kube-prometheus-stack

Introduction

kube-prometheus-stack uses Prometheus-operator and Prometheus to provide easy-to-use, end-to-end Kubernetes cluster monitoring capabilities.

Open source community: https://github.com/prometheus/prometheus

Constraints

By default, the kube-state-metrics component in the add-on does not collect labels and annotations of Kubernetes resources. To collect these labels and annotations, manually enable the collection function in the startup parameters and check whether the corresponding metrics are added to the collection whitelist of ServiceMonitor named kube-state-metrics. For details, see Collecting All Labels and Annotations of a Pod.

Installing the Add-on

1. Log in to the CCE console and access the cluster console. Choose Add-ons in the navigation pane, locate kube-prometheus-stack on the right, and click Install.
2. On the Install Add-on page, configure the specifications.
   • Deployment Mode: This parameter is available for the kube-prometheus-stack version 3.7.1 or later.
     • Agent mode: Data is not stored locally, requiring fewer resources than the server mode. However, this mode does not support HPA.
       

       In the agent mode, monitoring data is no longer stored locally. Therefore, AOM or a third-party monitoring system must be accessed.

     • Server mode: Data is stored locally, requiring more resources than the agent mode. In this mode, all kube-prometheus-stack functions are supported.
   • Containers: component instance created by the add-on. For details, see Components. You can select or customize a specification as required.

3. Configure related parameters.
   • Connect to Third Party: To report Prometheus data to a third-party monitoring system, enter the address and token of the third-party monitoring system and determine whether to skip certificate authentication.
   • Prometheus HA: The Prometheus-server, Prometheus-operator, thanos-query, custom-metrics-apiserver and alertmanager components are deployed in multi-instance mode in the cluster.
   • Install Grafana: Use Grafana to visualize monitoring data. grafana creates a 5 GiB storage volume by default. Uninstalling the add-on will not delete this volume. The default username and password for the first login are admin. You will be asked to change the password immediately after login.
   • Collection Period: period for collecting monitoring data.
   • Scheduling Policies: Support node affinity, taint, and tolerations. Multiple scheduling policies can be configured. If no affinity node label key or toleration node taint key is configured, this function is disabled by default.
     • Range: You can select the add-on pods for which the scheduling policy takes effect. By default, the scheduling policy takes effect for all pods. If a pod is specified, the scheduling policies configured for all pods are overwritten.
     • Affinity Node Label Key: Enter a node label key to set node affinity for the add-on pods.
     • Affinity Node Label Value: Enter a node label value to set node affinity for the add-on pods.
     • Toleration Node Taint Key: A component can be scheduled to a node that has the taint key you specify.

4. Click Install.

   After the add-on is installed, you may need to perform the following operations:

   • To use this add-on to provide system resource metrics (such as CPU and memory usage) for workload auto scaling, enable the Metric API. For details, see Providing Resource Metrics Through the Metrics API. After the configuration, use Prometheus to collect system resource metrics without repeatedly installing the metrics-server add-on.
   • To collect custom metrics, custom collection rules by referring to Configuring Collection Rules for Custom Metrics. After the configuration, the custom metrics can be monitored for alarming and used to create auto scaling policies. For details, see Monitoring Custom Metrics Using Prometheus.
   • If you also installed grafana when installing the add-on, you can view the dashboard. For details, see Accessing Grafana.

Components

All Kubernetes resources created during kube-prometheus-stack add-on installation are created in the namespace named monitoring.

Providing Resource Metrics Through the Metrics API

Resource metrics of containers and nodes, such as CPU and memory usage, can be obtained through the Kubernetes Metrics API. Resource metrics can be directly accessed, for example, by using the kubectl top command, or used by HPA or CustomedHPA policies for auto scaling.

The add-on can provide the Kubernetes Metrics API that is disabled by default. To enable the API, create the following APIService object:

apiVersion: apiregistration.k8s.io/v1
kind: APIService
metadata:
  labels:
    app: custom-metrics-apiserver
    release: cceaddon-prometheus
  name: v1beta1.metrics.k8s.io
spec:
  group: metrics.k8s.io
  groupPriorityMinimum: 100
  insecureSkipTLSVerify: true
  service:
    name: custom-metrics-apiserver
    namespace: monitoring
    port: 443
  version: v1beta1
  versionPriority: 100

You can save the object as a file, name it metrics-apiservice.yaml, and run the following command:

kubectl create -f metrics-apiservice.yaml

Run the kubectl top pod -n monitoring command. If the following information is displayed, the Metrics API can be accessed:

# kubectl top pod -n monitoring
NAME                                                      CPU(cores)   MEMORY(bytes)
......
custom-metrics-apiserver-d4f556ff9-l2j2m                  38m          44Mi
......

To uninstall the add-on, run the following kubectl command and delete the APIService object. Otherwise, the metrics-server add-on cannot be installed due to residual APIService resources.

kubectl delete APIService v1beta1.metrics.k8s.io

Configuring Collection Rules for Custom Metrics

The kube-prometheus-stack add-on of the new version does not provide custom metrics. That is, metric collection rules are no longer configured in the user-adapter-config ConfigMap (adapter-config in earlier versions). Add metric collection rules. For details about how to add rules, see Metrics Discovery and Presentation Configuration. If you upgrade the add-on from an earlier version to the new version, original configurations are inherited and used.

To use prometheus to monitor custom metrics, the application needs to provide a metric monitoring API. For details, see Prometheus Monitoring Data Collection.

1. Log in to the CCE console and access the cluster console. In the navigation pane, choose ConfigMaps and Secrets.
2. Switch to the monitoring namespace, find the user-adapter-config ConfigMap (adapter-config in earlier versions) on the ConfigMaps tab page, and click Update.
3. In Data, click Edit for the config.yaml file to add a custom metric collection rule under the rules field. Click OK.

   You can add multiple collection rules by adding multiple configurations under the rules field. For details, see Metrics Discovery and Presentation Configuration.

   Example custom metric rule:

   rules:
   # The rule matches the accumulated cAdvisor metric in seconds.
   - seriesQuery: '{__name__=~"^container_.*",container!="POD",namespace!="",pod!=""}'
     resources:
       # Specify pod and namespace resources.
       overrides:
         namespace:
           resource: namespace
         pod:
           resource: pod
     name:
       # Delete the container_ prefix and _seconds_total suffix, and use the content captured in .* as the metric name.
       matches: "^container_(.*)_seconds_total$"
     # Query metrics. .Series and .LabelMatchers are available in the Go language. Use separators << and >> to avoid conflicts with the Prometheus query language.
     metricsQuery: 'sum(rate(<<.Series>>{<<.LabelMatchers>>,container!="POD"}[2m])) by (<<.GroupBy>>)'

   

   In the preceding example, only basic pod metrics are collected. To collect custom metrics, see the official guide to add or modify rules.

4. Redeploy the custom-metrics-apiserver workload in the monitoring namespace.

Accessing Grafana

If Grafana is installed during add-on installation, you can access the node through the Service named grafana, which is a NodePort Service. If the node is accessed from an external network, you can bind an EIP to the node and access the node through the node port.

As shown in the following figure, the access address is http://{{Node EIP}}:30913.

Collecting All Labels and Annotations of a Pod

1. Log in to the CCE console and click the cluster name to access the cluster console. In the navigation pane, choose Workloads.
2. Switch to the monitoring namespace, find the kube-state-metrics workload on the Deployments tab page, and click Upgrade in the Operation column.
3. In the Lifecycle area of the container settings, edit the startup command.
   Add the following information to the end of the original kube-state-metrics startup parameter:

   --metric-labels-allowlist=pods=[*],nodes=[node,failure-domain.beta.kubernetes.io/zone,topology.kubernetes.io/zone]

   To collect annotations, add parameters in the startup parameters in the same way.

   --metric-annotations-allowlist=pods=[*],nodes=[node,failure-domain.beta.kubernetes.io/zone,topology.kubernetes.io/zone]

   

   When editing the startup command, do not modify other original startup parameters. Otherwise, the component may be abnormal.

4. kube-state-metrics starts to collect the labels/annotations of pods and nodes and checks whether kube_pod_labels/kube_pod_annotations is in the collection task of CloudScope.

   kubectl get servicemonitor kube-state-metrics -nmonitoring -oyaml | kube_pod_labels

For more kube-state-metrics startup parameters, see kube-state-metrics/cli-arguments.