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.
tip
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.
Using Dashboard as Component
One way to manage your customized dashboard is to use the component in KubeVela application like below.
apiVersion: core.oam.dev/v1beta1
kind: Application
metadata:
name: my-dashboard
spec:
components:
- name: my-dashboard
type: grafana-dashboard
properties:
uid: my-example-dashboard
data: |
{
"panels": [{
"gridPos": {
"h": 9,
"w": 12
},
"targets": [{
"datasource": {
"type": "prometheus",
"uid": "prometheus-vela"
},
"expr": "max(up) by (cluster)"
}],
"title": "Clusters",
"type": "timeseries"
}],
"title": "My Dashboard"
}
Using Dashboard as Trait
An alternative way for adding customized dashboard to your application is to use the grafana-dashboard trait.
apiVersion: core.oam.dev/v1beta1
kind: Application
metadata:
name: my-app
spec:
components:
- name: my-app
type: webservice
properties:
image: somefive/prometheus-client-example:new
traits:
- type: prometheus-scrape
- type: grafana-dashboard
properties:
data: |
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Import Dashboard from URL
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.
apiVersion: core.oam.dev/v1beta1
kind: Application
metadata:
name: my-dashboard
spec:
components: []
workflow:
steps:
- type: import-grafana-dashboard
name: import-grafana-dashboard
properties:
uid: my-dashboard
title: My Dashboard
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.
Using CUE to Generate Dashboards Dynamically
danger
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.
import (
"vela/op"
"vela/ql"
"strconv"
"math"
"regexp"
)
"create-dashboard": {
type: "workflow-step"
annotations: {}
labels: {}
description: "Create dashboard for application."
}
template: {
resources: ql.#CollectServiceEndpoints & {
app: {
name: context.name
namespace: context.namespace
filter: {}
}
} @step(1)
status: {
endpoints: *[] | [...{...}]
if resources.err == _|_ && resources.list != _|_ {
endpoints: [ for ep in resources.list if ep.endpoint.port == parameter.port {
name: "\(ep.ref.name):\(ep.ref.namespace):\(ep.cluster)"
portStr: strconv.FormatInt(ep.endpoint.port, 10)
if ep.cluster == "local" && ep.ref.kind == "Service" {
url: "http://\(ep.ref.name).\(ep.ref.namespace):\(portStr)"
}
if ep.cluster != "local" || ep.ref.kind != "Service" {
url: "http://\(ep.endpoint.host):\(portStr)"
}
}]
}
} @step(2)
getMetrics: op.#Steps & {
for ep in status.endpoints {
"\(ep.name)": op.#HTTPGet & {
url: ep.url + "/metrics"
}
}
} @step(3)
checkErrors: op.#Steps & {
for ep in status.endpoints if getMetrics["\(ep.name)"] != _|_ {
if getMetrics["\(ep.name)"].response.statusCode != 200 {
"\(ep.name)": op.#Steps & {
src: getMetrics["\(ep.name)"]
err: op.#Fail & {
message: "failed to get metrics for \(ep.name) from \(ep.url), code \(src.response.statusCode)"
}
}
}
}
} @step(4)
createDashboards: op.#Steps & {
for ep in status.endpoints if getMetrics["\(ep.name)"] != _|_ {
if getMetrics["\(ep.name)"].response.body != "" {
"\(ep.name)": dashboard & {
title: context.name
uid: "\(context.name)-\(context.namespace)"
description: "Auto-generated Dashboard"
metrics: *[] | [...{...}]
metrics: regexp.FindAllNamedSubmatch(#"""
# HELP \w+ (?P<desc>[^\n]+)\n# TYPE (?P<name>\w+) (?P<type>\w+)
"""#, getMetrics["\(ep.name)"].response.body, -1)
}
}
}
} @step(5)
applyDashboards: op.#Steps & {
for ep in status.endpoints if createDashboards["\(ep.name)"] != _|_ {
"\(ep.name)": op.#Apply & {
db: {for k, v in createDashboards["\(ep.name)"] if k != "metrics" {
"\(k)": v
}}
value: {
apiVersion: "o11y.prism.oam.dev/v1alpha1"
kind: "GrafanaDashboard"
metadata: name: "\(db.uid)@\(parameter.grafana)"
spec: db
}
}
}
} @step(6)
dashboard: {
title: *"Example Dashboard" | string
uid: *"" | string
description: *"" | string
metrics: [...{...}]
time: {
from: *"now-1h" | string
to: *"now" | string
}
refresh: *"30s" | string
templating: list: [{
type: "datasource"
name: "datasource"
label: "Data Source"
query: "prometheus"
hide: 2
}, {
type: "interval"
name: "rate_interval"
label: "Rate"
query: "3m,5m,10m,30m"
hide: 2
}]
panels: [for i, m in metrics {
title: m.name
type: "graph"
datasource: {
uid: "${datasource}"
type: "prometheus"
}
gridPos: {
w: 6
h: 8
x: math.Floor((i - y * 4) * 6)
y: math.Floor(i / 4)
}
description: m.desc
if m.type == "gauge" {
targets: [{
expr: "sum(\(m.name))"
}]
legend: show: false
}
if m.type == "counter" {
targets: [{
expr: "sum(rate(\(m.name)[$rate_interval]))"
}]
legend: show: false
}
if m.type == "histogram" || m.type == "summary" {
targets: [{
expr: "sum(rate(\(m.name)_sum[$rate_interval])) / sum(rate(\(m.name)_count[$rate_interval]))"
legendFormat: "avg"
}, {
expr: "histogram_quantile(0.75, sum(rate(\(m.name)_bucket[$rate_interval])) by (le))"
legendFormat: "p75"
}, {
expr: "histogram_quantile(0.99, sum(rate(\(m.name)_bucket[$rate_interval])) by (le))"
legendFormat: "p99"
}]
}
}]
}
parameter: {
port: *8080 | int
grafana: *"default" | string
}
}
Then you can create an application as follows.
apiVersion: core.oam.dev/v1beta1
kind: Application
metadata:
name: my-app
spec:
# the core workload
components:
- name: my-app
type: webservice
properties:
image: somefive/prometheus-client-example:new
traits:
- type: prometheus-scrape
# deploy and create dashboard automatically
workflow:
steps:
- type: deploy
name: deploy
properties:
policies: []
- type: create-dashboard
name: create-dashboard
This application will deploy your webservice first, and generate dashboard automatically according to the metrics collected from the webservice.