Customize Dashboard

In KubeVela, leveraging the power of Kubernetes Aggregated API layer, it is easy for users to manipulate dashboards on Grafana and make customizations to application visualizations.

Except for the pre-defined dashboards provided by the grafana addon as mentioned in the Out-of-the-box chapter, KubeVela users can deploy customized dashboards to their system as well.

Customize Dashboard - 图1tip

If you do not know how to build Grafana dashboards and export them as json data, you can refer to the following Grafana docs for details.

  1. Build your first dashboard
  2. Exporting a dashboard

One way to manage your customized dashboard is to use the component in KubeVela application like below.

  1. apiVersion: core.oam.dev/v1beta1
  2. kind: Application
  3. metadata:
  4.   name: my-dashboard
  5. spec:
  6.   components:
  7.     - name: my-dashboard
  8.       type: grafana-dashboard
  9.       properties:
  10.         uid: my-example-dashboard
  11.         data: |
  12.           {
  13.             "panels": [{
  14.                 "gridPos": {
  15.                     "h": 9,
  16.                     "w": 12
  17.                 },
  18.                 "targets": [{
  19.                     "datasource": {
  20.                         "type": "prometheus",
  21.                         "uid": "prometheus-vela"
  22.                     },
  23.                     "expr": "max(up) by (cluster)"
  24.                 }],
  25.                 "title": "Clusters",
  26.                 "type": "timeseries"
  27.             }],
  28.             "title": "My Dashboard"
  29.           }

An alternative way for adding customized dashboard to your application is to use the grafana-dashboard trait.

  1. apiVersion: core.oam.dev/v1beta1
  2. kind: Application
  3. metadata:
  4.   name: my-app
  5. spec:
  6.   components:
  7.     - name: my-app
  8.       type: webservice
  9.       properties:
  10.         image: somefive/prometheus-client-example:new
  11.       traits:
  12.         - type: prometheus-scrape
  13.         - type: grafana-dashboard
  14.           properties:
  15.             data: |
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Sometimes, you might already have some Grafana dashboards stored in OSS or served by other HTTP server. To import these dashboards in your system, you can leverage the import-grafana-dashboard workflow step as below.

  1. apiVersion: core.oam.dev/v1beta1
  2. kind: Application
  3. metadata:
  4.   name: my-dashboard
  5. spec:
  6.   components: []
  7.   workflow:
  8.     steps:
  9.       - type: import-grafana-dashboard
  10.         name: import-grafana-dashboard
  11.         properties: 
  12.           uid: my-dashboard
  13.           title: My Dashboard
  14.           url: https://kubevelacharts.oss-accelerate.aliyuncs.com/dashboards/up-cluster-dashboard.json

In the import-grafana-dashboard step, the application will download the dashboard json from the URL and create dashboards on Grafana with correct format.

Customize Dashboard - 图2danger

The following chapter requires you to have experiences in writing CUE language, especially complex KubeVela WorkflowStepDefinitions. Besides, it is also necessary to know the basic structure of Grafana dashboards. Without these knowledge, it would be relatively hard for users to understand the following section.

This demo is a proof of concept for Dashboard as Code with the use of KubeVela. There is still much work to be done for making it into practice, including dealing the late occurrence of some lazy metrics. It is not for production system for now. Do not expect it to work perfectly. It is welcome to submit your ideas and use cases on GitHub.

Details

With CUE, you can customize the process of creating dashboards. This will empower you to construct dashboards dynamically and combined with other actions. For example, you can make a WorkflowStepDefinition called create-dashboard, which finds the service created by the application itself and get the metrics from the exposed endpoint. Then, the step will build Grafana dashboard panels from those metrics automatically.

  1. import (
  2.     "vela/op"
  3.     "vela/ql"
  4.     "strconv"
  5.     "math"
  6.     "regexp"
  7. )
  9. "create-dashboard": {
  10.     type: "workflow-step"
  11.     annotations: {}
  12.     labels: {}
  13.     description: "Create dashboard for application."
  14. }
  15. template: {
  16.     resources: ql.#CollectServiceEndpoints & {
  17.         app: {
  18.             name:      context.name
  19.             namespace: context.namespace
  20.             filter: {}
  21.         }
  22.     } @step(1)
  24.     status: {
  25.         endpoints: *[] | [...{...}]
  26.         if resources.err == _|_ && resources.list != _|_ {
  27.             endpoints: [ for ep in resources.list if ep.endpoint.port == parameter.port {
  28.                 name: "\(ep.ref.name):\(ep.ref.namespace):\(ep.cluster)"
  29.                 portStr: strconv.FormatInt(ep.endpoint.port, 10)
  30.                 if ep.cluster == "local" && ep.ref.kind == "Service" {
  31.                     url: "http://\(ep.ref.name).\(ep.ref.namespace):\(portStr)"
  32.                 }
  33.                 if ep.cluster != "local" || ep.ref.kind != "Service" {
  34.                     url: "http://\(ep.endpoint.host):\(portStr)"
  35.                 }
  36.             }]
  37.         }
  38.     } @step(2)
  40.     getMetrics: op.#Steps & {
  41.         for ep in status.endpoints {
  42.             "\(ep.name)": op.#HTTPGet & {
  43.                 url: ep.url + "/metrics"
  44.             }
  45.         }
  46.     } @step(3)
  48.     checkErrors: op.#Steps & {
  49.         for ep in status.endpoints if getMetrics["\(ep.name)"] != _|_ {
  50.             if getMetrics["\(ep.name)"].response.statusCode != 200 {
  51.                 "\(ep.name)": op.#Steps & {
  52.                     src: getMetrics["\(ep.name)"]
  53.                     err: op.#Fail & {
  54.                         message: "failed to get metrics for \(ep.name) from \(ep.url), code \(src.response.statusCode)"
  55.                     }
  56.                 }
  57.             }
  58.         }
  59.     } @step(4)
  61.     createDashboards: op.#Steps & {
  62.         for ep in status.endpoints if getMetrics["\(ep.name)"] != _|_ {
  63.             if getMetrics["\(ep.name)"].response.body != "" {
  64.                 "\(ep.name)": dashboard & {
  65.                     title: context.name
  66.                     uid: "\(context.name)-\(context.namespace)"
  67.                     description: "Auto-generated Dashboard"
  68.                     metrics: *[] | [...{...}]
  69.                     metrics: regexp.FindAllNamedSubmatch(#"""
  70.                         # HELP \w+ (?P<desc>[^\n]+)\n# TYPE (?P<name>\w+) (?P<type>\w+)
  71.                         """#, getMetrics["\(ep.name)"].response.body, -1)
  72.                 }
  73.             }
  74.         }
  75.     } @step(5)
  77.     applyDashboards: op.#Steps & {
  78.         for ep in status.endpoints if createDashboards["\(ep.name)"] != _|_ {
  79.             "\(ep.name)": op.#Apply & {
  80.                 db: {for k, v in createDashboards["\(ep.name)"] if k != "metrics" {
  81.                     "\(k)": v
  82.                 }}
  83.                 value: {
  84.                     apiVersion: "o11y.prism.oam.dev/v1alpha1"
  85.                     kind:       "GrafanaDashboard"
  86.                     metadata: name: "\(db.uid)@\(parameter.grafana)"
  87.                     spec: db
  88.                 }
  89.             }
  90.         }
  91.     } @step(6)
  93.     dashboard: {
  94.         title: *"Example Dashboard" | string
  95.         uid: *"" | string
  96.         description: *"" | string
  97.         metrics: [...{...}]
  98.         time: {
  99.             from: *"now-1h" | string
  100.             to: *"now" | string
  101.         }
  102.         refresh: *"30s" | string
  103.         templating: list: [{
  104.             type: "datasource"
  105.             name: "datasource"
  106.             label: "Data Source"
  107.             query: "prometheus"
  108.             hide: 2
  109.         }, {
  110.             type: "interval"
  111.             name: "rate_interval"
  112.             label: "Rate"
  113.             query: "3m,5m,10m,30m"
  114.             hide: 2
  115.         }]
  117.         panels: [for i, m in metrics {
  118.             title: m.name
  119.             type: "graph"
  120.             datasource: {
  121.                 uid: "${datasource}"
  122.                 type: "prometheus"
  123.             }
  124.             gridPos: {
  125.                 w: 6
  126.                 h: 8
  127.                 x: math.Floor((i - y * 4) * 6)
  128.                 y: math.Floor(i / 4)
  129.             }
  130.             description: m.desc
  131.             if m.type == "gauge" {
  132.                 targets: [{
  133.                     expr: "sum(\(m.name))"
  134.                 }]
  135.                 legend: show: false
  136.             }
  137.             if m.type == "counter" {
  138.                 targets: [{
  139.                     expr: "sum(rate(\(m.name)[$rate_interval]))"
  140.                 }]
  141.                 legend: show: false
  142.             }
  143.             if m.type == "histogram" || m.type == "summary" {
  144.                 targets: [{
  145.                     expr: "sum(rate(\(m.name)_sum[$rate_interval])) / sum(rate(\(m.name)_count[$rate_interval]))"
  146.                     legendFormat: "avg"
  147.                 }, {
  148.                     expr: "histogram_quantile(0.75, sum(rate(\(m.name)_bucket[$rate_interval])) by (le))"
  149.                     legendFormat: "p75"
  150.                 }, {
  151.                     expr: "histogram_quantile(0.99, sum(rate(\(m.name)_bucket[$rate_interval])) by (le))"
  152.                     legendFormat: "p99"
  153.                 }]
  154.             }
  155.         }]
  156.     }
  158.     parameter: {
  159.         port: *8080 | int
  160.         grafana: *"default" | string
  161.     }
  162. }

Then you can create an application as follows.

  1. apiVersion: core.oam.dev/v1beta1
  2. kind: Application
  3. metadata:
  4.   name: my-app
  5. spec:
  6.   # the core workload
  7.   components:
  8.     - name: my-app
  9.       type: webservice
  10.       properties:
  11.         image: somefive/prometheus-client-example:new
  12.       traits:
  13.         - type: prometheus-scrape
  14.   # deploy and create dashboard automatically
  15.   workflow:
  16.     steps:
  17.       - type: deploy
  18.         name: deploy
  19.         properties:
  20.           policies: []
  21.       - type: create-dashboard
  22.         name: create-dashboard

This application will deploy your webservice first, and generate dashboard automatically according to the metrics collected from the webservice.

Last updated on Aug 4, 2023 by Daniel Higuero