部署prometheus、grafana、alertmanager,并且配置prometheus的动态、静态服务发现,实现对容器、物理节点、service、pod等资源指标监控,并在Grafana的web界面展示prometheus的监控指标,然后通过配置自定义告警规则,通过alertmanager实现qq、钉钉、微信报警。
prometheus特点
1.多维度数据模型
时间序列数据由metrics名称和键值对来组成
可以对数据进行聚合,切割等操作
所有的metrics都可以设置任意的多维标签。
2.灵活的查询语言(PromQL)可以对采集的metrics指标进行加法,乘法,连接等操作
3.可以直接在本地部署,不依赖其他分布式存储;
4.通过基于HTTP的pull方式采集时序数据;
5.可以通过中间网关pushgateway的方式把时间序列数据推送到prometheus server端;
6.可通过服务发现或者静态配置来发现目标服务对象(targets)。
7.有多种可视化图像界面,如Grafana等。
8.高效的存储,每个采样数据占3.5 bytes左右,300万的时间序列,30s间隔,保留60天,消耗磁盘大概200G。
prometheus组件介绍
1.Prometheus Server: 用于收集和存储时间序列数据。
2.Client Library: 客户端库,检测应用程序代码,当Prometheus抓取实例的HTTP端点时,客户端库会将所有跟踪的metrics指标的当前状态发送到prometheus server端。
3.Exporters: prometheus支持多种exporter,通过exporter可以采集metrics数据,然后发送到prometheus server端
4.Alertmanager: 从 Prometheus server 端接收到 alerts 后,会进行去重,分组,并路由到相应的接收方,发出报警,常见的接收方式有:电子邮件,微信,钉钉, slack等。
5.Grafana**:**监控仪表盘
6.pushgateway: 各个目标主机可上报数据到pushgatewy,然后prometheus server统一从pushgateway拉取数据。
prometheus工作流程:
1.Prometheus server可定期从活跃的(up)目标主机上(target)拉取监控指标数据,目标主机的监控数据可通过配置静态job或者服务发现的方式被prometheus server采集到,这种方式默认的pull方式拉取指标;也可通过pushgateway把采集的数据上报到prometheus server中;还可通过一些组件自带的exporter采集相应组件的数据;
2.Prometheus server把采集到的监控指标数据保存到本地磁盘或者数据库;
3.Prometheus采集的监控指标数据按时间序列存储,通过配置报警规则,把触发的报警发送到alertmanager
4.Alertmanager通过配置报警接收方,发送报警到邮件,微信或者钉钉等
5.Prometheus 自带的web ui界面提供PromQL查询语言,可查询监控数据
6.Grafana可接入prometheus数据源,把监控数据以图形化形式展示出
安装node-exporter组件
[root@master ~]# vim node-export.yaml
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: /
[root@master ~]# kubectl create namespace monitor-sa
namespace/monitor-sa created
[root@master ~]# kubectl apply -f node-export.yaml
daemonset.apps/node-exporter created
[root@master ~]# kubectl get pod -n monitor-sa -owide
NAME READY STATUS RESTARTS AGE IP NODE
node-exporter-jd8fh 1/1 Running 0 2m29s 192.168.1.12 node1
node-exporter-kq6dr 1/1 Running 0 2m29s 192.168.1.11 master
[root@master ~]# curl 192.168.1.12:9100/metrics
# HELP go_gc_duration_seconds A summary of the GC invocation durations.
# TYPE go_gc_duration_seconds summary
.....
# HELP 解释当前指标的含义
# TYPE 当前指标的数据类型
# HELP node_cpu_guest_seconds_total Seconds the cpus spent in guests (VMs) for each mode.
# TYPE node_cpu_guest_seconds_total counter
node_cpu_guest_seconds_total{cpu="0",mode="nice"} 0
node_cpu_guest_seconds_total{cpu="0",mode="user"} 0
node_cpu_guest_seconds_total{cpu="1",mode="nice"} 0
node_cpu_guest_seconds_total{cpu="1",mode="user"} 0
#创建一个sa账号
[root@master ~]# kubectl create serviceaccount monitor -n monitor-sa
serviceaccount/monitor created
[root@master ~]# kubectl get sa -n monitor-sa
NAME SECRETS AGE
monitor 1 13s
把sa账号monitor通过clusterrolebing绑定到clusterrole上
[root@master ~]# kubectl create clusterrolebinding monitor-clusterrolebinding -n monitor-sa --clusterrole=cluster-admin --serviceaccount=monitor-sa:monitor
2.创建数据目录 node1上操作如下命令:
[root@node1 ~]# mkdir /data
[root@node1 ~]# chmod 777 /data/
3.安装prometheus,以下步骤均在在k8s集群的master1节点操作
1)创建一个configmap存储卷,用来存放prometheus配置信息
[root@master ~]# vim prometheus-cfg.yaml
---
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:$2
- action: labelmap
regex: __meta_kubernetes_service_label_(.+)
- source_labels: [__meta_kubernetes_namespace]
action: replace
target_label: kubernetes_namespace
- source_labels: [__meta_kubernetes_service_name]
action: replace
target_label: kubernetes_name
promtheus-cfg.yaml文件需要手动修改部分${1} $1 $2
22行的replacement: ':9100'变成replacement: '${1}:9100'
42行的replacement: /api/v1/nodes//proxy/metrics/cadvisor变成
replacement: /api/v1/nodes/${1}/proxy/metrics/cadvisor
73行的replacement: 变成replacement: $1:$2
[root@master ~]# kubectl apply -f prometheus-cfg.yaml
configmap/prometheus-config created
[root@master ~]# kubectl get configmaps -n monitor-sa
NAME DATA AGE
prometheus-config 1 18s
2)通过deployment部署prometheus
cat >prometheus-deploy.yaml <
注意:在上面的prometheus-deploy.yaml文件有个nodeName字段,这个就是用来指定创建的这个prometheus的pod调度到哪个节点上,我们这里让nodeName=node1,也即是让pod调度到node1节点上,因为node1节点我们创建了数据目录/data,所以大家记住:你在k8s集群的哪个节点创建/data,就让pod调度到哪个节点。
[root@master ~]# kubectl apply -f prometheus-deploy.yaml
deployment.apps/prometheus-server created
[root@master ~]# kubectl get pod -n monitor-sa
NAME READY STATUS RESTARTS AGE
node-exporter-jd8fh 1/1 Running 0 5m
node-exporter-kq6dr 1/1 Running 0 5m
prometheus-server-86c6ff4ffb-ztxnt 1/1 Running 0 25s
3)给prometheus pod创建一个service
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
[root@master ~]# kubectl apply -f prometheus-svc.yaml
service/prometheus created
[root@master ~]# kubectl get svc -n monitor-sa
NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
prometheus NodePort 10.105.149.225 9090:30035/T 17s
prometheus热更新
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:
[root@master ~]# kubectl get pods -n monitor-sa -o wide | grep prometheus
prometheus-server-86 1/1 Running 0 7m25s 10.244.1.8
[root@master ~]# curl -X POST http://10.244.1.8:9090/-/reload
Grafana安装和配置
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
[root@node1 ~]# docker load -i heapster-grafana-amd64_v5_0_4.tar.gz
####grafana上传到node节点
[root@master ~]# kubectl apply -f grafana.yaml
[root@master ~]# kubectl get pods -n kube-system | grep grafana
monitoring-grafana-7d7f6cf5c6-hvb2m 1/1 Running 0 9s
[root@master ~]# kubectl get svc -n kube-system | grep grafana
NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
monitoring-grafana NodePort 10.108.0.93 80:31619/TCP 17s
访问192.168.1.12:31619,创建
安装配置kube-state-metrics组件
kube-state-metrics是什么?
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?我有多少job在运行中。
安装kube-state-metrics组件
1)创建sa,并对sa授权
[root@master ~]# kubectl create clusterrolebinding kubestate-clusterrolebinding -n kube-system --clusterrole=cluster-admin --serviceaccount=kube-system:kubestate
clusterrolebinding.rbac.authorization.k8s.io/kubestate-clusterrolebinding created
[root@master ~]# kubectl create sa kubestate -n kube-system
serviceaccount/kubestate created
2)安装kube-state-metrics组件
在k8s的master1节点生成一个kube-state-metrics-deploy.yaml文件
cat > kube-state-metrics-deploy.yaml <
[root@master ~]# kubectl apply -f kube-state-metrics-deploy.yaml
[root@master ~]# kubectl get pod -n kube-system | grep kube-state-metrics
kube-state-metrics-557f66c7d-bx5xp 1/1 Running 0 44s
3)创建service
在8s的master1节点生成一个kube-state-metrics-svc.yaml文件
cat >kube-state-metrics-svc.yaml <
[root@master ~]# kubectl apply -f kube-state-metrics-svc.yaml
service/kube-state-metrics created
[root@master ~]# kubectl get svc -n kube-system | grep kube-state-metrics
kube-state-metrics ClusterIP 10.101.125.39 8080/TCP 6s
**在grafana web界面导入Kubernetes Cluster (Prometheus)-1577674936972.json
安装和配置Alertmanager-发送报警到qq邮箱
在k8s的master1节点创建alertmanager-cm.yaml文件
cat >alertmanager-cm.yaml <
alertmanager配置文件解释说明:
smtp_smarthost: ‘smtp.163.com:25’ #用于发送邮件的邮箱的SMTP服务器地址+端口
smtp_from: ‘[email protected]’ #这是指定从哪个邮箱发送报警
smtp_auth_username: ‘15011572657’ #这是发送邮箱的认证用户,不是邮箱名
smtp_auth_password: ‘BDBPRMLNZGKWRFJP’ #这是发送邮箱的授权码而不是登录密码
email_configs: - to: ‘[email protected]’ #to后面指定发送到哪个邮箱
[root@master ~]# kubectl apply -f alertmanager-cm.yaml
[root@master ~]# kubectl get configmaps -n monitor-sa
NAME DATA AGE
alertmanager 1 78s
**在k8s的master1节点重新生成一个prometheus-cfg.yaml文件
**
cat prometheus-cfg.yaml
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:$2
source_labels:
- __address__
- __meta_kubernetes_pod_annotation_prometheus_io_port
target_label: __address__
- action: labelmap
regex: __meta_kubernetes_pod_label_(.+)
- action: replace
source_labels:
- __meta_kubernetes_namespace
target_label: kubernetes_namespace
- action: replace
source_labels:
- __meta_kubernetes_pod_name
target_label: kubernetes_pod_name
- job_name: 'kubernetes-schedule'
scrape_interval: 5s
static_configs:
- targets: ['192.168.1.11:10251']
- job_name: 'kubernetes-controller-manager'
scrape_interval: 5s
static_configs:
- targets: ['192.168.1.11:10252']
- job_name: 'kubernetes-kube-proxy'
scrape_interval: 5s
static_configs:
- targets: ['192.168.1.11:10249','192.168.1.12:10249']
- job_name: 'kubernetes-etcd'
scheme: https
tls_config:
ca_file: /var/run/secrets/kubernetes.io/k8s-certs/etcd/ca.crt
cert_file: /var/run/secrets/kubernetes.io/k8s-certs/etcd/server.crt
key_file: /var/run/secrets/kubernetes.io/k8s-certs/etcd/server.key
scrape_interval: 5s
static_configs:
- targets: ['192.168.1.11:2379']
rules.yml: |
groups:
- name: example
rules:
- alert: kube-proxy的cpu使用率大于80%
expr: rate(process_cpu_seconds_total{job=~"kubernetes-kube-proxy"}[1m]) * 100 > 80
for: 2s
labels:
severity: warnning
annotations:
description: "{{$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}}%)"
修改自己的集群IP
[root@master ~]# cat -n prometheus-cfg.yaml | grep 192.168
120 - targets: ['192.168.1.11:10251']
124 - targets: ['192.168.1.11:10252']
128 - targets: ['192.168.1.11:10249','192.168.1.12:10249']
137 - targets: ['192.168.1.11:2379']
方便查看报警修改的实际的报警指标
[root@master ~]# cat -n prometheus-cfg.yaml | grep "> 0"
178 expr: rate(process_cpu_seconds_total{job=~"kubernetes-controller-manager"}[1m]) * 100 > 0
222 expr: rate(process_cpu_seconds_total{k8s_app=~"kube-state-metrics"}[1m]) * 100 > 0
402 expr: kube_pod_container_status_restarts_total{namespace=~"kube-system|default|monitor-sa"} > 0
438 expr: rate(etcd_server_leader_changes_seen_total{job="kubernetes-etcd"}[1m]) > 0
447 expr: rate(etcd_server_proposals_failed_total{job="kubernetes-etcd"}[1m]) > 0
[root@master ~]# kubectl apply -f prometheus-cfg.yaml
[root@master ~]# kubectl get configmaps -n monitor-sa
NAME DATA AGE
alertmanager 1 18m
prometheus-config 2 3h29m
[root@master ~]# kubectl describe configmaps prometheus-config -n monitor-sa
在k8s的master1节点重新生成一个prometheus-deploy.yaml文件
cat >prometheus-deploy.yaml <
生成一个etcd-certs,这个在部署prometheus需要
[root@master ~]# 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
secret/etcd-certs created
[root@master ~]# kubectl apply -f prometheus-deploy.yaml
deployment.apps/prometheus-server configured
[root@master ~]# kubectl get pods -n monitor-sa | grep prometheus
prometheus-server-77c6dddd55-s4bzg 2/2 Running 0 13s
在k8s的master1节点重新生成一个alertmanager-svc.yaml文件
cat >alertmanager-svc.yaml <
[root@master ~]# kubectl apply -f alertmanager-svc.yaml
service/alertmanager created
[root@master ~]# kubectl get svc -n monitor-sa
NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
alertmanager NodePort 10.103.60.117 9093:30066/TCP 5s
prometheus NodePort 10.109.238.217 9090:31467/TCP
访问prometheus的web界面 192.168.1.11:30066
配置Alertmanager报警-发送报警到钉钉
2.安装钉钉的webhook插件,在k8s的master1节点操作
cd prometheus-webhook-dingtalk-0.3.0.linux-amd64
启动钉钉报警插件
[root@master ~]# tar zxvf prometheus-webhook-dingtalk-0.3.0.linux-amd64.tar.gz
[root@master ~]# cd prometheus-webhook-dingtalk-0.3.0.linux-amd64
[root@master prometheus-webhook-dingtalk-0.3.0.linux-amd64]# nohup ./prometheus-webhook-dingtalk --web.listen-address="0.0.0.0:8060" --ding.profile="cluster1=https://oapi.dingtalk.com/robot/send?access_token=67276f5bfaa874341d544bd" &
##网址就是上面的webhook地址
重新生成一个新的alertmanager-cm.yaml文件
cat >alertmanager-cm.yaml <
通过kubectl apply使配置生效,不起效的话删除重新apply
[root@master ~]# kubectl apply -f alertmanager-cm.yaml
[root@master ~]# kubectl apply -f prometheus-cfg.yaml
[root@master ~]# kubectl apply -f prometheus-deploy.yaml