k8s实战之部署Prometheus+Grafana可视化监控告警平台_telemetry 8081端口
从上面的图中可以看出Prometheus的界面可视化展示的功能较单一，不能满足需求，因此我们需要结合Grafana来进行可视化展示Prometheus监控数据，在上一章节，已经成功部署了Granfana，因此需要在使用的时候添加dashboard和Panel来设计展示相关的监控项，但是实际上在Granfana社区里面有很多成熟的模板，我们可以直接使用，然后根据自己的环境修改Panel中的查询语句来
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正文
static_configs:
-
targets:
-
192.168.73.135:9100 #ip地址根据自己的地址进行修改
-
192.168.73.138:9100
-
192.168.73.139:9100
-
192.168.73.140:9100
-
job_name: kubernetes-apiservers
kubernetes_sd_configs: -
role: endpoints
relabel_configs: -
action: keep
regex: default;kubernetes;https
source_labels: -
__meta_kubernetes_namespace
-
__meta_kubernetes_service_name
-
__meta_kubernetes_endpoint_port_name
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 -
job_name: kubernetes-nodes-kubelet
kubernetes_sd_configs: -
role: node
relabel_configs: -
action: labelmap
regex: _meta_kubernetes_node_label(.+)
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 -
job_name: kubernetes-nodes-cadvisor
kubernetes_sd_configs: -
role: node
relabel_configs: -
action: labelmap
regex: _meta_kubernetes_node_label(.+) -
target_label: metrics_path
replacement: /metrics/cadvisor
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 -
job_name: kubernetes-service-endpoints
kubernetes_sd_configs: -
role: endpoints
relabel_configs: -
action: keep
regex: true
source_labels: -
__meta_kubernetes_service_annotation_prometheus_io_scrape
-
action: replace
regex: (https?)
source_labels: -
__meta_kubernetes_service_annotation_prometheus_io_scheme
target_label: scheme -
action: replace
regex: (.+)
source_labels: -
__meta_kubernetes_service_annotation_prometheus_io_path
target_label: metrics_path -
action: replace
regex: ([^:]+)(?::\d+)?😭\d+)
replacement: $1:$2
source_labels: -
address
-
__meta_kubernetes_service_annotation_prometheus_io_port
target_label: address -
action: labelmap
regex: _meta_kubernetes_service_label(.+) -
action: replace
source_labels: -
__meta_kubernetes_namespace
target_label: kubernetes_namespace -
action: replace
source_labels: -
__meta_kubernetes_service_name
target_label: kubernetes_name -
job_name: kubernetes-services
kubernetes_sd_configs: -
role: service
metrics_path: /probe
params:
module: -
http_2xx
relabel_configs: -
action: keep
regex: true
source_labels: -
__meta_kubernetes_service_annotation_prometheus_io_probe
-
source_labels:
-
address
target_label: __param_target -
replacement: blackbox
target_label: address -
source_labels:
-
__param_target
target_label: instance -
action: labelmap
regex: _meta_kubernetes_service_label(.+) -
source_labels:
-
__meta_kubernetes_namespace
target_label: kubernetes_namespace -
source_labels:
-
__meta_kubernetes_service_name
target_label: kubernetes_name -
job_name: kubernetes-pods
kubernetes_sd_configs: -
role: pod
relabel_configs: -
action: keep
regex: true
source_labels: -
__meta_kubernetes_pod_annotation_prometheus_io_scrape
-
action: replace
regex: (.+)
source_labels: -
__meta_kubernetes_pod_annotation_prometheus_io_path
target_label: metrics_path -
action: replace
regex: ([^:]+)(?::\d+)?😭\d+)
replacement: $1:$2
source_labels: -
address
-
__meta_kubernetes_pod_annotation_prometheus_io_port
target_label: address -
action: labelmap
regex: _meta_kubernetes_pod_label(.+) -
action: replace
source_labels: -
__meta_kubernetes_namespace
target_label: kubernetes_namespace -
action: replace
source_labels: -
__meta_kubernetes_pod_name
target_label: kubernetes_pod_name
alerting:
alertmanagers: -
static_configs:
-
targets: [“alertmanager:80”]
创建
[root@k8s-master prometheus-k8s]# kubectl apply -f prometheus-configmap.yaml
3.5 有状态部署prometheus
这里使用storageclass进行动态供给，给prometheus的数据进行持久化，具体实现办法，可以查看之前的文章《k8s中的NFS动态存储供给》，除此之外可以使用静态供给的prometheus-statefulset-static-pv.yaml进行持久化
[root@k8s-master prometheus-k8s]# vim prometheus-statefulset.yaml
apiVersion: apps/v1
kind: StatefulSet
metadata:
name: prometheus
namespace: kube-system
labels:
k8s-app: prometheus
kubernetes.io/cluster-service: “true”
addonmanager.kubernetes.io/mode: Reconcile
version: v2.2.1
spec:
serviceName: “prometheus”
replicas: 1
podManagementPolicy: “Parallel”
updateStrategy:
type: “RollingUpdate”
selector:
matchLabels:
k8s-app: prometheus
template:
metadata:
labels:
k8s-app: prometheus
annotations:
scheduler.alpha.kubernetes.io/critical-pod: ‘’
spec:
priorityClassName: system-cluster-critical
serviceAccountName: prometheus
initContainers:
-
name: “init-chown-data”
image: “busybox:latest”
imagePullPolicy: “IfNotPresent”
command: [“chown”, “-R”, “65534:65534”, “/data”]
volumeMounts: -
name: prometheus-data
mountPath: /data
subPath: “”
containers: -
name: prometheus-server-configmap-reload
image: “jimmidyson/configmap-reload:v0.1”
imagePullPolicy: “IfNotPresent”
args: -
–volume-dir=/etc/config
-
–webhook-url=http://localhost:9090/-/reload
volumeMounts: -
name: config-volume
mountPath: /etc/config
readOnly: true
resources:
limits:
cpu: 10m
memory: 10Mi
requests:
cpu: 10m
memory: 10Mi -
name: prometheus-server
image: “prom/prometheus:v2.2.1”
imagePullPolicy: “IfNotPresent”
args: -
–config.file=/etc/config/prometheus.yml
-
–storage.tsdb.path=/data
-
–web.console.libraries=/etc/prometheus/console_libraries
-
–web.console.templates=/etc/prometheus/consoles
-
–web.enable-lifecycle
ports: -
containerPort: 9090
readinessProbe:
httpGet:
path: /-/ready
port: 9090
initialDelaySeconds: 30
timeoutSeconds: 30
livenessProbe:
httpGet:
path: /-/healthy
port: 9090
initialDelaySeconds: 30
timeoutSeconds: 30
based on 10 running nodes with 30 pods each
resources:
limits:
cpu: 200m
memory: 1000Mi
requests:
cpu: 200m
memory: 1000Mi
volumeMounts:
- name: config-volume
mountPath: /etc/config - name: prometheus-data
mountPath: /data
subPath: “” - name: prometheus-rules
mountPath: /etc/config/rules
terminationGracePeriodSeconds: 300
volumes:
- name: config-volume
configMap:
name: prometheus-config - name: prometheus-rules
configMap:
name: prometheus-rules
volumeClaimTemplates:
- metadata:
name: prometheus-data
spec:
storageClassName: managed-nfs-storage #存储类根据自己的存储类名字修改
accessModes: - ReadWriteOnce
resources:
requests:
storage: “16Gi”
创建
[root@k8s-master prometheus-k8s]# kubectl apply -f prometheus-statefulset.yaml
检查状态
[root@k8s-master prometheus-k8s]# kubectl get pod -n kube-system
NAME READY STATUS RESTARTS AGE
coredns-5bd5f9dbd9-wv45t 1/1 Running 1 8d
kubernetes-dashboard-7d77666777-d5ng4 1/1 Running 5 14d
prometheus-0 2/2 Running 6 14d
可以看到一个prometheus-0的pod，这就刚才使用statefulset控制器进行的有状态部署，两个容器的状态为Runing则是正常，如果不为Runing可以使用kubectl describe pod prometheus-0 -n kube-system查看报错详情
3.6 创建service暴露访问端口
此处使用nodePort固定一个访问端口，不适用随机端口，便于访问
[root@k8s-master prometheus-k8s]# vim prometheus-service.yaml
kind: Service
apiVersion: v1
metadata:
name: prometheus
namespace: kube-system
labels:
kubernetes.io/name: “Prometheus”
kubernetes.io/cluster-service: “true”
addonmanager.kubernetes.io/mode: Reconcile
spec:
type: NodePort
ports:
- name: http
port: 9090
protocol: TCP
targetPort: 9090
nodePort: 30090 #固定的对外访问的端口
selector:
k8s-app: prometheus
创建
[root@k8s-master prometheus-k8s]# kubectl apply -f prometheus-service.yaml
检查
[root@k8s-master prometheus-k8s]# kubectl get pod,svc -n kube-system
NAME READY STATUS RESTARTS AGE
pod/coredns-5bd5f9dbd9-wv45t 1/1 Running 1 8dpod/kubernetes-dashboard-7d77666777-d5ng4 1/1 Running 5 14dpod/prometheus-0 2/2 Running 6 14dNAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
service/kube-dns ClusterIP 10.0.0.2 53/UDP,53/TCP 13dservice/kubernetes-dashboard NodePort 10.0.0.127 443:30001/TCP 16dservice/prometheus NodePort 10.0.0.33 9090:30090/TCP 13d
3.7 web访问
使用任意一个NodeIP加端口进行访问，访问地址：http://NodeIP:Port ,此例就是：http://192.168.73.139:30090
访问成功的界面如图所示：
4 在K8S平台部署Grafana
通过上面的web访问，可以看出prometheus自带的UI界面是没有多少功能的，可视化展示的功能不完善，不能满足日常的监控所需，因此常常我们需要再结合Prometheus+Grafana的方式来进行可视化的数据展示
官网地址：
https://github.com/kubernetes/kubernetes/tree/master/cluster/addons/prometheus
https://grafana.com/grafana/download
刚才下载的项目中已经写好了Grafana的yaml，根据自己的环境进行修改
4.1 使用StatefulSet部署grafana
[root@k8s-master prometheus-k8s]# vim grafana.yaml
apiVersion: apps/v1
kind: StatefulSet
metadata:
name: grafana
namespace: kube-system
spec:
serviceName: “grafana”
replicas: 1
selector:
matchLabels:
app: grafana
template:
metadata:
labels:
app: grafana
spec:
containers:
- name: grafana
image: grafana/grafana
ports: - containerPort: 3000
protocol: TCP
resources:
limits:
cpu: 100m
memory: 256Mi
requests:
cpu: 100m
memory: 256Mi
volumeMounts: - name: grafana-data
mountPath: /var/lib/grafana
subPath: grafana
securityContext:
fsGroup: 472
runAsUser: 472
volumeClaimTemplates: - metadata:
name: grafana-data
spec:
storageClassName: managed-nfs-storage #和prometheus使用同一个存储类
accessModes: - ReadWriteOnce
resources:
requests:
storage: “1Gi”
apiVersion: v1
kind: Service
metadata:
name: grafana
namespace: kube-system
spec:
type: NodePort
ports:
- port : 80
targetPort: 3000
nodePort: 30091
selector:
app: grafana
4.2 Grafana的web访问
使用任意一个NodeIP加端口进行访问，访问地址：http://NodeIP:Port ,此例就是：http://192.168.73.139:30091
成功访问界面如下，会需要进行账号密码登陆，默认账号密码都为admin，登陆之后会让修改密码
登陆之后的界面如下
第一步需要进行数据源添加，点击create your first data source数据库图标，根据下图所示进行添加即可
第二步，添加完了之后点击底部的绿色的Save&Test，会成功提示Data sourse is working，则表示数据源添加成功
4.3 监控K8S集群中Pod、Node、资源对象数据的方法
1）Pod
kubelet的节点使用cAdvisor提供的metrics接口获取该节点所有Pod和容器相关的性能指标数据，安装kubelet默认就开启了
暴露接口地址：
https://NodeIP:10255/metrics/cadvisor
https://NodeIP:10250/metrics/cadvisor
2）Node
需要使用node_exporter收集器采集节点资源利用率。
https://github.com/prometheus/node_exporter
使用文档：https://prometheus.io/docs/guides/node-exporter/
使用node_exporter.sh脚本分别在所有服务器上部署node_exporter收集器，不需要修改可直接运行脚本
[root@k8s-master prometheus-k8s]# cat node_exporter.sh #!/bin/bashwget https://github.com/prometheus/node_exporter/releases/download/v0.17.0/node_exporter-0.17.0.linux-amd64.tar.gz
tar zxf node_exporter-0.17.0.linux-amd64.tar.gz
mv node_exporter-0.17.0.linux-amd64 /usr/local/node_exporter
cat </usr/lib/systemd/system/node_exporter.service
[Unit]
Description=https://prometheus.io
[Service]
Restart=on-failure
ExecStart=/usr/local/node_exporter/node_exporter --collector.systemd --collector.systemd.unit-whitelist=(docker|kubelet|kube-proxy|flanneld).service
[Install]
WantedBy=multi-user.target
EOF
systemctl daemon-reload
systemctl enable node_exporter
systemctl restart node_exporter
[root@k8s-master prometheus-k8s]# ./node_exporter.sh
检测node_exporter的进程，是否生效
[root@k8s-master prometheus-k8s]# ps -ef|grep node_exporter
root 6227 1 0 Oct08 ? 00:06:43 /usr/local/node_exporter/node_exporter --collector.systemd --collector.systemd.unit-whitelist=(docker|kubelet|kube-proxy|flanneld).service
root 118269 117584 0 23:27 pts/0 00:00:00 grep --color=auto node_exporter
3）资源对象
kube-state-metrics采集了k8s中各种资源对象的状态信息，只需要在master节点部署就行
https://github.com/kubernetes/kube-state-metrics
创建rbac的yaml对metrics进行授权
[root@k8s-master prometheus-k8s]# vim kube-state-metrics-rbac.yaml
apiVersion: v1
kind: ServiceAccount
metadata:
name: kube-state-metrics
namespace: kube-system
labels:
kubernetes.io/cluster-service: “true”
addonmanager.kubernetes.io/mode: Reconcile
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
name: kube-state-metrics
labels:
kubernetes.io/cluster-service: “true”
addonmanager.kubernetes.io/mode: Reconcile
rules:
- apiGroups: [“”]
resources: - configmaps
- secrets
- nodes
- pods
- services
- resourcequotas
- replicationcontrollers
- limitranges
- persistentvolumeclaims
- persistentvolumes
- namespaces
- endpoints
verbs: [“list”, “watch”] - apiGroups: [“extensions”]
resources: - daemonsets
- deployments
- replicasets
verbs: [“list”, “watch”] - apiGroups: [“apps”]
resources: - statefulsets
verbs: [“list”, “watch”] - apiGroups: [“batch”]
resources: - cronjobs
- jobs
verbs: [“list”, “watch”] - apiGroups: [“autoscaling”]
resources: - horizontalpodautoscalers
verbs: [“list”, “watch”]
apiVersion: rbac.authorization.k8s.io/v1
kind: Role
metadata:
name: kube-state-metrics-resizer
namespace: kube-system
labels:
kubernetes.io/cluster-service: “true”
addonmanager.kubernetes.io/mode: Reconcile
rules:
- apiGroups: [“”]
resources: - pods
verbs: [“get”] - apiGroups: [“extensions”]
resources: - deployments
resourceNames: [“kube-state-metrics”]
verbs: [“get”, “update”]
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata:
name: kube-state-metrics
labels:
kubernetes.io/cluster-service: “true”
addonmanager.kubernetes.io/mode: Reconcile
roleRef:
apiGroup: rbac.authorization.k8s.io
kind: ClusterRole
name: kube-state-metrics
subjects:
- kind: ServiceAccount
name: kube-state-metrics
namespace: kube-system
apiVersion: rbac.authorization.k8s.io/v1
kind: RoleBinding
metadata:
name: kube-state-metrics
namespace: kube-system
labels:
kubernetes.io/cluster-service: “true”
addonmanager.kubernetes.io/mode: Reconcile
roleRef:
apiGroup: rbac.authorization.k8s.io
kind: Role
name: kube-state-metrics-resizer
subjects:
- kind: ServiceAccount
name: kube-state-metrics
namespace: kube-system
[root@k8s-master prometheus-k8s]# kubectl apply -f kube-state-metrics-rbac.yaml
编写Deployment和ConfigMap的yaml进行metrics pod部署，不需要进行修改
[root@k8s-master prometheus-k8s]# cat kube-state-metrics-deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
name: kube-state-metrics
namespace: kube-system
labels:
k8s-app: kube-state-metrics
kubernetes.io/cluster-service: “true”
addonmanager.kubernetes.io/mode: Reconcile
version: v1.3.0
spec:
selector:
matchLabels:
k8s-app: kube-state-metrics
version: v1.3.0
replicas: 1
template:
metadata:
labels:
k8s-app: kube-state-metrics
version: v1.3.0
annotations:
scheduler.alpha.kubernetes.io/critical-pod: ‘’
spec:
priorityClassName: system-cluster-critical
serviceAccountName: kube-state-metrics
containers: - name: kube-state-metrics
image: lizhenliang/kube-state-metrics:v1.3.0
ports: - name: http-metrics
containerPort: 8080 - name: telemetry
containerPort: 8081
readinessProbe:
httpGet:
path: /healthz
port: 8080
initialDelaySeconds: 5
timeoutSeconds: 5 - name: addon-resizer
image: lizhenliang/addon-resizer:1.8.3
resources:
limits:
cpu: 100m
memory: 30Mi
requests:
cpu: 100m
memory: 30Mi
env: - name: MY_POD_NAME
valueFrom:
fieldRef:
fieldPath: metadata.name - name: MY_POD_NAMESPACE
valueFrom:
fieldRef:
fieldPath: metadata.namespace
volumeMounts: - name: config-volume
mountPath: /etc/config
command: - /pod_nanny
- –config-dir=/etc/config
- –container=kube-state-metrics
- –cpu=100m
- –extra-cpu=1m
- –memory=100Mi
- –extra-memory=2Mi
- –threshold=5
- –deployment=kube-state-metrics
volumes: - name: config-volume
configMap:
name: kube-state-metrics-config
Config map for resource configuration.
apiVersion: v1
kind: ConfigMap
metadata:
name: kube-state-metrics-config
namespace: kube-system
labels:
k8s-app: kube-state-metrics
kubernetes.io/cluster-service: “true”
addonmanager.kubernetes.io/mode: Reconcile
data:
NannyConfiguration: |-
apiVersion: nannyconfig/v1alpha1
kind: NannyConfiguration
[root@k8s-master prometheus-k8s]# kubectl apply -f kube-state-metrics-deployment.yaml
2.编写Service的yaml对metrics进行端口暴露
[root@k8s-master prometheus-k8s]# cat kube-state-metrics-service.yaml
apiVersion: v1
kind: Service
metadata:
name: kube-state-metrics
namespace: kube-system
labels:
kubernetes.io/cluster-service: “true”
addonmanager.kubernetes.io/mode: Reconcile
kubernetes.io/name: “kube-state-metrics”
annotations:
prometheus.io/scrape: ‘true’
spec:
ports:
- name: http-metrics
port: 8080
targetPort: http-metrics
protocol: TCP - name: telemetry
port: 8081
targetPort: telemetry
protocol: TCP
selector:
k8s-app: kube-state-metrics
[root@k8s-master prometheus-k8s]# kubectl apply -f kube-state-metrics-service.yaml
3.检查pod和svc的状态，可以看到正常运行了pod/kube-state-metrics-7c76bdbf68-kqqgd 和对外暴露了8080和8081端口
[root@k8s-master prometheus-k8s]# kubectl get pod,svc -n kube-system
NAME READY STATUS RESTARTS AGE
pod/alertmanager-5d75d5688f-fmlq6 2/2 Running 0 9dpod/coredns-5bd5f9dbd9-wv45t 1/1 Running 1 9dpod/grafana-0 1/1 Running 2 15dpod/kube-state-metrics-7c76bdbf68-kqqgd 2/2 Running 6 14dpod/kubernetes-dashboard-7d77666777-d5ng4 1/1 Running 5 16dpod/prometheus-0 2/2 Running 6 15dNAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
service/alertmanager ClusterIP 10.0.0.207 80/TCP 13dservice/grafana NodePort 10.0.0.74 80:30091/TCP 15dservice/kube-dns ClusterIP 10.0.0.2 53/UDP,53/TCP 14dservice/kube-state-metrics ClusterIP 10.0.0.194 8080/TCP,8081/TCP 14dservice/kubernetes-dashboard NodePort 10.0.0.127 443:30001/TCP 17dservice/prometheus NodePort 10.0.0.33 9090:30090/TCP 14d[root@k8s-master prometheus-k8s]#
5 使用Grafana可视化展示Prometheus监控数据
通常在使用Prometheus采集数据的时候我们需要监控K8S集群中Pod、Node、资源对象，因此我们需要安装对应的插件和资源采集器来提供api进行数据获取，在4.3中我们已经配置好，我们也可以使用Prometheus的UI界面中的Staus菜单下的Target中的各个采集器的状态情况，如图所示：
只有当我们各个Target的状态都是UP状态时，我们可以使用自带的的界面去获取到某一监控项的相关的数据，如图所示：
从上面的图中可以看出Prometheus的界面可视化展示的功能较单一，不能满足需求，因此我们需要结合Grafana来进行可视化展示Prometheus监控数据，在上一章节，已经成功部署了Granfana，因此需要在使用的时候添加dashboard和Panel来设计展示相关的监控项，但是实际上在Granfana社区里面有很多成熟的模板，我们可以直接使用，然后根据自己的环境修改Panel中的查询语句来获取数据
https://grafana.com/grafana/dashboards
推荐模板：
集群资源监控的模板号：3119，如图所示进行添加
当模板添加之后如果某一个Panel不显示数据，可以点击Panel上的编辑，查询PromQL语句，然后去Prometheus自己的界面上进行调试PromQL语句是否可以获取到值，最后调整之后的监控界面如图所示
资源状态监控：6417
同理，添加资源状态的监控模板，然后经过调整之后的监控界面如图所示，可以获取到k8s中各种资源状态的监控展示
Node监控：9276
同理，添加资源状态的监控模板，然后经过调整之后的监控界面如图所示，可以获取到各个node上的基本情况
6 在K8S中部署Alertmanager
6.1 部署Alertmanager的实现步骤
6.2 部署告警
我们以Email来进行实现告警信息的发送
首先需要准备一个发件邮箱，开启stmp发送功能
使用configmap存储告警规则，编写报警规则的yaml文件，可根据自己的实际情况进行修改和添加报警的规则，prometheus比zabbix就麻烦在这里，所有的告警规则需要自己去定义
[root@k8s-master prometheus-k8s]# vim prometheus-rules.yaml
apiVersion: v1
kind: ConfigMap
网上学习资料一大堆，但如果学到的知识不成体系，遇到问题时只是浅尝辄止，不再深入研究，那么很难做到真正的技术提升。
需要这份系统化的资料的朋友，可以添加V获取：vip1024b （备注软件测试）
一个人可以走的很快，但一群人才能走的更远！不论你是正从事IT行业的老鸟或是对IT行业感兴趣的新人，都欢迎加入我们的的圈子（技术交流、学习资源、职场吐槽、大厂内推、面试辅导），让我们一起学习成长！
y9ibG9nLmNzZG4ubmV0L3FxXzQwOTA3OTc3,size_16,color_FFFFFF,t_70)
6 在K8S中部署Alertmanager
6.1 部署Alertmanager的实现步骤
6.2 部署告警
我们以Email来进行实现告警信息的发送
首先需要准备一个发件邮箱，开启stmp发送功能
使用configmap存储告警规则，编写报警规则的yaml文件，可根据自己的实际情况进行修改和添加报警的规则，prometheus比zabbix就麻烦在这里，所有的告警规则需要自己去定义
[root@k8s-master prometheus-k8s]# vim prometheus-rules.yaml
apiVersion: v1
kind: ConfigMap
网上学习资料一大堆，但如果学到的知识不成体系，遇到问题时只是浅尝辄止，不再深入研究，那么很难做到真正的技术提升。
需要这份系统化的资料的朋友，可以添加V获取：vip1024b （备注软件测试）
[外链图片转存中…(img-qPcE2QIZ-1713328417129)]
一个人可以走的很快，但一群人才能走的更远！不论你是正从事IT行业的老鸟或是对IT行业感兴趣的新人，都欢迎加入我们的的圈子（技术交流、学习资源、职场吐槽、大厂内推、面试辅导），让我们一起学习成长！
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