Rising consumer lag is a signal to investigate. Start with a clear picture of the affected workload before changing capacity or configuration.
1. Define the business impact
Identify the consumer group, affected topics and the business process waiting on those events. Record when the increase began, whether it is continuing, and any recent deployment or traffic change. A delayed reporting job and a delayed customer transaction may need different responses.
2. Capture a read-only baseline
The Kafka 4.1 consumer-group tool can describe committed offsets, log-end offsets and lag by partition. The example below targets a local development broker and an example group. Adapt the connection and authentication settings for your environment; use the CLI help shipped with your Kafka distribution.
bin/kafka-consumer-groups.sh \
--bootstrap-server localhost:9092 \
--describe --group my-groupCompare several observations over time. Look for one partition diverging from the others, absent members, or changes in assignment. The same tool offers --members --verbose and --state with --describe for additional context.
3. Build an evidence timeline
Bring together consumer logs, deployment times, input traffic, processing latency and downstream service health. Ask which observation changed first. Avoid treating a single dashboard value as a complete diagnosis.
4. Make one measurable change
Write down the working hypothesis, the expected effect, the owner and the rollback plan. Test the smallest appropriate change and compare the result with your baseline. Keep the incident notes useful enough that the next engineer can follow the reasoning.
5. Close with an operational improvement
Capture the cause, the recovery action and the missing signal or runbook step. Turn the investigation into a concrete follow-up: a clearer alert, a deployment check, a capacity review or a focused team exercise.
Reference: Apache Kafka 4.1 — managing consumer groups. The command is an inspection example, not a remediation or offset-reset procedure.
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