6个步骤搞定云原生应用监控和告警(建议收藏)

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云原生系统搭建完毕之后,要建立可观测性和告警,有利于了解整个系统的运行状况。基于Prometheus搭建的云原生监控和告警是业内常用解决方案,每个云原生参与者都需要了解。

本文主要以springboot应用为例,讲解云原生应用监控和告警的实操,对于理论知识讲解不多。等朋友们把实操都理顺之后,再补充理论知识,就更容易理解整个体系了。

1、监控告警技术选型

kubernetes集群非常复杂,有容器基础资源指标、k8s集群Node指标、集群里的业务应用指标等等。面对大量需要监控的指标,传统监控方案Zabbix对于云原生监控的支持不是很好。

所以需要使用更适合云原生的监控告警方案prometheus,prometheus和云原生是密不可分的,并且prometheus现已成为云原生生态中监控的事实标准。下面来一步步搭建基于prometheus的监控告警方案。

prometheus的基本原理是:主动去**被监控的系统**拉取各项指标,然后汇总存入到自身的时序数据库,最后再通过图表展示出来,或者是根据告警规则触发告警。被监控的系统要主动暴露接口给prometheus去抓取指标。流程图如下:

2、前置准备

本文的操作前提是:需要安装好docker、kubernetes,在K8S集群里部署好一个springboot应用。

假设K8S集群有4个节点,分别是:k8s-master(10.20.1.21)、k8s-worker-1(10.20.1.22)、k8s-worker-2(10.20.1.23)、k8s-worker-3(10.20.1.24)。

3、安装Prometheus

3.1、在k8s-master节点创建命名空间

kubectl create ns monitoring

3.2、准备configmap文件

准备configmap文件prometheus-config.yaml,yaml文件中暂时只配置了对于prometheus本身指标的抓取任务。下文会继续补充这个yaml文件:

apiVersion: v1
kind: ConfigMap
metadata:
  name: prometheus-config
  namespace: monitoring
data:
  prometheus.yml: |
    global:
      scrape_interval: 15s
      scrape_timeout: 15s
    scrape_configs:
    - job_name: 'prometheus'
      static_configs:
      - targets: ['localhost:9090']

3.3、创建configmap

kubectl apply -f prometheus-config.yaml

3.4、准备prometheus的部署文件

准备prometheus的部署文件prometheus-deploy.yaml

apiVersion: apps/v1
kind: Deployment
metadata:
  name: prometheus
  namespace: monitoring
  labels:
    app: prometheus
spec:
  selector:
    matchLabels:
      app: prometheus
  template:
    metadata:
      labels:
        app: prometheus
    spec:
      serviceAccountName: prometheus
      containers:
        - image: prom/prometheus:v2.31.1
          name: prometheus
          securityContext:
            runAsUser: 0
          args:
            - "--config.file=/etc/prometheus/prometheus.yml"
            - "--storage.tsdb.path=/prometheus" # 指定tsdb数据路径
            - "--storage.tsdb.retention.time=24h"
            - "--web.enable-admin-api" # 控制对admin HTTP API的访问,其中包括删除时间序列等功能
            - "--web.enable-lifecycle" # 支持热更新,直接执行localhost:9090/-/reload立即生效
          ports:
            - containerPort: 9090
              name: http
          volumeMounts:
            - mountPath: "/etc/prometheus"
              name: config-volume
            - mountPath: "/prometheus"
              name: data
          resources:
            requests:
              cpu: 200m
              memory: 1024Mi
            limits:
              cpu: 200m
              memory: 1024Mi
        - image: jimmidyson/configmap-reload:v0.4.0  #prometheus配置动态加载
          name: prometheus-reload
          securityContext:
            runAsUser: 0
          args:
            - "--volume-dir=/etc/config"
            - "--webhook-url=http://localhost:9090/-/reload"
          volumeMounts:
            - mountPath: "/etc/config"
              name: config-volume
          resources:
            requests:
              cpu: 100m
              memory: 50Mi
            limits:
              cpu: 100m
              memory: 50Mi   
      volumes:
        - name: data
          persistentVolumeClaim:
            claimName: prometheus-data
        - configMap:
            name: prometheus-config
          name: config-volume

3.5、准备prometheus的存储文件

准备prometheus的存储文件prometheus-storage.yaml

apiVersion: storage.k8s.io/v1
kind: StorageClass
metadata:
  name: local-storage
provisioner: kubernetes.io/no-provisioner
volumeBindingMode: WaitForFirstConsumer
---
apiVersion: v1
kind: PersistentVolume
metadata:
  name: prometheus-local
  labels:
    app: prometheus
spec:
  accessModes:
    - ReadWriteOnce
  capacity:
    storage: 20Gi
  storageClassName: local-storage
  local:
    path: /data/k8s/prometheus  #确保该节点上存在此目录
  persistentVolumeReclaimPolicy: Retain
  nodeAffinity:
    required:
      nodeSelectorTerms:
        - matchExpressions:
            - key: kubernetes.io/hostname
              operator: In
              values:
                - k8s-worker-2
---
apiVersion: v1
kind: PersistentVolumeClaim
metadata:
  name: prometheus-data
  namespace: monitoring
spec:
  selector:
    matchLabels:
      app: prometheus
  accessModes:
    - ReadWriteOnce
  resources:
    requests:
      storage: 20Gi
  storageClassName: local-storage

这里我使用的是k8s-worker-2节点作为存储资源,读者们使用时要改成自己的节点名称,同时要在对应的节点下创建目录:/data/k8s/prometheus。最终时序数据库的数据会存储到此目录下,见下图:

上面的yaml中用到了pv、pvc、storageclass存储相关的知识,后面写篇文章讲解下,这里简单介绍下:pv、pvc、storageclass主要是为pod自动创建存储资源相关的组件。

3.6、创建存储资源

kubectl apply -f prometheus-storage.yaml

3.7、准备用户、角色、权限相关文件

准备用户、角色、权限相关文件prometheus-rbac.yaml

apiVersion: v1
kind: ServiceAccount
metadata:
  name: prometheus
  namespace: monitoring
---
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
  name: prometheus
rules:
  - apiGroups:
      - ""
    resources:
      - nodes
      - services
      - endpoints
      - pods
      - nodes/proxy
    verbs:
      - get
      - list
      - watch
  - apiGroups:
      - "extensions"
    resources:
      - ingresses
    verbs:
      - get
      - list
      - watch
  - apiGroups:
      - ""
    resources:
      - configmaps
      - nodes/metrics
    verbs:
      - get
  - nonResourceURLs:
      - /metrics
    verbs:
      - get
---
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata:
  name: prometheus
roleRef:
  apiGroup: rbac.authorization.k8s.io
  kind: ClusterRole
  name: prometheus
subjects:
  - kind: ServiceAccount
    name: prometheus
    namespace: monitoring

3.8、创建RBAC资源

kubectl apply -f prometheus-rbac.yaml

3.9、创建deployment资源

kubectl apply -f prometheus-deploy.yaml

3.10、准备service资源对象文件

准备service资源对象文件prometheus-svc.yaml。采用NortPort方式,供外部访问prometheus:

apiVersion: v1
kind: Service
metadata:
  name: prometheus
  namespace: monitoring
  labels:
    app: prometheus
spec:
  selector:
    app: prometheus
  type: NodePort
  ports:
    - name: web
      port: 9090
      targetPort: http

3.11、创建service对象:

kubectl apply -f prometheus-svc.yaml

3.12、访问prometheus

此时通过kubectl get svc -n monitoring获取暴露的端口号,通过K8S集群的任意节点+端口号就可以访问prometheus了。比如通过http://10.20.1.21:32459/访问,可以看到如下界面,通过targets可以看到上面prometheus-config.yaml文件中配置的被抓取对象:

至此prometheus安装完毕,下面继续安装grafana。

4、安装Grafana

prometheus的图表功能比较弱,一般使用grafana来展示prometheus的数据,下面开始安装grafana。

4.1、准备grafana部署文件

准备grafana部署文件grafana-deploy.yaml,这是一个all-in-one的文件,将Deployment、Service、PV、PVC的编排全部写在该文件中:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: grafana
  namespace: monitoring
spec:
  selector:
    matchLabels:
      app: grafana
  template:
    metadata:
      labels:
        app: grafana
    spec:
      volumes:
        - name: storage
          persistentVolumeClaim:
            claimName: grafana-data
      containers:
        - name: grafana
          image: grafana/grafana:8.3.3
          imagePullPolicy: IfNotPresent
          securityContext:
            runAsUser: 0
          ports:
            - containerPort: 3000
              name: grafana
          env:
            - name: GF_SECURITY_ADMIN_USER
              value: admin
            - name: GF_SECURITY_ADMIN_PASSWORD
              value: admin
          readinessProbe:
            failureThreshold: 10
            httpGet:
              path: /api/health
              port: 3000
              scheme: HTTP
            initialDelaySeconds: 60
            periodSeconds: 10
            successThreshold: 1
            timeoutSeconds: 30
          livenessProbe:
            failureThreshold: 3
            httpGet:
              path: /api/health
              port: 3000
              scheme: HTTP
            periodSeconds: 10
            successThreshold: 1
            timeoutSeconds: 1
          resources:
            limits:
              cpu: 400m
              memory: 1024Mi
            requests:
              cpu: 200m
              memory: 512Mi
          volumeMounts:
            - mountPath: /var/lib/grafana
              name: storage
---
apiVersion: v1
kind: Service
metadata:
  name: grafana
  namespace: monitoring
spec:
  type: NodePort
  ports:
    - port: 3000
  selector:
    app: grafana
---
apiVersion: v1
kind: PersistentVolume
metadata:
  name: grafana-local
  labels:
    app: grafana
spec:
  accessModes:
    - ReadWriteOnce
  capacity:
    storage: 1Gi
  storageClassName: local-storage
  local:
    path: /data/k8s/grafana #保证节点上创建好该目录
  persistentVolumeReclaimPolicy: Retain
  nodeAffinity:
    required:
      nodeSelectorTerms:
        - matchExpressions:
            - key: kubernetes.io/hostname
              operator: In
              values:
                - k8s-worker-2
---
apiVersion: v1
kind: PersistentVolumeClaim
metadata:
  name: grafana-data
  namespace: monitoring
spec:
  selector:
    matchLabels:
      app: grafana
  accessModes:
    - ReadWriteOnce
  resources:
    requests:
      storage: 1Gi
  storageClassName: local-storage

上文中依旧用到了PV、PVC、StorageClass的知识,节点亲和选择了k8s-worker-2节点,同时需要在该节点上创建改目录/data/k8s/grafana

4.2、部署grafana资源

kubectl apply -f grafana-deploy.yaml

4.3、访问grafana

查看对应的service端口映射:

通过链接http://10.20.1.21:31881/访问grafana,通过配置文件中的用户名和密码访问grafana,再导入prometheus的数据源:

5、配置数据抓取

5.1、配置抓取node数据

在抓取数据之前,需要在node节点上配置node-exporter,这样prometheus才能通过node-exporter暴露的接口抓取数据。

5.1.1、准备node-exporter的部署文件

准备node-exporter的部署文件node-exporter-daemonset.yaml

apiVersion: apps/v1
kind: DaemonSet
metadata:
  name: node-exporter
  namespace: kube-system
  labels:
    app: node-exporter
spec:
  selector:
    matchLabels:
      app: node-exporter
  template:
    metadata:
      labels:
        app: node-exporter
    spec:
      hostPID: true
      hostIPC: true
      hostNetwork: true
      nodeSelector:
        kubernetes.io/os: linux
      containers:
        - name: node-exporter
          image: prom/node-exporter:v1.3.1
          args:
            - --web.listen-address=$(HOSTIP):9100
            - --path.procfs=/host/proc
            - --path.sysfs=/host/sys
            - --path.rootfs=/host/root
            - --no-collector.hwmon # 禁用不需要的一些采集器
            - --no-collector.nfs
            - --no-collector.nfsd
            - --no-collector.nvme
            - --no-collector.dmi
            - --no-collector.arp
            - --collector.filesystem.ignored-mount-points=^/(dev|proc|sys|var/lib/containerd/.+|/var/lib/docker/.+|var/lib/kubelet/pods/.+)($|/)
            - --collector.filesystem.ignored-fs-types=^(autofs|binfmt_misc|cgroup|configfs|debugfs|devpts|devtmpfs|fusectl|hugetlbfs|mqueue|overlay|proc|procfs|pstore|rpc_pipefs|securityfs|sysfs|tracefs)$
          ports:
            - containerPort: 9100
          env:
            - name: HOSTIP
              valueFrom:
                fieldRef:
                  fieldPath: status.hostIP
          resources:
            requests:
              cpu: 150m
              memory: 200Mi
            limits:
              cpu: 300m
              memory: 400Mi
          securityContext:
            runAsNonRoot: true
            runAsUser: 65534
          volumeMounts:
            - name: proc
              mountPath: /host/proc
            - name: sys
              mountPath: /host/sys
            - name: root
              mountPath: /host/root
              mountPropagation: HostToContainer
              readOnly: true
      tolerations: # 添加容忍
        - operator: "Exists"
      volumes:
        - name: proc
          hostPath:
            path: /proc
        - name: dev
          hostPath:
            path: /dev
        - name: sys
          hostPath:
            path: /sys
        - name: root
          hostPath:
            path: /

5.1.2、部署node-exporter

kubectl apply -f  node-exporter-daemonset.yaml

5.1.3、prometheus接入抓取数据

在之前的prometheus-config.yaml文件中继续增加job-name,如下:

- job_name: kubernetes-nodes
  kubernetes_sd_configs:
  - role: node
  relabel_configs:
  - source_labels: [__address__]
    regex: '(.*):10250'
    replacement: '${1}:9100'
    target_label: __address__
    action: replace
  - action: labelmap
    regex: __meta_kubernetes_node_label_(.+)

完整的prometheus-config.yaml见文末。

prometheus-config.yaml文件修改完,稍等一会儿就可以看到页面多了几个target,如下图所示,这些都是被prometheus监控的对象:

5.2、配置抓取springboot actuator数据

5.2.1、配置springboot应用

  • springboot应用增加pom
<dependency>
    <groupId>org.springframework.boot</groupId>
    <artifactId>spring-boot-starter-actuator</artifactId>
</dependency>
<dependency>
    <groupId>io.micrometer</groupId>
    <artifactId>micrometer-registry-prometheus</artifactId>
</dependency>
  • springboot应用配置properties文件:
management.endpoint.health.probes.enabled=true
management.health.probes.enabled=true
management.endpoint.health.enabled=true
management.endpoint.health.show-details=always
management.endpoints.web.exposure.include=*
management.endpoints.web.exposure.exclude=env,beans
management.endpoint.shutdown.enabled=true
management.server.port=9090
  • 查看指标链接

配置完之后,重新打镜像部署到K8S集群,这里不做演示了。访问应用的/actuator/prometheus链接得到如下结果,将系统的指标信息暴露出来:

5.2.2、prometheus接入抓取数据

继续修改配置文件prometheus-config.yaml,如下:

- job_name: 'spring-actuator-many'
  metrics_path: '/actuator/prometheus'
  scrape_interval: 5s
  kubernetes_sd_configs:
  - role: pod
  relabel_configs:
  - source_labels: [__meta_kubernetes_namespace]
    separator: ;
    regex: 'test1'
    target_label: namespace
    action: keep
  - source_labels: [__address__]
    regex: '(.*):9090'
    target_label: __address__
    action: keep
  - action: labelmap
    regex: __meta_kubernetes_pod_label_(.+)

配置文件中的大概意思是,选择“端口是9090,namespace是test1”的pod资源进行监控。更多的语法,读者自行查阅prometheus官网。

稍等片刻,可以看到多了springboot应用的监控目标:

6、配置监控图表

指标数据都有了,接下来就是如何配置图表了。grafana提供了丰富的图表,可以在官网上自行选择。下文继续配置监控node的图表监控springboot应用的图表

配置图表有3种方式:json文件、输入图表id、输入json内容。配置界面如下图:

6.1、配置node监控图表

在上图的界面中选择输入图表id的方式,输入图表id8919,即可看到如下界面:

6.2、配置springboot应用的图表

在上图的界面中选择输入json内容的方式,输入此链接下的json内容https://img.mangod.top/blog/jvm-micrometer.json,即可看到如下图表:

至此k8s-node监控和springboot应用监控已经完成。如果还需要更多的监控,读者需要自行查阅资料。

7、安装告警alertmanager

监控完成之后,就是安装告警组件alertmanager了。可以选择在K8S集群下的任一节点使用docker安装。

7.1、安装alertmanager

7.1.1、拉取docker镜像

docker pull prom/alertmanager:v0.25.0

7.1.2、创建报警配置文件

创建报警配置文件alertmanager.yml之前,需要在安装alertmanager所在节点上创建目录/data/prometheus/alertmanager,在目录下创建文件alertmanager.yml,内容如下:

route:
  group_by: ['alertname']
  group_wait: 30s
  group_interval: 5m
  repeat_interval: 1h
  receiver: 'mail_163'
global:
  smtp_smarthost: 'smtp.qq.com:465'
  smtp_from: '294931067@qq.com'
  smtp_auth_username: '294931067@qq.com'
  # 此处是发送邮件的授权码,不是密码
  smtp_auth_password: '此处是授权码,比如sdfasdfsdffsfa'
  smtp_require_tls: false
receivers:
  - name: 'mail_163'
    email_configs:
      - to: 'yclxiao@163.com'
        send_resolved: true
inhibit_rules:
  - source_match:
      severity: 'critical'
    target_match:
      severity: 'warning'
    equal: ['alertname', 'dev', 'instance']

7.1.3、安装启动:

docker run --name alertmanager -d -p 9093:9093  -v /data/prometheus/alertmanager:/etc/alertmanager prom/alertmanager:v0.25.0

7.1.4、访问alertmanager

安装完毕之后,通过如下链接访问:http://10.20.1.21:9093/#/alerts,界面如下:

7.2、与prometheus关联

prometheus-configmap.yaml文件中增加如下配置,即可让prometheus与alertmanager关联起来,配置中的地址改成自己的prometheus地址。

7.3、配置触发告警规则

7.3.1、增加配置目录

prometheus-configmap.yaml文件中增加如下配置,即可增加触发告警的规则:

注意此处的文件目录/prometheus/是prometheus所在存储目录,我这里是安装在k8s-worker-2下,然后在prometheus的存储目录下建立/rules文件夹,如下图:

至此prometheus-config.yaml全部配置完毕,最后附上完整的prometheus-config.yaml

apiVersion: v1
kind: ConfigMap
metadata:
  name: prometheus-config
  namespace: monitoring
data:
  prometheus.yml: |
    global:
      scrape_interval: 15s
      scrape_timeout: 15s
    alerting:
      alertmanagers:
      - static_configs:
        - targets: 
          - 10.20.1.21:9093
    rule_files:
      - /prometheus/rules/*.rules
    scrape_configs:
    - job_name: 'prometheus'
      static_configs:
      - targets: ['localhost:9090']
    - job_name: "cadvisor"
      kubernetes_sd_configs:
        - role: node
      scheme: https
      tls_config:
        ca_file: /var/run/secrets/kubernetes.io/serviceaccount/ca.crt
        insecure_skip_verify: true
      bearer_token_file: /var/run/secrets/kubernetes.io/serviceaccount/token
      relabel_configs:
        - action: labelmap
          regex: __meta_kubernetes_node_label_(.+)
          replacement: $1
        - replacement: /metrics/cadvisor # <nodeip>/metrics -> <nodeip>/metrics/cadvisor
          target_label: __metrics_path__
    - job_name: kubernetes-nodes
      kubernetes_sd_configs:
      - role: node
      relabel_configs:
      - source_labels: [__address__]
        regex: '(.*):10250'
        replacement: '${1}:9100'
        target_label: __address__
        action: replace
      - action: labelmap
        regex: __meta_kubernetes_node_label_(.+)
    - job_name: 'spring-actuator-many'
      metrics_path: '/actuator/prometheus'
      scrape_interval: 5s
      kubernetes_sd_configs:
      - role: pod
      relabel_configs:
      - source_labels: [__meta_kubernetes_namespace]
        separator: ;
        regex: 'test1'
        target_label: namespace
        action: keep
      - source_labels: [__address__]
        regex: '(.*):9090'
        target_label: __address__
        action: keep
      - action: labelmap
        regex: __meta_kubernetes_pod_label_(.+)

7.3.2、配置触发告警规则

触发告警规则的目录已经定好了,接下来就是写具体规则了,在目录下创建2个触发告警的规则文件,如上图,文件中写了触发node节点告警规则触发springboot应用的告警规则,具体内容如下:

  • node节点告警规则-hoststats-alert.yaml
groups:
  - name: hostStatsAlert
    rules:
      - alert: hostCpuUsageAlert
        expr: sum(avg without (cpu)(irate(node_cpu_seconds_total{mode!='idle'}[5m]))) by (instance) > 0.85
        for: 1m
        labels:
          severity: page
        annotations:
          summary: "Instance {{ $labels.instance }} CPU usgae high"
          description: "{{ $labels.instance }} CPU usage above 85% (current value: {{ $value }})"
      - alert: hostMemUsageAlert
        expr: (node_memory_MemTotal - node_memory_MemAvailable)/node_memory_MemTotal > 0.85
        for: 1m
        labels:
          severity: page
        annotations:
          summary: "Instance {{ $labels.instance }} MEM usgae high"
          description: "{{ $labels.instance }} MEM usage above 85% (current value: {{ $value }})"
  • springboot应用告警规则-jvm-metrics-rules.yaml
groups:
  - name: jvm-metrics-rules
    rules:
      # 在5分钟里,GC花费时间超过10%
      - alert: GcTimeTooMuch
        expr: increase(jvm_gc_collection_seconds_sum[5m]) > 30
        for: 5m
        labels:
          severity: red
        annotations:
          summary: "{{ $labels.app }} GC时间占比超过10%"
          message: "ns:{{ $labels.namespace }} pod:{{ $labels.pod }} GC时间占比超过10%,当前值({{ $value }}%)"
      # GC次数太多
      - alert: GcCountTooMuch
        expr: increase(jvm_gc_collection_seconds_count[1m]) > 30
        for: 1m
        labels:
          severity: red
        annotations:
          summary: "{{ $labels.app }} 1分钟GC次数>30次"
          message: "ns:{{ $labels.namespace }} pod:{{ $labels.pod }} 1分钟GC次数>30次,当前值({{ $value }})"
      # FGC次数太多
      - alert: FgcCountTooMuch
        expr: increase(jvm_gc_collection_seconds_count{gc="ConcurrentMarkSweep"}[1h]) > 3
        for: 1m
        labels:
          severity: red
        annotations:
          summary: "{{ $labels.app }} 1小时的FGC次数>3次"
          message: "ns:{{ $labels.namespace }} pod:{{ $labels.pod }} 1小时的FGC次数>3次,当前值({{ $value }})"
      # 非堆内存使用超过80%
      - alert: NonheapUsageTooMuch
        expr: jvm_memory_bytes_used{job="spring-actuator-many", area="nonheap"} / jvm_memory_bytes_max * 100 > 80
        for: 1m
        labels:
          severity: red
        annotations:
          summary: "{{ $labels.app }} 非堆内存使用>80%"
          message: "ns:{{ $labels.namespace }} pod:{{ $labels.pod }} 非堆内存使用率>80%,当前值({{ $value }}%)"
      # 内存使用预警
      - alert: HeighMemUsage
        expr: process_resident_memory_bytes{job="spring-actuator-many"} / os_total_physical_memory_bytes * 100 > 15
        for: 1m
        labels:
          severity: red
        annotations:
          summary: "{{ $labels.app }} rss内存使用率大于85%"
          message: "ns:{{ $labels.namespace }} pod:{{ $labels.pod }} rss内存使用率大于85%,当前值({{ $value }}%)"
      # JVM高内存使用预警
      - alert: JavaHeighMemUsage
        expr: sum(jvm_memory_used_bytes{area="heap",job="spring-actuator-many"}) by(app,instance) / sum(jvm_memory_max_bytes{area="heap",job="spring-actuator-many"}) by(app,instance) * 100 > 85
        for: 1m
        labels:
          severity: red
        annotations:
          summary: "{{ $labels.app }} rss内存使用率大于85%"
          message: "ns:{{ $labels.namespace }} pod:{{ $labels.pod }} rss内存使用率大于85%,当前值({{ $value }}%)"
      # CPU使用预警
      - alert: JavaHeighCpuUsage
        expr: system_cpu_usage{job="spring-actuator-many"} * 100 > 85
        for: 1m
        labels:
          severity: red
        annotations:
          summary: "{{ $labels.app }} rss CPU使用率大于85%"
          message: "ns:{{ $labels.namespace }} pod:{{ $labels.pod }} rss内存使用率大于85%,当前值({{ $value }}%)"
  • 告警文件准备好之后,先重启alertmanager,再重启prometheus:

kubectl delete -f prometheus-deploy.yaml
kubectl apply -f prometheus-deploy.yaml
  • 查看界面

此时查看alertmanager的status,可以看到如下界面:

此时查看promethetus的rules,可以看到如下界面:

7.3.3、注意点

  • 改了alertmanager的告警配置要重启alertmanager才生效。
  • alertmanager.yml中的smtp_auth_password配置的是邮件发送的授权码,不是邮箱密码。邮箱的授权码的配置如下图,下图以QQ邮箱为例:

至此基于Prometheus和Grafana的监控和告警已经安装完毕。

8、测试告警

安装完毕后,简单测试下告警效果。有2种方式测试。

  • 方式1:将告警规则值调低,会收到如下邮件:

  • 方式2:通过命令cat /dev/zero>/dev/null拉高node节点的cpu或者拉高容器的cpu,,会收到如下邮件:

9、总结

本文主要讲解基于Prometheus + Grafana的云原生应用监控和告警的实战,助你快速搭建系统,希望对你有帮助!

本篇完结!感谢你的阅读,欢迎点赞 关注 收藏 私信!!!

原文链接: http://www.mangod.top/articles/2023/09/10/1694336145377.htmlhttps://mp.weixin.qq.com/s/bxRdOavM2BZEovEnoZ6wZw


标题:6个步骤搞定云原生应用监控和告警(建议收藏)
作者:程序员半支烟
地址:http://mangod.top/articles/2023/09/10/1694336145377.html