Prometheus+Grafana+Alertmanager搭建全方位的监控告警系统
prometheus安装和配置
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架构图
从上图可发现，Prometheus整个生态圈组成主要包括prometheus server，Exporter，pushgateway，alertmanager，grafana，Web ui界面，Prometheus server由三个部分组成，Retrieval，Storage，PromQL 。
- retrieval负责在活跃的target主机上抓取监控指标数据
- storage主要是把采集到的数据存储到磁盘中
- promQL是prometheus提供的查询语言模块
prometheus工作流程
- 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；
- 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；
- 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
- Alertmanager通过配置报警接收方，发送报警到邮件，微信或者钉钉等
- Prometheus 自带的web ui界面提供PromQL查询语言，可查询监控数据
- 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
node-exporter是采集机器（物理机、虚拟机、云主机等）的监控指标数据，能够采集到的指标包括CPU, 内存，磁盘，网络，文件数等信息。
实验环境
一个master节点，一个node节点。
在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
通过node-exporter采集数据
curl http://主机ip:9100/metrics
在k8s集群中部署promethues
创建namespace、sa账号，在k8s集群的master节点操作
kubectl create ns monitor-sa
kubectl create serviceaccount monitor -n monitor-sa
#把sa账号monitor通过clusterrolebing绑定到clusterrole上
kubectl create clusterrolebinding moniror-clusterrolebinding -n monitor-sa --clusterrole=cluster-admin --serviceaccount=monitor-sa:monitor
创建数据目录
# 在k8s集群的任何一个node节点操作，本实验在node1上操作
mkdir /data
chmod 777 /data/
安装prometheus，在master节点操作
#创建一个configmap存储卷，用来存放prometheus配置信息
#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: ':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//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: :
- 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
---
#通过deployment部署prometheus
#prometheus-deploy.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
name: prometheus-server
namespace: monitor-sa
labels:
app: prometheus
spec:
replicas: 1
selector:
matchLabels:
app: prometheus
component: server
template:
metadata:
labels:
app: prometheus
component: server
annotations:
prometheus.io/scrape: 'false'
spec:
nodeName: node1
serviceAccountName: monitor
containers:
- name: prometheus
image: prom/prometheus:v2.2.1
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
注意：通过上面命令生成的promtheus-cfg.yaml文件会有一些问题，$1和$2这种变量在文件里没有，需要在k8s的master1节点打开promtheus-cfg.yaml文件，手动把$1和$2这种变量写进文件里，promtheus-cfg.yaml文件需要手动修改部分如下：
22行的replacement: ':9100'变成replacement: '${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
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
#查看service在物理机映射的端口
kubectl get svc -n monitor-sa #访问prometheus web ui 界面
http://172.16.9.3:30426/graph
#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
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：
curl -X POST http://10.244.1.125:9090/-/reload#10.244.1.66是prometheus的pod的ip地址
#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
注意：
线上最好热加载，暴力删除可能造成监控数据的丢失
Grafana安装和配置
下载安装Grafana需要的镜像
上传heapster-grafana-amd64_v5_0_4.tar.gz镜像到k8s的各个master节点和k8s的各个node节点，然后在各个节点手动解压：
docker load -i heapster-grafana-amd64_v5_0_4.tar.gz
镜像所在的百度网盘地址如下：
链接：https://pan.baidu.com/s/1TmVGKxde_cEYrbjiETboEA 提取码：052u
在k8s的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
通过kubectl get sac -n cube-system看到grafana暴漏的苏主机端口是32351，我们可以访问k8s集群的master节点ip:32351即可访问grafana的web界面
Grafana界面接入prometheus数据源
登录Grafana,172.16.9.3:32351，账号密码都是admin
配置grafana界面，选择create your first data source
Name:Prometheus
Type:Prometheus
HTTP出的URL：http://prometheus.monitor-sa.svc:9090
点击左下角Save&Test，出现Data source is working，说明prometheus数据源成功的被grafana接入了。
导入监控模板，可在如下链接搜索
https://grafana.com/dashboards?dataSource=prometheus&search=kubernetes
也可直接导入node_exporter.json监控模板，这个可以把node节点指标显示出来，node_exporter.json在百度网盘地址如下：链接：https://pan.baidu.com/s/1vF1kAMRbxQkUGPlZt91MWg 提取码：kyd6
还可直接导入docker_rev1.json，可以把容器相关的数据展示出来
docker_rev1.json在百度网盘地址如下链接：https://pan.baidu.com/s/17o_nja5N2R-g9g5PkJ3aFA 提取码：vinv
导入监控模版步骤：点击左侧+号下面的Import，选择Upload json file,选择一个本地的json文件即可。
安装配置kube-state-metrics组件
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
创建sa，并对sa授权，在master节点操作
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
安装cube-state-metrics组件，在master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:v1.9.0
ports:
- containerPort: 8080
EOF
创建service，在master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
在Grafana web界面导入kubernetes Cluster和kubernetes cluster monitoring
链接：https://pan.baidu.com/s/1QAMqT8scsXx-lzEPI6MPgA
提取码：i4yd
安装和配置Alertmanager-发送报警到qq邮箱
在k8s的master节点创建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.163.com:25'
smtp_from: '15011572657@163.com'
smtp_auth_username: '15011572657'
smtp_auth_password: 'BDBPRMLNZGKWRFJP'
smtp_require_tls: false
route:
group_by: [alertname]
group_wait: 10s
group_interval: 10s
repeat_interval: 10m
receiver: default-receiver
receivers:
- name: 'default-receiver'
email_configs:
- to: 'y1486170457@qq.com'
send_resolved: true
EOF
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: '15011572657@163.com'
#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: 'y1486170457@qq.com'
#to后面指定发送到哪个邮箱，我发送到我的qq邮箱，大家需要写自己的邮箱地址，不应该跟smtp_from的邮箱名字重复
在master节点重新生成prometheus-cfg.yaml文件
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:$2
source_labels:
- __address__
- __meta_kubernetes_pod_annotation_prometheus_io_port
target_label: __address__
- action: labelmap
regex: __meta_kubernetes_pod_label_(.+)
- action: replace
source_labels:
- __meta_kubernetes_namespace
target_label: kubernetes_namespace
- action: replace
source_labels:
- __meta_kubernetes_pod_name
target_label: kubernetes_pod_name
- job_name: 'kubernetes-schedule'
scrape_interval: 5s
static_configs:
- targets: ['172.16.9.3:10251']
- job_name: 'kubernetes-controller-manager'
scrape_interval: 5s
static_configs:
- targets: ['172.16.9.3:10252']
- job_name: 'kubernetes-kube-proxy'
scrape_interval: 5s
static_configs:
- targets: ['172.16.9.3:10249','172.16.9.4:10249']
- job_name: 'kubernetes-etcd'
scheme: https
tls_config:
ca_file: /var/run/secrets/kubernetes.io/k8s-certs/etcd/ca.crt
cert_file: /var/run/secrets/kubernetes.io/k8s-certs/etcd/server.crt
key_file: /var/run/secrets/kubernetes.io/k8s-certs/etcd/server.key
scrape_interval: 5s
static_configs:
- targets: ['172.16.9.3:2379']
rules.yml: |
groups:
- name: example
rules:
- alert: kube-proxy的cpu使用率大于80%
expr: rate(process_cpu_seconds_total{job=~"kubernetes-kube-proxy"}[1m]) * 100 > 80
for: 2s
labels:
severity: warnning
annotations:
description: "{{$labels.instance}}的{{$labels.job}}组件的cpu使用率超过80%"
- alert: kube-proxy的cpu使用率大于90%
expr: rate(process_cpu_seconds_total{job=~"kubernetes-kube-proxy"}[1m]) * 100 > 90
for: 2s
labels:
severity: critical
annotations:
description: "{{$lables.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}}%)"
同样需要手动添加$的变量。
在k8smaster节点重新生成一个prometheus-deploy.yaml文件
cat >prometheus-deploy.yaml <<EOF
---
apiVersion: apps/v1
kind: Deployment
metadata:
name: prometheus-server
namespace: monitor-sa
labels:
app: prometheus
spec:
replicas: 1
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:
nodeName: node1
serviceAccountName: monitor
containers:
- name: prometheus
image: prom/prometheus:v2.2.1
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:v0.14.0
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
生成一个etch-certs，这个在部署prometheus需要
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
更新yaml文件，查看部署是否成功。
在k8smaster节点上重新生成一个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
#查看service在物理机映射的端口
kubectl get svc -n monitor-sa
访问prometheus界面，点击alerts，把controller-manager的cpu使用率大于90%展开，可看到status为FIRING，表示prometheus已经将告警发给alertmanager，在Alertmanager 中可以看到有一个 alert。
登录alertmanager web界面查看
配置alertmanager报警-发送报警到钉钉
创建钉钉机器人
打开电脑版钉钉，创建一个群，创建自定义机器人，按如下步骤创建
https://ding-doc.dingtalk.com/doc#/serverapi2/qf2nxq 我创建的机器人如下：
群设置-->智能群助手-->添加机器人-->自定义-->添加 机器人名称：kube-event
接收群组：钉钉报警测试 安全设置：
自定义关键词：cluster1 上面配置好之后点击完成即可，这样就会创建一个kube-event的报警机器人，创建机器人成功之后怎么查看webhook，按如下： 点击智能群助手，可以看到刚才创建的kube-event这个机器人，点击kube-event，就会进入到kube-event机器人的设置界面
出现如下内容：
机器人名称：kube-event
接受群组：钉钉报警测试
消息推送：开启
webhook：https://oapi.dingtalk.com/robot/send?access_token=9c03ff1f47b1d15a10d852398cafb84f8e81ceeb1ba557eddd8a79e5a5e5548e
安全设置：
自定义关键词：cluster1
安装钉钉的webhook插件，在master节点操作
tar zxvf prometheus-webhook-dingtalk-0.3.0.linux-amd64.tar.gz
#压缩包地址
#链接：https://pan.baidu.com/s/1_HtVZsItq2KsYvOlkIP9DQ
#提取码：d59o
cd prometheus-webhook-dingtalk-0.3.0.linux-amd64 #启动钉钉报警插件
nohup ./prometheus-webhook-dingtalk --web.listen-address="0.0.0.0:8060" --ding.profile="cluster1=https://oapi.dingtalk.com/robot/send?access_token=4372b6419ff1f198a9732dfb9f469f8c7eb7310dec00ede726a7ecd9d235c9b9" & #对原来的文件做备份
cp alertmanager-cm.yaml alertmanager-cm.yaml.bak #重新生成一个新的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.163.com:25'
smtp_from: '15011572657@163.com'
smtp_auth_username: '15011572657'
smtp_auth_password: 'BDBPRMLNZGKWRFJP'
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://192.168.124.16:8060/dingtalk/cluster1/send'
send_resolved: true
EOF #通过kubectl apply使配置生效
kubectl delete -f alertmanager-cm.yaml
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
#通过上面步骤，就可以实现钉钉报警了
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