controls the speed by requesting a specific number of items (N), and the Publisher respects that demand. This demand control creates a smooth data flow, prevents overload, and ensures predictable performance. Backpressure keeps reactive systems stable and is critical for platforms like High-traffic APIs, Streaming platforms, and Event-driven microservices. We also highlight frameworks such as Project Reactor, Spring WebFlux, and RxJava, which support backpressure by following the Reactive Streams specification. Remember: A fast tank plus a thin pipe equals disaster; a controlled valve equals backpressure