[Argo] 01 - Create Argo Workflows

Ref: [K8S] 03 - k8s YAML, NameSpace & Pod

Ref: Argo Workflows概述，安装部署，服务的发布、加密方式

Ref: Argo Workflows —— Kubernetes的工作流引擎入门

主要还是对工作流的深入理解和实践～

 

  Argo工作流被实现为 Kubernetes CRD (自定义资源定义)。

  定义工作流，其中每个步骤都是一个容器。

 

 

安装 Argo

首先，需要一个Kubernetes集群和kubectl设置。 简单的安装步骤：

kubectl create ns argo
kubectl apply -n argo -f https://raw.githubusercontent.com/argoproj/argo-workflows/stable/manifests/quick-start-postgres.yaml  <---- 资源清单文件

Ref: https://github.com/argoproj/argo-workflows/releases/

# Download the binary
curl -sLO https://github.com/argoproj/argo-workflows/releases/download/v3.4.0-rc4/argo-linux-amd64.gz

# Unzip
gunzip argo-linux-amd64.gz

# Make binary executable
chmod +x argo-linux-amd64

# Move binary to path
mv ./argo-linux-amd64 /usr/local/bin/argo

# Test installation
argo version

# Controller and server
kubectl create namespace argo
kubectl apply -n argo -f https://github.com/argoproj/argo-workflows/releases/download/v3.4.0-rc4/install.yaml

 

  • 新的 namespace
jeffrey@unsw-ThinkPad-T490:~$ sudo kubectl create ns argo
[sudo] password for jeffrey: 
namespace/argo created

jeffrey@unsw-ThinkPad-T490:~$ kubectl get namespaces
NAME                 STATUS   AGE
argo                 Active   32s
default              Active   9h
kube-node-lease      Active   9h
kube-public          Active   9h
kube-system          Active   9h
local-path-storage   Active   9h

jeffrey@unsw-ThinkPad-T490:~$ kubectl get pods -n kube-system
NAME                                              READY   STATUS    RESTARTS   AGE
coredns-565d847f94-d8p8b                          1/1     Running   0          9h
coredns-565d847f94-jgvx9                          1/1     Running   0          9h
etcd-mycluster-control-plane                      1/1     Running   0          9h
kindnet-7cql6                                     1/1     Running   0          9h
kube-apiserver-mycluster-control-plane            1/1     Running   0          9h
kube-controller-manager-mycluster-control-plane   1/1     Running   0          9h
kube-proxy-675z6                                  1/1     Running   0          9h
kube-scheduler-mycluster-control-plane            1/1     Running   0          9h

jeffrey@unsw-ThinkPad-T490:~$ kubectl get pods -n argo
No resources found in argo namespace.
  • 新的 resources

根据资源文件，创建了如下资源，等待几分钟后，才能全部启动："ready"。

jeffrey@unsw-ThinkPad-T490:~$ kubectl get pods -n argo
NAME                                   READY   STATUS              RESTARTS   AGE
argo-server-58f7cd6b78-mchd5           0/1     ContainerCreating   0          16s
httpbin-57cc54477b-m2hlp               0/1     ContainerCreating   0          16s
minio-559d785589-2hrv9                 0/1     ContainerCreating   0          16s
postgres-6596775946-kzbwd              0/1     ContainerCreating   0          16s
workflow-controller-64d599d7c6-qlllq   0/1     ContainerCreating   0          16s

jeffrey@unsw-ThinkPad-T490:~$ kubectl get pods -n argo
NAME                                   READY   STATUS    RESTARTS        AGE
argo-server-58f7cd6b78-mchd5           1/1     Running   2 (2m36s ago)   5m32s
httpbin-57cc54477b-m2hlp               1/1     Running   0               5m32s
minio-559d785589-2hrv9                 1/1     Running   0               5m32s
postgres-6596775946-kzbwd              1/1     Running   0               5m32s
workflow-controller-64d599d7c6-qlllq   1/1     Running   2 (2m37s ago)   5m32s

 

 

资源清单描述3种工作流

这里demo了三个基本的例子。

Ref: Argo Workflows and Pipelines - CI/CD, Machine Learning, and Other Kubernetes Workflows【视频讲解】

Download from https://github.com/vfarcic/argo-workflows-demo【相关代码】

  • 加载一个简单的镜像
ubuntu@ip-172-30-5-71:~/argo-workflows/argo-workflows-demo$ cat workflows/silly.yaml 
---------------------------------------------------------------------------------------
apiVersion: argoproj.io/v1alpha1
kind: Workflow ----> 创建的不是pod，也不是namespace，这里却是个workflow
metadata:
  generateName: very-
  labels:
    workflows.argoproj.io/archive-strategy: "false"
spec:
  entrypoint: silly
  serviceAccountName: workflow
  templates:
  - name: silly
    container:
      image: alpine:latest
      command: [ls]
      args: ["-l"]

 

  • 并行 工作流

Ref: https://youtu.be/UMaivwrAyTA?t=329

”- -“ 的意思在以上链接中有讲解。

ubuntu@ip-172-30-5-71:~/argo-workflows/argo-workflows-demo$ cat workflows/parallel.yaml 
----------------------------------------------------------------------------------------
apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
  generateName: parallel-
  labels:
    workflows.argoproj.io/archive-strategy: "false"
spec:
  entrypoint: hello
  serviceAccountName: workflow
  templates:
  - name: hello
    steps:
    - - name: ls
        template: template-ls
    - - name: sleep-a
        template: template-sleep  # 与sleep-b并行
      - name: sleep-b
        template: template-sleep
    - - name: delay
        template: template-delay  # sleep-a, sleep-b 全部结束后，再执行delay
    - - name: sleep
        template: template-sleep
  - name: template-ls
    container:
      image: alpine
      command: [ls]
      args: ["-l"]
  - name: template-sleep
    script:
      image: alpine
      command: [sleep]
      args: ["10"]
  - name: template-delay
    suspend:
      duration: "600s"

 

  • DAG 工作流
ubuntu@ip-172-30-5-71:~/argo-workflows/argo-workflows-demo$ cat workflows/dag.yaml 
apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
  generateName: dag-
  labels:
    workflows.argoproj.io/archive-strategy: "false"
spec:
  entrypoint: full
  serviceAccountName: workflow
  volumes:
  - name: kaniko-secret
    secret:
      secretName: regcred
      items:
        - key: .dockerconfigjson
          path: config.json
  templates:
  - name: full
    dag:
      tasks:
      - name: task-a
        template: my-task  # 与 task-b, task-c 同时执行
        arguments:
          parameters:
          - name: message
            value: This is task-a
      - name: task-b
        template: my-task
        arguments:
          parameters:
          - name: message
            value: This is task-b
      - name: task-c
        template: my-task
        arguments:
          parameters:
          - name: message
            value: This is task-c
      - name: task-d
        template: my-task
        arguments:
          parameters:
          - name: message
            value: This is task-d
        dependencies:
        - task-a
      - name: task-e
        template: my-task
        arguments:
          parameters:
          - name: message
            value: This is task-e
        dependencies:
        - task-a
      - name: task-f
        template: my-task
        arguments:
          parameters:
          - name: message
            value: This is task-f
        dependencies:
        - task-a
        - task-e
      - name: task-g
        template: my-task
        arguments:
          parameters:
          - name: message
            value: This is task-g
  - name: my-task
    inputs:
      parameters:
      - name: message
    container:
      image: alpine
      command: [echo]
      args:
      - "{{inputs.parameters.message}}"

 

 

 

 

Argo Workflows-Kubernetes的工作流引擎

阅读笔记，了解一些基本概念。

 

 mino是进行制品仓库。

Artifacts

Argo 支持接入对象存储，做全局的 Artifact 仓库，本地可以使用 MinIO.

使用对象存储存储 Artifact，最大的好处就是可以在 Pod 之间随意传数据，Pod 可以完全分布式地运行在 Kubernetes 集群的任何节点上。

Goto: [Argo] 07 - Parameters passing（For more details please check this）

另外也可以考虑借助 Artifact 仓库实现跨流水线的缓存复用（未测试），提升构建速度。

 

 配置一个server端的ingress，即可访问UI。

apiVersion: traefik.containo.us/v1alpha1
kind: IngressRoute
metadata:
  name: minio
  namespace: argo
spec:
  entryPoints:
  - web
  routes:
  - match: Host(`minio-test.coolops.cn`)
    kind: Rule
    services:
    - name: minio
      port: 9000

 

 

核心概念

三级定义: 要了解 Argo 定义的 CRD，先从其中的三级定义入手。概念上的从大到小 分别为 WorkflowTemplate，Workflow，template。这些资源的命名有些相似，所以会稍微有些迷惑性。

  • Workflow

Workflow是Argo中最重要的资源，其主要有两个重要功能：

    • 它定义要执行的工作流
    • 它存储工作流程的状态

要执行的工作流定义在Workflow.spec字段中，其主要包括templates和entrypoint。

apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
  generateName: hello-world-  # Workflow的配置名称
spec:
  entrypoint: whalesay        # 解析whalesay templates
  templates:
  - name: whalesay            # 定义whalesay templates，和entrypoint保持一致
    container:                # 定义一个容器，输出"helloworld"
      image: docker/whalesay
      command: [cowsay]
      args: ["hello world"]

 

  • Templates

Templates 是列表结构，主要分为两类：

    • 定义具体的工作流
    • 调用其他模板提供并行控制

 

定义具体的工作流

    • Container

- name: whalesay            
    container:                
      image: docker/whalesay
      command: [cowsay]
      args: ["hello world"]

    • Script

Script是Container的另一种包装实现，其定义方式和Container相同，只是增加了source字段用于自定义脚本，如下：

- name: gen-random-int
    script:
      image: python:alpine3.6
      command: [python]
      source: |
        import random
        i = random.randint(1, 100)
        print(i)

脚本的输出结果会根据调用方式自动导出到

{{tasks.<NAME>.outputs.result}}
{{steps.<NAME>.outputs.result}}

    • Resource 

Resource主要用于直接在K8S集群上 执行集群资源操作，可以 get, create, apply, delete, replace,  patch 集群资源。

如下在集群中创建一个ConfigMap类型资源：（ ConfigMap 是 Kubernetes 用来向应用 Pod 中注入配置数据的方法）

- name: k8s-owner-reference
    resource:
      action: create
      manifest: |
        apiVersion: v1
        kind: ConfigMap
        metadata:
          generateName: owned-eg-
        data:
          some: value

    • Suspend

Suspend主要用于暂停，可以暂停一段时间，也可以手动恢复，命令使用 argo resume 进行恢复。定义格式如下：

- name: delay
    suspend:
      duration: "20s"

 

调用其他模板提供并行控制

    • Steps

Steps主要是通过定义一系列步骤来定义任务，其结构是"list of lists"；

外部列表将顺序执行，内部列表将并行执行。如下：

（step1和step2a是顺序执行，而step2a和step2b是并行执行）

- name: hello-hello-hello
    steps:
    - - name: step1
        template: prepare-data
    - - name: step2a
        template: run-data-first-half
      - name: step2b
        template: run-data-second-half

(a) 通过When来进行条件判断。

apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
  generateName: coinflip-
spec:
  entrypoint: coinflip
  templates:
  - name: coinflip
    steps:
    - - name: flip-coin
        template: flip-coin
- - name: heads
        template: heads
        when: "{{steps.flip-coin.outputs.result}} == heads"
      - name: tails
        template: tails
        when: "{{steps.flip-coin.outputs.result}} == tails"
  - name: flip-coin
    script:
      image: python:alpine3.6
      command: [python]
      source: |
        import random
        result = "heads" if random.randint(0,1) == 0 else "tails"
        print(result)
  - name: heads
    container:
      image: alpine:3.6
      command: [sh, -c]
      args: ["echo \"it was heads\""]
  - name: tails
    container:
      image: alpine:3.6
      command: [sh, -c]
      args: ["echo \"it was tails\""]

(b) 进行循环操作。

Ref: Kubernetes 工作流引擎：Argo（1）

apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
  generateName: loops-
spec:
  entrypoint: loop-example
  templates:
  - name: loop-example
    steps:
    - - name: print-message
        template: whalesay
        arguments:
          parameters:
          - name: message
            value: "{{item}}"
        withItems:    # <---- 执行了两次该step，生成了两个pods
        - hello world
        - goodbye world
  - name: whalesay
    inputs:
      parameters:
      - name: message
    container:
      image: docker/whalesay:latest
      command: [cowsay]
      args: ["{{inputs.parameters.message}}"]

 

Ref: Kubernetes 工作流引擎：Argo（1）【强烈推荐过一遍】

图文讲解，不错的样子！

(1) NodePort 类型的 Service。

对一些应用的某些部分（如前端），可能希望将其暴露给 Kubernetes 集群外部的 IP 地址。

Kubernetes ServiceTypes 允许指定你所需要的 Service 类型，默认是 ClusterIP。

Type 的取值以及行为如下：

ClusterIP：通过集群的内部 IP 暴露服务，选择该值时服务只能够在集群内部访问。 这也是默认的 ServiceType。

NodePort：通过每个节点上的 IP 和静态端口（NodePort）暴露服务。 NodePort 服务会路由到自动创建的 ClusterIP 服务。 通过请求 <节点 IP>:<节点端口>，你可以从集群的外部访问一个 NodePort 服务。

LoadBalancer：使用云提供商的负载均衡器向外部暴露服务。 外部负载均衡器可以将流量路由到自动创建的 NodePort 服务和 ClusterIP 服务上。

ExternalName：通过返回 CNAME 和对应值，可以将服务映射到 externalName 字段的内容（例如，foo.bar.example.com）。 无需创建任何类型代理。
ClusterIP乃四个类型之一
$ kubectl get pods -n argo
NAME                                   READY   STATUS              RESTARTS   AGE
argo-ui-76c6cf75b4-vh6w6               0/1     ContainerCreating   0          14s
workflow-controller-69f6ff7cbc-5pqbj   0/1     ContainerCreating   0          14s
-------------------------------------------------------------------------------------------------
$ kubectl get pods -n argo
NAME                                   READY   STATUS    RESTARTS   AGE
argo-ui-76c6cf75b4-vh6w6               1/1     Running   0          10m
workflow-controller-69f6ff7cbc-5pqbj   1/1     Running   0          10m
-------------------------------------------------------------------------------------------------
$ kubectl get svc -n argo
NAME      TYPE        CLUSTER-IP      EXTERNAL-IP   PORT(S)   AGE
argo-ui   ClusterIP   10.97.124.167   <none>        80/TCP    10m
-------------------------------------------------------------------------------------------------
$ kubectl edit svc argo-ui -n argo
kind: Service
metadata:
......
spec:
......
  sessionAffinity: None
  type: NodePort
......
service/argo-ui edited
-------------------------------------------------------------------------------------------------
$ kubectl get svc -n argo
NAME      TYPE       CLUSTER-IP      EXTERNAL-IP   PORT(S)        AGE
argo-ui   NodePort   10.97.124.167   <none>        80:32686/TCP   12m
-------------------------------------------------------------------------------------------------
$ kubectl get crd |grep argo
workflows.argoproj.io                         2019-09-10T03:27:41Z
-------------------------------------------------------------------------------------------------
$ kubectl api-versions |grep argo
argoproj.io/v1alpha1
具体操作

(2) 如果出现了一个权限错误，Argo 官网上给出的解决方案是给 default:default 绑定上 admin 的 clusterrole 权限： 

$ kubectl create rolebinding default-admin --clusterrole=admin --serviceaccount=default:default
View Code

(3) 递归操作。

# coinflip-recursive is a variation of the coinflip example.
 # This is an example of a dynamic workflow which extends
 # indefinitely until it achieves a desired result. In this
 # example, the 'flip-coin' step is recursively repeated until
 # the result of the step is "heads".
 apiVersion: argoproj.io/v1alpha1
 kind: Workflow
 metadata:
   generateName: coinflip-recursive-
 spec:
   entrypoint: coinflip
   templates:
   - name: coinflip
     steps:
     - - name: flip-coin
         template: flip-coin
     - - name: heads
         template: heads
         when: "{{steps.flip-coin.outputs.result}} == heads"
       - name: tails
         template: coinflip
         when: "{{steps.flip-coin.outputs.result}} == tails"
 
   - name: flip-coin
     script:
       image: python:alpine3.6
       command: [python]
       source: |
         import random
         result = "heads" if random.randint(0,1) == 0 else "tails"
         print(result)
 
   - name: heads
     container:
       image: alpine:3.6
       command: [sh, -c]
       args: ["echo \"it was heads\""]
View Code

每一次递归调用都会产生一个 Pod！

 

    • Dag

Ref: Argo Workflows-Kubernetes的工作流引擎（下）

 

  • Variables 变量

首先在spec字段定义arguments，定义变量message，其值是hello world，然后在templates字段中需要先定义一个inputs字段，用于templates的输入参数，然后在使用"{{}}"形式引用变量。

apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
  generateName: hello-world-parameters-
spec:
  entrypoint: whalesay
  arguments:
    parameters:
      - name: message    # <----　定义变量
        value: hello world
  templates:
    - name: whalesay
      inputs:
        parameters:
          - name: message
      container:
        image: docker/whalesay
        command: [ cowsay ]
        args: [ "{{inputs.parameters.message}}" ]

变量还可以进行一些函数运算，主要有：

    • filter：过滤
    • asInt：转换为Int
    • asFloat：转换为Float
    • string：转换为String
    • toJson：转换为Json

例子：

filter([1, 2], { # > 1})
asInt(inputs.parameters["my-int-param"])
asFloat(inputs.parameters["my-float-param"])
string(1)
toJson([1, 2])

更多语法可以访问https://github.com/antonmedv/expr/blob/master/docs/Language-Definition.md进行学习。

 

  • 制品库

在安装argo的时候，已经安装了mino作为制品库。

For more details, please check [Argo] 07 - Parameters passing - Artifacts

 

  • WorkflowTemplate

WorkflowTemplate 相当于是 Workflow 的模板库，和 Workflow 一样，也由 template 组成。用户在创建完 WorkflowTemplate 后，可以通过直接提交它们来执行 Workflow。 

Ref: Argo Workflows-Kubernetes的工作流引擎（下）

(1) 定义了了一个 WorkflowTemplate。

apiVersion: argoproj.io/v1alpha1
kind: WorkflowTemplate
metadata:
  name: workflow-template-1
spec:
  entrypoint: whalesay-template
  arguments:
    parameters:
      - name: message
        value: hello world
  templates:
    - name: whalesay-template
      inputs:
        parameters:
          - name: message
      container:
        image: docker/whalesay
        command: [cowsay]
        args: ["{{inputs.parameters.message}}"]
kind: WorkflowTemplate

 (2) 加载

jeff@unsw-ThinkPad-T490:Argo$ argo template create workflowtemplate.yaml -n argo
Name:                workflow-template-1
Namespace:           argo
Created:             Sat Sep 17 17:55:12 +1000 (now)
jeffrey@unsw-ThinkPad-T490:Argo$ argo submit call_workflowtemplate.yaml -n argo
Name:                workflow-template-hello-world-5mrr4
Namespace:           argo
ServiceAccount:      unset (will run with the default ServiceAccount)
Status:              Pending
Created:             Sat Sep 17 17:56:32 +1000 (now)
Progress:

 

分析call_workflowtemplate.yaml 如下所示。

apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
  generateName: workflow-template-hello-world-
spec:
  entrypoint: whalesay
  templates:
  - name: whalesay
    steps:                                # 引用模板必须在steps/dag/template下
      - - name: call-whalesay-template
          templateRef:                    # 应用模板字段
            name: workflow-template-1     # WorkflowTemplate名
            template: whalesay-template   # 具体的template名
          arguments:                      # 参数
            parameters:
            - name: message
              value: "hello world"

 

  • ClusterWorkflowTemplate

ClusterWorkflowTemplate创建的是一个集群范围内的WorkflowTemplate，其他workflow可以引用它。

(1) 定义了了一个 ClusterWorkflowTemplate。

apiVersion: argoproj.io/v1alpha1
kind: ClusterWorkflowTemplate
metadata:
  name: cluster-workflow-template-whalesay-template
spec:
  templates:
  - name: whalesay-template
    inputs:
      parameters:
      - name: message
    container:
      image: docker/whalesay
      command: [cowsay]
      args: ["{{inputs.parameters.message}}"]
kind: ClusterWorkflowTemplate

 (2) 加载

jeffrey@unsw-ThinkPad-T490:Argo$ argo cluster-template create clusterWorkflowtemplate.yaml -n argo
Name:                cluster-workflow-template-whalesay-template
Created:             Sat Sep 17 18:07:32 +1000 (now)

jeffrey@unsw-ThinkPad-T490:Argo$ argo submit call_clusterWorkflowtemplate.yaml -n argo
Name:                workflow-template-hello-world-c7b4c
Namespace:           argo
ServiceAccount:      unset (will run with the default ServiceAccount)
Status:              Pending
Created:             Sat Sep 17 18:09:19 +1000 (now)
Progress:

 

分析call_clusterWorkflowtemplate.yaml 如下所示。

apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
  generateName: workflow-template-hello-world-
spec:
  entrypoint: whalesay
  templates:
  - name: whalesay
    steps:          
      - - name: call-whalesay-template
          templateRef:                                          # 引用模板
            name: cluster-workflow-template-whalesay-template   # ClusterWorkflow名
            template: whalesay-template                         # 具体的模板名
            clusterScope: true                                  # 表示是ClusterWorkflow
          arguments:                                            #  参数
            parameters:
            - name: message
              value: "hello world"

 

 

 

 实践CI/CD的整个流程很简单，即：拉代码->编译->构建镜像->上传镜像->部署

 

apiVersion: argoproj.io/v1alpha1
kind: WorkflowTemplate
metadata:
  annotations:
    workflows.argoproj.io/description: |
      Checkout out from Git, build and deploy application.
    workflows.argoproj.io/maintainer: '@joker'
    workflows.argoproj.io/tags: java, git
    workflows.argoproj.io/version: '>= 2.9.0'
  name: devops-java 
spec:
  entrypoint: main
  arguments:
    parameters:
      - name: repo
        value: gitlab-test.coolops.cn/root/springboot-helloworld.git
      - name: branch
        value: master
      - name: image
        value: registry.cn-hangzhou.aliyuncs.com/rookieops/myapp:202103101613
      - name: cache-image
        value: registry.cn-hangzhou.aliyuncs.com/rookieops/myapp
      - name: dockerfile
        value: Dockerfile
      - name: devops-cd-repo
        value: gitlab-test.coolops.cn/root/devops-cd.git
      - name: gitlabUsername
        value: devops
      - name: gitlabPassword
        value: devops123456
  templates:
    - name: main
      steps:
        - - name: Checkout
            template: Checkout
        - - name: Build
            template: Build
        - - name: BuildImage
            template: BuildImage
        - - name: Deploy
            template: Deploy
    # 拉取代码
    - name: Checkout
      script:
        image: registry.cn-hangzhou.aliyuncs.com/rookieops/maven:3.5.0-alpine
        workingDir: /work
        command:
        - sh
        source: |
          git clone --branch {{workflow.parameters.branch}} http://{{workflow.parameters.gitlabUsername}}:{{workflow.parameters.gitlabPassword}}@{{workflow.parameters.repo}} .
        volumeMounts:
          - mountPath: /work
            name: work
    # 编译打包  
    - name: Build
      script:
        image: registry.cn-hangzhou.aliyuncs.com/rookieops/maven:3.5.0-alpine
        workingDir: /work
        command:
        - sh
        source: mvn -B clean package -Dmaven.test.skip=true -Dautoconfig.skip
        volumeMounts:
          - mountPath: /work
            name: work
    # 构建镜像  
    - name: BuildImage
      volumes:
      - name: docker-config
        secret:
          secretName: docker-config
      container:
        image: registry.cn-hangzhou.aliyuncs.com/rookieops/kaniko-executor:v1.5.0
        workingDir: /work
        args:
          - --context=.
          - --dockerfile={{workflow.parameters.dockerfile}}
          - --destination={{workflow.parameters.image}}
          - --skip-tls-verify
          - --reproducible
          - --cache=true
          - --cache-repo={{workflow.parameters.cache-image}}
        volumeMounts:
          - mountPath: /work
            name: work
          - name: docker-config
            mountPath: /kaniko/.docker/
    # 部署  
    - name: Deploy
      script:
        image: registry.cn-hangzhou.aliyuncs.com/rookieops/kustomize:v3.8.1
        workingDir: /work
        command:
        - sh
        source: |
           git remote set-url origin http://{{workflow.parameters.gitlabUsername}}:{{workflow.parameters.gitlabPassword}}@{{workflow.parameters.devops-cd-repo}}
           git config --global user.name "Administrator"
           git config --global user.email "coolops@163.com"
           git clone http://{{workflow.parameters.gitlabUsername}}:{{workflow.parameters.gitlabPassword}}@{{workflow.parameters.devops-cd-repo}} /work/devops-cd
           cd /work/devops-cd
           git pull
           cd /work/devops-cd/devops-simple-java
           kustomize edit set image {{workflow.parameters.image}}
           git commit -am 'image update'
           git push origin master
        volumeMounts:
          - mountPath: /work
            name: work
  volumeClaimTemplates:
    - name: work
      metadata:
        name: work
      spec:
        storageClassName: nfs-client-storageclass
        accessModes: [ "ReadWriteOnce" ]
        resources:
          requests:
            storage: 1Gi
View Code

(1) Annotations用于非识别信息，即 Kubernetes 不关心的元数据。因此，注解键和值没有约束。因此，如果您想为其他人添加有关给定资源的信息，则注解是更好的选择。

apiVersion: argoproj.io/v1alpha1
kind: WorkflowTemplate
metadata:
  annotations:
    workflows.argoproj.io/description: |Checkout out from Git, build and deploy application.
    workflows.argoproj.io/maintainer: '@joker'
    workflows.argoproj.io/tags: java, git
    workflows.argoproj.io/version: '>= 2.9.0'
  name: devops-java

 

 (2) Initial parameters

spec:
  entrypoint: main
  arguments:
    parameters:
      - name: repo
        value: gitlab-test.coolops.cn/root/springboot-helloworld.git
      - name: branch
        value: master
      - name: image
        value: registry.cn-hangzhou.aliyuncs.com/rookieops/myapp:202103101613
      - name: cache-image
        value: registry.cn-hangzhou.aliyuncs.com/rookieops/myapp
      - name: dockerfile
        value: Dockerfile
      - name: devops-cd-repo
        value: gitlab-test.coolops.cn/root/devops-cd.git
      - name: gitlabUsername
        value: devops
      - name: gitlabPassword
        value: devops123456

 

(3)

  templates:
    - name: main
      steps:
        - - name: Checkout
            template: Checkout
        - - name: Build
            template: Build
        - - name: BuildImage
            template: BuildImage
        - - name: Deploy
            template: Deploy

 

(3.1)

    # 拉取代码
    - name: Checkout
      script:
        image: registry.cn-hangzhou.aliyuncs.com/rookieops/maven:3.5.0-alpine
        workingDir: /work  # ----> 默认就下载到了这里
        command:
        - sh
        source: |
          git clone --branch {{workflow.parameters.branch}} http://{{workflow.parameters.gitlabUsername}}:{{workflow.parameters.gitlabPassword}}@{{workflow.parameters.repo}} .
        volumeMounts:
          - mountPath: /work
            name: work

 

(3.2)

    # 编译打包  
    - name: Build
      script:
        image: registry.cn-hangzhou.aliyuncs.com/rookieops/maven:3.5.0-alpine
        workingDir: /work
        command:
        - sh
        source: mvn -B clean package -Dmaven.test.skip=true -Dautoconfig.skip
        volumeMounts:
          - mountPath: /work
            name: work

 

(3.3) 特点，镜像中 build 镜像。

    # 构建镜像
    - name: BuildImage
      volumes:
      - name: docker-config
        secret:
          secretName: docker-config
      container:
        image: registry.cn-hangzhou.aliyuncs.com/rookieops/kaniko-executor:v1.5.0
        workingDir: /work
        args:
          - --context=.
          - --dockerfile={{workflow.parameters.dockerfile}}
          - --destination={{workflow.parameters.image}}
          - --skip-tls-verify
          - --reproducible
          - --cache=true
          - --cache-repo={{workflow.parameters.cache-image}}
        volumeMounts:
          - mountPath: /work
            name: work
          - name: docker-config
            mountPath: /kaniko/.docker/

 

(3.4)

    # 部署  
    - name: Deploy
      script:
        image: registry.cn-hangzhou.aliyuncs.com/rookieops/kustomize:v3.8.1
        workingDir: /work
        command:
        - sh
        source: |
           git remote set-url origin http://{{workflow.parameters.gitlabUsername}}:{{workflow.parameters.gitlabPassword}}@{{workflow.parameters.devops-cd-repo}}
           git config --global user.name "Administrator"
           git config --global user.email "coolops@163.com"
           git clone http://{{workflow.parameters.gitlabUsername}}:{{workflow.parameters.gitlabPassword}}@{{workflow.parameters.devops-cd-repo}} /work/devops-cd
           cd /work/devops-cd
           git pull
           cd /work/devops-cd/devops-simple-java
           kustomize edit set image {{workflow.parameters.image}}
           git commit -am 'image update'
           git push origin master
        volumeMounts:
          - mountPath: /work
            name: work

 

 

End.

posted @ 2022-09-11 10:28  郝壹贰叁  阅读(451)  评论(0)  编辑  收藏  举报