上一篇我们介绍了如何监控Kube-Proxy，Kube-Proxy的/metrics接口没有认证，相对比较容易，这一篇我们介绍一下Kubelet，Kubelet的监控相比Kube-Proxy增加了认证机制，相对更复杂一些。

Kubelet 端口说明

如果你有多台Node节点，可以批量执行ss -tlnp|grep kubelet看一下，Kubelet 监听两个固定端口(我的环境，你的环境可能不同)，一个是10248，一个是10250，通过下面的命令可以知道，10248是健康检查的端口：

[root@tt-fc-dev01.nj ~]# ps aux|grep kubeletroot      163490  0.0  0.0  12136  1064 pts/1    S+   13:34   0:00 grep --color=auto kubeletroot      166673  3.2  1.0 3517060 81336 ?       Ssl  Aug16 4176:52 /usr/bin/kubelet --bootstrap-kubeconfig=/etc/kubernetes/bootstrap-kubelet.conf --kubeconfig=/etc/kubernetes/kubelet.conf --config=/var/lib/kubelet/config.yaml --hostname-override=10.206.0.16 --network-plugin=cni --pod-infra-container-image=registry.aliyuncs.com/google_containers/pause:3.6[root@tt-fc-dev01.nj ~]# cat /var/lib/kubelet/config.yaml | grep 102healthzPort: 10248[root@tt-fc-dev01.nj ~]# curl localhost:10248/healthzok

我们再看一下 10250，10250实际是Kubelet的默认端口，/metrics接口就是在这个端口暴露的，我们请求一下：

[root@tt-fc-dev01.nj ~]# curl localhost:10250/metricsClient sent an HTTP request to an HTTPS server.[root@tt-fc-dev01.nj ~]# curl https://localhost:10250/metricscurl: (60) SSL certificate problem: self signed certificate in certificate chainMore details here: https://curl.haxx.se/docs/sslcerts.htmlcurl failed to verify the legitimacy of the server and therefore could notestablish a secure connection to it. To learn more about this situation andhow to fix it, please visit the web page mentioned above.[root@tt-fc-dev01.nj ~]# curl -k https://localhost:10250/metricsUnauthorized

-k表示不校验SSL证书是否正确，最后的命令可以看到返回了 Unauthorized，表示认证失败，我们先来解决一下认证问题。认证是 Kubernetes 的一个知识点，这里先不展开（你需要Google一下了解基本常识），直接实操。

认证信息

下面的信息可以保存为 auth.yaml，创建了 ClusterRole、ServiceAccount、ClusterRoleBinding。

---apiVersion: rbac.authorization.k8s.io/v1kind: ClusterRolemetadata:  name: categraf-daemonsetrules:- apiGroups:  - ""  resources:  - nodes/metrics  - nodes/stats  - nodes/proxy  verbs:  - get---apiVersion: v1kind: ServiceAccountmetadata:  name: categraf-daemonset  namespace: flashcat---apiVersion: rbac.authorization.k8s.io/v1kind: ClusterRoleBindingmetadata:  name: categraf-daemonsetroleRef:  apiGroup: rbac.authorization.k8s.io  kind: ClusterRole  name: categraf-daemonsetsubjects:- kind: ServiceAccount  name: categraf-daemonset  namespace: flashcat

ClusterRole是个全局概念，不属于任一个namespace，定义了很多权限点，都是读权限，监控嘛，读权限就可以了，ServiceAccount则是namespace颗粒度的一个概念，这里我们创建了一个名为categraf-daemonset的ServiceAccount，然后绑定到ClusterRole上面，具备了各种查询权限。apply一下即可：

[work@tt-fc-dev01.nj yamls]$ kubectl apply -f auth.yamlclusterrole.rbac.authorization.k8s.io/categraf-daemonset createdserviceaccount/categraf-daemonset createdclusterrolebinding.rbac.authorization.k8s.io/categraf-daemonset created[work@tt-fc-dev01.nj yamls]$ kubectl get ClusterRole | grep categraf-daemoncategraf-daemonset                                                     2022-11-14T03:53:54Z[work@tt-fc-dev01.nj yamls]$ kubectl get sa -n flashcatNAME                 SECRETS   AGEcategraf-daemonset   1         90mdefault              1         4d23h[work@tt-fc-dev01.nj yamls]$ kubectl get ClusterRoleBinding -n flashcat | grep categraf-daemoncategraf-daemonset ClusterRole/categraf-daemonset 91m

测试权限

上面的命令行输出可以看出来，我们已经成功创建了 ServiceAccount，把ServiceAccount的内容打印出来看一下：

[root@tt-fc-dev01.nj qinxiaohui]# kubectl get sa categraf-daemonset -n flashcat -o yamlapiVersion: v1kind: ServiceAccountmetadata:  annotations:    kubectl.kubernetes.io/last-applied-configuration: |      {"apiVersion":"v1","kind":"ServiceAccount","metadata":{"annotations":{},"name":"categraf-daemonset","namespace":"flashcat"}}  creationTimestamp: "2022-11-14T03:53:54Z"  name: categraf-daemonset  namespace: flashcat  resourceVersion: "120570510"  uid: 22f5a785-871c-4454-b82e-12bf104450a0secrets:- name: categraf-daemonset-token-7mccq

注意最后两行，这个ServiceAccount实际是关联了一个Secret，我们再看看这个Secret的内容：

[root@tt-fc-dev01.nj qinxiaohui]# kubectl get secret categraf-daemonset-token-7mccq -n flashcat -o yamlapiVersion: v1data:  ca.crt: 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  namespace: Zmxhc2hjYXQ=  token: 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kind: Secretmetadata:  annotations:    kubernetes.io/service-account.name: categraf-daemonset    kubernetes.io/service-account.uid: 22f5a785-871c-4454-b82e-12bf104450a0  creationTimestamp: "2022-11-14T03:53:54Z"  name: categraf-daemonset-token-7mccq  namespace: flashcat  resourceVersion: "120570509"  uid: 0a228da5-6e60-4b22-beff-65cc56683e41type: kubernetes.io/service-account-token

我们把这个token字段拿到，然后base64转码一下，作为Bearer Token来请求测试一下：

[root@tt-fc-dev01.nj qinxiaohui]# token=`kubectl get secret categraf-daemonset-token-7mccq -n flashcat -o jsonpath={.data.token} | base64 -d`[root@tt-fc-dev01.nj qinxiaohui]# curl -s -k -H "Authorization: Bearer $token" https://localhost:10250/metrics > aaaa[root@tt-fc-dev01.nj qinxiaohui]# head -n 5 aaaa# HELP apiserver_audit_event_total [ALPHA] Counter of audit events generated and sent to the audit backend.# TYPE apiserver_audit_event_total counterapiserver_audit_event_total 0# HELP apiserver_audit_requests_rejected_total [ALPHA] Counter of apiserver requests rejected due to an error in audit logging backend.# TYPE apiserver_audit_requests_rejected_total counterapiserver_audit_requests_rejected_total 0

通了！

这就说明我们创建的ServiceAccount是好使的，后面我们把 Categraf 作为采集器搞成 Daemonset，再为 Categraf 这个 Daemonset 指定 ServiceAccountName，Kubernetes就会自动把 Token 的内容挂到 Daemonset 的目录里，下面开始实操。

升级 Daemonset

上一篇咱们为 Kube-Proxy 的采集准备了 Daemonset，咱们就继续修改这个 Daemonset，让这个 Daemonset 不但可以采集 Kube-Proxy，也可以采集 Kubelet，先给 Categraf 准备一下相关的配置，可以把下面的内容保存为 categraf-configmap-v2.yaml

---kind: ConfigMapmetadata:  name: categraf-configapiVersion: v1data:  config.toml: |    [global]    hostname = "$HOSTNAME"    interval = 15    providers = ["local"]    [writer_opt]    batch = 2000    chan_size = 10000    [[writers]]    url = "http://10.206.0.16:19000/prometheus/v1/write"    timeout = 5000    dial_timeout = 2500    max_idle_conns_per_host = 100    ---kind: ConfigMapmetadata:  name: categraf-input-prometheusapiVersion: v1data:  prometheus.toml: |    [[instances]]    urls = ["http://127.0.0.1:10249/metrics"]    labels = { job="kube-proxy" }    [[instances]]    urls = ["https://127.0.0.1:10250/metrics"]    bearer_token_file = "/var/run/secrets/kubernetes.io/serviceaccount/token"    use_tls = true    insecure_skip_verify = true    labels = { job="kubelet" }    [[instances]]    urls = ["https://127.0.0.1:10250/metrics/cadvisor"]    bearer_token_file = "/var/run/secrets/kubernetes.io/serviceaccount/token"    use_tls = true    insecure_skip_verify = true    labels = { job="cadvisor" }

apply 一下，让新的配置生效：

[work@tt-fc-dev01.nj yamls]$ kubectl apply -f categraf-configmap-v2.yaml -n flashcatconfigmap/categraf-config unchangedconfigmap/categraf-input-prometheus configured

Categraf 的 Daemonset 需要把 ServiceAccountName 给绑定上，上一讲咱们用的 yaml 命名为：categraf-daemonset-v1.yaml ，咱们升级一下这个文件到 categraf-daemonset-v2.yaml 版本，内容如下：

apiVersion: apps/v1kind: DaemonSetmetadata:  labels:    app: categraf-daemonset  name: categraf-daemonsetspec:  selector:    matchLabels:      app: categraf-daemonset  template:    metadata:      labels:        app: categraf-daemonset    spec:      containers:      - env:        - name: TZ          value: Asia/Shanghai        - name: HOSTNAME          valueFrom:            fieldRef:              apiVersion: v1              fieldPath: spec.nodeName        - name: HOSTIP          valueFrom:            fieldRef:              apiVersion: v1              fieldPath: status.hostIP        image: flashcatcloud/categraf:v0.2.18        imagePullPolicy: IfNotPresent        name: categraf        volumeMounts:        - mountPath: /etc/categraf/conf          name: categraf-config        - mountPath: /etc/categraf/conf/input.prometheus          name: categraf-input-prometheus      hostNetwork: true      serviceAccountName: categraf-daemonset      restartPolicy: Always      tolerations:      - effect: NoSchedule        operator: Exists      volumes:      - configMap:          name: categraf-config        name: categraf-config      - configMap:          name: categraf-input-prometheus        name: categraf-input-prometheus

这里跟 v1 版本相比，唯一的变化，就是加了serviceAccountName: categraf-daemonset这个配置，把原来的 Daemonset 删掉，从新创建一下：

[work@tt-fc-dev01.nj yamls]$ kubectl delete ds categraf-daemonset -n flashcatdaemonset.apps "categraf-daemonset" deleted[work@tt-fc-dev01.nj yamls]$ kubectl apply -f categraf-daemonset-v2.yaml -n flashcatdaemonset.apps/categraf-daemonset created# waiting...[work@tt-fc-dev01.nj yamls]$ kubectl get pods -n flashcatNAME                       READY   STATUS    RESTARTS   AGEcategraf-daemonset-d8jt8   1/1     Running   0          37scategraf-daemonset-fpx8v   1/1     Running   0          43scategraf-daemonset-mp468   1/1     Running   0          32scategraf-daemonset-s775l   1/1     Running   0          40scategraf-daemonset-wxkjk   1/1     Running   0          47scategraf-daemonset-zwscc   1/1     Running   0          35s

好了，我们去检查一下数据是否成功采集上来了：

上面这个指标是 Kubelet 自身的，即从 Kubelet 的/metrics接口采集的，我们再来看一个 cAdvisor 的，即从/metrics/cadvisor接口采集的：

看起来数据都上来了，导入监控大盘看看效果。

导入仪表盘

分成两部分，一个是 Kubelet 自身的仪表盘，JSON配置在这里，截图效果如下：

另外一个是Pod容器相关的大盘，JSON配置在这里(感谢张健老师悉心整理)

监控指标说明

之前孔飞老师整理的 Kubelet 相关指标的中文解释，我也一并附到这里，供大家参考：

# HELP go_gc_duration_seconds A summary of the pause duration of garbage collection cycles.# TYPE go_gc_duration_seconds summarygc的时间统计(summary指标)# HELP go_goroutines Number of goroutines that currently exist.# TYPE go_goroutines gaugegoroutine 数量# HELP go_threads Number of OS threads created.# TYPE go_threads gauge线程数量# HELP kubelet_cgroup_manager_duration_seconds [ALPHA] Duration in seconds for cgroup manager operations. Broken down by method.# TYPE kubelet_cgroup_manager_duration_seconds histogram操作cgroup的时长分布，按照操作类型统计# HELP kubelet_containers_per_pod_count [ALPHA] The number of containers per pod.# TYPE kubelet_containers_per_pod_count histogrampod中container数量的统计(spec.containers的数量)# HELP kubelet_docker_operations_duration_seconds [ALPHA] Latency in seconds of Docker operations. Broken down by operation type.# TYPE kubelet_docker_operations_duration_seconds histogram操作docker的时长分布，按照操作类型统计# HELP kubelet_docker_operations_errors_total [ALPHA] Cumulative number of Docker operation errors by operation type.# TYPE kubelet_docker_operations_errors_total counter操作docker的错误累计次数，按照操作类型统计# HELP kubelet_docker_operations_timeout_total [ALPHA] Cumulative number of Docker operation timeout by operation type.# TYPE kubelet_docker_operations_timeout_total counter操作docker的超时统计，按照操作类型统计# HELP kubelet_docker_operations_total [ALPHA] Cumulative number of Docker operations by operation type.# TYPE kubelet_docker_operations_total counter操作docker的累计次数，按照操作类型统计# HELP kubelet_eviction_stats_age_seconds [ALPHA] Time between when stats are collected, and when pod is evicted based on those stats by eviction signal# TYPE kubelet_eviction_stats_age_seconds histogram驱逐操作的时间分布，按照驱逐信号(原因)分类统计# HELP kubelet_evictions [ALPHA] Cumulative number of pod evictions by eviction signal# TYPE kubelet_evictions counter驱逐次数统计，按照驱逐信号(原因)统计# HELP kubelet_http_inflight_requests [ALPHA] Number of the inflight http requests# TYPE kubelet_http_inflight_requests gauge请求kubelet的inflight请求数,按照method path server_type统计, 注意与每秒的request数区别开# HELP kubelet_http_requests_duration_seconds [ALPHA] Duration in seconds to serve http requests# TYPE kubelet_http_requests_duration_seconds histogram请求kubelet的请求时间统计, 按照method path server_type统计# HELP kubelet_http_requests_total [ALPHA] Number of the http requests received since the server started# TYPE kubelet_http_requests_total counter请求kubelet的请求数统计,按照method path server_type统计# HELP kubelet_managed_ephemeral_containers [ALPHA] Current number of ephemeral containers in pods managed by this kubelet. Ephemeral containers will be ignored if disabled by the EphemeralContainers feature gate, and this number will be 0.# TYPE kubelet_managed_ephemeral_containers gauge当前kubelet管理的临时容器数量# HELP kubelet_network_plugin_operations_duration_seconds [ALPHA] Latency in seconds of network plugin operations. Broken down by operation type.# TYPE kubelet_network_plugin_operations_duration_seconds histogram网络插件的操作耗时分布 ，按照操作类型(operation_type)统计, 如果 --feature-gates=EphemeralContainers=false, 否则一直为0 # HELP kubelet_network_plugin_operations_errors_total [ALPHA] Cumulative number of network plugin operation errors by operation type.# TYPE kubelet_network_plugin_operations_errors_total counter网络插件累计操作错误数统计，按照操作类型(operation_type)统计# HELP kubelet_network_plugin_operations_total [ALPHA] Cumulative number of network plugin operations by operation type.# TYPE kubelet_network_plugin_operations_total counter网络插件累计操作数统计，按照操作类型(operation_type)统计# HELP kubelet_node_name [ALPHA] The node's name. The count is always 1.# TYPE kubelet_node_name gaugenode name# HELP kubelet_pleg_discard_events [ALPHA] The number of discard events in PLEG.# TYPE kubelet_pleg_discard_events counterPLEG(pod lifecycle event generator) 丢弃的event数统计# HELP kubelet_pleg_last_seen_seconds [ALPHA] Timestamp in seconds when PLEG was last seen active.# TYPE kubelet_pleg_last_seen_seconds gaugePLEG上次活跃的时间戳# HELP kubelet_pleg_relist_duration_seconds [ALPHA] Duration in seconds for relisting pods in PLEG.# TYPE kubelet_pleg_relist_duration_seconds histogramPLEG relist pod时间分布 # HELP kubelet_pleg_relist_interval_seconds [ALPHA] Interval in seconds between relisting in PLEG.# TYPE kubelet_pleg_relist_interval_seconds histogramPLEG relist 间隔时间分布# HELP kubelet_pod_start_duration_seconds [ALPHA] Duration in seconds for a single pod to go from pending to running.# TYPE kubelet_pod_start_duration_seconds histogrampod启动时间(从pending到running)分布, kubelet watch到pod时到pod中contianer都running后, watch各种source channel的pod变更# HELP kubelet_pod_worker_duration_seconds [ALPHA] Duration in seconds to sync a single pod. Broken down by operation type: create, update, or sync# TYPE kubelet_pod_worker_duration_seconds histogrampod状态变化的时间分布， 按照操作类型(create update sync)统计, worker就是kubelet中处理一个pod的逻辑工作单位# HELP kubelet_pod_worker_start_duration_seconds [ALPHA] Duration in seconds from seeing a pod to starting a worker.# TYPE kubelet_pod_worker_start_duration_seconds histogramkubelet watch到pod到worker启动的时间分布# HELP kubelet_run_podsandbox_duration_seconds [ALPHA] Duration in seconds of the run_podsandbox operations. Broken down by RuntimeClass.Handler.# TYPE kubelet_run_podsandbox_duration_seconds histogram启动sandbox的时间分布# HELP kubelet_run_podsandbox_errors_total [ALPHA] Cumulative number of the run_podsandbox operation errors by RuntimeClass.Handler.# TYPE kubelet_run_podsandbox_errors_total counter启动sanbox出现error的总数 # HELP kubelet_running_containers [ALPHA] Number of containers currently running# TYPE kubelet_running_containers gauge当前containers运行状态的统计, 按照container状态统计，created running exited# HELP kubelet_running_pods [ALPHA] Number of pods that have a running pod sandbox# TYPE kubelet_running_pods gauge当前处于running状态pod数量# HELP kubelet_runtime_operations_duration_seconds [ALPHA] Duration in seconds of runtime operations. Broken down by operation type.# TYPE kubelet_runtime_operations_duration_seconds histogram容器运行时的操作耗时(container在create list exec remove stop等的耗时)# HELP kubelet_runtime_operations_errors_total [ALPHA] Cumulative number of runtime operation errors by operation type.# TYPE kubelet_runtime_operations_errors_total counter容器运行时的操作错误数统计(按操作类型统计)# HELP kubelet_runtime_operations_total [ALPHA] Cumulative number of runtime operations by operation type.# TYPE kubelet_runtime_operations_total counter容器运行时的操作总数统计(按操作类型统计)# HELP kubelet_started_containers_errors_total [ALPHA] Cumulative number of errors when starting containers# TYPE kubelet_started_containers_errors_total counterkubelet启动容器错误总数统计(按code和container_type统计)code包括ErrImagePull ErrImageInspect ErrImagePull ErrRegistryUnavailable ErrInvalidImageName等container_type一般为"container" "podsandbox"# HELP kubelet_started_containers_total [ALPHA] Cumulative number of containers started# TYPE kubelet_started_containers_total counterkubelet启动容器总数# HELP kubelet_started_pods_errors_total [ALPHA] Cumulative number of errors when starting pods# TYPE kubelet_started_pods_errors_total counterkubelet启动pod遇到的错误总数(只有创建sandbox遇到错误才会统计)# HELP kubelet_started_pods_total [ALPHA] Cumulative number of pods started# TYPE kubelet_started_pods_total counterkubelet启动的pod总数 # HELP process_cpu_seconds_total Total user and system CPU time spent in seconds.# TYPE process_cpu_seconds_total counter统计cpu使用率# HELP process_max_fds Maximum number of open file descriptors.# TYPE process_max_fds gauge允许进程打开的最大fd数# HELP process_open_fds Number of open file descriptors.# TYPE process_open_fds gauge当前打开的fd数量# HELP process_resident_memory_bytes Resident memory size in bytes.# TYPE process_resident_memory_bytes gauge进程驻留内存大小# HELP process_start_time_seconds Start time of the process since unix epoch in seconds.# TYPE process_start_time_seconds gauge进程启动时间# HELP rest_client_request_duration_seconds [ALPHA] Request latency in seconds. Broken down by verb and URL.# TYPE rest_client_request_duration_seconds histogram请求apiserver的耗时统计(按照url和请求类型统计verb)# HELP rest_client_requests_total [ALPHA] Number of HTTP requests, partitioned by status code, method, and host.# TYPE rest_client_requests_total counter请求apiserver的总次数(按照返回码code和请求类型method统计)# HELP storage_operation_duration_seconds [ALPHA] Storage operation duration# TYPE storage_operation_duration_seconds histogram存储操作耗时(按照存储plugin(configmap emptydir hostpath 等 )和operation_name分类统计)# HELP volume_manager_total_volumes [ALPHA] Number of volumes in Volume Manager# TYPE volume_manager_total_volumes gauge本机挂载的volume数量统计(按照plugin_name和state统计plugin_name包括"host-path" "empty-dir" "configmap" "projected")state(desired_state_of_world期状态/actual_state_of_world实际状态)

下面是 cAdvisor 指标梳理：

# HELP container_cpu_cfs_periods_total Number of elapsed enforcement period intervals.# TYPE container_cpu_cfs_periods_total countercfs时间片总数, 完全公平调度的时间片总数(分配到cpu的时间片数)# HELP container_cpu_cfs_throttled_periods_total Number of throttled period intervals.# TYPE container_cpu_cfs_throttled_periods_total counter容器被throttle的时间片总数# HELP container_cpu_cfs_throttled_seconds_total Total time duration the container has been throttled.# TYPE container_cpu_cfs_throttled_seconds_total counter容器被throttle的时间# HELP container_file_descriptors Number of open file descriptors for the container.# TYPE container_file_descriptors gauge容器打开的fd数# HELP container_memory_usage_bytes Current memory usage in bytes, including all memory regardless of when it was accessed# TYPE container_memory_usage_bytes gauge容器内存使用量，单位byte # HELP container_network_receive_bytes_total Cumulative count of bytes received# TYPE container_network_receive_bytes_total counter容器入方向的流量# HELP container_network_transmit_bytes_total Cumulative count of bytes transmitted# TYPE container_network_transmit_bytes_total counter容器出方向的流量# HELP container_spec_cpu_period CPU period of the container.# TYPE container_spec_cpu_period gauge容器的cpu调度单位时间# HELP container_spec_cpu_quota CPU quota of the container.# TYPE container_spec_cpu_quota gauge容器的cpu规格 ，除以单位调度时间可以计算核数# HELP container_spec_memory_limit_bytes Memory limit for the container.# TYPE container_spec_memory_limit_bytes gauge容器的内存规格，单位byte# HELP container_threads Number of threads running inside the container# TYPE container_threads gauge容器当前的线程数# HELP container_threads_max Maximum number of threads allowed inside the container, infinity if value is zero# TYPE container_threads_max gauge允许容器启动的最大线程数

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关于作者

本文作者秦晓辉、孔飞，快猫星云监控技术爱好者，文章内容是快猫技术团队共同沉淀的结晶，作者做了编辑整理，我们会持续输出监控、稳定性保障相关的技术文章，文章可转载，转载请注明出处，尊重技术人员的成果。