Kafka Deep Dive for System Design Interviews
Imagine you're designing a system that needs to handle millions of events per second. Orders flow in from an e-commerce platform, clicks stream from a website, and logs pour in from hundreds of microservices. You need to capture all of this data reliably, process it in real-time, and feed it to multiple downstream systems, each with different requirements and processing speeds.
This is exactly the kind of problem Apache Kafka was built to solve. Originally developed at LinkedIn to handle their massive data pipelines, Kafka has become the de facto standard for high-throughput event streaming. Companies like Netflix, Uber, and Airbnb rely on it to process trillions of messages daily.
But here's what many engineers miss: Kafka is not just a message queue. It's a distributed commit log, a fundamentally different abstraction that enables capabilities traditional message brokers can't match, like message replay, multiple independent consumers, and durable event storage.
This chapter covers the practical knowledge you need to discuss Kafka confidently. We'll explore producer configurations, consumer group mechanics, partitioning strategies, replication, and common patterns like event sourcing and exactly-once processing.