Overview

This repo demonstrates examples of JMX monitoring stacks that can monitor Confluent Cloud and Confluent Platform. While Confluent Cloud UI and Confluent Control Center provides an opinionated view of Apache Kafka monitoring, JMX monitoring stacks serve a larger purpose to our users, allowing them to setup monitoring across multiple parts of their organization, many outside of Kafka, and to have a single pane of glass.

Repo provides metrics and dashboards for:

Caution

The examples in this repo may not be complete. They serve only to demonstrate how the integration works with Confluent Cloud and Confluent Platform.

How to use with Confluent Cloud

The demo with Confluent Cloud needs a Confluent Cloud cluster and you (as a user) are required to gather some details before spinning up the Confluent Cloud monitoring solution. Please refer to this README for detailed steps to run a Confluent Cloud based sample dashboard.

How to use with Confluent cp-ansible

To add JMX exporter configurations from this project into Confluent cp-ansible add the following configurations:

    zookeeper_jmxexporter_config_source_path: ../jmx-monitoring-stacks/shared-assets/jmx-exporter/zookeeper.yml
    kafka_broker_jmxexporter_config_source_path: ../jmx-monitoring-stacks/shared-assets/jmx-exporter/kafka_broker.yml
    schema_registry_jmxexporter_config_source_path: ../jmx-monitoring-stacks/shared-assets/jmx-exporter/confluent_schemaregistry.yml
    kafka_connect_jmxexporter_config_source_path: ../jmx-monitoring-stacks/shared-assets/jmx-exporter/kafka_connect.yml
    kafka_rest_jmxexporter_config_source_path: ../jmx-monitoring-stacks/shared-assets/jmx-exporter/confluent_rest.yml
    ksql_jmxexporter_config_source_path: ../jmx-monitoring-stacks/shared-assets/jmx-exporter/confluent_ksql.yml

Add and execute the Ansible template task here to generate the Prometheus configuration for your Ansible inventory.

How to use with Kubernetes and Confluent for Kubernetes Operator (CFK)

To add JMX exporter configurations to your Kubernetes workspace, please refer to this README

How to use with cp-demo

This repo is intended to work smoothly with Confluent cp-demo.

Make sure you have enough system resources on the local host to run this. Verify in the advanced Docker preferences settings that the memory available to Docker is at least 8 GB (default is 2 GB).

NOTE: jq is required to be installed on your machine to run the demo.

NOTE: If there is interest to test Kafka Lag Exporter (included on the monitoring stacks) make sure to use JDK 8 when running the demo, as it requires JDK8-generated certificates for the container to work (seglo/kafka-lag-exporter#270).

Ensure that cp-demo is not already running on the local host.
Decide which monitoring stack to demo: either jmxexporter-prometheus-grafana, metricbeat-elastic-kibana, or jmxexporter-newrelic and set the MONITORING_STACK variable accordingly.

CP_DEMO_VERSION=7.5.0-post

# Set one of these
MONITORING_STACK=jmxexporter-prometheus-grafana
MONITORING_STACK=metricbeat-elastic-kibana
MONITORING_STACK=jmxexporter-newrelic

Clone cp-demo and checkout a branch (tested branches are from 7.2.0-post).

[[ -d "cp-demo" ]] || git clone https://github.com/confluentinc/cp-demo.git
(cd cp-demo && git fetch && git checkout $CP_DEMO_VERSION && git pull)

Clone jmx-monitoring-stacks and checkout main branch.

[[ -d "jmx-monitoring-stacks" ]] || git clone https://github.com/confluentinc/jmx-monitoring-stacks.git
(cd jmx-monitoring-stacks && git fetch && git checkout main && git pull)

Start the monitoring solution with the STACK selected. This command also starts cp-demo, you do not need to start cp-demo separately.

${MONITORING_STACK}/start.sh

NOTE: New Relic requires a License Key to be added in ${MONITORING_STACK}/start.sh

Stop the monitoring solution. This command also stops cp-demo, you do not need to stop cp-demo separately.

${MONITORING_STACK}/stop.sh

How to use with Apache Kafka client applications (producers, consumers, kafka streams applications)

For an example that showcases how to monitor Apache Kafka client applications, and steps through various failure scenarios to see how they are reflected in the provided metrics, see the Observability for Apache Kafka® Clients to Confluent Cloud tutorial.