You’re building a real-time chat app, and the architect suggests using Apache Kafka. How would you explain Kafka’s role and how it helps in this case?

If we’re building a real-time chat application and the architect suggests using Apache Kafka, I would explain that Kafka acts as a high-throughput, low-latency messaging system that helps manage the flow of messages between users and services in a scalable and fault-tolerant way.

In this case, Kafka would be used as a message broker. Every time a user sends a message, it gets published to a Kafka topic. Consumers — which could be chat services, storage layers, analytics engines, or notification services — can then subscribe to those topics and process the messages in real time.

This architecture has several advantages:

• Decoupling: The sender and receiver don’t need to be connected directly. Kafka sits in the middle and handles message delivery reliably.

  
  

• Scalability: As the number of users grows, Kafka can handle millions of messages per second by distributing load across partitions and brokers.

  
  

• Durability: Messages can be stored for a configurable retention period, which means they can be replayed or reprocessed if needed.

  
  

• Fault Tolerance: Even if parts of the system go down, Kafka ensures messages aren’t lost, which is critical for real-time apps.

  
  

So in a real-time chat app, Kafka ensures that messages are reliably transmitted, even under heavy load, and supports adding new features like message archiving, moderation, or analytics without impacting the core messaging flow.