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Chapter 9. Introducing Metrics

This section describes how to monitor AMQ Streams Kafka, Zookeeper and Kafka Connect clusters using Prometheus to provide monitoring data for example Grafana dashboards.

In order to run the example Grafana dashboards, you must:

  1. Add metrics configuration to your Kafka cluster resource
  2. Deploy Prometheus and Prometheus Alertmanager
  3. Deploy Grafana
Note

The resources referenced in this section are intended as a starting point for setting up monitoring, but they are provided as examples only. If you require further support on configuring and running Prometheus or Grafana in production, try reaching out to their respective communities.

Additional resources

9.1. Example Metrics files

You can find the example metrics configuration files in the examples/metrics directory. 

metrics
├── grafana-install
│   ├── grafana.yaml 1
├── grafana-dashboards 2
│   ├── strimzi-kafka-connect.json
│   ├── strimzi-kafka.json
│   └── strimzi-zookeeper.json
│   └── strimzi-kafka-exporter.json 3
├── kafka-connect-metrics.yaml 4
├── kafka-metrics.yaml 5
├── prometheus-additional-properties
│   └── prometheus-additional.yaml 6
├── prometheus-alertmanager-config
│   └── alert-manager-config.yaml 7
└── prometheus-install
    ├── alert-manager.yaml 8
    ├── prometheus-rules.yaml 9
    ├── prometheus.yaml 10
    └── strimzi-service-monitor.yaml 11
1
Installation file for the Grafana image
2
Grafana dashboard configuration
3
Grafana dashboard configuration specific to Kafka Exporter
4
Metrics configuration that defines Prometheus JMX Exporter relabeling rules for Kafka Connect
5
Metrics configuration that defines Prometheus JMX Exporter relabeling rules for Kafka and Zookeeper
6
Configuration to add roles for service monitoring
7
Hook definitions for sending notifications through Alertmanager
8
Resources for deploying and configuring Alertmanager
9
Alerting rules examples for use with Prometheus Alertmanager (deployed with Prometheus)
10
Installation file for the Prometheus image
11
Prometheus job definitions to scrape metrics data

9.2. Prometheus metrics

AMQ Streams uses the Prometheus JMX Exporter to expose JMX metrics from Kafka and Zookeeper using an HTTP endpoint, which is then scraped by the Prometheus server.

9.2.1. Prometheus metrics configuration

AMQ Streams provides example configuration files for Grafana.

Grafana dashboards are dependent on Prometheus JMX Exporter relabeling rules, which are defined for:

  • Kafka and Zookeeper as a Kafka resource configuration in the example kafka-metrics.yaml file
  • Kafka Connect as KafkaConnect and KafkaConnectS2I resources in the example kafka-connect-metrics.yaml file

A label is a name-value pair. Relabeling is the process of writing a label dynamically. For example, the value of a label may be derived from the name of a Kafka server and client ID.

Note

We show metrics configuration using kafka-metrics.yaml in this section, but the process is the same when configuring Kafka Connect using the kafka-connect-metrics.yaml file.

Additional resources

For more information on the use of relabeling, see Configuration in the Prometheus documentation.

9.2.2. Prometheus metrics deployment options

To apply the example metrics configuration of relabeling rules to your Kafka cluster, do one of the following:

9.2.3. Copying Prometheus metrics configuration to a Kafka resource

To use Grafana dashboards for monitoring, you can copy the example metrics configuration to a Kafka resource.

Procedure

Execute the following steps for each Kafka resource in your deployment.

  1. Update the Kafka resource in an editor. 

    oc edit kafka my-cluster
  2. Copy the example configuration in kafka-metrics.yaml to your own Kafka resource definition.
  3. Save the file, exit the editor and wait for the updated resource to be reconciled.

9.2.4. Deploying a Kafka cluster with Prometheus metrics configuration

To use Grafana dashboards for monitoring, you can deploy an example Kafka cluster with metrics configuration.

Procedure

  • Deploy the Kafka cluster with the metrics configuration: 

    oc apply -f kafka-metrics.yaml

9.3. Prometheus

Prometheus provides an open source set of components for systems monitoring and alert notification.

We describe here how you can use the CoreOS Prometheus Operator to run and manage a Prometheus server that is suitable for use in production environments, but with the correct configuration you can run any Prometheus server.

Note

The Prometheus server configuration uses service discovery to discover the pods in the cluster from which it gets metrics. For this feature to work correctly, the service account used for running the Prometheus service pod must have access to the API server so it can retrieve the pod list.

For more information, see Discovering services.

9.3.1. Prometheus configuration

AMQ Streams provides example configuration files for the Prometheus server.

A Prometheus image is provided for deployment:

  • prometheus.yaml

Additional Prometheus-related configuration is also provided in the following files:

  • prometheus-additional.yaml
  • prometheus-rules.yaml
  • strimzi-service-monitor.yaml

For Prometheus to obtain monitoring data:

Then use the configuration files to:

Alerting rules

The prometheus-rules.yaml file provides example alerting rule examples for use with Alertmanager.

9.3.2. Prometheus resources

When you apply the Prometheus configuration, the following resources are created in your OpenShift cluster and managed by the Prometheus Operator:

  • A ClusterRole that grants permissions to Prometheus to read the health endpoints exposed by the Kafka and Zookeeper pods, cAdvisor and the kubelet for container metrics.
  • A ServiceAccount for the Prometheus pods to run under.
  • A ClusterRoleBinding which binds the ClusterRole to the ServiceAccount.
  • A Deployment to manage the Prometheus Operator pod.
  • A ServiceMonitor to manage the configuration of the Prometheus pod.
  • A Prometheus to manage the configuration of the Prometheus pod.
  • A PrometheusRule to manage alerting rules for the Prometheus pod.
  • A Secret to manage additional Prometheus settings.
  • A Service to allow applications running in the cluster to connect to Prometheus (for example, Grafana using Prometheus as datasource).

9.3.3. Deploying the Prometheus Operator

To deploy the Prometheus Operator to your Kafka cluster, apply the YAML resource files from the Prometheus CoreOS repository.

Procedure

  1. Download the resource files from the repository and replace the example namespace with your own:

    On Linux, use: 

    curl -s https://raw.githubusercontent.com/coreos/prometheus-operator/master/example/rbac/prometheus-operator/prometheus-operator-deployment.yaml | sed -e 's/namespace: .\*/namespace: my-namespace/' > prometheus-operator-deployment.yaml
    curl -s https://raw.githubusercontent.com/coreos/prometheus-operator/master/example/rbac/prometheus-operator/prometheus-operator-cluster-role.yaml > prometheus-operator-cluster-role.yaml
    curl -s https://raw.githubusercontent.com/coreos/prometheus-operator/master/example/rbac/prometheus-operator/prometheus-operator-cluster-role-binding.yaml | sed -e 's/namespace: .*/namespace: my-namespace/' > prometheus-operator-cluster-role-binding.yaml
    curl -s https://raw.githubusercontent.com/coreos/prometheus-operator/master/example/rbac/prometheus-operator/prometheus-operator-service-account.yaml | sed -e 's/namespace: .*/namespace: my-namespace/' > prometheus-operator-service-account.yaml

    On MacOS, use: 

    curl -s https://raw.githubusercontent.com/coreos/prometheus-operator/master/example/rbac/prometheus-operator/prometheus-operator-deployment.yaml | sed -e '' 's/namespace: .\*/namespace: my-namespace/' > prometheus-operator-deployment.yaml
    curl -s https://raw.githubusercontent.com/coreos/prometheus-operator/master/example/rbac/prometheus-operator/prometheus-operator-cluster-role.yaml > prometheus-operator-cluster-role.yaml
    curl -s https://raw.githubusercontent.com/coreos/prometheus-operator/master/example/rbac/prometheus-operator/prometheus-operator-cluster-role-binding.yaml | sed -e '' 's/namespace: .*/namespace: my-namespace/' > prometheus-operator-cluster-role-binding.yaml
    curl -s https://raw.githubusercontent.com/coreos/prometheus-operator/master/example/rbac/prometheus-operator/prometheus-operator-service-account.yaml | sed -e '' 's/namespace: .*/namespace: my-namespace/' > prometheus-operator-service-account.yaml
    Note

    If it is not required, you can manually remove the spec.template.spec.securityContext property from the prometheus-operator-deployment.yaml file.

  2. Deploy the Prometheus Operator: 

    oc apply -f prometheus-operator-deployment.yaml
oc apply -f prometheus-operator-cluster-role.yaml
oc apply -f prometheus-operator-cluster-role-binding.yaml
oc apply -f prometheus-operator-service-account.yaml

9.3.4. Deploying Prometheus

To deploy Prometheus to your Kafka cluster to obtain monitoring data, apply the example resource file for the Prometheus docker image and the YAML files for Prometheus-related resources.

The deployment process creates a ClusterRoleBinding and discovers an Alertmanager instance in the namespace specified for the deployment.

Note

By default, the Prometheus Operator only supports jobs that include an endpoints role for service discovery. Targets are discovered and scraped for each endpoint port address. For endpoint discovery, the port address may be derived from service (role: service) or pod (role: pod) discovery.

Prerequisites

Procedure

  1. Modify the Prometheus installation file (prometheus.yaml) according to the namespace Prometheus is going to be installed in:

    On Linux, use: 

    sed -i 's/namespace: .*/namespace: my-namespace/' prometheus.yaml

    On MacOS, use: 

    sed -i '' 's/namespace: .*/namespace: my-namespace/' prometheus.yaml
  2. Edit the ServiceMonitor resource in strimzi-service-monitor.yaml to define Prometheus jobs that will scrape the metrics data.

  3. To use another role:

    1. Create a Secret resource: 

      oc create secret generic additional-scrape-configs --from-file=prometheus-additional.yaml
    2. Edit the additionalScrapeConfigs property in the prometheus.yaml file to include the name of the Secret and the YAML file (prometheus-additional.yaml) that contains the additional configuration.

  4. Edit the prometheus-rules.yaml file that creates sample alert notification rules:

    On Linux, use: 

    sed -i 's/namespace: .*/namespace: my-namespace/' prometheus-rules.yaml

    On MacOS, use: 

    sed -i '' 's/namespace: .*/namespace: my-namespace/' prometheus-rules.yaml

  5. Deploy the Prometheus resources: 

    oc apply -f strimzi-service-monitor.yaml
oc apply -f prometheus-rules.yaml
oc apply -f prometheus.yaml

9.4. Prometheus Alertmanager

Prometheus Alertmanager is a plugin for handling alerts and routing them to a notification service. Alertmanager supports an essential aspect of monitoring, which is to be notified of conditions that indicate potential issues based on alerting rules.

9.4.1. Alertmanager configuration

AMQ Streams provides example configuration files for Prometheus Alertmanager.

A configuration file defines the resources for deploying Alertmanager:

  • alert-manager.yaml

An additional configuration file provides the hook definitions for sending notifications from your Kafka cluster.

  • alert-manager-config.yaml

For Alertmanger to handle Prometheus alerts, use the configuration files to:

9.4.2. Alerting rules

Alerting rules provide notifications about specific conditions observed in the metrics. Rules are declared on the Prometheus server, but Prometheus Alertmanager is responsible for alert notifications.

Prometheus alerting rules describe conditions using PromQL expressions that are continuously evaluated.

When an alert expression becomes true, the condition is met and the Prometheus server sends alert data to the Alertmanager. Alertmanager then sends out a notification using the communication method configured for its deployment.

Alertmanager can be configured to use email, chat messages or other notification methods.

Additional resources

For more information about setting up alerting rules, see Configuration in the Prometheus documentation.

9.4.3. Alerting rule examples

Example alerting rules for Kafka and Zookeeper metrics are provided with AMQ Streams for use in a Prometheus deployment.

General points about the alerting rule definitions:

  • A for property is used with the rules to determine the period of time a condition must persist before an alert is triggered.
  • A tick is a basic Zookeeper time unit, which is measured in milliseconds and configured using the tickTime parameter of Kafka.spec.zookeeper.config. For example, if Zookeeper tickTime=3000, 3 ticks (3 x 3000) equals 9000 milliseconds.
  • The availability of the ZookeeperRunningOutOfSpace metric and alert is dependent on the OpenShift configuration and storage implementation used. Storage implementations for certain platforms may not be able to supply the information on available space required for the metric to provide an alert.

Kafka alerting rules

UnderReplicatedPartitions
Gives the number of partitions for which the current broker is the lead replica but which have fewer replicas than the min.insync.replicas configured for their topic. This metric provides insights about brokers that host the follower replicas. Those followers are not keeping up with the leader. Reasons for this could include being (or having been) offline, and over-throttled interbroker replication. An alert is raised when this value is greater than zero, providing information on the under-replicated partitions for each broker.
AbnormalControllerState
Indicates whether the current broker is the controller for the cluster. The metric can be 0 or 1. During the life of a cluster, only one broker should be the controller and the cluster always needs to have an active controller. Having two or more brokers saying that they are controllers indicates a problem. If the condition persists, an alert is raised when the sum of all the values for this metric on all brokers is not equal to 1, meaning that there is no active controller (the sum is 0) or more than one controller (the sum is greater than 1).
UnderMinIsrPartitionCount
Indicates that the minimum number of in-sync replicas (ISRs) for a lead Kafka broker, specified using min.insync.replicas, that must acknowledge a write operation has not been reached. The metric defines the number of partitions that the broker leads for which the in-sync replicas count is less than the minimum in-sync. An alert is raised when this value is greater than zero, providing information on the partition count for each broker that did not achieve the minimum number of acknowledgments.
OfflineLogDirectoryCount
Indicates the number of log directories which are offline (for example, due to a hardware failure) so that the broker cannot store incoming messages anymore. An alert is raised when this value is greater than zero, providing information on the number of offline log directories for each broker.
KafkaRunningOutOfSpace
Indicates the remaining amount of disk space that can be used for writing data. An alert is raised when this value is lower than 5GiB, providing information on the disk that is running out of space for each persistent volume claim. The threshold value may be changed in prometheus-rules.yaml.

Zookeeper alerting rules

AvgRequestLatency
Indicates the amount of time it takes for the server to respond to a client request. An alert is raised when this value is greater than 10 (ticks), providing the actual value of the average request latency for each server.
OutstandingRequests
Indicates the number of queued requests in the server. This value goes up when the server receives more requests than it can process. An alert is raised when this value is greater than 10, providing the actual number of outstanding requests for each server.
ZookeeperRunningOutOfSpace
Indicates the remaining amount of disk space that can be used for writing data to Zookeeper. An alert is raised when this value is lower than 5GiB., providing information on the disk that is running out of space for each persistent volume claim.

9.4.4. Deploying Alertmanager

To deploy Alertmanager, apply the example configuration files.

The sample configuration provided with AMQ Streams configures the Alertmanager to send notifications to a Slack channel.

The following resources are defined on deployment:

  • An Alertmanager to manage the Alertmanager pod.
  • A Secret to manage the configuration of the Alertmanager.
  • A Service to provide an easy to reference hostname for other services to connect to Alertmanager (such as Prometheus).

Prerequisites

Procedure

  1. Create a Secret resource from the Alertmanager configuration file (alert-manager-config.yaml): 

    oc create secret generic alertmanager-alertmanager --from-file=alert-manager-config.yaml

  2. Update the alert-manager-config.yaml file to replace the:

    • slack_api_url property with the actual value of the Slack API URL related to the application for the Slack workspace
    • channel property with the actual Slack channel on which to send notifications

  3. Deploy Alertmanager: 

    oc apply -f alert-manager.yaml

9.5. Grafana

Grafana provides visualizations of Prometheus metrics.

You can deploy and enable the example Grafana dashboards provided with AMQ Streams.

9.5.1. Grafana configuration

AMQ Streams provides example dashboard configuration files for Grafana.

A Grafana docker image is provided for deployment:

  • grafana.yaml

Example dashboards are also provided as JSON files:

  • strimzi-kafka.json
  • strimzi-kafka-connect.json
  • strimzi-zookeeper.json

The example dashboards are a good starting point for monitoring key metrics, but they do not represent all available metrics. You may need to modify the example dashboards or add other metrics, depending on your infrastructure.

For Grafana to present the dashboards, use the configuration files to:

9.5.2. Deploying Grafana

To deploy Grafana to provide visualizations of Prometheus metrics, apply the example configuration file.

Prerequisites

Procedure

  1. Deploy Grafana: 

    oc apply -f grafana.yaml
  2. Enable the Grafana dashboards.

9.5.3. Enabling the example Grafana dashboards

Set up a Prometheus data source and example dashboards to enable Grafana for monitoring.

Note

No alert notification rules are defined.

When accessing a dashboard, you can use the port-forward command to forward traffic from the Grafana pod to the host.

For example, you can access the Grafana user interface by:

  1. Running oc port-forward grafana-1-fbl7s 3000:3000
  2. Pointing a browser to http://localhost:3000
Note

The name of the Grafana pod is different for each user.

Procedure

  1. Access the Grafana user interface using admin/admin credentials.

    On the initial view choose to reset the password.

    Grafana login

  2. Click the Add data source button.

    Grafana home

  3. Add Prometheus as a data source.

  4. Click Add to test the connection to the data source.

    Add Prometheus data source

  5. Click Dashboards, then Import to open the Import Dashboard window and import the example dashboards (or paste the JSON).

    Add Grafana dashboard

After importing the dashboards, the Grafana dashboard homepage presents Kafka and Zookeeper dashboards.

When the Prometheus server has been collecting metrics for a AMQ Streams cluster for some time, the dashboards are populated.

Figure 9.1. Kafka dashboard

Kafka dashboard

Figure 9.2. Zookeeper dashboard

Zookeeper dashboard
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