How Apache Kafka messaging system work using a Food Delivery Service illustration
A look at the application of this popular pub-sub message queue system
This post also avalable in my Medium
Intro
In today’s digital world, applications are constantly generating a massive, never-ending flow of data — from user clicks and financial transactions to IoT sensor readings. Handling this high-volume, real-time data stream is a monumental challenge. Come to the rescue is Apache Kafka, an enterprise-grade distributed event streaming platform designed to drink from this firehose of information without breaking a sweat.
Lets examine a use-case of Kafka system in the fast-paced world of online food ordering (sorry if you are reading this before lunch time). Think of Door Dash, Uber Eat. etc. — every tap, every confirmed order, and every delivery status update creates a massive, never-ending flow of data. From the moment you hit “order” to when your meal arrives at your door, countless pieces of information are flying around in real-time. Handling this high-volume, continuous stream of data for thousands of simultaneous orders is a monumental challenge for any food delivery platform.
So how does the Kafka architectural design actually work and able to handle such heavy load? Let’s break down the core architecture from producer to consumer.
Also, I came across a very nice diagram of Apache Kafka definition in the blog ByteByteGo which would help us visualize this from a holistic system architect point of view:
