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1. Kubernetes与微服务概述

1.1 Kubernetes简介

Kubernetes（简称K8s）是一个开源的容器编排平台，由Google基于其内部系统Borg的经验开发并于2014年开源。它自动化了容器化应用的部署、扩展和管理，为容器化应用提供了声明式配置和自动化。Kubernetes已成为云原生应用开发和部署的事实标准，支持跨多个主机的容器集群管理，提供应用容器的快速部署、扩展、维护和故障转移。

1.2 微服务架构概述

微服务架构是一种将应用程序设计为小型、自治服务集合的架构风格，每个服务实现特定业务功能，运行在自己的进程中，通过轻量级机制（通常是HTTP/REST API）通信。微服务架构支持独立部署、扩展和维护，使团队能够使用最适合其服务的技术栈。

1.3 Kubernetes与微服务的结合

Kubernetes为微服务架构提供了理想的运行环境：

• 服务发现与负载均衡：Kubernetes内置服务发现和负载均衡机制，简化了微服务间的通信。
• 自动扩展：基于CPU/内存使用率或自定义指标自动扩展服务实例数量。
• 自我修复：自动重启失败的容器，替换和重新调度容器，确保服务高可用。
• 配置管理：集中管理应用配置，支持配置热更新。
• 存储编排：自动挂载本地或云存储系统，为微服务提供持久化存储。

2. Kubernetes核心架构与组件

2.1 Kubernetes架构概览

Kubernetes采用主从（Master-Node）架构，主要由控制平面（Control Plane）和工作节点（Worker Nodes）组成：

• 控制平面：负责集群管理，包括API服务器、调度器、控制器管理器和etcd。
• 工作节点：运行应用容器，包括kubelet、kube-proxy和容器运行时。

2.2 核心组件详解

API服务器（kube-apiserver）

• API服务器是Kubernetes控制平面的前端，处理REST操作，验证和更新API对象的状态。
• 所有组件（包括用户、控制器、节点等）都通过API服务器交互。
• 水平可扩展，通常部署多个实例以实现高可用。

# API服务器配置示例
apiVersion: v1
kind: Pod
metadata:
name: kube-apiserver
namespace: kube-system
spec:
containers:
- command:
- kube-apiserver
- --advertise-address=192.168.1.100
- --allow-privileged=true
- --authorization-mode=Node,RBAC
- --client-ca-file=/etc/kubernetes/pki/ca.crt
- --enable-admission-plugins=NodeRestriction
- --enable-bootstrap-token-auth=true
image: k8s.gcr.io/kube-apiserver:v1.22.0

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etcd

• etcd是分布式键值存储，保存集群的配置和状态信息。
• 提供高可用、强一致性的数据存储。
• 需要定期备份以防止数据丢失。

# etcd备份示例
ETCDCTL_API=3 etcdctl snapshot save snapshot.db \
--endpoints=https://127.0.0.1:2379 \
--cacert=/etc/kubernetes/pki/etcd/ca.crt \
--cert=/etc/kubernetes/pki/etcd/server.crt \
--key=/etc/kubernetes/pki/etcd/server.key
# etcd恢复示例
ETCDCTL_API=3 etcdctl snapshot restore snapshot.db \
--data-dir /var/lib/etcd-restore \
--initial-cluster master-1=https://192.168.1.100:2380 \
--initial-advertise-peer-urls https://192.168.1.100:2380

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调度器（kube-scheduler）

• 调度器负责将Pod分配到合适的节点上。
• 根据资源需求、硬件约束、亲和性和反亲和性规则、数据位置、工作负载间干扰等因素做出调度决策。

# 自定义调度器配置示例
apiVersion: kubescheduler.config.k8s.io/v1beta1
kind: KubeSchedulerConfiguration
profiles:
- schedulerName: custom-scheduler
plugins:
score:
disabled:
- name: NodeResourcesBalancedAllocation
enabled:
- name: CustomScorer

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控制器管理器（kube-controller-manager）

• 控制器管理器运行核心控制器进程，包括节点控制器、副本控制器、端点控制器等。
• 监控集群状态，并在实际状态与期望状态不符时采取行动。

# 控制器管理器配置示例
apiVersion: v1
kind: Pod
metadata:
name: kube-controller-manager
namespace: kube-system
spec:
containers:
- command:
- kube-controller-manager
- --allocate-node-cidrs=true
- --authentication-kubeconfig=/etc/kubernetes/controller-manager.conf
- --authorization-kubeconfig=/etc/kubernetes/controller-manager.conf
- --cluster-cidr=10.244.0.0/16
image: k8s.gcr.io/kube-controller-manager:v1.22.0

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kubelet

• kubelet是运行在每个节点上的代理，确保容器在Pod中运行。
• 与API服务器通信，接收Pod规范并确保容器健康运行。
• 管理节点上的容器生命周期。

# kubelet配置示例 (/etc/systemd/system/kubelet.service.d/10-kubeadm.conf)
[Service]
Environment="KUBELET_KUBECONFIG_ARGS=--bootstrap-kubeconfig=/etc/kubernetes/bootstrap-kubelet.conf --kubeconfig=/etc/kubernetes/kubelet.conf"
Environment="KUBELET_CONFIG_ARGS=--config=/var/lib/kubelet/config.yaml"
ExecStart=
ExecStart=/usr/bin/kubelet $KUBELET_KUBECONFIG_ARGS $KUBELET_CONFIG_ARGS $KUBELET_KUBEADM_ARGS $KUBELET_EXTRA_ARGS

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kube-proxy

• kube-proxy维护节点上的网络规则，实现Kubernetes服务概念。
• 负责为服务提供负载均衡，支持多种代理模式：userspace、iptables和IPVS。

# kube-proxy配置示例
apiVersion: kubeproxy.config.k8s.io/v1alpha1
bindAddress: 0.0.0.0
clientConnection:
acceptContentTypes: ""
burst: 10
contentType: application/vnd.kubernetes.protobuf
kubeconfig: /var/lib/kube-proxy/kubeconfig.conf
qps: 5
clusterCIDR: "10.244.0.0/16"
configSyncPeriod: 15m0s
conntrack:
maxPerCore: 32768
min: 131072
tcpCloseWaitTimeout: 1h0m0s
tcpEstablishedTimeout: 24h0m0s
mode: "ipvs"

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容器运行时

• 容器运行时负责运行容器，如Docker、containerd、CRI-O等。
• Kubernetes通过容器运行时接口（CRI）与容器运行时交互。

# containerd配置示例
cat <<EOF | sudo tee /etc/modules-load.d/containerd.conf
overlay
br_netfilter
EOF
sudo modprobe overlay
sudo modprobe br_netfilter
cat <<EOF | sudo tee /etc/sysctl.d/99-kubernetes-cri.conf
net.bridge.bridge-nf-call-iptables = 1
net.ipv4.ip_forward = 1
net.bridge.bridge-nf-call-ip6tables = 1
EOF
sudo sysctl --system
sudo mkdir -p /etc/containerd
sudo containerd config default | sudo tee /etc/containerd/config.toml
sudo systemctl restart containerd

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3. Kubernetes安装与配置

3.1 部署方式选择

Kubernetes有多种部署方式，根据需求选择合适的方案：

• kubeadm：官方推荐的部署工具，适合生产环境，提供引导式安装流程。
• 二进制部署：手动下载和配置各个组件，适合了解Kubernetes内部工作原理。
• 托管Kubernetes服务：如GKE、EKS、AKS，云提供商提供的托管服务，简化运维。
• 第三方工具：如Rancher、OpenShift，提供额外的管理功能和用户界面。

3.2 使用kubeadm部署Kubernetes集群

# 在所有节点上执行
# 关闭防火墙
sudo systemctl stop firewalld
sudo systemctl disable firewalld
# 禁用SELinux
sudo setenforce 0
sudo sed -i 's/^SELINUX=enforcing$/SELINUX=permissive/' /etc/selinux/config
# 禁用swap
sudo swapoff -a
sudo sed -i '/swap/d' /etc/fstab
# 安装Docker
sudo yum install -y yum-utils
sudo yum-config-manager --add-repo https://download.docker.com/linux/centos/docker-ce.repo
sudo yum install -y containerd.io docker-ce docker-ce-cli
sudo mkdir -p /etc/docker
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
sudo systemctl enable docker
sudo systemctl daemon-reload
sudo systemctl restart docker
# 安装kubeadm, kubelet, kubectl
cat <<EOF | sudo tee /etc/yum.repos.d/kubernetes.repo
[kubernetes]
name=Kubernetes
baseurl=https://packages.cloud.google.com/yum/repos/kubernetes-el7-\$basearch
enabled=1
gpgcheck=1
repo_gpgcheck=1
gpgkey=https://packages.cloud.google.com/yum/doc/yum-key.gpg https://packages.cloud.google.com/yum/doc/rpm-package-key.gpg
exclude=kubelet kubeadm kubectl
EOF
sudo yum install -y kubelet kubeadm kubectl --disableexcludes=kubernetes
sudo systemctl enable --now kubelet
# 配置内核参数
cat <<EOF | sudo tee /etc/sysctl.d/k8s.conf
net.bridge.bridge-nf-call-ip6tables = 1
net.bridge.bridge-nf-call-iptables = 1
net.ipv4.ip_forward = 1
EOF
sudo sysctl --system

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# 在主节点上执行
sudo kubeadm init --pod-network-cidr=10.244.0.0/16 --apiserver-advertise-address=<master-ip>
# 配置kubectl
mkdir -p $HOME/.kube
sudo cp -i /etc/kubernetes/admin.conf $HOME/.kube/config
sudo chown $(id -u):$(id -g) $HOME/.kube/config
# 安装网络插件（以Flannel为例）
kubectl apply -f https://raw.githubusercontent.com/coreos/flannel/master/Documentation/kube-flannel.yml

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# 在工作节点上执行（使用kubeadm init输出的join命令）
sudo kubeadm join <master-ip>:6443 --token <token> --discovery-token-ca-cert-hash <hash>

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3.3 高可用配置

# 配置负载均衡器（以HAProxy为例）
cat <<EOF | sudo tee /etc/haproxy/haproxy.cfg
frontend kubernetes-frontend
bind *:6443
mode tcp
option tcplog
default_backend kubernetes-backend
backend kubernetes-backend
mode tcp
balance roundrobin
option tcp-check
server master1 192.168.1.100:6443 check
server master2 192.168.1.101:6443 check
server master3 192.168.1.102:6443 check
EOF
sudo systemctl restart haproxy
# 初始化第一个控制平面节点
sudo kubeadm init --control-plane-endpoint "LOAD_BALANCER_DNS:LOAD_BALANCER_PORT" --upload-certs --pod-network-cidr=10.244.0.0/16
# 加入其他控制平面节点
sudo kubeadm join LOAD_BALANCER_DNS:LOAD_BALANCER_PORT --token <token> --discovery-token-ca-cert-hash <hash> --control-plane --certificate-key <key>

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4. Kubernetes核心概念与资源对象

4.1 Pod

Pod是Kubernetes中最小的可部署单元，包含一个或多个紧密关联的容器。Pod中的容器共享网络命名空间和存储卷，可以方便地相互通信。

# Pod示例
apiVersion: v1
kind: Pod
metadata:
name: myapp-pod
labels:
app: myapp
spec:
containers:
- name: myapp-container
image: busybox
command: ['sh', '-c', 'echo Hello Kubernetes! && sleep 3600']
- name: sidecar-container
image: fluentd
volumeMounts:
- name: log-volume
mountPath: /var/log
volumes:
- name: log-volume
emptyDir: {}

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4.2 Deployment

Deployment管理Pod的部署和扩展，提供声明式更新和回滚功能。Deployment使用ReplicaSet确保指定数量的Pod副本在运行。

# Deployment示例
apiVersion: apps/v1
kind: Deployment
metadata:
name: nginx-deployment
labels:
app: nginx
spec:
replicas: 3
selector:
matchLabels:
app: nginx
template:
metadata:
labels:
app: nginx
spec:
containers:
- name: nginx
image: nginx:1.14.2
ports:
- containerPort: 80
resources:
requests:
memory: "64Mi"
cpu: "250m"
limits:
memory: "128Mi"
cpu: "500m"
# 使用节点亲和性调度
affinity:
nodeAffinity:
requiredDuringSchedulingIgnoredDuringExecution:
nodeSelectorTerms:
- matchExpressions:
- key: disktype
operator: In
values:
- ssd

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4.3 Service

Service为Pod提供稳定的网络端点，实现负载均衡和服务发现。Kubernetes支持多种Service类型：ClusterIP、NodePort、LoadBalancer和ExternalName。

# Service示例
apiVersion: v1
kind: Service
metadata:
name: my-service
spec:
selector:
app: myapp
ports:
- protocol: TCP
port: 80
targetPort: 9376
# ClusterIP类型（默认）
type: ClusterIP

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# NodePort Service示例
apiVersion: v1
kind: Service
metadata:
name: my-nodeport-service
spec:
type: NodePort
selector:
app: myapp
ports:
- port: 80
targetPort: 80
# 可选，默认自动分配
nodePort: 30007

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4.4 ConfigMap与Secret

ConfigMap用于存储非敏感配置数据，Secret用于存储敏感数据如密码、API密钥等。

# ConfigMap示例
apiVersion: v1
kind: ConfigMap
metadata:
name: app-config
data:
database_url: "jdbc:mysql://db.example.com:3306/mydb"
api_endpoint: "https://api.example.com/v1"
log_level: "INFO"

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# Secret示例
apiVersion: v1
kind: Secret
metadata:
name: db-secret
type: Opaque
data:
# echo -n "admin" | base64
username: YWRtaW4=
# echo -n "password123" | base64
password: cGFzc3dvcmQxMjM=

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# 在Pod中使用ConfigMap和Secret
apiVersion: v1
kind: Pod
metadata:
name: configmap-secret-pod
spec:
containers:
- name: myapp
image: myapp:1.0
envFrom:
- configMapRef:
name: app-config
env:
- name: DB_PASSWORD
valueFrom:
secretKeyRef:
name: db-secret
key: password
volumeMounts:
- name: config-volume
mountPath: /etc/config
volumes:
- name: config-volume
configMap:
name: app-config

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4.5 Volume与PersistentVolume

Volume为Pod提供持久化存储，PersistentVolume（PV）和PersistentVolumeClaim（PVC）提供存储资源抽象。

# PersistentVolume示例
apiVersion: v1
kind: PersistentVolume
metadata:
name: pv-nfs
spec:
capacity:
storage: 10Gi
accessModes:
- ReadWriteMany
nfs:
path: /data
server: nfs-server.example.com

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# PersistentVolumeClaim示例
apiVersion: v1
kind: PersistentVolumeClaim
metadata:
name: pvc-nfs
spec:
accessModes:
- ReadWriteMany
resources:
requests:
storage: 5Gi

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# 在Pod中使用PVC
apiVersion: v1
kind: Pod
metadata:
name: pod-with-pvc
spec:
containers:
- name: myapp
image: myapp:1.0
volumeMounts:
- name: nfs-storage
mountPath: /data
volumes:
- name: nfs-storage
persistentVolumeClaim:
claimName: pvc-nfs

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4.6 Namespace

Namespace将集群划分为多个虚拟集群，实现资源隔离和多租户。

# Namespace示例
apiVersion: v1
kind: Namespace
metadata:
name: development
labels:
name: development

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# 设置默认命名空间
kubectl config set-context --current --namespace=development

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4.7 Ingress

Ingress管理外部访问集群内服务的HTTP和HTTPS路由，提供负载均衡、SSL终止和基于名称的虚拟主机。

# Ingress示例
apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
name: my-ingress
annotations:
nginx.ingress.kubernetes.io/rewrite-target: /
spec:
rules:
- host: myapp.example.com
http:
paths:
- path: /
pathType: Prefix
backend:
service:
name: my-service
port:
number: 80

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5. 微服务部署实践

5.1 微服务架构设计原则

在Kubernetes上部署微服务时，应遵循以下设计原则：

• 单一职责：每个微服务专注于特定业务功能。
• 松耦合：服务间通过API通信，避免共享数据库。
• 高内聚：相关功能应组织在同一服务中。
• 有界上下文：明确服务边界和责任范围。
• 去中心化治理：各团队可自主选择技术栈。
• 自动化部署：实现CI/CD流水线，支持快速迭代。
• 容错设计：服务应具备故障隔离和恢复能力。

5.2 微服务部署模式

每个微服务部署为单个容器，这是最常见的模式。

# 单容器微服务部署示例
apiVersion: apps/v1
kind: Deployment
metadata:
name: user-service
spec:
replicas: 3
selector:
matchLabels:
app: user-service
template:
metadata:
labels:
app: user-service
spec:
containers:
- name: user-service
image: myregistry/user-service:1.0.0
ports:
- containerPort: 8080
env:
- name: DB_HOST
value: "mysql-service"
- name: DB_PORT
value: "3306"
livenessProbe:
httpGet:
path: /health
port: 8080
initialDelaySeconds: 30
periodSeconds: 10
readinessProbe:
httpGet:
path: /ready
port: 8080
initialDelaySeconds: 5
periodSeconds: 5
---
apiVersion: v1
kind: Service
metadata:
name: user-service
spec:
selector:
app: user-service
ports:
- protocol: TCP
port: 80
targetPort: 8080

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某些微服务可能需要多个容器协同工作，如应用容器和日志收集容器。

# 多容器微服务部署示例
apiVersion: apps/v1
kind: Deployment
metadata:
name: payment-service
spec:
replicas: 2
selector:
matchLabels:
app: payment-service
template:
metadata:
labels:
app: payment-service
spec:
containers:
- name: payment-app
image: myregistry/payment-service:1.0.0
ports:
- containerPort: 8080
volumeMounts:
- name: log-volume
mountPath: /var/log/payment
- name: log-collector
image: fluent/fluentd:v1.14-1
volumeMounts:
- name: log-volume
mountPath: /var/log/payment
- name: config-volume
mountPath: /fluentd/etc
volumes:
- name: log-volume
emptyDir: {}
- name: config-volume
configMap:
name: fluentd-config
---
apiVersion: v1
kind: ConfigMap
metadata:
name: fluentd-config
data:
fluent.conf: |
<source>
@type tail
path /var/log/payment/*.log
pos_file /var/log/payment.log.pos
tag payment.*
format json
</source>
<match payment.**>
@type elasticsearch
host elasticsearch-service
port 9200
index_name payment
type_name _doc
</match>

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5.3 微服务通信模式

使用REST或gRPC进行同步通信，通过Kubernetes Service实现服务发现。

# REST API服务示例
apiVersion: apps/v1
kind: Deployment
metadata:
name: order-service
spec:
replicas: 2
template:
spec:
containers:
- name: order-service
image: myregistry/order-service:1.0.0
env:
- name: USER_SERVICE_URL
value: "http://user-service"
- name: PRODUCT_SERVICE_URL
value: "http://product-service"

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// Go服务调用示例
func getUserInfo(userID string) (*User, error) {
userServiceURL := os.Getenv("USER_SERVICE_URL")
if userServiceURL == "" {
userServiceURL = "http://user-service.default.svc.cluster.local"
}
resp, err := http.Get(fmt.Sprintf("%s/users/%s", userServiceURL, userID))
if err != nil {
return nil, fmt.Errorf("failed to call user service: %v", err)
}
defer resp.Body.Close()
if resp.StatusCode != http.StatusOK {
return nil, fmt.Errorf("user service returned non-200 status: %d", resp.StatusCode)
}
var user User
if err := json.NewDecoder(resp.Body).Decode(&user); err != nil {
return nil, fmt.Errorf("failed to decode user response: %v", err)
}
return &user, nil
}

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使用消息队列实现异步通信，如Kafka、RabbitMQ等。

# Kafka部署示例
apiVersion: apps/v1
kind: StatefulSet
metadata:
name: kafka
spec:
serviceName: kafka
replicas: 3
selector:
matchLabels:
app: kafka
template:
metadata:
labels:
app: kafka
spec:
containers:
- name: kafka
image: confluentinc/cp-kafka:latest
ports:
- containerPort: 9092
env:
- name: KAFKA_BROKER_ID
valueFrom:
fieldRef:
fieldPath: metadata.name
- name: KAFKA_ZOOKEEPER_CONNECT
value: "zookeeper:2181"
- name: KAFKA_ADVERTISED_LISTENERS
value: "PLAINTEXT://kafka-0.kafka.default.svc.cluster.local:9092,kafka-1.kafka.default.svc.cluster.local:9092,kafka-2.kafka.default.svc.cluster.local:9092"
volumeMounts:
- name: data
mountPath: /var/lib/kafka/data
volumeClaimTemplates:
- metadata:
name: data
spec:
accessModes: [ "ReadWriteOnce" ]
resources:
requests:
storage: 10Gi
---
apiVersion: v1
kind: Service
metadata:
name: kafka
spec:
ports:
- port: 9092
name: server
clusterIP: None
selector:
app: kafka

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// Go Kafka生产者示例
func produceOrderEvent(order Order) error {
broker := os.Getenv("KAFKA_BROKERS")
if broker == "" {
broker = "kafka:9092"
}
config := sarama.NewConfig()
config.Producer.Return.Successes = true
producer, err := sarama.NewSyncProducer([]string{broker}, config)
if err != nil {
return fmt.Errorf("failed to create Kafka producer: %v", err)
}
defer producer.Close()
orderJSON, err := json.Marshal(order)
if err != nil {
return fmt.Errorf("failed to marshal order: %v", err)
}
msg := &sarama.ProducerMessage{
Topic: "orders",
Value: sarama.StringEncoder(orderJSON),
}
_, _, err = producer.SendMessage(msg)
if err != nil {
return fmt.Errorf("failed to send message: %v", err)
}
return nil
}

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5.4 微服务配置管理

使用ConfigMap和Secret管理不同环境的配置。

# 开发环境ConfigMap
apiVersion: v1
kind: ConfigMap
metadata:
name: app-config-dev
namespace: development
data:
log_level: "DEBUG"
database_url: "jdbc:mysql://dev-db:3306/myapp_dev"
feature_flags: "new-ui,experimental-api"

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# 生产环境ConfigMap
apiVersion: v1
kind: ConfigMap
metadata:
name: app-config-prod
namespace: production
data:
log_level: "INFO"
database_url: "jdbc:mysql://prod-db.cluster.example.com:3306/myapp_prod"
feature_flags: "new-ui"

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# 使用ConfigMap的部署
apiVersion: apps/v1
kind: Deployment
metadata:
name: myapp
namespace: development
spec:
template:
spec:
containers:
- name: myapp
image: myregistry/myapp:1.0.0
envFrom:
- configMapRef:
name: app-config-dev

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使用配置中心如Spring Cloud Config、Consul或Apollo实现动态配置更新。

# Spring Cloud Config Server部署
apiVersion: apps/v1
kind: Deployment
metadata:
name: config-server
spec:
replicas: 1
selector:
matchLabels:
app: config-server
template:
metadata:
labels:
app: config-server
spec:
containers:
- name: config-server
image: myregistry/config-server:1.0.0
ports:
- containerPort: 8888
env:
- name: SPRING_PROFILES_ACTIVE
value: "native"
- name: SPRING_CLOUD_CONFIG_SERVER_NATIVE_SEARCH_LOCATIONS
value: "file:/config"
volumeMounts:
- name: config-repo
mountPath: /config
volumes:
- name: config-repo
gitRepo:
repository: https://github.com/myorg/config-repo.git
revision: main
---
apiVersion: v1
kind: Service
metadata:
name: config-server
spec:
selector:
app: config-server
ports:
- protocol: TCP
port: 8888
targetPort: 8888

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# 使用配置中心的微服务
apiVersion: apps/v1
kind: Deployment
metadata:
name: user-service
spec:
template:
spec:
containers:
- name: user-service
image: myregistry/user-service:1.0.0
env:
- name: SPRING_PROFILES_ACTIVE
value: "kubernetes"
- name: SPRING_CLOUD_CONFIG_URI
value: "http://config-server:8888"
- name: SPRING_CLOUD_CONFIG_NAME
value: "user-service"
livenessProbe:
httpGet:
path: /actuator/health
port: 8080
initialDelaySeconds: 60
periodSeconds: 30

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5.5 微服务监控与日志

部署Prometheus和Grafana实现微服务监控。

# Prometheus部署
apiVersion: apps/v1
kind: Deployment
metadata:
name: prometheus
spec:
replicas: 1
selector:
matchLabels:
app: prometheus
template:
metadata:
labels:
app: prometheus
spec:
containers:
- name: prometheus
image: prom/prometheus:v2.30.0
ports:
- containerPort: 9090
volumeMounts:
- name: prometheus-config
mountPath: /etc/prometheus
- name: prometheus-data
mountPath: /prometheus
volumes:
- name: prometheus-config
configMap:
name: prometheus-config
- name: prometheus-data
emptyDir: {}
---
apiVersion: v1
kind: ConfigMap
metadata:
name: prometheus-config
data:
prometheus.yml: |
global:
scrape_interval: 15s
scrape_configs:
- 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

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# Grafana部署
apiVersion: apps/v1
kind: Deployment
metadata:
name: grafana
spec:
replicas: 1
selector:
matchLabels:
app: grafana
template:
metadata:
labels:
app: grafana
spec:
containers:
- name: grafana
image: grafana/grafana:8.2.0
ports:
- containerPort: 3000
env:
- name: GF_SECURITY_ADMIN_PASSWORD
valueFrom:
secretKeyRef:
name: grafana-secret
key: admin-password
volumeMounts:
- name: grafana-data
mountPath: /var/lib/grafana
volumes:
- name: grafana-data
emptyDir: {}
---
apiVersion: v1
kind: Service
metadata:
name: grafana
spec:
selector:
app: grafana
ports:
- protocol: TCP
port: 80
targetPort: 3000
type: NodePort

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部署EFK（Elasticsearch、Fluentd、Kibana）栈实现日志收集和分析。

# Elasticsearch部署
apiVersion: apps/v1
kind: StatefulSet
metadata:
name: elasticsearch
spec:
serviceName: elasticsearch
replicas: 3
selector:
matchLabels:
app: elasticsearch
template:
metadata:
labels:
app: elasticsearch
spec:
containers:
- name: elasticsearch
image: docker.elastic.co/elasticsearch/elasticsearch:7.15.0
ports:
- containerPort: 9200
name: rest
- containerPort: 9300
name: inter-node
env:
- name: discovery.zen.minimum_master_nodes
value: "2"
- name: discovery.seed_hosts
value: "elasticsearch-0.elasticsearch,elasticsearch-1.elasticsearch,elasticsearch-2.elasticsearch"
- name: cluster.initial_master_nodes
value: "elasticsearch-0,elasticsearch-1,elasticsearch-2"
- name: ES_JAVA_OPTS
value: "-Xms512m -Xmx512m"
volumeMounts:
- name: data
mountPath: /usr/share/elasticsearch/data
volumeClaimTemplates:
- metadata:
name: data
spec:
accessModes: [ "ReadWriteOnce" ]
resources:
requests:
storage: 10Gi

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# Fluentd DaemonSet部署
apiVersion: apps/v1
kind: DaemonSet
metadata:
name: fluentd
spec:
selector:
matchLabels:
name: fluentd
template:
metadata:
labels:
name: fluentd
spec:
tolerations:
- key: node-role.kubernetes.io/master
effect: NoSchedule
containers:
- name: fluentd
image: fluent/fluentd-kubernetes-daemonset:v1.14-debian-elasticsearch7-1
env:
- name: FLUENT_ELASTICSEARCH_HOST
value: "elasticsearch"
- name: FLUENT_ELASTICSEARCH_PORT
value: "9200"
- name: FLUENT_ELASTICSEARCH_SCHEME
value: "http"
resources:
limits:
memory: 512Mi
requests:
cpu: 100m
memory: 200Mi
volumeMounts:
- name: varlog
mountPath: /var/log
- name: varlibdockercontainers
mountPath: /var/lib/docker/containers
readOnly: true
terminationGracePeriodSeconds: 30
volumes:
- name: varlog
hostPath:
path: /var/log
- name: varlibdockercontainers
hostPath:
path: /var/lib/docker/containers

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# Kibana部署
apiVersion: apps/v1
kind: Deployment
metadata:
name: kibana
spec:
replicas: 1
selector:
matchLabels:
app: kibana
template:
metadata:
labels:
app: kibana
spec:
containers:
- name: kibana
image: docker.elastic.co/kibana/kibana:7.15.0
ports:
- containerPort: 5601
env:
- name: ELASTICSEARCH_HOSTS
value: http://elasticsearch:9200
---
apiVersion: v1
kind: Service
metadata:
name: kibana
spec:
selector:
app: kibana
ports:
- protocol: TCP
port: 5601
targetPort: 5601
type: NodePort

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6. 企业级Kubernetes应用技巧

6.1 多环境管理

使用命名空间隔离不同环境，并配置网络策略和资源配额。

# 开发环境命名空间
apiVersion: v1
kind: Namespace
metadata:
name: development
labels:
name: development
env: dev
---
# 生产环境命名空间
apiVersion: v1
kind: Namespace
metadata:
name: production
labels:
name: production
env: prod

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# 资源配额示例
apiVersion: v1
kind: ResourceQuota
metadata:
name: dev-quota
namespace: development
spec:
hard:
requests.cpu: "4"
requests.memory: 8Gi
limits.cpu: "10"
limits.memory: 16Gi
persistentvolumeclaims: "5"
requests.storage: "50Gi"

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# 网络策略示例
apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
name: dev-network-policy
namespace: development
spec:
podSelector: {}
policyTypes:
- Ingress
- Egress
ingress:
- from:
- namespaceSelector:
matchLabels:
name: development
- namespaceSelector:
matchLabels:
name: shared-services
egress:
- to:
- namespaceSelector:
matchLabels:
name: development
- namespaceSelector:
matchLabels:
name: shared-services

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使用Argo CD或Flux CD实现GitOps工作流，管理多环境部署。

# Argo CD Application示例
apiVersion: argoproj.io/v1alpha1
kind: Application
metadata:
name: myapp-dev
namespace: argocd
spec:
project: default
source:
repoURL: https://github.com/myorg/myapp-k8s-manifests.git
targetRevision: HEAD
path: overlays/development
destination:
server: https://kubernetes.default.svc
namespace: development
syncPolicy:
automated:
prune: true
selfHeal: true
---
apiVersion: argoproj.io/v1alpha1
kind: Application
metadata:
name: myapp-prod
namespace: argocd
spec:
project: default
source:
repoURL: https://github.com/myorg/myapp-k8s-manifests.git
targetRevision: HEAD
path: overlays/production
destination:
server: https://kubernetes.default.svc
namespace: production
syncPolicy:
automated:
prune: true
selfHeal: true
syncOptions:
- Validate=false

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6.2 安全最佳实践

使用基于角色的访问控制（RBAC）限制用户和服务账户的权限。

# 角色定义
apiVersion: rbac.authorization.k8s.io/v1
kind: Role
metadata:
namespace: development
name: developer-role
rules:
- apiGroups: [""]
resources: ["pods", "services", "configmaps", "secrets"]
verbs: ["get", "list", "watch", "create", "update", "patch", "delete"]
- apiGroups: ["apps"]
resources: ["deployments", "replicasets"]
verbs: ["get", "list", "watch", "create", "update", "patch", "delete"]

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# 角色绑定
apiVersion: rbac.authorization.k8s.io/v1
kind: RoleBinding
metadata:
name: developer-binding
namespace: development
subjects:
- kind: User
name: developer1
apiGroup: rbac.authorization.k8s.io
roleRef:
kind: Role
name: developer-role
apiGroup: rbac.authorization.k8s.io

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# 集群角色定义
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
name: cluster-admin-view
rules:
- apiGroups: [""]
resources: ["nodes", "namespaces"]
verbs: ["get", "list", "watch"]

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使用Pod安全策略（Pod Security Policies）或Pod安全准入控制器（Pod Security Admission）增强Pod安全性。

# Pod安全策略示例
apiVersion: policy/v1beta1
kind: PodSecurityPolicy
metadata:
name: restricted-psp
spec:
privileged: false
allowPrivilegeEscalation: false
requiredDropCapabilities:
- ALL
volumes:
- 'configMap'
- 'emptyDir'
- 'projected'
- 'secret'
- 'downwardAPI'
- 'persistentVolumeClaim'
runAsUser:
rule: 'MustRunAsNonRoot'
seLinux:
rule: 'RunAsAny'
fsGroup:
rule: 'RunAsAny'

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# Pod安全标准（使用Pod Security Admission）
apiVersion: v1
kind: Namespace
metadata:
name: secure-namespace
labels:
pod-security.kubernetes.io/enforce: restricted
pod-security.kubernetes.io/enforce-version: v1.24
pod-security.kubernetes.io/audit: restricted
pod-security.kubernetes.io/audit-version: v1.24
pod-security.kubernetes.io/warn: restricted
pod-security.kubernetes.io/warn-version: v1.24

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使用网络策略控制Pod间通信，实现零信任网络。

# 默认拒绝所有入站和出站流量
apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
name: default-deny-all
namespace: production
spec:
podSelector: {}
policyTypes:
- Ingress
- Egress

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# 允许特定命名空间间的通信
apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
name: allow-api-to-db
namespace: production
spec:
podSelector:
matchLabels:
app: database
policyTypes:
- Ingress
ingress:
- from:
- namespaceSelector:
matchLabels:
name: api
- podSelector:
matchLabels:
app: api-service
ports:
- protocol: TCP
port: 3306

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6.3 高可用与灾备

使用多区域部署实现高可用和灾备。

# 多区域部署示例（使用反亲和性）
apiVersion: apps/v1
kind: Deployment
metadata:
name: critical-service
spec:
replicas: 6
template:
spec:
affinity:
podAntiAffinity:
requiredDuringSchedulingIgnoredDuringExecution:
- labelSelector:
matchExpressions:
- key: app
operator: In
values:
- critical-service
topologyKey: "kubernetes.io/hostname"
podAntiAffinity:
preferredDuringSchedulingIgnoredDuringExecution:
- weight: 100
podAffinityTerm:
labelSelector:
matchExpressions:
- key: app
operator: In
values:
- critical-service
topologyKey: "topology.kubernetes.io/zone"
containers:
- name: critical-service
image: myregistry/critical-service:1.0.0
ports:
- containerPort: 8080

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使用Velero实现集群备份与恢复。

# 安装Velero
velero install \
--provider aws \
--bucket velero-backups \
--secret-file ./credentials-velero \
--use-volume-snapshots=true \
--plugins velero/velero-plugin-for-aws:v1.3.0
# 创建备份
velero backup create my-backup --include-namespaces production
# 计划备份
velero schedule create daily-backup --schedule="0 2 * * *" --include-namespaces production
# 恢复备份
velero restore create --from-backup my-backup

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6.4 自动扩缩容

使用HPA根据CPU、内存或自定义指标自动调整Pod数量。

# HPA示例
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
name: myapp-hpa
spec:
scaleTargetRef:
apiVersion: apps/v1
kind: Deployment
name: myapp
minReplicas: 2
maxReplicas: 10
metrics:
- type: Resource
resource:
name: cpu
target:
type: Utilization
averageUtilization: 50
- type: Resource
resource:
name: memory
target:
type: Utilization
averageUtilization: 70
- type: Pods
pods:
metric:
name: packets-per-second
target:
type: AverageValue
averageValue: 1k
- type: Object
object:
metric:
name: requests-per-second
describedObject:
apiVersion: networking.k8s.io/v1beta1
kind: Ingress
name: myapp-ingress
target:
type: Value
value: 10k

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使用VPA自动调整Pod的资源请求和限制。

# VPA示例
apiVersion: autoscaling.k8s.io/v1
kind: VerticalPodAutoscaler
metadata:
name: myapp-vpa
spec:
targetRef:
apiVersion: "apps/v1"
kind: Deployment
name: myapp
updatePolicy:
updateMode: "Auto"
resourcePolicy:
containerPolicies:
- containerName: "*"
minAllowed:
cpu: "100m"
memory: "50Mi"
maxAllowed:
cpu: "1"
memory: "500Mi"
controlledResources: ["cpu", "memory"]

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使用集群自动扩缩容器根据资源需求调整节点数量。

# 集群自动扩缩容配置示例
apiVersion: "autoscaling.openshift.io/v1"
kind: "ClusterAutoscaler"
metadata:
name: "default"
spec:
podPriorityThreshold: -10
resourceLimits:
maxNodesTotal: 50
cores:
min: 20
max: 100
memory:
min: 80
max: 400
scaleDown:
enabled: true
delayAfterAdd: "10m"
delayAfterDelete: "5m"
delayAfterFailure: "3m"
unneededTime: "30m"

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7. 实际项目部署难题及解决方案

7.1 资源管理难题

问题：如何合理设置Pod的资源请求和限制，避免资源浪费或性能问题？

解决方案：

1. 使用监控工具收集历史资源使用数据
2. 进行负载测试确定资源需求
3. 使用VPA自动调整资源配置
4. 实施资源配额和限制范围

# LimitRange示例
apiVersion: v1
kind: LimitRange
metadata:
name: resource-limits
namespace: development
spec:
limits:
- default:
cpu: "500m"
memory: "512Mi"
defaultRequest:
cpu: "250m"
memory: "256Mi"
type: Container

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# 使用kubectl top命令监控资源使用
kubectl top pods --all-namespaces
kubectl top nodes
# 使用Prometheus查询资源使用历史
# 容器CPU使用率
sum(rate(container_cpu_usage_seconds_total{image!="", container!="POD"}[5m])) by (namespace, pod)
# 容器内存使用量
sum(container_memory_working_set_bytes{image!="", container!="POD"}) by (namespace, pod)

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问题：如何处理资源争用，确保关键服务的性能？

解决方案：

1. 使用Kubernetes的QoS类别（Guaranteed、Burstable、BestEffort）
2. 为关键服务设置更高的优先级
3. 使用节点亲和性和反亲和性优化资源分配

# Guaranteed QoS示例（设置相等的requests和limits）
apiVersion: v1
kind: Pod
metadata:
name: guaranteed-pod
spec:
containers:
- name: guaranteed-container
image: nginx
resources:
requests:
memory: "200Mi"
cpu: "500m"
limits:
memory: "200Mi"
cpu: "500m"

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# PriorityClass示例
apiVersion: scheduling.k8s.io/v1
kind: PriorityClass
metadata:
name: high-priority
value: 1000000
globalDefault: false
description: "This priority class should be used for critical service pods only."
---
apiVersion: v1
kind: Pod
metadata:
name: critical-pod
spec:
priorityClassName: high-priority
containers:
- name: critical-container
image: myregistry/critical-service:1.0.0

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7.2 网络通信难题

问题：微服务间如何实现高效的服务发现？

解决方案：

1. 使用Kubernetes内置的DNS服务
2. 实现服务网格（如Istio、Linkerd）
3. 使用Headless Service进行有状态服务发现

# Headless Service示例
apiVersion: v1
kind: Service
metadata:
name: stateful-service
spec:
clusterIP: None # Headless Service
selector:
app: stateful-app
ports:
- port: 80
targetPort: 8080

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// Go服务发现示例
func discoverServiceEndpoints(serviceName, namespace string) ([]string, error) {
// 使用Kubernetes客户端库查询服务端点
config, err := rest.InClusterConfig()
if err != nil {
return nil, fmt.Errorf("failed to get in-cluster config: %v", err)
}
clientset, err := kubernetes.NewForConfig(config)
if err != nil {
return nil, fmt.Errorf("failed to create clientset: %v", err)
}
endpoints, err := clientset.CoreV1().Endpoints(namespace).Get(context.TODO(), serviceName, metav1.GetOptions{})
if err != nil {
return nil, fmt.Errorf("failed to get endpoints: %v", err)
}
var addresses []string
for _, subset := range endpoints.Subsets {
for _, port := range subset.Ports {
for _, addr := range subset.Addresses {
addresses = append(addresses, fmt.Sprintf("%s:%d", addr.IP, port.Port))
}
}
}
return addresses, nil
}

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问题：如何优化微服务间的网络通信性能？

解决方案：

1. 使用本地缓存减少网络请求
2. 实现连接池和HTTP/2
3. 使用服务网格优化通信路径
4. 部署网络策略优化流量路由

# Istio服务网格配置示例
apiVersion: networking.istio.io/v1alpha3
kind: DestinationRule
metadata:
name: user-service
spec:
host: user-service
trafficPolicy:
connectionPool:
tcp:
maxConnections: 100
connectTimeout: 30ms
tcpKeepalive:
time: 7200s
interval: 75s
http:
http1MaxPendingRequests: 100
http2MaxRequests: 1000
maxRetries: 3
idleTimeout: 90s
h2UpgradePolicy: UPGRADE
outlierDetection:
consecutiveGatewayErrors: 5
interval: 30s
baseEjectionTime: 30s
maxEjectionPercent: 50

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// HTTP/2客户端示例
func createHTTP2Client() *http.Client {
transport := &http.Transport{
ForceAttemptHTTP2: true,
MaxIdleConns: 100,
IdleConnTimeout: 90 * time.Second,
TLSClientConfig: &tls.Config{
InsecureSkipVerify: true, // 仅用于测试环境
},
}
return &http.Client{
Transport: transport,
Timeout: 30 * time.Second,
}
}
// 使用连接池示例
var httpClient = &http.Client{
Transport: &http.Transport{
MaxIdleConns: 100,
MaxIdleConnsPerHost: 10,
IdleConnTimeout: 90 * time.Second,
},
Timeout: 30 * time.Second,
}

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7.3 存储管理难题

问题：如何为有状态微服务提供可靠的持久化存储？

解决方案：

1. 使用StatefulSet管理有状态应用
2. 配置持久化卷和持久化卷声明
3. 实现存储类（StorageClass）支持动态卷供应
4. 使用分布式存储系统（如Ceph、GlusterFS）

# StatefulSet示例
apiVersion: apps/v1
kind: StatefulSet
metadata:
name: cassandra
spec:
serviceName: cassandra
replicas: 3
selector:
matchLabels:
app: cassandra
template:
metadata:
labels:
app: cassandra
spec:
containers:
- name: cassandra
image: cassandra:3.11
ports:
- containerPort: 9042
name: cql
volumeMounts:
- name: cassandra-data
mountPath: /var/lib/cassandra
volumeClaimTemplates:
- metadata:
name: cassandra-data
spec:
accessModes: [ "ReadWriteOnce" ]
storageClassName: fast-ssd
resources:
requests:
storage: 10Gi

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# StorageClass示例
apiVersion: storage.k8s.io/v1
kind: StorageClass
metadata:
name: fast-ssd
provisioner: kubernetes.io/gce-pd
parameters:
type: pd-ssd
replication-type: regional-pd
allowVolumeExpansion: true
volumeBindingMode: WaitForFirstConsumer

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问题：如何实现微服务数据的可靠备份与恢复？

解决方案：

1. 使用Velero备份Kubernetes资源和持久化卷
2. 实现数据库专用备份工具（如pg_dump、mysqldump）
3. 定期测试恢复流程
4. 实现异地备份

# Velero备份配置示例
apiVersion: velero.io/v1
kind: Backup
metadata:
name: daily-backup
namespace: velero
spec:
includedNamespaces:
- production
includedResources:
- persistentvolumeclaims
- persistentvolumes
- deployments
- statefulsets
- configmaps
- secrets
ttl: 168h0m0s # 7天
storageLocation: default
volumeSnapshotLocations:
- default

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# 数据库备份脚本示例
#!/bin/bash
# MySQL备份
mysqldump -h $DB_HOST -u $DB_USER -p$DB_PASSWORD --all-databases | gzip > /backups/mysql-$(date +%Y%m%d).sql.gz
# PostgreSQL备份
pg_dump -h $DB_HOST -U $DB_USER -d $DB_NAME | gzip > /backups/postgres-$(date +%Y%m%d).sql.gz
# MongoDB备份
mongodump --host $DB_HOST --username $DB_USER --password $DB_PASSWORD --out /backups/mongodb-$(date +%Y%m%d)
tar -czf /backups/mongodb-$(date +%Y%m%d).tar.gz /backups/mongodb-$(date +%Y%m%d)
rm -rf /backups/mongodb-$(date +%Y%m%d)
# 上传到云存储
aws s3 cp /backups/ s3://$S3_BUCKET/$(date +%Y%m%d)/ --recursive

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7.4 配置管理难题

问题：如何确保不同环境（开发、测试、生产）的配置一致性？

解决方案：

1. 使用Kustomize管理环境差异
2. 实现配置模板和参数化
3. 使用GitOps工作流同步配置
4. 配置版本控制和审计

# Kustomize基础配置
# base/deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
name: myapp
spec:
replicas: 2
selector:
matchLabels:
app: myapp
template:
metadata:
labels:
app: myapp
spec:
containers:
- name: myapp
image: myregistry/myapp:1.0.0
ports:
- containerPort: 8080
env:
- name: LOG_LEVEL
value: "INFO"

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# Kustomize覆盖配置（开发环境）
# overlays/development/kustomization.yaml
apiVersion: kustomize.config.k8s.io/v1beta1
kind: Kustomization
resources:
- ../../base
patchesStrategicMerge:
- |-
apiVersion: apps/v1
kind: Deployment
metadata:
name: myapp
spec:
replicas: 1
template:
spec:
containers:
- name: myapp
env:
- name: LOG_LEVEL
value: "DEBUG"
- name: DATABASE_URL
value: "jdbc:mysql://dev-db:3306/myapp_dev"

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# Kustomize覆盖配置（生产环境）
# overlays/production/kustomization.yaml
apiVersion: kustomize.config.k8s.io/v1beta1
kind: Kustomization
resources:
- ../../base
patchesStrategicMerge:
- |-
apiVersion: apps/v1
kind: Deployment
metadata:
name: myapp
spec:
replicas: 5
template:
spec:
containers:
- name: myapp
env:
- name: LOG_LEVEL
value: "WARN"
- name: DATABASE_URL
value: "jdbc:mysql://prod-db.cluster.example.com:3306/myapp_prod"
resources:
requests:
memory: "512Mi"
cpu: "500m"
limits:
memory: "1Gi"
cpu: "1000m"

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问题：如何安全地管理微服务中的敏感信息（如密码、API密钥）？

解决方案：

1. 使用Kubernetes Secret对象
2. 集成外部密钥管理系统（如HashiCorp Vault）
3. 实现密钥轮换策略
4. 使用密钥注入工具（如Sealed Secrets）

# 使用外部密钥管理系统的示例
apiVersion: v1
kind: Secret
metadata:
name: db-credentials
annotations:
vault.security.banzaicloud.io/vault-role: "database"
vault.security.banzaicloud.io/vault-path: "database/creds/myapp"
type: Opaque

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# Sealed Secret示例
# 1. 创建普通Secret
apiVersion: v1
kind: Secret
metadata:
name: my-secret
type: Opaque
data:
username: YWRtaW4=
password: MWYyZDFlMmU2N2Rm
# 2. 使用kubeseal工具加密
kubeseal --format yaml --cert mycert.pem < my-secret.yaml > my-sealed-secret.yaml
# 3. 生成的SealedSecret可以安全地提交到代码库
apiVersion: bitnami.com/v1alpha1
kind: SealedSecret
metadata:
creationTimestamp: null
name: my-secret
namespace: my-namespace
spec:
encryptedData:
password: AgBy3i4OJSWK+PiTySYZZA9rO43cGDEq4KuP1j4oFQm6S8Qw7JF5Yg1x8uyRVlMG6yBhU6G9XuVqYzY6j5Z5J5O5i9j7k5l5m5n5o5p5q5r5s5t5u5v5w==
username: AgBy3i4OJSWK+PiTySYZZA9rO43cGDEq4KuP1j4oFQm6S8Qw7JF5Yg1x8uyRVlMG6yBhU6G9XuVqYzY6j5Z5J5O5i9j7k5l5m5n5o5p5q5r5s5t5u5v5w==
template:
metadata:
creationTimestamp: null
name: my-secret
namespace: my-namespace
type: Opaque

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8. Kubernetes运维效率提升策略

8.1 自动化运维

使用Jenkins、GitLab CI或GitHub Actions实现自动化CI/CD流水线。

# GitHub Actions示例
name: Build and Deploy to Kubernetes
on:
push:
branches: [ main ]
env:
REGISTRY: ghcr.io
IMAGE_NAME: ${{ github.repository }}
jobs:
build-and-push:
runs-on: ubuntu-latest
permissions:
contents: read
packages: write
steps:
- name: Checkout repository
uses: actions/checkout@v2
- name: Log in to the Container registry
uses: docker/login-action@f054a8b539a109f9f41c372932f1ae047eff08c9
with:
registry: ${{ env.REGISTRY }}
username: ${{ github.actor }}
password: ${{ secrets.GITHUB_TOKEN }}
- name: Extract metadata
id: meta
uses: docker/metadata-action@98669ae865ea3cffbcbaa878cf57c20bbf1c6c38
with:
images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
- name: Build and push Docker image
uses: docker/build-push-action@ad44023a93711e3deb337508980b4b5e9bcdc5dc
with:
context: .
push: true
tags: ${{ steps.meta.outputs.tags }}
labels: ${{ steps.meta.outputs.labels }}
deploy:
needs: build-and-push
runs-on: ubuntu-latest
steps:
- name: Checkout repository
uses: actions/checkout@v2
- name: Setup kubectl
uses: azure/setup-kubectl@v1
with:
version: '1.22.0'
- name: Configure kubeconfig
run: |
mkdir -p $HOME/.kube
echo "${{ secrets.KUBECONFIG }}" | base64 -d > $HOME/.kube/config
chmod 600 $HOME/.kube/config
- name: Deploy to Kubernetes
run: |
# 更新镜像标签
sed -i "s|IMAGE_TAG|${{ github.sha }}|g" k8s/deployment.yaml
# 应用配置
kubectl apply -f k8s/
# 验证部署
kubectl rollout status deployment/myapp

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使用Shell脚本或Python编写自动化运维脚本，提高日常操作效率。

#!/bin/bash
# Kubernetes集群健康检查脚本
# 检查节点状态
echo "=== 检查节点状态 ==="
kubectl get nodes
# 检查系统Pod状态
echo -e "\n=== 检查系统Pod状态 ==="
kubectl get pods -n kube-system
# 检查所有命名空间中的异常Pod
echo -e "\n=== 检查异常Pod ==="
kubectl get pods --all-namespaces --field-selector=status.phase!=Running,status.phase!=Succeeded
# 检查未就绪的Deployment
echo -e "\n=== 检查未就绪的Deployment ==="
kubectl get deployments --all-namespaces -o custom-columns=NAME:.metadata.namespace/.metadata.name,READY:.status.readyReplicas,UP_TO_DATE:.status.updatedReplicas,AVAILABLE:.status.availableReplicas | awk '$2!=$3 || $2!=$4'
# 检查资源使用情况
echo -e "\n=== 检查资源使用情况 ==="
kubectl top nodes
# 检查证书过期情况
echo -e "\n=== 检查证书过期情况 ==="
kubectl get secrets -n kube-system -o jsonpath='{range .items[*]}{.metadata.name}{"\t"}{.data.ca\.crt}{"\n"}{end}' | while read -r secret_name ca_crt_base64; do
if [ -n "$ca_crt_base64" ]; then
echo "$ca_crt_base64" | base64 -d | openssl x509 -noout -dates | grep notAfter | awk -F'=' '{print $2}' | xargs -I {} date -d {} -D "%b %d %H:%M:%S %Y %Z" +"%Y-%m-%d" | while read -r expiry_date; do
current_date=$(date +"%Y-%m-%d")
days_left=$(( ($(date -d "$expiry_date" +%s) - $(date -d "$current_date" +%s)) / 86400 ))
if [ "$days_left" -lt 30 ]; then
echo "警告: 证书 $secret_name 将在 $days_left 天后过期 ($expiry_date)"
fi
done
fi
done

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#!/usr/bin/env python3
# Kubernetes资源清理脚本
from kubernetes import client, config
import datetime
import argparse
def cleanup_completed_jobs(namespace, older_than_days):
config.load_kube_config()
batch_v1 = client.BatchV1Api()
now = datetime.datetime.now()
cutoff_date = now - datetime.timedelta(days=older_than_days)
print(f"清理 {namespace} 命名空间中早于 {older_than_days} 天的已完成Job...")
jobs = batch_v1.list_namespaced_job(namespace)
count = 0
for job in jobs.items:
if job.status.completion_time and job.status.completion_time < cutoff_date:
print(f"删除Job: {job.metadata.name} (完成时间: {job.status.completion_time})")
batch_v1.delete_namespaced_job(job.metadata.name, namespace)
count += 1
print(f"已删除 {count} 个Job")
def cleanup_failed_pods(namespace, older_than_days):
config.load_kube_config()
core_v1 = client.CoreV1Api()
now = datetime.datetime.now()
cutoff_date = now - datetime.timedelta(days=older_than_days)
print(f"清理 {namespace} 命名空间中早于 {older_than_days} 天的失败Pod...")
pods = core_v1.list_namespaced_pod(namespace)
count = 0
for pod in pods.items:
if pod.status.phase == "Failed" and pod.status.start_time and pod.status.start_time < cutoff_date:
print(f"删除Pod: {pod.metadata.name} (状态: {pod.status.phase}, 开始时间: {pod.status.start_time})")
core_v1.delete_namespaced_pod(pod.metadata.name, namespace)
count += 1
print(f"已删除 {count} 个Pod")
def main():
parser = argparse.ArgumentParser(description="Kubernetes资源清理工具")
parser.add_argument("--namespace", required=True, help="命名空间")
parser.add_argument("--older-than", type=int, default=7, help="清理早于N天的资源")
parser.add_argument("--jobs", action="store_true", help="清理已完成的Job")
parser.add_argument("--pods", action="store_true", help="清理失败的Pod")
args = parser.parse_args()
if args.jobs:
cleanup_completed_jobs(args.namespace, args.older_than)
if args.pods:
cleanup_failed_pods(args.namespace, args.older_than)
if __name__ == "__main__":
main()

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8.2 监控与告警

构建基于Prometheus、Grafana和AlertManager的全方位监控体系。

# AlertManager配置示例
apiVersion: v1
kind: ConfigMap
metadata:
name: alertmanager-config
namespace: monitoring
data:
config.yml: |
global:
resolve_timeout: 5m
smtp_smarthost: 'smtp.example.com:587'
smtp_from: 'alerts@example.com'
smtp_auth_username: 'alerts@example.com'
smtp_auth_password: 'password'
route:
group_by: ['alertname', 'cluster', 'service']
group_wait: 30s
group_interval: 5m
repeat_interval: 12h
receiver: 'web.hook'
routes:
- match:
alertname: DeadMansSwitch
receiver: 'deadmansswitch'
- match:
service: database
receiver: 'database-pager'
- match_re:
service: ^(api|web).*
receiver: 'webhook'
receivers:
- name: 'deadmansswitch'
- name: 'web.hook'
webhook_configs:
- url: 'http://127.0.0.1:5001/'
- name: 'database-pager'
email_configs:
- to: 'database-team@example.com'
- name: 'webhook'
webhook_configs:
- url: 'http://webhook.example.com/alerts'

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# Prometheus告警规则示例
apiVersion: v1
kind: ConfigMap
metadata:
name: prometheus-alerts
namespace: monitoring
data:
alerts.yml: |
groups:
- name: kubernetes-apps
rules:
- alert: PodCrashLooping
expr: rate(kube_pod_container_status_restarts_total[15m]) * 60 > 0
for: 15m
labels:
severity: critical
annotations:
summary: "Pod {{ $labels.pod }} is crash looping"
description: "Pod {{ $labels.pod }} ({{ $labels.namespace }}) is in crash loop back-off state."
- alert: PodNotReady
expr: sum by (namespace, pod) (kube_pod_status_ready{condition="false"}) == 1
for: 10m
labels:
severity: warning
annotations:
summary: "Pod {{ $labels.pod }} is not ready"
description: "Pod {{ $labels.pod }} ({{ $labels.namespace }}) has been in a non-ready state for more than 10 minutes."
- name: kubernetes-resources
rules:
- alert: NodeMemoryUsage
expr: (1 - (node_memory_MemAvailable_bytes / node_memory_MemTotal_bytes)) * 100 > 85
for: 5m
labels:
severity: warning
annotations:
summary: "Node memory usage is high"
description: "Node {{ $labels.instance }} memory usage is above 85% (current value: {{ $value }}%)."
- alert: NodeDiskUsage
expr: (1 - (node_filesystem_avail_bytes{fstype!="tmpfs"} / node_filesystem_size_bytes{fstype!="tmpfs"})) * 100 > 85
for: 5m
labels:
severity: warning
annotations:
summary: "Node disk usage is high"
description: "Node {{ $labels.instance }} disk usage is above 85% (current value: {{ $value }}%)."

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使用ELK或EFK栈实现日志分析与告警。

# Elasticsearch告警规则示例（使用ElastAlert）
apiVersion: v1
kind: ConfigMap
metadata:
name: elastalert-config
namespace: logging
data:
config.yaml: |
rules_folder: /etc/elastalert/rules
run_every:
seconds: 10
buffer_time:
minutes: 15
es_host: elasticsearch
es_port: 9200
writeback_index: elastalert_status
writeback_alias: elastalert_alerts
alert_time_limit:
days: 2

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# 告警规则示例
apiVersion: v1
kind: ConfigMap
metadata:
name: elastalert-rules
namespace: logging
data:
error_frequency.yaml: |
name: High Error Frequency
type: frequency
index: kubernetes-*
num_events: 50
timeframe:
minutes: 5
filter:
- query:
query_string:
query: "log_level: ERROR"
alert:
- "email"
email: "devops@example.com"
alert_text: "High error frequency detected in service {0}"
alert_text_args: ["kubernetes.pod_name"]
alert_subject: "High Error Frequency Alert"

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8.3 运维工具链

使用Rancher、Lens或kubectl插件实现多集群管理。

# 安装kubectl插件kubectx和kubens
git clone https://github.com/ahmetb/kubectx /opt/kubectx
ln -sf /opt/kubectx/kubectx /usr/local/bin/kubectx
ln -sf /opt/kubectx/kubens /usr/local/bin/kubens
# 配置多集群访问
kubectl config view --flatten > ~/.kube/config
# 使用kubectx切换集群
kubectx prod-cluster
kubectx dev-cluster
# 使用kubens切换命名空间
kubens production
kubens development

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# Rancher集群配置示例
apiVersion: provisioning.cattle.io/v1
kind: Cluster
metadata:
name: production-cluster
namespace: fleet-default
spec:
rkeConfig:
machinePools:
- name: control-plane
controlPlaneRole: true
etcdRole: true
quantity: 3
machineConfigRef:
name: control-plane-config
namespace: fleet-default
- name: worker
workerRole: true
quantity: 5
machineConfigRef:
name: worker-config
namespace: fleet-default

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使用Helm、Kustomize或Operator SDK简化资源管理。

# Helm Chart示例
# Chart.yaml
apiVersion: v2
name: myapp
description: A Helm chart for my application
version: 0.1.0
appVersion: 1.0.0
# values.yaml
replicaCount: 3
image:
repository: myregistry/myapp
pullPolicy: IfNotPresent
tag: "1.0.0"
service:
type: ClusterIP
port: 80
ingress:
enabled: true
annotations:
kubernetes.io/ingress.class: nginx
hosts:
- host: myapp.example.com
paths: ["/"]
resources:
limits:
cpu: 100m
memory: 128Mi
requests:
cpu: 100m
memory: 128Mi
autoscaling:
enabled: true
minReplicas: 3
maxReplicas: 10
targetCPUUtilizationPercentage: 80

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# Operator示例（使用Operator SDK）
# api/v1alpha1/myapp_types.go
package v1alpha1
import (
metav1 "k8s.io/apimachinery/pkg/apis/meta/v1"
)
// MyAppSpec defines the desired state of MyApp
type MyAppSpec struct {
// Size is the size of the myapp deployment
Size int32 `json:"size"`
// Image is the container image to use
Image string `json:"image"`
// Port is the port the container should expose
Port int32 `json:"port"`
}
// MyAppStatus defines the observed state of MyApp
type MyAppStatus struct {
// Nodes represents the current nodes in the myapp cluster
Nodes []string `json:"nodes"`
}
// +kubebuilder:object:root=true
// +kubebuilder:subresource:status
// MyApp is the Schema for the myapps API
type MyApp struct {
metav1.TypeMeta `json:",inline"`
metav1.ObjectMeta `json:"metadata,omitempty"`
Spec MyAppSpec `json:"spec,omitempty"`
Status MyAppStatus `json:"status,omitempty"`
}
// +kubebuilder:object:root=true
// MyAppList contains a list of MyApp
type MyAppList struct {
metav1.TypeMeta `json:",inline"`
metav1.ListMeta `json:"metadata,omitempty"`
Items []MyApp `json:"items"`
}
func init() {
SchemeBuilder.Register(&MyApp{}, &MyAppList{})
}

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// controllers/myapp_controller.go
package controllers
import (
"context"
"fmt"
"time"
appsv1 "k8s.io/api/apps/v1"
corev1 "k8s.io/api/core/v1"
"k8s.io/apimachinery/pkg/api/errors"
"k8s.io/apimachinery/pkg/api/resource"
metav1 "k8s.io/apimachinery/pkg/apis/meta/v1"
"k8s.io/apimachinery/pkg/types"
"k8s.io/apimachinery/pkg/runtime"
ctrl "sigs.k8s.io/controller-runtime"
"sigs.k8s.io/controller-runtime/pkg/client"
myappv1alpha1 "myapp.domain.com/api/v1alpha1"
)
// MyAppReconciler reconciles a MyApp object
type MyAppReconciler struct {
client.Client
Scheme *runtime.Scheme
}
// +kubebuilder:rbac:groups=myapp.domain.com,resources=myapps,verbs=get;list;watch;create;update;patch;delete
// +kubebuilder:rbac:groups=myapp.domain.com,resources=myapps/status,verbs=get;update;patch
// +kubebuilder:rbac:groups=apps,resources=deployments,verbs=get;list;watch;create;update;patch;delete
// +kubebuilder:rbac:groups=core,resources=services,verbs=get;list;watch;create;update;patch;delete
func (r *MyAppReconciler) Reconcile(ctx context.Context, req ctrl.Request) (ctrl.Result, error) {
reqLogger := log.WithValues("Request.Namespace", req.Namespace, "Request.Name", req.Name)
reqLogger.Info("Reconciling MyApp")
// Fetch the MyApp instance
myapp := &myappv1alpha1.MyApp{}
err := r.Get(ctx, req.NamespacedName, myapp)
if err != nil {
if errors.IsNotFound(err) {
// Request object not found, could have been deleted after reconcile request.
// Owned objects are automatically garbage collected. For additional cleanup logic use finalizers.
// Return and don't requeue
reqLogger.Info("MyApp resource not found. Ignoring since object must be deleted.")
return ctrl.Result{}, nil
}
// Error reading the object - requeue the request.
reqLogger.Error(err, "Failed to get MyApp")
return ctrl.Result{}, err
}
// Check if the deployment already exists, if not create a new one
found := &appsv1.Deployment{}
err = r.Get(ctx, types.NamespacedName{Name: myapp.Name, Namespace: myapp.Namespace}, found)
if err != nil && errors.IsNotFound(err) {
// Define a new deployment
dep := r.deploymentForMyApp(myapp)
reqLogger.Info("Creating a new Deployment", "Deployment.Namespace", dep.Namespace, "Deployment.Name", dep.Name)
err = r.Create(ctx, dep)
if err != nil {
reqLogger.Error(err, "Failed to create new Deployment", "Deployment.Namespace", dep.Namespace, "Deployment.Name", dep.Name)
return ctrl.Result{}, err
}
// Deployment created successfully - return and requeue
return ctrl.Result{Requeue: true}, nil
} else if err != nil {
reqLogger.Error(err, "Failed to get Deployment")
return ctrl.Result{}, err
}
// Ensure the deployment size is the same as the spec
size := myapp.Spec.Size
if *found.Spec.Replicas != size {
found.Spec.Replicas = &size
err = r.Update(ctx, found)
if err != nil {
reqLogger.Error(err, "Failed to update Deployment", "Deployment.Namespace", found.Namespace, "Deployment.Name", found.Name)
return ctrl.Result{}, err
}
// Spec updated - return and requeue
return ctrl.Result{Requeue: true}, nil
}
// Update the MyApp status with the pod names
// List the pods for this myapp's deployment
podList := &corev1.PodList{}
listOpts := []client.ListOption{
client.InNamespace(myapp.Namespace),
client.MatchingLabels(labelsForMyApp(myapp.Name)),
}
if err = r.List(ctx, podList, listOpts...); err != nil {
reqLogger.Error(err, "Failed to list pods", "MyApp.Namespace", myapp.Namespace, "MyApp.Name", myapp.Name)
return ctrl.Result{}, err
}
podNames := getPodNames(podList.Items)
// Update status.Nodes if needed
if !reflect.DeepEqual(podNames, myapp.Status.Nodes) {
myapp.Status.Nodes = podNames
err := r.Status().Update(ctx, myapp)
if err != nil {
reqLogger.Error(err, "Failed to update MyApp status")
return ctrl.Result{}, err
}
}
return ctrl.Result{RequeueAfter: time.Minute * 5}, nil
}
func (r *MyAppReconciler) deploymentForMyApp(m *myappv1alpha1.MyApp) *appsv1.Deployment {
labels := labelsForMyApp(m.Name)
replicas := m.Spec.Size
dep := &appsv1.Deployment{
ObjectMeta: metav1.ObjectMeta{
Name: m.Name,
Namespace: m.Namespace,
},
Spec: appsv1.DeploymentSpec{
Replicas: &replicas,
Selector: &metav1.LabelSelector{
MatchLabels: labels,
},
Template: corev1.PodTemplateSpec{
ObjectMeta: metav1.ObjectMeta{
Labels: labels,
},
Spec: corev1.PodSpec{
Containers: []corev1.Container{{
Image: m.Spec.Image,
Name: m.Name,
Ports: []corev1.ContainerPort{{
ContainerPort: m.Spec.Port,
Name: "http",
}},
Resources: corev1.ResourceRequirements{
Limits: corev1.ResourceList{
corev1.ResourceCPU: resource.MustParse("100m"),
corev1.ResourceMemory: resource.MustParse("100Mi"),
},
Requests: corev1.ResourceList{
corev1.ResourceCPU: resource.MustParse("50m"),
corev1.ResourceMemory: resource.MustParse("50Mi"),
},
},
}},
},
},
},
}
// Set MyApp instance as the owner and controller
ctrl.SetControllerReference(m, dep, r.Scheme)
return dep
}
func labelsForMyApp(name string) map[string]string {
return map[string]string{"app": "myapp", "myapp_cr": name}
}
func getPodNames(pods []corev1.Pod) []string {
var podNames []string
for _, pod := range pods {
podNames = append(podNames, pod.Name)
}
return podNames
}
func (r *MyAppReconciler) SetupWithManager(mgr ctrl.Manager) error {
return ctrl.NewControllerManagedBy(mgr).
For(&myappv1alpha1.MyApp{}).
Owns(&appsv1.Deployment{}).
Complete(r)
}

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9. 高级主题与最佳实践

9.1 服务网格

部署和使用Istio服务网格增强微服务通信管理。

# Istio Operator部署
apiVersion: install.istio.io/v1alpha1
kind: IstioOperator
metadata:
namespace: istio-system
name: istio-controlplane
spec:
profile: default
components:
pilot:
k8s:
env:
- name: PILOT_TRACE_SAMPLING
value: "100"
ingressGateways:
- name: istio-ingressgateway
enabled: true
k8s:
serviceAnnotations:
service.beta.kubernetes.io/aws-load-balancer-type: nlb
service:
type: LoadBalancer
ports:
- port: 80
targetPort: 8080
name: http2
- port: 443
targetPort: 8443
name: https
egressGateways:
- name: istio-egressgateway
enabled: true
values:
global:
meshID: mesh1
network: network1

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# Istio VirtualService示例
apiVersion: networking.istio.io/v1alpha3
kind: VirtualService
metadata:
name: reviews
spec:
hosts:
- reviews
http:
- match:
- headers:
end-user:
exact: jason
fault:
delay:
percentage:
value: 100
fixedDelay: 7s
route:
- destination:
host: reviews
subset: v2
- route:
- destination:
host: reviews
subset: v3

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# Istio DestinationRule示例
apiVersion: networking.istio.io/v1alpha3
kind: DestinationRule
metadata:
name: reviews
spec:
host: reviews
trafficPolicy:
connectionPool:
tcp:
maxConnections: 100
connectTimeout: 30ms
tcpKeepalive:
time: 7200s
interval: 75s
http:
http1MaxPendingRequests: 100
http2MaxRequests: 1000
maxRetries: 3
idleTimeout: 90s
h2UpgradePolicy: UPGRADE
outlierDetection:
consecutiveGatewayErrors: 5
interval: 30s
baseEjectionTime: 30s
maxEjectionPercent: 50
subsets:
- name: v1
labels:
version: v1
- name: v2
labels:
version: v2
- name: v3
labels:
version: v3

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部署和使用Linkerd服务网格，提供轻量级服务网格解决方案。

# 安装Linkerd CLI
curl -sL https://run.linkerd.io/install | sh
# 验证Kubernetes集群配置
linkerd check --pre
# 安装Linkerd控制平面
linkerd install | kubectl apply -f -
# 验证安装
linkerd check
# 注入Linkerd到命名空间
kubectl get namespaces -l linkerd.io/control-plane-ns=linkerd
kubectl annotate namespace default linkerd.io/inject=enabled

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# Linkerd服务配置示例
apiVersion: split.smi-spec.io/v1alpha1
kind: TrafficSplit
metadata:
name: reviews-split
spec:
service: reviews
backends:
- service: reviews-v1
weight: 50
- service: reviews-v2
weight: 50

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9.2 Serverless架构

使用Knative在Kubernetes上构建Serverless应用。

# Knative Service示例
apiVersion: serving.knative.dev/v1
kind: Service
metadata:
name: hello
spec:
template:
spec:
containers:
- image: gcr.io/knative-samples/helloworld-go
ports:
- containerPort: 8080
env:
- name: TARGET
value: "Knative"

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# Knative事件示例
apiVersion: sources.knative.dev/v1
kind: ContainerSource
metadata:
name: heartbeat-source
spec:
template:
spec:
containers:
- image: gcr.io/knative-releases/knative.dev/eventing/cmd/heartbeats
name: heartbeats
args:
- --period=1
env:
- name: POD_NAME
value: "heartbeats"
- name: POD_NAMESPACE
value: "default"
sink:
ref:
apiVersion: serving.knative.dev/v1
kind: Service
name: event-display

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使用OpenFaaS构建Serverless函数。

# OpenFaaS函数示例
apiVersion: openfaas.com/v1
kind: Function
metadata:
name: echo
namespace: openfaas-fn
spec:
name: echo
image: functions/echo:latest
labels:
com.openfaas.scale.min: "2"
com.openfaas.scale.max: "10"
com.openfaas.scale.factor: "20"
environment:
write_debug: "true"

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# OpenFaaS异步函数示例
apiVersion: openfaas.com/v1
kind: Function
metadata:
name: async-echo
namespace: openfaas-fn
spec:
name: async-echo
image: functions/async-echo:latest
labels:
com.openfaas.scale.min: "1"
com.openfaas.scale.max: "5"
annotations:
com.openfaas.async: "true"
com.openfaas.topic: "echo-topic"

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9.3 多集群管理

使用Kubernetes Federation v2实现多集群管理。

# KubeFed控制平面部署
apiVersion: types.kubefed.io/v1beta1
kind: KubeFedCluster
metadata:
name: cluster2
namespace: kube-federation-system
spec:
apiEndpoint: https://cluster2.example.com
secretRef:
name: cluster2-secret
caBundle: LS0tLS1CRUdJTiBDRVJUSUZJQ0FURS0tLS0t...

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# 联邦资源示例
apiVersion: types.kubefed.io/v1beta1
kind: FederatedDeployment
metadata:
name: myapp
namespace: default
spec:
template:
spec:
replicas: 3
selector:
matchLabels:
app: myapp
template:
metadata:
labels:
app: myapp
spec:
containers:
- name: myapp
image: myregistry/myapp:1.0.0
ports:
- containerPort: 8080
placement:
clusters:
- name: cluster1
- name: cluster2
overrides:
- clusterName: cluster2
clusterOverrides:
- path: "spec.replicas"
value: 5

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使用Rancher实现多集群统一管理。

# Rancher集群配置
apiVersion: provisioning.cattle.io/v1
kind: Cluster
metadata:
name: cluster1
namespace: fleet-default
spec:
rkeConfig:
machinePools:
- name: control-plane
controlPlaneRole: true
etcdRole: true
quantity: 3
machineConfigRef:
name: control-plane-config
namespace: fleet-default
- name: worker
workerRole: true
quantity: 5
machineConfigRef:
name: worker-config
namespace: fleet-default
---
apiVersion: provisioning.cattle.io/v1
kind: Cluster
metadata:
name: cluster2
namespace: fleet-default
spec:
rkeConfig:
machinePools:
- name: control-plane
controlPlaneRole: true
etcdRole: true
quantity: 3
machineConfigRef:
name: control-plane-config
namespace: fleet-default
- name: worker
workerRole: true
quantity: 5
machineConfigRef:
name: worker-config
namespace: fleet-default

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# Rancher多集群项目
apiVersion: management.cattle.io/v3
kind: Project
metadata:
name: multi-cluster-project
namespace: fleet-default
spec:
clusterName: cluster1
description: "Multi-cluster project"
displayName: "Multi-cluster Project"
projectTemplateId: local:p-xxxxx

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9.4 GitOps实践

使用Argo CD实现GitOps工作流。

# Argo CD Application示例
apiVersion: argoproj.io/v1alpha1
kind: Application
metadata:
name: myapp
namespace: argocd
spec:
project: default
source:
repoURL: https://github.com/myorg/myapp-k8s-manifests.git
targetRevision: HEAD
path: overlays/production
destination:
server: https://kubernetes.default.svc
namespace: production
syncPolicy:
automated:
prune: true
selfHeal: true
syncOptions:
- Validate=false

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# Argo CD App of Apps模式
apiVersion: argoproj.io/v1alpha1
kind: Application
metadata:
name: root-app
namespace: argocd
spec:
project: default
source:
repoURL: https://github.com/myorg/myapp-k8s-manifests.git
targetRevision: HEAD
path: apps
destination:
server: https://kubernetes.default.svc
namespace: argocd
syncPolicy:
automated:
prune: true
selfHeal: true

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使用Flux CD实现GitOps工作流。

# Flux HelmRelease示例
apiVersion: helm.toolkit.fluxcd.io/v2beta1
kind: HelmRelease
metadata:
name: myapp
namespace: production
spec:
interval: 5m
chart:
spec:
chart: myapp
version: "1.0.0"
sourceRef:
kind: HelmRepository
name: myapp-charts
namespace: flux-system
values:
image:
repository: myregistry/myapp
tag: "1.0.0"
replicaCount: 3
service:
type: ClusterIP
port: 80
ingress:
enabled: true
annotations:
kubernetes.io/ingress.class: nginx
hosts:
- host: myapp.example.com
paths: ["/"]

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# Flux Kustomization示例
apiVersion: kustomize.toolkit.fluxcd.io/v1beta2
kind: Kustomization
metadata:
name: myapp
namespace: production
spec:
interval: 5m
path: "./overlays/production"
prune: true
sourceRef:
kind: GitRepository
name: myapp-repo
namespace: flux-system
validation: client
healthChecks:
- kind: Deployment
name: myapp
namespace: production

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10. 总结与展望

10.1 Kubernetes最佳实践总结

在本文中，我们全面探讨了Kubernetes微服务部署的各个方面，从基础概念到高级应用。以下是关键最佳实践的总结：

1. 资源管理：合理设置Pod的资源请求和限制使用LimitRange和ResourceQuota控制资源使用实施HPA和VPA实现自动扩缩容
2. 合理设置Pod的资源请求和限制
3. 使用LimitRange和ResourceQuota控制资源使用
4. 实施HPA和VPA实现自动扩缩容
5. 网络与通信：使用Service实现服务发现和负载均衡实施NetworkPolicy控制网络流量考虑使用服务网格（如Istio或Linkerd）增强微服务通信
6. 使用Service实现服务发现和负载均衡
7. 实施NetworkPolicy控制网络流量
8. 考虑使用服务网格（如Istio或Linkerd）增强微服务通信
9. 存储管理：为有状态应用使用StatefulSet和持久化存储实现定期备份和恢复策略使用StorageClass支持动态卷供应
10. 为有状态应用使用StatefulSet和持久化存储
11. 实现定期备份和恢复策略
12. 使用StorageClass支持动态卷供应
13. 配置管理：使用ConfigMap和Secret管理配置实现多环境配置管理（如Kustomize）安全管理敏感信息（如Sealed Secrets）
14. 使用ConfigMap和Secret管理配置
15. 实现多环境配置管理（如Kustomize）
16. 安全管理敏感信息（如Sealed Secrets）
17. 安全实践：实施RBAC控制访问权限使用Pod安全策略或Pod安全准入控制器定期更新和扫描容器镜像
18. 实施RBAC控制访问权限
19. 使用Pod安全策略或Pod安全准入控制器
20. 定期更新和扫描容器镜像
21. 监控与日志：建立全面的监控体系（如Prometheus和Grafana）实现集中式日志收集和分析（如EFK栈）设置有效的告警机制
22. 建立全面的监控体系（如Prometheus和Grafana）
23. 实现集中式日志收集和分析（如EFK栈）
24. 设置有效的告警机制
25. 自动化运维：实现CI/CD流水线自动化部署使用GitOps工作流管理配置开发自动化运维脚本和工具
26. 实现CI/CD流水线自动化部署
27. 使用GitOps工作流管理配置
28. 开发自动化运维脚本和工具
29. 高可用与灾备：实现多区域部署提高可用性定期测试备份和恢复流程建立灾难恢复计划
30. 实现多区域部署提高可用性
31. 定期测试备份和恢复流程
32. 建立灾难恢复计划

资源管理：

• 合理设置Pod的资源请求和限制
• 使用LimitRange和ResourceQuota控制资源使用
• 实施HPA和VPA实现自动扩缩容

网络与通信：

• 使用Service实现服务发现和负载均衡
• 实施NetworkPolicy控制网络流量
• 考虑使用服务网格（如Istio或Linkerd）增强微服务通信

存储管理：

• 为有状态应用使用StatefulSet和持久化存储
• 实现定期备份和恢复策略
• 使用StorageClass支持动态卷供应

配置管理：

• 使用ConfigMap和Secret管理配置
• 实现多环境配置管理（如Kustomize）
• 安全管理敏感信息（如Sealed Secrets）

安全实践：

• 实施RBAC控制访问权限
• 使用Pod安全策略或Pod安全准入控制器
• 定期更新和扫描容器镜像

监控与日志：

• 建立全面的监控体系（如Prometheus和Grafana）
• 实现集中式日志收集和分析（如EFK栈）
• 设置有效的告警机制

自动化运维：

• 实现CI/CD流水线自动化部署
• 使用GitOps工作流管理配置
• 开发自动化运维脚本和工具

高可用与灾备：

• 实现多区域部署提高可用性
• 定期测试备份和恢复流程
• 建立灾难恢复计划

10.2 Kubernetes生态系统发展趋势

Kubernetes生态系统正在快速发展，以下是一些值得关注的趋势：

1. 边缘计算：K3s等轻量级Kubernetes发行版在边缘场景的应用KubeEdge等边缘计算框架的成熟5G与Kubernetes结合的边缘应用
2. K3s等轻量级Kubernetes发行版在边缘场景的应用
3. KubeEdge等边缘计算框架的成熟
4. 5G与Kubernetes结合的边缘应用
5. Serverless：Knative、OpenFaaS等Serverless框架的普及事件驱动架构在Kubernetes上的应用FaaS（函数即服务）与微服务的结合
6. Knative、OpenFaaS等Serverless框架的普及
7. 事件驱动架构在Kubernetes上的应用
8. FaaS（函数即服务）与微服务的结合
9. 服务网格：服务网格技术的标准化（如SMI规范）服务网格与无代理模式的演进服务网格在多集群环境中的应用
10. 服务网格技术的标准化（如SMI规范）
11. 服务网格与无代理模式的演进
12. 服务网格在多集群环境中的应用
13. GitOps：GitOps成为云原生应用部署的主流模式GitOps工具链的成熟和多样化GitOps与DevSecOps的融合
14. GitOps成为云原生应用部署的主流模式
15. GitOps工具链的成熟和多样化
16. GitOps与DevSecOps的融合
17. AI/ML与Kubernetes：Kubeflow等机器学习平台的发展GPU资源调度和管理的优化AI驱动的智能运维（AIOps）
18. Kubeflow等机器学习平台的发展
19. GPU资源调度和管理的优化
20. AI驱动的智能运维（AIOps）
21. 安全增强：零信任网络架构的普及机密计算在Kubernetes上的应用安全策略即代码（Policy as Code）的实践
22. 零信任网络架构的普及
23. 机密计算在Kubernetes上的应用
24. 安全策略即代码（Policy as Code）的实践

边缘计算：

• K3s等轻量级Kubernetes发行版在边缘场景的应用
• KubeEdge等边缘计算框架的成熟
• 5G与Kubernetes结合的边缘应用

Serverless：

• Knative、OpenFaaS等Serverless框架的普及
• 事件驱动架构在Kubernetes上的应用
• FaaS（函数即服务）与微服务的结合

服务网格：

• 服务网格技术的标准化（如SMI规范）
• 服务网格与无代理模式的演进
• 服务网格在多集群环境中的应用

GitOps：

• GitOps成为云原生应用部署的主流模式
• GitOps工具链的成熟和多样化
• GitOps与DevSecOps的融合

AI/ML与Kubernetes：

• Kubeflow等机器学习平台的发展
• GPU资源调度和管理的优化
• AI驱动的智能运维（AIOps）

安全增强：

• 零信任网络架构的普及
• 机密计算在Kubernetes上的应用
• 安全策略即代码（Policy as Code）的实践

10.3 持续学习与提升

Kubernetes技术栈庞大且不断演进，持续学习至关重要。以下是一些建议：

1. 官方资源：定期阅读Kubernetes官方文档和博客参与Kubernetes社区活动和讨论关注CNCF（云原生计算基金会）的项目和活动
2. 定期阅读Kubernetes官方文档和博客
3. 参与Kubernetes社区活动和讨论
4. 关注CNCF（云原生计算基金会）的项目和活动
5. 实践项目：在个人项目中应用Kubernetes技术参与开源项目贡献搭建实验环境测试新功能
6. 在个人项目中应用Kubernetes技术
7. 参与开源项目贡献
8. 搭建实验环境测试新功能
9. 认证与培训：考取CKA、CKAD、CKS等Kubernetes认证参加专业培训课程参与技术会议和研讨会
10. 考取CKA、CKAD、CKS等Kubernetes认证
11. 参加专业培训课程
12. 参与技术会议和研讨会
13. 社区参与：加入本地Kubernetes用户组参与线上技术分享和讨论关注Kubernetes专家的博客和社交媒体
14. 加入本地Kubernetes用户组
15. 参与线上技术分享和讨论
16. 关注Kubernetes专家的博客和社交媒体

官方资源：

• 定期阅读Kubernetes官方文档和博客
• 参与Kubernetes社区活动和讨论
• 关注CNCF（云原生计算基金会）的项目和活动

实践项目：

• 在个人项目中应用Kubernetes技术
• 参与开源项目贡献
• 搭建实验环境测试新功能

认证与培训：

• 考取CKA、CKAD、CKS等Kubernetes认证
• 参加专业培训课程
• 参与技术会议和研讨会

社区参与：

• 加入本地Kubernetes用户组
• 参与线上技术分享和讨论
• 关注Kubernetes专家的博客和社交媒体

通过持续学习和实践，您可以不断提升Kubernetes技能，更好地应对企业级容器编排平台的挑战，提高运维效率，为业务发展提供强有力的技术支持。