Kafka monitoring provides real-time visibility into your Apache Kafka clusters to ensure reliable data streaming and prevent costly downtime in distributed systems. Using a collector-based approach, you get comprehensive monitoring through a flexible, vendor-neutral solution that works across self-hosted environments and Kubernetes with Strimzi.
Collector options
New Relic supports two OpenTelemetry Collector distributions for Kafka monitoring, both offering identical functionality with the same configuration files and monitoring capabilities.
- NRDOT Collector (recommended): New Relic's distribution of OpenTelemetry Collector with New Relic support for assistance. Download from NRDOT Collector releases.
- OpenTelemetry Collector: The upstream community distribution. Download from OpenTelemetry Collector releases.
Choose the collector that best fits your support and operational requirements, then proceed to set up monitoring for your environment.
Monitor your Kafka clusters with comprehensive dashboards showing cluster health, broker status, topic metrics, and consumer group performance.
Why Kafka monitoring?
- Prevent outages - Get alerts for broker failures, under-replicated partitions, and offline topics before they cause downtime
- Optimize performance - Identify consumer lag, slow producers, and network bottlenecks that affect data processing speed
- Plan capacity - Track resource usage, message rates, and connection counts to scale proactively
- Ensure data integrity - Monitor replication health and partition balance to prevent data loss
Common use case
Whether you're streaming financial transactions, processing IoT sensor data, or handling microservices communication, Kafka monitoring helps you catch issues before they impact your business. Get alerted when consumer lag spikes threaten real-time dashboards, when broker failures risk data loss, or when network bottlenecks slow down critical data pipelines. This monitoring is essential for e-commerce platforms, real-time analytics systems, and any application where message delivery delays or failures can affect user experience or business operations.
Get started
Choose your Kafka environment to begin monitoring. Each setup guide includes prerequisites, configuration steps, and troubleshooting tips.
How it works
Kafka monitoring works by deploying a collector alongside your Kafka cluster to continuously gather performance data. The collector uses multiple specialized components to capture comprehensive metrics from different parts of your Kafka infrastructure.
Three components work together to collect your data:
- Kafka metrics receiver - Connects to your Kafka cluster to monitor overall health, including consumer lag, topic metrics, and partition status
- OpenTelemetry Java Agent - Runs alongside each Kafka broker to collect detailed JVM performance metrics and operational data, then sends this information to the collector
- OTLP receiver - Receives and processes the detailed broker metrics from the Java Agent
Data flow:
- OpenTelemetry Java Agent attaches to Kafka brokers as a Java agent via
-javaagentparameter - Java Agent collects JMX metrics from Kafka brokers using custom configuration rules
- Metrics are sent via OTLP (gRPC) protocol to the OpenTelemetry Collector on port 4317
- Kafka metrics receiver collects cluster-level metrics directly from Kafka via the bootstrap port
- Data is processed, enriched, and batched for efficient transmission
- Metrics are exported to New Relic via the OTLP exporter
- New Relic automatically creates entities and populates dashboards
What you get: Key metrics include consumer lag, broker health, request rates, network throughput, partition replication status, resource utilization, and JVM performance data.
For complete metric names, descriptions, and alerting recommendations, see Kafka metrics reference.
Optional: Add application-level monitoring
The monitoring setup above tracks your Kafka cluster health and performance. To get the full picture of how data flows through your system, you can also monitor the applications that send and receive messages from Kafka.
Application monitoring adds:
- Request latencies from your apps to Kafka
- Throughput metrics at the application level
- Error rates and distributed traces
- Complete visibility from producers → brokers → consumers
Quick setup: Use the OpenTelemetry Java Agent for zero-code Kafka instrumentation. For advanced configuration, see the Kafka instrumentation documentation.
Next steps
Ready to start monitoring your Kafka clusters?
Set up monitoring:
- Self-hosted Kafka - Monitor Kafka running on physical or virtual machines
- Kubernetes with Strimzi - Monitor Kafka deployed on Kubernetes
After setup:
- Find and query your data - Navigate New Relic UI and write NRQL queries
- Explore Kafka metrics - Complete metrics reference with alerting recommendations