Retention Policies

Introduction

What is Retention Policies? Retention controls how long Kafka keeps records by time, size, or compaction.

Understanding the topic

What happens — Retention Policies:

• Plan partitions and replication factor.
• Configure retention and compaction.
• Monitor ISR, lag, disk.
• Scale brokers and partitions with load.

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 retention-policies --partitions 6 --replication-factor 3
kafka-console-producer --bootstrap-server localhost:9092 --topic retention-policies
kafka-console-consumer --bootstrap-server localhost:9092 --topic retention-policies --from-beginning
kafka-consumer-groups --bootstrap-server localhost:9092 --describe --group retention-policies-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

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