超详细实操-基于容器的本地工作流引擎-argo(一)
文章目录
- 1. 什么是Argo工作流？
- 2. 部署argo
- 2.1 创建名称空间，下载yaml文件
- 3. 如何使用 Argo workflow
- 3.1 Hello World!
- 3.2 Parameters参数
- 3.3 Steps步骤
- 3.4 DAG(有向无环图)
- 3.5 Artifacts工件（制品）
- 3.6 工作流结构
- 3.7 Loops 循环
- 3.8 条件
- 3.9 secrets
- 3.10 Scripts & Results
- 3.11 Output Parameters
- 3.12 Retrying Failed or Errored Steps
- 3.13 Recursion(递归)
- 3.14 Exit handlers(退出处理)
- 3.15 Timeouts
- 总结
- 参考文档
1. 什么是Argo工作流？
Argo Workflows是一个开源的容器本地工作流引擎，用于在kubernetes上协调运行作业。Argo Workflows是基于kubernetes CRD实现的
功能：
-
定义工作流，其中工作流中的每一个步骤都是一个容器
-
将多个步骤工作流建模成一系列的任务，或者使用有向无环图（DAG）捕获任务间的依赖关系
-
使用kubernetes上的Argo Workflows可以在短时间内轻松操作大量计算密集型作业
-
不需要配置复杂的软件开发产品就可以在kubernetes本地环境中运行CI/CD
Argo是Cloud Native Computing Foundation（CNCF）托管的项目。
2. 部署argo
官网部署介绍
-
创建名称空间并且运行安装文件（安装文件从网上下载）
-
分配权限
-
设置port-forward 访问
kubectl port-forward 通过端口转发映射本地端口到指定的应用端口，从而访问集群中的应用程序(Pod).
-
安装 Argo CLI 命令行客户端
实操演示
2.1 创建名称空间，下载yaml文件
kubectl create ns argo
//在安装yaml文件的目录下，执行
kubectl apply -n argo -f .
注意：
1. yaml文件中的进行镜像吗，默认从docker hub中获取。注意网络。可以事先下载好
2. 注意deployment的节点选择 确保相应节点包换对应标签。也可以去掉节点选择，随机调度
下载install.yaml,Argo版本下载是当时最新版本的2.9.0
# This is an auto-generated file. DO NOT EDIT
apiVersion: apiextensions.k8s.io/v1beta1
kind: CustomResourceDefinition
metadata:
name: clusterworkflowtemplates.argoproj.io
spec:
group: argoproj.io
names:
kind: ClusterWorkflowTemplate
plural: clusterworkflowtemplates
shortNames:
- clusterwftmpl
- cwft
scope: Cluster
version: v1alpha1
---
apiVersion: apiextensions.k8s.io/v1beta1
kind: CustomResourceDefinition
metadata:
name: cronworkflows.argoproj.io
spec:
group: argoproj.io
names:
kind: CronWorkflow
plural: cronworkflows
shortNames:
- cronwf
- cwf
scope: Namespaced
version: v1alpha1
---
apiVersion: apiextensions.k8s.io/v1beta1
kind: CustomResourceDefinition
metadata:
name: workflows.argoproj.io
spec:
additionalPrinterColumns:
- JSONPath: .status.phase
description: Status of the workflow
name: Status
type: string
- JSONPath: .status.startedAt
description: When the workflow was started
format: date-time
name: Age
type: date
group: argoproj.io
names:
kind: Workflow
plural: workflows
shortNames:
- wf
scope: Namespaced
version: v1alpha1
---
apiVersion: apiextensions.k8s.io/v1beta1
kind: CustomResourceDefinition
metadata:
name: workflowtemplates.argoproj.io
spec:
group: argoproj.io
names:
kind: WorkflowTemplate
plural: workflowtemplates
shortNames:
- wftmpl
scope: Namespaced
version: v1alpha1
---
apiVersion: v1
kind: ServiceAccount
metadata:
name: argo
---
apiVersion: v1
kind: ServiceAccount
metadata:
name: argo-server
---
apiVersion: rbac.authorization.k8s.io/v1
kind: Role
metadata:
name: argo-role
rules:
- apiGroups:
- ""
resources:
- secrets
verbs:
- get
---
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
labels:
rbac.authorization.k8s.io/aggregate-to-admin: "true"
name: argo-aggregate-to-admin
rules:
- apiGroups:
- argoproj.io
resources:
- workflows
- workflows/finalizers
- workflowtemplates
- workflowtemplates/finalizers
- cronworkflows
- cronworkflows/finalizers
- clusterworkflowtemplates
- clusterworkflowtemplates/finalizers
verbs:
- create
- delete
- deletecollection
- get
- list
- patch
- update
- watch
---
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
labels:
rbac.authorization.k8s.io/aggregate-to-edit: "true"
name: argo-aggregate-to-edit
rules:
- apiGroups:
- argoproj.io
resources:
- workflows
- workflows/finalizers
- workflowtemplates
- workflowtemplates/finalizers
- cronworkflows
- cronworkflows/finalizers
- clusterworkflowtemplates
- clusterworkflowtemplates/finalizers
verbs:
- create
- delete
- deletecollection
- get
- list
- patch
- update
- watch
---
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
labels:
rbac.authorization.k8s.io/aggregate-to-view: "true"
name: argo-aggregate-to-view
rules:
- apiGroups:
- argoproj.io
resources:
- workflows
- workflows/finalizers
- workflowtemplates
- workflowtemplates/finalizers
- cronworkflows
- cronworkflows/finalizers
- clusterworkflowtemplates
- clusterworkflowtemplates/finalizers
verbs:
- get
- list
- watch
---
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
name: argo-cluster-role
rules:
- apiGroups:
- ""
resources:
- pods
- pods/exec
verbs:
- create
- get
- list
- watch
- update
- patch
- delete
- apiGroups:
- ""
resources:
- configmaps
verbs:
- get
- watch
- list
- apiGroups:
- ""
resources:
- persistentvolumeclaims
verbs:
- create
- delete
- apiGroups:
- argoproj.io
resources:
- workflows
- workflows/finalizers
verbs:
- get
- list
- watch
- update
- patch
- delete
- create
- apiGroups:
- argoproj.io
resources:
- workflowtemplates
- workflowtemplates/finalizers
- clusterworkflowtemplates
- clusterworkflowtemplates/finalizers
verbs:
- get
- list
- watch
- apiGroups:
- ""
resources:
- serviceaccounts
verbs:
- get
- list
- apiGroups:
- argoproj.io
resources:
- cronworkflows
- cronworkflows/finalizers
verbs:
- get
- list
- watch
- update
- patch
- delete
- apiGroups:
- ""
resources:
- events
verbs:
- create
- patch
- apiGroups:
- policy
resources:
- poddisruptionbudgets
verbs:
- create
- get
- delete
---
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
name: argo-server-cluster-role
rules:
- apiGroups:
- ""
resources:
- configmaps
verbs:
- get
- watch
- list
- apiGroups:
- ""
resources:
- secrets
verbs:
- get
- apiGroups:
- ""
resources:
- pods
- pods/exec
- pods/log
verbs:
- get
- list
- watch
- delete
- apiGroups:
- argoproj.io
resources:
- workflows
- workflowtemplates
- cronworkflows
- clusterworkflowtemplates
verbs:
- create
- get
- list
- watch
- update
- patch
- delete
---
apiVersion: rbac.authorization.k8s.io/v1
kind: RoleBinding
metadata:
name: argo-binding
roleRef:
apiGroup: rbac.authorization.k8s.io
kind: Role
name: argo-role
subjects:
- kind: ServiceAccount
name: argo
---
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata:
name: argo-binding
roleRef:
apiGroup: rbac.authorization.k8s.io
kind: ClusterRole
name: argo-cluster-role
subjects:
- kind: ServiceAccount
name: argo
namespace: argo
---
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata:
name: argo-server-binding
roleRef:
apiGroup: rbac.authorization.k8s.io
kind: ClusterRole
name: argo-server-cluster-role
subjects:
- kind: ServiceAccount
name: argo-server
namespace: argo
---
apiVersion: v1
kind: ConfigMap
metadata:
name: workflow-controller-configmap
---
apiVersion: v1
kind: Service
metadata:
name: argo-server
spec:
ports:
- name: web
port: 2746
targetPort: 2746
selector:
app: argo-server
---
apiVersion: v1
kind: Service
metadata:
name: workflow-controller-metrics
spec:
ports:
- name: metrics
port: 9090
protocol: TCP
targetPort: 9090
selector:
app: workflow-controller
---
apiVersion: apps/v1
kind: Deployment
metadata:
name: argo-server
spec:
selector:
matchLabels:
app: argo-server
template:
metadata:
labels:
app: argo-server
spec:
containers:
- args:
- server
image: argoproj/argocli:v2.9.0
name: argo-server
ports:
- containerPort: 2746
name: web
readinessProbe:
httpGet:
path: /
port: 2746
scheme: HTTP
initialDelaySeconds: 10
periodSeconds: 20
nodeSelector:
kubernetes.io/os: linux
serviceAccountName: argo-server
---
apiVersion: apps/v1
kind: Deployment
metadata:
name: workflow-controller
spec:
selector:
matchLabels:
app: workflow-controller
template:
metadata:
labels:
app: workflow-controller
spec:
containers:
- args:
- --configmap
- workflow-controller-configmap
- --executor-image
- argoproj/argoexec:v2.9.0
command:
- workflow-controller
image: argoproj/workflow-controller:v2.9.0
name: workflow-controller
nodeSelector:
kubernetes.io/os: linux
serviceAccountName: argo
可以看到argo安装yaml文件中有很多的kubernetes 资源对象。
- CustomResourceDefinition
- ServiceAccount、ClusterRole、RoleBinding
- ConfigMap
- Service
- Deployment
安装完成后，为了访问方便，我们将 argo-ui 改成 NodePort 类型的 Service（当然也可以创建 Ingress 对象通过域名进行访问）：
[root@mydevops ~]# kubectl get svc -n argo
NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
argo-server NodePort 10.109.54.114 <none> 2746:30006/TCP 12d
workflow-controller-metrics ClusterIP 10.96.121.111 <none> 9090/TCP 12d
[root@mydevops ~]# kubectl get svc -n argo argo-server -oyaml
apiVersion: v1
kind: Service
metadata:
annotations:
kubectl.kubernetes.io/last-applied-configuration: |
{"apiVersion":"v1","kind":"Service","metadata":{"annotations":{},"name":"argo-server","namespace":"argo"},"spec":{"ports":[{"name":"web","port":2746,"targetPort":2746}],"selector":{"app":"argo-server"}}}
creationTimestamp: 2020-07-03T08:40:58Z
name: argo-server
namespace: argo
resourceVersion: "76349"
selfLink: /api/v1/namespaces/argo/services/argo-server
uid: eac4c614-bd08-11ea-90d4-000c2944bd9b
spec:
clusterIP: 10.109.54.114
externalTrafficPolicy: Cluster
ports:
- name: web
nodePort: 30006
port: 2746
protocol: TCP
targetPort: 2746
selector:
app: argo-server
sessionAffinity: None
type: NodePort
status:
loadBalancer: {}
[root@mydevops ~]# kubectl get pod -n argo
NAME READY STATUS RESTARTS AGE
argo-server-bcdd887f6-f44tk 1/1 Running 0 10m
workflow-controller-69bb45b59d-dxkjh 1/1 Running 0 10m
安装部署完成后可以浏览器访问UI页面:
也可以安装argo 命令行客户端：
# Download the binary
curl -sLO https://github.com/argoproj/argo/releases/download/v2.9.0/argo-linux-amd64
# Make binary executable
chmod +x argo-linux-amd64
# Move binary to path
mv ./argo-linux-amd64 /usr/local/bin/argo
# Test installation
argo version
//本地演示
[root@mydevops ~]# argo version
argo: v2.9.0
BuildDate: 2020-07-02T00:49:55Z
GitCommit: d67d3b1dbc61ebc5789806794ccd7e2debd71ffc
GitTreeState: clean
GitTag: v2.9.0
GoVersion: go1.13.4
Compiler: gc
Platform: linux/amd64
3. 如何使用 Argo workflow
Argo是一个开源项目，它为Kubernetes提供容器本地工作流。Argo工作流中的每一步都被定义为一个容器。
下面通过演练官网示例学习语法：
关于Argo ClI |
argo or kubectlArgo实现为Kubernetes CRD(自定义资源)。因此，可以使用kubectl管理Argo工作流，但是argo命令行提供更加丰富的功能：如参数替换、yaml文件语法校验等等
argo示例argo submit hello-world.yaml # submit a workflow spec to Kubernetes argo list # list current workflows argo get hello-world-xxx # get info about a specific workflow argo logs -w hello-world-xxx # get logs from all steps in a workflow argo logs hello-world-xxx-yyy # get logs from a specific step in a workflow argo delete hello-world-xxx # delete workflow
kubectl示例kubectl create -f hello-world.yaml kubectl get wf kubectl get wf hello-world-xxx kubectl get po --selector=workflows.argoproj.io/workflow=hello-world-xxx --show-all # similar to argo kubectl logs hello-world-xxx-yyy -c main kubectl delete wf hello-world-xxx
3.1 Hello World!
使用dokcer Hub提供的镜像
[root@mydevops ~]# docker run docker/whalesay cowsay "hello world"
_____________
< hello world >
-------------
\
\
\
## .
## ## ## ==
## ## ## ## ===
/""""""""""""""""___/ ===
~~~ {~~ ~~~~ ~~~ ~~~~ ~~ ~ / ===- ~~~
\______ o __/
\ \ __/
\____\______/
现在开始使用argo在kubernetes上运行这个容器
apiVersion: argoproj.io/v1alpha1
kind: Workflow # new type of k8s spec
metadata:
generateName: hello-world- # name of the workflow spec
spec:
# 调用whalesay模板，这个工作流yaml,
# 主要学习entrypoint、templates
# 在Workflow中 入口entrypoint决定从哪个模板开始
# templates定义所有的模板内容
entrypoint: whalesay
templates:
- name: whalesay # name of the template
container:
image: docker/whalesay
command: [cowsay]
args: ["hello world"]
resources: # limit the resources
limits:
memory: 32Mi
cpu: 100m
运行结果：
[root@mydevops examples]# argo submit hello-world.yaml
Name: hello-world-wpqhc
Namespace: default
ServiceAccount: default
Status: Pending
Created: Thu Jul 16 17:16:28 +0800 (now)
[root@mydevops examples]# argo list
NAME STATUS AGE DURATION PRIORITY
hello-world-wpqhc Running 6s 6s 0
[root@mydevops examples]# kubectl get pod
NAME READY STATUS RESTARTS AGE
hello-world-wpqhc 0/2 Completed 0 47s
[root@mydevops examples]# argo get hello-world-wpqhc
Name: hello-world-wpqhc
Namespace: default
ServiceAccount: default
Status: Succeeded
Conditions:
Completed True
Created: Thu Jul 16 17:16:28 +0800 (1 minute ago)
Started: Thu Jul 16 17:16:28 +0800 (1 minute ago)
Finished: Thu Jul 16 17:16:45 +0800 (50 seconds ago)
Duration: 17 seconds
ResourcesDuration: 6s*(1 cpu),5s*(100Mi memory)
STEP TEMPLATE PODNAME DURATION MESSAGE
✔ hello-world-wpqhc whalesay hello-world-wpqhc 15s
[root@mydevops examples]# argo logs hello-world-wpqhc
hello-world-wpqhc: _____________
hello-world-wpqhc: < hello world >
hello-world-wpqhc: -------------
hello-world-wpqhc: \
hello-world-wpqhc: \
hello-world-wpqhc: \
hello-world-wpqhc: ## .
hello-world-wpqhc: ## ## ## ==
hello-world-wpqhc: ## ## ## ## ===
hello-world-wpqhc: /""""""""""""""""___/ ===
hello-world-wpqhc: ~~~ {~~ ~~~~ ~~~ ~~~~ ~~ ~ / ===- ~~~
hello-world-wpqhc: \______ o __/
hello-world-wpqhc: \ \ __/
hello-world-wpqhc: \____\______/
UI页面显示：
3.2 Parameters参数
带有参数的yaml文件
apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
generateName: hello-world-parameters-
spec:
# 调用 whalesay
entrypoint: whalesay
# 定义参数message 值"hello world"
arguments:
parameters:
- name: message
value: hello world
templates:
- name: whalesay
inputs:
parameters:
- name: message # parameter declaration
container:
# run cowsay with that message input parameter as args
image: docker/whalesay
command: [cowsay]
args: ["{{inputs.parameters.message}}"]
运行结果：
#如果工作流yaml文件包含输入参数，可以利用argo -p 给参数赋值
#argo --parameter-file params.yaml 指定参数文件
#argo --entrypoint whalesay-caps 指定工作流其实模板
[root@mydevops examples]# argo submit arguments-parameters.yaml -p message="goodbye world"
Name: hello-world-parameters-ml4x4
Namespace: default
ServiceAccount: default
Status: Pending
Created: Thu Jul 16 17:33:47 +0800 (now)
Parameters:
message: goodbye world
[root@mydevops examples]# kubectl get pod
NAME READY STATUS RESTARTS AGE
hello-world-parameters-ml4x4 0/2 ContainerCreating 0 12s
hello-world-wpqhc 0/2 Completed 0 17m
[root@mydevops examples]# argo list hello-world-parameters-ml4x4
NAME STATUS AGE DURATION PRIORITY
hello-world-parameters-ml4x4 Succeeded 3m 17s 0
hello-world-wpqhc Succeeded 21m 17s 0
[root@mydevops examples]# argo logs hello-world-parameters-ml4x4
hello-world-parameters-ml4x4: _______________
hello-world-parameters-ml4x4: < goodbye world >
hello-world-parameters-ml4x4: ---------------
hello-world-parameters-ml4x4: \
hello-world-parameters-ml4x4: \
hello-world-parameters-ml4x4: \
hello-world-parameters-ml4x4: ## .
hello-world-parameters-ml4x4: ## ## ## ==
hello-world-parameters-ml4x4: ## ## ## ## ===
hello-world-parameters-ml4x4: /""""""""""""""""___/ ===
hello-world-parameters-ml4x4: ~~~ {~~ ~~~~ ~~~ ~~~~ ~~ ~ / ===- ~~~
hello-world-parameters-ml4x4: \______ o __/
hello-world-parameters-ml4x4: \ \ __/
hello-world-parameters-ml4x4: \____\______/
3.3 Steps步骤
在这个例子中，我们将看到如何创建多步骤工作流，如何在工作流规范中定义多个模板，以及如何创建嵌套工作流。
steps的yaml文件
apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
generateName: steps-
spec:
entrypoint: hello-hello-hello
#两个包含容器的模板：hello-hello-hello and whalesay
templates:
- name: hello-hello-hello
# 这个模板中包含很多步骤，每个步骤运行一个容器
# 步骤按照排列顺序，从上往下，同一个数组内的步骤，是并行执行
steps:
- - name: hello1
template: whalesay
arguments:
parameters:
- name: message
value: "hello1"
- - name: hello2a
template: whalesay
arguments:
parameters:
- name: message
value: "hello2a"
- name: hello2b
template: whalesay
arguments:
parameters:
- name: message
value: "hello2b"
- name: whalesay
inputs:
parameters:
- name: message
container:
image: docker/whalesay
command: [cowsay]
args: ["{{inputs.parameters.message}}"]
运行结果：
[root@mydevops examples]# argo watch steps-9cvhn
# Watch the latest workflow:
argo watch @latest
Name: steps-9cvhn
Namespace: default
ServiceAccount: default
Status: Succeeded
Conditions:
Completed True
Created: Fri Jul 17 10:12:20 +0800 (5 minutes ago)
Started: Fri Jul 17 10:12:20 +0800 (5 minutes ago)
Finished: Fri Jul 17 10:12:58 +0800 (4 minutes ago)
Duration: 38 seconds
ResourcesDuration: 20s*(1 cpu),20s*(100Mi memory)
STEP TEMPLATE PODNAME DURATION MESSAGE
✔ steps-9cvhn hello-hello-hello
├---✔ hello1 whalesay steps-9cvhn-374755726 16s
└-·-✔ hello2a whalesay steps-9cvhn-1017544751 16s
└-✔ hello2b whalesay steps-9cvhn-1034322370 19s
3.4 DAG(有向无环图)
作为步骤的替代方法，DAG可以通过指定每个任务的依赖关系，将工作流定义为一个有向无环图(DAG)。对于复杂的工作流，这可以更简单地维护，并在运行任务时允许最大的并行性。
在下面的工作流中，步骤A首先运行，因为它没有依赖关系。一旦A完成，步骤B和C将并行运行。最后，一旦B和C完成，就可以运行步骤D
apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
generateName: dag-diamond-
spec:
entrypoint: diamond
templates:
- name: echo
inputs:
parameters:
- name: message
container:
image: alpine:3.7
command: [echo, "{{inputs.parameters.message}}"]
- name: diamond
dag:
tasks:
- name: A
template: echo
arguments:
parameters: [{name: message, value: A}]
- name: B
dependencies: [A]
template: echo
arguments:
parameters: [{name: message, value: B}]
- name: C
dependencies: [A]
template: echo
arguments:
parameters: [{name: message, value: C}]
- name: D
dependencies: [B, C]
template: echo
arguments:
parameters: [{name: message, value: D}]
DAG有一个内置的快速失败结束特性，一旦检测到一个DAG节点失败，它就会停止调度新步骤。然后，在DAG自身失败之前，它会等待所有DAG节点完成。FailFast标志默认为true，如果设置为false，它将允许DAG运行DAG的所有分支直到完成(成功或失败)，而不考虑DAG中分支的失败结果。
运行结果：
[root@mydevops examples]# argo list
NAME STATUS AGE DURATION PRIORITY
dag-diamond-4z69w Succeeded 12m 40s 0
steps-9cvhn Succeeded 31m 38s 0
hello-world-parameters-ml4x4 Succeeded 17h 17s 0
hello-world-wpqhc Succeeded 17h 17s 0
[root@mydevops examples]# argo watch dag-diamond-4z69w
Name: dag-diamond-4z69w
Namespace: default
ServiceAccount: default
Status: Succeeded
Conditions:
Completed True
Created: Fri Jul 17 10:30:46 +0800 (12 minutes ago)
Started: Fri Jul 17 10:30:46 +0800 (12 minutes ago)
Finished: Fri Jul 17 10:31:26 +0800 (12 minutes ago)
Duration: 40 seconds
ResourcesDuration: 3s*(1 cpu),3s*(100Mi memory)
STEP TEMPLATE PODNAME DURATION MESSAGE
✔ dag-diamond-4z69w diamond
├-✔ A echo dag-diamond-4z69w-1804781070 11s
├-✔ B echo dag-diamond-4z69w-1788003451 12s
├-✔ C echo dag-diamond-4z69w-1771225832 12s
└-✔ D echo dag-diamond-4z69w-1888669165 11s
3.5 Artifacts工件（制品）
注意:需要配置一个工件存储库来运行制品的示例
argo的输入输出–output和input输出目录或文件到下一步骤，有部分场景需要使用output把目录或者文件传递到下一个步骤。
argo提供了两种方式
- 一种是参数方式parameter
- 一种是工件方式artifacts
各自适用于不同的场景，参数方式是把某个文本的内容读取出来传递给下一步骤。工件方式可以传递文件本身或者文件目录。
工件方式需要有一个中转文件的地方，所以需要给argo配置一个存储引擎。
目前argo支持三种类型的存储：
aws的s3，gcs(Google Cloud Storage),MinIO如果没有配置存储引擎，会报如下错误：
controller is not configured with a default archive location
这里我们部署一个开源的存储引擎-MinIO
官网部署方式：
由于安装helm以及添加helm仓库，网络原因，下载比较慢。我会直接使用yaml文件安装。或者按照官网使用docker部署一个简易版的minIO
在运行工作流时，生成或使用工件的步骤是非常常见的。通常，一个步骤的输出工件可以用作后续步骤的输入工件。
工件示例的yaml文件
apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
generateName: artifact-passing-
spec:
entrypoint: artifact-example
templates:
- name: artifact-example
steps:
- - name: generate-artifact
template: whalesay
- - name: consume-artifact
template: print-message
arguments:
artifacts:
# bind message to the hello-art artifact
# generated by the generate-artifact step
- name: message
from: "{{steps.generate-artifact.outputs.artifacts.hello-art}}"
- name: whalesay
container:
image: docker/whalesay:latest
command: [sh, -c]
args: ["cowsay hello world | tee /tmp/hello_world.txt"]
outputs:
artifacts:
# generate hello-art artifact from /tmp/hello_world.txt
# artifacts can be directories as well as files
- name: hello-art
path: /tmp/hello_world.txt
- name: print-message
inputs:
artifacts:
# unpack the message input artifact
# and put it at /tmp/message
- name: message
path: /tmp/message
container:
image: alpine:latest
command: [sh, -c]
args: ["cat /tmp/message"]
运行结果：
[root@mydevops examples]# argo -n argo list
NAME STATUS AGE DURATION PRIORITY
artifact-passing-sp8zc Running 14s 14s 0
[root@mydevops examples]# argo -n argo watch artifact-passing-sp8zc
Name: artifact-passing-sp8zc
Namespace: argo
ServiceAccount: default
Status: Succeeded
Conditions:
Completed True
Created: Fri Jul 17 15:48:08 +0800 (1 minute ago)
Started: Fri Jul 17 15:48:08 +0800 (1 minute ago)
Finished: Fri Jul 17 15:48:48 +0800 (1 minute ago)
Duration: 40 seconds
ResourcesDuration: 13s*(1 cpu),13s*(100Mi memory)
STEP TEMPLATE PODNAME DURATION MESSAGE
✔ artifact-passing-sp8zc artifact-example
├---✔ generate-artifact whalesay artifact-passing-sp8zc-3656188962 18s
└---✔ consume-artifact print-message artifact-passing-sp8zc-1545141922 20s
3.6 工作流结构
通过上述几个示例，对工作流的yaml文件的书写结构应该有了一个初步的认识。
先总结一下工作流模板的基础架构：
- Kubernetes header 头信息包括Kubernetes 元信息
- Spec body spec结构体包含工作流调用入口以及模板定义
- Entrypoint invocation with optionally arguments 可选参数的入口调用
- List of template definitions 模板列表的定义
- Name of the template 模板名字
- Optionally a list of inputs（可选的）输入列表
- Optionally a list of outputs （可选的）输出列表
- Container invocation (leaf template) or a list of steps 容器调用(叶模板)或步骤列表
- 对于每个步骤，都有一个模板调用
总的来说，工作流规范由一组Argo template组成，其中每个template由一个可选的输入部分、一个可选的输出部分和一个容器调用或一个步骤列表组成，其中每个步骤调用另一个template。
注意，工作流规范的容器部分将接受与pod规范的容器部分相同的选项，包括但不限于environment variables, secrets, and volume mounts. Similarly, for volume claims and volumes。
3.7 Loops 循环
在编写工作流时，能够迭代一组输入通常非常有用
apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
generateName: loops-param-arg-
spec:
entrypoint: loop-param-arg-example
arguments:
parameters:
- name: os-list # a list of items
value: |
[
{ "image": "debian", "tag": "9.1" },
{ "image": "debian", "tag": "8.9" },
{ "image": "alpine", "tag": "3.6" },
{ "image": "ubuntu", "tag": "17.10" }
]
templates:
- name: loop-param-arg-example
inputs:
parameters:
- name: os-list
steps:
- - name: test-linux
template: cat-os-release
arguments:
parameters:
- name: image
value: "{{item.image}}"
- name: tag
value: "{{item.tag}}"
withParam: "{{inputs.parameters.os-list}}" # parameter specifies the list to iterate over
# This template is the same as in the previous example
- name: cat-os-release
inputs:
parameters:
- name: image
- name: tag
container:
image: "{{inputs.parameters.image}}:{{inputs.parameters.tag}}"
command: [cat]
args: [/etc/os-release]
运行结果：
3.8 条件
argo工作流也可以根据条件去执行具体的步骤，下面演示扔硬币流程。
条件示例yaml文件：
apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
generateName: coinflip-
spec:
entrypoint: coinflip
templates:
- name: coinflip
steps:
# flip a coin
- - name: flip-coin
template: flip-coin
# evaluate the result in parallel
- - name: heads
template: heads # call heads template if "heads"
when: "{{steps.flip-coin.outputs.result}} == heads"
- name: tails
template: tails # call tails template if "tails"
when: "{{steps.flip-coin.outputs.result}} == tails"
# Return heads or tails based on a random number
- 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\""]
运行结果：
3.9 secrets
Argo支持与Kubernetes Pod规范相同的secrets语法和机制，允许访问作为环境变量或volume mounts的secrets
示例的yaml文件
# To run this example, first create the secret by running:
# kubectl create secret generic my-secret --from-literal=mypassword=S00perS3cretPa55word
apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
generateName: secret-example-
spec:
entrypoint: whalesay
# To access secrets as files, add a volume entry in spec.volumes[] and
# then in the container template spec, add a mount using volumeMounts.
volumes:
- name: my-secret-vol
secret:
secretName: my-secret # name of an existing k8s secret
templates:
- name: whalesay
container:
image: alpine:3.7
command: [sh, -c]
args: ['
echo "secret from env: $MYSECRETPASSWORD";
echo "secret from file: `cat /secret/mountpath/mypassword`"
']
# To access secrets as environment variables, use the k8s valueFrom and
# secretKeyRef constructs.
env:
- name: MYSECRETPASSWORD # name of env var
valueFrom:
secretKeyRef:
name: my-secret # name of an existing k8s secret
key: mypassword # 'key' subcomponent of the secret
volumeMounts:
- name: my-secret-vol # mount file containing secret at /secret/mountpath
mountPath: "/secret/mountpath"
运行结果：
[root@mydevops examples]# kubectl get secrets -n argo my-minio-cred -oyaml
apiVersion: v1
data:
accessKey: YWRtaW4=
secretKey: MTIzMTIzMTIz
kind: Secret
metadata:
annotations:
kubectl.kubernetes.io/last-applied-configuration: |
{"apiVersion":"v1","data":{"accessKey":"YWRtaW4=","secretKey":"MTIzMTIzMTIz"},"kind":"Secret","metadata":{"annotations":{},"name":"my-minio-cred","namespace":"argo"}}
creationTimestamp: 2020-07-09T04:24:48Z
name: my-minio-cred
namespace: argo
resourceVersion: "143571"
selfLink: /api/v1/namespaces/argo/secrets/my-minio-cred
uid: 1fef3aa5-c19c-11ea-8646-000c2944bd9b
type: Opaque
[root@mydevops examples]# argo logs -n argo secret-example-ft25s
secret-example-ft25s: secret from env: admin
secret-example-ft25s: secret from file: admin
3.10 Scripts & Results
通常，我们只需要一个执行工作流规范中指定脚本的模板。
yaml文件：
apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
generateName: scripts-bash-
spec:
entrypoint: bash-script-example
templates:
- name: bash-script-example
steps:
- - name: generate
template: gen-random-int-bash
- - name: print
template: print-message
arguments:
parameters:
- name: message
value: "{{steps.generate.outputs.result}}" # The result of the here-script
- name: gen-random-int-bash
script:
image: debian:9.4
command: [bash]
source: | # Contents of the here-script
cat /dev/urandom | od -N2 -An -i | awk -v f=1 -v r=100 '{printf "%i\n", f + r * $1 / 65536}'
- name: gen-random-int-python
script:
image: python:alpine3.6
command: [python]
source: |
import random
i = random.randint(1, 100)
print(i)
- name: gen-random-int-javascript
script:
image: node:9.1-alpine
command: [node]
source: |
var rand = Math.floor(Math.random() * 100);
console.log(rand);
- name: print-message
inputs:
parameters:
- name: message
container:
image: alpine:latest
command: [sh, -c]
args: ["echo result was: {{inputs.parameters.message}}"]
运行结果：
[root@mydevops examples]# argo logs scripts-bash-twsqv
scripts-bash-twsqv-4196821547: 19
scripts-bash-twsqv-202958314: result was: 19
3.11 Output Parameters
argo的输入输出–output和input输出目录或文件到下一步骤，有部分场景需要使用output把目录或者文件传递到下一个步骤。
argo提供了两种方式
- 一种是参数方式parameter
- 一种是工件方式artifacts
这里介绍参数的输出方式。工件输出方式，请参考3.5
apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
generateName: output-parameter-
spec:
entrypoint: output-parameter
templates:
- name: output-parameter
steps:
- - name: generate-parameter
template: whalesay
- - name: consume-parameter
template: print-message
arguments:
parameters:
# Pass the hello-param output from the generate-parameter step as the message input to print-message
#DAG模板调用参数-{{tasks.generate-parameter.outputs.parameter .hello-param}}。
- name: message
value: "{{steps.generate-parameter.outputs.parameters.hello-param}}"
- name: whalesay
container:
image: docker/whalesay:latest
command: [sh, -c]
args: ["echo -n hello world > /tmp/hello_world.txt"] # generate the content of hello_world.txt
outputs:
parameters:
- name: hello-param # name of output parameter
valueFrom:
path: /tmp/hello_world.txt # set the value of hello-param to the contents of this hello-world.txt
- name: print-message
inputs:
parameters:
- name: message
container:
image: docker/whalesay:latest
command: [cowsay]
args: ["{{inputs.parameters.message}}"]
运行结果：
3.12 Retrying Failed or Errored Steps
重试失败或者错误步骤，工作流yaml文件中提供一个retryStrategy字段来描述失败或者错误的工作流情况
limit最大的重试次数retryPolicy重试策略，有Always、OnFailure(default)、OnErrorbackoff指定重试时间
当提供的是空的 retryStrategy (i.e. retryStrategy: {}) ，经会一直重试，知道成功运行工作流
# This example demonstrates the use of retry back offs
apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
generateName: retry-backoff-
spec:
entrypoint: retry-backoff
templates:
- name: retry-backoff
retryStrategy:
limit: 10
retryPolicy: "Always"
backoff:
duration: "1" # Must be a string. Default unit is seconds. Could also be a Duration, e.g.: "2m", "6h", "1d"
factor: 2
maxDuration: "1m" # Must be a string. Default unit is seconds. Could also be a Duration, e.g.: "2m", "6h", "1d"
container:
image: python:alpine3.6
command: ["python", -c]
# fail with a 66% probability
args: ["import random; import sys; exit_code = random.choice([0, 1, 1]); sys.exit(exit_code)"]
3.13 Recursion(递归)
工作流中的模板可以递归地相互调用!在示例中我们可以继续使用抛硬币yaml文件，知道出现正面停止步骤
apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
generateName: coinflip-recursive-
spec:
entrypoint: coinflip
templates:
- name: coinflip
steps:
# flip a coin
- - name: flip-coin
template: flip-coin
# evaluate the result in parallel
- - name: heads
template: heads # call heads template if "heads"
when: "{{steps.flip-coin.outputs.result}} == heads"
- name: tails # keep flipping coins if "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\""]
运行结果：
第一次：
第二次：
第一次抛硬币，第二次出现正面结束
第二次抛硬币，第三次出现正面结束
3.14 Exit handlers(退出处理)
退出处理程序是始终在工作流结束时执行的模板，无论成功或失败
- 在工作流运行后进行清理
- 发送工作流状态通知
- 发布通过/失败状态到webhook结果 (e.g. GitHub build result)
- 重新提交或提交另一个工作流
apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
generateName: exit-handlers-
spec:
entrypoint: intentional-fail
onExit: exit-handler # invoke exit-hander template at end of the workflow
templates:
# primary workflow template
- name: intentional-fail
container:
image: alpine:latest
command: [sh, -c]
args: ["echo intentional failure; exit 1"]
# Exit handler templates
# After the completion of the entrypoint template, the status of the
# workflow is made available in the global variable {{workflow.status}}.
# {{workflow.status}} will be one of: Succeeded, Failed, Error
- name: exit-handler
steps:
- - name: notify
template: send-email
- name: celebrate
template: celebrate
when: "{{workflow.status}} == Succeeded"
- name: cry
template: cry
when: "{{workflow.status}} != Succeeded"
- name: send-email
container:
image: alpine:latest
command: [sh, -c]
args: ["echo send e-mail: {{workflow.name}} {{workflow.status}}"]
- name: celebrate
container:
image: alpine:latest
command: [sh, -c]
args: ["echo hooray!"]
- name: cry
container:
image: alpine:latest
command: [sh, -c]
args: ["echo boohoo!"]
3.15 Timeouts
可以为工作流设置超时时间，使用activeDeadlineSeconds字段
# To enforce a timeout for a container template, specify a value for activeDeadlineSeconds.
apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
generateName: timeouts-
spec:
entrypoint: sleep
templates:
- name: sleep
container:
image: alpine:latest
command: [sh, -c]
args: ["echo sleeping for 1m; sleep 60; echo done"]
activeDeadlineSeconds: 10 # terminate container template after 10 seconds
总结
argo工作流有着非常丰富的功能，不仅可以进行上述操作，还可以操作kubernetes的资源，创建伴生容器等等操作。在持续集成中工作流的一个流行应用程序。目前，Argo不提供自动启动CI作业的事件触发器，将来版本中会出现。现在可以通过编写cron作业来检查新的提交并启动所需的工作流，或者使用现有的Jenkins服务器启动工作流。
参考文档
https://github.com/argoproj/argo/blob/master/examples/README.md