Beyond Pub/Sub: Why Scaling Real-Time Delivery Is Harder Than You Think

A technical deep dive into what breaks, and how to fix it.

At first glance, Pub/Sub seems like a clean solution to real-time delivery: decoupled components, flexible consumers, event-driven UX.

But once you try to scale it to thousands of clients, session-aware personalization, and multiple delivery channels, you’ll find yourself buried in workarounds.

This post breaks down why that happens and how Diffusion changes the equation.

The Cracks in Traditional Pub/Sub

Let’s break down what typically goes wrong:

1. Over-publishing and client-side filtering

Most Pub/Sub systems treat data like a firehose. You publish everything, and clients are left to filter out the 90% they don’t need. That:

• Wastes bandwidth

• Eats mobile battery

• Increases client-side complexity

• Exposes you to unnecessary security risks

What you want is intent-based streaming, where the server knows what each consumer needs, and only sends that.

2. No built-in concept of identity or session context

Traditional brokers don’t understand users. They just route messages based on static topics.

To enforce permissions, you need to:

• Build complex ACL layers around the broker

• Maintain topic structures that map to user entitlements

• Reinvent context-awareness over and over

It’s brittle, opaque, and a nightmare to audit.

3. Latency grows with load, and with location

With systems like Kafka, RabbitMQ, or Redis Streams, high fan-out requires:

• Spinning up more consumers

• Caching downstream

• Load balancing across regions

This adds both architectural weight and delivery delay, especially when you’re sending the same JSON blob to 10,000 clients.

What Real-Time Should Actually Look Like

Here’s how Diffusion addresses these problems out of the box:

Smart Fan-Out

Publish once, stream to many, but each stream is tailored.

• Clients only get what they’re entitled to

• Streams can be filtered, throttled, or transformed per user

• Delta updates reduce payload size dramatically

Session-Aware Architecture

Each connection carries identity and entitlement metadata.

• No need to duplicate data across topics

• Built-in security filters at the topic/view level

• Supports dynamic client sessions and reconnections

Low-latency delivery over efficient WebSockets

Diffusion streams JSON as compact binary, with full compression and delta encoding.

• Drastically reduces payload size

• Keeps data fresh even over slow or mobile networks

• Avoids wasteful polling or constant GET requests

Built-In Latest Value Cache

New subscribers get the current value immediately, not just updates going forward. That means:

• Faster cold starts

• No “blank screen” until next message arrives

• Less logic for engineers to build manually

How This Plays With Your Stack

You don’t have to rip out your Pub/Sub system entirely. Diffusion works as a real-time edge server:

• Feeds from Kafka, databases, REST APIs, or internal systems

• Delivers to clients (web, mobile, kiosk, etc.) via filtered WebSocket streams

• Hosts on-prem, in your cloud, or via Diffusion Cloud (SaaS)

It acts as the final mile, delivering real-time UX without forcing you to rebuild your backend.

Want to see it in action? Start your free trial of Diffusion Cloud: diffusiondata.com/news/free-trial