• Install (Community Edition)

Monitoring a Single K8s Cluster

Created：2023-10-04 Last Modified：2025-04-02

This document was translated by ChatGPT

#1. Introduction

If you have deployed applications in a K8s cluster, this chapter explains how to use DeepFlow for monitoring. DeepFlow can collect observability signals (AutoMetrics, AutoTracing, AutoProfiling) from all Pods without interference and automatically injects K8s resources and K8s custom labels tags (AutoTagging) into all observability data based on information obtained from the apiserver.

#2. Preparation

#2.1 Deployment Topology

#2.2 Storage Class

We recommend using Persistent Volumes to store data for MySQL and ClickHouse to avoid unnecessary maintenance costs. You can provide a default Storage Class or add the parameter --set global.storageClass=<your storageClass> to select a Storage Class for creating PVC.

You may choose OpenEBS (opens new window) for creating PVC:

kubectl apply -f https://openebs.github.io/charts/openebs-operator.yaml
## config default storage class
kubectl patch storageclass openebs-hostpath  -p '{"metadata": {"annotations":{"storageclass.kubernetes.io/is-default-class":"true"}}}'
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#3. Deploy DeepFlow

Install DeepFlow using Helm:

helm repo add deepflow https://deepflowio.github.io/deepflow
helm repo update deepflow # use `helm repo update` when helm < 3.7.0
helm install deepflow -n deepflow deepflow/deepflow --version 6.6.018 --create-namespace
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helm repo add deepflow https://deepflow-ce.oss-cn-beijing.aliyuncs.com/chart/stable
helm repo update deepflow # use `helm repo update` when helm < 3.7.0
cat << EOF > values-custom.yaml
global:
  image:
      repository: registry.cn-beijing.aliyuncs.com/deepflow-ce
EOF
helm install deepflow -n deepflow deepflow/deepflow --version 6.6.018 --create-namespace -f values-custom.yaml
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Note:

  • Use helm --set global.storageClass to specify the storageClass
  • Use helm --set global.replicas to specify the number of replicas for deepflow-server and clickhouse
  • We recommend saving the contents of helm's --set parameters in a separate yaml file, refer to the Advanced Configuration section.

#4. Download deepflow-ctl

deepflow-ctl is a command-line tool for managing DeepFlow. It is recommended to download it to the K8s Node where deepflow-server is located for subsequent use:

# Set temporary variable
Version=v6.6

# Download using the variable
curl -o /usr/bin/deepflow-ctl \
  "https://deepflow-ce.oss-cn-beijing.aliyuncs.com/bin/ctl/$Version/linux/$(arch | sed 's|x86_64|amd64|' | sed 's|aarch64|arm64|')/deepflow-ctl"

# Add execute permission
chmod a+x /usr/bin/deepflow-ctl
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#5. Access the Grafana Page

The output from executing helm to deploy DeepFlow provides commands to obtain the URL and password for accessing Grafana. Example output:

NODE_PORT=$(kubectl get --namespace deepflow -o jsonpath="{.spec.ports[0].nodePort}" services deepflow-grafana)
NODE_IP=$(kubectl get nodes -o jsonpath="{.items[0].status.addresses[0].address}")
echo -e "Grafana URL: http://$NODE_IP:$NODE_PORT  \nGrafana auth: admin:deepflow"
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Example output after executing the above command:

Grafana URL: http://10.1.2.3:31999
Grafana auth: admin:deepflow
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#6. Next Steps