Conquer Apache Kafka 2025 – Dive into Data Streaming Dominance!

The placement of replicas in Kafka is designed to ensure high availability and fault tolerance for the data being processed. By default, each replica for a partition is placed on separate brokers. This means that if one broker fails, there are other brokers that have copies of the same data, allowing the system to continue functioning without data loss or downtime.

This approach also promotes data redundancy, which is crucial for maintaining the reliability of the messaging system. With each replica stored on different brokers, it mitigates the risk of data loss due to hardware failures, network issues, or other unforeseen problems that may affect an individual broker.

In contrast, placing all replicas on the same broker would create a single point of failure, compromising the system's robustness. Randomly distributing replicas among brokers does not necessarily guarantee that critical partitions are adequately protected against failures. Additionally, utilizing broker load to assign replicas may not effectively focus on ensuring high availability if certain brokers become overloaded, which could also lead to performance bottlenecks.

Thus, the practice of placing each replica on separate brokers is foundational to Kafka’s design philosophy of achieving resilience, performance, and scalability in distributed data systems.