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Version: v1.5

Automated Observability

Observability is critical for infrastructures and applications. Without observability system, it is hard to identify what happens when system broke down. On contrary, a strong observabilty system can not only provide confidences for operators but can also help developers quickly locate the performance bottleneck or the weak points inside the whole system.

To help users build their own observability system, KubeVela provides a list of addons, including

  • prometheus-server: A server records metrics in time series with flexible queries supported.
  • kube-state-metrics: A metrics collector for the Kubernetes system.
  • node-exporter: A metrics collector for the running Kubernetes nodes.
  • grafana: A web application that provides analytics and interactive visualizations.

More addons for logging and tracing will be introduced in later versions.


  1. The observability suite includes several addons which requires some computation resources to work properly. The recommended installation resources for you cluster is 2 core + 4 Gi memory.

  2. The required KubeVela version (both Server-side controller and Client-side CLI) for installation is not below v1.5.0-beta.4.

Quick Start

To enable the addon suites, you simply needs to run the vela addon enable commands as below.


If your KubeVela is multi-cluster scenario, see the multi-cluster installation section below.

  1. Install the kube-state-metrics addon
vela addon enable kube-state-metrics
  1. Install the node-exporter addon
vela addon enable node-exporter
  1. Install the prometheus-server
vela addon enable prometheus-server
  1. Install the grafana addon.
vela addon enable grafana
  1. Access your grafana through port-forward.
kubectl port-forward svc/grafana -n o11y-system 8080:3000

Now you can access your grafana by access http://localhost:8080 in your browser. The default username and password are admin and kubevela respectively.

Automated Dashboards

There are four automated dashboards you can navigate and view your system.

KubeVela Application

This dashboard shows the basic information for one application.

URL: http://localhost:8080/d/application-overview/kubevela-applications


The KubeVela Application dashboard shows the overview of the metadata for the application. It directly accesses the Kubernetes API to retrieve the runtime application information, where you can use it as an entrance.

The Basic Information section extracts key information into panels and give you the most straightforward view for the current application.

The Related Resource section shows those resources that work together with the application itself, including the managed resources, the recorded ResourceTrackers and the revisions.

Kubernetes Deployemnt

This dashboard shows the overview of native deployments. You can navigate deployments across clusters.

URL: http://localhost:8080/d/deployment-overview/kubernetes-deployment


The Kubernetes Deployment dashboard gives you the detail running status for the deployment.

The Pods panel shows the pods that the deployment itself is currently managing.

The Replicas panel shows how the number of replicas changes, which can be used to diagnose when and how your deployment shifted to undesired state.

The Pod section includes the details for the resource usage (including the CPU / Memory / Network / Storage) which can be used to identify if the pod is facing resource pressure or making/receiving unexpected traffics.

KubeVela System

This dashboard shows the overview of the KubeVela system. It can be used to see if KubeVela controller is healthy.

URL: http://localhost:8080/d/kubevela-system/kubevela-system


The KubeVela System dashboard gives you the running details of the KubeVela core modules, including the controller and the cluster-gateway. Other modules like velaux or prism are expected to be added in the future.

The Computation Resource section shows the usage for core modules. It can be used to track if there is any memory leak (if the memory usage is continuously increasing) or under high pressure (the cpu usage is always very high). If the memory usage hits the resource limit, the corresponding module will be killed and restarted, which indicates the lack of computation resource. You should add more CPU/Memory for them.

The Controller section includes a wide range of panels which can help you to diagnose the bottleneck of the KubeVela controller in your scenario.

The Controller Queue and Controller Queue Add Rate panels show you the controller working queue changes. If the controller queue is increasing continuously, it means there are too much applications or application changes in the system, and the controller is unable to handle them in time. Then it means there is performance issues for KubeVela controller. A temporary increase for the controller queue is tolerable, but keeping for a long time will lead to memory increase which will finally cause Out-Of-Memory problems.

Reconcile Rate and Average Reconcile Time panels give you the overview of the controller status. If reconcile rate is steady and average reconcile time is reasonable (like under 500ms, depending on your scenario), your KubeVela controller is healthy. If the controller queue add rate is increasing but the reconcile rate does not go up, it will gradually lead to increase for the controller queue and cause troubles. There are various cases that your controller is unhealthy:

  1. Reconcile is healthy but there are too much applications, you will find everything is okay except the controller queue metrics increasing. Check your CPU/Memory usage for the controller. You might need to add more computation resources.
  2. Reconcile is not healthy due to too much errors. You will find lots of errors in the Reconcile Rate panel. This means your system is continuously facing process errors for application. It could be caused by invalid application configurations or unexpected errors while running workflows. Check application details and see which applications are causing errors.
  3. Reconcile is not healthy due to long reconcile times. You need to check ApplicationController Reconcile Time panel and see whether it is a common case (the average reconcile time is high), or only part of your applications have problems (the p95 reconcile time is high). For the former case, usually it is caused by either insufficient CPU (CPU usage is high) or too much requests and rate limited by kube-apiserver (check ApplicationController Client Request Throughput and ApplicationController Client Request Average Time panel and see which resource requests is slow or excessive). For the later case you need to check which application is large and uses lots of time for reconciliations.

Sometimes you might need refer to ApplicationController Reconcile Stage Time and see if there is some special reconcile stages are abnormal. For example, GCResourceTrackers use lots of time means there might be blockings for recycling resource in KubeVela system.

The Application section shows the overview of the applications in your whole KubeVela system. It can be used to see the changes of the application numbers and the used workflow steps. The Workflow Initialize Rate is an auxiliary panel which can be used to see how frequent new workflow execution is launched. The Workflow Average Complete Time can further show how much time it costs to finish the whole workflow.

Kubernetes APIServer

This dashboard shows the running status of all Kubernetes apiservers.

URL: http://localhost:8080/d/kubernetes-apiserver/kubernetes-apiserver


The Kubernetes APIServer dashboard helps you to see the most fundamental part for your Kubernetes system. If your Kubernetes APIServer is not running healthy, all of your controllers and modules in your Kubernetes system will be abnormal and unable to handle requests successfully. So it is important to make sure everything is fine in this dashboard.

The Requests section includes a series of panels which shows the QPS and latency for various kind of requests. Usually your APIServer could fail to respond if it is flooded by too much requests. At this time, you can see which type of requests is causing trouble.

The WorkQueue section shows the process status of the Kubernetes APIServer. If the Queue Size is large, it means the number of requests is out of the process capability of your Kubernetes APIServer.

The Watches section shows the number of watches in your Kubernetes APIServer. Compared to other types of requests, WATCH requests will continuously consume computation resources in Kubernetes APIServer, so it will be helpful to keep the number of watches limited.


The above installation process can be customized in several ways.

Multi-cluster installation

If you want to install observability addons in multi-cluster scenario, make sure your Kubernetes clusters support LoadBalancer service and are mutatually accessible.

By default, the installation process for kube-state-metrics, node-exporter and prometheus-server are natually multi-cluster supported (they will be automatically installed to all clusters). But to let your grafana on the control plane to be able to access prometheus-server in managed clusters, you need to use the following command to enable prometheus-server.

vela addon enable prometheus-server thanos=true serviceType=LoadBalancer

This will install thanos sidecar & query along with prometheus-server. Then enable grafana, you will be able to see aggregated prometheus metrics now.

You can also choose which clusters to install addons by using commands as below

vela addon enable kube-state-metrics clusters=\{local,c2\}

If you add new clusters to your control plane after addons being installed, you need to re-enable the addon to let it take effect.

Custom Prometheus configuration

If you want to make customization to your prometheus-server installation, you can put your configuration into a individual ConfigMap, like my-prom in namespace o11y-system. To distribute your custom config to all clusters, you can also use a KubeVela Application to do the job.

Recording rules

For example, if you want to add some recording rules to all your prometheus server configurations in all clusters, you can firstly create an application to distribute your recording rules as below.

# my-prom.yaml
kind: Application
name: my-prom
namespace: o11y-system
- type: k8s-objects
name: my-prom
- apiVersion: v1
kind: ConfigMap
name: my-prom
namespace: o11y-system
my-recording-rules.yml: |
- name: example
- record: apiserver:requests:rate5m
expr: sum(rate(apiserver_request_total{job="kubernetes-nodes"}[5m]))
- type: topology
name: topology
clusterLabelSelector: {}

Then you need to add customConfig parameter to the enabling process of the prometheus-server addon, like

vela addon enable prometheus-server thanos=true serviceType=LoadBalancer storage=1G customConfig=my-prom

Then you will be able to see the recording rules configuration being delivered into all prome

Alerting rules & Other configurations

To make customization to other configurations like alerting rules, the process is same with the recording rules example shown above. You only need to change/add prometheus configurations in the application.

my-alerting-rules.yml: |
- name: example
- alert: HighApplicationQueueDepth
expr: sum(workqueue_depth{app_kubernetes_io_name="vela-core",name="application"}) > 100
for: 10m
summary: High Application Queue Depth


Customize Grafana credential

If you want to change the default username and password for Grafana, you can run the following command

vela addon enable grafana adminUser=super-user adminPassword=PASSWORD

This will change your default admin user to super-user and its password to PASSWORD.

Custom storage

If you want your prometheus-server and grafana to persist data in volumes, you can also specify storage parameter for your installation, like

vela addon enable prometheus-server storage=1G

This will create PersistentVolumeClaims and let the addon use the provided storage. The storage will not be automatically recycled even if the addon is disabled. You need to clean up the storage manually.

Integrate other Prometheus & Grafana

Sometimes, you might already have Prometheus & Grafana instances. They might be built by other tools, or come from cloud providers. Follow the below guide to integrate with existing systems.

Integrate Prometheus

If you already have external prometheus service and you want to connect it to Grafana (established by vela addon), you can create a GrafanaDatasource to register it through KubeVela application.

kind: Application
name: register-prometheus
- type: grafana-datasource
name: my-prometheus
access: proxy
basicAuth: false
isDefault: false
name: MyPrometheus
readOnly: true
withCredentials: true
httpHeaderName1: Authorization
tlsSkipVerify: true
httpHeaderValue1: <token of your prometheus access>
type: prometheus
url: <my-prometheus url>

For example, if you are using the Prometheus service on Alibaba Cloud (ARMS), you can go to the Prometheus setting page and find the access url & access token.


You need to ensure your grafana access is already available. You can run kubectl get grafana default and see if it exists.

Integrate Grafana

If you already have existing Grafana, similar to Prometheus integration, you can create a Grafana access through KubeVela application.

kind: Application
name: register-grafana
- type: grafana-access
name: my-grafana
name: my-grafana
endpoint: <my-grafana url>
token: <access token>

To get your grafana access, you can go into your Grafana instance and configure API keys.


Then copy the token into your grafana registration configuration.


After the application is successfully dispatched, you can check the registration by running the following command.

> kubectl get grafana
default http://grafana.o11y-system:3000 BasicAuth
my-grafana BearerToken

Now you can manage your dashboard and datasource on your grafana instance through the native Kubernetes API as well.

# show all the dashboard you have
kubectl get grafanadashboard -l grafana=my-grafana
# show all the datasource you have
kubectl get grafanadatasource -l grafana=my-grafana

For more details, you can refer to vela-prism.

Integrate Other Tools or Systems

There are a wide range of community tools or eco-systems that users can leverage for building their observability system, such as prometheus-operator or DataDog. By far, KubeVela does not have existing best practices for those integration. We may integrate with those popular projects through KubeVela addons in the future. We are also welcome to community contributions for broader explorations and more connections.


Compared with Helm

Although it is possible to install these resources into your Kubernetes system through Helm, one of the major benefits for using KubeVela addon to install them is that it supports multi-cluster delivery natively, which means, once you add your managed clusters into the KubeVela control plane, you would be able to install, upgrade or uninstall these addons in all clusters through one line.

Compared with legacy observability addon

The old KubeVela observability addon install prometheus, grafana and other widgets as a whole. The latest observability addon suites (after KubeVela v1.5.0) divides it into multiple parts, which allows users to only install part of the suite.

Besides, the old observability addon relies on the fluxcd addon to install the widgets as Helm release. The latest version, uses the native webservice component in KubeVela, which allows more flexible customizations.


KubeVela will integrate more observability addons in the future, such as logging and tracing addons. Community operators like prometheus-operator also provide alternative ways for managing observability application, which are intended to be included in KubeVela addons as well. We are also welcome to more integrations through KubeVela addon ecosystem.