Grafana 備份恢復教程

本文轉載自查看原文 2020-12-16 14:03 528 docker

原文鏈接：https://fuckcloudnative.io/posts/how-to-back-up-all-of-your-grafana-dashboards/

目前我們 k8s 集群的 Grafana 使用 ceph 作為持久化存儲，一但我將 Grafana 的 Deployment 刪除重建之后，之前的所有數據都會丟失，重建的 PV 會映射到后端存儲的新位置。萬幸的是，我真的手欠重建了，還沒有提前備份。。。萬幸個鬼啊我。

在我歷經 250 分鍾重建 Dashboard 之后，心里久久不能平靜，一句 MMP 差點就要脫口而出。

1. 低級方案

再這樣下去我真的要變成 250 了，這怎么能忍，立馬打開 Google 研究了一把 Grafana 備份的各種騷操作，發現大部分備份方案都是通過 shell 腳本調用 Grafana 的 API 來導出各種配置。備份腳本大部分都集中在這個 gist 中：

https://gist.github.com/crisidev/bd52bdcc7f029be2f295

我挑選出幾個比較好用的，大家也可以自行挑選其他的。

導出腳本

#!/bin/bash

# Usage:
#
# export_grafana_dashboards.sh https://admin:REDACTED@grafana.dedevsecops.com

create_slug () {
  echo "$1" | iconv -t ascii//TRANSLIT | sed -r s/[^a-zA-Z0-9]+/-/g | sed -r s/^-+\|-+$//g | tr A-Z a-z
}

full_url=$1
username=$(echo "${full_url}" | cut -d/ -f 3 | cut -d: -f 1)
base_url=$(echo "${full_url}" | cut -d@ -f 2)
folder=$(create_slug "${username}-${base_url}")

mkdir "${folder}"
for db_uid in $(curl -s "${full_url}/api/search" | jq -r .[].uid); do
  db_json=$(curl -s "${full_url}/api/dashboards/uid/${db_uid}")
  db_slug=$(echo "${db_json}" | jq -r .meta.slug)
  db_title=$(echo "${db_json}" | jq -r .dashboard.title)
  filename="${folder}/${db_slug}.json"
  echo "Exporting \"${db_title}\" to \"${filename}\"..."
  echo "${db_json}" | jq -r . > "${filename}"
done
echo "Done"

這個腳本比較簡單，直接導出了所有 Dashboard 的 json 配置，也沒有標記目錄信息，如果你用它導出的配置來恢復 Grafana，所有的 Dashboard 都會導入到 Grafana 的 General 目錄下，不太友好。

導入腳本

grafana-dashboard-importer.sh

#!/bin/bash
#
# add the "-x" option to the shebang line if you want a more verbose output
#
#
OPTSPEC=":hp:t:k:"

show_help() {
cat << EOF
Usage: $0 [-p PATH] [-t TARGET_HOST] [-k API_KEY]
Script to import dashboards into Grafana
    -p      Required. Root path containing JSON exports of the dashboards you want imported.
    -t      Required. The full URL of the target host
    -k      Required. The API key to use on the target host

    -h      Display this help and exit.
EOF
}

###### Check script invocation options ######
while getopts "$OPTSPEC" optchar; do
    case "$optchar" in
        h)
            show_help
            exit
            ;;
        p)
            DASH_DIR="$OPTARG";;
        t)
            HOST="$OPTARG";;
        k)
            KEY="$OPTARG";;
        \?)
          echo "Invalid option: -$OPTARG" >&2
          exit 1
          ;;
        :)
          echo "Option -$OPTARG requires an argument." >&2
          exit 1
          ;;
    esac
done

if [ -z "$DASH_DIR" ] || [ -z "$HOST" ] || [ -z "$KEY" ]; then
    show_help
    exit 1
fi

# set some colors for status OK, FAIL and titles
SETCOLOR_SUCCESS="echo -en \\033[0;32m"
SETCOLOR_FAILURE="echo -en \\033[1;31m"
SETCOLOR_NORMAL="echo -en \\033[0;39m"
SETCOLOR_TITLE_PURPLE="echo -en \\033[0;35m" # purple

# usage log "string to log" "color option"
function log_success() {
   if [ $# -lt 1 ]; then
       ${SETCOLOR_FAILURE}
       echo "Not enough arguments for log function! Expecting 1 argument got $#"
       exit 1
   fi

   timestamp=$(date "+%Y-%m-%d %H:%M:%S %Z")

   ${SETCOLOR_SUCCESS}
   printf "[%s] $1\n" "$timestamp"
   ${SETCOLOR_NORMAL}
}

function log_failure() {
   if [ $# -lt 1 ]; then
       ${SETCOLOR_FAILURE}
       echo "Not enough arguments for log function! Expecting 1 argument got $#"
       exit 1
   fi

   timestamp=$(date "+%Y-%m-%d %H:%M:%S %Z")

   ${SETCOLOR_FAILURE}
   printf "[%s] $1\n" "$timestamp"
   ${SETCOLOR_NORMAL}
}

function log_title() {
   if [ $# -lt 1 ]; then
       ${SETCOLOR_FAILURE}
       log_failure "Not enough arguments for log function! Expecting 1 argument got $#"
       exit 1
   fi

   ${SETCOLOR_TITLE_PURPLE}
   printf "|-------------------------------------------------------------------------|\n"
   printf "|%s|\n" "$1";
   printf "|-------------------------------------------------------------------------|\n"
   ${SETCOLOR_NORMAL}
}

if [ -d "$DASH_DIR" ]; then
    DASH_LIST=$(find "$DASH_DIR" -mindepth 1 -name \*.json)
    if [ -z "$DASH_LIST" ]; then
        log_title "----------------- $DASH_DIR contains no JSON files! -----------------"
        log_failure "Directory $DASH_DIR does not appear to contain any JSON files for import. Check your path and try again."
        exit 1
    else
        FILESTOTAL=$(echo "$DASH_LIST" | wc -l)
        log_title "----------------- Starting import of $FILESTOTAL dashboards -----------------"
    fi
else
    log_title "----------------- $DASH_DIR directory not found! -----------------"
    log_failure "Directory $DASH_DIR does not exist. Check your path and try again."
    exit 1
fi

NUMSUCCESS=0
NUMFAILURE=0
COUNTER=0

for DASH_FILE in $DASH_LIST; do
    COUNTER=$((COUNTER + 1))
    echo "Import $COUNTER/$FILESTOTAL: $DASH_FILE..."
    RESULT=$(cat "$DASH_FILE" | jq '. * {overwrite: true, dashboard: {id: null}}' | curl -s -X POST -H "Content-Type: application/json" -H "Authorization: Bearer $KEY" "$HOST"/api/dashboards/db -d @-)
    if [[ "$RESULT" == *"success"* ]]; then
        log_success "$RESULT"
        NUMSUCCESS=$((NUMSUCCESS + 1))
    else
        log_failure "$RESULT"
        NUMFAILURE=$((NUMFAILURE + 1))
    fi
done

log_title "Import complete. $NUMSUCCESS dashboards were successfully imported. $NUMFAILURE dashboard imports failed.";
log_title "------------------------------ FINISHED ---------------------------------";

導入腳本需要目標機器上的 Grafana 已經啟動，而且需要提供管理員 API Key。登錄 Grafana Web 界面，打開 API Keys：

新建一個 API Key，角色選擇 Admin，過期時間自己調整：

導入方式：

$ ./grafana-dashboard-importer.sh -t http://<grafana_svc_ip>:<grafana_svc_port> -k <api_key> -p <backup folder>

其中 -p 參數指定的是之前導出的 json 所在的目錄。

目前的方案痛點在於只能備份 Dashboard，不能備份其他的配置（例如，數據源、用戶、秘鑰等），而且沒有將 Dashboard 和目錄對應起來，即不支持備份 Folder。下面介紹一個比較完美的備份恢復方案，支持所有配置的備份恢復，簡直不要太香。

2. 高級方案

更高級的方案已經有人寫好了，項目地址是：

https://github.com/ysde/grafana-backup-tool

該備份工具支持以下幾種配置：

目錄
Dashboard
數據源
Grafana 告警頻道（Alert Channel）
組織（Organization）
用戶（User）

使用方法很簡單，跑個容器就好了嘛，不過作者提供的 Dockerfile 我不是很滿意，自己修改了點內容：

FROM alpine:latest

LABEL maintainer="grafana-backup-tool Docker Maintainers https://fuckcloudnative.io"

ENV ARCHIVE_FILE ""

RUN echo "@edge http://dl-cdn.alpinelinux.org/alpine/edge/community" >> /etc/apk/repositories; \
    apk --no-cache add python3 py3-pip py3-cffi py3-cryptography ca-certificates bash git; \
    git clone https://github.com/ysde/grafana-backup-tool /opt/grafana-backup-tool; \
    cd /opt/grafana-backup-tool; \
    pip3 --no-cache-dir install .; \
    chown -R 1337:1337 /opt/grafana-backup-tool

WORKDIR /opt/grafana-backup-tool

USER 1337

只有 Dockerfile 不行，還得通過 CI/CD 自動構建並推送到 docker.io。不要問我用什么，當然是白嫖 GitHub Action，workflow 內容如下：

#=================================================
# https://github.com/yangchuansheng/docker-image
# Description: Build and push grafana-backup-tool Docker image
# Lisence: MIT
# Author: Ryan
# Blog: https://fuckcloudnative.io
#=================================================

name: Build and push grafana-backup-tool Docker image

# Controls when the action will run. Triggers the workflow on push or pull request
# events but only for the master branch
on:
  push:
    branches: [ master ]
    paths: 
      - 'grafana-backup-tool/Dockerfile'
      - '.github/workflows/grafana-backup-tool.yml'
  pull_request:
    branches: [ master ]
    paths: 
      - 'grafana-backup-tool/Dockerfile'
  #watch:
    #types: started

# A workflow run is made up of one or more jobs that can run sequentially or in parallel
jobs:
  # This workflow contains a single job called "build"
  build:
    # The type of runner that the job will run on
    runs-on: ubuntu-latest

    # Steps represent a sequence of tasks that will be executed as part of the job
    steps:
    # Checks-out your repository under $GITHUB_WORKSPACE, so your job can access it
    - uses: actions/checkout@v2

    - name: Set up QEMU
      uses: docker/setup-qemu-action@v1

    - name: Set up Docker Buildx
      uses: docker/setup-buildx-action@v1

    - name: Login to DockerHub
      uses: docker/login-action@v1 
      with:
        username: ${{ secrets.DOCKER_USERNAME }}
        password: ${{ secrets.DOCKER_PASSWORD }}
        
    - name: Login to GitHub Package Registry
      env:
        username: ${{ github.repository_owner }}
        password: ${{ secrets.GHCR_TOKEN }}
      run: echo ${{ env.password }} | docker login ghcr.io -u ${{ env.username }} --password-stdin  

    # Runs a single command using the runners shell
    - name: Build and push Docker images to docker.io and ghcr.io
      uses: docker/build-push-action@v2
      with:
        file: 'grafana-backup-tool/Dockerfile'
        platforms: linux/386,linux/amd64,linux/arm/v6,linux/arm/v7,linux/arm64,linux/ppc64le,linux/s390x
        context: grafana-backup-tool
        push: true
        tags: |
          yangchuansheng/grafana-backup-tool:latest
          ghcr.io/yangchuansheng/grafana-backup-tool:latest

    #- name: Update repo description
      #uses: peter-evans/dockerhub-description@v2
      #env:
        #DOCKERHUB_USERNAME: ${{ secrets.DOCKER_USERNAME }}
        #DOCKERHUB_PASSWORD: ${{ secrets.DOCKER_PASSWORD }}
        #DOCKERHUB_REPOSITORY: yangchuansheng/grafana-backup-tool
        #README_FILEPATH: grafana-backup-tool/readme.md

這里我不打算解釋 workflow 的內容，有點基礎的應該都能看懂，實在不行，以后我會單獨寫文章解釋（又可以繼續水文了~）。這個 workflow 實現的功能就是自動構建各個 CPU 架構的鏡像，並推送到 docker.io 和 ghcr.io，特么的真香！

就問爽不爽？

你可以直接關注我的倉庫：

https://github.com/yangchuansheng/docker-image

構建好鏡像后，就可以直接運行容器來進行備份和恢復操作了。如果你想在集群內操作，可以通過 Deployment 或 Job 來實現；如果你想在本地或 k8s 集群外操作，可以選擇 docker run，我不反對，你也可以選擇 docker-compose，這都沒問題。但我要告訴你一個更騷的辦法，可以騷到讓你無法自拔。

首先需要在本地或集群外安裝 Podman，如果操作系統是 Win10，可以考慮通過 WSL 來安裝；如果操作系統是 Linux，那就不用說了；如果操作系統是 MacOS，請參考我的上篇文章：在 macOS 中使用 Podman。

裝好了 Podman 之后，就可以進行騷操作了，請睜大眼睛。

先編寫一個 Deployment 配置清單（什么？Deployment？是的，你沒聽錯）：

grafana-backup-deployment.yaml

apiVersion: apps/v1
kind: Deployment
metadata:
  name: grafana-backup
  labels:
    app: grafana-backup
spec:
  replicas: 1
  selector:
    matchLabels:
      app: grafana-backup
  template:
    metadata:
      labels:
        app: grafana-backup
    spec:
      containers:
      - name: grafana-backup
        image: yangchuansheng/grafana-backup-tool:latest
        imagePullPolicy: IfNotPresent
        command: ["/bin/bash"]
        tty: true
        stdin: true
        env:
        - name: GRAFANA_TOKEN
          value: "eyJr0NkFBeWV1QVpMNjNYWXA3UXNOM2JWMWdZOTB2ZFoiLCJuIjoiYWRtaW4iLCJpZCI6MX0="
        - name: GRAFANA_URL
          value: "http://<grafana_ip>:<grafana_port>"
        - name: GRAFANA_ADMIN_ACCOUNT
          value: "admin"
        - name: GRAFANA_ADMIN_PASSWORD
          value: "admin"
        - name: VERIFY_SSL
          value: "False"
        volumeMounts:
        - mountPath: /opt/grafana-backup-tool
          name: data
      volumes:
      - name: data
        hostPath:
          path: /mnt/manifest/grafana/backup

這里面的環境變量根據自己的實際情況修改，一定不要照抄我的！

不要一臉懵逼，我先來解釋一下為什么要准備這個 Deployment 配置清單，因為 Podman 可以直接通過這個配置清單運行容器，命令如下：

$ podman play kube grafana-backup-deployment.yaml

我第一次見到這個操作的時候也不禁連連我艹，這也可以？確實可以，不過呢，Podman 只是將其翻譯一下，跑個容器而已，並不是真正運行 Deployment，因為它沒有控制器啊，但是，還是真香！

想象一下，你可以將 k8s 集群中的配置清單拿到本地或測試機器直接跑，再也不用 k8s 集群准備一份 yaml，docker-compose 再准備一份 yaml 了，一份 yaml 走天下，服不服？

docker-compose 混到今天這個地步，也是蠻可憐的。

細心的讀者應該能發現上面的配置清單有點奇怪，Dockerfile 也有點奇怪。Dockerfile 中沒有寫 CMD 或 ENTRYPOINT，Deployment 中直接將啟動命令設置為 bash，這是因為在我之前測試的過程中發現該鏡像啟動的容器有點問題，它會陷入一個循環，備份完了之后又會繼續備份，不斷重復，導致備份目錄下生成了一坨壓縮包。目前還沒找到比較好的解決辦法，只能將容器的啟動命令設置為 bash，等容器運行后再進入容器進行備份操作：

$ podman pod ls
POD ID        NAME                  STATUS   CREATED        # OF CONTAINERS  INFRA ID
728aec216d66  grafana-backup-pod-0  Running  3 minutes ago  2                92aa0824fe7d

$ podman ps
CONTAINER ID  IMAGE                                      COMMAND    CREATED        STATUS            PORTS   NAMES
b523fa8e4819  yangchuansheng/grafana-backup-tool:latest  /bin/bash  3 minutes ago  Up 3 minutes ago          grafana-backup-pod-0-grafana-backup
92aa0824fe7d  k8s.gcr.io/pause:3.2                                  3 minutes ago  Up 3 minutes ago          728aec216d66-infra

$ podman exec -it grafana-backup-pod-0-grafana-backup bash
bash-5.0$ grafana-backup save
...
...
########################################

backup folders at: _OUTPUT_/folders/202012111556
backup datasources at: _OUTPUT_/datasources/202012111556
backup dashboards at: _OUTPUT_/dashboards/202012111556
backup alert_channels at: _OUTPUT_/alert_channels/202012111556
backup organizations at: _OUTPUT_/organizations/202012111556
backup users at: _OUTPUT_/users/202012111556

created archive at: _OUTPUT_/202012111556.tar.gz

默認情況下會備份所有的組件，你也可以指定備份的組件：

$ grafana-backup save --components=<folders,dashboards,datasources,alert-channels,organizations,users>

比如，我只想備份 Dashboards 和 Folders：

$ grafana-backup save --components=folders,dashboards

當然，你也可以全部備份，恢復的時候再選擇自己想恢復的組件：

$ grafana-backup restore --components=folders,dashboards

至此，再也不用怕 Dashboard 被改掉或刪除啦。

最后提醒一下，Prometheus Operator 項目中的 Grafana 通過 Provisioning 的方式預導入了一些默認的 Dashboards，這本來沒有什么問題，但 grafana-backup-tool 工具無法忽略跳過已經存在的配置，如果恢復的過程中遇到已經存在的配置，會直接報錯退出。本來這也很好解決，一般情況下到 Grafana Web 界面中刪除所有的 Dashboard 就好了，但通過 Provisioning 導入的 Dashboard 是無法刪除的，這就很尷尬了。

在作者修復這個 bug 之前，要想解決這個問題，有兩個辦法：

第一個辦法是在恢復之前將 Grafana Deployment 中關於 Provisioning 的配置全部刪除，就是這些配置：

        volumeMounts:
        - mountPath: /etc/grafana/provisioning/datasources
          name: grafana-datasources
          readOnly: false
        - mountPath: /etc/grafana/provisioning/dashboards
          name: grafana-dashboards
          readOnly: false
        - mountPath: /grafana-dashboard-definitions/0/apiserver
          name: grafana-dashboard-apiserver
          readOnly: false
        - mountPath: /grafana-dashboard-definitions/0/cluster-total
          name: grafana-dashboard-cluster-total
          readOnly: false
        - mountPath: /grafana-dashboard-definitions/0/controller-manager
          name: grafana-dashboard-controller-manager
          readOnly: false
        - mountPath: /grafana-dashboard-definitions/0/k8s-resources-cluster
          name: grafana-dashboard-k8s-resources-cluster
          readOnly: false
        - mountPath: /grafana-dashboard-definitions/0/k8s-resources-namespace
          name: grafana-dashboard-k8s-resources-namespace
          readOnly: false
        - mountPath: /grafana-dashboard-definitions/0/k8s-resources-node
          name: grafana-dashboard-k8s-resources-node
          readOnly: false
        - mountPath: /grafana-dashboard-definitions/0/k8s-resources-pod
          name: grafana-dashboard-k8s-resources-pod
          readOnly: false
        - mountPath: /grafana-dashboard-definitions/0/k8s-resources-workload
          name: grafana-dashboard-k8s-resources-workload
          readOnly: false
        - mountPath: /grafana-dashboard-definitions/0/k8s-resources-workloads-namespace
          name: grafana-dashboard-k8s-resources-workloads-namespace
          readOnly: false
        - mountPath: /grafana-dashboard-definitions/0/kubelet
          name: grafana-dashboard-kubelet
          readOnly: false
        - mountPath: /grafana-dashboard-definitions/0/namespace-by-pod
          name: grafana-dashboard-namespace-by-pod
          readOnly: false
        - mountPath: /grafana-dashboard-definitions/0/namespace-by-workload
          name: grafana-dashboard-namespace-by-workload
          readOnly: false
        - mountPath: /grafana-dashboard-definitions/0/node-cluster-rsrc-use
          name: grafana-dashboard-node-cluster-rsrc-use
          readOnly: false
        - mountPath: /grafana-dashboard-definitions/0/node-rsrc-use
          name: grafana-dashboard-node-rsrc-use
          readOnly: false
        - mountPath: /grafana-dashboard-definitions/0/nodes
          name: grafana-dashboard-nodes
          readOnly: false
        - mountPath: /grafana-dashboard-definitions/0/persistentvolumesusage
          name: grafana-dashboard-persistentvolumesusage
          readOnly: false
        - mountPath: /grafana-dashboard-definitions/0/pod-total
          name: grafana-dashboard-pod-total
          readOnly: false
        - mountPath: /grafana-dashboard-definitions/0/prometheus-remote-write
          name: grafana-dashboard-prometheus-remote-write
          readOnly: false
        - mountPath: /grafana-dashboard-definitions/0/prometheus
          name: grafana-dashboard-prometheus
          readOnly: false
        - mountPath: /grafana-dashboard-definitions/0/proxy
          name: grafana-dashboard-proxy
          readOnly: false
        - mountPath: /grafana-dashboard-definitions/0/scheduler
          name: grafana-dashboard-scheduler
          readOnly: false
        - mountPath: /grafana-dashboard-definitions/0/statefulset
          name: grafana-dashboard-statefulset
          readOnly: false
        - mountPath: /grafana-dashboard-definitions/0/workload-total
          name: grafana-dashboard-workload-total
          readOnly: false
...
...
      volumes:
      - name: grafana-datasources
        secret:
          secretName: grafana-datasources
      - configMap:
          name: grafana-dashboards
        name: grafana-dashboards
      - configMap:
          name: grafana-dashboard-apiserver
        name: grafana-dashboard-apiserver
      - configMap:
          name: grafana-dashboard-cluster-total
        name: grafana-dashboard-cluster-total
      - configMap:
          name: grafana-dashboard-controller-manager
        name: grafana-dashboard-controller-manager
      - configMap:
          name: grafana-dashboard-k8s-resources-cluster
        name: grafana-dashboard-k8s-resources-cluster
      - configMap:
          name: grafana-dashboard-k8s-resources-namespace
        name: grafana-dashboard-k8s-resources-namespace
      - configMap:
          name: grafana-dashboard-k8s-resources-node
        name: grafana-dashboard-k8s-resources-node
      - configMap:
          name: grafana-dashboard-k8s-resources-pod
        name: grafana-dashboard-k8s-resources-pod
      - configMap:
          name: grafana-dashboard-k8s-resources-workload
        name: grafana-dashboard-k8s-resources-workload
      - configMap:
          name: grafana-dashboard-k8s-resources-workloads-namespace
        name: grafana-dashboard-k8s-resources-workloads-namespace
      - configMap:
          name: grafana-dashboard-kubelet
        name: grafana-dashboard-kubelet
      - configMap:
          name: grafana-dashboard-namespace-by-pod
        name: grafana-dashboard-namespace-by-pod
      - configMap:
          name: grafana-dashboard-namespace-by-workload
        name: grafana-dashboard-namespace-by-workload
      - configMap:
          name: grafana-dashboard-node-cluster-rsrc-use
        name: grafana-dashboard-node-cluster-rsrc-use
      - configMap:
          name: grafana-dashboard-node-rsrc-use
        name: grafana-dashboard-node-rsrc-use
      - configMap:
          name: grafana-dashboard-nodes
        name: grafana-dashboard-nodes
      - configMap:
          name: grafana-dashboard-persistentvolumesusage
        name: grafana-dashboard-persistentvolumesusage
      - configMap:
          name: grafana-dashboard-pod-total
        name: grafana-dashboard-pod-total
      - configMap:
          name: grafana-dashboard-prometheus-remote-write
        name: grafana-dashboard-prometheus-remote-write
      - configMap:
          name: grafana-dashboard-prometheus
        name: grafana-dashboard-prometheus
      - configMap:
          name: grafana-dashboard-proxy
        name: grafana-dashboard-proxy
      - configMap:
          name: grafana-dashboard-scheduler
        name: grafana-dashboard-scheduler
      - configMap:
          name: grafana-dashboard-statefulset
        name: grafana-dashboard-statefulset
      - configMap:
          name: grafana-dashboard-workload-total
        name: grafana-dashboard-workload-total

第二個辦法就是刪除 Prometheus Operator 自帶的 Grafana，自己通過 Helm 或者 manifest 部署不使用 Provisioning 的 Grafana。

如果你既不想刪除 Provisioning 的配置，也不想自己部署 Grafana，那只能使用上文提到的低級方案了。

Kubernetes 1.18.2 1.17.5 1.16.9 1.15.12離線安裝包發布地址http://store.lameleg.com ，歡迎體驗。使用了最新的sealos v3.3.6版本。作了主機名解析配置優化，lvscare 掛載/lib/module解決開機啟動ipvs加載問題，修復lvscare社區netlink與3.10內核不兼容問題,sealos生成百年證書等特性。更多特性 https://github.com/fanux/sealos 。歡迎掃描下方的二維碼加入釘釘群，釘釘群已經集成sealos的機器人實時可以看到sealos的動態。

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