背景
由于容器化和微服务的大力发展，Kubernetes基本已经统一了容器管理方案，当我们使用Kubernetes来进行容器化管理的时候，全面监控Kubernetes也就成了我们第一个需要探索的问题。我们需要监控kubernetes的ingress、service、deployment、pod……等等服务，以达到随时掌握Kubernetes集群的内部状况。
此文章是Prometheus监控系列的第二篇，基于上一篇讲解了怎么对Kubernetes集群实施Prometheus监控。
K8s编排文件可参考 https://github.com/xianyuLuo/prometheus-monitor-kubernetes
Prometheus部署
在k8s上部署Prometheus十分简单，下面给的例子中将Prometheus部署到prometheus命名空间。
部署——数据采集
将kube-state-metrics和prometheus分开部署，先部署prometheus。
Prometheus
prometheus-rbac.yaml
apiVersion: rbac.authorization.k8s.io/v1beta1
kind: ClusterRole
metadata:
name: prometheus
rules:
- apiGroups: [""]
resources:
- nodes
- nodes/proxy
- services
- endpoints
- pods
verbs: ["get", "list", "watch"]
- apiGroups:
- extensions
resources:
- ingresses
verbs: ["get", "list", "watch"]
- nonResourceURLs: ["/metrics"]
verbs: ["get"]
---
apiVersion: v1
kind: ServiceAccount
metadata:
name: prometheus
namespace: prometheus
---
apiVersion: rbac.authorization.k8s.io/v1beta1
kind: ClusterRoleBinding
metadata:
name: prometheus
roleRef:
apiGroup: rbac.authorization.k8s.io
kind: ClusterRole
name: prometheus
subjects:
- kind: ServiceAccount
name: prometheus
namespace: prometheus
prometheus.rbac.yml定义了Prometheus容器访问k8s apiserver所需的ServiceAccount、ClusterRole以及ClusterRoleBinding。
prometheus-config-configmap.yaml
apiVersion: v1
kind: ConfigMap
metadata:
name: prometheus-config
namespace: prometheus
data:
prometheus.yml: |
global:
scrape_interval: 15s
evaluation_interval: 15s
scrape_configs:
- job_name: 'kubernetes-apiservers'
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-nodes'
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/${1}/proxy/metrics
- job_name: 'kubernetes-cadvisor'
kubernetes_sd_configs:
- role: node
scheme: https
tls_config:
ca_file: /var/run/secrets/kubernetes.io/serviceaccount/ca.crt
bearer_token_file: /var/run/secrets/kubernetes.io/serviceaccount/token
relabel_configs:
- action: labelmap
regex: __meta_kubernetes_node_label_(.+)
- target_label: __address__
replacement: kubernetes.default.svc:443
- source_labels: [__meta_kubernetes_node_name]
regex: (.+)
target_label: __metrics_path__
replacement: /api/v1/nodes/${1}/proxy/metrics/cadvisor
- job_name: 'kubernetes-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-services'
kubernetes_sd_configs:
- role: service
metrics_path: /probe
params:
module: [http_2xx]
relabel_configs:
- source_labels: [__meta_kubernetes_service_annotation_prometheus_io_probe]
action: keep
regex: true
- source_labels: [__address__]
target_label: __param_target
- target_label: __address__
replacement: blackbox-exporter.example.com:9115
- source_labels: [__param_target]
target_label: instance
- action: labelmap
regex: __meta_kubernetes_service_label_(.+)
- source_labels: [__meta_kubernetes_namespace]
target_label: kubernetes_namespace
- source_labels: [__meta_kubernetes_service_name]
target_label: kubernetes_name
- job_name: 'kubernetes-ingresses'
kubernetes_sd_configs:
- role: ingress
relabel_configs:
- source_labels: [__meta_kubernetes_ingress_annotation_prometheus_io_probe]
action: keep
regex: true
- source_labels: [__meta_kubernetes_ingress_scheme,__address__,__meta_kubernetes_ingress_path]
regex: (.+);(.+);(.+)
replacement: ${1}://${2}${3}
target_label: __param_target
- target_label: __address__
replacement: blackbox-exporter.example.com:9115
- source_labels: [__param_target]
target_label: instance
- action: labelmap
regex: __meta_kubernetes_ingress_label_(.+)
- source_labels: [__meta_kubernetes_namespace]
target_label: kubernetes_namespace
- source_labels: [__meta_kubernetes_ingress_name]
target_label: kubernetes_name
- job_name: 'kubernetes-pods'
kubernetes_sd_configs:
- role: pod
relabel_configs:
- source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_scrape]
action: keep
regex: true
- source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_path]
action: replace
target_label: __metrics_path__
regex: (.+)
- source_labels: [__address__, __meta_kubernetes_pod_annotation_prometheus_io_port]
action: replace
regex: ([^:]+)(?::\d+)?;(\d+)
replacement: $1:$2
target_label: __address__
- action: labelmap
regex: __meta_kubernetes_pod_label_(.+)
- source_labels: [__meta_kubernetes_namespace]
action: replace
target_label: kubernetes_namespace
- source_labels: [__meta_kubernetes_pod_name]
action: replace
target_label: kubernetes_pod_name
prometheus-config-configmap.yaml定义了prometheus的配置文件，以configmap的形式使用。
prometheus-dep.yaml
apiVersion: apps/v1beta2
kind: Deployment
metadata:
name: prometheus-dep
namespace: prometheus
spec:
replicas: 1
selector:
matchLabels:
app: prometheus-dep
template:
metadata:
labels:
app: prometheus-dep
spec:
containers:
- image: prom/prometheus:v2.3.2
name: prometheus
command:
- "/bin/prometheus"
args:
- "--config.file=/etc/prometheus/prometheus.yml"
- "--storage.tsdb.path=/prometheus"
- "--storage.tsdb.retention=1d"
ports:
- containerPort: 9090
protocol: TCP
volumeMounts:
- mountPath: "/prometheus"
name: data
- mountPath: "/etc/prometheus"
name: config-volume
resources:
requests:
cpu: 100m
memory: 100Mi
limits:
cpu: 500m
memory: 2500Mi
serviceAccountName: prometheus
imagePullSecrets:
- name: regsecret
volumes:
- name: data
emptyDir: {}
- name: config-volume
configMap:
name: prometheus-config
prometheus-dep.yaml定义了prometheus的部署，这里使用–storage.tsdb.retention参数，监控数据只保留1天，因为最终监控数据会统一汇总。 limits资源限制根据集群大小进行适当调整。
prometheus-svc.yaml
kind: Service
apiVersion: v1
metadata:
name: prometheus-svc
namespace: prometheus
spec:
type: NodePort
ports:
- port: 9090
targetPort: 9090
nodePort: 30090
selector:
app: prometheus-dep
prometheus-svc.yaml定义Prometheus的Service，需要将Prometheus以NodePort、LoadBalancer或Ingress暴露到集群外部，这样外部的Prometheus才能访问它。这里采用的NodePort，所以只需要访问集群中有外网地址的任意一台服务器的30090端口就可以使用prometheus。
kube-state-metrics
prometheus部署成功后，接着再部署kube-state-metrics作为prometheus的一个exporter来使用，提供deployment、daemonset、cronjob等服务的监控数据。
kube-state-metrics-rbac.yaml
apiVersion: v1
kind: ServiceAccount
metadata:
name: kube-state-metrics
namespace: prometheus
---
apiVersion: rbac.authorization.k8s.io/v1
# kubernetes versions before 1.8.0 should use rbac.authorization.k8s.io/v1beta1
kind: Role
metadata:
namespace: prometheus
name: kube-state-metrics-resizer
rules:
- apiGroups: [""]
resources:
- pods
verbs: ["get"]
- apiGroups: ["extensions"]
resources:
- deployments
resourceNames: ["kube-state-metrics"]
verbs: ["get", "update"]
---
apiVersion: rbac.authorization.k8s.io/v1
# kubernetes versions before 1.8.0 should use rbac.authorization.k8s.io/v1beta1
kind: ClusterRole
metadata:
name: kube-state-metrics
rules:
- apiGroups: [""]
resources:
- configmaps
- secrets
- 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
# kubernetes versions before 1.8.0 should use rbac.authorization.k8s.io/v1beta1
kind: RoleBinding
metadata:
name: kube-state-metrics
namespace: prometheus
roleRef:
apiGroup: rbac.authorization.k8s.io
kind: Role
name: kube-state-metrics-resizer
subjects:
- kind: ServiceAccount
name: kube-state-metrics
namespace: prometheus
---
apiVersion: rbac.authorization.k8s.io/v1
# kubernetes versions before 1.8.0 should use rbac.authorization.k8s.io/v1beta1
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: prometheus
kube-state-metrics-rbac.yaml定义了kube-state-metrics访问k8s apiserver所需的ServiceAccount和ClusterRole及ClusterRoleBinding。
kube-state-metrics-dep.yaml
apiVersion: apps/v1beta2
# Kubernetes versions after 1.9.0 should use apps/v1
# Kubernetes versions before 1.8.0 should use apps/v1beta1 or extensions/v1beta1
# addon-resizer描述：https://github.com/kubernetes/autoscaler/tree/master/addon-resizer
kind: Deployment
metadata:
name: kube-state-metrics
namespace: prometheus
spec:
selector:
matchLabels:
k8s-app: kube-state-metrics
replicas: 1
template:
metadata:
labels:
k8s-app: kube-state-metrics
spec:
serviceAccountName: kube-state-metrics
containers:
- name: kube-state-metrics
image: xianyuluo/kube-state-metrics:v1.3.1
ports:
- name: http-metrics
containerPort: 8080
- name: telemetry
containerPort: 8081
readinessProbe:
httpGet:
path: /healthz
port: 8080
initialDelaySeconds: 5
timeoutSeconds: 5
- name: addon-resizer
image: xianyuluo/addon-resizer:1.7
resources:
limits:
cpu: 100m
memory: 30Mi
requests:
cpu: 100m
memory: 30Mi
env:
- name: MY_POD_NAME
valueFrom:
fieldRef:
fieldPath: metadata.name
- name: MY_POD_NAMESPACE
valueFrom:
fieldRef:
fieldPath: metadata.namespace
command:
- /pod_nanny
- --container=kube-state-metrics
- --cpu=100m
- --extra-cpu=1m
- --memory=100Mi
- --extra-memory=2Mi
- --threshold=5
- --deployment=kube-state-metrics
kube-state-metrics-dep.yaml定义了kube-state-metrics的部署。
kube-state-metrics-svc.yaml
apiVersion: v1
kind: Service
metadata:
name: kube-state-metrics
namespace: prometheus
labels:
k8s-app: kube-state-metrics
annotations:
prometheus.io/scrape: 'true'
spec:
ports:
- name: http-metrics
port: 8080
targetPort: http-metrics
protocol: TCP
- name: telemetry
port: 8081
targetPort: telemetry
protocol: TCP
selector:
k8s-app: kube-state-metrics
kube-state-metrics-svc.yaml定义了kube-state-metrics的暴露方式，这里只需要使用默认的ClusterIP就可以了，因为它只提供给集群内部的promethes访问。
k8s集群中的prometheus监控到这儿就已经全部OK了，接下来还需要做的是汇总数据、展示数据及告警规则配置。
部署——数据汇总
prometheus-server
prometheus-server和前面prometheus的步骤基本相同，需要针对configmap、数据存储时间（一般为30d）、svc类型做些许改变，同时增加 rule.yaml。
prometheus-server不需要kube-state-metrics。prometheus-server可以部署在任意k8s集群，或者部署在K8s集群外部都可以。
prometheus-rbac.yaml (内容和上面的一致，namespace为prometheus-server)
......
prometheus-server-config-configmap.yaml
apiVersion: v1
kind: ConfigMap
metadata:
name: prometheus-server-config
namespace: prometheus-server
data:
prometheus.yml: |
global:
scrape_interval: 30s # Set the scrape interval to every 15 seconds. Default is every 1 minute.
evaluation_interval: 30s # Evaluate rules every 15 seconds. The default is every 1 minute.
scrape_timeout: 30s
# Alertmanager configuration
alerting:
alertmanagers:
- static_configs:
- targets: ["x.x.com:80"]
rule_files:
- "/etc/prometheus/rule.yml"
scrape_configs:
- job_name: 'federate-k8scluster-1'
scrape_interval: 30s
honor_labels: true
metrics_path: '/federate'
params:
'match[]':
- '{job=~"kubernetes-.*"}'
static_configs:
- targets: ['x.x.x.x:30090']
labels:
k8scluster: xxxx-k8s
- job_name: 'federate-k8scluster-2'
scrape_interval: 30s
honor_labels: true
metrics_path: '/federate'
params:
'match[]':
- '{job=~"kubernetes-.*"}'
static_configs:
- targets: ['x.x.x.x:30090']
labels:
k8scluster: yyyy-k8s
global：全局配置。设置了收集数据频率、超时等
alerting：告警配置。指定了prometheus将满足告警规则的信息发送到哪儿？告警规则在rule_files定义
rule_files：定义的告警规则文件
scrape_configs：监控数据刮取配置。定义了2个job，分别是federate-k8scluster-1、federate-k8scluster-2。其中federate-k8scluster-1配置了去x.x.x.x30090采集数据，并且要匹配job名为”kubernetes-“开头。注意下面的labels，这个是自己定义的，它的作用在于给每一条刮取过来的监控数据都加上一个 k8scluster: xxxx-k8s 的Key-Value，xxxx一般指定为项目代码。这样我们可以在多个集群数据中区分该条数据是属于哪一个k8s集群，这对于后面的展示和告警都非常有利。
prometheus-server-rule-configmap.yaml
apiVersion: v1
kind: ConfigMap
metadata:
name: prometheus-server-rule-config
namespace: prometheus-server
data:
rule.yml: |
groups:
- name: kubernetes
rules:
- alert: PodDown
expr: kube_pod_status_phase{phase="Unknown"} == 1 or kube_pod_status_phase{phase="Failed"} == 1
for: 1m
labels:
severity: error
service: prometheus_bot
receiver_group: "{{ $labels.k8scluster}}_{{ $labels.namespace }}"
annotations:
summary: Pod Down
k8scluster: "{{ $labels.k8scluster}}"
namespace: "{{ $labels.namespace }}"
pod: "{{ $labels.pod }}"
container: "{{ $labels.container }}"
- alert: PodRestart
expr: changes(kube_pod_container_status_restarts_total{pod !~ "analyzer.*"}[10m]) > 0
for: 1m
labels:
severity: error
service: prometheus_bot
receiver_group: "{{ $labels.k8scluster}}_{{ $labels.namespace }}"
annotations:
summary: Pod Restart
k8scluster: "{{ $labels.k8scluster}}"
namespace: "{{ $labels.namespace }}"
pod: "{{ $labels.pod }}"
container: "{{ $labels.container }}"
- alert: NodeUnschedulable
expr: kube_node_spec_unschedulable == 1
for: 5m
labels:
severity: error
service: prometheus_bot
receiver_group: "{{ $labels.k8scluster}}_{{ $labels.namespace }}"
annotations:
summary: Node Unschedulable
k8scluster: "{{ $labels.k8scluster}}"
node: "{{ $labels.node }}"
- alert: NodeStatusError
expr: kube_node_status_condition{condition="Ready", status!="true"} == 1
for: 5m
labels:
severity: error
service: prometheus_bot
receiver_group: "{{ $labels.k8scluster}}_{{ $labels.namespace }}"
annotations:
summary: Node Status Error
k8scluster: "{{ $labels.k8scluster}}"
node: "{{ $labels.node }}"
- alert: DaemonsetUnavailable
expr: kube_daemonset_status_number_unavailable > 0
for: 5m
labels:
severity: error
service: prometheus_bot
receiver_group: "{{ $labels.k8scluster}}_{{ $labels.namespace }}"
annotations:
summary: Daemonset Unavailable
k8scluster: "{{ $labels.k8scluster}}"
namespace: "{{ $labels.namespace }}"
daemonset: "{{ $labels.daemonset }}"
- alert: JobFailed
expr: kube_job_status_failed == 1
for: 5m
labels:
severity: error
service: prometheus_bot
receiver_group: "{{ $labels.k8scluster}}_{{ $labels.namespace }}"
annotations:
summary: Job Failed
k8scluster: "{{ $labels.k8scluster}}"
namespace: "{{ $labels.namespace }}"
job: "{{ $labels.exported_job }}"
rule.yaml定义了告警规则。此文件中定义了 PodDown、PodRestart、NodeUnschedulable、NodeStatusError、DaemonsetUnavailable、JobFailed 共6条规则。
alert：名称
expr：表达式。prometheus的SQL语句
for：时间范围
annotations：告警消息，其中 {{*}} 为Prometheus内部变量
prometheus-server-dep.yaml (参考上面的prometheus-dep.yaml做些许调整)
apiVersion: apps/v1beta2
kind: Deployment
metadata:
name: prometheus-server-dep
namespace: prometheus-server
spec:
replicas: 1
selector:
matchLabels:
app: prometheus-server-dep
......
args:
- "--config.file=/etc/prometheus/prometheus.yml"
- "--storage.tsdb.path=/prometheus"
- "--web.console.libraries=/usr/share/prometheus/console_libraries"
- "--web.console.templates=/usr/share/prometheus"
- "--storage.tsdb.retention=30d"
- "--web.enable-lifecycle"
......
volumeMounts:
- mountPath: "/prometheus"
name: data
- mountPath: "/etc/prometheus/prometheus.yml"
name: server-config-volume
subPath: prometheus.yml
- mountPath: "/etc/prometheus/rule.yml"
name: rule-config-volume
subPath: rule.yml
......
volumes:
- name: data
emptyDir: {}
- name: server-config-volume
configMap:
name: prometheus-server-config
- name: rule-config-volume
configMap:
name: prometheus-server-rule-config
volumes.data这里使用的是emptyDir，这样其实不妥，应该单独挂载一块盘来存储汇总数据。可使用pv实现。
prometheus-server-svc.yaml (参考上面的prometheus-svc.yaml做些许调整)
kind: Service
apiVersion: v1
metadata:
name: prometheus-server-svc
namespace: prometheus-server
spec:
type: LoadBalancer
ports:
- port: 80
targetPort: 9090
selector:
app: prometheus-server-dep
到这儿，数据采集和数据汇总就已经OK了。
Prometheus-server部署成功之后，在浏览器中可以看到监控数据汇总信息了
Status –> Configuration 中可以看到Prometheus-server的配置
Status –> Rules 中可以看到规则文件内容
Status –> Targets 中可以看到刮取目标的状态信息
告警配置
遵循上篇文章中的架构，告警使用Prometheus官方提供的组件Alertmanager
alertmanager-config-configmap.yaml
apiVersion: v1
kind: ConfigMap
metadata:
name: alertmanager-config
namespace: prometheus
data:
config.yml: |
global:
resolve_timeout: 5m
route:
receiver: default
group_wait: 30s
group_interval: 5m
repeat_interval: 4h
group_by: ['alertname', 'k8scluster', 'node', 'container', 'exported_job', 'daemonset']
routes:
- receiver: send_msg_warning
group_wait: 60s
match:
severity: warning
receivers:
- name: default
webhook_configs:
- url: 'http://msg.x.com/xxx/'
send_resolved: true
http_config:
bearer_token: 'xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx'
- name: send_msg_warning
webhook_configs:
- url: 'http://msg.x.com/xxx/'
send_resolved: true
http_config:
bearer_token: 'xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx'
alertmanager-config-configmap.yaml定义了alertmanager的配置文件
route：路由。分级匹配，然后交给指定 receivers，其中route.group_by中的k8scluster是prometheus-server-config.yaml中自定义的标签
receivers：发送。这里使用webhook方式发送给自研的send_msg模块
email、wechat、webhook、slack等发送方式配置请见官网文档：https://prometheus.io/docs/alerting/configuration/
alertmanager-dep.yaml
apiVersion: apps/v1beta2
kind: Deployment
metadata:
name: alertmanager-dep
namespace: alertmanager
spec:
replicas: 1
selector:
matchLabels:
app: alertmanager-dep
template:
metadata:
labels:
app: alertmanager-dep
spec:
containers:
- image: prom/alertmanager:v0.15.2
name: alertmanager
args:
- "--config.file=/etc/alertmanager/config.yml"
- "--storage.path=/alertmanager"
- "--data.retention=720h"
volumeMounts:
- mountPath: "/alertmanager"
name: data
- mountPath: "/etc/alertmanager"
name: config-volume
resources:
requests:
cpu: 100m
memory: 100Mi
limits:
cpu: 500m
memory: 2500Mi
volumes:
- name: data
emptyDir: {}
- name: config-volume
configMap:
name: alertmanager-config
alertmanager-dep.yaml定义了Alertmanager的部署。
展示
遵循上篇文章中的架构，展示使用开源的Grafana。Grafana的部署方式就不详细描述了，下面展示两个Dashboard
kubernetes-deployment-dashboard，展示了大多关于deployment的信息。左上角的Cluster选项就是利用prometheus-server-config.yaml中自定义的labels.k8scluster标签实现的。
kubernetes-pod-dashboard，展示的都是关于pod和container的信息，包括CPU、mem使用监控。此页面数据量一般比较大。左上角的Cluster选项也是利用prometheus-server-config.yaml中自定义的labels.k8scluster做的。
kubernetes-deployment-dashboard下载地址：https://grafana.com/dashboards/9730
kubernetes-pod-dashboard下载地址：https://grafana.com/dashboards/9729
结束
详细监控Kubernetes集群本身就是一项复杂的工作，好在有Prometheus、Grafana、kube-state-metrics这些优秀的开源工具，才让我们的工作复杂度得以缓解，Thanks。
此文章也是“使用prometheus完美监控kubernetes集群”系列的第二篇，如果在部署过程中遇到问题或者有不理解的地方，欢迎随时后台留言。