Apache Kafka Practice Exam 2025 – The Complete All-in-One Guide for Exam Success!

Increasing the number of partitions for a Kafka topic enhances throughput by enabling more parallel processing of messages. Each partition in Kafka can be treated as an independent log where messages are stored in the order they are received. When a topic has multiple partitions, it allows more consumers to process messages concurrently, as each consumer can be assigned to a distinct partition. This parallelism significantly boosts the system's ability to handle large volumes of data more efficiently.

While fewer partitions may lead to simpler data management and the potential for coordinated processing, having many partitions primarily drives performance improvements. It is important to note that while increasing partitions can improve throughput, it can also introduce complexity regarding message ordering because messages sent to different partitions are processed independently. Thus, partitioning needs to be thoughtfully designed to align with the application’s requirements for message ordering and consumption.