Watermill is a Go library for working efficiently with message streams. It is intended as a library for building event-driven applications, enabling event sourcing, CQRS, RPC over messages, sagas. Why? Lack of standard messaging library There are many third party and standard library tools which help to implement standardized RPC or HTTP communication in Golang. There are also multiple third party HTTP routers and frameworks. But when you want to build a message-driven application, there are no libraries which are infrastructure-agnostic and not opinionated.
Let me start by thanking all contributors for feedback on Watermill - it drives us to add new features. Thanks! It’s been almost a month since the initial release of Watermill. However, it’s just the beginning and we are still working hard to ship new features. What is new in Watermill 0.2? Documentation - watermill.io Godoc is great. However, it’s functionality is sometimes too limited to express more complicated documentation.
This post is a quick how-to for starting a new project in Go. It features: Hot code reloading Running multiple Docker containers with Docker Compose Using Go Modules for managing dependencies It’s best to show the above working together with an example project. We’re going to set up two separate services communicating with messages over NATS. The first one will receive messages on an HTTP endpoint and then publish them to a NATS topic.
54 days of work, 12,909 lines of code, 47 Monsters and 42 KFC Twisters later finally it is Watermill v0.3.0! To keep it short, let’s go through the changes. One important thing: at the end of this post there is a 3 question survey. Please take a moment to fill it out, it will help us make Watermill even better. CQRS component One of the most important parts of the v0.
If you compare MySQL or PostgreSQL with Kafka or RabbitMQ, at first, it seems they are entirely different software. And usually, that’s true, as you would use them for quite different tasks. What they have in common is processing streams of data, and they specialize in specific ways of doing it. While Kafka and RabbitMQ are popular examples of Pub/Subs (also known as message queues or stream processing platforms), I’d like to share some patterns for using SQL databases as Pub/Subs as well.