Prometheus 的 Dashboard 虽然号称拥有多种多样的图表,但是实在太简陋了,一般都用专业的 Grafana 工具来出图
- 官网：https://grafana.com/
- 文档：https://grafana.com/docs/grafana/latest/
- Github: https://github.com/grafana/grafana
- Dashboard: https://grafana.com/grafana/dashboards/
1 使用炫酷的 grafana 出图
1.1 部署 Grafana
1.1.1 准备镜像
docker pull grafana/grafana:5.4.2
docker tag 6f18ddf9e552 harbor.zq.com/infra/grafana:v5.4.2
docker push harbor.zq.com/infra/grafana:v5.4.2
准备目录
mkdir /data/k8s-yaml/grafana
cd /data/k8s-yaml/grafana
1.1.2 准备 RBAC 资源清单
cat >rbac.yaml <<'EOF'
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
labels:
addonmanager.kubernetes.io/mode: Reconcile
kubernetes.io/cluster-service: "true"
name: grafana
rules:
- apiGroups:
- "*"
resources:
- namespaces
- deployments
- pods
verbs:
- get
- list
- watch
---
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata:
labels:
addonmanager.kubernetes.io/mode: Reconcile
kubernetes.io/cluster-service: "true"
name: grafana
roleRef:
apiGroup: rbac.authorization.k8s.io
kind: ClusterRole
name: grafana
subjects:
- kind: User
name: k8s-node
EOF
1.1.3 准备 Deployment 资源清单
cat >dp.yaml <<'EOF'
apiVersion: extensions/v1beta1
kind: Deployment
metadata:
labels:
app: grafana
name: grafana
name: grafana
namespace: infra
spec:
progressDeadlineSeconds: 600
replicas: 1
revisionHistoryLimit: 7
selector:
matchLabels:
name: grafana
strategy:
rollingUpdate:
maxSurge: 1
maxUnavailable: 1
type: RollingUpdate
template:
metadata:
labels:
app: grafana
name: grafana
spec:
containers:
- name: grafana
image: harbor.zq.com/infra/grafana:v5.4.2
imagePullPolicy: IfNotPresent
ports:
- containerPort: 3000
protocol: TCP
volumeMounts:
- mountPath: /var/lib/grafana
name: data
imagePullSecrets:
- name: harbor
securityContext:
runAsUser: 0
volumes:
- nfs:
server: hdss7-200
path: /data/nfs-volume/grafana
name: data
EOF
创建 Grafana 数据目录
mkdir /data/nfs-volume/grafana
1.1.4 准备 SVC 资源清单
cat >svc.yaml <<'EOF'
apiVersion: v1
kind: Service
metadata:
name: grafana
namespace: infra
spec:
ports:
- port: 3000
protocol: TCP
targetPort: 3000
selector:
app: grafana
EOF
1.1.5 准备 Ingress 资源清单
cat >ingress.yaml <<'EOF'
apiVersion: extensions/v1beta1
kind: Ingress
metadata:
name: grafana
namespace: infra
spec:
rules:
- host: grafana.zq.com
http:
paths:
- path: /
backend:
serviceName: grafana
servicePort: 3000
EOF
1.1.6 域名解析
vi /var/named/zq.com.zone
grafana A 10.4.7.10
systemctl restart named
1.1.7 应用资源配置清单
kubectl apply -f http://k8s-yaml.zq.com/grafana/rbac.yaml
kubectl apply -f http://k8s-yaml.zq.com/grafana/dp.yaml
kubectl apply -f http://k8s-yaml.zq.com/grafana/svc.yaml
kubectl apply -f http://k8s-yaml.zq.com/grafana/ingress.yaml
1.2 使用 Grafana 出图
1.2.1 浏览器访问验证
默认用户名密码: admin/admin
能成功访问表示安装成功
进入后立即修改管理员密码为 admin123
1.2.2 进入容器安装插件
Grafana 确认启动好以后,需要进入 Grafana 容器内部,安装以下插件
kubectl -n infra exec -it grafana-d6588db94-xr4s6 /bin/bash
# 以下命令在容器内执行
grafana-cli plugins install grafana-kubernetes-app
grafana-cli plugins install grafana-clock-panel
grafana-cli plugins install grafana-piechart-panel
grafana-cli plugins install briangann-gauge-panel
grafana-cli plugins install natel-discrete-panel
1.2.3 配置数据源
添加数据源,依次点击：左侧锯齿图标–>add data source–>Prometheus
添加完成后重启 Grafana
kubectl -n infra delete pod grafana-7dd95b4c8d-nj5cx
1.2.4 添加K8S集群信息
启用 K8S 插件,依次点击：左侧锯齿图标–>Plugins–>kubernetes–>Enable
新建 Cluster,依次点击：左侧 K8S 图标–>New Cluster
1.2.5 查看 K8S 集群数据和图表
添加完需要稍等几分钟，在没有取到数据之前，会报 Http Forbidden，没关系，等一会就好。大概 2-5 分钟。
点击 Cluster Dashboard
2 配置 Alert 告警插件
2.1 部署 Alert 插件
2.1.1 准备 Docker 镜像
docker pull docker.io/prom/alertmanager:v0.14.0
docker tag 23744b2d645c harbor.zq.com/infra/alertmanager:v0.14.0
docker push harbor.zq.com/infra/alertmanager:v0.14.0
准备目录
mkdir /data/k8s-yaml/alertmanager
cd /data/k8s-yaml/alertmanager
2.1.2 准备 ConfigMap 资源清单
cat >cm.yaml <<'EOF'
apiVersion: v1
kind: ConfigMap
metadata:
name: alertmanager-config
namespace: infra
data:
config.yml: |-
global:
# 在没有报警的情况下声明为已解决的时间
resolve_timeout: 5m
# 配置邮件发送信息
smtp_smarthost: 'smtp.163.com:25'
smtp_from: 'xxx@163.com'
smtp_auth_username: 'xxx@163.com'
smtp_auth_password: 'xxxxxx'
smtp_require_tls: false
templates:
- '/etc/alertmanager/*.tmpl'
# 所有报警信息进入后的根路由，用来设置报警的分发策略
route:
# 这里的标签列表是接收到报警信息后的重新分组标签，例如，接收到的报警信息里面有许多具有 cluster=A 和 alertname=LatncyHigh 这样的标签的报警信息将会批量被聚合到一个分组里面
group_by: ['alertname', 'cluster']
# 当一个新的报警分组被创建后，需要等待至少group_wait时间来初始化通知，这种方式可以确保您能有足够的时间为同一分组来获取多个警报，然后一起触发这个报警信息。
group_wait: 30s
# 当第一个报警发送后，等待'group_interval'时间来发送新的一组报警信息。
group_interval: 5m
# 如果一个报警信息已经发送成功了，等待'repeat_interval'时间来重新发送他们
repeat_interval: 5m
# 默认的receiver：如果一个报警没有被一个route匹配，则发送给默认的接收器
receiver: default
receivers:
- name: 'default'
email_configs:
- to: 'xxxx@qq.com'
send_resolved: true
html: ''
headers: { Subject: " " }
email.tmpl: |
告警程序: prometheus_alert <br>
告警级别: <br>
告警类型: <br>
故障主机: <br>
告警主题: <br>
触发时间: <br>
告警程序: prometheus_alert <br>
告警级别: <br>
告警类型: <br>
故障主机: <br>
告警主题: <br>
触发时间: <br>
恢复时间: <br>
EOF
2.1.3 准备 Deployment 资源清单
cat >dp.yaml <<'EOF'
apiVersion: extensions/v1beta1
kind: Deployment
metadata:
name: alertmanager
namespace: infra
spec:
replicas: 1
selector:
matchLabels:
app: alertmanager
template:
metadata:
labels:
app: alertmanager
spec:
containers:
- name: alertmanager
image: harbor.zq.com/infra/alertmanager:v0.14.0
args:
- "--config.file=/etc/alertmanager/config.yml"
- "--storage.path=/alertmanager"
ports:
- name: alertmanager
containerPort: 9093
volumeMounts:
- name: alertmanager-cm
mountPath: /etc/alertmanager
volumes:
- name: alertmanager-cm
configMap:
name: alertmanager-config
imagePullSecrets:
- name: harbor
EOF
2.1.4 准备 SVC 资源清单
cat >svc.yaml <<'EOF'
apiVersion: v1
kind: Service
metadata:
name: alertmanager
namespace: infra
spec:
selector:
app: alertmanager
ports:
- port: 80
targetPort: 9093
EOF
2.1.5 应用资源配置清单
kubectl apply -f http://k8s-yaml.zq.com/alertmanager/cm.yaml
kubectl apply -f http://k8s-yaml.zq.com/alertmanager/dp.yaml
kubectl apply -f http://k8s-yaml.zq.com/alertmanager/svc.yaml
2.2 K8S 使用 Alert 报警
2.2.1 K8S 创建基础报警规则文件
cat >/data/nfs-volume/prometheus/etc/rules.yml <<'EOF'
groups:
- name: hostStatsAlert
rules:
- alert: hostCpuUsageAlert
expr: sum(avg without (cpu)(irate(node_cpu{mode!='idle'}[5m]))) by (instance) > 0.85
for: 5m
labels:
severity: warning
annotations:
summary: " CPU usage above 85% (current value: %)"
- alert: hostMemUsageAlert
expr: (node_memory_MemTotal - node_memory_MemAvailable)/node_memory_MemTotal > 0.85
for: 5m
labels:
severity: warning
annotations:
summary: " MEM usage above 85% (current value: %)"
- alert: OutOfInodes
expr: node_filesystem_free{fstype="overlay",mountpoint ="/"} / node_filesystem_size{fstype="overlay",mountpoint ="/"} * 100 < 10
for: 5m
labels:
severity: warning
annotations:
summary: "Out of inodes (instance )"
description: "Disk is almost running out of available inodes (< 10% left) (current value: )"
- alert: OutOfDiskSpace
expr: node_filesystem_free{fstype="overlay",mountpoint ="/rootfs"} / node_filesystem_size{fstype="overlay",mountpoint ="/rootfs"} * 100 < 10
for: 5m
labels:
severity: warning
annotations:
summary: "Out of disk space (instance )"
description: "Disk is almost full (< 10% left) (current value: )"
- alert: UnusualNetworkThroughputIn
expr: sum by (instance) (irate(node_network_receive_bytes[2m])) / 1024 / 1024 > 100
for: 5m
labels:
severity: warning
annotations:
summary: "Unusual network throughput in (instance )"
description: "Host network interfaces are probably receiving too much data (> 100 MB/s) (current value: )"
- alert: UnusualNetworkThroughputOut
expr: sum by (instance) (irate(node_network_transmit_bytes[2m])) / 1024 / 1024 > 100
for: 5m
labels:
severity: warning
annotations:
summary: "Unusual network throughput out (instance )"
description: "Host network interfaces are probably sending too much data (> 100 MB/s) (current value: )"
- alert: UnusualDiskReadRate
expr: sum by (instance) (irate(node_disk_bytes_read[2m])) / 1024 / 1024 > 50
for: 5m
labels:
severity: warning
annotations:
summary: "Unusual disk read rate (instance )"
description: "Disk is probably reading too much data (> 50 MB/s) (current value: )"
- alert: UnusualDiskWriteRate
expr: sum by (instance) (irate(node_disk_bytes_written[2m])) / 1024 / 1024 > 50
for: 5m
labels:
severity: warning
annotations:
summary: "Unusual disk write rate (instance )"
description: "Disk is probably writing too much data (> 50 MB/s) (current value: )"
- alert: UnusualDiskReadLatency
expr: rate(node_disk_read_time_ms[1m]) / rate(node_disk_reads_completed[1m]) > 100
for: 5m
labels:
severity: warning
annotations:
summary: "Unusual disk read latency (instance )"
description: "Disk latency is growing (read operations > 100ms) (current value: )"
- alert: UnusualDiskWriteLatency
expr: rate(node_disk_write_time_ms[1m]) / rate(node_disk_writes_completedl[1m]) > 100
for: 5m
labels:
severity: warning
annotations:
summary: "Unusual disk write latency (instance )"
description: "Disk latency is growing (write operations > 100ms) (current value: )"
- name: http_status
rules:
- alert: ProbeFailed
expr: probe_success == 0
for: 1m
labels:
severity: error
annotations:
summary: "Probe failed (instance )"
description: "Probe failed (current value: )"
- alert: StatusCode
expr: probe_http_status_code <= 199 OR probe_http_status_code >= 400
for: 1m
labels:
severity: error
annotations:
summary: "Status Code (instance )"
description: "HTTP status code is not 200-399 (current value: )"
- alert: SslCertificateWillExpireSoon
expr: probe_ssl_earliest_cert_expiry - time() < 86400 * 30
for: 5m
labels:
severity: warning
annotations:
summary: "SSL certificate will expire soon (instance )"
description: "SSL certificate expires in 30 days (current value: )"
- alert: SslCertificateHasExpired
expr: probe_ssl_earliest_cert_expiry - time() <= 0
for: 5m
labels:
severity: error
annotations:
summary: "SSL certificate has expired (instance )"
description: "SSL certificate has expired already (current value: )"
- alert: BlackboxSlowPing
expr: probe_icmp_duration_seconds > 2
for: 5m
labels:
severity: warning
annotations:
summary: "Blackbox slow ping (instance )"
description: "Blackbox ping took more than 2s (current value: )"
- alert: BlackboxSlowRequests
expr: probe_http_duration_seconds > 2
for: 5m
labels:
severity: warning
annotations:
summary: "Blackbox slow requests (instance )"
description: "Blackbox request took more than 2s (current value: )"
- alert: PodCpuUsagePercent
expr: sum(sum(label_replace(irate(container_cpu_usage_seconds_total[1m]),"pod","$1","container_label_io_kubernetes_pod_name", "(.*)"))by(pod) / on(pod) group_right kube_pod_container_resource_limits_cpu_cores *100 )by(container,namespace,node,pod,severity) > 80
for: 5m
labels:
severity: warning
annotations:
summary: "Pod cpu usage percent has exceeded 80% (current value: %)"
EOF
2.2.2 K8S 更新配置
在 Prometheus 配置文件中追加配置：
cat >>/data/nfs-volume/prometheus/etc/prometheus.yml <<'EOF'
alerting:
alertmanagers:
- static_configs:
- targets: ["alertmanager"]
rule_files:
- "/data/etc/rules.yml"
EOF
重载配置：
curl -X POST http://prometheus.zq.com/-/reload
以上这些就是我们的告警规则
2.2.3 测试告警
把 test 命名空间里的 dubbo-demo-service 给停掉
Blackbox 里信息已报错, Alert 里面项目变黄了
等到 Alert 中项目变为红色的时候就开会发邮件告警
如果需要自己定制告警规则和告警内容，需要研究一下 PromQL，自己修改配置文件。