Designing a Real-Time Chat System at Scale

<h1> Message Queues Explained: Why LinkedIn Built Kafka and Changed Async Communication Forever </h1> <blockquote> <p><strong>Covers:</strong> Point-to-Point vs Pub-Sub, Kafka

<p><em>Originally published on <a href="https://tamiz.pro/insights/resilient-realtime-systems-websockets-redis-high-availability" rel="noopener noreferrer">tamiz.pro</a>.</em></p>

<p>Every mainstream messenger makes your phone number your identity. Change apps,<br /> change phones — it follows you. Join a group with strangers and you've handed<br /> every

<ul> <li> <strong>Book:</strong> <a href="https://www.amazon.com/dp/B0GX2SQ594" rel="noopener noreferrer">System Design Pocket Guide: Interviews</a> </li> <li> <strong>Also by

<p>Short answer: when notifications are lost while a user is offline, debug the durable record and cursor before the live connection. For a financial chat room that must survive

<h2> 1. Intro — The Problem We Actually Had </h2> <p>Building real-time systems is often presented as a distributed systems problem.</p> <p>Kafka, stream processors, event buses,

<blockquote> <p>The pager went off at 2:14 AM.</p> </blockquote> <p>If you’ve spent any time operating distributed systems, you know that sound. It doesn’t just wake you up; it

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