基于 Prometheus+Grafana+Alertmanager 搭建 K8S 云监控告警平台(附配置告警至QQ、钉钉)
- 一、机器规划
- 二、部署安装 node-exporter、prometheus、Grafana、kube-state-metrics
- 三、安装和配置 Alertmanager -- 发送告警到 QQ 邮箱
- 四、配置 Alertmanager 报警 -- 发送告警到钉钉
一、机器规划
角色 | 主机名 | ip 地址 |
---|---|---|
master | k8s-master1 | 192.168.112.10 |
node | k8s-node1 | 192.168.112.20 |
node | k8s-node2 | 192.168.112.30 |
平台 | VMware Workstation |
---|---|
操作系统 | CentOS Linux release 7.9.2009 (Core) |
内存、CPU | 4C4G |
磁盘大小 | 20G SCSI |
二、部署安装 node-exporter、prometheus、Grafana、kube-state-metrics
1、创建 monitor-sa 命名空间
master 节点操作
kubectl create ns monitor-sa
2、安装node-exporter组件
master 节点操作
cat >> node-export.yaml <<EOF
apiVersion: apps/v1
kind: DaemonSet
metadata:
name: node-exporter
namespace: monitor-sa
labels:
name: node-exporter
spec:
selector:
matchLabels:
name: node-exporter
template:
metadata:
labels:
name: node-exporter
spec:
hostPID: true
hostIPC: true
hostNetwork: true
containers:
- name: node-exporter
image: prom/node-exporter:v0.16.0
ports:
- containerPort: 9100
resources:
requests:
cpu: 0.15
securityContext:
privileged: true
args:
- --path.procfs
- /host/proc
- --path.sysfs
- /host/sys
- --collector.filesystem.ignored-mount-points
- '"^/(sys|proc|dev|host|etc)($|/)"'
volumeMounts:
- name: dev
mountPath: /host/dev
- name: proc
mountPath: /host/proc
- name: sys
mountPath: /host/sys
- name: rootfs
mountPath: /rootfs
tolerations:
- key: "node-role.kubernetes.io/master"
operator: "Exists"
effect: "NoSchedule"
volumes:
- name: proc
hostPath:
path: /proc
- name: dev
hostPath:
path: /dev
- name: sys
hostPath:
path: /sys
- name: rootfs
hostPath:
path: /
EOF
2.1、说明
- 主机命名空间共享 (
hostPID
,hostIPC
,hostNetwork
)hostPID: true
: 允许 Pod 使用主机的 PID 命名空间。Pod 可以看到主机上的所有进程hostIPC: true
: 允许 Pod 使用主机的 IPC 命名空间。Pod 可以与其他在主机上运行的进程共享 IPC 资源(如信号量、消息队列等)。hostNetwork: true
: 允许 Pod 使用主机的网络命名空间。Pod 将使用主机的网络接口
- 命令行参数 (
args
) --path.procfs /host/proc
: 指定node-exporter
应该从/host/proc
路径读取进程文件系统的数据。这使得node-exporter
可以访问宿主机的进程信息。--path.sysfs /host/sys
: 指定node-exporter
应该从/host/sys
路径读取系统文件系统的数据。这使得node-exporter
可以访问宿主机的系统信息。--collector.filesystem.ignored-mount-points "^/(sys|proc|dev|host|etc)($|/)"
: 指定哪些文件系统的挂载点应该被忽略,不被node-exporter
收集。这里忽略了/sys
,/proc
,/dev
,/host
, 和/etc
这些挂载点,避免收集不必要的数据。- 挂载点 (
volumeMounts
和volumes
)/proc
挂载- 宿主机路径:
/proc
- 容器内路径:
/host/proc
- 作用: 让
node-exporter
访问宿主机的进程文件系统。
- 宿主机路径:
/dev
挂载- 宿主机路径:
/dev
- 容器内路径:
/host/dev
- 作用: 让
node-exporter
访问宿主机的设备文件。
- 宿主机路径:
/sys
挂载- 宿主机路径:
/sys
- 容器内路径:
/host/sys
- 作用: 让
node-exporter
访问宿主机的系统文件系统。
- 宿主机路径:
/
挂载- 宿主机路径:
/
- 容器内路径:
/rootfs
- 作用: 让
node-exporter
访问宿主机的根文件系统。
- 宿主机路径:
- 容忍度 (
tolerations
)key: "node-role.kubernetes.io/master"
: 指定容忍的污点键。operator: "Exists"
: 表示只要存在该污点键,无论值是什么,都予以容忍。effect: "NoSchedule"
: 表示即使节点上有这种污点,也不会阻止 Pod 被调度到该节点上。
2.2、应用资源清单
kubectl apply -f node-export.yaml
kubectl get pods -n monitor-sa -l name=node-exporter
2.3、通过node-exporter采集数据
node-export默认的监听端口是9100,可以看到当前主机获取到的所有监控数据
# curl http://<master-ip>:9100/metrics
curl http://192.168.112.10:9100/metrics
3、k8s 集群中部署 prometheus
3.1、创建一个 sa 账号
kubectl create serviceaccount monitor -n monitor-sa
3.2、将 sa 账号 monitor 通过 clusterrolebing 绑定到 clusterrole 上
kubectl create clusterrolebinding monitor-clusterrolebinding -n monitor-sa --clusterrole=cluster-admin --serviceaccount=monitor-sa:monitor
3.3、创建数据目录
所有 node 节点
mkdir /data && chmod 777 /data/
3.4、安装prometheus
master 节点操作
3.4.1、将 prometheus.yml
文件以 ConfigMap 的形式进行管理
cat >> prometheus-cfg.yaml << 'EOF'
---
kind: ConfigMap
apiVersion: v1
metadata:
labels:
app: prometheus
name: prometheus-config
namespace: monitor-sa
data:
prometheus.yml: |
global:
scrape_interval: 15s
scrape_timeout: 10s
evaluation_interval: 1m
scrape_configs:
- job_name: 'kubernetes-node'
kubernetes_sd_configs:
- role: node
relabel_configs:
- source_labels: [__address__]
regex: '(.*):10250'
replacement: '${1}:9100'
target_label: __address__
action: replace
- action: labelmap
regex: __meta_kubernetes_node_label_(.+)
- job_name: 'kubernetes-node-cadvisor'
kubernetes_sd_configs:
- role: node
scheme: https
tls_config:
ca_file: /var/run/secrets/kubernetes.io/serviceaccount/ca.crt
bearer_token_file: /var/run/secrets/kubernetes.io/serviceaccount/token
relabel_configs:
- action: labelmap
regex: __meta_kubernetes_node_label_(.+)
- target_label: __address__
replacement: kubernetes.default.svc:443
- source_labels: [__meta_kubernetes_node_name]
regex: (.+)
target_label: __metrics_path__
replacement: '/api/v1/nodes/${1}/proxy/metrics/cadvisor'
- job_name: 'kubernetes-apiserver'
kubernetes_sd_configs:
- role: endpoints
scheme: https
tls_config:
ca_file: /var/run/secrets/kubernetes.io/serviceaccount/ca.crt
bearer_token_file: /var/run/secrets/kubernetes.io/serviceaccount/token
relabel_configs:
- source_labels: [__meta_kubernetes_namespace, __meta_kubernetes_service_name, __meta_kubernetes_endpoint_port_name]
action: keep
regex: default;kubernetes;https
- job_name: 'kubernetes-service-endpoints'
kubernetes_sd_configs:
- role: endpoints
relabel_configs:
- source_labels: [__meta_kubernetes_service_annotation_prometheus_io_scrape]
action: keep
regex: true
- source_labels: [__meta_kubernetes_service_annotation_prometheus_io_scheme]
action: replace
target_label: __scheme__
regex: (https?)
- source_labels: [__meta_kubernetes_service_annotation_prometheus_io_path]
action: replace
target_label: __metrics_path__
regex: (.+)
- source_labels: [__address__, __meta_kubernetes_service_annotation_prometheus_io_port]
action: replace
target_label: __address__
regex: ([^:]+)(?::\d+)?;(\d+)
replacement: '$1:$2'
- action: labelmap
regex: __meta_kubernetes_service_label_(.+)
- source_labels: [__meta_kubernetes_namespace]
action: replace
target_label: kubernetes_namespace
- source_labels: [__meta_kubernetes_service_name]
action: replace
target_label: kubernetes_name
EOF
3.4.2、应用 cm 资源清单
kubectl apply -f prometheus-cfg.yaml
kubectl get cm prometheus-config -n monitor-sa -o yaml
需要确保 cm 正确解析了变量 $1、$2
不然 prometheus 获取不到对应的 IP 地址会无法正常监控
3.4.3、通过 Deployment 部署 prometheus
cat >> prometheus-deploy.yaml << EOF
---
apiVersion: apps/v1
kind: Deployment
metadata:
name: prometheus-server
namespace: monitor-sa
labels:
app: prometheus
spec:
replicas: 2
selector:
matchLabels:
app: prometheus
component: server
#matchExpressions:
#- {key: app, operator: In, values: [prometheus]}
#- {key: component, operator: In, values: [server]}
template:
metadata:
labels:
app: prometheus
component: server
annotations:
prometheus.io/scrape: 'false'
spec:
affinity:
podAntiAffinity:
requiredDuringSchedulingIgnoredDuringExecution:
- labelSelector:
matchExpressions:
- key: app
operator: In
values:
- prometheus
- key: component
operator: In
values:
- server
topologyKey: kubernetes.io/hostname
serviceAccountName: monitor
containers:
- name: prometheus
image: quay.io/prometheus/prometheus:latest
imagePullPolicy: IfNotPresent
command:
- prometheus
- --config.file=/etc/prometheus/prometheus.yml
- --storage.tsdb.path=/prometheus
- --storage.tsdb.retention=720h
ports:
- containerPort: 9090
protocol: TCP
volumeMounts:
- mountPath: /etc/prometheus/prometheus.yml
name: prometheus-config
subPath: prometheus.yml
- mountPath: /prometheus/
name: prometheus-storage-volume
volumes:
- name: prometheus-config
configMap:
name: prometheus-config
items:
- key: prometheus.yml
path: prometheus.yml
mode: 0644
- name: prometheus-storage-volume
hostPath:
path: /data
type: Directory
EOF
3.4.4、应用 prometheus 资源清单
kubectl apply -f prometheus-deploy.yaml
3.4.5、给 prometheus 的 pod 创建一个 svc
cat > prometheus-svc.yaml << EOF
---
apiVersion: v1
kind: Service
metadata:
name: prometheus
namespace: monitor-sa
labels:
app: prometheus
spec:
type: NodePort
ports:
- port: 9090
targetPort: 9090
protocol: TCP
selector:
app: prometheus
component: server
EOF
3.4.6、应用 svc 资源清单
kubectl get svc -n monitor-sa -o wide
通过上面可以看到service在宿主机上映射的端口是30172,这样我们访问k8s集群的k8s-master1节点的ip:30172,就可以访问到prometheus的web ui界面了
3.5、访问prometheus UI界面
# <k8s-master1 IP>:32032
192.168.112.10:32032
3.6、查看配置的服务发现
点击页面的Status->Targets,可看到如下,说明我们配置的服务发现可以正常采集数据
4、prometheus热更新
4.1、热加载 prometheus
#为了每次修改配置文件可以热加载prometheus,也就是不停止prometheus,就可以使配置生效,如修改prometheus-cfg.yaml,想要使配置生效可用如下热加载命令:
curl -X POST http://<prometheus-pod-ip>:9090/-/reload
kubectl get pods -n monitor-sa -l app=prometheus -o wide
4.2、暴力重启 prometheus
热加载速度比较慢,可以暴力重启prometheus
如修改上面的prometheus-cfg.yaml文件之后,可执行如下强制删除
kubectl delete -f prometheus-cfg.yaml
kubectl delete -f prometheus-deploy.yaml
# 然后再通过apply更新
kubectl apply -f prometheus-cfg.yaml
kubectl apply -f prometheus-deploy.yaml
线上最好热加载,暴力删除可能造成监控数据的丢失
5、Grafana安装和配置
5.1、下载 Grafana 需要的镜像
链接:https://pan.baidu.com/s/1TmVGKxde_cEYrbjiETboEA
提取码:052u
5.2、在 k8s 集群各个节点导入 Grafana 镜像
docker load -i heapster-grafana-amd64_v5_0_4.tar.gz
docker images | grep grafana
5.3、master 节点创建 grafana.yaml
cat >> grafana.yaml << EOF
apiVersion: apps/v1
kind: Deployment
metadata:
name: monitoring-grafana
namespace: kube-system
spec:
replicas: 1
selector:
matchLabels:
task: monitoring
k8s-app: grafana
template:
metadata:
labels:
task: monitoring
k8s-app: grafana
spec:
containers:
- name: grafana
image: k8s.gcr.io/heapster-grafana-amd64:v5.0.4
ports:
- containerPort: 3000
protocol: TCP
volumeMounts:
- mountPath: /etc/ssl/certs
name: ca-certificates
readOnly: true
- mountPath: /var
name: grafana-storage
env:
- name: INFLUXDB_HOST
value: monitoring-influxdb
- name: GF_SERVER_HTTP_PORT
value: "3000"
# The following env variables are required to make Grafana accessible via
# the kubernetes api-server proxy. On production clusters, we recommend
# removing these env variables, setup auth for grafana, and expose the grafana
# service using a LoadBalancer or a public IP.
- name: GF_AUTH_BASIC_ENABLED
value: "false"
- name: GF_AUTH_ANONYMOUS_ENABLED
value: "true"
- name: GF_AUTH_ANONYMOUS_ORG_ROLE
value: Admin
- name: GF_SERVER_ROOT_URL
# If you're only using the API Server proxy, set this value instead:
# value: /api/v1/namespaces/kube-system/services/monitoring-grafana/proxy
value: /
volumes:
- name: ca-certificates
hostPath:
path: /etc/ssl/certs
- name: grafana-storage
emptyDir: {}
---
apiVersion: v1
kind: Service
metadata:
labels:
# For use as a Cluster add-on (https://github.com/kubernetes/kubernetes/tree/master/cluster/addons)
# If you are NOT using this as an addon, you should comment out this line.
kubernetes.io/cluster-service: 'true'
kubernetes.io/name: monitoring-grafana
name: monitoring-grafana
namespace: kube-system
spec:
# In a production setup, we recommend accessing Grafana through an external Loadbalancer
# or through a public IP.
# type: LoadBalancer
# You could also use NodePort to expose the service at a randomly-generated port
# type: NodePort
ports:
- port: 80
targetPort: 3000
selector:
k8s-app: grafana
type: NodePort
EOF
5.4、查看 Grafana 的 pod 和 svc
5.5、查看 Grafana UI 界面
# <master-ip>:<grafana-svc-port>
192.168.112.10:31455
5.6、给 Grafana 接入 Prometheus 数据源
选择 Create your first data source |
---|
Name: Prometheus |Type: Prometheus|HTTP 处的URL写 如下:http://prometheus.monitor-sa.svc:9090 |
点击左下角 Save & Test,出现如下 Data source is working,说明 prometheus 数据源成功的被 grafana 接入了 |
5.7、获取监控模板
- 可以在 Grafana Dashboard 官网搜索需要的
Grafana dashboards | Grafana Labs
- 也可以直接克隆 Github 仓库,获取 node_exporter.json 、 docker_rev1.json 监控模板
git clone git@github.com:misakivv/Grafana-Dashboard.git
5.8、导入监控模板
依次点击左侧栏的 + 号下方的 Import |
---|
选择 Upload json file,选择一个本地的node_exporter.json 文件 |
导入后 Options 选项中会出现 Name 是自动生成的,Prometheus 是需要我们选择 Prometheus的 |
点击 Import 即可出现如下界面 |
按照如上操作,导入docker_rev1.json监控模板 |
6、安装配置 kube-state-metrics 组件
6.1、什么是 kube-state-metrics
kube-state-metrics通过监听API Server生成有关资源对象的状态指标,比如Deployment、Node、Pod,需要注意的是kube-state-metrics只是简单的提供一个metrics数据,并不会存储这些指标数据,所以我们可以使用Prometheus来抓取这些数据然后存储,主要关注的是业务相关的一些元数据,比如Deployment、Pod、副本状态等;调度了多少个replicas?现在可用的有几个?多少个Pod是running/stopped/terminated状态?Pod重启了多少次?有多少job在运行中。
6.2、创建 sa ,并进行授权
k8s-master1 节点编写一个 kube-state-metrics-rbac.yaml 文件
cat >> kube-state-metrics-rbac.yaml << EOF
---
apiVersion: v1
kind: ServiceAccount
metadata:
name: kube-state-metrics
namespace: kube-system
---
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
name: kube-state-metrics
rules:
- apiGroups: [""]
resources: ["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: ClusterRoleBinding
metadata:
name: kube-state-metrics
roleRef:
apiGroup: rbac.authorization.k8s.io
kind: ClusterRole
name: kube-state-metrics
subjects:
- kind: ServiceAccount
name: kube-state-metrics
namespace: kube-system
EOF
kubectl get sa,clusterrole,clusterrolebinding -n kube-system | grep kube-state-metrics
6.3、创建并应用 kube-state-metrics-deploy.yaml 文件
k8s-master1 节点操作
cat > kube-state-metrics-deploy.yaml <<EOF
apiVersion: apps/v1
kind: Deployment
metadata:
name: kube-state-metrics
namespace: kube-system
spec:
replicas: 1
selector:
matchLabels:
app: kube-state-metrics
template:
metadata:
labels:
app: kube-state-metrics
spec:
serviceAccountName: kube-state-metrics
containers:
- name: kube-state-metrics
# image: gcr.io/google_containers/kube-state-metrics-amd64:v1.3.1
image: quay.io/coreos/kube-state-metrics:latest
ports:
- containerPort: 8080
EOF
kubectl apply -f kube-state-metrics-deploy.yaml
kubectl get pods -n kube-system -l app=kube-state-metrics -w
拉取 kube-state-metrics 指定镜像版本失败时可以选择在集群各个节点上
docker pull quay.io/coreos/kube-state-metrics:latest
拉取最新 tag 版本
6.4、创建并应用 kube-state-metrics-svc.yaml 文件
k8s-master1 节点操作
cat >> kube-state-metrics-svc.yaml <<EOF
apiVersion: v1
kind: Service
metadata:
annotations:
prometheus.io/scrape: 'true'
name: kube-state-metrics
namespace: kube-system
labels:
app: kube-state-metrics
spec:
ports:
- name: kube-state-metrics
port: 8080
protocol: TCP
selector:
app: kube-state-metrics
EOF
kubectl apply -f kube-state-metrics-svc.yaml
kubectl get svc -n kube-system -l app=kube-state-metrics
6.5、获取 kube-state-metrics json 文件
git clone git@github.com:misakivv/Grafana-Dashboard.git
6.6、向 Grafana 导入 kube-state-metrics json 文件
点击左侧栏 + 号的 Import |
---|
点击 Upload .json File,上传 Kubernetes Cluster (Prometheus)-1577674936972.json |
查看 |
**同样的导入 Kubernetes cluster monitoring (via Prometheus) (k8s 1.16)-1577691996738.json ** |
三、安装和配置 Alertmanager -- 发送告警到 QQ 邮箱
1、将 alertmanager-cm.yaml 文件以 cm 形式进行管理
k8s-master1 节点操作
cat >> alertmanager-cm.yaml << EOF
kind: ConfigMap
apiVersion: v1
metadata:
name: alertmanager
namespace: monitor-sa
data:
alertmanager.yml: |-
global:
resolve_timeout: 1m
smtp_smarthost: 'smtp.qq.com:465'
smtp_from: '2830909671@qq.com'
smtp_auth_username: '2830909671@qq.com'
smtp_auth_password: 'ajjgpgwwfkpcdgih'
smtp_require_tls: false
route:
group_by: [alertname]
group_wait: 5s
group_interval: 5s
repeat_interval: 5m
receiver: default-receiver
receivers:
- name: 'default-receiver'
email_configs:
- to: 'misakikk@qq.com'
send_resolved: true
EOF
kubectl apply -f alertmanager-cm.yaml
kubectl get cm alertmanager -n monitor-sa
1.1、alertmanager配置文件说明
smtp_smarthost: 'smtp.qq.com:465'
#用于发送邮件的邮箱的SMTP服务器地址+端口。QQ 邮箱 SMTP 服务地址,官方地址为 smtp.qq.com 端口为 465 或 587,同时要设置开启 POP3/SMTP 服务。
smtp_from: '2830909671@qq.com'
#这是指定从哪个邮箱发送报警
smtp_auth_password: 'ajjgpgwwfkpcdgih'
#这是发送邮箱的授权码而不是登录密码
email_configs:
- to: 'misakikk@qq.com'
#to后面指定发送到哪个邮箱
2、重新生成并应用 prometheus-cfg.yaml 文件
k8s-master1 节点操作
cat > prometheus-cfg.yaml << 'EOF'
kind: ConfigMap
apiVersion: v1
metadata:
labels:
app: prometheus
name: prometheus-config
namespace: monitor-sa
data:
prometheus.yml: |
rule_files:
- /etc/prometheus/rules.yml
alerting:
alertmanagers:
- static_configs:
- targets: ["localhost:9093"]
global:
scrape_interval: 15s
scrape_timeout: 10s
evaluation_interval: 1m
scrape_configs:
- job_name: 'kubernetes-node'
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: 'kubernetes-node-cadvisor'
kubernetes_sd_configs:
- role: node
scheme: https
tls_config:
ca_file: /var/run/secrets/kubernetes.io/serviceaccount/ca.crt
bearer_token_file: /var/run/secrets/kubernetes.io/serviceaccount/token
relabel_configs:
- action: labelmap
regex: __meta_kubernetes_node_label_(.+)
- target_label: __address__
replacement: kubernetes.default.svc:443
- source_labels: [__meta_kubernetes_node_name]
regex: (.+)
target_label: __metrics_path__
replacement: '/api/v1/nodes/${1}/proxy/metrics/cadvisor'
- job_name: 'kubernetes-apiserver'
kubernetes_sd_configs:
- role: endpoints
scheme: https
tls_config:
ca_file: /var/run/secrets/kubernetes.io/serviceaccount/ca.crt
bearer_token_file: /var/run/secrets/kubernetes.io/serviceaccount/token
relabel_configs:
- source_labels: [__meta_kubernetes_namespace, __meta_kubernetes_service_name, __meta_kubernetes_endpoint_port_name]
action: keep
regex: default;kubernetes;https
- job_name: 'kubernetes-service-endpoints'
kubernetes_sd_configs:
- role: endpoints
relabel_configs:
- source_labels: [__meta_kubernetes_service_annotation_prometheus_io_scrape]
action: keep
regex: true
- source_labels: [__meta_kubernetes_service_annotation_prometheus_io_scheme]
action: replace
target_label: __scheme__
regex: (https?)
- source_labels: [__meta_kubernetes_service_annotation_prometheus_io_path]
action: replace
target_label: __metrics_path__
regex: (.+)
- source_labels: [__address__, __meta_kubernetes_service_annotation_prometheus_io_port]
action: replace
target_label: __address__
regex: ([^:]+)(?::\d+)?;(\d+)
replacement: '$1:$2'
- action: labelmap
regex: __meta_kubernetes_service_label_(.+)
- source_labels: [__meta_kubernetes_namespace]
action: replace
target_label: kubernetes_namespace
- source_labels: [__meta_kubernetes_service_name]
action: replace
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:$2'
source_labels:
- __address__
- __meta_kubernetes_pod_annotation_prometheus_io_port
target_label: __address__
- action: labelmap
regex: __meta_kubernetes_pod_label_(.+)
- action: replace
source_labels:
- __meta_kubernetes_namespace
target_label: kubernetes_namespace
- action: replace
source_labels:
- __meta_kubernetes_pod_name
target_label: kubernetes_pod_name
- job_name: 'kubernetes-schedule'
scrape_interval: 5s
static_configs:
- targets: ['192.168.112.10:10259']
- job_name: 'kubernetes-controller-manager'
scrape_interval: 5s
static_configs:
- targets: ['192.168.112.10:10257']
- job_name: 'kubernetes-kube-proxy'
scrape_interval: 5s
static_configs:
- targets: ['192.168.112.10:10249','192.168.112.20:10249','192.168.112.30:10249']
- job_name: 'kubernetes-etcd'
scheme: https
tls_config:
ca_file: /var/run/secrets/kubernetes.io/k8s-certs/etcd/ca.crt
cert_file: /var/run/secrets/kubernetes.io/k8s-certs/etcd/server.crt
key_file: /var/run/secrets/kubernetes.io/k8s-certs/etcd/server.key
scrape_interval: 5s
static_configs:
- targets: ['192.168.112.10:2381']
rules.yml: |
groups:
- name: example
rules:
- alert: kube-proxy的cpu使用率大于80%
expr: rate(process_cpu_seconds_total{job=~"kubernetes-kube-proxy"}[1m]) * 100 > 80
for: 2s
labels:
severity: warnning
annotations:
description: "{{$labels.instance}}的{{$labels.job}}组件的cpu使用率超过80%"
- alert: kube-proxy的cpu使用率大于90%
expr: rate(process_cpu_seconds_total{job=~"kubernetes-kube-proxy"}[1m]) * 100 > 90
for: 2s
labels:
severity: critical
annotations:
description: "{{$labels.instance}}的{{$labels.job}}组件的cpu使用率超过90%"
- alert: scheduler的cpu使用率大于80%
expr: rate(process_cpu_seconds_total{job=~"kubernetes-schedule"}[1m]) * 100 > 80
for: 2s
labels:
severity: warnning
annotations:
description: "{{$labels.instance}}的{{$labels.job}}组件的cpu使用率超过80%"
- alert: scheduler的cpu使用率大于90%
expr: rate(process_cpu_seconds_total{job=~"kubernetes-schedule"}[1m]) * 100 > 90
for: 2s
labels:
severity: critical
annotations:
description: "{{$labels.instance}}的{{$labels.job}}组件的cpu使用率超过90%"
- alert: controller-manager的cpu使用率大于80%
expr: rate(process_cpu_seconds_total{job=~"kubernetes-controller-manager"}[1m]) * 100 > 80
for: 2s
labels:
severity: warnning
annotations:
description: "{{$labels.instance}}的{{$labels.job}}组件的cpu使用率超过80%"
- alert: controller-manager的cpu使用率大于90%
expr: rate(process_cpu_seconds_total{job=~"kubernetes-controller-manager"}[1m]) * 100 > 0
for: 2s
labels:
severity: critical
annotations:
description: "{{$labels.instance}}的{{$labels.job}}组件的cpu使用率超过90%"
- alert: apiserver的cpu使用率大于80%
expr: rate(process_cpu_seconds_total{job=~"kubernetes-apiserver"}[1m]) * 100 > 80
for: 2s
labels:
severity: warnning
annotations:
description: "{{$labels.instance}}的{{$labels.job}}组件的cpu使用率超过80%"
- alert: apiserver的cpu使用率大于90%
expr: rate(process_cpu_seconds_total{job=~"kubernetes-apiserver"}[1m]) * 100 > 90
for: 2s
labels:
severity: critical
annotations:
description: "{{$labels.instance}}的{{$labels.job}}组件的cpu使用率超过90%"
- alert: etcd的cpu使用率大于80%
expr: rate(process_cpu_seconds_total{job=~"kubernetes-etcd"}[1m]) * 100 > 80
for: 2s
labels:
severity: warnning
annotations:
description: "{{$labels.instance}}的{{$labels.job}}组件的cpu使用率超过80%"
- alert: etcd的cpu使用率大于90%
expr: rate(process_cpu_seconds_total{job=~"kubernetes-etcd"}[1m]) * 100 > 90
for: 2s
labels:
severity: critical
annotations:
description: "{{$labels.instance}}的{{$labels.job}}组件的cpu使用率超过90%"
- alert: kube-state-metrics的cpu使用率大于80%
expr: rate(process_cpu_seconds_total{k8s_app=~"kube-state-metrics"}[1m]) * 100 > 80
for: 2s
labels:
severity: warnning
annotations:
description: "{{$labels.instance}}的{{$labels.k8s_app}}组件的cpu使用率超过80%"
value: "{{ $value }}%"
threshold: "80%"
- alert: kube-state-metrics的cpu使用率大于90%
expr: rate(process_cpu_seconds_total{k8s_app=~"kube-state-metrics"}[1m]) * 100 > 0
for: 2s
labels:
severity: critical
annotations:
description: "{{$labels.instance}}的{{$labels.k8s_app}}组件的cpu使用率超过90%"
value: "{{ $value }}%"
threshold: "90%"
- alert: coredns的cpu使用率大于80%
expr: rate(process_cpu_seconds_total{k8s_app=~"kube-dns"}[1m]) * 100 > 80
for: 2s
labels:
severity: warnning
annotations:
description: "{{$labels.instance}}的{{$labels.k8s_app}}组件的cpu使用率超过80%"
value: "{{ $value }}%"
threshold: "80%"
- alert: coredns的cpu使用率大于90%
expr: rate(process_cpu_seconds_total{k8s_app=~"kube-dns"}[1m]) * 100 > 90
for: 2s
labels:
severity: critical
annotations:
description: "{{$labels.instance}}的{{$labels.k8s_app}}组件的cpu使用率超过90%"
value: "{{ $value }}%"
threshold: "90%"
- alert: kube-proxy打开句柄数>600
expr: process_open_fds{job=~"kubernetes-kube-proxy"} > 600
for: 2s
labels:
severity: warnning
annotations:
description: "{{$labels.instance}}的{{$labels.job}}打开句柄数>600"
value: "{{ $value }}"
- alert: kube-proxy打开句柄数>1000
expr: process_open_fds{job=~"kubernetes-kube-proxy"} > 1000
for: 2s
labels:
severity: critical
annotations:
description: "{{$labels.instance}}的{{$labels.job}}打开句柄数>1000"
value: "{{ $value }}"
- alert: kubernetes-schedule打开句柄数>600
expr: process_open_fds{job=~"kubernetes-schedule"} > 600
for: 2s
labels:
severity: warnning
annotations:
description: "{{$labels.instance}}的{{$labels.job}}打开句柄数>600"
value: "{{ $value }}"
- alert: kubernetes-schedule打开句柄数>1000
expr: process_open_fds{job=~"kubernetes-schedule"} > 1000
for: 2s
labels:
severity: critical
annotations:
description: "{{$labels.instance}}的{{$labels.job}}打开句柄数>1000"
value: "{{ $value }}"
- alert: kubernetes-controller-manager打开句柄数>600
expr: process_open_fds{job=~"kubernetes-controller-manager"} > 600
for: 2s
labels:
severity: warnning
annotations:
description: "{{$labels.instance}}的{{$labels.job}}打开句柄数>600"
value: "{{ $value }}"
- alert: kubernetes-controller-manager打开句柄数>1000
expr: process_open_fds{job=~"kubernetes-controller-manager"} > 1000
for: 2s
labels:
severity: critical
annotations:
description: "{{$labels.instance}}的{{$labels.job}}打开句柄数>1000"
value: "{{ $value }}"
- alert: kubernetes-apiserver打开句柄数>600
expr: process_open_fds{job=~"kubernetes-apiserver"} > 600
for: 2s
labels:
severity: warnning
annotations:
description: "{{$labels.instance}}的{{$labels.job}}打开句柄数>600"
value: "{{ $value }}"
- alert: kubernetes-apiserver打开句柄数>1000
expr: process_open_fds{job=~"kubernetes-apiserver"} > 1000
for: 2s
labels:
severity: critical
annotations:
description: "{{$labels.instance}}的{{$labels.job}}打开句柄数>1000"
value: "{{ $value }}"
- alert: kubernetes-etcd打开句柄数>600
expr: process_open_fds{job=~"kubernetes-etcd"} > 600
for: 2s
labels:
severity: warnning
annotations:
description: "{{$labels.instance}}的{{$labels.job}}打开句柄数>600"
value: "{{ $value }}"
- alert: kubernetes-etcd打开句柄数>1000
expr: process_open_fds{job=~"kubernetes-etcd"} > 1000
for: 2s
labels:
severity: critical
annotations:
description: "{{$labels.instance}}的{{$labels.job}}打开句柄数>1000"
value: "{{ $value }}"
- alert: coredns
expr: process_open_fds{k8s_app=~"kube-dns"} > 600
for: 2s
labels:
severity: warnning
annotations:
description: "插件{{$labels.k8s_app}}({{$labels.instance}}): 打开句柄数超过600"
value: "{{ $value }}"
- alert: coredns
expr: process_open_fds{k8s_app=~"kube-dns"} > 1000
for: 2s
labels:
severity: critical
annotations:
description: "插件{{$labels.k8s_app}}({{$labels.instance}}): 打开句柄数超过1000"
value: "{{ $value }}"
- alert: kube-proxy
expr: process_virtual_memory_bytes{job=~"kubernetes-kube-proxy"} > 2000000000
for: 2s
labels:
severity: warnning
annotations:
description: "组件{{$labels.job}}({{$labels.instance}}): 使用虚拟内存超过2G"
value: "{{ $value }}"
- alert: scheduler
expr: process_virtual_memory_bytes{job=~"kubernetes-schedule"} > 2000000000
for: 2s
labels:
severity: warnning
annotations:
description: "组件{{$labels.job}}({{$labels.instance}}): 使用虚拟内存超过2G"
value: "{{ $value }}"
- alert: kubernetes-controller-manager
expr: process_virtual_memory_bytes{job=~"kubernetes-controller-manager"} > 2000000000
for: 2s
labels:
severity: warnning
annotations:
description: "组件{{$labels.job}}({{$labels.instance}}): 使用虚拟内存超过2G"
value: "{{ $value }}"
- alert: kubernetes-apiserver
expr: process_virtual_memory_bytes{job=~"kubernetes-apiserver"} > 2000000000
for: 2s
labels:
severity: warnning
annotations:
description: "组件{{$labels.job}}({{$labels.instance}}): 使用虚拟内存超过2G"
value: "{{ $value }}"
- alert: kubernetes-etcd
expr: process_virtual_memory_bytes{job=~"kubernetes-etcd"} > 2000000000
for: 2s
labels:
severity: warnning
annotations:
description: "组件{{$labels.job}}({{$labels.instance}}): 使用虚拟内存超过2G"
value: "{{ $value }}"
- alert: kube-dns
expr: process_virtual_memory_bytes{k8s_app=~"kube-dns"} > 2000000000
for: 2s
labels:
severity: warnning
annotations:
description: "插件{{$labels.k8s_app}}({{$labels.instance}}): 使用虚拟内存超过2G"
value: "{{ $value }}"
- alert: HttpRequestsAvg
expr: sum(rate(rest_client_requests_total{job=~"kubernetes-kube-proxy|kubernetes-kubelet|kubernetes-schedule|kubernetes-control-manager|kubernetes-apiservers"}[1m])) > 1000
for: 2s
labels:
team: admin
annotations:
description: "组件{{$labels.job}}({{$labels.instance}}): TPS超过1000"
value: "{{ $value }}"
threshold: "1000"
- alert: Pod_restarts
expr: kube_pod_container_status_restarts_total{namespace=~"kube-system|default|monitor-sa"} > 0
for: 2s
labels:
severity: warnning
annotations:
description: "在{{$labels.namespace}}名称空间下发现{{$labels.pod}}这个pod下的容器{{$labels.container}}被重启,这个监控指标是由{{$labels.instance}}采集的"
value: "{{ $value }}"
threshold: "0"
- alert: Pod_waiting
expr: kube_pod_container_status_waiting_reason{namespace=~"kube-system|default"} == 1
for: 2s
labels:
team: admin
annotations:
description: "空间{{$labels.namespace}}({{$labels.instance}}): 发现{{$labels.pod}}下的{{$labels.container}}启动异常等待中"
value: "{{ $value }}"
threshold: "1"
- alert: Pod_terminated
expr: kube_pod_container_status_terminated_reason{namespace=~"kube-system|default|monitor-sa"} == 1
for: 2s
labels:
team: admin
annotations:
description: "空间{{$labels.namespace}}({{$labels.instance}}): 发现{{$labels.pod}}下的{{$labels.container}}被删除"
value: "{{ $value }}"
threshold: "1"
- alert: Etcd_leader
expr: etcd_server_has_leader{job="kubernetes-etcd"} == 0
for: 2s
labels:
team: admin
annotations:
description: "组件{{$labels.job}}({{$labels.instance}}): 当前没有leader"
value: "{{ $value }}"
threshold: "0"
- alert: Etcd_leader_changes
expr: rate(etcd_server_leader_changes_seen_total{job="kubernetes-etcd"}[1m]) > 0
for: 2s
labels:
team: admin
annotations:
description: "组件{{$labels.job}}({{$labels.instance}}): 当前leader已发生改变"
value: "{{ $value }}"
threshold: "0"
- alert: Etcd_failed
expr: rate(etcd_server_proposals_failed_total{job="kubernetes-etcd"}[1m]) > 0
for: 2s
labels:
team: admin
annotations:
description: "组件{{$labels.job}}({{$labels.instance}}): 服务失败"
value: "{{ $value }}"
threshold: "0"
- alert: Etcd_db_total_size
expr: etcd_debugging_mvcc_db_total_size_in_bytes{job="kubernetes-etcd"} > 10000000000
for: 2s
labels:
team: admin
annotations:
description: "组件{{$labels.job}}({{$labels.instance}}):db空间超过10G"
value: "{{ $value }}"
threshold: "10G"
- alert: Endpoint_ready
expr: kube_endpoint_address_not_ready{namespace=~"kube-system|default"} == 1
for: 2s
labels:
team: admin
annotations:
description: "空间{{$labels.namespace}}({{$labels.instance}}): 发现{{$labels.endpoint}}不可用"
value: "{{ $value }}"
threshold: "1"
- name: 物理节点状态-监控告警
rules:
- alert: 物理节点cpu使用率
expr: 100-avg(irate(node_cpu_seconds_total{mode="idle"}[5m])) by(instance)*100 > 90
for: 2s
labels:
severity: ccritical
annotations:
summary: "{{ $labels.instance }}cpu使用率过高"
description: "{{ $labels.instance }}的cpu使用率超过90%,当前使用率[{{ $value }}],需要排查处理"
- alert: 物理节点内存使用率
expr: (node_memory_MemTotal_bytes - (node_memory_MemFree_bytes + node_memory_Buffers_bytes + node_memory_Cached_bytes)) / node_memory_MemTotal_bytes * 100 > 90
for: 2s
labels:
severity: critical
annotations:
summary: "{{ $labels.instance }}内存使用率过高"
description: "{{ $labels.instance }}的内存使用率超过90%,当前使用率[{{ $value }}],需要排查处理"
- alert: InstanceDown
expr: up == 0
for: 2s
labels:
severity: critical
annotations:
summary: "{{ $labels.instance }}: 服务器宕机"
description: "{{ $labels.instance }}: 服务器延时超过2分钟"
- alert: 物理节点磁盘的IO性能
expr: 100-(avg(irate(node_disk_io_time_seconds_total[1m])) by(instance)* 100) < 60
for: 2s
labels:
severity: critical
annotations:
summary: "{{$labels.mountpoint}} 流入磁盘IO使用率过高!"
description: "{{$labels.mountpoint }} 流入磁盘IO大于60%(目前使用:{{$value}})"
- alert: 入网流量带宽
expr: ((sum(rate (node_network_receive_bytes_total{device!~'tap.*|veth.*|br.*|docker.*|virbr*|lo*'}[5m])) by (instance)) / 100) > 102400
for: 2s
labels:
severity: critical
annotations:
summary: "{{$labels.mountpoint}} 流入网络带宽过高!"
description: "{{$labels.mountpoint }}流入网络带宽持续5分钟高于100M. RX带宽使用率{{$value}}"
- alert: 出网流量带宽
expr: ((sum(rate (node_network_transmit_bytes_total{device!~'tap.*|veth.*|br.*|docker.*|virbr*|lo*'}[5m])) by (instance)) / 100) > 102400
for: 2s
labels:
severity: critical
annotations:
summary: "{{$labels.mountpoint}} 流出网络带宽过高!"
description: "{{$labels.mountpoint }}流出网络带宽持续5分钟高于100M. RX带宽使用率{{$value}}"
- alert: TCP会话
expr: node_netstat_Tcp_CurrEstab > 1000
for: 2s
labels:
severity: critical
annotations:
summary: "{{$labels.mountpoint}} TCP_ESTABLISHED过高!"
description: "{{$labels.mountpoint }} TCP_ESTABLISHED大于1000%(目前使用:{{$value}}%)"
- alert: 磁盘容量
expr: 100-(node_filesystem_free_bytes{fstype=~"ext4|xfs"}/node_filesystem_size_bytes {fstype=~"ext4|xfs"}*100) > 80
for: 2s
labels:
severity: critical
annotations:
summary: "{{$labels.mountpoint}} 磁盘分区使用率过高!"
description: "{{$labels.mountpoint }} 磁盘分区使用大于80%(目前使用:{{$value}}%)"
EOF
注意:
除了
kube-proxy
默认在每个节点的10249
端口上暴露其指标其余的
kubernetes-schedule
、kubernetes-controller-manager
、kubernetes-etcd
这些组件Pod 的容器需要根据自己的 k8s 集群情况进行修改
kubectl apply -f prometheus-cfg.yaml
kubectl get cm prometheus-config -n monitor-sa -o yaml
同样的还是需要检查 cm 文件中是否正确解析了 $1 $2
3、重新生成 prometheus-deploy.yaml 文件
k8s-master1 节点操作
cat > prometheus-deploy.yaml << EOF
---
apiVersion: apps/v1
kind: Deployment
metadata:
name: prometheus-server
namespace: monitor-sa
labels:
app: prometheus
spec:
replicas: 2
selector:
matchLabels:
app: prometheus
component: server
#matchExpressions:
#- {key: app, operator: In, values: [prometheus]}
#- {key: component, operator: In, values: [server]}
template:
metadata:
labels:
app: prometheus
component: server
annotations:
prometheus.io/scrape: 'false'
spec:
affinity:
podAntiAffinity:
requiredDuringSchedulingIgnoredDuringExecution:
- labelSelector:
matchExpressions:
- key: app
operator: In
values:
- prometheus
- key: component
operator: In
values:
- server
topologyKey: kubernetes.io/hostname
serviceAccountName: monitor
containers:
- name: prometheus
image: quay.io/prometheus/prometheus:latest
imagePullPolicy: IfNotPresent
command:
- "/bin/prometheus"
args:
- "--config.file=/etc/prometheus/prometheus.yml"
- "--storage.tsdb.path=/prometheus"
- "--storage.tsdb.retention=24h"
- "--web.enable-lifecycle"
ports:
- containerPort: 9090
protocol: TCP
volumeMounts:
- mountPath: /etc/prometheus
name: prometheus-config
- mountPath: /prometheus/
name: prometheus-storage-volume
- name: k8s-certs
mountPath: /var/run/secrets/kubernetes.io/k8s-certs/etcd/
- name: alertmanager
image: prom/alertmanager:latest
imagePullPolicy: IfNotPresent
args:
- "--config.file=/etc/alertmanager/alertmanager.yml"
- "--log.level=debug"
ports:
- containerPort: 9093
protocol: TCP
name: alertmanager
volumeMounts:
- name: alertmanager-config
mountPath: /etc/alertmanager
- name: alertmanager-storage
mountPath: /alertmanager
- name: localtime
mountPath: /etc/localtime
volumes:
- name: prometheus-config
configMap:
name: prometheus-config
- name: prometheus-storage-volume
hostPath:
path: /data
type: Directory
- name: k8s-certs
secret:
secretName: etcd-certs
- name: alertmanager-config
configMap:
name: alertmanager
- name: alertmanager-storage
hostPath:
path: /data/alertmanager
type: DirectoryOrCreate
- name: localtime
hostPath:
path: /usr/share/zoneinfo/Asia/Shanghai
EOF
3.1、创建一个名为 etcd-certs 的 Secret
kubectl -n monitor-sa create secret generic etcd-certs --from-file=/etc/kubernetes/pki/etcd/server.key --from-file=/etc/kubernetes/pki/etcd/server.crt --from-file=/etc/kubernetes/pki/etcd/ca.crt
3.2、应用 prometheus-deploy.yaml 文件
kubectl apply -f prometheus-deploy.yaml
kubectl get pods -n monitor-sa
4、重新生成并创建 alertmanager-svc.yaml 文件
cat >alertmanager-svc.yaml <<EOF
---
apiVersion: v1
kind: Service
metadata:
labels:
name: prometheus
kubernetes.io/cluster-service: 'true'
name: alertmanager
namespace: monitor-sa
spec:
ports:
- name: alertmanager
nodePort: 30066
port: 9093
protocol: TCP
targetPort: 9093
selector:
app: prometheus
sessionAffinity: None
type: NodePort
EOF
kubectl apply -f alertmanager-svc.yaml
kubectl get svc alertmanager -n monitor-sa
5、访问 prometheus UI 界面
5.1、【error】kube-controller-manager、etcd、kube-proxy、kube-scheduler 组件 connection refused
5.1.1、kube-proxy
默认情况下,该服务监听端口只提供给127.0.0.1,需修改为0.0.0.0
kubectl edit cm/kube-proxy -n kube-system
- 编辑文件,将文件修改允许0.0.0.0即可,保存
metricsBindAddress: 0.0.0.0:10249
- 删除重建 kube-proxy 的 pod
kubectl delete pod -l k8s-app=kube-proxy -n kube-system
- 效果
5.1.2、kube-controller-manager
事先说明:到这一步我试过网上很多方法都没有成功获取到数据,所以我重新创建了 sa 慎用,仅供参考
- 修改 kube-controller-manager 的 yaml 文件
默认监听本地修改为 0.0.0.0
- --bind-address=127.0.0.1
# 修改为
- --bind-address=0.0.0.0
- 创建ServiceAccount
创建一个新的ServiceAccount,用于Prometheus访问 kube-controller-manager
。
cat > prom-sa << EOF
apiVersion: v1
kind: ServiceAccount
metadata:
name: prometheus-sa
namespace: monitor-sa
EOF
- 创建ClusterRole
创建一个ClusterRole,定义Prometheus所需的权限。
cat > porm-role << EOF
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
name: prometheus-role
rules:
- nonResourceURLs:
- "/metrics"
verbs:
- get
EOF
- 创建ClusterRoleBinding
将ServiceAccount绑定到ClusterRole。
cat > prom-bind.yaml << EOF
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata:
name: prometheus-binding
subjects:
- kind: ServiceAccount
name: prometheus-sa
namespace: monitor-sa
roleRef:
kind: ClusterRole
name: prometheus-role
apiGroup: rbac.authorization.k8s.io
EOF
- 获取ServiceAccount的Token
获取ServiceAccount的Token,以便在Prometheus配置中使用。
TOKEN=$(kubectl get secret $(kubectl get sa prometheus-sa -n monitor-sa -o json | jq -r '.secrets[].name') -n monitor-sa -o json | jq -r '.data.token' | base64 --decode)
- 修改Prometheus配置文件(cm)
- job_name: 'kubernetes-controller-manager'
scheme: https
tls_config:
insecure_skip_verify: true # 禁用证书验证
authorization:
credentials: eyJhbGciOiJSUzI1NiIsImtpZCI6IkFEWVNqaWlueWVDMzBUcTZvQk9MRkpxQ0diLWRGWkNoaWlpZkgwR21NcEkifQ.eyJpc3MiOiJrdWJlcm5ldGVzL3NlcnZpY2VhY2NvdW50Iiwia3ViZXJuZXRlcy5pby9zZXJ2aWNlYWNjb3VudC9uYW1lc3BhY2UiOiJtb25pdG9yLXNhIiwia3ViZXJuZXRlcy5pby9zZXJ2aWNlYWNjb3VudC9zZWNyZXQubmFtZSI6InByb21ldGhldXMtc2EtdG9rZW4tbnQ5bm4iLCJrdWJlcm5ldGVzLmlvL3NlcnZpY2VhY2NvdW50L3NlcnZpY2UtYWNjb3VudC5uYW1lIjoicHJvbWV0aGV1cy1zYSIsImt1YmVybmV0ZXMuaW8vc2VydmljZWFjY291bnQvc2VydmljZS1hY2NvdW50LnVpZCI6IjQ4YTA5NDExLTAwMmYtNDE0Ni05YzY4LTBiNmVjOWYzYWZlZCIsInN1YiI6InN5c3RlbTpzZXJ2aWNlYWNjb3VudDptb25pdG9yLXNhOnByb21ldGhldXMtc2EifQ.DNgCjTVxsrGDltvQZG-x7qPQrh369SO_e0faGrrhjgkBLS4q2sh85wkaBNNZcIjxZcVk7ZU9gQmQkM3AIgGIcIURpQGDMgVVI_xF1JV8iQWe-nL1yHnQAXDjyMAd1826wVvMH8LSKqdKfPVaMHN8t0LScX5yHonSJUqoevxi7Mm7tiUd33IlMQ6xH6M8Tu8bsg-fOVmL6nnGpC1tPgaZy8M_GA_Kh9j8SwHXi4Yd9r75eOSa3J6N4KF6n-EPKxnGmXDooA60G94YptsDFCQMi1t4TLAFR1FKraycWHwPbIwviUZTvA1WXbkiHnh0R6q-y0hHJVbAi_ZXagVXKZFBaw # 替换为实际的Token值
scrape_interval: 5s
static_configs:
- targets: ['192.168.112.10:10257']
- 重启Prometheus
更新配置后,重启Prometheus以应用新的配置。
kubectl rollout restart deployment/prometheus-server -n monitor-sa
- 效果
5.1.3、kube-schedule
和 kube-controller-manager 操作一致
- 效果
5.1.4、etcd
- 修改创建 etcd 的 yaml 文件
添加 master 节点 ip + etcd port
vim /etc/kubernetes/manifests/etcd.yaml
- --listen-metrics-urls=http://127.0.0.1:2381,http://192.168.112.10:2381
- 修改 prometheus.yaml 文件
改为 http
- 效果
6、点击Alerts,查看
7、把controller-manager的cpu使用率大于90%展开
FIRING表示prometheus已经将告警发给alertmanager
在Alertmanager 中可以看到有 alert。
8、登录 alertmanager UI
<master-ip>:svc-alertmanager-port
192.168.112.10:30066
9、登录 QQ 邮箱查看告警信息
四、配置 Alertmanager 报警 -- 发送告警到钉钉
1、手机端拉群
因为 PC 端不好操作
2、创建自定义机器人
群设置 |
---|
机器人 |
添加机器人 |
自定义 |
添加 |
机器人名字、安全设置 |
保管好 Webhook |
3、获取钉钉的 Webhook 插件
master 节点操作
git clone git@github.com:misakivv/prometheus-webhook-dingtalk.git
cd prometheus-webhook-dingtalk
tar zxvf prometheus-webhook-dingtalk-0.3.0.linux-amd64.tar.gz
cd prometheus-webhook-dingtalk-0.3.0.linux-amd64
4、启动钉钉告警插件
nohup ./prometheus-webhook-dingtalk --web.listen-address="0.0.0.0:8060" --ding.profile="cluster1=https://oapi.dingtalk.com/robot/send?access_token=feb3df2c6a987c8c1466c16eb90f4c2d3817c481aacf15cecc46f588f2716f25" &
5、对 alertmanager-cm.yaml 文件做备份
cp alertmanager-cm.yaml alertmanager-cm.yaml.bak
6、重新生成新的 alertmanager-cm.yaml 文件
cat >alertmanager-cm.yaml <<EOF
kind: ConfigMap
apiVersion: v1
metadata:
name: alertmanager
namespace: monitor-sa
data:
alertmanager.yml: |-
global:
resolve_timeout: 1m
smtp_smarthost: 'smtp.qq.com:465'
smtp_from: '2830909671@qq.com'
smtp_auth_username: '2830909671@qq.com'
smtp_auth_password: 'ajjgpgwwfkpcdgih'
smtp_require_tls: false
route:
group_by: [alertname]
group_wait: 10s
group_interval: 10s
repeat_interval: 10m
receiver: cluster1
receivers:
- name: cluster1
webhook_configs:
- url: 'http://192.168.112.10:8060/dingtalk/cluster1/send'
send_resolved: true
EOF
7、重建资源以生效
kubectl delete cm alertmanager -n monitor-sa
kubectl apply -f alertmanager-cm.yaml
kubectl delete -f prometheus-cfg.yaml
kubectl apply -f prometheus-cfg.yaml
kubectl delete -f prometheus-deploy.yaml
kubectl apply -f prometheus-deploy.yaml
8、效果
暂时先这样,其实 alertmanager 还有静默、去重、抑制等功能,下一篇再共同学习