Interview Questions and Answers

As the main role of the Leader is to perform the task of all read and write requests for the partition, whereas Followers passively replicate the leader. Hence, at the time of Leader failing, one of the Followers takeover the role of the Leader. Basically, this entire process ensures load balancing of the servers.

Because of Replication, we can be sure that published messages are not lost and can be consumed in the event of any machine error, program error or frequent software upgrades.

Simply, it implies that the Follower cannot fetch data as fast as data accumulated by the Leader.

Whenever the Kafka Producer attempts to send messages at a pace that the Broker cannot handle at that time QueueFullException typically occurs. However, to collaboratively handle the increased load, users will need to add enough brokers, since the Producer doesn’t block.

However, retention period retains all the published records within the Kafka cluster. It doesn’t check whether they have been consumed or not. Moreover, the records can be discarded by using a configuration setting for the retention period. And, it results as it can free up some space.

For our cluster, Kafka MirrorMaker offers geo-replication. Basically, messages are replicated across multiple data centers or cloud regions, with MirrorMaker. So, it can be used in active/passive scenarios for backup and recovery; or also to place data closer to our users, or support data locality requirements.

We can easily deploy Kafka as a multi-tenant solution. However, by configuring which topics can produce or consume data, Multi-tenancy is enabled. Also, it provides operations support for quotas.