There’s something magical about building apps that feel alive. When a message appears instantly in a chat. When notifications pop up the moment something happens. When multiple people can edit the same document together without friction. That’s the power of real time applications.
But behind the magic is a story of architecture. A journey where each step solves one problem, but often creates the next. Let’s walk through that journey, from the simplest setups to production ready systems.
The beauty of Starting Simple
Let's start where most of us do, putting everything in one place.
Your app handles WebSocket connections, processes business logic,
talks to the database, and sends real time updates all from the same
server. It's like having one super capable person running your
entire operation.
And honestly, this setup works well at the start.Deployment is simple. Debugging is easier since everything lives in one place. Fast Real time updates as there’s no extra network hop. For early users, it feels almost magical.
When Your Success Becomes Your Problem
Success brings more users. And more users, means your single server starts juggling a lot more balls. Each WebSocket connection needs memory and processing power. Your database queries take longer. Background tasks compete for attention.
Suddenly, everything starts slowing down together. When you need to
deploy a small bug fix, all your users get disconnected. When your
database gets busy, your real time updates lag. It's like having one
person trying to answer phones, cook meals, and greet customers all
at once.
Breaking Things Apart: Microservices
Breaking things apart solves these problems. Scaling one giant server is expensive and wasteful, need more payment processing power? You have to scale everything, even parts that don't need it. Plus, deploying a tiny notification fix kicks off every user.
The natural next step is breaking things apart. Each microservice
gets its own responsibility, user management, payments,
notifications and each one handles its own WebSocket connections
too. When something happens, services broadcast events to each other
using a message system like RabbitMQ.
This feels cleaner. Services can scale independently,
deployments don't kill everything, and your code is cleaner with
each service focused on its specific job. RabbitMQ makes sure all
services stay in sync, and you can add more instances when you need
them.
The WebSocket Headache
But wait now you have a different challenge. Microservices come with trafdeoffs, with 5 services and 3 instances each, you suddenly have 15 WebSocket servers running. That's a lot of overhead for something that used to be simple. Plus, scaling becomes tricky when you need more instances just to handle WebSocket connections, not business logic.
And let's talk about your frontend team, they're not happy. Now
they need to manage multiple WebSocket connections, figure out which
service to connect to for what data, and handle all the complexity
that brings.
One Gateway to Rule Them All
Here's where the socket gateway pattern comes in place. Instead of each service managing its own WebSocket connections, you create one dedicated service just for real time communication. Think of it as a
specialized receptionist who knows exactly where to route every
message.
Your frontend connects to just one place, making development much
simpler. The gateway listens for events from all your services and
delivers them to the right users. Meanwhile, your business services
can focus on what they do best, without worrying about WebSocket
management.
The Empty Room Problem
Multiple gateway instances got a problem. When a service broadcasts an event, every gateway instance receives it even
if most of them don't have the target users connected. It's like
shouting an announcement in every room of a building, even when the
person you're looking for is only in one room.
Imagine sending a notification to 1,000 users across 10 gateway
instances. Each instance has to check if it has those users
connected, resulting in 10,000 lookups where most come back empty.
Your system spends more time looking for users it doesn't have than
delivering messages to users it does have.
Smart Routing with Redis
The solution is simple and elegant. Each gateway registers its connected users in Redis, creating a shared directory of who’s where. When a service needs to send an update, it looks up the user in Redis and sends the event directly to the right gateway. No guessing, no broadcasting.
Connections are dynamic, but Redis keeps everything up to date. Users can move between instances, gateways can scale out, and messages always find their destination. With optional optimizations like pub/sub and TTLs, the system stays fast, precise, and scalable.
Scaling real time systems always brings new challenges. How do you keep messages fast and reliable as users and regions grow? If you have any suggestions, tips, or ideas, I’d love to hear from you, connect with me on LinkedIn.
Top comments (1)
great work !!