Metrics

Introduction

What is Metrics? Kafka exposes JMX metrics.

Understanding the topic

What happens — Metrics:

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

Visual explanation

Pipeline view:

Topic partitions
  ↓
replicas across brokers
  ↓
ISR health
  ↓
disk + network + controller
  ↓
SLO dashboards

Step-by-step explanation

1. Create/configure — kafka-topics or API.
2. Replicate — Followers stay in ISR.
3. Observe — Metrics and alerts.
4. Scale — Brokers/partitions/consumers.

Informative example

Example:

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

Execution workflow

Best practices

• Use replication factor 3 for critical topics.
• Set min.insync.replicas with acks=all.
• Alert on under-replicated partitions, offline partitions, disk usage, and lag.
• Document topic ownership and cleanup policy.

Common mistakes

• Changing partitions without understanding ordering impact.
• Ignoring hot partitions caused by skewed keys.
• Letting retention grow until disks become the outage.

Summary

Metrics — Operate Kafka through SLOs: durability, availability, latency, lag, disk headroom, and recovery time.