KLogic
📊 Monitoring Guide

Kafka Monitoring Fundamentals

Master the essential principles of Apache Kafka monitoring with comprehensive coverage of key metrics, alerting strategies, and observability best practices.

Published: August 3, 2025 • 20 min read • Fundamentals Guide

Why Kafka Monitoring is Critical

Apache Kafka powers mission-critical data pipelines in modern organizations. Without proper monitoring, issues can cascade quickly, causing data loss, performance degradation, and business impact.

Business Impact

Kafka outages can cost enterprises $100K-$1M per hour in lost revenue and productivity

MTTR Reduction

Proper monitoring reduces mean time to resolution from hours to minutes

Proactive Prevention

Identify and resolve issues before they impact production systems

The Four Pillars of Kafka Monitoring

Essential monitoring domains for comprehensive Kafka observability

Broker Health

Monitor broker availability, resource utilization, and cluster stability to ensure your Kafka infrastructure remains healthy and performant.

Key Metrics

CPU Utilization< 80%
Memory Usage< 85%
Disk Usage< 70%
Network I/OMonitor

Producer Performance

Track producer throughput, latency, and error rates to ensure data ingestion meets business requirements and SLA commitments.

Key Metrics

Records/secThroughput
Batch SizeEfficiency
Request Latency< 100ms
Error Rate< 0.1%

Consumer Monitoring

Monitor consumer group health, lag, and processing rates to ensure downstream applications receive data in a timely and reliable manner.

Key Metrics

Consumer Lag< 1000 msgs
Processing RateMonitor
RebalancingTrack Freq
Commit Latency< 50ms

Topic Management

Track topic-level metrics including partition distribution, replication status, and storage usage to optimize data organization and performance.

Key Metrics

Partition CountBalanced
Replication Factor≥ 3
Size per Partition< 25GB
Under-replicated0 partitions

Essential Kafka Metrics to Monitor

Critical metrics that provide insight into Kafka cluster health and performance

Broker-Level Metrics

UnderReplicatedPartitions

Number of partitions that don't have enough replicas. Should always be 0.

Alert: > 0 partitions

ActiveControllerCount

Number of active controllers. Exactly one broker should be the controller.

Expected: 1 per cluster

OfflinePartitionsCount

Number of partitions without an active leader. Critical metric for availability.

Alert: > 0 partitions

Broker Health Dashboard

Under-replicated Partitions0
Active Controller1
Offline Partitions0
ISR Shrinks/sec0.2

Producer Performance

Record Send Rate15.2K
Avg Request Latency45ms
Record Error Rate0.02%
Record Retry Rate0.8/sec

Producer Metrics

record-send-rate

Average number of records sent per second. Key throughput indicator.

Monitor: Baseline trends

request-latency-avg

Average request latency in milliseconds. Impacts end-to-end processing time.

Target: < 100ms

record-error-rate

Rate of failed record sends. High error rates indicate system issues.

Alert: > 1%

Kafka Alerting Strategy

Build effective alerting that catches issues without alert fatigue

Critical Alerts

Immediate response required. Page on-call engineers for cluster-wide impact.

Offline partitions > 0
Under-replicated partitions > 10
Broker down > 5 minutes

Warning Alerts

Attention needed within business hours. May indicate developing issues.

Consumer lag > 10K messages
Disk usage > 70%
Producer error rate > 1%

Informational

Track trends and patterns. Send to dashboards and logging systems.

Throughput changes > 50%
New topics created
Rebalancing events

Kafka Monitoring Tool Categories

Understanding different approaches to Kafka monitoring

JMX-Based Monitoring

Traditional approach using JMX metrics exposed by Kafka brokers. Requires custom configuration and metric collection setup.

Complete metric coverage
Real-time data access
Requires custom dashboards
Complex alert setup
# Example JMX query
kafka.server:type=BrokerTopicMetrics,name=MessagesInPerSec

Specialized Kafka Platforms

Purpose-built monitoring solutions designed specifically for Kafka environments with pre-configured dashboards and intelligent alerting.

Pre-built Kafka dashboards
Intelligent alerting rules
Kafka-specific insights
Minimal setup required
KLogic Benefits: AI-powered insights, proactive alerting, comprehensive topology visualization

Kafka Monitoring Best Practices

Proven strategies for effective Kafka monitoring

1. Start with Golden Signals

Focus on the four golden signals of monitoring: latency, traffic, errors, and saturation. These provide the foundation for understanding system health.

Latency
Traffic
Errors
Saturation

2. Monitor at Multiple Levels

Implement monitoring at cluster, broker, topic, and application levels for comprehensive visibility.

1

Cluster Health

Overall cluster status, controller election, partition distribution

2

Broker Performance

CPU, memory, disk, network utilization per broker

3

Topic Metrics

Per-topic throughput, partition sizes, replication status

4

Application Level

Producer/consumer performance, processing latency, business metrics

3. Implement Proactive Alerting

Set up alerts that catch issues before they impact users. Use baseline-based alerts and trend analysis for early detection.

Reactive Alerts

Partition offline
Alert after the problem occurs

Proactive Alerts

ISR shrink rate increasing
Alert before partition becomes offline

Master Kafka Monitoring Today

Put these monitoring fundamentals into practice with KLogic's intelligent Kafka monitoring platform. Get started with pre-configured dashboards and AI-powered insights.

Free 14-day trial • Pre-built dashboards • Intelligent alerting • AI-powered insights