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部署步骤
3台Linux机器(这里我用的是Ubuntu的虚拟机) 初始化3台机器 三台机器安装Docker、Kubeadm 部署Master 部署网络插件Weave 部署两个Worker 部署可视化插件Dashboard 部署存储插件Rook
系统初始化
关闭Swap$ swapoff -a$ sed -i '/ swap / s/^\(.*\)$/#\1/g' /etc/fstab关闭防火墙$ ufw disable$ cat <<EOF | sudo tee /etc/modules-load.d/k8s.confbr_netfilterEOF$ cat <<EOF | sudo tee /etc/sysctl.d/k8s.confnet.bridge.bridge-nf-call-ip6tables = 1net.bridge.bridge-nf-call-iptables = 1EOF$ sysctl --system$ cat >> /etc/hosts << EOF172.16.56.133 k8s-master172.16.56.134 k8s-node-01172.16.56.135 k8s-node-02EOF
安装Kubeadm
$ curl https://mirrors.aliyun.com/kubernetes/apt/doc/apt-key.gpg | apt-key add -$ cat <<EOF > /etc/apt/sources.list.d/kubernetes.listdeb https://mirrors.aliyun.com/kubernetes/apt/ kubernetes-xenial mainEOF$ apt-get update$ apt-get install -y docker.io kubeadm
上述命令执行完成以后,kubeadm、kubelet、kubectl、kubernetes-cni这些二进制文件都会被自动安装好。
修改Docker配置
$ cat <<EOF | sudo tee /etc/docker/daemon.json{ "exec-opts": ["native.cgroupdriver=systemd"], "log-driver": "json-file", "log-opts": { "max-size": "100m" }, "storage-driver": "overlay2"}EOF$ systemctl restart docker
提前准备镜像
$ kubeadm config images list

set -o errexitset -o nounsetset -o pipefail##这里定义版本,按照上面得到的列表自己改一下版本号KUBE_VERSION=v1.23.6KUBE_PAUSE_VERSION=3.6ETCD_VERSION=3.5.1-0DNS_VERSION=v1.8.6##这是原始仓库名,最后需要改名成这个GCR_URL=k8s.gcr.io##这里就是写你要使用的仓库DOCKERHUB_URL=k8simage##这里是镜像列表,新版本要把coredns改成coredns/corednsimages=(kube-proxy:${KUBE_VERSION}kube-scheduler:${KUBE_VERSION}kube-controller-manager:${KUBE_VERSION}kube-apiserver:${KUBE_VERSION}pause:${KUBE_PAUSE_VERSION}etcd:${ETCD_VERSION}coredns:${DNS_VERSION})##这里是拉取和改名的循环语句for imageName in ${images[@]} ; do docker pull $DOCKERHUB_URL/$imageName docker tag $DOCKERHUB_URL/$imageName $GCR_URL/$imageName docker rmi $DOCKERHUB_URL/$imageNamedonedocker tag k8s.gcr.io/coredns:v1.8.6 k8s.gcr.io/coredns/coredns:v1.8.6docker rmi k8s.gcr.io/coredns:v1.8.6
上述脚本保存成pull_k8s_image.sh。
$ chmod +x ./pull_k8s_image.sh$ ./pull_k8s_image.sh
Kubeadm Master
$ kubeadm init$ mkdir -p $HOME/.kube$ sudo cp -i /etc/kubernetes/admin.conf $HOME/.kube/config$ sudo chown $(id -u):$(id -g) $HOME/.kube/config
Master在安装成功以后还会输出以下内容:
$ kubeadm join 172.16.56.133:6443 --token b19ftk.uc7t11gdkou56kdj \ --discovery-token-ca-cert-hash sha256:a57125da9f1180ee1ce322cf04d28503be59746135aca20c1a97ca6aaa5a86d6
上述内容主要是Worker Node加入集群的时候使用。
kubeam在init命令后主要有以下逻辑:
一系列的检查工作,确定本台机器可以部署Kubernetes 生成Kubernetes对外提供服务的所需的各种证书和对应的目录 为其他组件生成访问kube-apiserver所需的配置文件 为Master组件生成Pod配置文件,kube-apiserver、kube-controller-manager、kube-scheduler,根据配置文件会自动创建Pod kubeadm通过localhost:6443/healthz这个健康检测URL,等待master的组件完全启动 为集群生成一个bootstrap token,只要拥有该token,任何一个安装了kubelet和kubeadm的节点都可以通过kubeadm join命令加入集群 token生成以后,kubeadm会将ca.crt等Master节点的信息通过ConfigMap保存到etcd中,该ConfigMap的名字是cluster-info 安装默认插件,默认安装kube-proxy和coredns,用来提供整个集群的服务发现和负载均衡
生成的证书存放在哪里?
/etc/kubernetes/pki目录下:

其他组件访问kube-apiserver的配置文件生成在哪里?
/etc/kubernetes/xxx.conf

Master组件的YAML定义存放在哪里?
/etc/kubernetes/manifests/目录下:

当kubelet启动时,会检测该目录下的所有Pod文件,并加载启动他们,该启动方法称之为Static Pod。
$ kubectl get nodes$ kubectl describe node k8s-master


通过上图可以看出,Node处于NotReady状态的原因是未部署任何的网络插件。
# 查看k8s默认工作空间(kube-system)的各个Pod的状态$ kubectl get pods -n kube-system

部署网络插件
$ docker pull weaveworks/weave-kube:2.8.1$ docker tag weaveworks/weave-kube:2.8.1 ghcr.io/weaveworks/launcher/weave-kube:2.8.1$ docker rmi weaveworks/weave-kube:2.8.1$ docker pull weaveworks/weave-npc:2.8.1$ docker tag weaveworks/weave-npc:2.8.1 ghcr.io/weaveworks/launcher/weave-npc:2.8.1$ docker rmi weaveworks/weave-npc:2.8.1$ kubectl apply -f "https://cloud.weave.works/k8s/net?k8s-version=$(kubectl version | base64 | tr -d '\n')"$ kubectl get pods -n kube-system$ kubectl get nodes


在我们的网络插件部署成功以后,coredns的Pod状态变为Running,并且master节点也变为了Ready状态。
Kubernetes WorkNode部署
$ docker pull k8simage/pause:3.6$ docker tag k8simage/pause:3.6 k8s.gcr.io/pause:3.6$ docker rmi k8simage/pause:3.6$ docker pull k8simage/kube-proxy:v1.23.6$ docker tag k8simage/kube-proxy:v1.23.6 k8s.gcr.io/kube-proxy:v1.23.6$ docker rmi k8simage/kube-proxy:v1.23.6$ docker pull weaveworks/weave-kube:2.8.1$ docker tag weaveworks/weave-kube:2.8.1 ghcr.io/weaveworks/launcher/weave-kube:2.8.1$ docker rmi weaveworks/weave-kube:2.8.1$ docker pull weaveworks/weave-npc:2.8.1$ docker tag weaveworks/weave-npc:2.8.1 ghcr.io/weaveworks/launcher/weave-npc:2.8.1$ docker rmi weaveworks/weave-npc:2.8.1$ kubeadm join 172.16.56.133:6443 --token b19ftk.uc7t11gdkou56kdj \ --discovery-token-ca-cert-hash sha256:a57125da9f1180ee1ce322cf04d28503be59746135aca20c1a97ca6aaa5a86d6
两个WorkNode部署成功以后我们在Master节点上执行以下命令:
$ kubectl get nodes

通过Taint/Toleration调整Master执行 Pod的策略
默认情况下我们通常不允许在master节点上运行用的Pod,我们可以借助Kubernetes的Taint/Toleration实现该目标。
Taint/Toleration的原理?
一旦某个节点被加上了一个Taint,即被“打上了污点”,那么所有Pod就都不能在这个节点上运行,因为Kubernetes的Pod都有“洁癖”。除非,有个别的Pod 声明自己能容忍这个“污点”,即声明了Toleration,它才可以在这个节点上运行。
如何打污点?
$ kubectl taint nodes node1 foo=bar:NoSchedule
node1节点上就会增加一个键值对格式的Taint,即:foo=bar:NoSchedule。其中值里面的NoSchedule,意味着这个Taint只会在调度新Pod时产生作用,而不会影响已经在node1上运行的Pod,哪怕它们没有Toleration。
查看master节点上的Taint?
$ kubectl describe node k8s-master

Master节点默认被加上了node-role.kubernetes.io/master:NoSchedule这样一个“污点”,其中键是node-role.kubernetes.io/master,而没有提供值。
部署可视化插件
$ kubectl create clusterrolebinding serviceaccount-cluster-admin --clusterrole=cluster-admin --group=system:serviceaccount$ kubectl create clusterrolebinding serviceaccount-cluster-admin --clusterrole=cluster-admin --user=system:serviceaccount:kubernetes-dashboard:kubernetes-dashboard$ kubectl apply -f https://raw.githubusercontent.com/kubernetes/dashboard/v2.5.0/aio/deploy/recommended.yaml
上述命令可以直接安装可视化界面,如果暂时不能科学冲浪,那么可以使用下面的YAML进行部署:
apiVersion: v1kind: Namespacemetadata: name: kubernetes-dashboard---apiVersion: v1kind: ServiceAccountmetadata: labels: k8s-app: kubernetes-dashboard name: kubernetes-dashboard namespace: kubernetes-dashboard---kind: ServiceapiVersion: v1metadata: labels: k8s-app: kubernetes-dashboard name: kubernetes-dashboard namespace: kubernetes-dashboardspec: ports: - port: 443 targetPort: 8443 selector: k8s-app: kubernetes-dashboard---apiVersion: v1kind: Secretmetadata: labels: k8s-app: kubernetes-dashboard name: kubernetes-dashboard-certs namespace: kubernetes-dashboardtype: Opaque---apiVersion: v1kind: Secretmetadata: labels: k8s-app: kubernetes-dashboard name: kubernetes-dashboard-csrf namespace: kubernetes-dashboardtype: Opaquedata: csrf: ""---apiVersion: v1kind: Secretmetadata: labels: k8s-app: kubernetes-dashboard name: kubernetes-dashboard-key-holder namespace: kubernetes-dashboardtype: Opaque---kind: ConfigMapapiVersion: v1metadata: labels: k8s-app: kubernetes-dashboard name: kubernetes-dashboard-settings namespace: kubernetes-dashboard---kind: RoleapiVersion: rbac.authorization.k8s.io/v1metadata: labels: k8s-app: kubernetes-dashboard name: kubernetes-dashboard namespace: kubernetes-dashboardrules: # Allow Dashboard to get, update and delete Dashboard exclusive secrets. - apiGroups: [""] resources: ["secrets"] resourceNames: ["kubernetes-dashboard-key-holder", "kubernetes-dashboard-certs", "kubernetes-dashboard-csrf"] verbs: ["get", "update", "delete"] # Allow Dashboard to get and update 'kubernetes-dashboard-settings' config map. - apiGroups: [""] resources: ["configmaps"] resourceNames: ["kubernetes-dashboard-settings"] verbs: ["get", "update"] # Allow Dashboard to get metrics. - apiGroups: [""] resources: ["services"] resourceNames: ["heapster", "dashboard-metrics-scraper"] verbs: ["proxy"] - apiGroups: [""] resources: ["services/proxy"] resourceNames: ["heapster", "http:heapster:", "https:heapster:", "dashboard-metrics-scraper", "http:dashboard-metrics-scraper"] verbs: ["get"]---kind: ClusterRoleapiVersion: rbac.authorization.k8s.io/v1metadata: labels: k8s-app: kubernetes-dashboard name: kubernetes-dashboardrules: # Allow Metrics Scraper to get metrics from the Metrics server - apiGroups: ["metrics.k8s.io"] resources: ["pods", "nodes"] verbs: ["get", "list", "watch"]---apiVersion: rbac.authorization.k8s.io/v1kind: RoleBindingmetadata: labels: k8s-app: kubernetes-dashboard name: kubernetes-dashboard namespace: kubernetes-dashboardroleRef: apiGroup: rbac.authorization.k8s.io kind: Role name: kubernetes-dashboardsubjects: - kind: ServiceAccount name: kubernetes-dashboard namespace: kubernetes-dashboard---apiVersion: rbac.authorization.k8s.io/v1kind: ClusterRoleBindingmetadata: name: kubernetes-dashboardroleRef: apiGroup: rbac.authorization.k8s.io kind: ClusterRole name: kubernetes-dashboardsubjects: - kind: ServiceAccount name: kubernetes-dashboard namespace: kubernetes-dashboard---kind: DeploymentapiVersion: apps/v1metadata: labels: k8s-app: kubernetes-dashboard name: kubernetes-dashboard namespace: kubernetes-dashboardspec: replicas: 1 revisionHistoryLimit: 10 selector: matchLabels: k8s-app: kubernetes-dashboard template: metadata: labels: k8s-app: kubernetes-dashboard spec: securityContext: seccompProfile: type: RuntimeDefault containers: - name: kubernetes-dashboard image: kubernetesui/dashboard:v2.5.0 imagePullPolicy: Always ports: - containerPort: 8443 protocol: TCP args: - --auto-generate-certificates - --namespace=kubernetes-dashboard # Uncomment the following line to manually specify Kubernetes API server Host # If not specified, Dashboard will attempt to auto discover the API server and connect # to it. Uncomment only if the default does not work. # - --apiserver-host=http://my-address:port volumeMounts: - name: kubernetes-dashboard-certs mountPath: /certs # Create on-disk volume to store exec logs - mountPath: /tmp name: tmp-volume livenessProbe: httpGet: scheme: HTTPS path: / port: 8443 initialDelaySeconds: 30 timeoutSeconds: 30 securityContext: allowPrivilegeEscalation: false readOnlyRootFilesystem: true runAsUser: 1001 runAsGroup: 2001 volumes: - name: kubernetes-dashboard-certs secret: secretName: kubernetes-dashboard-certs - name: tmp-volume emptyDir: {} serviceAccountName: kubernetes-dashboard nodeSelector: "kubernetes.io/os": linux # Comment the following tolerations if Dashboard must not be deployed on master tolerations: - key: node-role.kubernetes.io/master effect: NoSchedule---kind: ServiceapiVersion: v1metadata: labels: k8s-app: dashboard-metrics-scraper name: dashboard-metrics-scraper namespace: kubernetes-dashboardspec: ports: - port: 8000 targetPort: 8000 selector: k8s-app: dashboard-metrics-scraper---kind: DeploymentapiVersion: apps/v1metadata: labels: k8s-app: dashboard-metrics-scraper name: dashboard-metrics-scraper namespace: kubernetes-dashboardspec: replicas: 1 revisionHistoryLimit: 10 selector: matchLabels: k8s-app: dashboard-metrics-scraper template: metadata: labels: k8s-app: dashboard-metrics-scraper spec: securityContext: seccompProfile: type: RuntimeDefault containers: - name: dashboard-metrics-scraper image: kubernetesui/metrics-scraper:v1.0.7 ports: - containerPort: 8000 protocol: TCP livenessProbe: httpGet: scheme: HTTP path: / port: 8000 initialDelaySeconds: 30 timeoutSeconds: 30 volumeMounts: - mountPath: /tmp name: tmp-volume securityContext: allowPrivilegeEscalation: false readOnlyRootFilesystem: true runAsUser: 1001 runAsGroup: 2001 serviceAccountName: kubernetes-dashboard nodeSelector: "kubernetes.io/os": linux # Comment the following tolerations if Dashboard must not be deployed on master tolerations: - key: node-role.kubernetes.io/master effect: NoSchedule volumes: - name: tmp-volume emptyDir: {}

如何访问dashboard?
在master节点执行以下命令$ kubectl proxy --address=0.0.0.0在本机执行以下命令$ ssh -L 8001:172.16.56.133:8001 k8s@172.16.56.133
浏览器访问http://localhost:8001/api/v1/namespaces/kubernetes-dashboard/services/https:kubernetes-dashboard:/proxy/#/login

如何获取Token?
$ kubectl -n kubernetes-dashboard describe secret $(kubectl -n kubernetes-dashboard get secret | grep kubernetes-dashboard-token | awk '{print $1}')

登录成功以后就可以看到如下页面:

部署存储插件
容器的持久化存储是用来保持容器状态的重要手段,存储插件会在容器里挂载一个基于网络或者其他机制的远程数据卷,使得容器里创建的文件实际上是保存在远程存储服务器上,这样无论在哪个宿主机上启动新的容器,都可以请求挂载指定持久化存储卷,从而访问到数据卷里面的内容。
$ docker pull objectscale/csi-node-driver-registrar:v2.5.0$ docker tag objectscale/csi-node-driver-registrar:v2.5.0 k8s.gcr.io/sig-storage/csi-node-driver-registrar:v2.5.0$ docker rmi objectscale/csi-node-driver-registrar:v2.5.0$ docker pull objectscale/csi-provisioner:v3.1.0$ docker tag objectscale/csi-provisioner:v3.1.0 k8s.gcr.io/sig-storage/csi-provisioner:v3.1.0$ docker rmi objectscale/csi-provisioner:v3.1.0$ docker pull objectscale/csi-resizer:v1.4.0$ docker tag objectscale/csi-resizer:v1.4.0 k8s.gcr.io/sig-storage/csi-resizer:v1.4.0$ docker rmi objectscale/csi-resizer:v1.4.0$ docker pull longhornio/csi-attacher:v3.2.1$ docker tag longhornio/csi-attacher:v3.2.1 k8s.gcr.io/sig-storage/csi-attacher:v3.4.0$ docker rmi longhornio/csi-attacher:v3.2.1$ docker pull longhornio/csi-snapshotter:v3.0.3$ docker tag longhornio/csi-snapshotter:v3.0.3 k8s.gcr.io/sig-storage/csi-snapshotter:v5.0.1$ docker rmi longhornio/csi-snapshotter:v3.0.3上面的命令均是为了准备镜像(如果可以科学冲浪的同学请忽略),直接执行下面的命令即可。$ git clone --single-branch --branch v1.9.2 https://github.com/rook/rook.git$ cd rook/deploy/examples$ kubectl apply -f crds.yaml -f common.yaml -f operator.yaml$ kubectl apply -f cluster.yaml
当我们部署存储插件以后,整个Kubernetes集群就搭建完成了,一个崭新的Kubernetes集群就映入眼帘。

本期Kubernetes集群搭建就到这,扫码关注置顶,更多内容我们下期再见!





