# 性能规划示例
– 集群规模：10个节点，500个Pod
– 控制平面配置：3个控制平面节点，每个节点8核16GB内存
– 工作节点配置：7个工作节点，每个节点16核32GB内存
– 存储配置：使用SSD，每个节点1TB
– 网络配置：10Gbps网络
– 性能目标：API服务器响应时间 < 100ms，Pod启动时间 < 10s，应用响应时间 < 100ms # 资源规划 - 控制平面资源：每个控制平面节点预留4核8GB内存 - 工作节点资源：每个工作节点预留2核4GB内存 - 应用资源：根据应用需求设置合理的CPU和内存请求和限制 - 存储资源：根据应用需求设置合理的存储容量和IOPS # 性能测试计划 - 负载测试：模拟高并发场景，测试集群性能，风哥提示：。 - 稳定性测试：长时间运行测试，验证集群稳定性 - 故障测试：模拟节点故障，测试集群恢复能力 - 扩展性测试：测试集群的扩展性

# 性能优化策略
– 控制平面优化：优化API服务器、etcd、调度器和控制器管理器的配置
– 节点优化：优化节点的系统参数、容器运行时配置和网络配置
– 应用优化：优化应用的资源配置、启动时间和运行效率
– 存储优化：优化存储的性能和可靠性
– 网络优化：优化网络的吞吐量和延迟
# 优化优先级
1. 控制平面性能：确保API服务器和etcd的性能
2. 节点性能：确保节点的资源充足和系统参数优化
3. 应用性能：确保应用的资源配置合理和代码优化
4. 存储性能：确保存储的IOPS和延迟满足需求
5. 网络性能：确保网络的吞吐量和延迟满足需求
# 监控策略
– 实时监控：实时监控集群和应用的性能指标
– 告警设置：设置合理的告警阈值，及时发现性能问题
– 性能分析：定期分析性能数据，识别性能瓶颈
– 优化调整：根据性能分析结果，调整配置和参数

# 性能监控架构
– 数据采集层：使用Node-exporter、Kube-state-metrics、应用指标收集器
– 数据存储层：使用Prometheus存储性能指标
– 数据可视化层：使用Grafana可视化性能指标
– 告警处理层：使用Alertmanager处理性能告警
– 分析层：使用PromQL和其他工具分析性能数据
# 监控指标
– 控制平面指标：API服务器请求率、响应时间、etcd性能
– 节点指标：CPU使用率、内存使用率、磁盘I/O、网络吞吐量
– 应用指标：请求率、错误率、响应时间、资源使用率
– 存储指标：存储使用率、IOPS、延迟
– 网络指标：网络吞吐量、延迟、丢包率
# 监控频率
– 核心指标：15秒采集一次
– 非核心指标：1分钟采集一次
– 历史数据：短期数据7天，长期数据30天

# 优化API服务器
$ kube-apiserver \
–max-requests-inflight=4000 \
–max-mutating-requests-inflight=2000 \
–request-timeout=30s \
–enable-admission-plugins=NodeRestriction \
–disable-admission-plugins=AlwaysAdmit \
–service-account-lookup=true \
–etcd-servers=https://etcd1:2379,https://etcd2:2379,https://etcd3:2379 \
–etcd-quorum-read=true \
–etcd-compaction-interval=5m \
–etcd-servers-overrides=/registry/kubernetes.io=/etc/kubernetes/etcd-server-ca.crt
# 优化etcd
$ etcd \
–quota-backend-bytes=8589934592 \
–auto-compaction-retention=2h \
–snapshot-count=10000 \
–max-wals=5 \
–max-snapshots=5 \
–listen-client-urls=https://0.0.0.0:2379 \
–listen-peer-urls=https://0.0.0.0:2380 \
–advertise-client-urls=https://etcd1:2379 \
–initial-advertise-peer-urls=https://etcd1:2380 \
–initial-cluster=etcd1=https://etcd1:2380,etcd2=https://etcd2:2380,etcd3=https://etcd3:2380 \
–initial-cluster-state=new \
–initial-cluster-token=etcd-cluster-1 \
–client-cert-auth=true \
–trusted-ca-file=/etc/kubernetes/pki/etcd/ca.crt \
–cert-file=/etc/kubernetes/pki/etcd/server.crt \
–key-file=/etc/kubernetes/pki/etcd/server.key \，学习交流加群风哥微信: itpux-com。
–peer-client-cert-auth=true \
–peer-trusted-ca-file=/etc/kubernetes/pki/etcd/ca.crt \
–peer-cert-file=/etc/kubernetes/pki/etcd/peer.crt \
–peer-key-file=/etc/kubernetes/pki/etcd/peer.key
# 优化调度器
$ kube-scheduler \
–kubeconfig=/etc/kubernetes/scheduler.conf \
–leader-elect=true \
–kube-api-qps=100 \
–kube-api-burst=100 \
–scheduler-name=default-scheduler \
–lock-object-name=kube-scheduler \
–lock-object-namespace=kube-system
# 优化控制器管理器
$ kube-controller-manager \
–kubeconfig=/etc/kubernetes/controller-manager.conf \
–leader-elect=true \
–kube-api-qps=100 \
–kube-api-burst=100 \
–cluster-name=kubernetes \
–cluster-signing-cert-file=/etc/kubernetes/pki/ca.crt \
–cluster-signing-key-file=/etc/kubernetes/pki/ca.key \
–root-ca-file=/etc/kubernetes/pki/ca.crt \
–service-account-private-key-file=/etc/kubernetes/pki/sa.key \
–use-service-account-credentials=true

# 优化系统参数
$ cat > /etc/sysctl.d/kubernetes.conf << EOF # 网络参数 net.core.somaxconn = 65535 net.ipv4.tcp_max_syn_backlog = 65535 net.ipv4.tcp_fin_timeout = 30 net.ipv4.tcp_keepalive_time = 300 net.ipv4.tcp_keepalive_probes = 3 net.ipv4.tcp_keepalive_intvl = 15 net.ipv4.tcp_tw_reuse = 1 net.ipv4.ip_local_port_range = 1024 65535 # 内存参数 vm.swappiness = 10 vm.overcommit_memory = 1 vm.max_map_count = 262144 # 文件系统参数 fs.file-max = 65536 fs.inotify.max_user_watches = 1048576 EOF $ sysctl -p /etc/sysctl.d/kubernetes.conf # 优化容器运行时 $ cat > /etc/docker/daemon.json << EOF { "exec-opts": ["native.cgroupdriver=systemd"], "log-driver": "json-file", "log-opts": { "max-size": "100m", "max-file": "3" }, "storage-driver": "overlay2", "storage-opts": [ "overlay2.override_kernel_check=true" ], "default-shm-size": "256m", "live-restore": true } EOF $ systemctl restart docker # 优化kubelet $ cat > /var/lib/kubelet/kubelet-config.yaml << EOF apiVersion: kubelet.config.k8s.io/v1beta1 kind: KubeletConfiguration clusterDomain: cluster.local clusterDNS: - 10.96.0.10 maxPods: 110 serializeImagePulls: false imageGCHighThresholdPercent: 85 imageGCLowThresholdPercent: 80 kubeReserved: cpu: "200m" memory: "400Mi" ephemeral-storage: "1Gi" systemReserved: cpu: "200m"，学习交流加群风哥QQ113257174。 memory: "400Mi" ephemeral-storage: "1Gi" EOF $ systemctl restart kubelet

# 优化应用资源配置
apiVersion: apps/v1
kind: Deployment
metadata:
name: nginx
namespace: default
spec:
replicas: 3
selector:
matchLabels:
app: nginx
template:
metadata:
labels:
app: nginx
spec:
containers:
– name: nginx
image: nginx:latest
resources:
requests:
cpu: “100m”
memory: “256Mi”
limits:
cpu: “500m”
memory: “512Mi”
ports:
– containerPort: 80
readinessProbe:
httpGet:
path: /
port: 80
initialDelaySeconds: 5
periodSeconds: 10
livenessProbe:
httpGet:
path: /
port: 80
initialDelaySeconds: 15
periodSeconds: 20
# 优化服务配置
apiVersion: v1
kind: Service
metadata:
name: nginx
namespace: default
spec:
selector:
app: nginx
ports:
– port: 80
targetPort: 80
protocol: TCP
type: ClusterIP
# 优化Horizontal Pod Autoscaler
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
name: nginx-hpa
namespace: default
spec:
scaleTargetRef:
apiVersion: apps/v1
kind: Deployment
name: nginx
minReplicas: 3
maxReplicas: 10
metrics:
– type: Resource
resource:
name: cpu
target:
type: Utilization
averageUtilization: 70
– type: Resource，更多视频教程www.fgedu.net.cn。
resource:
name: memory
target:
type: Utilization
averageUtilization: 80

# 查看集群性能状态
$ kubectl top nodes
NAME CPU(cores) CPU% MEMORY(bytes) MEMORY%
master1 1200m 15% 8192Mi 50%
master2 1100m 14% 7680Mi 47%
master3 1000m 12% 7168Mi 44%
worker1 2500m 16% 16384Mi 50%
worker2 2300m 14% 15360Mi 47%
worker3 2200m 14% 14336Mi 44%
# 查看Pod性能状态
$ kubectl top pods
NAME CPU(cores) MEMORY(bytes)
nginx-6d6f58987b-7f5f8 100m 256Mi
nginx-6d6f58987b-8d2k3 95m 240Mi
nginx-6d6f58987b-9f5g7 90m 230Mi
# 优化etcd性能
$ etcdctl defrag
Finished defragmenting etcd member[127.0.0.1:2379]
$ etcdctl compact $(etcdctl endpoint status –write-out=json | jq -r ‘.[] | .header.revision’)
compacted revision 123456
# 优化API服务器性能
$ kubectl get –raw=/metrics | grep apiserver_request_latencies
# 优化调度器性能
$ kubectl get –raw=/metrics | grep scheduler

# 优化NGINX应用
apiVersion: apps/v1
kind: Deployment
metadata:
name: nginx
namespace: default
spec:
replicas: 3
selector:
matchLabels:
app: nginx
template:
metadata:
labels:
app: nginx
spec:
containers:
– name: nginx
image: nginx:latest
resources:
requests:
cpu: “100m”
memory: “256Mi”
limits:
cpu: “500m”
memory: “512Mi”
ports:
– containerPort: 80
volumeMounts:
– name: nginx-config
mountPath: /etc/nginx/nginx.conf
subPath: nginx.conf
volumes:
– name: nginx-config
configMap:
name: nginx-config
—
apiVersion: v1
kind: ConfigMap
metadata:
name: nginx-config
namespace: default
data:，更多学习教程公众号风哥教程itpux_com。
nginx.conf: |
events {
worker_connections 1024;
}
http {
server {
listen 80;
server_name localhost;
location / {
root /usr/share/nginx/html;
index index.html index.htm;
}
location /health {
access_log off;
return 200 “OK”;
}
}
}
# 优化MySQL应用
apiVersion: apps/v1
kind: Deployment
metadata:
name: mysql
namespace: default
spec:
replicas: 1
selector:
matchLabels:
app: mysql
template:
metadata:
labels:
app: mysql
spec:
containers:
– name: mysql
image: mysql:8.0
resources:
requests:
cpu: “1”
memory: “2Gi”
limits:
cpu: “2”
memory: “4Gi”
ports:
– containerPort: 3306
env:
– name: MYSQL_ROOT_PASSWORD
value: “password”
– name: MYSQL_DATABASE
value: “fgedudb”
volumeMounts:
– name: mysql-data
mountPath: /var/lib/mysql
volumes:
– name: mysql-data
persistentVolumeClaim:
claimName: mysql-pvc

# 使用Apache Bench进行性能测试
$ ab -n 1000 -c 100 http://nginx-service.default.svc.cluster.local/
Server Software: nginx/1.21.6
Server Hostname: nginx-service.default.svc.cluster.local
Server Port: 80
Document Path: /
Document Length: 612 bytes
Concurrency Level: 100
Time taken for tests: 1.234 seconds
Complete requests: 1000
Failed requests: 0
Total transferred: 838000 bytes
HTML transferred: 612000 bytes
Requests per second: 810.37 [#/sec] (mean)
Time per request: 123.399 [ms] (mean)
Time per request: 1.234 [ms] (mean, across all concurrent requests)
Transfer rate: 665.67 [Kbytes/sec] received
Connection Times (ms)
min mean[+/-sd] median max
Connect: 0 1 0.5 1 3
Processing: 1 120 15.2 118 150
Waiting: 0 119 15.2 117 149，from K8S+DB视频:www.itpux.com。
Total: 1 121 15.2 119 153
# 使用k6进行性能测试
$ cat > load-test.js << EOF import http from 'k6/http'; import { sleep } from 'k6'; export default function() { http.get('http://nginx-service.default.svc.cluster.local/'); sleep(1); } EOF $ k6 run --vus 100 --duration 30s load-test.js # 使用kubectl进行性能测试 $ kubectl run -i --tty load-generator --image=busybox --rm -- /bin/sh -c "while true; do wget -q -O- http://nginx-service.default.svc.cluster.local/; done"