Apache Kafka - Real-time Stream Processing (Master Class) - Kafka Work Distribution Architecture - Part 2

The video tutorial explains the roles of leader and follower brokers in a Kafka cluster. It covers how brokers manage leader and follower partitions, the responsibilities of leaders in handling producer and consumer requests, and the role of followers in replicating messages. The concept of the ISR list is introduced, detailing how it helps maintain synchronization between leaders and followers. The video also discusses handling leader failures, message commitment, and the importance of setting a minimum number of in-sync replicas to ensure data consistency.

What are the two types of partitions managed by a Kafka broker?

• Producer and Consumer

• Leader and Follower

• Primary and Secondary

• Master and Slave

What is the primary responsibility of a leader broker in Kafka?

• To handle producer and consumer requests

• To backup data

• To manage network traffic

• To store metadata

How do follower brokers maintain synchronization with the leader?

• By managing consumer requests

• By storing data locally

• By copying messages from the leader

• By sending periodic updates

What is the ISR list in Kafka?

• A list of all brokers

• A list of in-sync replicas

• A list of consumer groups

• A list of producer requests

What does the term 'not too far' refer to in Kafka synchronization?

• The distance between data centers

• The time margin for followers to sync

• The number of messages in a queue

• The size of the ISR list

Why might a follower be removed from the ISR list?

• It is more than 10 seconds behind

• It has too many messages

• It is handling consumer requests

• It is a leader broker

What happens to uncommitted messages if a leader broker fails?

• They are lost permanently

• They are resent by the producer

• They are ignored

• They are stored in a backup

How can Kafka ensure data is not lost when a leader fails?

• By using committed messages

• By increasing network bandwidth

• By storing data in the cloud

• By reducing the number of replicas

What is the purpose of setting a minimum number of in-sync replicas?

• To ensure data is written to multiple replicas

• To increase data redundancy

• To reduce storage costs

• To speed up data processing

What is the consequence of having fewer in-sync replicas than the minimum required?

• The broker will accept more messages

• The leader becomes read-only

• The system crashes

• Data is automatically deleted