In Kafka, multiple consumer groups can simultaneously process data from the same topic, but their data processing is independent of each other. Each consumer group can have one or more consumer instances that work together to consume data from the topic. This design enables horizontal scalability and fault tolerance. I will explain this process in detail with examples.
Consumer Groups and Partitions Relationship
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Partition Assignment:
- Kafka topics are partitioned into multiple partitions, enabling data to be distributed across brokers and processed in parallel.
- Each consumer group is responsible for consuming all data from the topic, while partitions represent logical divisions of this data.
- Consumer groups in Kafka automatically assign partitions to consumer instances, even when the number of partitions exceeds the number of consumer instances, allowing each consumer instance to handle multiple partitions.
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Independence of Multiple Consumer Groups:
- Each consumer group independently maintains an offset to track its progress, enabling different consumer groups to be at distinct read positions within the topic.
- This mechanism allows different applications or services to consume the same data stream independently without interference.
Example Illustration
Assume an e-commerce platform where order information is stored in a Kafka topic named orders with 5 partitions. Now, there are two consumer groups:
- Consumer Group A: Responsible for real-time calculation of order totals.
- Consumer Group B: Responsible for processing order data to generate shipping notifications.
Although both groups subscribe to the same topic orders, they operate independently as distinct consumer groups, allowing them to process the same data stream without interference:
- Group A can have 3 consumer instances, each handling a portion of the partitions.
- Group B can have 2 consumer instances, which will evenly distribute the 5 partitions according to the partition assignment algorithm.
In this way, each group can independently process data based on its business logic and processing speed without interference.
Conclusion
By using different consumer groups to process different partitions of the same topic, Kafka supports robust parallel data processing capabilities and high application flexibility. Each consumer group can independently consume data according to its processing speed and business requirements, which is essential for building highly available and scalable real-time data processing systems.