Kubernetes-监控Kubelet
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 kubelet
root 163490 0.0 0.0 12136 1064 pts/1 S+ 13:34 0:00 grep --color=auto kubelet
root 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 102
healthzPort: 10248
[root@tt-fc-dev01.nj ~]# curl localhost:10248/healthz
ok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/metrics
Client sent an HTTP request to an HTTPS server.
[root@tt-fc-dev01.nj ~]# curl https://localhost:10250/metrics
curl: (60) SSL certificate problem: self signed certificate in certificate chain
More details here: https://curl.haxx.se/docs/sslcerts.html
curl failed to verify the legitimacy of the server and therefore could not
establish a secure connection to it. To learn more about this situation and
how to fix it, please visit the web page mentioned above.
[root@tt-fc-dev01.nj ~]# curl -k https://localhost:10250/metrics
Unauthorized-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/v1
kind: ClusterRole
metadata:
name: categraf-daemonset
rules:
- apiGroups:
- ""
resources:
- nodes/metrics
- nodes/stats
- nodes/proxy
verbs:
- get
---
apiVersion: v1
kind: ServiceAccount
metadata:
name: categraf-daemonset
namespace: flashcat
---
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata:
name: categraf-daemonset
roleRef:
apiGroup: rbac.authorization.k8s.io
kind: ClusterRole
name: categraf-daemonset
subjects:
- kind: ServiceAccount
name: categraf-daemonset
namespace: flashcatClusterRole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.yaml
clusterrole.rbac.authorization.k8s.io/categraf-daemonset created
serviceaccount/categraf-daemonset created
clusterrolebinding.rbac.authorization.k8s.io/categraf-daemonset created
[work@tt-fc-dev01.nj yamls]$ kubectl get ClusterRole | grep categraf-daemon
categraf-daemonset 2022-11-14T03:53:54Z
[work@tt-fc-dev01.nj yamls]$ kubectl get sa -n flashcat
NAME SECRETS AGE
categraf-daemonset 1 90m
default 1 4d23h
[work@tt-fc-dev01.nj yamls]$ kubectl get ClusterRoleBinding -n flashcat | grep categraf-daemon
categraf-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 yaml
apiVersion: v1
kind: ServiceAccount
metadata:
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-12bf104450a0
secrets:
- 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 yaml
apiVersion: v1
data:
ca.crt: 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
namespace: Zmxhc2hjYXQ=
token: ZXlKaGJHY2lPaUpTVXpJMU5pSXNJbXRwWkNJNklqRTJZVTlNU2pObFFVbEhlbmhDV1dsVmFIcEVTRlZVWVdoZlZVaDZSbmd6TUZGZlVWUjJUR0pzVUVraWZRLmV5SnBjM01pT2lKcmRXSmxjbTVsZEdWekwzTmxjblpwWTJWaFkyTnZkVzUwSWl3aWEzVmlaWEp1WlhSbGN5NXBieTl6WlhKMmFXTmxZV05qYjNWdWRDOXVZVzFsYzNCaFkyVWlPaUptYkdGemFHTmhkQ0lzSW10MVltVnlibVYwWlhNdWFXOHZjMlZ5ZG1salpXRmpZMjkxYm5RdmMyVmpjbVYwTG01aGJXVWlPaUpqWVhSbFozSmhaaTFrWVdWdGIyNXpaWFF0ZEc5clpXNHROMjFqWTNFaUxDSnJkV0psY201bGRHVnpMbWx2TDNObGNuWnBZMlZoWTJOdmRXNTBMM05sY25acFkyVXRZV05qYjNWdWRDNXVZVzFsSWpvaVkyRjBaV2R5WVdZdFpHRmxiVzl1YzJWMElpd2lhM1ZpWlhKdVpYUmxjeTVwYnk5elpYSjJhV05sWVdOamIzVnVkQzl6WlhKMmFXTmxMV0ZqWTI5MWJuUXVkV2xrSWpvaU1qSm1OV0UzT0RVdE9EY3hZeTAwTkRVMExXSTRNbVV0TVRKaVpqRXdORFExTUdFd0lpd2ljM1ZpSWpvaWMzbHpkR1Z0T25ObGNuWnBZMlZoWTJOdmRXNTBPbVpzWVhOb1kyRjBPbU5oZEdWbmNtRm1MV1JoWlcxdmJuTmxkQ0o5Lm03czJ2Z1JuZDJzMDJOUkVwakdpc0JYLVBiQjBiRjdTRUFqb2RjSk9KLWh6YWhzZU5FSDFjNGNDbXotMDN5Z1Rkal9NT1VKaWpCalRmaW9FSWpGZHRCS0hEMnNjNXlkbDIwbjU4VTBSVXVDemRYQl9tY0J1WDlWWFM2bE5zYVAxSXNMSGdscV9Sbm5XcDZaNmlCaWp6SU05QUNuckY3MGYtd1FZTkVLc2MzdGhubmhSX3E5MkdkZnhmdGU2NmhTRGthdGhPVFRuNmJ3ZnZMYVMxV1JCdEZ4WUlwdkJmVXpkQ1FBNVhRYVNPck00RFluTE5uVzAxWDNqUGVZSW5ka3NaQ256cmV6Tnp2OEt5VFRTSlJ2VHVKMlZOU2lHaDhxTEgyZ3IzenhtQm5Qb1d0czdYeFhBTkJadG0yd0E2OE5FXzY0SlVYS0tfTlhfYmxBbFViakwtUQ==
kind: Secret
metadata:
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-65cc56683e41
type: 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 $token" https://localhost:10250/metrics > aaaa
[root@tt-fc-dev01.nj qinxiaohui]# head -n 5 aaaa
# HELP apiserver_audit_event_total [ALPHA] Counter of audit events generated and sent to the audit backend.
# TYPE apiserver_audit_event_total counter
apiserver_audit_event_total 0
# HELP apiserver_audit_requests_rejected_total [ALPHA] Counter of apiserver requests rejected due to an error in audit logging backend.
# TYPE apiserver_audit_requests_rejected_total counter
apiserver_audit_requests_rejected_total 0通了！
这就说明我们创建的ServiceAccount是好使的，后面我们把 Categraf 作为采集器搞成 Daemonset，再为 Categraf 这个 Daemonset 指定 ServiceAccountName，Kubernetes就会自动把 Token 的内容挂到 Daemonset 的目录里，下面开始实操。
升级 Daemonset
上一篇咱们为 Kube-Proxy 的采集准备了 Daemonset，咱们就继续修改这个 Daemonset，让这个 Daemonset 不但可以采集 Kube-Proxy，也可以采集 Kubelet，先给 Categraf 准备一下相关的配置，可以把下面的内容保存为 categraf-configmap-v2.yaml
---
kind: ConfigMap
metadata:
name: categraf-config
apiVersion: v1
data:
config.toml: |
[global]
hostname = "$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: ConfigMap
metadata:
name: categraf-input-prometheus
apiVersion: v1
data:
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 flashcat
configmap/categraf-config unchanged
configmap/categraf-input-prometheus configuredCategraf 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/v1
kind: DaemonSet
metadata:
labels:
app: categraf-daemonset
name: categraf-daemonset
spec:
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 flashcat
daemonset.apps "categraf-daemonset" deleted
[work@tt-fc-dev01.nj yamls]$ kubectl apply -f categraf-daemonset-v2.yaml -n flashcat
daemonset.apps/categraf-daemonset created
# waiting...
[work@tt-fc-dev01.nj yamls]$ kubectl get pods -n flashcat
NAME READY STATUS RESTARTS AGE
categraf-daemonset-d8jt8 1/1 Running 0 37s
categraf-daemonset-fpx8v 1/1 Running 0 43s
categraf-daemonset-mp468 1/1 Running 0 32s
categraf-daemonset-s775l 1/1 Running 0 40s
categraf-daemonset-wxkjk 1/1 Running 0 47s
categraf-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 summary
gc的时间统计(summary指标)
# HELP go_goroutines Number of goroutines that currently exist.
# TYPE go_goroutines gauge
goroutine 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 histogram
pod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 gauge
node name
# HELP kubelet_pleg_discard_events [ALPHA] The number of discard events in PLEG.
# TYPE kubelet_pleg_discard_events counter
PLEG(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 gauge
PLEG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 histogram
PLEG 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 histogram
PLEG 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 histogram
pod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 histogram
pod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 histogram
kubelet 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 counter
kubelet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 counter
kubelet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 counter
kubelet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 counter
kubelet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 counter
cfs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
