简单4步，利用Prometheus Operator实现自定义指标监控

简单4步，利用Prometheus Operator实现自定义指标监控

本文来自Rancher Labs

在过去的文章中，我们花了相当大的篇幅来聊关于监控的话题。这是因为当你正在管理Kubernetes集群时，一切都会以极快的速度发生变化。因此有一个工具来监控集群的健康状态和资源指标极为重要。

在Rancher 2.5中，我们引入了基于Prometheus Operator的新版监控，它可以提供Prometheus以及相关监控组件的原生Kubernetes部署和管理。Prometheus Operator可以让你监控集群节点、Kubernetes组件和应用程序工作负载的状态和进程。同时，它还能够通过Prometheus收集的指标来定义告警并且创建自定义仪表盘，通过Grafana可以轻松地可视化收集到的指标。你可以访问下列链接获取更多关于新版监控组件的细节：

Operator来抓取自定义指标并利用这些指标进行高级工作负载管理。

安装Prometheus

现在让我们部署一个从应用层暴露自定义指标的示例工作负载。该工作负载暴露了一个简单的应用程序，该应用程序已经使用Prometheus client_golang库进行了检测，并在/metric端点上提供了一些自定义指标。

它有两个指标：

http_requests_total apps/v1 kind: Deployment metadata: labels: app.kubernetes.io/name: prometheus-example-app name: prometheus-example-app spec: replicas: 1 selector: matchLabels: app.kubernetes.io/name: prometheus-example-app template: metadata: labels: app.kubernetes.io/name: prometheus-example-app spec: containers: - name: prometheus-example-app image: gmehta3/demo-app:metrics ports: - name: web containerPort: 8080 --- apiVersion: v1 kind: Service metadata: name: prometheus-example-app labels: app.kubernetes.io/name: prometheus-example-app spec: selector: app.kubernetes.io/name: prometheus-example-app ports: - protocol: TCP port: 8080 targetPort: 8080 name: web --- apiVersion: networking.k8s.io/v1beta1 kind: Ingress metadata: name: prometheus-example-app spec: rules: - host: hpa.demo http: paths: - path: / backend: serviceName: prometheus-example-app servicePort: 8080

部署ServiceMonitor

你可以访问以下链接查看完整的ServiceMonitor规范：

现在，我们来部署ServiceMonitor，Prometheus用它来收集组成prometheus-example-app Kubernetes服务的pod。 ```apiVersion: monitoring.coreos.com/v1kind: ServiceMonitormetadata:name: prometheus-example-appspec:selector:matchLabels:app.kubernetes.io/name: prometheus-example-appendpoints:

port: web   如你所见，现在用户可以在Rancher监控中浏览ServiceMonitor。      不久之后，新的service monitor和服务相关联的pod应该会反映在Prometheus服务发现中。      我们也能够在Prometheus中看到指标。        ## 部署Grafana仪表盘    

在Rancher 2.5中，监控可以让用户将Grafana仪表盘存储为cattle-dashboards命名空间中的ConfigMaps。

用户或集群管理员现在可以在这一命名空间中添加更多的仪表盘以扩展Grafana的自定义仪表盘。

Dashboard ConfigMap Example

apiVersion: v1 kind: ConfigMap metadata: name: prometheus-example-app-dashboard namespace: cattle-dashboards labels: grafana_dashboard: "1" data: prometheus-example-app.json: | { "annotations": { "list": [ { "builtIn": 1, "datasource": "-- Grafana --", "enable": true, "hide": true, "iconColor": "rgba(0, 211, 255, 1)", "name": "Annotations & Alerts", "type": "dashboard" } ] }, "editable": true, "gnetId": null, "graphTooltip": 0, "links": [], "panels": [ { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": null, "fieldConfig": { "defaults": { "custom": {} }, "overrides": [] }, "fill": 1, "fillGradient": 0, "gridPos": { "h": 9, "w": 12, "x": 0, "y": 0 }, "hiddenSeries": false, "id": 2, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 1, "nullPointMode": "null", "percentage": false, "pluginVersion": "7.1.5", "pointradius": 2, "points": false, "renderer": "flot", "seriesOverrides": [], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "expr": "rate(http_requests_total{code=\"200\",service=\"prometheus-example-app\"}[5m])", "instant": false, "interval": "", "legendFormat": "", "refId": "A" } ], "thresholds": [], "timeFrom": null, "timeRegions": [], "timeShift": null, "title": "http_requests_total_200", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "buckets": null, "mode": "time", "name": null, "show": true, "values": [] }, "yaxes": [ { "format": "short", "label": null, "logBase": 1, "max": null, "min": null, "show": true }, { "format": "short", "label": null, "logBase": 1, "max": null, "min": null, "show": true } ], "yaxis": { "align": false, "alignLevel": null } }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": null, "description": "", "fieldConfig": { "defaults": { "custom": {} }, "overrides": [] }, "fill": 1, "fillGradient": 0, "gridPos": { "h": 8, "w": 12, "x": 0, "y": 9 }, "hiddenSeries": false, "id": 4, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 1, "nullPointMode": "null", "percentage": false, "pluginVersion": "7.1.5", "pointradius": 2, "points": false, "renderer": "flot", "seriesOverrides": [], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "expr": "rate(http_requests_total{code!=\"200\",service=\"prometheus-example-app\"}[5m])", "interval": "", "legendFormat": "", "refId": "A" } ], "thresholds": [], "timeFrom": null, "timeRegions": [], "timeShift": null, "title": "http_requests_total_not_200", "tooltip": { "shared": true, "sort": 0, "value_type": "individual" }, "type": "graph", "xaxis": { "buckets": null, "mode": "time", "name": null, "show": true, "values": [] }, "yaxes": [ { "format": "short", "label": null, "logBase": 1, "max": null, "min": null, "show": true }, { "format": "short", "label": null, "logBase": 1, "max": null, "min": null, "show": true } ], "yaxis": { "align": false, "alignLevel": null } } ], "schemaVersion": 26, "style": "dark", "tags": [], "templating": { "list": [] }, "time": { "from": "now-15m", "to": "now" }, "timepicker": { "refresh_intervals": [ "5s", "10s", "30s", "1m", "5m", "15m", "30m", "1h", "2h", "1d" ] }, "timezone": "", "title": "prometheus example app", "version": 1 }

自定义指标的HPA

这一部分假设你已经将prometheus-adapter作为监控的一部分安装完毕了。实际上，在默认情况下，监控安装程序会安装prometheus-adapter。

用户现在可以创建一个HPA spec，如下所示：

apiVersion: autoscaling/v2beta2 kind: HorizontalPodAutoscaler metadata: name: prometheus-example-app-hpa spec: scaleTargetRef: apiVersion: apps/v1 kind: Deployment name: prometheus-example-app minReplicas: 1 maxReplicas: 5 metrics: - type: Object object: describedObject: kind: Service name: prometheus-example-app metric: name: http_requests target: averageValue: "5" type: AverageValue

你可以查看以下链接获取关于HPA的更多信息：

-c 10 -n 5000 结

在本文中，我们探讨了Rancher 2.5中新监控的灵活性。开发人员和集群管理员可以利用该堆栈来监控它们的工作负载，部署可视化，并利用Kubernetes内可用的高级工作负载管理功能。

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