Understanding Consumer Groups, Partition Assignments, and Rebalances in Kafka

In Kafka, consumer groups play a crucial role in achieving scalability and fault tolerance when consuming messages from topics. In this article, we’ll dive into the concepts of consumer groups, partition assignments, and rebalances in Kafka.

Consumer Groups

A consumer group is a logical grouping of one or more consumers that work together to consume messages from one or more Kafka topics. Consumers within a group coordinate with each other to distribute the workload and ensure that each message is processed exactly once by a single consumer in the group.

Key points about consumer groups:

  • Consumers in a group are identified by a unique group.id.
  • Each consumer in a group is assigned a subset of partitions to consume from.
  • Consumers within a group can horizontally scale by adding or removing instances.

Partition Assignments

Partition assignment is the process of distributing the partitions of a topic among the consumers in a consumer group. Kafka provides different partition assignment strategies:

  1. Range Assignor: The range assignor divides the partitions evenly among the consumers in the group based on their numerical order. For example, if there are 10 partitions and 3 consumers, the first consumer gets partitions 0-2, the second gets 3-5, and the third gets 6-9.

  2. RoundRobin Assignor: The round-robin assignor assigns partitions to consumers in a circular manner. It distributes the partitions evenly across the consumers, ensuring a balanced workload distribution.

  3. StickyAssignor: The sticky assignor aims to minimize partition movement during rebalances while still ensuring a fair distribution. It assigns partitions to consumers based on their previous assignments, if possible, to reduce the number of partition reassignments.

The partition assignment strategy can be configured using the partition.assignment.strategy property in the consumer configuration.


Rebalancing is the process of redistributing the partition assignments among the consumers in a consumer group when there is a change in the group membership. Rebalances occur in the following scenarios:

  1. A new consumer joins the group.
  2. An existing consumer leaves the group (either voluntarily or due to a failure).
  3. New partitions are added to an existing topic.

During a rebalance, the following steps occur:

  1. The group coordinator (a Kafka broker) receives the rebalance request.
  2. The coordinator selects a leader consumer from the group to perform the rebalance.
  3. The leader consumer fetches the current partition assignments and the list of active consumers.
  4. The leader consumer executes the partition assignment strategy to generate a new partition assignment plan.
  5. The leader consumer sends the new partition assignments to the group coordinator.
  6. The coordinator distributes the new assignments to all the consumers in the group.
  7. Each consumer starts consuming from its newly assigned partitions.

Rebalances can have an impact on the performance of the consumers, as they need to revoke their current assignments, wait for the new assignments, and potentially seek to new offsets in the partitions.

Rebalance Listener

Kafka provides a rebalance listener API that allows consumers to execute custom logic before and after a rebalance. The rebalance listener is useful for performing tasks such as:

  • Committing offsets before a rebalance to ensure data consistency.
  • Cleaning up any resources or state associated with the revoked partitions.
  • Initializing any resources or state required for the newly assigned partitions.

Here’s an example of using a rebalance listener in a Kafka consumer:

private class RebalanceHandler implements ConsumerRebalanceListener {
    public void onPartitionsRevoked(Collection<TopicPartition> partitions) {
        // Perform cleanup tasks before partitions are revoked

    public void onPartitionsAssigned(Collection<TopicPartition> partitions) {
        // Perform initialization tasks for newly assigned partitions

// ...

consumer.subscribe(topics, new RebalanceHandler());

Best Practices

  1. Choose the right partition assignment strategy: Consider the characteristics of your consumer group and the desired workload distribution when selecting the partition assignment strategy.

  2. Handle rebalances gracefully: Use the rebalance listener to perform necessary cleanup and initialization tasks during rebalances. Ensure that your consumers can handle rebalances without data loss or inconsistencies.

  3. Monitor consumer group performance: Keep track of consumer group metrics such as consumer lag, partition assignments, and rebalance frequencies. Use tools like Kafka Manager or Confluent Control Center to monitor the health and performance of your consumer groups.

  4. Scale consumers responsibly: When scaling the number of consumers in a group, consider the impact on rebalances and the overall performance of the group. Avoid excessive consumer churn, which can lead to frequent rebalances and reduced throughput.

  5. Commit offsets regularly: Regularly commit offsets to ensure that the consumers’ progress is persisted and to minimize the impact of rebalances. Use the enable.auto.commit property or manual offset commits based on your application’s requirements.