Kubernetes 监控 Harbor
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如果我们的 harbor 服务主机或者 harbor 服务及组件出现异常，我们该如何快速处理呢？
Harbor v2.2及以上版本支持配置 Prometheus 监控 Harbor，所以你的 harbor 版本必须要大于 2.2。
本篇文章以二进制的方式简单的部署 Prometheus 相关服务，可以帮助你快速的的实现 Prometheus 对 harbor 的监控。
Prometheus 监控 Harbor（二进制版）
一、部署说明
在 harbor 服务主机上部署:
- prometheus
- node-exporter
- grafana
- alertmanager
harbor 版本：2.4.2 主机：192.168.2.22
二、Harbor 启用 metrics 服务
2.1 停止 Harbor 服务
$ cd /app/harbor $ docker-compose down
2.2 修改 harbor.yml 配置
修改 harbor 的配置文件中 metrics 参数，启用harbor-exporter组件。
$ cat harbor.yml### metrics配置部分metric: enabled: true #是否启用，需要修改为true（启用） port: 9099 #默认的端口为9090，与prometheus的端口会冲突（所以需要修改下） path: /metrics
对 harbor 不熟悉的建议对配置文件备份下！
2.3 配置注入组件
$ ./prepre
2.4 install 安装 harbor
$ ./install.sh --with-notary --with-trivy --with-chartmuseum$ docker-compose psNAME COMMAND SERVICE STATUS PORTSchartmuseum "./docker-entrypoint…" chartmuseum running (healthy) harbor-core "/harbor/entrypoint.…" core running (healthy) harbor-db "/docker-entrypoint.…" postgresql running (healthy) harbor-exporter "/harbor/entrypoint.…" exporter running
可以看到多了 harbor-exporter 组件。
三、Harbor 指标说明
在前面启用了 harbor-exporter 监控组件后，可以通过 curl 命令去查看 harbor 暴露了哪些指标。
harbor 暴露了以下 4 个关键组件的指标数据。
3.1 harbor-exporter 组件指标
exporter组件指标与 Harbor 实例配置相关，并从 Harbor 数据库中收集一些数据。指标可在
<harbor_instance>:<metrics_port>/<metrics_path>查看
$ curl http://192.168.2.22:9099/metrics
1）harbor_project_total
harbor_project_total 采集了公共和私人项目总共数量。
$ curl http://192.168.2.22:9099/metrics | grep harbor_project_total# HELP harbor_project_total Total projects number# TYPE harbor_project_total gaugeharbor_project_total{public="true"} 1 # 公共项目的数量为“1”harbor_project_total{public="false"} 1 #私有项目的数量
2）harbor_project_repo_total
项目（Project）中的存储库总数。
$ curl http://192.168.2.22:9099/metrics | grep harbor_project_repo_total# HELP harbor_project_repo_total Total project repos number# TYPE harbor_project_repo_total gaugeharbor_project_repo_total{project_name="library",public="true"} 0
3）harbor_project_member_total
项目中的成员总数
$ curl http://192.168.2.22:9099/metrics | grep harbor_project_member_total# HELP harbor_project_member_total Total members number of a project# TYPE harbor_project_member_total gaugeharbor_project_member_total{project_name="library"} 1 #项目library下有“1”个用户
4）harbor_project_quota_usage_byte
一个项目的总使用资源
$ curl http://192.168.2.22:9099/metrics | grep harbor_project_quota_usage_byte# HELP harbor_project_quota_usage_byte The used resource of a project# TYPE harbor_project_quota_usage_byte gaugeharbor_project_quota_usage_byte{project_name="library"} 0
5）harbor_project_quota_byte
项目中设置的配额
$ curl http://192.168.2.22:9099/metrics | grep harbor_project_quota_byte# HELP harbor_project_quota_byte The quota of a project# TYPE harbor_project_quota_byte gaugeharbor_project_quota_byte{project_name="library"} -1 #-1 表示不限制
6）harbor_artifact_pulled
项目中镜像拉取的总数
$ curl http://192.168.2.22:9099/metrics | grep harbor_artifact_pulled# HELP harbor_artifact_pulled The pull number of an artifact# TYPE harbor_artifact_pulled gaugeharbor_artifact_pulled{project_name="library"} 0
7）harbor_project_artifact_total
项目中的工件类型总数，artifact_type , project_name, public ( true, false)
$ curl http://192.168.2.22:9099/metrics | grep harbor_project_artifact_total
8）harbor_health
Harbor状态
$ curl http://192.168.2.22:9099/metrics | grep harbor_health# HELP harbor_health Running status of Harbor# TYPE harbor_health gaugeharbor_health 1 #1表示正常，0表示异常
9）harbor_system_info
Harbor 实例的信息，auth_mode ( db_auth, ldap_auth, uaa_auth, http_auth, oidc_auth),harbor_version, self_registration( true, false)
$ curl http://192.168.2.22:9099/metrics | grep harbor_system_info# HELP harbor_system_info Information of Harbor system# TYPE harbor_system_info gaugeharbor_system_info{auth_mode="db_auth",harbor_version="v2.4.2-ef2e2e56",self_registration="false"} 1
10）harbor_up
Harbor 组件运行状态，组件 ( chartmuseum, core, database, jobservice, portal, redis, registry, registryctl, trivy)
$ curl http://192.168.2.22:9099/metrics | grep harbor_upharbor_up Running status of harbor component# TYPE harbor_up gaugeharbor_up{component="chartmuseum"} 1harbor_up{component="core"} 1harbor_up{component="database"} 1harbor_up{component="jobservice"} 1harbor_up{component="portal"} 1harbor_up{component="redis"} 1harbor_up{component="registry"} 1harbor_up{component="registryctl"} 1harbor_up{component="trivy"} 1 #Trivy扫描器运行状态
11）harbor_task_queue_size
队列中每种类型的任务总数，
$ curl http://192.168.2.22:9099/metrics | grep harbor_task_queue_size# HELP harbor_task_queue_size Total number of tasks# TYPE harbor_task_queue_size gaugeharbor_task_queue_size{type="DEMO"} 0harbor_task_queue_size{type="GARBAGE_COLLECTION"} 0harbor_task_queue_size{type="IMAGE_GC"} 0harbor_task_queue_size{type="IMAGE_REPLICATE"} 0harbor_task_queue_size{type="IMAGE_SCAN"} 0harbor_task_queue_size{type="IMAGE_SCAN_ALL"} 0harbor_task_queue_size{type="P2P_PREHEAT"} 0harbor_task_queue_size{type="REPLICATION"} 0harbor_task_queue_size{type="RETENTION"} 0harbor_task_queue_size{type="SCHEDULER"} 0harbor_task_queue_size{type="SLACK"} 0harbor_task_queue_size{type="WEBHOOK"} 0
12）harbor_task_queue_latency
多久前要处理的下一个作业按类型排入队列
$ curl http://192.168.2.22:9099/metrics | grep harbor_task_queue_latency# HELP harbor_task_queue_latency how long ago the next job to be processed was enqueued# TYPE harbor_task_queue_latency gaugeharbor_task_queue_latency{type="DEMO"} 0harbor_task_queue_latency{type="GARBAGE_COLLECTION"} 0harbor_task_queue_latency{type="IMAGE_GC"} 0harbor_task_queue_latency{type="IMAGE_REPLICATE"} 0harbor_task_queue_latency{type="IMAGE_SCAN"} 0harbor_task_queue_latency{type="IMAGE_SCAN_ALL"} 0harbor_task_queue_latency{type="P2P_PREHEAT"} 0harbor_task_queue_latency{type="REPLICATION"} 0harbor_task_queue_latency{type="RETENTION"} 0harbor_task_queue_latency{type="SCHEDULER"} 0harbor_task_queue_latency{type="SLACK"} 0harbor_task_queue_latency{type="WEBHOOK"} 0
13）harbor_task_scheduled_total
计划任务数
$ curl http://192.168.2.22:9099/metrics | grep harbor_task_scheduled_total# HELP harbor_task_scheduled_total total number of scheduled job# TYPE harbor_task_scheduled_total gaugeharbor_task_scheduled_total 0
14）harbor_task_concurrency
池（Total)上每种类型的并发任务总数
$ curl http://192.168.2.22:9099/metrics | grep harbor_task_concurrencyharbor_task_concurrency{pool="d4053262b74f0a7b83bc6add",type="GARBAGE_COLLECTION"} 0
3.2 harbor-core 组件指标
以下是从 Harbor core 组件中提取的指标，获取格式：
<harbor_instance>:<metrics_port>/<metrics_path>?comp=core.
1）harbor_core_http_inflight_requests
请求总数，操作（Harbor API operationId 中的值。一些遗留端点没有，因此标签值为）operationId``unknown
harbor-core 组件的指标
$ curl http://192.168.2.22:9099/metrics?comp=core | grep harbor_core_http_inflight_requests# HELP harbor_core_http_inflight_requests The total number of requests# TYPE harbor_core_http_inflight_requests gaugeharbor_core_http_inflight_requests 0
2）harbor_core_http_request_duration_seconds
请求的持续时间，
方法 ( GET, POST, HEAD, PATCH, PUT), 操作 ( Harbor APIoperationId 中的 值。一些遗留端点没有, 所以标签值为), 分位数 operationId``unknown
$ curl http://192.168.2.22:9099/metrics?comp=core | grep harbor_core_http_request_duration_seconds# HELP harbor_core_http_request_duration_seconds The time duration of the requests# TYPE harbor_core_http_request_duration_seconds summaryharbor_core_http_request_duration_seconds{method="GET",operation="GetHealth",quantile="0.5"} 0.001797115harbor_core_http_request_duration_seconds{method="GET",operation="GetHealth",quantile="0.9"} 0.010445204harbor_core_http_request_duration_seconds{method="GET",operation="GetHealth",quantile="0.99"} 0.010445204
3）harbor_core_http_request_total
请求总数
方法（GET, POST, HEAD, PATCH, PUT），操作（[Harbor API operationId 中的 值。一些遗留端点没有，因此标签值为）operationId``unknown
$ curl http://192.168.2.22:9099/metrics?comp=core | grep harbor_core_http_request_total# HELP harbor_core_http_request_total The total number of requests# TYPE harbor_core_http_request_total counterharbor_core_http_request_total{code="200",method="GET",operation="GetHealth"} 14harbor_core_http_request_total{code="200",method="GET",operation="GetInternalconfig"} 1harbor_core_http_request_total{code="200",method="GET",operation="GetPing"} 176harbor_core_http_request_total{code="200",method="GET",operation="GetSystemInfo"} 14
3.3 registry 组件指标
注册表，以下是从 Docker 发行版中提取的指标，查看指标方式：
<harbor_instance>:<metrics_port>/<metrics_path>?comp=registry.
1）registry_http_in_flight_requests
进行中的 HTTP 请求，处理程序
$ curl http://192.168.2.22:9099/metrics?comp=registry | grep registry_http_in_flight_requests# HELP registry_http_in_flight_requests The in-flight HTTP requests# TYPE registry_http_in_flight_requests gaugeregistry_http_in_flight_requests{handler="base"} 0registry_http_in_flight_requests{handler="blob"} 0registry_http_in_flight_requests{handler="blob_upload"} 0registry_http_in_flight_requests{handler="blob_upload_chunk"} 0registry_http_in_flight_requests{handler="catalog"} 0registry_http_in_flight_requests{handler="manifest"} 0registry_http_in_flight_requests{handler="tags"} 0
2）registry_http_request_duration_seconds
HTTP 请求延迟（以秒为单位），处理程序、方法( ,,,, GET) POST,文件 HEADPATCHPUT
$ curl http://192.168.2.22:9099/metrics?comp=registry | grep registry_http_request_duration_seconds
3）registry_http_request_size_bytes
HTTP 请求大小（以字节为单位）。
$ curl http://192.168.2.22:9099/metrics?comp=registry | grep registry_http_request_size_bytes
3.4 jobservice 组件指标
以下是从 Harbor Jobservice 提取的指标，
可在<harbor_instance>:<metrics_port>/<metrics_path>?comp=jobservice.查看
1）harbor_jobservice_info
Jobservice 的信息,
$ curl http://192.168.2.22:9099/metrics?comp=jobservice | grep harbor_jobservice_info# HELP harbor_jobservice_info the information of jobservice# TYPE harbor_jobservice_info gaugeharbor_jobservice_info{node="f47de52e23b7:172.18.0.11",pool="35f1301b0e261d18fac7ba41",workers="10"} 1
2）harbor_jobservice_task_total
每个作业类型处理的任务数
$ curl http://192.168.2.22:9099/metrics?comp=jobservice | grep harbor_jobservice_task_tota
3）harbor_jobservice_task_process_time_seconds
任务处理时间的持续时间，即任务从开始执行到任务结束用了多少时间。
$ curl http://192.168.2.22:9099/metrics?comp=jobservice | grep harbor_jobservice_task_process_time_seconds
四、部署 Prometheus Server（二进制）
4.1 创建安装目录
$ mkdir /etc/prometheus
4.2 下载安装包
$ wget https://github.com/prometheus/prometheus/releases/download/v2.36.2/prometheus-2.36.2.linux-amd64.tar.gz -c$ tar zxvf prometheus-2.36.2.linux-amd64.tar.gz -C /etc/prometheus$ cp prometheus-2.36.2.linux-amd64/{prometheus,promtool} /usr/local/bin/$ prometheus --version #查看版本prometheus, version 2.36.2 (branch: HEAD, revision: d7e7b8e04b5ecdc1dd153534ba376a622b72741b) build user: root@f051ce0d6050 build date: 20220620-13:21:35 go version: go1.18.3 platform: linux/amd64
4.3 修改配置文件
在 prometheus 的配置文件中指定获取 harbor 采集的指标数据。
$ cp prometheus-2.36.2.linux-amd64/prometheus.yml /etc/prometheus/$ cat <<EOF > /etc/prometheus/prometheus.ymlglobal: scrape_interval: 15s evaluation_interval: 15s## 指定Alertmanagers地址alerting: alertmanagers: - static_configs: - targets: ["192.168.2.10:9093"] #填写Alertmanagers地址 ## 配置告警规则文件rule_files: #指定告警规则 - /etc/prometheus/rules.yml scrape_configs: - job_name: "prometheus" static_configs: - targets: ["localhost:9090"] - job_name: 'node-exporter' static_configs: - targets: - '192.168.2.22:9100' - job_name: "harbor-exporter" scrape_interval: 20s static_configs: - targets: ['192.168.2.22:9099'] - job_name: 'harbor-core' params: comp: ['core'] static_configs: - targets: ['192.168.2.22:9099'] - job_name: 'harbor-registry' params: comp: ['registry'] static_configs: - targets: ['192.168.2.22:9099'] - job_name: 'harbor-jobservice' params: comp: ['jobservice'] static_configs: - targets: ['192.168.2.22:9099']EOF
4.4 语法检查
检测配置文件的语法是否正确！
$ promtool check config /etc/prometheus/prometheus.ymlChecking /etc/prometheus/prometheus.yml SUCCESS: /etc/prometheus/prometheus.yml is valid prometheus config file syntax Checking /etc/prometheus/rules.yml SUCCESS: 6 rules found
4.5 创建服务启动文件
$ cat <<EOF > /usr/lib/systemd/system/prometheus.service[Unit]Description=Prometheus ServiceDocumentation=https://prometheus.io/docs/introduction/overview/wants=network-online.targetAfter=network-online.target [Service]Type=simpleUser=rootGroup=rootExecStart=/usr/local/bin/prometheus --config.file=/etc/prometheus/prometheus.yml [Install]WantedBy=multi-user.targetEOF
4.6 启动服务
$ systemctl daemon-reload$ systemctl enable --now prometheus.service$ systemctl status prometheus.service
4.7 浏览器访问 Prometheus UI
在浏览器地址栏输入主机 IP:9090 访问 Prometheus UI 管理界面。
五、部署 node-exporter
node-exporter服务可采集主机的cpu、内存、磁盘等资源指标。
5.1 下载安装包
$ wget https://github.com/prometheus/node_exporter/releases/download/v1.2.2/node_exporter-1.2.2.linux-amd64.tar.gz$ tar zxvf node_exporter-1.2.2.linux-amd64.tar.gz$ cp node_exporter-1.2.2.linux-amd64/node_exporter /usr/local/bin/$ node_exporter --versionnode_exporter, version 1.2.2 (branch: HEAD, revision: 26645363b486e12be40af7ce4fc91e731a33104e) build user: root@b9cb4aa2eb17 build date: 20210806-13:44:18 go version: go1.16.7 platform: linux/amd64
5.2 创建服务启动文件
$ cat <<EOF > /usr/lib/systemd/system/node-exporter.service[Unit]Description=Prometheus Node ExporterAfter=network.target [Service]ExecStart=/usr/local/bin/node_exporter#User=prometheus [Install]WantedBy=multi-user.targetEOF
5.3 启动服务
$ systemctl daemon-reload$ systemctl enable --now node-exporter.service$ systemctl status node-exporter.service$ ss -ntulp | grep node_exportertcp LISTEN 0 128 :::9100 :::* users:(("node_exporter",pid=36218,fd=3)
5.4 查看 node 指标
通过 curl 获取 node-exporter 服务采集到的监控数据。
$ curl http://localhost:9100/metrics
六、Grafana 部署与仪表盘设计
二进制部署 Grafana v8.4.4 服务。
6.1 下载安装包
$ wget https://dl.grafana.com/enterprise/release/grafana-enterprise-8.4.4.linux-amd64.tar.gz -c $ tar zxvf grafana-enterprise-8.4.4.linux-amd64.tar.gz -C /etc/$ mv /etc/grafana-8.4.4 /etc/grafana$ cp -a /etc/grafana/bin/{grafana-cli,grafana-server} /usr/local/bin/#安装依赖包$ yum install -y fontpackages-filesystem.noarch libXfont libfontenc lyx-fonts.noarch xorg-x11-font-utils
6.2 安装插件
- 安装 grafana 时钟插件
$ grafana-cli plugins install grafana-clock-panel
- 安装 Zabbix 插件
$ grafana-cli plugins install alexanderzobnin-zabbix-app
- 安装服务器端图像渲染组件
$ yum install -y fontconfig freetype* urw-fonts
6.3 创建服务启动文件
$ cat <<EOF >/usr/lib/systemd/system/grafana.service[Service]Type=notifyExecStart=/usr/local/bin/grafana-server -homepath /etc/grafanaRestart=on-failure [Install]WantedBy=multi-user.targetEOF
-homepath：指定 grafana 的工作目录
6.4 启动 grafana 服务
$ systemctl daemon-reload$ systemctl enable --now grafana.service$ systemctl status grafana.service$ ss -ntulp | grep grafana-servertcp LISTEN 0 128 :::3000 :::* users:(("grafana-server",pid=120140,fd=9))
6.5 配置数据源
在浏览器地址栏输入主机 IP 和 grafana 服务端口访问 Grafana UI 界面后，添加 Prometheus 数据源。
默认用户密码：admin/admin
6.6 导入 json 模板
一旦您配置了Prometheus服务器以收集您的 Harbor 指标，您就可以使用 Grafana 来可视化您的数据。Harbor 存储库中提供了一个 示例 Grafana 仪表板，可帮助您开始可视化 Harbor 指标。
Harbor 官方提供了一个 grafana 的 json 文件模板。下载：
https://github.com/goharbor/harbor/blob/main/contrib/grafana-dashborad/metrics-example.json
七、部署 AlertManager 服务（扩展）
Alertmanager 是一个独立的告警模块，接收 Prometheus 等客户端发来的警报，之后通过分组、删除重复等处理，并将它们通过路由发送给正确的接收器；
7.1 下载安装包
$ wget https://github.com/prometheus/alertmanager/releases/download/v0.23.0/alertmanager-0.23.0.linux-amd64.tar.gz$ tar zxvf alertmanager-0.23.0.linux-amd64.tar.gz$ cp alertmanager-0.23.0.linux-amd64/{alertmanager,amtool} /usr/local/bin/
7.2 修改配置文件
$ mkdir /etc/alertmanager$ cat /etc/alertmanager/alertmanager.ymlglobal: resolve_timeout: 5m route: group_by: ['alertname'] group_wait: 10s group_interval: 10s repeat_interval: 1h receiver: 'web.hook'receivers:- name: 'web.hook' webhook_configs: - url: 'http://127.0.0.1:5001/'inhibit_rules: - source_match: severity: 'critical' target_match: severity: 'warning' equal: ['alertname', 'dev', 'instance']
7.3 创建服务启动文件
$ cat <<EOF >/usr/lib/systemd/system/alertmanager.service[Unit]Description=alertmanagerfter=network.target [Service]ExecStart=/usr/local/bin/alertmanager --config.file=/etc/alertmanager/alertmanager.ymlExecReload=/bin/kill -HUP $MAINPIDKillMode=processRestart=on-failure [Install]WantedBy=multi-user.targetEOF
7.4 启动服务
$ systemctl daemon-reload$ systemctl enable --now alertmanager.service$ systemctl status alertmanager.service$ ss -ntulp | grep alertmanager
7.5 配置告警规则
前面在 Prometheus server 的配置文件中中指定了告警规则的文件为/etc/prometheus/rules.yml。
$ cat /etc/prometheus/rules.ymlgroups: - name: Warning rules: - alert: NodeMemoryUsage expr: 100 - (node_memory_MemFree_bytes + node_memory_Cached_bytes + node_memory_Buffers_bytes) / node_memory_MemTotal_bytes*100 > 80 for: 1m labels: status: Warning annotations: summary: "{{$labels.instance}}: 内存使用率过高" description: "{{$labels.instance}}: 内存使用率大于 80% (当前值: {{ $value }}" - alert: NodeCpuUsage expr: (1-((sum(increase(node_cpu_seconds_total{mode="idle"}[1m])) by (instance)) / (sum(increase(node_cpu_seconds_total[1m])) by (instance)))) * 100 > 70 for: 1m labels: status: Warning annotations: summary: "{{$labels.instance}}: CPU使用率过高" description: "{{$labels.instance}}: CPU使用率大于 70% (当前值: {{ $value }}" - alert: NodeDiskUsage expr: 100 - node_filesystem_free_bytes{fstype=~"xfs|ext4"} / node_filesystem_size_bytes{fstype=~"xfs|ext4"} * 100 > 80 for: 1m labels: status: Warning annotations: summary: "{{$labels.instance}}: 分区使用率过高" description: "{{$labels.instance}}: 分区使用大于 80% (当前值: {{ $value }}" - alert: Node-UP expr: up{job='node-exporter'} == 0 for: 1m labels: status: Warning annotations: summary: "{{$labels.instance}}: 服务宕机" description: "{{$labels.instance}}: 服务中断超过1分钟" - alert: TCP expr: node_netstat_Tcp_CurrEstab > 1000 for: 1m labels: status: Warning annotations: summary: "{{$labels.instance}}: TCP连接过高" description: "{{$labels.instance}}: 连接大于1000 (当前值: {{$value}})" - alert: IO expr: 100 - (avg(irate(node_disk_io_time_seconds_total[1m])) by(instance)* 100) < 60 for: 1m labels: status: Warning annotations: summary: "{{$labels.instance}}: 流入磁盘IO使用率过高" description: "{{$labels.instance}}:流入磁盘IO大于60% (当前值:{{$value}})"
