Apache Kafka Practice

When a new broker joins a Kafka cluster, the usual behavior involves reconfiguring the cluster to redistribute data and load among all the brokers. This process is important for maintaining a balanced system as it ensures that the workload is evenly spread out, which can enhance performance and fault tolerance.

Kafka utilizes partitions for topics, and each partition can have a leader broker responsible for handling all reads and writes for that partition, as well as replicas for redundancy. When a new broker arrives, the cluster leader will often initiate reassignment of partitions, which may involve moving some partitions to the new broker to optimize the cluster's resource utilization. This reassignment is generally done based on a rebalance strategy that considers how much data the brokers are currently handling.

The other choices do not accurately describe the typical behavior within Kafka. For instance, a new broker does not immediately take on the role of a leader; leadership is determined based on the current partition assignments and replication factors existing in the cluster. Additionally, existing topics are preserved rather than deleted when a new broker joins. The cluster does not halt during this process; instead, it continues to serve requests while rebalancing occurs in the background.