Imagine you’re working on a high-traffic e-commerce application where millions of orders are placed every minute, triggering real-time notifications, and your system needs to process these millions of notifications in real time.
This is where Kafka Consumer Groups come in. By running multiple consumers into a group, Kafka enables you to distribute the processing load across different consumers, allowing you to scale seamlessly to handle millions of messages per minute. In this blog, we’ll explore how consumer groups work in Kafka and how they help you manage large-scale data streams with ease.
What is a Kafka Consumer Group?
A consumer group in Kafka is a group of one or more consumer instances that work together to consume messages from Kafka topics. These consumers need not be running on the same machine. And these consumers need not be connected to each other (they could be deployed on machines that are completely disconnected from machines where other consumers are deployed).
Each consumer in the group is assigned a subset of partitions, ensuring that each message is processed by only one consumer from the group. This enables horizontal scalability, as the incoming messages/events are evenly distributed across multiple consumers.
For example consider a topic with 4 partitions. This topic can be consumed by a group of up to 4 consumers.
- If the group has fewer consumers than partitions, some consumers will handle multiple partitions.
- If there are more consumers than partitions, some consumers will remain idle.
Few more things to note about consumer groups –
- A consumer can be a part of only 1 consumer group. In other words, a consumer cannot belong to more than 1 consumer groups.
- Multiple consumer groups can independently consume data from same topic without affecting each other.
- Adding consumers to consumer group is easy – just ensure that all consumers have the same
group.idproperty. For a step-by-step guide on creating Kafka consumer application, refer my other blog here – How to Build Your First Kafka Consumer: A Step-by-Step Tutorial. - Consumers running as part of a consumer group could be deployed anywhere, as long as they are able to reach Kafka brokers.
- Consumers running as part of a consumer group need not talk to other consumers in the same group.
Key Benefits of Using Consumer Groups
- Load Balancing – Kafka distributes partitions among the consumers in a group, ensuring that no single consumer is overloaded.
- Fault Tolerance – If a consumer in the group crashes or disconnects, Kafka automatically reassigns its partitions to the remaining consumers.
- Scalability – Adding more consumers to a group increases the processing capacity of the consumers, as the load is shared among more instances.
- Exclusive Message Processing – Each message is processed only once by only one consumer within the group, eliminating duplication.
Consumer Group Example
Let’s break down how consumer groups work with an example:
- Topic:
orders - Partitions: 4
- Consumer Group:
order-processing-group
Scenario 1: Two Consumers
Each consumer will be assigned two partitions:
- Consumer 1 → Partition 0, Partition 1
- Consumer 2 → Partition 2, Partition 3
Scenario 2: Adding a Third Consumer
When a new consumer joins, Kafka rebalances the group:
- Consumer 1 → Partition 0
- Consumer 2 → Partition 1
- Consumer 3 → Partition 2, Partition 3
Scenario 3: A Consumer Fails
If Consumer 2 crashes, its partitions are reassigned:
- Consumer 1 → Partition 0, Partition 1
- Consumer 3 → Partition 2, Partition 3
Conclusion
Kafka consumer groups provide a way to distribute message processing workload across multiple consumers, enabling scalability and fault tolerance. However the number of consumers in the consumer group is limited by the number of partitions in the topic. For a comprehensive guide on topics, partitions and offsets, check out my other blog here – Kafka Essentials: How Topics, Partitions, and Offsets Work Together.
