Rebalancing

Introduction

What is Rebalancing? Rebalancing redistributes partitions when consumers join, leave, fail heartbeats, or topic partitions change.

Understanding the topic

What happens — Rebalancing:

• Group membership changes trigger rebalance.
• Partitions revoked then reassigned.
• Processing pauses during rebalance.
• Cooperative sticky reduces stop-the-world.

Visual explanation

Pipeline view:

Consumer polls records from topic partitions
  ↓
Process business logic (DB, API, etc.)
  ↓
Commit offset after success
  ↓
Monitor consumer lag

Step-by-step explanation

1. Subscribe — Consumer joins a group and receives partition assignments.
2. Poll — Fetch records in batches with consumer.poll().
3. Process — Run business logic for each record.
4. Commit — Save offset after successful side effects.
5. Repeat — Continue polling; rebalance if group membership changes.

Informative example

Example:

kafka-topics --bootstrap-server localhost:9092 --create --topic rebalancing --partitions 6 --replication-factor 3
kafka-console-producer --bootstrap-server localhost:9092 --topic rebalancing
kafka-console-consumer --bootstrap-server localhost:9092 --topic rebalancing --from-beginning
kafka-consumer-groups --bootstrap-server localhost:9092 --describe --group rebalancing-service

Execution workflow

Best practices

• Use manual commits for critical side effects.
• Make consumers idempotent with event IDs or unique constraints.
• Keep processing under max.poll.interval.ms or use pause/resume patterns.
• Send poison messages to DLT with failure metadata.

Common mistakes

• Committing before database writes complete.
• Scaling consumers beyond partition count and expecting more throughput.
• Blocking the poll loop with slow downstream calls.

Summary

Rebalancing — Commit offsets only after successful processing; make handlers idempotent; monitor lag per partition.