Search Results for "messaging"

GenStage is a specification and set of behaviours for building demand-driven data pipelines on the BEAM. It formalizes the roles of producers, consumers, and producer-consumers, using back-pressure so that fast producers don’t overwhelm downstream stages. Developers implement callbacks like handle_demand and handle_events to control how items are emitted, transformed, and consumed across asynchronous boundaries. Because stages are OTP processes, you gain fault tolerance, supervised restarts,...

Phoenix is a high-performance, productive web development framework written in Elixir. It runs on the Erlang VM (BEAM). It is designed to support both traditional request/response web applications. It also supports real-time, soft-real-time applications via WebSockets, channels, PubSub, and presence features. Phoenix emphasizes fault tolerance, scalability, and developer productivity. It provides tools like code generators, LiveView integration, templating, routing, and a flexible plug...

...The GitHub repository refers to the original open-source Fishjam media server, which was a general-purpose media server written in Elixir and is no longer actively maintained. The broader Fishjam project has since evolved into a platform for WebRTC-based live video, audio, and messaging infrastructure. It is designed for use cases such as video conferencing, live streaming, classrooms, telehealth, gameplay, collaboration, and interactive media products. Fishjam focuses on reducing the amount of WebRTC infrastructure that teams need to build themselves. Its main value is giving developers a practical foundation for real-time communication products with sub-second media delivery.

...The library offers a global name registry, conflict resolution for name ownership, and hooks for reacting to membership changes. Its design embraces OTP principles, using supervised processes and fault-tolerant messaging to keep the registry resilient under failures. Developers can co-locate Swarm with their existing supervision trees to scale workers horizontally without writing custom clustering code. Typical use cases include globally unique workers, sharded consumers, and presence-like coordination where node churn is expected. By turning registry and handoff into configuration and callbacks, Swarm reduces the complexity of multi-node deployments.