自定义监控大盘
在 KubeVela 中，借助 Kubernetes 原生的 Aggregated API Layer，KubeVela 用户可以比较轻易地在集群中操作修改 Grafana 上的监控大盘。
除了 开箱即用 章节中 grafana 插件预置的监控大盘外，KubeVela 用户同样也可以在系统中部署自定义大盘。
提示
如果你还不了解如何在 Grafana 上创建大盘并将它们以 JSON 格式导出，你可以阅读下列 Grafana 官方文档来学习。
使用应用组件部署监控大盘
一种部署监控大盘的方式是在 KubeVela 应用中使用相应的组件类型。
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"
}
使用应用运维特征部署监控大盘
除了组件外，用户也可以将监控大盘放在运维特征中进行部署。
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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通过 URL 导入监控大盘
有时，你可能已经把编写好的 Grafana 监控大盘存储在了 OSS 或者其他 HTTP 服务器上。在 KubeVela 的应用中，你同样也可以使用 import-grafana-dashboard 工作流步骤来将大盘导入。
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
在 import-grafana-dashboard 步骤中, 应用首先将会从 URL 上下载监控大盘的数据，然后将它创建到 Grafana 上。
使用 CUE 来动态生成监控大盘
危险
下面的章节将会要求您具有一定的 CUE 语言开发经验，尤其是 KubeVela 的工作流自定义步骤的开发经验。此外，Grafana 监控大盘基础数据结构的相关知识也是必要的。如果缺少这些知识，下面的内容将会比较难理解。
下文是一个在 KubeVela 中实践 Dashboard as Code 的样例。当你在实际环境中使用它时，根据场景可能存在额外的问题需要注意，比如部分指标的延迟出现。目前该技术尚未使用在生产环境中，它是否能够在不同的环境中顺利运行也尚未验证。欢迎将你的想法和用例提交在 GitHub 上进行讨论。
Details
你可以使用 CUE 来定制监控大盘的生成过程。它允许你根据其他动作行为的结果来动态创建监控大盘的表盘内容。例如，你可以用如下的代码创建一个 create-dashboard 工作流步骤定义，它会自动从应用提供的服务端口中找到相应的指标内容并为不同类型的指标生成相应的折线图。
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
}
}
然后你可以按照如下方式创建一个应用。
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
该应用会首先部署 webservice 组件，然后自动采集它的指标并为其生成监控大盘。