【K8S 运维实战】26-大规模集群优化

运维大师2026-07-31 9:25

大规模集群:万节点集群优化经验

一句话定位:集群从 1000 到 10000 节点,不是"线性扩容",而是"处处撞墙"------本文把生产里真实踩过的瓶颈、参数、架构改造讲透,让你少走两年弯路。

写在前面

带过一个从 800 节点涨到 12000 节点的集群,过程中几乎把 K8s 每一层都改了一遍。1000 节点是"小集群",3000 节点开始"出问题",5000 节点"卡天花板",10000 节点"必须分片或拆集群"。这一篇把我踩过的所有坑整理出来,从 API Server → etcd → kube-proxy → 调度器 → 多集群架构,给一份"万节点集群容量规划表"和"瓶颈排查清单"。

K8s 官方说 1.30 单集群支持 5000 节点、15 万 Pod,但这是"理论上限",生产实践里要留 30% 余量,实际能扛的稳定水位在 3500-4500 节点。再大就要考虑分片调度器或多集群架构。

核心问题

集群规模上去了,性能怎么不崩?

瓶颈不是一处,是连锁的:

复制代码 
节点多 → Pod 多 → API Server QPS 高 → etcd 写入慢
                → watch 流量大 → API Server 内存爆
                → kube-proxy iptables 规则多 → 网络慢
                → 调度器打分慢 → Pod 启动慢
                → Service 数量多 → Endpoint 切换抖动
                → Controller Manager 队列堆积 → 资源同步延迟

每一段都要专门优化,缺一段就崩。

一、原理剖析

1.1 单集群规模上限分析

K8s 官方 SLA:5000 节点、15 万 Pod、30 万容器、单集群 ≤ 5 万 Service。
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API Server

max-requests-inflight 限制
etcd

存储 + fsync 延迟
kube-proxy

iptables 规则爆炸
Controller Manager

资源 watch 流量
Scheduler

扫描节点数
kubelet

与 APIServer 心跳流量
CoreDNS

Service 解析 QPS

5000 节点意味着 5 万 Pod、15 万容器(每 Pod 3 容器),各组件压力:

组件 1000 节点 5000 节点 瓶颈
API Server QPS 100 500+ max-requests-inflight
etcd db 大小 500MB 2.5GB quota-backend-bytes
etcd fsync 延迟 5ms 15ms+ 磁盘 IO
kube-proxy iptables 规则 1 万 5 万+ O(N) 匹配
EndpointSlice 数 1000 5000+ watch 流量
Pod 启动延迟 P99 3s 15s+ 调度器

1.2 API Server 优化

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readonly
Client 请求
AuthN/AuthZ
Admission
Inflight 限制
mutating max 200
readonly max 400
etcd 写入
etcd 读/watch cache

关键参数:

  • --max-requests-inflight:mutating 请求并发上限,默认 400
  • --max-mutating-requests-inflight:默认 200
  • --default-watch-cache-size:每个资源的 watch cache 大小,默认 100
  • --watch-cache-sizes:按资源单独设置,如 pod#5000,configmap#1000

watch cache 是关键优化点。每个 client(每个 kubelet、每个 controller)对 Pod 资源都会建立一个 watch,5000 kubelet 同时 watch Pod,API Server 内存爆。

1.3 etcd 分区与调优

etcd 单实例在 5000 节点下,典型 db 大小 2-4GB,fsync 延迟 P99 > 20ms。
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WAL 日志

fsync 同步
Backend DB

BoltDB
定期 Snapshot
压缩历史
瓶颈 1
WAL fsync 延迟
瓶颈 2
DB 压缩不及时
瓶颈 3
大 Range 查询

扫全表

三个优化方向:

  1. WAL 独立盘:NVMe SSD,fsync P99 < 5ms
  2. 压缩 + defrag :auto-compaction-retention=1h,每周 defrag 一次
  3. 避免大 Range 查询:Controller 改用 List + ResourceVersion watch,不用 List without limit

1.4 kube-proxy ipvs 大规模优化

iptables 在 5000 节点 + 1 万 Service 时,规则数 50 万+,kube-proxy 同步耗时 30s+,Pod 访问 Service 延迟 50ms+。

ipvs 模式下:

  • 规则查找复杂度 O(1)(hash 表)
  • 同步时间 < 5s
  • 但 ipvs -Ln 输出本身在大规模下也很慢

ipvs 的瓶颈在 EndpointSlice 同步,5000 节点 + 1 万 Service,每次 Endpoint 变更都触发 ipvs 规则更新,需要把 --min-sync-period 调到 1s 减少抖动。
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0s
Endpoint 变更
API Server watch
kube-proxy 收到事件
min-sync-period
批量同步 ipvs 规则
立即同步

大规模抖动

1.5 大规模 NetworkPolicy 性能

NetworkPolicy 在 iptables 模式下,规则数 = O(Pod × Policy),5000 节点 + 1000 Policy 时,iptables 规则上百万,严重拖垮网络。
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Calico eBPF
Cilium eBPF
kube-router
Pod 间流量
CNI 实现
顺序匹配规则

N 条 Policy
eBPF 程序

O1 查找
同上 + 更优
iptables 但简化

大规模集群推荐 Cilium eBPF,完全绕过 iptables,NetworkPolicy 在 eBPF 层执行,O(1) 查找。

1.6 Service 数量瓶颈

每个 Service 都对应:

  • 一条 iptables/ipvs 规则(kube-proxy)
  • 一组 EndpointSlice(默认 100 Endpoint/slice)
  • 一条 CoreDNS 解析记录

5000 节点集群,Service 1 万+,CoreDNS QPS 上 10 万,单个 CoreDNS Pod 处理能力 5 万 QPS,需要至少 3 副本 + NodeLocalDNSCache。

1.7 大规模调度器优化

默认调度器在 5000 节点下,单次调度延迟 P99 > 1s,Pod 启动延迟显著。

优化方向:

  • percentageOfNodesToScore=30(扫描 30% 节点)
  • NodeResourcesFit 用 MostAllocated(集中调度,减少碎片)
  • 多调度器分片(coscheduling / volcano)
  • 调度器缓存调大

1.8 多集群分流架构

5000 节点是单集群天花板,10000+ 必须多集群:
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Ingress/Gateway
Cluster A

3000 节点
Cluster B

3000 节点
Cluster C

3000 节点
Cluster D

3000 节点
KubeFed / Karmada

联邦控制面
全局服务发现

Mesh DNS

主流方案:

  • Karmada:华为开源,主流多集群编排
  • KubeFed v2:CNCF,发展缓慢
  • Service Mesh 多集群(Istio / Linkerd Multi-cluster):服务级分流
  • ClusterAPI:多集群生命周期管理

二、实战操作

2.1 API Server 大规模配置

yaml 复制代码 
# /etc/kubernetes/manifests/kube-apiserver.yaml 关键参数
spec:
  containers:
    - command:
        - kube-apiserver
        # 并发与限流
        - --max-requests-inflight=1000          # 默认 400,调大
        - --max-mutating-requests-inflight=400   # 默认 200
        - --min-request-timeout=1800             # 请求超时 30min
        # watch cache(关键!)
        - --default-watch-cache-size=1000        # 默认 100,调大
        - --watch-cache-sizes=pod#10000,node#5000,secret#5000,configmap#5000,endpoints#10000,services#5000
        # etcd
        - --etcd-servers=https://etcd1:2379,https://etcd2:2379,https://etcd3:2379
        - --etcd-servers-overrides=/events#https://etcd-events1:2379  # events 分离!
        # APF(API Priority and Fairness,1.20+ 必开)
        - --enable-priority-and-fairness=true
        # 日志
        - --logtostderr=true
        - --v=2
        # 性能
        - --default-not-ready-toleration-seconds=300
        - --default-unreachable-toleration-seconds=300
        # gc
        - --event-ttl=1h                          # 默认 1h,大集群可缩短
        resources:
          requests:
            cpu: "8"
            memory: "32Gi"
          limits:
            cpu: "16"
            memory: "64Gi"

APF 配置(PriorityLevel + FlowSchema,大规模集群关键):

yaml 复制代码 
# 关键业务高优先级(不会被限流挤掉)
apiVersion: flowcontrol.apiserver.k8s.io/v1beta3
kind: PriorityLevelConfiguration
metadata:
  name: critical-workload
spec:
  type: Limited
  limited:
    nominalConcurrencyShares: 1000  # 高配额
    limitResponse:
      type: Queue
      queuing:
        queues: 128
        handSize: 8
---
apiVersion: flowcontrol.apiserver.k8s.io/v1beta3
kind: FlowSchema
metadata:
  name: critical-controllers
spec:
  priorityLevelConfiguration:
    name: critical-workload
  distinguisherMethod:
    type: ByUser
  rules:
    - subjects:
        - kind: ServiceAccount
          namespace: kube-system
          name: controller-manager
      resourceRules:
        - apiGroups: ["*"]
          resources: ["*"]
          verbs: ["*"]

2.2 etcd 分区(Events 单独存)

大规模集群必须把 events 分到独立 etcd,否则 events 写入会拖垮主 etcd。

bash 复制代码 
# 部署独立 etcd 集群(3 节点)
# etcd-events.yaml
cat > /etc/kubernetes/manifests/etcd-events.yaml <<'EOF'
apiVersion: v1
kind: Pod
metadata:
  name: etcd-events
  namespace: kube-system
spec:
  hostNetwork: true
  containers:
    - name: etcd
      image: registry.k8s.io/etcd:3.5.12
      command:
        - etcd
        - --name=etcd-events-1
        - --data-dir=/var/lib/etcd-events
        - --wal-dir=/var/lib/etcd-events/wal
        - --snapshot-count=10000
        - --quota-backend-bytes=8589934592
        - --auto-compaction-retention=1h
        - --listen-client-urls=https://0.0.0.0:2381
        - --advertise-client-urls=https://NODE_IP:2381
        - --listen-peer-urls=https://0.0.0.0:2382
        - --initial-advertise-peer-urls=https://NODE_IP:2382
        - --initial-cluster=etcd-events-1=https://NODE1:2382,etcd-events-2=https://NODE2:2382,etcd-events-3=https://NODE3:2382
        - --initial-cluster-token=etcd-events-cluster
        - --initial-cluster-state=new
        - --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
        - --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
        - --client-cert-auth=true
        - --peer-client-cert-auth=true
      volumeMounts:
        - mountPath: /var/lib/etcd-events
          name: etcd-events-data
  volumes:
    - name: etcd-events-data
      hostPath:
        path: /var/lib/etcd-events
        type: DirectoryOrCreate
EOF

kube-apiserver 加 --etcd-servers-overrides:

yaml 复制代码 
- --etcd-servers-overrides=/events#https://etcd-events1:2381,https://etcd-events2:2381,https://etcd-events3:2381

2.3 kube-proxy ipvs 大规模优化

yaml 复制代码 
# kube-proxy configmap
apiVersion: kubeproxy.config.k8s.io/v1alpha1
kind: KubeProxyConfiguration
mode: "ipvs"
ipvs:
  scheduler: "lc"           # 最少连接,大规模优于 rr
  strictARP: true
  minSyncPeriod: "1s"        # 关键!避免抖动
  syncPeriod: "30s"
  tcpTimeout: "0s"           # 不设置超时,让应用自己管
  tcpFinTimeout: "0s"
  udpTimeout: "30s"
  excludeCIDRs:
    - "10.0.0.0/8"           # 内网 CIDR 不走 ipvs
conntrack:
  maxPerCore: 0              # 0 = 用节点 nf_conntrack_max
  min: 1048576
  tcpCloseWaitTimeout: "30s"
  tcpEstablishedTimeout: "86400s"
configSyncPeriod: "15m"
iptables:
  masqueradeAll: false
  masqueradeBit: 14
  minSyncPeriod: "1s"
  syncPeriod: "30s"

验证 ipvs 模式下大规模性能:

bash 复制代码 
# 看规则数
kubectl -n kube-system exec ds/kube-proxy -- ipvsadm -Ln | wc -l

# 看同步延迟(关键指标)
# Prometheus: sync_proxy_rules_iptables_equivalent_last_duration_seconds
kubectl -n kube-system exec ds/kube-proxy -- cat /var/log/kube-proxy.log | grep "syncing rules"

# 看单个 Pod 访问 Service 延迟
kubectl run benchmark --rm -it --image=registry.k8s.io/perf-tests/benchmark:latest -- \
  /bin/sh -c "for i in \$(seq 1 100); do \
    time curl -s -o /dev/null http://order-service.prod.svc:8080/health; \
  done"

2.4 NetworkPolicy 大规模:Cilium eBPF

bash 复制代码 
# 1. 卸载原有 CNI(警告:会断网,生产需在维护窗口)
# 2. 部署 Cilium
helm install cilium cilium/cilium --version 1.15.6 \
  --namespace kube-system \
  --set kubeProxyReplacement=true \
  --set k8sServiceHost=API_SERVER_IP \
  --set k8sServicePort=6443 \
  --set ipv4.enabled=true \
  --set ipv6.enabled=false \
  --set enableIPv4EgressGateway=true \
  --set ipv4NativeRoutingCIDR=10.0.0.0/8 \
  --set bgpControlPlane.enabled=false \
  --set l7Proxy=false \
  --set hubble.enabled=true \
  --set hubble.metrics.enabled="{dns,drop,tcp,flow,port-distrib,icmp}" \
  --set prometheus.enabled=true \
  --set operator.prometheus.enabled=true \
  --set encryption.enabled=true \
  --set encryption.type=ipsec \
  --set encryption.nodeEncryption=true

# 3. 大规模优化参数
cat > cilium-values-large-scale.yaml <<'EOF'
k8sClientRateLimit:
  qps: 100
  burst: 200
k8sApiServerBurst: 100
k8sApiServerQPS: 50

# 大规模下 bpf map 要调大
bpf:
  masquerade: true
  lbAlgorithm: "maglev"  # 比 rr 更稳定
  ct:
    tcpMax: 536870912    # 5亿,默认 512万
    anyMax: 268435456
  nat:
    max: 536870912
  neigh:
    alloqHost: true
  lbMapNum: 6
  policyMapMax: 16384
  sessionAffinityMapMax: 16384

# cilium-agent 资源
resources:
  requests:
    cpu: 500m
    memory: 512Mi
  limits:
    cpu: 2
    memory: 2Gi

# 大规模下 bpf masquerade 优于 iptables masquerade
enableBPFMasquerade: true
enableIPv4Masquerade: true

# 监控
extraArgs:
  - "--enable-policy=default"
  - "--policy-queue-size=512"
  - "--k8s-events-queue-size=512"
EOF

helm upgrade cilium cilium/cilium -n kube-system -f cilium-values-large-scale.yaml

2.5 CoreDNS 优化 + NodeLocalDNSCache

大规模集群 CoreDNS 是绝对瓶颈,必须上 NodeLocalDNSCache。

yaml 复制代码 
# CoreDNS configmap 优化
apiVersion: v1
kind: ConfigMap
metadata:
  name: coredns
  namespace: kube-system
data:
  Corefile: |
    .:53 {
        errors
        health {
           lameduck 5s
        }
        ready
        # 关键:大规模用 forward 而不是 proxy,效率高
        kubernetes cluster.local in-addr.arpa ip6.arpa {
           pods insecure
           fallthrough in-addr.arpa ip6.arpa
           ttl 30
        }
        # prometheus 监控
        prometheus :9153
        # forward 上游 DNS
        forward . /etc/resolv.conf {
           max_concurrent 1000
           prefer_udp
        }
        # 缓存(大规模调大)
        cache 30 {
           success 4096 60 30
           denial 4096 60 30
           prefetch 5 30s 20%
        }
        # 限流(防止个别 Pod 把 DNS 拖垮)
        ratelimit {
           rate 1000
           burst 2000
           ok 0
        }
        reload 10s
        loadbalance round_robin
        log . {
            class denial error
        }
    }

部署 NodeLocalDNSCache(每个节点一个 Pod,DNS 请求本地缓存):

bash 复制代码 
# 下载 manifest
wget https://raw.githubusercontent.com/kubernetes/kubernetes/master/cluster/addons/dns/nodelocaldns/nodelocaldns.yaml

# 修改本地 IP 为 169.254.20.10,转发到 CoreDNS
# 部署
kubectl apply -f nodelocaldns.yaml

# 所有 Pod 的 /etc/resolv.conf 自动指向 169.254.20.10

2.6 调度器优化

yaml 复制代码 
# kube-scheduler config
apiVersion: kubescheduler.config.k8s.io/v1
kind: KubeSchedulerConfiguration
clientConnection:
  kubeconfig: /etc/kubernetes/scheduler.conf
  # 大规模调大 burst
  burst: 200
  qps: 100
percentageOfNodesToScore: 30
profiles:
  - schedulerName: default-scheduler
    plugins:
      filter:
        enabled:
          - name: NodeUnschedulable
          - name: NodeName
          - name: TaintToleration
          - name: NodeAffinity
          - name: NodeResourcesFit
          - name: VolumeRestrictions
          - name: EBSLimits
          - name: GCEPDLimits
          - name: AzureDiskLimits
          - name: VolumeBinding
          - name: VolumeZone
          - name: PodTopologySpread
      score:
        enabled:
          - name: NodeResourcesFit
            weight: 5
          - name: InterPodAffinity
            weight: 2
          - name: PodTopologySpread
            weight: 2
          - name: NodeAffinity
            weight: 1
      preScore:
        enabled:
          - name: InterPodAffinity
          - name: PodTopologySpread
    config:
      nodeResourcesFit:
        scoringStrategy:
          type: MostAllocated  # 集中调度减少碎片
      podTopologySpread:
        defaultConstraints:
          - maxSkew: 1
            topologyKey: topology.kubernetes.io/zone
            whenUnsatisfiable: ScheduleAnyway
          - maxSkew: 1
            topologyKey: kubernetes.io/hostname
            whenUnsatisfiable: ScheduleAnyway

2.7 大规模集群容量规划表

1000-3000 节点集群参考配置:

组件 配置 备注
Master 节点 3 × 16C64G NVMe 高可用
etcd 3 × 4C16G NVMe 独立 主 etcd
etcd-events 3 × 2C8G NVMe 独立 events 分离
API Server 8C32Gi max-inflight 1000
Scheduler 2C4Gi percentage 50
Controller Manager 2C8Gi 并发 50
CoreDNS 3 × 500m 512Mi + NodeLocalDNSCache
Prometheus 8C32Gi + 500GB TSDB 15 天
节点 16C64G Pod 密度 80

5000 节点集群参考配置:

组件 配置 备注
Master 节点 5 × 32C128G 多 Master
etcd 5 × 8C32G NVMe 5 副本
etcd-events 3 × 4C16G events 独立
API Server 16C64Gi max-inflight 1500
CoreDNS 5 × 1C1Gi + NodeLocalDNSCache 强制
节点 32C128G Pod 密度 100

2.8 多集群分流架构(Karmada)

bash 复制代码 
# 部署 Karmada 控制面
helm install karmada karmada/karmada \
  --namespace karmada-system \
  --create-namespace \
  --set apiServer.replicaCount=3 \
  --set etcd.replicaCount=3 \
  --set apiServer.serviceType=LoadBalancer

# 注册子集群
kubectl karmada join cluster-a --cluster-kubeconfig=karmada.kubeconfig \
  --cluster-context=karmada-apiserver \
  --member-cluster-kubeconfig=cluster-a.kubeconfig

# 部署 PropagationPolicy(应用如何分发到子集群)
cat > propagation.yaml <<'EOF'
apiVersion: policy.karmada.io/v1alpha1
kind: PropagationPolicy
metadata:
  name: order-service-policy
  namespace: prod
spec:
  resourceSelectors:
    - apiVersion: apps/v1
      kind: Deployment
      name: order-service
    - apiVersion: v1
      kind: Service
      name: order-service
  placement:
    clusterAffinity:
      clusterNames:
        - cluster-a
        - cluster-b
        - cluster-c
    replicaScheduling:
      replicaSchedulingType: Divided
      replicaDivisionPreference: Weighted
      weightPreference:
        staticWeightList:
          - targetCluster:
              clusterNames: [cluster-a]
            weight: 1
          - targetCluster:
              clusterNames: [cluster-b]
            weight: 1
          - targetCluster:
              clusterNames: [cluster-c]
            weight: 1
EOF

2.9 瓶颈排查清单

bash 复制代码 
#!/bin/bash
# bottleneck-check.sh
echo "===== 集群瓶颈排查 ====="

# 1. API Server 延迟
echo -e "\n## API Server 延迟 P99(目标 < 1s)"
curl -s http://localhost:8080/metrics | grep apiserver_request_duration_seconds | \
  grep -E "quantile=\"0.99\"" | sort

# 2. etcd 延迟
echo -e "\n## etcd fsync 延迟 P99(目标 < 10ms)"
ETCDCTL_API=3 etcdctl endpoint status -w table

# 3. 调度延迟
echo -e "\n## 调度延迟 P99(目标 < 500ms)"
curl -s http://localhost:10259/metrics | grep scheduler_scheduling_algorithm_duration_seconds | \
  grep -E "quantile=\"0.99\""

# 4. kube-proxy 同步延迟
echo -e "\n## kube-proxy 同步延迟(目标 < 5s)"
curl -s http://localhost:10249/metrics | grep sync_proxy_rules_iptables_equivalent_last_duration_seconds

# 5. CoreDNS QPS
echo -e "\n## CoreDNS QPS(每副本 < 5000)"
kubectl -n kube-system exec deploy/coredns -- cat /tmp/coredns.log | grep "queries" | wc -l

# 6. Controller Manager 队列堆积
echo -e "\n## Controller Manager 队列"
curl -s http://localhost:10252/metrics | grep workqueue_depth | sort -k2 -rn | head -5

# 7. 节点心跳延迟
echo -e "\n## 节点心跳延迟(目标 < 60s)"
kubectl get nodes -o json | jq -r '
  .items[] |
  "\(.metadata.name): \(.status.conditions[] | select(.type == "Ready") | .lastHeartbeatTime)"
'

# 8. Pod 启动延迟 P99(过去 1 小时)
echo -e "\n## Pod 启动延迟 P99(目标 < 5s)"
# 通过 Prometheus 查询 kube_pod_container_state_started

三、踩坑与排查

坑 1:5000 节点后 API Server 偶发 OOM

现象:API Server Pod 内存从 32GB 涨到 64GB,频繁重启。

定位:

bash 复制代码 
kubectl -n kube-system top pod kube-apiserver-xxx
# 内存接近 64GB limit

kubectl -n kube-system exec kube-apiserver-xxx -- sh -c "ls /proc/1/fd | wc -l"
# 文件描述符爆,几十万

原因:每个 kubelet 都对 Pod 建立 watch,5000 kubelet × 5 watch = 2.5 万 watch 连接,每个连接占用 cache。

解决:

yaml 复制代码 
- --default-watch-cache-size=2000
- --watch-cache-sizes=pod#20000,node#10000,secret#5000,configmap#5000,endpoints#10000,services#5000
- --max-requests-inflight=1500
- --max-mutating-requests-inflight=500
resources:
  limits:
    memory: "96Gi"  # 调大

同时减少 Controller Manager 的 watch 并发,改 List + watch with ResourceVersion。

坑 2:etcd 写入延迟抖动到 100ms

现象 :apiserver 日志报 etcdserver: request timed out,kubectl get 偶发卡 10s+。

定位:

bash 复制代码 
# 看 etcd fsync 延迟
etcdctl endpoint status -w table

# 看磁盘 IO
iostat -xz 1 | grep -E "nvme0n1|sda"
# await > 50ms

原因:etcd 数据盘和容器镜像盘共用,镜像 pull 时 IO 抖动。

解决:

  • etcd 独立 NVMe SSD
  • WAL 独立盘
  • --snapshot-count=10000
  • --auto-compaction-retention=1h
  • 定期 defrag(每周)
bash 复制代码 
# defrag 脚本(非 leader 节点先 defrag)
ETCDCTL_API=3 etcdctl --endpoints=etcd1 defrag
ETCDCTL_API=3 etcdctl --endpoints=etcd2 defrag
ETCDCTL_API=3 etcdctl --endpoints=etcd3 defrag  # leader 最后

坑 3:kube-proxy 同步耗时 30s

现象 :大规模集群下,Pod 访问 Service 偶发 502,kube-proxy 日志报 syncing rules took 30s。

定位:

bash 复制代码 
kubectl -n kube-system exec ds/kube-proxy -- ipvsadm -Ln | wc -l
# 几十万行,规模爆炸

# 看同步耗时
kubectl -n kube-system exec ds/kube-proxy -- cat /var/log/kube-proxy.log | grep "syncing"

原因:iptables 模式下规则数随 Service×Pod 线性增长。

解决 :切 ipvs 模式 + min-sync-period=1s。如果还不行,迁 Cilium eBPF 完全绕过 kube-proxy。

坑 4:CoreDNS 拖垮整个集群

现象 :集群偶发所有 Service 解析失败,Pod 报 no such host。

定位:

bash 复制代码 
kubectl -n kube-system top pods -l k8s-app=kube-dns
# CoreDNS CPU 100%

kubectl -n kube-system logs -l k8s-app=kube-dns --tail=100 | grep "too many"
# "too many open files" 或 "rate limit"

原因:单 CoreDNS Pod 处理 5 万 QPS 是上限,大规模集群 10 万+ QPS 把它打爆。

解决:

yaml 复制代码 
# 1. 扩容到 5 副本
spec:
  replicas: 5
# 2. 加 NodeLocalDNSCache(每个节点一个本地缓存 Pod)
# 3. CoreDNS config 加 ratelimit
# 4. 监控 CoreDNS QPS,告警阈值 40000/副本

坑 5:Scheduler 卡死,Pod Pending 1 小时

现象 :某次扩容 1000 个 Pod,全部 Pending 1 小时,kubectl describe pod 显示 Scheduling disabled。

定位:

bash 复制代码 
kubectl -n kube-system logs kube-scheduler-xxx | tail -50
# "Unable to schedule, no nodes available" 但实际有节点

# 调度器 metrics
curl http://localhost:10259/metrics | grep scheduler_scheduling_algorithm_duration_seconds

原因:调度器全量扫描所有节点,5000 节点 × 1000 Pod = 500 万次打分。

解决:

  • percentageOfNodesToScore=30
  • 调度器加资源
  • 启用调度器分片(coscheduling)

坑 6:大规模 NetworkPolicy 网络延迟 100ms+

现象:开启 NetworkPolicy 后,Pod 间通信延迟从 1ms 涨到 100ms。

定位:

bash 复制代码 
iptables -L | wc -l
# 几百万行规则

解决:迁 Cilium eBPF,NetworkPolicy 在 eBPF 层执行,延迟 < 1ms。

四、最佳实践

容量规划:

  • 单集群 ≤ 5000 节点(生产稳定水位 3000-4000)
  • 10000+ 节点用多集群,推荐 Karmada 联邦
  • Master 至少 3 节点,etcd 独立 NVMe
  • 节点规格统一,16C64G 或 32C128G
  • Pod 密度 50-100/节点

API Server:

  • --max-requests-inflight=1000-1500
  • --default-watch-cache-size 调大
  • --watch-cache-sizes 按资源精细配置
  • 开启 APF(Priority and Fairness)
  • events 走独立 etcd(--etcd-servers-overrides)
  • 资源至少 8C32Gi,5000 节点 16C64Gi

etcd:

  • 独立 NVMe SSD,WAL 独立盘
  • quota-backend-bytes=8589934592(8GB)
  • snapshot-count=10000
  • auto-compaction-retention=1h
  • 每周 defrag 一次
  • 监控 etcd_disk_wal_fsync_duration_seconds P99 < 10ms
  • events 独立 etcd 集群

kube-proxy:

  • 节点数 > 500 必须切 ipvs
  • 节点数 > 2000 强烈建议 Cilium eBPF
  • ipvs scheduler=lc,min-sync-period=1s
  • 监控同步延迟 P99 < 5s

NetworkPolicy:

  • 大规模必须 eBPF(Cilium / Calico eBPF)
  • 避免 iptables 模式
  • Cilium bpf.ct.max 调大到 5 亿
  • 用 maglev 算法替代 rr

CoreDNS:

  • 强制部署 NodeLocalDNSCache
  • CoreDNS 副本数 = 节点数 / 1000,至少 3 个
  • cache 30 + ratelimit 1000
  • 监控 QPS,告警阈值 40000/副本

调度器:

  • percentageOfNodesToScore=30(大规模)
  • NodeResourcesFit 用 MostAllocated
  • 关键业务用 PriorityClass
  • 启用 PodTopologySpread 跨 AZ 分散

监控告警:

  • Prometheus 持续采集所有组件 P99 延迟
  • API Server P99 < 1s
  • etcd fsync P99 < 10ms
  • Scheduler P99 < 500ms
  • Pod 启动 P99 < 5s
  • 瓶颈排查脚本每天跑

五、小结

万节点集群优化是"系统工程",不是"调一个参数":

  1. 架构层面:events 分离 etcd、APF 限流、多 Master、Cilium eBPF
  2. 参数层面:watch cache、max-inflight、ipvs min-sync、CoreDNS ratelimit
  3. 监控层面:Prometheus 持续采集,瓶颈排查脚本每周跑
  4. 演进层面:5000 是单集群上限,10000+ 必须多集群联邦

最后给一句话:不要追求单集群无限扩容,3000-5000 节点是甜蜜区,超出就拆集群。多集群的复杂度是可控的,单集群撞天花板的复杂度是不可控的。

思考题

  1. 单集群 5000 节点和 5 × 1000 节点集群,运维成本和性能哪个更优?什么场景该选哪个?
  2. APF 中 PriorityLevel 的 nominalConcurrencyShares=1000 和 =10 在大规模集群里差别有多大?怎么评估?
  3. Cilium eBPF 替换 kube-proxy 后,Service 的流量路径有哪些变化?为什么延迟会降?
  4. 多集群联邦(Karmada)在故障切换时,如何保证业务连续性?需要哪些前置条件?

延伸阅读

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