高可用Kubernetes集群-15. 部署Kubernetes集群统一日志管理

参考文档:

  1. Github:https://github.com/kubernetes/kubernetes/tree/master/cluster/addons/fluentd-elasticsearch

Kubernetes推荐采用Fluentd+Elasticsearch+Kibana对系统与容器日志进行采集,查询并展现。

一.环境

1. 基础环境

组件

版本

Remark

kubernetes

v1.9.2

 

fluentd-elasticsearch

v2.0.4

 

elasticsearch

v5.6.4

 

kibana

5.6.4

 

2. 原理

  1. 容器运行输出的到控制台的日志,以*-json.log的命名方式存放到/var/lib/docker/containers目录;
  2. 在各Node上运行fluentd服务(同logstash),采集所在节点/var/log与/var/lib/docker/containers两个目录下的日志;
  3. fluentd采集的日志数据汇总到elasticsearch集群;
  4. kibana展示与交互。

二.部署Kubernetes集群性能监控

1. 准备images

kubernetes部署服务时,为避免部署时发生pull镜像超时的问题,建议提前将相关镜像pull到相关所有节点(以下以kubenode1为例),或搭建本地镜像系统。

  1. 基础环境已做了镜像加速,可参考:http://www.cnblogs.com/netonline/p/7420188.html
  2. 需要从gcr.io pull的镜像,已利用Docker Hub的"Create Auto-Build GitHub"功能(Docker Hub利用GitHub上的Dockerfile文件build镜像),在个人的Docker Hub build成功,可直接pull到本地使用。

 

[root@kubenode1 ~]# docker pull netonline/fluentd-elasticsearch:v2.0.4

[root@kubenode1 ~]# docker pull netonline/elasticsearch:v5.6.4

[root@kubenode1 ~]# docker pull netonline/kibana:5.6.4

 

# elastic需要vm.max_map_count不低于262144,利于轻量级的alpine linux初始化1个基础容器保证需求

[root@kubenode1 ~]# docker pull alpine:3.6

2. 下载yaml范本

# release下载页:https://github.com/kubernetes/kubernetes/tree/master/cluster/addons/fluentd-elasticsearch

[root@kubenode1 ~]# mkdir -p /usr/local/src/yaml/efk

[root@kubenode1 ~]# cd /usr/local/src/yaml/efk

 

# fluentd-es-configmap.

[root@kubenode1 efk]# wget https://raw.githubusercontent.com/kubernetes/kubernetes/master/cluster/addons/fluentd-elasticsearch/fluentd-es-configmap.yaml

 

# fluentd-es-ds

[root@kubenode1 efk]# wget https://raw.githubusercontent.com/kubernetes/kubernetes/master/cluster/addons/fluentd-elasticsearch/fluentd-es-ds.yaml

 

# es-statefulset

[root@kubenode1 efk]# wget https://raw.githubusercontent.com/kubernetes/kubernetes/master/cluster/addons/fluentd-elasticsearch/es-statefulset.yaml

 

# es-service.

[root@kubenode1 efk]# wget https://raw.githubusercontent.com/kubernetes/kubernetes/master/cluster/addons/fluentd-elasticsearch/es-service.yaml

 

# kibana-deployment

[root@kubenode1 efk]# wget https://raw.githubusercontent.com/kubernetes/kubernetes/master/cluster/addons/fluentd-elasticsearch/kibana-deployment.yaml

 

# kibana-service

[root@kubenode1 efk]# wget https://raw.githubusercontent.com/kubernetes/kubernetes/master/cluster/addons/fluentd-elasticsearch/kibana-service.yaml

 

本实验使用yaml文件(修改版):https://github.com/Netonline2016/kubernetes/tree/master/addons/fluentd-elasticsearch

3. es-statefulset.yaml

es-statefulset.yaml由4个模块组成:ServiceAccout,ClusterRole,ClusterRoleBinding,StatefulSet。

其中ServiceAccout,ClusterRole,ClusterRoleBinding等3个模块定义了1个新的ClusterRole权限,并完成ClusterRoleBinding,授权到ServiceAccout。这3个模块默认不修改。

1)StatefulSet

默认不需要修改ServiceAccount部分,设置ServiceAccount资源,获取rbac中定义的权限。

StatefulSet是Deployment/RC的一个特殊变种,主要面向有状态的服务,特性如下:

  1. StatefulSet中每个Pod都有稳定的,唯一的网络标识,可以用来发现集群内的其他成员;如StatefulSet的名字为elasticsearch,则第一个Pod为elasticsearch-0,第二个Pod为elasticsearch-1,依次类推;
  2. StatefulSet控制的Pod副本的启停顺序是受控的,操作第n个Pod时,前n-1个Pod已运行且状态ready;
  3. StatefulSet里的Pod采用稳定的持久化的存储卷,通过PV/PVC实现,删除Pod时间默认不会删除与StatefulSet相关的存储卷,保证了数据安全。

 

# 修改处:第76行,变更镜像名;

# 设定为StatefulSet资源,副本数为2,采用持久化存储;

# 使用init Container在应用启动之前做初始化操作

[root@kubenode1 ~]# cd /usr/local/src/yaml/efk/

[root@kubenode1 efk]# sed -i 's|k8s.gcr.io/elasticsearch:v5.6.4|netonline/elasticsearch:v5.6.4|g' es-statefulset.yaml

[root@kubenode1 efk]# cat es-statefulset.yaml

# Elasticsearch deployment itself

apiVersion: apps/v1

kind: StatefulSet

metadata:

name: elasticsearch-logging

namespace: kube-system

labels:

k8s-app: elasticsearch-logging

version: v5.6.4

kubernetes.io/cluster-service: "true"

addonmanager.kubernetes.io/mode: Reconcile

spec:

serviceName: elasticsearch-logging

replicas: 2

selector:

matchLabels:

k8s-app: elasticsearch-logging

version: v5.6.4

template:

metadata:

labels:

k8s-app: elasticsearch-logging

version: v5.6.4

kubernetes.io/cluster-service: "true"

spec:

serviceAccountName: elasticsearch-logging

containers:

- image: netonline/elasticsearch:v5.6.4

name: elasticsearch-logging

resources:

# need more cpu upon initialization, therefore burstable class

limits:

cpu: 1000m

requests:

cpu: 100m

ports:

- containerPort: 9200

name: db

protocol: TCP

- containerPort: 9300

name: transport

protocol: TCP

volumeMounts:

- name: elasticsearch-logging

mountPath: /data

env:

- name: "NAMESPACE"

valueFrom:

fieldRef:

fieldPath: metadata.namespace

volumes:

- name: elasticsearch-logging

emptyDir: {}

# Elasticsearch requires vm.max_map_count to be at least 262144.

# If your OS already sets up this number to a higher value, feel free

# to remove this init container.

initContainers:

- image: alpine:3.6

command: ["/sbin/sysctl", "-w", "vm.max_map_count=262144"]

name: elasticsearch-logging-init

securityContext:

privileged: true

4. es-service.yaml

es-service.yaml默认不需要修改。

5. fluentd-es-configmap.yaml

fluentd-es-configmap.yaml设定一个ConfigMap资源,以volume的形式挂载为fluentd服务内部的文件,默认不需要修改。

6. fluentd-es-ds.yaml

fluentd-es-ds.yaml由4个模块组成:ServiceAccout,ClusterRole,ClusterRoleBinding,StatefulSet。

其中ServiceAccout,ClusterRole,ClusterRoleBinding等3个模块定义了1个新的ClusterRole: fluentd-es权限,并完成ClusterRoleBinding: fluentd-es,授权到ServiceAccout: fluentd-es。这3个模块默认不修改。

1)DaemonSet

fluentd需要在每个Node上运行,有以下3种方式实现:

  1. 直接在Node上部署fluentd服务;
  2. 通过kubelet的--config参数,为每个Node加载fluentd Pod;
  3. 通过DaemonSet资源设定fluentd Pod在每个Node运行(官方推荐)。

 

# 修改处:第16行,变更镜像名;

# nodeSelector:标签设置为"true",设定DaemonSet调度Pod只能调度到含有标签" beta.kubernetes.io/fluentd-ds-ready"的节点,需要在相应节点设置标签

[root@kubenode1 efk]# sed -i 's|k8s.gcr.io/fluentd-elasticsearch:v2.0.4|netonline/fluentd-elasticsearch:v2.0.4|g' fluentd-es-ds.yaml

[root@kubenode1 efk]# cat fluentd-es-ds.yaml

……

apiVersion: apps/v1

kind: DaemonSet

metadata:

name: fluentd-es-v2.0.4

namespace: kube-system

labels:

k8s-app: fluentd-es

version: v2.0.4

kubernetes.io/cluster-service: "true"

addonmanager.kubernetes.io/mode: Reconcile

spec:

selector:

matchLabels:

k8s-app: fluentd-es

version: v2.0.4

template:

metadata:

labels:

k8s-app: fluentd-es

kubernetes.io/cluster-service: "true"

version: v2.0.4

# This annotation ensures that fluentd does not get evicted if the node

# supports critical pod annotation based priority scheme.

# Note that this does not guarantee admission on the nodes (#40573).

annotations:

scheduler.alpha.kubernetes.io/critical-pod: ''

spec:

priorityClassName: system-node-critical

serviceAccountName: fluentd-es

containers:

- name: fluentd-es

image: netonline/fluentd-elasticsearch:v2.0.4

env:

- name: FLUENTD_ARGS

value: --no-supervisor -q

resources:

limits:

memory: 500Mi

requests:

cpu: 100m

memory: 200Mi

volumeMounts:

- name: varlog

mountPath: /var/log

- name: varlibdockercontainers

mountPath: /var/lib/docker/containers

readOnly: true

- name: libsystemddir

mountPath: /host/lib

readOnly: true

- name: config-volume

mountPath: /etc/fluent/config.d

nodeSelector:

beta.kubernetes.io/fluentd-ds-ready: "true"

terminationGracePeriodSeconds: 30

volumes:

- name: varlog

hostPath:

path: /var/log

- name: varlibdockercontainers

hostPath:

path: /var/lib/docker/containers

# It is needed to copy systemd library to decompress journals

- name: libsystemddir

hostPath:

path: /usr/lib64

- name: config-volume

configMap:

name: fluentd-es-config-v0.1.4

2)设置标签

# 所有期望运行fluentd Pod的节点都需要设置标签

[root@kubenode1 ~]# kubectl get nodes

[root@kubenode1 ~]# kubectl label nodes 172.30.200.21 beta.kubernetes.io/fluentd-ds-ready=true

[root@kubenode1 ~]# kubectl label nodes 172.30.200.22 beta.kubernetes.io/fluentd-ds-ready=true

[root@kubenode1 ~]# kubectl label nodes 172.30.200.23 beta.kubernetes.io/fluentd-ds-ready=true

7. kibana-deployment.yaml

# 修改处:第22行,变更镜像名;

[root@kubenode1 efk]# sed -i 's|docker.elastic.co/kibana/kibana:5.6.4|netonline/kibana:5.6.4|g' kibana-deployment.yaml

8. kibana-service.yaml

默认不需要修改Service部分。

三.验证

1. 启动服务

[root@kubenode1 ~]# cd /usr/local/src/yaml/efk/

[root@kubenode1 efk]# kubectl create -f .

2. 查看服务

# 查看statefulset,daemonset与deployment

[root@kubenode1 ~]# kubectl get statefulset -n kube-system

[root@kubenode1 ~]# kubectl get daemonset -n kube-system

[root@kubenode1 ~]# kubectl get deployment -n kube-system | grep kibana

 

# 查看elasticsearch与kibana的Pod运行状态;

# 有状态的Pod命名是有规律的

[root@kubenode1 ~]# kubectl get pods -n kube-system | grep -E 'elasticsearch|kibana'

 

# 查看fluentd的Pod运行状态,"-o wide"参数可显示Pod运行节点;

# 期望运行的节点都运行了fluentd Pod服务

[root@kubenode1 ~]# kubectl get pods -n kube-system -o wide | grep fluentd

 

# 查看service运行状态

[root@kubenode1 ~]# kubectl get svc -n kube-system | grep -E 'elasticsearch|kibana'

 

# kibana Pod第一次启动时有一定的初始化操作来优化并cache状态页面,时间一般在10~20分钟内;

# 通过日志查看,"-f"参数类似于"tailf";

[root@kubenode1 ~]# kubectl log kibana-logging-5d4b6ddfc7-szx6d -n kube-system -f

3. 访问elasticsearch

# 访问elasticsearch,采用kube-apiserver方式,也可以使用kubecet proxy代理的方式(同dashboard方式)

[root@kubenode1 ~]# kubectl cluster-info

 

浏览器访问访问elasticsearch,返回json文档:https://172.30.200.10:6443/api/v1/namespaces/kube-system/services/elasticsearch-logging/proxy/

4. 访问kibana

访问kibana,同样采用kube-apiserver方式,也可以使用kubecet proxy代理的方式(同dashboard方式)

浏览器访问访问kibana:https://172.30.200.10:6443/api/v1/namespaces/kube-system/services/kibana-logging/proxy

同ELK,第一次进入kibana,需要进行初始化配置,默认"Create"即可;

 

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转载自www.cnblogs.com/netonline/p/9004916.html