Monitoring and Metrics in Kafka

Monitoring and metrics are crucial for maintaining a healthy and performant Kafka cluster. Kafka provides a rich set of metrics that allow you to gain insights into the state and performance of your cluster, brokers, topics, and clients. In this article, we’ll explore the key aspects of monitoring and metrics in Kafka.

JMX Metrics

Kafka exposes a wide range of metrics through Java Management Extensions (JMX). These metrics provide detailed information about various components of the Kafka system. Some important JMX metrics include:

  1. Broker Metrics: Metrics related to the Kafka broker, such as request rates, request latencies, network throughput, and disk usage.

  2. Topic Metrics: Metrics specific to Kafka topics, such as message production rates, consumption rates, and lag.

  3. Consumer Group Metrics: Metrics related to consumer groups, including consumer lag, commit rates, and offset management.

  4. Producer Metrics: Metrics specific to Kafka producers, such as record send rates, request latencies, and batch sizes.

  5. Connector Metrics: Metrics related to Kafka Connect connectors, including task status, offset commit frequencies, and error rates.

To access JMX metrics, you can use tools like JConsole or JMX exporters that integrate with monitoring systems like Prometheus.

Kafka Metrics API

Kafka also provides a Metrics API that allows you to programmatically access and report metrics from your Kafka applications. The Metrics API enables you to instrument your producers, consumers, and streams applications to collect custom metrics.

Here’s an example of using the Metrics API in a Kafka producer:

public class MetricsProducer {
    private static final String TOPIC = "my-topic";
    private static final String BOOTSTRAP_SERVERS = "localhost:9092";

    public static void main(String[] args) {
        Properties props = new Properties();
        props.put(ProducerConfig.CLIENT_ID_CONFIG, "MetricsProducer");
        props.put(ProducerConfig.METRIC_REPORTER_CLASSES_CONFIG, "org.apache.kafka.common.metrics.JmxReporter");

        KafkaProducer<String, String> producer = new KafkaProducer<>(props);

        // Create a custom metric
        Metric customMetric = new Metric("custom_metric", "Custom Metric", "messages");

        // Send records and update the custom metric
        for (int i = 0; i < 10; i++) {
            producer.send(new ProducerRecord<>(TOPIC, "key-" + i, "value-" + i));


In this example, we create a custom metric using the Metrics API and increment it each time a record is sent. The custom metric can be accessed through JMX or other monitoring systems.

Monitoring Tools

There are several popular monitoring tools that can be used with Kafka:

  1. Kafka Manager: A web-based tool for managing and monitoring Kafka clusters. It provides a user-friendly interface for viewing cluster metrics, managing topics, and performing administrative tasks.

  2. Prometheus: A powerful monitoring system and time series database. Prometheus can scrape Kafka metrics exposed through JMX exporters or the Kafka Metrics API.

  3. Grafana: A data visualization platform that integrates well with Prometheus. Grafana allows you to create interactive dashboards to visualize Kafka metrics and monitor the health and performance of your cluster.

  4. Datadog: A cloud-based monitoring and analytics platform that provides Kafka monitoring capabilities. Datadog offers pre-built dashboards and integrations for monitoring Kafka metrics, logs, and events.

These tools provide comprehensive monitoring capabilities, allowing you to track key metrics, set up alerts, and gain visibility into the performance and health of your Kafka cluster.

Best Practices

Here are some best practices for monitoring and metrics in Kafka:

  1. Monitor key metrics: Focus on monitoring key metrics such as broker health, topic performance, consumer group lag, and producer/consumer throughput. These metrics provide insights into the overall health and performance of your Kafka system.

  2. Set up alerts: Define alerts for critical metrics and thresholds. Alerts help you proactively identify and address issues before they impact your system’s performance or availability.

  3. Use dashboards: Create dashboards to visualize Kafka metrics and provide a centralized view of your cluster’s health and performance. Dashboards make it easier to spot trends, anomalies, and potential issues.

  4. Monitor client metrics: In addition to monitoring the Kafka cluster itself, it’s important to monitor metrics from Kafka clients (producers, consumers, and streams applications) to ensure they are performing optimally and not experiencing any issues.

  5. Regularly review and optimize: Regularly review your Kafka metrics and monitoring setup to identify areas for improvement. Fine-tune your monitoring configuration, update dashboards, and optimize alert thresholds based on your specific requirements and usage patterns.