Multi Cluster Application

KubeVela is by design a full functional Continuous Delivery (CD) platform with fine grained support for hybrid/multi-cloud/multi-cluster deployment. This section will introduce how to deliver multi-cluster application with KubeVela policies and workflow.

There are many scenarios that developers or system operators need to deploy and manage applications across multiple clusters.

  • For scalability, a single Kubernetes cluster has its limit around 5K nodes or less, it is unable to handle the large scale application load.
  • For stability/availability, one single application can be deployed in multiple clusters for backup, which provides more stability and availability.
  • For security, application might need to be deployed in different zones/areas as government policy requires.

Multi Cluster Application - 图1

KubeVela leverages the Cluster-Gateway for multi-cluster, it’s installed automatically along with KubeVela chart. By default, it will directly connect to the clusters by using the kubeconfig as secret. You can also enable the Open Cluster Management for the PULL mode.

The following guide will introduce how to manage applications across clusters on KubeVela.

Please make sure you have clusters in your control plane, in general, this work should be done by operator engineers. If you’re a DevOps engineer or trying KubeVela, you can refer to manage cluster docs to learn how to join clusters.

For the rest docs, we assume you have clusters with the following names:

  1. $ vela cluster list
  2. CLUSTER                 TYPE            ENDPOINT                ACCEPTED        LABELS
  3. local                   Internal        -                       true                  
  4. cluster-beijing         X509Certificate <ENDPOINT_BEIJING>      true                  
  5. cluster-hangzhou-1      X509Certificate <ENDPOINT_HANGZHOU_1>   true                  
  6. cluster-hangzhou-2      X509Certificate <ENDPOINT_HANGZHOU_2>   true

Multi Cluster Application - 图2note

By default, the hub cluster where KubeVela locates is registered as the local cluster. You can use it like a managed cluster in spite that you cannot detach it or modify it.

To deliver your application into multiple clusters, you simply need to configure which clusters you want to deploy through the topology policy. For example, you can deploy an nginx webservice in hangzhou clusters by running the following commands

  1. $ cat <<EOF | vela up -f -
  2. apiVersion: core.oam.dev/v1beta1
  3. kind: Application
  4. metadata:
  5.   name: basic-topology
  6.   namespace: examples
  7. spec:
  8.   components:
  9.     - name: nginx-basic
  10.       type: webservice
  11.       properties:
  12.         image: nginx
  13.       traits:
  14.         - type: expose
  15.           properties:
  16.             port: [80]
  17.   policies:
  18.     - name: topology-hangzhou-clusters
  19.       type: topology
  20.       properties:
  21.         clusters: ["cluster-hangzhou-1", "cluster-hangzhou-2"]
  22. EOF

You can check the deploy result by running vela status

  1. vela status basic-topology -n examples

expected output

  1. About:
  3.   Name:         basic-topology               
  4.   Namespace:    examples                     
  5.   Created at:   2022-04-08 14:37:54 +0800 CST
  6.   Status:       workflowFinished             
  8. Workflow:
  10.   mode: DAG
  11.   finished: true
  12.   Suspend: false
  13.   Terminated: false
  14.   Steps
  15.   - id:3mvz5i8elj
  16.     name:deploy-topology-hangzhou-clusters
  17.     type:deploy
  18.     phase:succeeded 
  19.     message:
  21. Services:
  23.   - Name: nginx-basic  
  24.     Cluster: cluster-hangzhou-1  Namespace: examples
  25.     Type: webservice
  26.     Healthy Ready:1/1
  27.     Traits:
  28.       ✅ expose
  29.   - Name: nginx-basic  
  30.     Cluster: cluster-hangzhou-2  Namespace: examples
  31.     Type: webservice
  32.     Healthy Ready:1/1
  33.     Traits:
  34.       ✅ expose

You can debugging the above deployed nginx webservice by running the following vela CLI commands. You can play with your pods in managed clusters directly on the hub cluster, without switching KubeConfig context. If you have multiple clusters in on application, the CLI command will ask you to choose one interactively.

  • vela status as shown above can give you an overview of your deployed multi-cluster application. Example usage is shown above.
  • vela status --pod can list the pod status of your application.
  • vela logs shows pod logs in managed clusters.
  1. $ vela logs basic-topology -n examples 
  2. ? You have 2 deployed resources in your app. Please choose one: Cluster: cluster-hangzhou-1 | Namespace: examples | Kind: Deployment | Name: nginx-basic
  3. + nginx-basic-dfb6dcf8d-km5vk › nginx-basic
  4. nginx-basic-dfb6dcf8d-km5vk nginx-basic 2022-04-08T06:38:10.540430392Z /docker-entrypoint.sh: /docker-entrypoint.d/ is not empty, will attempt to perform configuration
  5. nginx-basic-dfb6dcf8d-km5vk nginx-basic 2022-04-08T06:38:10.540742240Z /docker-entrypoint.sh: Looking for shell scripts in /docker-entrypoint.d/
  • vela exec helps you execute commands in pods in managed clusters.
  1. $ vela exec basic-topology -n examples -it -- ls 
  2. ? You have 2 deployed resources in your app. Please choose one: Cluster: cluster-hangzhou-1 | Namespace: examples | Kind: Deployment | Name: nginx-basic
  3. bin   docker-entrypoint.d   home   media  proc  sbin  tmp
  4. boot  docker-entrypoint.sh  lib    mnt    root  srv   usr
  5. dev   etc                   lib64  opt    run   sys   var
  • vela port-forward can discover and forward ports of pods or services in managed clusters to your local endpoint.
  1. $ vela port-forward basic-topology -n examples 8080:80
  2. ? You have 4 deployed resources in your app. Please choose one: Cluster: cluster-hangzhou-1 | Namespace: examples | Kind: Deployment | Name: nginx-basic
  3. Forwarding from 127.0.0.1:8080 -> 80
  4. Forwarding from [::1]:8080 -> 80
  6. Forward successfully! Opening browser ...
  7. Handling connection for 8080

The following figure displays the architecture of a multi-cluster application. All the configurations (including Application, Policy and Workflow) lives in the hub cluster. Only the resources (like deployment or service) will be dispatched in to managed clusters.

The policies mainly takes charge of describing the destination of the resources and how they should be overridden. The real executor of the resource dispatch is the workflow. In each deploy workflow step, it will refer to some policies, override the default configuration, and dispatch the resources.

multi-cluster-arch

The most straightforward way to configure the deploy destination is to write cluster names inside the topology policy. Sometimes, it will be more easy to select clusters by labels, like filtering all clusters in hangzhou:

  1. apiVersion: core.oam.dev/v1beta1
  2. kind: Application
  3. metadata:
  4.   name: label-selector-topology
  5.   namespace: examples
  6. spec:
  7.   components:
  8.     - name: nginx-label-selector
  9.       type: webservice
  10.       properties:
  11.         image: nginx
  12.   policies:
  13.     - name: topology-hangzhou-clusters
  14.       type: topology
  15.       properties:
  16.         clusterLabelSelector:
  17.           region: hangzhou

If you want to deploy application components into the control plane cluster, you can use the local cluster. Besides, you can also deploy your application components in another namespace other than the application’s original namespace.

  1. apiVersion: core.oam.dev/v1beta1
  2. kind: Application
  3. metadata:
  4.   name: local-ns-topology
  5.   namespace: examples
  6. spec:
  7.   components:
  8.     - name: nginx-local-ns
  9.       type: webservice
  10.       properties:
  11.         image: nginx
  12.   policies:
  13.     - name: topology-local
  14.       type: topology
  15.       properties:
  16.         clusters: ["local"]
  17.         namespace: examples-alternative

Multi Cluster Application - 图4tip

Sometimes, for security issues, you might want to disable this behavior and retrict the resources to be deployed within the same namespace of the application. This can be done by setting --allow-cross-namespace-resource=false in the bootstrap parameter of the KubeVela controller.

By default, if you declare multiple topology policies in the application, the application components will be deployed in all destinations following the order of the policies.

If you want to control the deploy process, like changing the order or adding manual approval, you can use the deploy workflow step explicitly in the workflow to achieve that.

  1. apiVersion: core.oam.dev/v1beta1
  2. kind: Application
  3. metadata:
  4.   name: deploy-workflowstep
  5.   namespace: examples
  6. spec:
  7.   components:
  8.     - name: nginx-deploy-workflowstep
  9.       type: webservice
  10.       properties:
  11.         image: nginx
  12.   policies:
  13.     - name: topology-hangzhou-clusters
  14.       type: topology
  15.       properties:
  16.         clusterLabelSelector:
  17.           region: hangzhou
  18.     - name: topology-local
  19.       type: topology
  20.       properties:
  21.         clusters: ["local"]
  22.         namespace: examples-alternative
  23.   workflow:
  24.     steps:
  25.       - type: deploy
  26.         name: deploy-local
  27.         properties:
  28.           policies: ["topology-local"]
  29.       - type: deploy
  30.         name: deploy-hangzhou
  31.         properties:
  32.           # require manual approval before running this step
  33.           auto: false
  34.           policies: ["topology-hangzhou-clusters"]

You can also deploy application components with different topology policies concurrently, by filling these topology policies in one deploy step.

  1. apiVersion: core.oam.dev/v1beta1
  2. kind: Application
  3. metadata:
  4.   name: deploy-concurrently
  5.   namespace: examples
  6. spec:
  7.   components:
  8.     - name: nginx-deploy-concurrently
  9.       type: webservice
  10.       properties:
  11.         image: nginx
  12.   policies:
  13.     - name: topology-hangzhou-clusters
  14.       type: topology
  15.       properties:
  16.         clusterLabelSelector:
  17.           region: hangzhou
  18.     - name: topology-local
  19.       type: topology
  20.       properties:
  21.         clusters: ["local"]
  22.         namespace: examples-alternative
  23.   workflow:
  24.     steps:
  25.       - type: deploy
  26.         name: deploy-all
  27.         properties:
  28.           policies: ["topology-local", "topology-hangzhou-clusters"]

There are times that you want to make changes to the configuration in some clusters, rather than use the default configuration declared in the application’s components field. For example, using a different container image or changing the default number of replicas.

The override policy is able to help you make customizations in different clusters. You can use it together with the topology policy in the deploy workflow step.

In the following example, the application will deploy a default nginx webservice in the local cluster. Then it will deploy a high-available nginx webservice with the legacy image nginx:1.20 and 3 replicas in hangzhou clusters.

  1. apiVersion: core.oam.dev/v1beta1
  2. kind: Application
  3. metadata:
  4.   name: deploy-with-override
  5.   namespace: examples
  6. spec:
  7.   components:
  8.     - name: nginx-with-override
  9.       type: webservice
  10.       properties:
  11.         image: nginx
  12.   policies:
  13.     - name: topology-hangzhou-clusters
  14.       type: topology
  15.       properties:
  16.         clusterLabelSelector:
  17.           region: hangzhou
  18.     - name: topology-local
  19.       type: topology
  20.       properties:
  21.         clusters: ["local"]
  22.         namespace: examples-alternative
  23.     - name: override-nginx-legacy-image
  24.       type: override
  25.       properties:
  26.         components:
  27.           - name: nginx-with-override
  28.             properties:
  29.               image: nginx:1.20
  30.     - name: override-high-availability
  31.       type: override
  32.       properties:
  33.         components:
  34.           - type: webservice
  35.             traits:
  36.               - type: scaler
  37.                 properties:
  38.                   replicas: 3
  39.   workflow:
  40.     steps:
  41.       - type: deploy
  42.         name: deploy-local
  43.         properties:
  44.           policies: ["topology-local"]
  45.       - type: deploy
  46.         name: deploy-hangzhou
  47.         properties:
  48.           policies: ["topology-hangzhou-clusters", "override-nginx-legacy-image", "override-high-availability"]

Multi Cluster Application - 图5note

The override policy is used to modify the basic configuration. Therefore, it is designed to be used together with topology policy. If you do not want to use topology policy, you can directly write configurations in the component part instead of using the override policy. If you misuse the override policy in the deploy workflow step without topology policy, no error will be reported but you will find nothing is deployed.

The override policy has many advanced capabilities, such as adding new component or selecting components to use. The following example will first deploy an nginx webservice with nginx:1.20 image to local cluster. Then two nginx webservices with nginx and nginx:stable images will be deployed to hangzhou clusters respectively.

  1. apiVersion: core.oam.dev/v1beta1
  2. kind: Application
  3. metadata:
  4.   name: advance-override
  5.   namespace: examples
  6. spec:
  7.   components:
  8.     - name: nginx-advance-override-legacy
  9.       type: webservice
  10.       properties:
  11.         image: nginx:1.20
  12.     - name: nginx-advance-override-latest
  13.       type: webservice
  14.       properties:
  15.         image: nginx
  16.   policies:
  17.     - name: topology-hangzhou-clusters
  18.       type: topology
  19.       properties:
  20.         clusterLabelSelector:
  21.           region: hangzhou
  22.     - name: topology-local
  23.       type: topology
  24.       properties:
  25.         clusters: ["local"]
  26.         namespace: examples-alternative
  27.     - name: override-nginx-legacy
  28.       type: override
  29.       properties:
  30.         selector: ["nginx-advance-override-legacy"]
  31.     - name: override-nginx-latest
  32.       type: override
  33.       properties:
  34.         selector: ["nginx-advance-override-latest", "nginx-advance-override-stable"]
  35.         components:
  36.           - name: nginx-advance-override-stable
  37.             type: webservice
  38.             properties:
  39.               image: nginx:stable
  40.   workflow:
  41.     steps:
  42.       - type: deploy
  43.         name: deploy-local
  44.         properties:
  45.           policies: ["topology-local", "override-nginx-legacy"]
  46.       - type: deploy
  47.         name: deploy-hangzhou
  48.         properties:
  49.           policies: ["topology-hangzhou-clusters", "override-nginx-latest"]

Sometimes, you may want to use the same policy across multiple applications or reuse previous workflow to deploy different resources. To reduce the repeated code, you can leverage the external policies and workflow and refer to them in your applications.

Multi Cluster Application - 图6caution

You can only refer to Policy and Workflow within your application’s namespace.

  1. apiVersion: core.oam.dev/v1alpha1
  2. kind: Policy
  3. metadata:
  4.   name: topology-hangzhou-clusters
  5.   namespace: examples
  6. type: topology
  7. properties:
  8.   clusterLabelSelector:
  9.     region: hangzhou
  10. ---
  11. apiVersion: core.oam.dev/v1alpha1
  12. kind: Policy
  13. metadata:
  14.   name: override-high-availability-webservice
  15.   namespace: examples
  16. type: override
  17. properties:
  18.   components:
  19.     - type: webservice
  20.       traits:
  21.         - type: scaler
  22.           properties:
  23.             replicas: 3
  24. ---
  25. apiVersion: core.oam.dev/v1alpha1
  26. kind: Workflow
  27. metadata:
  28.   name: make-release-in-hangzhou
  29.   namespace: examples
  30. steps:
  31.   - type: deploy
  32.     name: deploy-hangzhou
  33.     properties:
  34.       auto: false
  35.       policies: ["override-high-availability-webservice", "topology-hangzhou-clusters"]
  1. apiVersion: core.oam.dev/v1beta1
  2. kind: Application
  3. metadata:
  4.   name: external-policies-and-workflow
  5.   namespace: examples
  6. spec:
  7.   components:
  8.     - name: nginx-external-policies-and-workflow
  9.       type: webservice
  10.       properties:
  11.         image: nginx
  12.   workflow:
  13.     ref: make-release-in-hangzhou

Multi Cluster Application - 图7note

The internal policies will be loaded first. External policies will only be used when there is no corresponding policy inside the application.

In the following example, we can reuse topology-hangzhou-clusters policy and make-release-in-hangzhou workflow but modify the override-high-availability-webservice policy by injecting the same-named policy inside the new application.

  1. apiVersion: core.oam.dev/v1beta1
  2. kind: Application
  3. metadata:
  4.   name: nginx-stable-ultra
  5.   namespace: examples
  6. spec:
  7.   components:
  8.     - name: nginx-stable-ultra
  9.       type: webservice
  10.       properties:
  11.         image: nginx:stable
  12.   policies:
  13.     - name: override-high-availability-webservice
  14.       type: override
  15.       properties:
  16.         components:
  17.           - type: webservice
  18.             traits:
  19.               - type: scaler
  20.                 properties:
  21.                   replicas: 5
  22.   workflow:
  23.     ref: make-release-in-hangzhou

The multi-cluster feature and combine with the customized workflow steps to provide a powerful way for multi-cluster scheduling.

In the following example, we’ll deploy the task first into the local cluster and default namespace, then check the deploy status by read-object step, after that we’ll deploy the task into the prod namespace according the status.

  1. apiVersion: core.oam.dev/v1beta1
  2. kind: Application
  3. metadata:
  4.   name: deploy-with-override
  5. spec:
  6.   components:
  7.     - name: mytask
  8.       type: task
  9.       properties:
  10.         image: bash
  11.         count: 1
  12.         cmd: ["echo",  "hello world"]
  13.   policies:
  14.     - name: target-default
  15.       type: topology
  16.       properties:
  17.         clusters: ["local"]
  18.         namespace: "default"
  19.     - name: target-prod
  20.       type: topology
  21.       properties:
  22.         clusters: ["local"]
  23.         namespace: "prod"
  24.     - name: override-annotations-1
  25.       type: override
  26.       properties:
  27.         components:
  28.           - type: task
  29.             traits:
  30.             - type: annotations
  31.               properties:
  32.                 "description": "01 cron task - 1"
  33.     - name: override-annotations-2
  34.       type: override
  35.       properties:
  36.         components:
  37.           - type: task          
  38.             traits:
  39.             - type: annotations
  40.               properties:
  41.                 "description": "02 cron task - 2"                
  42.   workflow:
  43.     steps:
  44.       - type: deploy
  45.         name: deploy-01
  46.         properties:
  47.           policies: ["target-default", "override-annotations-1"]
  48.       - name: read-object
  49.         type: read-object
  50.         outputs:
  51.           - name: ready
  52.             valueFrom: output.value.status["ready"]
  53.         properties:
  54.           apiVersion: batch/v1
  55.           kind: Job
  56.           name: mytask
  57.           namespace: default
  58.           cluster: local        
  59.       - type: deploy
  60.         name: deploy-02
  61.         inputs:
  62.           - from: ready      
  63.         if: inputs["ready"] == 0
  64.         properties:
  65.           policies: ["target-prod", "override-annotations-2"]

KubeVela Application v1.3 uses different policies and workflow steps to configure and managing multi-cluster applications.

The outdated env-binding policy and deploy2env workflow step in old versions are kept now and might be deprecated in the future.

The new policies and workflow steps can cover all the use-cases in old versions so it is possible to upgrade all your applications while maintaining the same capabilities. Upgrade tools are not available now but will come out before deprecation happens.

If you already have applications running in production environment and do not want to change them, KubeVela v1.3 is also compatible for it. It is NOT necessary to migrate old multi-cluster applications to new ones.

In this section, we introduced how KubeVela delivering micro services in multiple clusters, the whole process can be easily modeled as a declarative deployment plan.

No more ad-hoc scripts or glue code, KubeVela will get the application delivery workflow done with full automation and determinism. Most importantly, KubeVela expects you keep using the CI solutions you are already familiar with and KubeVela is fully complementary to them as the CD control plane.

Last updated on Aug 4, 2023 by Daniel Higuero