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Raft Kafka on k8s 部署实战操作

Raft Kafka on k8s 部署实战操作

部署 Raft Kafka(Kafka 3.3.1 及以上版本引入的 KRaft 模式)在 Kubernetes (k8s) 上,可以简化 Kafka 集群的管理,因为它不再依赖于 Zookeeper。

在这里插入图片描述
关于Raft Kafka 介绍和物理机部署可以参考我以下文章:

以下是部署 Raft Kafka 集群的基本步骤:

1)准备 Kubernetes 集群

确保你有一个运行中的 Kubernetes 集群,并且已经配置了 kubectl 命令行工具。
部署教程如下:

2)安装 helm

下载地址:https://github.com/helm/helm/releases

# 下载包
wget https://get.helm.sh/helm-v3.9.4-linux-amd64.tar.gz
# 解压压缩包
tar -xf helm-v3.9.4-linux-amd64.tar.gz
# 制作软连接
ln -s /opt/helm/linux-amd64/helm /usr/local/bin/helm
# 验证
helm version
helm help
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3)配置 Helm chart

如果你使用 Bitnami 的 Kafka Helm chart,你需要创建一个 values.yaml 文件来配置 Kafka 集群。在该文件中,你可以启用 KRaft 模式并配置其他设置,如认证、端口等。

# 添加下载源
helm repo add bitnami https://charts.bitnami.com/bitnami
# 下载
helm pull bitnami/kafka --version 26.0.0
# 解压
tar -xf kafka-26.0.0.tgz

# 修改配置
vi kafka/values.yaml
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以下是一个 values.yaml 的示例配置:

image:
  registry: registry.cn-hangzhou.aliyuncs.com
  repository: bigdata_cloudnative/kafka
  tag: 3.6.0-debian-11-r0

listeners:
  client:
    containerPort: 9092
    # 默认是带鉴权的,SASL_PLAINTEXT
    protocol: PLAINTEXT
    name: CLIENT
    sslClientAuth: ""

controller:
  replicaCount: 3 # 控制器的数量
  persistence:
  storageClass: "kafka-controller-local-storage"
  size: "10Gi"
  # 目录需要提前在宿主机上创建
  local:
    - name: kafka-controller-0
      host: "local-168-182-110"
      path: "/opt/bigdata/servers/kraft/kafka-controller/data1"
    - name: kafka-controller-1
      host: "local-168-182-111"
      path: "/opt/bigdata/servers/kraft/kafka-controller/data1"
    - name: kafka-controller-2
      host: "local-168-182-112"
      path: "/opt/bigdata/servers/kraft/kafka-controller/data1"

broker:
  replicaCount: 3  # 代理的数量
  persistence:
  storageClass: "kafka-broker-local-storage"
  size: "10Gi"
  # 目录需要提前在宿主机上创建
  local:
    - name: kafka-broker-0
      host: "local-168-182-110"
      path: "/opt/bigdata/servers/kraft/kafka-broker/data1"
    - name: kafka-broker-1
      host: "local-168-182-111"
      path: "/opt/bigdata/servers/kraft/kafka-broker/data1"
    - name: kafka-broker-2
      host: "local-168-182-112"
      path: "/opt/bigdata/servers/kraft/kafka-broker/data1"

service:
  type: NodePort
  nodePorts:
    #NodePort 默认范围是 30000-32767
    client: "32181"
    tls: "32182"




# Enable Prometheus to access ZooKeeper metrics endpoint
metrics:
  enabled: true

kraft:
  enabled: true
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添加以下几个文件:

  • kafka/templates/broker/pv.yaml
{{- range .Values.broker.persistence.local }}
---
apiVersion: v1
kind: PersistentVolume
metadata:
  name: {{ .name }}
  labels:
    name: {{ .name }}
spec:
  storageClassName: {{ $.Values.broker.persistence.storageClass }}
  capacity:
    storage: {{ $.Values.broker.persistence.size }}
  accessModes:
    - ReadWriteOnce
  local:
    path: {{ .path }}
  nodeAffinity:
    required:
      nodeSelectorTerms:
        - matchExpressions:
            - key: kubernetes.io/hostname
              operator: In
              values:
                - {{ .host }}
---
{{- end }}
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  • kafka/templates/broker/storage-class.yaml
kind: StorageClass
apiVersion: storage.k8s.io/v1
metadata:
  name: {{ .Values.broker.persistence.storageClass }}
provisioner: kubernetes.io/no-provisioner
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  • kafka/templates/controller-eligible/pv.yaml
{{- range .Values.controller.persistence.local }}
---
apiVersion: v1
kind: PersistentVolume
metadata:
  name: {{ .name }}
  labels:
    name: {{ .name }}
spec:
  storageClassName: {{ $.Values.controller.persistence.storageClass }}
  capacity:
    storage: {{ $.Values.controller.persistence.size }}
  accessModes:
    - ReadWriteOnce
  local:
    path: {{ .path }}
  nodeAffinity:
    required:
      nodeSelectorTerms:
        - matchExpressions:
            - key: kubernetes.io/hostname
              operator: In
              values:
                - {{ .host }}
---
{{- end }}
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  • kafka/templates/controller-eligible/storage-class.yaml
kind: StorageClass
apiVersion: storage.k8s.io/v1
metadata:
  name: {{ .Values.controller.persistence.storageClass }}
provisioner: kubernetes.io/no-provisioner
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4)使用 Helm 部署 Kafka 集群

# 先准备好镜像
docker pull docker.io/bitnami/kafka:3.6.0-debian-11-r0
docker tag docker.io/bitnami/kafka:3.6.0-debian-11-r0 registry.cn-hangzhou.aliyuncs.com/bigdata_cloudnative/kafka:3.6.0-debian-11-r0
docker push registry.cn-hangzhou.aliyuncs.com/bigdata_cloudnative/kafka:3.6.0-debian-11-r0

# 开始安装
helm install kraft ./kafka -n kraft --create-namespace
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NOTES

[root@local-168-182-110 KRaft-on-k8s]# helm upgrade kraft 
Release "kraft" has been upgraded. Happy Helming!
NAME: kraft
LAST DEPLOYED: Sun Mar 24 20:05:04 2024
NAMESPACE: kraft
STATUS: deployed
REVISION: 3
TEST SUITE: None
NOTES:
CHART NAME: kafka
CHART VERSION: 26.0.0
APP VERSION: 3.6.0

** Please be patient while the chart is being deployed **

Kafka can be accessed by consumers via port 9092 on the following DNS name from within your cluster:

    kraft-kafka.kraft.svc.cluster.local

Each Kafka broker can be accessed by producers via port 9092 on the following DNS name(s) from within your cluster:

    kraft-kafka-controller-0.kraft-kafka-controller-headless.kraft.svc.cluster.local:9092
    kraft-kafka-controller-1.kraft-kafka-controller-headless.kraft.svc.cluster.local:9092
    kraft-kafka-controller-2.kraft-kafka-controller-headless.kraft.svc.cluster.local:9092
    kraft-kafka-broker-0.kraft-kafka-broker-headless.kraft.svc.cluster.local:9092
    kraft-kafka-broker-1.kraft-kafka-broker-headless.kraft.svc.cluster.local:9092
    kraft-kafka-broker-2.kraft-kafka-broker-headless.kraft.svc.cluster.local:9092

To create a pod that you can use as a Kafka client run the following commands:

    kubectl run kraft-kafka-client --restart='Never' --image registry.cn-hangzhou.aliyuncs.com/bigdata_cloudnative/kafka:3.6.0-debian-11-r0 --namespace kraft --command -- sleep infinity
    kubectl exec --tty -i kraft-kafka-client --namespace kraft -- bash

    PRODUCER:
        kafka-console-producer.sh \
            --broker-list kraft-kafka-controller-0.kraft-kafka-controller-headless.kraft.svc.cluster.local:9092,kraft-kafka-controller-1.kraft-kafka-controller-headless.kraft.svc.cluster.local:9092,kraft-kafka-controller-2.kraft-kafka-controller-headless.kraft.svc.cluster.local:9092,kraft-kafka-broker-0.kraft-kafka-broker-headless.kraft.svc.cluster.local:9092,kraft-kafka-broker-1.kraft-kafka-broker-headless.kraft.svc.cluster.local:9092,kraft-kafka-broker-2.kraft-kafka-broker-headless.kraft.svc.cluster.local:9092 \
            --topic test

    CONSUMER:
        kafka-console-consumer.sh \
            --bootstrap-server kraft-kafka.kraft.svc.cluster.local:9092 \
            --topic test \
            --from-beginning

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在这里插入图片描述

5)测试验证

# 创建客户端
kubectl run kraft-kafka-client --restart='Never' --image registry.cn-hangzhou.aliyuncs.com/bigdata_cloudnative/kafka:3.6.0-debian-11-r0 --namespace kraft --command -- sleep infinity
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创建客户端

kafka-topics.sh --create --topic test --bootstrap-server kraft-kafka-controller-0.kraft-kafka-controller-headless.kraft.svc.cluster.local:9092  --partitions 3 --replication-factor 2

# 查看详情
kafka-topics.sh --describe --bootstrap-server kraft-kafka-controller-0.kraft-kafka-controller-headless.kraft.svc.cluster.local:9092  --topic test

# 删除topic
kafka-topics.sh --delete --topic test --bootstrap-server kraft-kafka-controller-0.kraft-kafka-controller-headless.kraft.svc.cluster.local:9092
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生产者和消费者

# 生产者
kafka-console-producer.sh \
    --broker-list kraft-kafka-controller-0.kraft-kafka-controller-headless.kraft.svc.cluster.local:9092,kraft-kafka-controller-1.kraft-kafka-controller-headless.kraft.svc.cluster.local:9092,kraft-kafka-controller-2.kraft-kafka-controller-headless.kraft.svc.cluster.local:9092,kraft-kafka-broker-0.kraft-kafka-broker-headless.kraft.svc.cluster.local:9092,kraft-kafka-broker-1.kraft-kafka-broker-headless.kraft.svc.cluster.local:9092,kraft-kafka-broker-2.kraft-kafka-broker-headless.kraft.svc.cluster.local:9092 \
    --topic test

# 消费者
kafka-console-consumer.sh \
    --bootstrap-server kraft-kafka.kraft.svc.cluster.local:9092 \
    --topic test \
    --from-beginning
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6)更新集群

helm upgrade kraft ./kafka -n kraft
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7)删除集群

helm uninstall kraft -n kraft
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Raft Kafka on k8s 部署实战操作就先到这里了,有任何疑问也可关注我公众号:大数据与云原生技术分享,进行技术交流,如本篇文章对您有所帮助,麻烦帮忙一键三连(点赞、转发、收藏)~

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