Consumer Offsets and Delivery Semantics

The video tutorial explains consumer offsets in Kafka, detailing how they are stored and committed. It introduces delivery semantics, including at most once, at least once, and exactly once, and discusses their implications. The tutorial also covers the concept of item potent consumers, especially in the context of exactly once semantics, and highlights the importance of choosing the right delivery semantics for different workflows.

What is the primary purpose of committing consumer offsets in Kafka?

• To improve the security of the data

• To ensure data is stored in multiple locations

• To allow consumers to resume reading from the last processed message after a failure

• To increase the speed of data processing

In the given example, what happens when a consumer commits its offset at 4262?

• The consumer can resume reading from offset 4262 after a failure

• The consumer stops processing data

• The consumer starts reading from the beginning of the topic

• The consumer deletes all previous data

Which delivery semantic ensures that a message is processed at least once?

• Exactly once

• At least once

• None of the above

• At most once

What is a key requirement for a processing system to handle 'at least once' delivery semantics effectively?

• The system must be secure

• The system must be distributed

• The system must be idempotent

• The system must be fast

Which delivery semantic is considered the 'Holy Grail' and can be achieved using Kafka Streams API?

• At most once

• At least once

• None of the above

• Exactly once