⚙️ TIL How Slack Handles Millions of Messages

I've been researching more into how Slack manages their massive distributed system, specifically their implementation of Command Query Responsibility Segregation (CQRS), and I thought it was pretty fascinating.

For those who aren't familiar, CQRS is basically separating your read and write operations into different models.

Slack handles millions of concurrent users all sending messages, uploading files, and searching through message history simultaneously. By splitting reads and writes, they can optimize each path separately:

1. Write operations (sending messages, uploading files, creating channels) can be optimized for consistency and durability. Meanwhile, read operations (loading message history, searching, checking user status) can be tuned for speed and scale.

2. This lets them handle different scaling requirements for reads vs writes. Reading message history is WAY more common than writing new messages, so they can scale those operations independently.

BUT (and there's always a but in distributed systems) this comes with some interesting challenges:

• Eventual consistency means what you write might not be immediately available to read
• Network failures can cause all sorts of fun problems
• Data synchronization between read and write models needs careful handling

What I find most interesting is how they've managed to make all this complexity completely invisible to end users. When's the last time you noticed message delivery issues in Slack?