Consumer Lag

Introduction

What is Consumer Lag? Consumer lag is the gap between latest offsets and committed consumer offsets.

Understanding the topic

What happens — Consumer Lag:

• Identify topic, partition, offset scope.
• Check broker health and ISR.
• Inspect consumer errors and deploys.
• Remediate: scale, DLT, replay, or fix schema.

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

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