Apache Kafka Practice

The purpose of the 'Scaling Consumers' approach in Kafka is to distribute the workload by increasing the number of consumer instances. This is essential for efficiently handling large volumes of data and ensuring that messages are processed in a timely manner. By adding more consumer instances, you can parallelize the processing of messages, which leads to better throughput and lower latency.

Kafka topics are divided into partitions, and each consumer in a consumer group reads from one or more of these partitions. If you have more consumer instances than partitions, some consumers will remain idle. Therefore, scaling consumers enables optimal resource utilization and makes it possible for the system to process messages more effectively under heavy loads.

Other options do not align with the concept of scaling consumers. Reducing the number of consumers would lead to heavier loads on fewer instances, which is counterproductive. Improving data compression pertains to optimizing storage and bandwidth rather than scaling consumer instances. Enhancing message retention focuses on how long messages are stored in Kafka, which is unrelated to the workload distribution among consumer instances.