Apache Kafka - Achieving Exactly-Once Semantics (EOS)

In distributed systems, message delivery guarantees are one of the most critical design considerations. Every pipeline must answer a deceptively simple question: how many times will this message be processed?

Traditionally, systems have offered two guarantees:

• • At-most-once delivery: Messages are processed zero or one time. This avoids duplicates but risks data loss if failures occur.
• • At-least-once delivery: Messages are processed one or more times. This ensures reliability but introduces duplicates that downstream systems must handle.

In domains like payments, fraud detection, and compliance auditing, duplicates or lost events can have severe consequences.

Apache Kafka - The Architecture to Handle Billions of Events Per Day

The modern digital world operates on an unprecedented volume of data. Every click, transaction, sensor reading, and user interaction is an event, and the systems designed to process them must handle this stream in real-time, reliably, and at massive scale.

The Scale Challenge

• • LinkedIn, Kafka’s creator, processes over 7 trillion messages per day.
• • Uber handles trillions of messages and multiple petabytes of data daily, from calculating ETAs to driver-rider matching.
• • FinTech companies handle over a billion events every day to process transactions and real-time monitoring. In this deep dive, we will move past the basic definitions of Kafka and explore the specific architectural patterns and configuration tuning strategies that allow Kafka to manage billions of events per day.

The Three Pillars of Kafka Scalability

Kafka’s remarkable scaling ability is rooted in a design philosophy that treats data not as records in a database, but as an immutable, distributed commit log. This simple, yet powerful, concept allows it to sustain immense throughput.