Connecting Kafka Exporter

Application Scenario

When using Kafka, you need to monitor their running, for example, checking the cluster status and whether messages are stacked. The Prometheus monitoring function monitors Kafka running using Exporter in the CCE container scenario. This section describes how to deploy Kafka Exporter and implement alarm access.

You are advised to use CCE for unified Exporter management.

Prerequisites

A CCE cluster has been created and Kafka has been installed.
Your service has been connected for Prometheus monitoring and a CCE cluster has also been connected. For details, see Prometheus Instance for CCE.
You have uploaded the kafka_exporter image to SoftWare Repository for Container (SWR). For details, see Uploading an Image Through a Container Engine Client.

Deploying Kafka Exporter

Log in to the CCE console.
Click the connected cluster. The cluster management page is displayed.

Perform the following operations to deploy Exporter:

Deploy Kafka Exporter.

In the navigation pane, choose Workloads. In the upper right corner, click Create Workload. Then select the Deployment workload and select a desired namespace to deploy Kafka Exporter. YAML configuration example for deploying Exporter:

apiVersion: apps/v1
kind: Deployment
metadata:
  labels:
    k8s-app: kafka-exporter # Change the name based on service requirements. You are advised to add the Kafka instance information, for example, ckafka-2vrgx9fd-kafka-exporter.
  name: kafka-exporter # Change the name based on service requirements. You are advised to add the Kafka instance information, for example, ckafka-2vrgx9fd-kafka-exporter.
  namespace: default # Namespace of an existing cluster
spec:
  replicas: 1
  selector:
    matchLabels:
      k8s-app: kafka-exporter # Change the name based on service requirements. You are advised to add the Kafka instance information, for example, ckafka-2vrgx9fd-kafka-exporter.
  template:
    metadata:
      labels:
        k8s-app: kafka-exporter # Change the name based on service requirements. You are advised to add the Kafka instance information, for example, ckafka-2vrgx9fd-kafka-exporter.
    spec:
      containers:
      - args:
        - --kafka.server=120.46.215.4:30092 # Address of the Kafka instance
        image: swr.cn-north-4.myhuaweicloud.com/mall-swarm-demo/kafka-exporter:latest
        imagePullPolicy: IfNotPresent
        name: kafka-exporter
        ports:
        - containerPort: 9308
          name: metric-port # Required when you configure a capture task
        securityContext:
          privileged: false
        terminationMessagePath: /dev/termination-log
        terminationMessagePolicy: File
      dnsPolicy: ClusterFirst
      imagePullSecrets:
      - name: default-secret
      restartPolicy: Always
      schedulerName: default-scheduler
      securityContext: {}
      terminationGracePeriodSeconds: 30
---
apiVersion: v1
kind: Service
metadata:
  name: kafka-exporter
spec:
  type: NodePort
  selector:
    k8s-app: kafka-exporter
  ports:
    - protocol: TCP
      nodePort: 30091
      port: 9308
      targetPort: 9308

For more details about Exporter parameters, see kafka-exporter.

Check whether Kafka Exporter is successfully deployed.
1. On the Deployments tab page, click the Deployment created in 3.a. In the pod list, choose More > View Logs in the Operation column. The Exporter is successfully started and its access address is exposed.
1. Perform verification using one of the following methods:
  - Log in to a cluster node and run either of the following commands:
```
curl http://{Cluster IP address}:9308/metrics
```
```
curl http://{Private IP address of any node in the cluster}:30091/metrics
```
  - In the instance list, choose More > Remote Login in the Operation column and run the following command:
```
curl http://localhost:9308/metric
```
  - Access http://{Public IP address of any node in the cluster}:30091/metrics.
    Figure 1 Accessing a cluster node

Collecting Service Data of the CCE Cluster

Add PodMonitor to configure a collection rule for monitoring the service data of applications deployed in the CCE cluster.

In the following example, metrics are collected every 30s. Therefore, you can check the reported metrics on the AOM page about 30s later.

Configuration information:

apiVersion: monitoring.coreos.com/v1
kind: PodMonitor
metadata:
  name: kafka-exporter
  namespace: default 
spec:
  namespaceSelector:
    matchNames:
      - default # Namespace where Exporter is located.
  podMetricsEndpoints:
  - interval: 30s
    path: /metrics
    port: metric-port
  selector:
    matchLabels:
      k8s-app: kafka-exporter

Verifying that Metrics Can Be Reported to AOM

Log in to the AOM 2.0 console.
In the navigation pane on the left, choose Prometheus Monitoring > Instances.
Click the Prometheus instance connected to the CCE cluster. The instance details page is displayed.
On the Metrics tab page of the Service Discovery page, select your target cluster.
Select job {namespace}/kafka-exporter to query custom metrics starting with kafka.

Setting a Dashboard and Alarm Rule on AOM

By setting a dashboard, you can monitor CCE cluster data on the same screen. By setting an alarm rule, you can detect cluster faults and implement warning in a timely manner.

Setting a dashboard
1. Log in to the AOM 2.0 console.
2. In the navigation pane, choose Dashboard. On the displayed page, click Add Dashboard to add a dashboard. For details, see Creating a Dashboard.
3. On the Dashboard page, select a Prometheus instance for CCE and click Add Graph. For details, see Adding a Graph to a Dashboard.
Setting an alarm rule
1. Log in to the AOM 2.0 console.
2. In the navigation pane, choose Alarm Management > Alarm Rules.
3. Click Create Alarm Rule to set an alarm rule. For details, see Creating a Metric Alarm Rule.