Kubernetes教程FG030-数据仓库与分析在Kubernetes中的应用实战解析
本文档风哥主要介绍数据仓库与分析在Kubernetes中的应用，包括数据仓库概述、数据分析概述、Kubernetes中的数据仓库、数据仓库规划、数据分析规划、最佳实践规划、数据仓库实施方案、数据分析实施方案、集成实施方案、数据仓库案例、数据分析案例、集成案例等内容，风哥教程参考Kubernetes官方文档和数据仓库相关文档，适合DevOps工程师和数据工程师在学习和测试中使用，如果要应用于生产环境则需要自行确认。
Part01-基础概念与理论知识
1.1 数据仓库概述
数据仓库是一个面向主题的、集成的、相对稳定的、反映历史变化的数据集合，用于支持管理决策。数据仓库的主要特点包括：
- 面向主题：数据仓库围绕业务主题组织数据，如销售、市场、财务等
- 集成：数据仓库将来自不同数据源的数据整合在一起
- 相对稳定：数据仓库中的数据一旦加载，很少修改
- 反映历史变化：数据仓库存储历史数据，支持时间序列分析
1.2 数据分析概述
数据分析是指对数据进行收集、处理、分析和可视化，以提取有价值的信息和洞察。数据分析的主要类型包括：
- 描述性分析：描述过去发生的事情
- 诊断性分析：解释为什么发生
- 预测性分析：预测未来可能发生的事情
- 指导性分析：建议应该做什么
1.3 Kubernetes中的数据仓库
Kubernetes中的数据仓库是指在Kubernetes集群中部署和运行的数据仓库系统，如PostgreSQL、MySQL、MongoDB、Elasticsearch等。Kubernetes提供了以下优势：
- 容器化部署：数据仓库系统可以容器化，便于部署和管理
- 弹性伸缩：根据负载自动扩展数据仓库服务
- 高可用性：通过副本和状态管理确保数据仓库服务的可用性
- 存储管理：通过PersistentVolume和StorageClass管理数据存储
- 网络管理：通过Service和Ingress管理网络访问
- 安全管理：通过RBAC和网络策略管理访问权限
Part02-生产环境规划与建议
2.1 数据仓库规划
生产环境Kubernetes数据仓库规划：
# 数据仓库规划
– 存储规划：
– 选择合适的存储类型：PersistentVolume、StorageClass
– 配置存储容量：根据数据量和增长速度
– 考虑存储性能：IOPS、吞吐量
– 实施存储备份和恢复策略
– 计算资源规划：
– 为数据仓库服务分配足够的CPU和内存
– 考虑数据处理和查询性能
– 配置资源请求和限制
– 实施资源配额管理
– 网络规划：
– 配置网络策略，限制不必要的网络访问
– 考虑网络带宽和延迟
– 实施网络隔离，提高安全性
– 配置服务发现和负载均衡
– 安全规划：
– 配置RBAC权限，限制访问
– 实施网络策略，限制网络访问
– 配置Pod安全策略，限制Pod特权
– 加密敏感数据，保护数据安全
– 监控和告警：
– 部署Prometheus和Grafana，监控数据仓库服务
– 配置告警规则，及时通知异常情况
– 建立监控仪表盘，直观查看系统状态
– 定期检查监控配置，确保监控有效性
– 备份和恢复：
– 实施数据备份策略，定期备份数据
– 配置备份存储，确保备份数据安全
– 测试备份恢复流程，确保备份有效性
– 建立灾难恢复计划，应对突发情况
– 扩展性规划：
– 考虑数据量增长，规划存储扩展
– 考虑查询负载增长，规划计算资源扩展
– 实施自动扩缩容，根据负载调整资源
– 规划高可用架构，确保服务可用性
– 版本管理：
– 规划数据仓库版本升级策略
– 测试版本兼容性，确保升级安全
– 建立版本回滚机制，应对升级失败
– 记录版本变更，便于审计和问题排查
– 存储规划：
– 选择合适的存储类型：PersistentVolume、StorageClass
– 配置存储容量：根据数据量和增长速度
– 考虑存储性能：IOPS、吞吐量
– 实施存储备份和恢复策略
– 计算资源规划：
– 为数据仓库服务分配足够的CPU和内存
– 考虑数据处理和查询性能
– 配置资源请求和限制
– 实施资源配额管理
– 网络规划：
– 配置网络策略，限制不必要的网络访问
– 考虑网络带宽和延迟
– 实施网络隔离，提高安全性
– 配置服务发现和负载均衡
– 安全规划：
– 配置RBAC权限，限制访问
– 实施网络策略，限制网络访问
– 配置Pod安全策略，限制Pod特权
– 加密敏感数据，保护数据安全
– 监控和告警：
– 部署Prometheus和Grafana，监控数据仓库服务
– 配置告警规则，及时通知异常情况
– 建立监控仪表盘，直观查看系统状态
– 定期检查监控配置，确保监控有效性
– 备份和恢复：
– 实施数据备份策略，定期备份数据
– 配置备份存储，确保备份数据安全
– 测试备份恢复流程，确保备份有效性
– 建立灾难恢复计划，应对突发情况
– 扩展性规划：
– 考虑数据量增长，规划存储扩展
– 考虑查询负载增长，规划计算资源扩展
– 实施自动扩缩容，根据负载调整资源
– 规划高可用架构，确保服务可用性
– 版本管理：
– 规划数据仓库版本升级策略
– 测试版本兼容性，确保升级安全
– 建立版本回滚机制，应对升级失败
– 记录版本变更，便于审计和问题排查
2.2 数据分析规划
生产环境Kubernetes数据分析规划：
# 数据分析规划
– 工具选择：
– 选择合适的数据分析工具：Spark、Flink、Hive等
– 考虑工具的性能和扩展性
– 评估工具的兼容性和集成能力
– 选择适合业务需求的工具
– 计算资源规划：
– 为数据分析任务分配足够的CPU和内存
– 考虑数据处理和分析性能
– 配置资源请求和限制
– 实施资源配额管理
– 存储规划：
– 选择合适的存储类型：HDFS、S3、NFS等
– 配置存储容量：根据数据量和增长速度
– 考虑存储性能：IOPS、吞吐量
– 实施存储备份和恢复策略
– 网络规划：
– 配置网络策略，限制不必要的网络访问
– 考虑网络带宽和延迟
– 实施网络隔离，提高安全性
– 配置服务发现和负载均衡
– 安全规划：
– 配置RBAC权限，限制访问
– 实施网络策略，限制网络访问
– 配置Pod安全策略，限制Pod特权
– 加密敏感数据，保护数据安全
– 监控和告警：
– 部署Prometheus和Grafana，监控数据分析服务
– 配置告警规则，及时通知异常情况
– 建立监控仪表盘，直观查看系统状态
– 定期检查监控配置，确保监控有效性
– 扩展性规划：
– 考虑数据量增长，规划存储扩展
– 考虑分析负载增长，规划计算资源扩展
– 实施自动扩缩容，根据负载调整资源
– 规划高可用架构，确保服务可用性
– 版本管理：
– 规划数据分析工具版本升级策略
– 测试版本兼容性，确保升级安全
– 建立版本回滚机制，应对升级失败
– 记录版本变更，便于审计和问题排查
– 工具选择：
– 选择合适的数据分析工具：Spark、Flink、Hive等
– 考虑工具的性能和扩展性
– 评估工具的兼容性和集成能力
– 选择适合业务需求的工具
– 计算资源规划：
– 为数据分析任务分配足够的CPU和内存
– 考虑数据处理和分析性能
– 配置资源请求和限制
– 实施资源配额管理
– 存储规划：
– 选择合适的存储类型：HDFS、S3、NFS等
– 配置存储容量：根据数据量和增长速度
– 考虑存储性能：IOPS、吞吐量
– 实施存储备份和恢复策略
– 网络规划：
– 配置网络策略，限制不必要的网络访问
– 考虑网络带宽和延迟
– 实施网络隔离，提高安全性
– 配置服务发现和负载均衡
– 安全规划：
– 配置RBAC权限，限制访问
– 实施网络策略，限制网络访问
– 配置Pod安全策略，限制Pod特权
– 加密敏感数据，保护数据安全
– 监控和告警：
– 部署Prometheus和Grafana，监控数据分析服务
– 配置告警规则，及时通知异常情况
– 建立监控仪表盘，直观查看系统状态
– 定期检查监控配置，确保监控有效性
– 扩展性规划：
– 考虑数据量增长，规划存储扩展
– 考虑分析负载增长，规划计算资源扩展
– 实施自动扩缩容，根据负载调整资源
– 规划高可用架构，确保服务可用性
– 版本管理：
– 规划数据分析工具版本升级策略
– 测试版本兼容性，确保升级安全
– 建立版本回滚机制，应对升级失败
– 记录版本变更，便于审计和问题排查
2.3 最佳实践规划
生产环境Kubernetes数据仓库与分析的最佳实践规划：
# 最佳实践规划
– 架构设计：
– 采用微服务架构，模块化设计
– 实施分层架构，分离存储和计算
– 考虑数据流转和处理流程
– 规划数据备份和恢复策略
– 性能优化：
– 优化数据存储结构，提高查询性能
– 配置合理的资源分配，提高计算效率
– 实施数据分区和索引，加速数据访问
– 优化查询语句，减少执行时间
– 安全管理：
– 实施最小权限原则，限制访问权限
– 加密敏感数据，保护数据安全
– 定期安全审计，发现和修复安全漏洞
– 配置网络隔离，提高安全性
– 监控和告警：
– 部署全面的监控系统，监控系统状态
– 配置合理的告警规则，及时发现异常
– 建立监控仪表盘，直观查看系统状态
– 定期分析监控数据，优化系统性能
– 自动化运维：
– 实施CI/CD流程，自动化部署和升级
– 配置自动扩缩容，根据负载调整资源
– 建立自动化备份和恢复流程
– 自动化测试和验证，确保系统可靠性
– 文档和流程：
– 编写详细的架构文档，指导系统设计
– 建立操作手册，指导日常运维
– 记录常见问题和解决方案，便于故障处理
– 定期更新文档，保持文档时效性
– 团队协作：
– 建立跨团队协作机制，共同维护系统
– 明确责任分工，确保系统稳定运行
– 定期召开技术会议，讨论系统优化
– 分享经验和知识，提高团队能力
– 持续改进：
– 定期评估系统性能，发现改进点
– 实施优化措施，提高系统性能和可靠性
– 跟踪技术发展，引入新的技术和方法
– 持续学习，提高团队技术水平
– 架构设计：
– 采用微服务架构，模块化设计
– 实施分层架构，分离存储和计算
– 考虑数据流转和处理流程
– 规划数据备份和恢复策略
– 性能优化：
– 优化数据存储结构，提高查询性能
– 配置合理的资源分配，提高计算效率
– 实施数据分区和索引，加速数据访问
– 优化查询语句，减少执行时间
– 安全管理：
– 实施最小权限原则，限制访问权限
– 加密敏感数据，保护数据安全
– 定期安全审计，发现和修复安全漏洞
– 配置网络隔离，提高安全性
– 监控和告警：
– 部署全面的监控系统，监控系统状态
– 配置合理的告警规则，及时发现异常
– 建立监控仪表盘，直观查看系统状态
– 定期分析监控数据，优化系统性能
– 自动化运维：
– 实施CI/CD流程，自动化部署和升级
– 配置自动扩缩容，根据负载调整资源
– 建立自动化备份和恢复流程
– 自动化测试和验证，确保系统可靠性
– 文档和流程：
– 编写详细的架构文档，指导系统设计
– 建立操作手册，指导日常运维
– 记录常见问题和解决方案，便于故障处理
– 定期更新文档，保持文档时效性
– 团队协作：
– 建立跨团队协作机制，共同维护系统
– 明确责任分工，确保系统稳定运行
– 定期召开技术会议，讨论系统优化
– 分享经验和知识，提高团队能力
– 持续改进：
– 定期评估系统性能，发现改进点
– 实施优化措施，提高系统性能和可靠性
– 跟踪技术发展，引入新的技术和方法
– 持续学习，提高团队技术水平
Part03-生产环境项目实施方案
3.1 数据仓库实施方案
生产环境Kubernetes数据仓库实施方案：
# 数据仓库实施方案
– PostgreSQL部署：
1. 创建StorageClass：
$ cat > storageclass.yaml << 'EOF' apiVersion: storage.k8s.io/v1 kind: StorageClass metadata: name: postgres-storage provisioner: kubernetes.io/aws-ebs parameters: type: gp2 iopsPerGB: "10" reclaimPolicy: Retain allowVolumeExpansion: true EOF $ kubectl apply -f storageclass.yaml 2. 创建PersistentVolumeClaim： $ cat > pvc.yaml << 'EOF' apiVersion: v1 kind: PersistentVolumeClaim metadata: name: postgres-pvc spec: storageClassName: postgres-storage accessModes: - ReadWriteOnce resources: requests: storage: 100Gi EOF $ kubectl apply -f pvc.yaml 3. 部署PostgreSQL： $ cat > postgres-deployment.yaml << 'EOF' apiVersion: apps/v1 kind: Deployment metadata: name: postgres namespace: default spec: replicas: 1 selector: matchLabels: app: postgres template: metadata: labels: app: postgres spec: containers: - name: postgres image: postgres:14 ports: - containerPort: 5432 env: - name: POSTGRES_USER value: "fgedu" - name: POSTGRES_PASSWORD value: "fgedu123" - name: POSTGRES_DB value: "fgedudb" volumeMounts: - name: postgres-data mountPath: /var/lib/postgresql/data volumes: - name: postgres-data persistentVolumeClaim: claimName: postgres-pvc --- apiVersion: v1 kind: Service metadata: name: postgres namespace: default spec: selector: app: postgres ports: - port: 5432 targetPort: 5432 EOF $ kubectl apply -f postgres-deployment.yaml 4. 验证PostgreSQL部署： $ kubectl get pods -l app=postgres $ kubectl get services postgres - MySQL部署： 1. 创建StorageClass： $ cat > storageclass.yaml << 'EOF' apiVersion: storage.k8s.io/v1 kind: StorageClass metadata: name: mysql-storage provisioner: kubernetes.io/aws-ebs parameters: type: gp2 iopsPerGB: "10" reclaimPolicy: Retain allowVolumeExpansion: true EOF $ kubectl apply -f storageclass.yaml 2. 创建PersistentVolumeClaim： $ cat > pvc.yaml << 'EOF' apiVersion: v1 kind: PersistentVolumeClaim metadata: name: mysql-pvc spec: storageClassName: mysql-storage accessModes: - ReadWriteOnce resources: requests: storage: 100Gi EOF $ kubectl apply -f pvc.yaml 3. 部署MySQL： $ cat > mysql-deployment.yaml << 'EOF' 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 ports: - containerPort: 3306 env: - name: MYSQL_ROOT_PASSWORD value: "fgedu123" - name: MYSQL_DATABASE value: "fgedudb" - name: MYSQL_USER value: "fgedu" - name: MYSQL_PASSWORD value: "fgedu123" volumeMounts: - name: mysql-data mountPath: /var/lib/mysql volumes: - name: mysql-data persistentVolumeClaim: claimName: mysql-pvc --- apiVersion: v1 kind: Service metadata: name: mysql namespace: default spec: selector: app: mysql ports: - port: 3306 targetPort: 3306 EOF $ kubectl apply -f mysql-deployment.yaml 4. 验证MySQL部署： $ kubectl get pods -l app=mysql $ kubectl get services mysql - MongoDB部署： 1. 创建StorageClass： $ cat > storageclass.yaml << 'EOF' apiVersion: storage.k8s.io/v1 kind: StorageClass metadata: name: mongodb-storage provisioner: kubernetes.io/aws-ebs parameters: type: gp2 iopsPerGB: "10" reclaimPolicy: Retain allowVolumeExpansion: true EOF $ kubectl apply -f storageclass.yaml 2. 创建PersistentVolumeClaim： $ cat > pvc.yaml << 'EOF' apiVersion: v1 kind: PersistentVolumeClaim metadata: name: mongodb-pvc spec: storageClassName: mongodb-storage accessModes: - ReadWriteOnce resources: requests: storage: 100Gi EOF $ kubectl apply -f pvc.yaml 3. 部署MongoDB： $ cat > mongodb-deployment.yaml << 'EOF' apiVersion: apps/v1 kind: Deployment metadata: name: mongodb namespace: default spec: replicas: 1 selector: matchLabels: app: mongodb template: metadata: labels: app: mongodb spec: containers: - name: mongodb image: mongo:5.0 ports: - containerPort: 27017 env: - name: MONGO_INITDB_ROOT_USERNAME value: "fgedu" - name: MONGO_INITDB_ROOT_PASSWORD value: "fgedu123" - name: MONGO_INITDB_DATABASE value: "fgedudb" volumeMounts: - name: mongodb-data mountPath: /data/db volumes: - name: mongodb-data persistentVolumeClaim: claimName: mongodb-pvc --- apiVersion: v1 kind: Service metadata: name: mongodb namespace: default spec: selector: app: mongodb ports: - port: 27017 targetPort: 27017 EOF $ kubectl apply -f mongodb-deployment.yaml 4. 验证MongoDB部署： $ kubectl get pods -l app=mongodb $ kubectl get services mongodb - Elasticsearch部署：，风哥提示：。 1. 创建StorageClass： $ cat > storageclass.yaml << 'EOF' apiVersion: storage.k8s.io/v1 kind: StorageClass metadata: name: elasticsearch-storage provisioner: kubernetes.io/aws-ebs parameters: type: gp2 iopsPerGB: "10" reclaimPolicy: Retain allowVolumeExpansion: true EOF $ kubectl apply -f storageclass.yaml 2. 创建PersistentVolumeClaim： $ cat > pvc.yaml << 'EOF' apiVersion: v1 kind: PersistentVolumeClaim metadata: name: elasticsearch-pvc spec: storageClassName: elasticsearch-storage accessModes: - ReadWriteOnce resources: requests: storage: 100Gi EOF $ kubectl apply -f pvc.yaml 3. 部署Elasticsearch： $ cat > elasticsearch-deployment.yaml << 'EOF' apiVersion: apps/v1 kind: Deployment metadata: name: elasticsearch namespace: default spec: replicas: 1 selector: matchLabels: app: elasticsearch template: metadata: labels: app: elasticsearch spec: containers: - name: elasticsearch image: elasticsearch:7.17.0 ports: - containerPort: 9200 - containerPort: 9300 env: - name: discovery.type value: "single-node" - name: ES_JAVA_OPTS value: "-Xms1g -Xmx1g" - name: ELASTIC_PASSWORD value: "fgedu123" volumeMounts: - name: elasticsearch-data mountPath: /usr/share/elasticsearch/data volumes: - name: elasticsearch-data persistentVolumeClaim: claimName: elasticsearch-pvc --- apiVersion: v1 kind: Service metadata: name: elasticsearch namespace: default spec: selector: app: elasticsearch ports: - port: 9200 targetPort: 9200 - port: 9300 targetPort: 9300 EOF $ kubectl apply -f elasticsearch-deployment.yaml 4. 验证Elasticsearch部署： $ kubectl get pods -l app=elasticsearch $ kubectl get services elasticsearch
– PostgreSQL部署：
1. 创建StorageClass：
$ cat > storageclass.yaml << 'EOF' apiVersion: storage.k8s.io/v1 kind: StorageClass metadata: name: postgres-storage provisioner: kubernetes.io/aws-ebs parameters: type: gp2 iopsPerGB: "10" reclaimPolicy: Retain allowVolumeExpansion: true EOF $ kubectl apply -f storageclass.yaml 2. 创建PersistentVolumeClaim： $ cat > pvc.yaml << 'EOF' apiVersion: v1 kind: PersistentVolumeClaim metadata: name: postgres-pvc spec: storageClassName: postgres-storage accessModes: - ReadWriteOnce resources: requests: storage: 100Gi EOF $ kubectl apply -f pvc.yaml 3. 部署PostgreSQL： $ cat > postgres-deployment.yaml << 'EOF' apiVersion: apps/v1 kind: Deployment metadata: name: postgres namespace: default spec: replicas: 1 selector: matchLabels: app: postgres template: metadata: labels: app: postgres spec: containers: - name: postgres image: postgres:14 ports: - containerPort: 5432 env: - name: POSTGRES_USER value: "fgedu" - name: POSTGRES_PASSWORD value: "fgedu123" - name: POSTGRES_DB value: "fgedudb" volumeMounts: - name: postgres-data mountPath: /var/lib/postgresql/data volumes: - name: postgres-data persistentVolumeClaim: claimName: postgres-pvc --- apiVersion: v1 kind: Service metadata: name: postgres namespace: default spec: selector: app: postgres ports: - port: 5432 targetPort: 5432 EOF $ kubectl apply -f postgres-deployment.yaml 4. 验证PostgreSQL部署： $ kubectl get pods -l app=postgres $ kubectl get services postgres - MySQL部署： 1. 创建StorageClass： $ cat > storageclass.yaml << 'EOF' apiVersion: storage.k8s.io/v1 kind: StorageClass metadata: name: mysql-storage provisioner: kubernetes.io/aws-ebs parameters: type: gp2 iopsPerGB: "10" reclaimPolicy: Retain allowVolumeExpansion: true EOF $ kubectl apply -f storageclass.yaml 2. 创建PersistentVolumeClaim： $ cat > pvc.yaml << 'EOF' apiVersion: v1 kind: PersistentVolumeClaim metadata: name: mysql-pvc spec: storageClassName: mysql-storage accessModes: - ReadWriteOnce resources: requests: storage: 100Gi EOF $ kubectl apply -f pvc.yaml 3. 部署MySQL： $ cat > mysql-deployment.yaml << 'EOF' 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 ports: - containerPort: 3306 env: - name: MYSQL_ROOT_PASSWORD value: "fgedu123" - name: MYSQL_DATABASE value: "fgedudb" - name: MYSQL_USER value: "fgedu" - name: MYSQL_PASSWORD value: "fgedu123" volumeMounts: - name: mysql-data mountPath: /var/lib/mysql volumes: - name: mysql-data persistentVolumeClaim: claimName: mysql-pvc --- apiVersion: v1 kind: Service metadata: name: mysql namespace: default spec: selector: app: mysql ports: - port: 3306 targetPort: 3306 EOF $ kubectl apply -f mysql-deployment.yaml 4. 验证MySQL部署： $ kubectl get pods -l app=mysql $ kubectl get services mysql - MongoDB部署： 1. 创建StorageClass： $ cat > storageclass.yaml << 'EOF' apiVersion: storage.k8s.io/v1 kind: StorageClass metadata: name: mongodb-storage provisioner: kubernetes.io/aws-ebs parameters: type: gp2 iopsPerGB: "10" reclaimPolicy: Retain allowVolumeExpansion: true EOF $ kubectl apply -f storageclass.yaml 2. 创建PersistentVolumeClaim： $ cat > pvc.yaml << 'EOF' apiVersion: v1 kind: PersistentVolumeClaim metadata: name: mongodb-pvc spec: storageClassName: mongodb-storage accessModes: - ReadWriteOnce resources: requests: storage: 100Gi EOF $ kubectl apply -f pvc.yaml 3. 部署MongoDB： $ cat > mongodb-deployment.yaml << 'EOF' apiVersion: apps/v1 kind: Deployment metadata: name: mongodb namespace: default spec: replicas: 1 selector: matchLabels: app: mongodb template: metadata: labels: app: mongodb spec: containers: - name: mongodb image: mongo:5.0 ports: - containerPort: 27017 env: - name: MONGO_INITDB_ROOT_USERNAME value: "fgedu" - name: MONGO_INITDB_ROOT_PASSWORD value: "fgedu123" - name: MONGO_INITDB_DATABASE value: "fgedudb" volumeMounts: - name: mongodb-data mountPath: /data/db volumes: - name: mongodb-data persistentVolumeClaim: claimName: mongodb-pvc --- apiVersion: v1 kind: Service metadata: name: mongodb namespace: default spec: selector: app: mongodb ports: - port: 27017 targetPort: 27017 EOF $ kubectl apply -f mongodb-deployment.yaml 4. 验证MongoDB部署： $ kubectl get pods -l app=mongodb $ kubectl get services mongodb - Elasticsearch部署：，风哥提示：。 1. 创建StorageClass： $ cat > storageclass.yaml << 'EOF' apiVersion: storage.k8s.io/v1 kind: StorageClass metadata: name: elasticsearch-storage provisioner: kubernetes.io/aws-ebs parameters: type: gp2 iopsPerGB: "10" reclaimPolicy: Retain allowVolumeExpansion: true EOF $ kubectl apply -f storageclass.yaml 2. 创建PersistentVolumeClaim： $ cat > pvc.yaml << 'EOF' apiVersion: v1 kind: PersistentVolumeClaim metadata: name: elasticsearch-pvc spec: storageClassName: elasticsearch-storage accessModes: - ReadWriteOnce resources: requests: storage: 100Gi EOF $ kubectl apply -f pvc.yaml 3. 部署Elasticsearch： $ cat > elasticsearch-deployment.yaml << 'EOF' apiVersion: apps/v1 kind: Deployment metadata: name: elasticsearch namespace: default spec: replicas: 1 selector: matchLabels: app: elasticsearch template: metadata: labels: app: elasticsearch spec: containers: - name: elasticsearch image: elasticsearch:7.17.0 ports: - containerPort: 9200 - containerPort: 9300 env: - name: discovery.type value: "single-node" - name: ES_JAVA_OPTS value: "-Xms1g -Xmx1g" - name: ELASTIC_PASSWORD value: "fgedu123" volumeMounts: - name: elasticsearch-data mountPath: /usr/share/elasticsearch/data volumes: - name: elasticsearch-data persistentVolumeClaim: claimName: elasticsearch-pvc --- apiVersion: v1 kind: Service metadata: name: elasticsearch namespace: default spec: selector: app: elasticsearch ports: - port: 9200 targetPort: 9200 - port: 9300 targetPort: 9300 EOF $ kubectl apply -f elasticsearch-deployment.yaml 4. 验证Elasticsearch部署： $ kubectl get pods -l app=elasticsearch $ kubectl get services elasticsearch
3.2 数据分析实施方案
生产环境Kubernetes数据分析实施方案。，风哥提示：。
# 数据分析实施方案
– Spark部署：
1. 部署Spark Master：
$ cat > spark-master-deployment.yaml << 'EOF' apiVersion: apps/v1 kind: Deployment metadata: name: spark-master namespace: default spec: replicas: 1 selector: matchLabels: app: spark-master template: metadata: labels: app: spark-master spec: containers: - name: spark-master image: bitnami/spark:3.3.0 ports: - containerPort: 7077 - containerPort: 8080 env: - name: SPARK_MODE value: "master" --- apiVersion: v1 kind: Service metadata: name: spark-master namespace: default spec: selector: app: spark-master ports: - port: 7077 targetPort: 7077 - port: 8080 targetPort: 8080 EOF $ kubectl apply -f spark-master-deployment.yaml 2. 部署Spark Worker： $ cat > spark-worker-deployment.yaml << 'EOF' apiVersion: apps/v1 kind: Deployment metadata: name: spark-worker namespace: default spec: replicas: 2 selector: matchLabels: app: spark-worker template: metadata: labels: app: spark-worker spec: containers: - name: spark-worker image: bitnami/spark:3.3.0 ports: - containerPort: 8081 env: - name: SPARK_MODE value: "worker" - name: SPARK_MASTER_URL value: "spark://spark-master:7077" EOF $ kubectl apply -f spark-worker-deployment.yaml 3. 验证Spark部署： $ kubectl get pods -l app=spark-master $ kubectl get pods -l app=spark-worker $ kubectl get services spark-master - Flink部署： 1. 部署Flink JobManager： $ cat > flink-jobmanager-deployment.yaml << 'EOF' apiVersion: apps/v1 kind: Deployment metadata: name: flink-jobmanager namespace: default spec: replicas: 1 selector: matchLabels: app: flink-jobmanager template: metadata: labels: app: flink-jobmanager spec: containers: - name: flink-jobmanager image: flink:1.15.0 ports: - containerPort: 8081 - containerPort: 6123 command: - /opt/flink/bin/jobmanager.sh args: - start-foreground --- apiVersion: v1 kind: Service metadata: name: flink-jobmanager namespace: default spec: selector: app: flink-jobmanager ports: - port: 8081 targetPort: 8081 - port: 6123 targetPort: 6123 EOF $ kubectl apply -f flink-jobmanager-deployment.yaml 2. 部署Flink TaskManager： $ cat > flink-taskmanager-deployment.yaml << 'EOF' apiVersion: apps/v1 kind: Deployment metadata: name: flink-taskmanager namespace: default spec: replicas: 2 selector: matchLabels: app: flink-taskmanager template: metadata: labels: app: flink-taskmanager，学习交流加群风哥微信: itpux-com。 spec: containers: - name: flink-taskmanager image: flink:1.15.0 ports: - containerPort: 6121 - containerPort: 6122 command: - /opt/flink/bin/taskmanager.sh args: - start-foreground env: - name: JOB_MANAGER_RPC_ADDRESS value: "flink-jobmanager" EOF $ kubectl apply -f flink-taskmanager-deployment.yaml 3. 验证Flink部署： $ kubectl get pods -l app=flink-jobmanager $ kubectl get pods -l app=flink-taskmanager $ kubectl get services flink-jobmanager - Hive部署： 1. 部署Hive Metastore： $ cat > hive-metastore-deployment.yaml << 'EOF' apiVersion: apps/v1 kind: Deployment metadata: name: hive-metastore namespace: default spec: replicas: 1 selector: matchLabels: app: hive-metastore template: metadata: labels: app: hive-metastore spec: containers: - name: hive-metastore image: apache/hive:3.1.3 ports: - containerPort: 9083 env: - name: HIVE_METASTORE_HOST value: "hive-metastore" - name: HIVE_METASTORE_PORT value: "9083" - name: HIVE_METASTORE_DATABASE_TYPE value: "postgres" - name: HIVE_METASTORE_CONNECTION_URL value: "jdbc:postgresql://postgres:5432/hive" - name: HIVE_METASTORE_CONNECTION_USERNAME value: "fgedu" - name: HIVE_METASTORE_CONNECTION_PASSWORD value: "fgedu123" --- apiVersion: v1 kind: Service metadata: name: hive-metastore namespace: default spec: selector: app: hive-metastore ports: - port: 9083 targetPort: 9083 EOF $ kubectl apply -f hive-metastore-deployment.yaml 2. 部署Hive Server2： $ cat > hive-server2-deployment.yaml << 'EOF' apiVersion: apps/v1 kind: Deployment metadata: name: hive-server2 namespace: default spec: replicas: 1 selector: matchLabels: app: hive-server2 template: metadata: labels: app: hive-server2 spec: containers: - name: hive-server2 image: apache/hive:3.1.3 ports: - containerPort: 10000 - containerPort: 10002 env: - name: HIVE_SERVER2_HOST value: "hive-server2" - name: HIVE_SERVER2_PORT value: "10000" - name: HIVE_METASTORE_URI value: "thrift://hive-metastore:9083" --- apiVersion: v1 kind: Service metadata: name: hive-server2 namespace: default spec: selector: app: hive-server2 ports: - port: 10000 targetPort: 10000 - port: 10002 targetPort: 10002 EOF $ kubectl apply -f hive-server2-deployment.yaml 3. 验证Hive部署： $ kubectl get pods -l app=hive-metastore $ kubectl get pods -l app=hive-server2 $ kubectl get services hive-metastore $ kubectl get services hive-server2
– Spark部署：
1. 部署Spark Master：
$ cat > spark-master-deployment.yaml << 'EOF' apiVersion: apps/v1 kind: Deployment metadata: name: spark-master namespace: default spec: replicas: 1 selector: matchLabels: app: spark-master template: metadata: labels: app: spark-master spec: containers: - name: spark-master image: bitnami/spark:3.3.0 ports: - containerPort: 7077 - containerPort: 8080 env: - name: SPARK_MODE value: "master" --- apiVersion: v1 kind: Service metadata: name: spark-master namespace: default spec: selector: app: spark-master ports: - port: 7077 targetPort: 7077 - port: 8080 targetPort: 8080 EOF $ kubectl apply -f spark-master-deployment.yaml 2. 部署Spark Worker： $ cat > spark-worker-deployment.yaml << 'EOF' apiVersion: apps/v1 kind: Deployment metadata: name: spark-worker namespace: default spec: replicas: 2 selector: matchLabels: app: spark-worker template: metadata: labels: app: spark-worker spec: containers: - name: spark-worker image: bitnami/spark:3.3.0 ports: - containerPort: 8081 env: - name: SPARK_MODE value: "worker" - name: SPARK_MASTER_URL value: "spark://spark-master:7077" EOF $ kubectl apply -f spark-worker-deployment.yaml 3. 验证Spark部署： $ kubectl get pods -l app=spark-master $ kubectl get pods -l app=spark-worker $ kubectl get services spark-master - Flink部署： 1. 部署Flink JobManager： $ cat > flink-jobmanager-deployment.yaml << 'EOF' apiVersion: apps/v1 kind: Deployment metadata: name: flink-jobmanager namespace: default spec: replicas: 1 selector: matchLabels: app: flink-jobmanager template: metadata: labels: app: flink-jobmanager spec: containers: - name: flink-jobmanager image: flink:1.15.0 ports: - containerPort: 8081 - containerPort: 6123 command: - /opt/flink/bin/jobmanager.sh args: - start-foreground --- apiVersion: v1 kind: Service metadata: name: flink-jobmanager namespace: default spec: selector: app: flink-jobmanager ports: - port: 8081 targetPort: 8081 - port: 6123 targetPort: 6123 EOF $ kubectl apply -f flink-jobmanager-deployment.yaml 2. 部署Flink TaskManager： $ cat > flink-taskmanager-deployment.yaml << 'EOF' apiVersion: apps/v1 kind: Deployment metadata: name: flink-taskmanager namespace: default spec: replicas: 2 selector: matchLabels: app: flink-taskmanager template: metadata: labels: app: flink-taskmanager，学习交流加群风哥微信: itpux-com。 spec: containers: - name: flink-taskmanager image: flink:1.15.0 ports: - containerPort: 6121 - containerPort: 6122 command: - /opt/flink/bin/taskmanager.sh args: - start-foreground env: - name: JOB_MANAGER_RPC_ADDRESS value: "flink-jobmanager" EOF $ kubectl apply -f flink-taskmanager-deployment.yaml 3. 验证Flink部署： $ kubectl get pods -l app=flink-jobmanager $ kubectl get pods -l app=flink-taskmanager $ kubectl get services flink-jobmanager - Hive部署： 1. 部署Hive Metastore： $ cat > hive-metastore-deployment.yaml << 'EOF' apiVersion: apps/v1 kind: Deployment metadata: name: hive-metastore namespace: default spec: replicas: 1 selector: matchLabels: app: hive-metastore template: metadata: labels: app: hive-metastore spec: containers: - name: hive-metastore image: apache/hive:3.1.3 ports: - containerPort: 9083 env: - name: HIVE_METASTORE_HOST value: "hive-metastore" - name: HIVE_METASTORE_PORT value: "9083" - name: HIVE_METASTORE_DATABASE_TYPE value: "postgres" - name: HIVE_METASTORE_CONNECTION_URL value: "jdbc:postgresql://postgres:5432/hive" - name: HIVE_METASTORE_CONNECTION_USERNAME value: "fgedu" - name: HIVE_METASTORE_CONNECTION_PASSWORD value: "fgedu123" --- apiVersion: v1 kind: Service metadata: name: hive-metastore namespace: default spec: selector: app: hive-metastore ports: - port: 9083 targetPort: 9083 EOF $ kubectl apply -f hive-metastore-deployment.yaml 2. 部署Hive Server2： $ cat > hive-server2-deployment.yaml << 'EOF' apiVersion: apps/v1 kind: Deployment metadata: name: hive-server2 namespace: default spec: replicas: 1 selector: matchLabels: app: hive-server2 template: metadata: labels: app: hive-server2 spec: containers: - name: hive-server2 image: apache/hive:3.1.3 ports: - containerPort: 10000 - containerPort: 10002 env: - name: HIVE_SERVER2_HOST value: "hive-server2" - name: HIVE_SERVER2_PORT value: "10000" - name: HIVE_METASTORE_URI value: "thrift://hive-metastore:9083" --- apiVersion: v1 kind: Service metadata: name: hive-server2 namespace: default spec: selector: app: hive-server2 ports: - port: 10000 targetPort: 10000 - port: 10002 targetPort: 10002 EOF $ kubectl apply -f hive-server2-deployment.yaml 3. 验证Hive部署： $ kubectl get pods -l app=hive-metastore $ kubectl get pods -l app=hive-server2 $ kubectl get services hive-metastore $ kubectl get services hive-server2
3.3 集成实施方案
生产环境Kubernetes数据仓库与分析的集成实施方案。
# 集成实施方案
– Airflow部署：
1. 创建StorageClass：
$ cat > storageclass.yaml << 'EOF' apiVersion: storage.k8s.io/v1 kind: StorageClass metadata: name: airflow-storage provisioner: kubernetes.io/aws-ebs parameters: type: gp2 iopsPerGB: "10" reclaimPolicy: Retain allowVolumeExpansion: true EOF $ kubectl apply -f storageclass.yaml 2. 创建PersistentVolumeClaim： $ cat > pvc.yaml << 'EOF' apiVersion: v1 kind: PersistentVolumeClaim metadata: name: airflow-pvc spec: storageClassName: airflow-storage accessModes: - ReadWriteOnce resources: requests: storage: 100Gi EOF $ kubectl apply -f pvc.yaml 3. 部署Airflow： $ cat > airflow-deployment.yaml << 'EOF' apiVersion: apps/v1 kind: Deployment metadata: name: airflow-webserver namespace: default spec: replicas: 1 selector: matchLabels: app: airflow-webserver template: metadata: labels: app: airflow-webserver spec: containers: - name: airflow-webserver image: apache/airflow:2.5.0 ports: - containerPort: 8080 env: - name: AIRFLOW__CORE__EXECUTOR value: "LocalExecutor" - name: AIRFLOW__DATABASE__SQL_ALCHEMY_CONN value: "postgresql+psycopg2://fgedu:fgedu123@postgres:5432/airflow" - name: AIRFLOW__WEBSERVER__SECRET_KEY value: "fgedu-secret-key" volumeMounts: - name: airflow-data mountPath: /opt/airflow/dags volumes: - name: airflow-data persistentVolumeClaim: claimName: airflow-pvc --- apiVersion: v1 kind: Service metadata: name: airflow-webserver namespace: default spec: selector: app: airflow-webserver ports: - port: 8080 targetPort: 8080 EOF $ kubectl apply -f airflow-deployment.yaml 4. 初始化Airflow数据库： $ kubectl exec -it $(kubectl get pods -l app=airflow-webserver -o jsonpath="{.items[0].metadata.name}") -- airflow db init $ kubectl exec -it $(kubectl get pods -l app=airflow-webserver -o jsonpath="{.items[0].metadata.name}") -- airflow users create --username admin --firstname Admin --lastname User --role Admin --email admin@example.com --password admin 5. 验证Airflow部署： $ kubectl get pods -l app=airflow-webserver $ kubectl get services airflow-webserver - JupyterHub部署： 1. 创建StorageClass： $ cat > storageclass.yaml << 'EOF' apiVersion: storage.k8s.io/v1 kind: StorageClass metadata: name: jupyterhub-storage provisioner: kubernetes.io/aws-ebs parameters: type: gp2 iopsPerGB: "10" reclaimPolicy: Retain allowVolumeExpansion: true EOF，学习交流加群风哥QQ113257174。 $ kubectl apply -f storageclass.yaml 2. 部署JupyterHub： $ cat > jupyterhub-deployment.yaml << 'EOF' apiVersion: apps/v1 kind: Deployment metadata: name: jupyterhub namespace: default spec: replicas: 1 selector: matchLabels: app: jupyterhub template: metadata: labels: app: jupyterhub spec: containers: - name: jupyterhub image: jupyterhub/jupyterhub:2.3.1 ports: - containerPort: 8000 env: - name: JUPYTERHUB_API_TOKEN value: "fgedu-api-token" - name: JUPYTERHUB_COOKIE_SECRET value: "fgedu-cookie-secret" --- apiVersion: v1 kind: Service metadata: name: jupyterhub namespace: default spec: selector: app: jupyterhub ports: - port: 8000 targetPort: 8000 EOF $ kubectl apply -f jupyterhub-deployment.yaml 3. 验证JupyterHub部署： $ kubectl get pods -l app=jupyterhub $ kubectl get services jupyterhub - Grafana部署： 1. 创建StorageClass： $ cat > storageclass.yaml << 'EOF' apiVersion: storage.k8s.io/v1 kind: StorageClass metadata: name: grafana-storage provisioner: kubernetes.io/aws-ebs parameters: type: gp2 iopsPerGB: "10" reclaimPolicy: Retain allowVolumeExpansion: true EOF $ kubectl apply -f storageclass.yaml 2. 创建PersistentVolumeClaim： $ cat > pvc.yaml << 'EOF' apiVersion: v1 kind: PersistentVolumeClaim metadata: name: grafana-pvc spec: storageClassName: grafana-storage accessModes: - ReadWriteOnce resources: requests: storage: 100Gi EOF $ kubectl apply -f pvc.yaml 3. 部署Grafana： $ cat > grafana-deployment.yaml << 'EOF' apiVersion: apps/v1 kind: Deployment metadata: name: grafana namespace: default spec: replicas: 1 selector: matchLabels: app: grafana template: metadata: labels: app: grafana spec: containers: - name: grafana image: grafana/grafana:8.5.0 ports: - containerPort: 3000 volumeMounts: - name: grafana-data mountPath: /var/lib/grafana volumes: - name: grafana-data persistentVolumeClaim: claimName: grafana-pvc --- apiVersion: v1 kind: Service metadata: name: grafana namespace: default spec: selector: app: grafana ports: - port: 3000 targetPort: 3000 EOF $ kubectl apply -f grafana-deployment.yaml 4. 验证Grafana部署： $ kubectl get pods -l app=grafana $ kubectl get services grafana
– Airflow部署：
1. 创建StorageClass：
$ cat > storageclass.yaml << 'EOF' apiVersion: storage.k8s.io/v1 kind: StorageClass metadata: name: airflow-storage provisioner: kubernetes.io/aws-ebs parameters: type: gp2 iopsPerGB: "10" reclaimPolicy: Retain allowVolumeExpansion: true EOF $ kubectl apply -f storageclass.yaml 2. 创建PersistentVolumeClaim： $ cat > pvc.yaml << 'EOF' apiVersion: v1 kind: PersistentVolumeClaim metadata: name: airflow-pvc spec: storageClassName: airflow-storage accessModes: - ReadWriteOnce resources: requests: storage: 100Gi EOF $ kubectl apply -f pvc.yaml 3. 部署Airflow： $ cat > airflow-deployment.yaml << 'EOF' apiVersion: apps/v1 kind: Deployment metadata: name: airflow-webserver namespace: default spec: replicas: 1 selector: matchLabels: app: airflow-webserver template: metadata: labels: app: airflow-webserver spec: containers: - name: airflow-webserver image: apache/airflow:2.5.0 ports: - containerPort: 8080 env: - name: AIRFLOW__CORE__EXECUTOR value: "LocalExecutor" - name: AIRFLOW__DATABASE__SQL_ALCHEMY_CONN value: "postgresql+psycopg2://fgedu:fgedu123@postgres:5432/airflow" - name: AIRFLOW__WEBSERVER__SECRET_KEY value: "fgedu-secret-key" volumeMounts: - name: airflow-data mountPath: /opt/airflow/dags volumes: - name: airflow-data persistentVolumeClaim: claimName: airflow-pvc --- apiVersion: v1 kind: Service metadata: name: airflow-webserver namespace: default spec: selector: app: airflow-webserver ports: - port: 8080 targetPort: 8080 EOF $ kubectl apply -f airflow-deployment.yaml 4. 初始化Airflow数据库： $ kubectl exec -it $(kubectl get pods -l app=airflow-webserver -o jsonpath="{.items[0].metadata.name}") -- airflow db init $ kubectl exec -it $(kubectl get pods -l app=airflow-webserver -o jsonpath="{.items[0].metadata.name}") -- airflow users create --username admin --firstname Admin --lastname User --role Admin --email admin@example.com --password admin 5. 验证Airflow部署： $ kubectl get pods -l app=airflow-webserver $ kubectl get services airflow-webserver - JupyterHub部署： 1. 创建StorageClass： $ cat > storageclass.yaml << 'EOF' apiVersion: storage.k8s.io/v1 kind: StorageClass metadata: name: jupyterhub-storage provisioner: kubernetes.io/aws-ebs parameters: type: gp2 iopsPerGB: "10" reclaimPolicy: Retain allowVolumeExpansion: true EOF，学习交流加群风哥QQ113257174。 $ kubectl apply -f storageclass.yaml 2. 部署JupyterHub： $ cat > jupyterhub-deployment.yaml << 'EOF' apiVersion: apps/v1 kind: Deployment metadata: name: jupyterhub namespace: default spec: replicas: 1 selector: matchLabels: app: jupyterhub template: metadata: labels: app: jupyterhub spec: containers: - name: jupyterhub image: jupyterhub/jupyterhub:2.3.1 ports: - containerPort: 8000 env: - name: JUPYTERHUB_API_TOKEN value: "fgedu-api-token" - name: JUPYTERHUB_COOKIE_SECRET value: "fgedu-cookie-secret" --- apiVersion: v1 kind: Service metadata: name: jupyterhub namespace: default spec: selector: app: jupyterhub ports: - port: 8000 targetPort: 8000 EOF $ kubectl apply -f jupyterhub-deployment.yaml 3. 验证JupyterHub部署： $ kubectl get pods -l app=jupyterhub $ kubectl get services jupyterhub - Grafana部署： 1. 创建StorageClass： $ cat > storageclass.yaml << 'EOF' apiVersion: storage.k8s.io/v1 kind: StorageClass metadata: name: grafana-storage provisioner: kubernetes.io/aws-ebs parameters: type: gp2 iopsPerGB: "10" reclaimPolicy: Retain allowVolumeExpansion: true EOF $ kubectl apply -f storageclass.yaml 2. 创建PersistentVolumeClaim： $ cat > pvc.yaml << 'EOF' apiVersion: v1 kind: PersistentVolumeClaim metadata: name: grafana-pvc spec: storageClassName: grafana-storage accessModes: - ReadWriteOnce resources: requests: storage: 100Gi EOF $ kubectl apply -f pvc.yaml 3. 部署Grafana： $ cat > grafana-deployment.yaml << 'EOF' apiVersion: apps/v1 kind: Deployment metadata: name: grafana namespace: default spec: replicas: 1 selector: matchLabels: app: grafana template: metadata: labels: app: grafana spec: containers: - name: grafana image: grafana/grafana:8.5.0 ports: - containerPort: 3000 volumeMounts: - name: grafana-data mountPath: /var/lib/grafana volumes: - name: grafana-data persistentVolumeClaim: claimName: grafana-pvc --- apiVersion: v1 kind: Service metadata: name: grafana namespace: default spec: selector: app: grafana ports: - port: 3000 targetPort: 3000 EOF $ kubectl apply -f grafana-deployment.yaml 4. 验证Grafana部署： $ kubectl get pods -l app=grafana $ kubectl get services grafana
Part04-生产案例与实战讲解
4.1 数据仓库案例
生产环境Kubernetes数据仓库的案例。
# 案例：PostgreSQL数据仓库部署
# 场景：在Kubernetes集群中部署PostgreSQL作为数据仓库，用于存储和管理业务数据
# 问题：
– 需要在Kubernetes集群中部署PostgreSQL数据仓库
– 需要确保数据持久化和高可用性
– 需要配置网络访问和安全管理
# 解决方案：
1. 创建StorageClass：
$ cat > storageclass.yaml << 'EOF' apiVersion: storage.k8s.io/v1 kind: StorageClass metadata: name: postgres-storage provisioner: kubernetes.io/aws-ebs parameters: type: gp2 iopsPerGB: "10" reclaimPolicy: Retain allowVolumeExpansion: true EOF $ kubectl apply -f storageclass.yaml 2. 创建PersistentVolumeClaim： $ cat > pvc.yaml << 'EOF' apiVersion: v1 kind: PersistentVolumeClaim metadata: name: postgres-pvc spec: storageClassName: postgres-storage accessModes: - ReadWriteOnce resources: requests: storage: 100Gi EOF $ kubectl apply -f pvc.yaml 3. 部署PostgreSQL： $ cat > postgres-deployment.yaml << 'EOF' apiVersion: apps/v1 kind: Deployment metadata: name: postgres namespace: default spec: replicas: 1 selector: matchLabels: app: postgres template: metadata: labels: app: postgres spec: containers: - name: postgres image: postgres:14 ports: - containerPort: 5432 env: - name: POSTGRES_USER value: "fgedu" - name: POSTGRES_PASSWORD value: "fgedu123" - name: POSTGRES_DB value: "fgedudb" volumeMounts: - name: postgres-data mountPath: /var/lib/postgresql/data volumes: - name: postgres-data persistentVolumeClaim: claimName: postgres-pvc --- apiVersion: v1 kind: Service metadata: name: postgres namespace: default spec: selector: app: postgres ports: - port: 5432 targetPort: 5432 EOF $ kubectl apply -f postgres-deployment.yaml 4. 验证PostgreSQL部署： $ kubectl get pods -l app=postgres NAME READY STATUS RESTARTS AGE postgres-6d6f58987b-7f5f8 1/1 Running 0 5m $ kubectl get services postgres NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE postgres ClusterIP 10.96.123.45 5432/TCP 5m
5. 连接PostgreSQL：
$ kubectl run -it –rm –image=postgres:14 postgres-client — psql -h postgres -U fgedu -d fgedudb
Password for user fgedu: fgedu123
psql (14.0 (Debian 14.0-1.pgdg110+1))
Type “help” for help.
fgedudb=> CREATE TABLE fgedu_users (，更多视频教程www.fgedu.net.cn。
fgedudb(> id SERIAL PRIMARY KEY,
fgedudb(> name VARCHAR(100),
fgedudb(> email VARCHAR(100),
fgedudb(> created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
fgedudb(> );
CREATE TABLE
fgedudb=> INSERT INTO fgedu_users (name, email) VALUES (‘Alice’, ‘alice@example.com’);
INSERT 0 1
fgedudb=> INSERT INTO fgedu_users (name, email) VALUES (‘Bob’, ‘bob@example.com’);
INSERT 0 1
fgedudb=> SELECT * FROM fgedu_users;
id | name | email | created_at
—-+——-+———————+—————————-
1 | Alice | alice@example.com | 2024-01-01 00:00:00.000000
2 | Bob | bob@example.com | 2024-01-01 00:00:00.000000
(2 rows)
fgedudb=> \q
# 案例：MySQL数据仓库部署
# 场景：在Kubernetes集群中部署MySQL作为数据仓库，用于存储和管理业务数据
# 问题：
– 需要在Kubernetes集群中部署MySQL数据仓库
– 需要确保数据持久化和高可用性
– 需要配置网络访问和安全管理
# 解决方案：
1. 创建StorageClass：
$ cat > storageclass.yaml << 'EOF' apiVersion: storage.k8s.io/v1 kind: StorageClass metadata: name: mysql-storage provisioner: kubernetes.io/aws-ebs parameters: type: gp2 iopsPerGB: "10" reclaimPolicy: Retain allowVolumeExpansion: true EOF $ kubectl apply -f storageclass.yaml 2. 创建PersistentVolumeClaim： $ cat > pvc.yaml << 'EOF' apiVersion: v1 kind: PersistentVolumeClaim metadata: name: mysql-pvc spec: storageClassName: mysql-storage accessModes: - ReadWriteOnce resources: requests: storage: 100Gi EOF $ kubectl apply -f pvc.yaml 3. 部署MySQL： $ cat > mysql-deployment.yaml << 'EOF' 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 ports: - containerPort: 3306 env: - name: MYSQL_ROOT_PASSWORD value: "fgedu123" - name: MYSQL_DATABASE value: "fgedudb" - name: MYSQL_USER value: "fgedu" - name: MYSQL_PASSWORD value: "fgedu123" volumeMounts: - name: mysql-data mountPath: /var/lib/mysql volumes: - name: mysql-data persistentVolumeClaim: claimName: mysql-pvc --- apiVersion: v1 kind: Service metadata: name: mysql namespace: default spec: selector: app: mysql ports: - port: 3306 targetPort: 3306 EOF $ kubectl apply -f mysql-deployment.yaml 4. 验证MySQL部署： $ kubectl get pods -l app=mysql NAME READY STATUS RESTARTS AGE mysql-6d6f58987b-7f5f8 1/1 Running 0 5m $ kubectl get services mysql NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE mysql ClusterIP 10.96.123.46 3306/TCP 5m
5. 连接MySQL：
$ kubectl run -it –rm –image=mysql:8.0 mysql-client — mysql -h mysql -u fgedu -pfgedu123 fgedudb
mysql: [Warning] Using a password on the command line interface can be insecure.
Welcome to the MySQL monitor. Commands end with ; or \g.
Your MySQL connection id is 10
Server version: 8.0.28 MySQL Community Server – GPL
Copyright (c) 2000, 2022, Oracle and/or its affiliates.
Oracle is a registered trademark of Oracle Corporation and/or its
affiliates. Other names may be trademarks of their respective
owners.
Type ‘help;’ or ‘\h’ for help. Type ‘\c’ to clear the current input statement.
mysql> CREATE TABLE fgedu_users (
-> id INT AUTO_INCREMENT PRIMARY KEY,
-> name VARCHAR(100),
-> email VARCHAR(100),
-> created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
-> );
Query OK, 0 rows affected (0.01 sec)
mysql> INSERT INTO fgedu_users (name, email) VALUES (‘Alice’, ‘alice@example.com’);
Query OK, 1 row affected (0.00 sec)
mysql> INSERT INTO fgedu_users (name, email) VALUES (‘Bob’, ‘bob@example.com’);
Query OK, 1 row affected (0.00 sec)
mysql> SELECT * FROM fgedu_users;
+—-+——-+———————+———————+
| id | name | email | created_at |
+—-+——-+———————+———————+
| 1 | Alice | alice@example.com | 2024-01-01 00:00:00 |
| 2 | Bob | bob@example.com | 2024-01-01 00:00:00 |
+—-+——-+———————+———————+
2 rows in set (0.00 sec)
mysql> exit
Bye
# 场景：在Kubernetes集群中部署PostgreSQL作为数据仓库，用于存储和管理业务数据
# 问题：
– 需要在Kubernetes集群中部署PostgreSQL数据仓库
– 需要确保数据持久化和高可用性
– 需要配置网络访问和安全管理
# 解决方案：
1. 创建StorageClass：
$ cat > storageclass.yaml << 'EOF' apiVersion: storage.k8s.io/v1 kind: StorageClass metadata: name: postgres-storage provisioner: kubernetes.io/aws-ebs parameters: type: gp2 iopsPerGB: "10" reclaimPolicy: Retain allowVolumeExpansion: true EOF $ kubectl apply -f storageclass.yaml 2. 创建PersistentVolumeClaim： $ cat > pvc.yaml << 'EOF' apiVersion: v1 kind: PersistentVolumeClaim metadata: name: postgres-pvc spec: storageClassName: postgres-storage accessModes: - ReadWriteOnce resources: requests: storage: 100Gi EOF $ kubectl apply -f pvc.yaml 3. 部署PostgreSQL： $ cat > postgres-deployment.yaml << 'EOF' apiVersion: apps/v1 kind: Deployment metadata: name: postgres namespace: default spec: replicas: 1 selector: matchLabels: app: postgres template: metadata: labels: app: postgres spec: containers: - name: postgres image: postgres:14 ports: - containerPort: 5432 env: - name: POSTGRES_USER value: "fgedu" - name: POSTGRES_PASSWORD value: "fgedu123" - name: POSTGRES_DB value: "fgedudb" volumeMounts: - name: postgres-data mountPath: /var/lib/postgresql/data volumes: - name: postgres-data persistentVolumeClaim: claimName: postgres-pvc --- apiVersion: v1 kind: Service metadata: name: postgres namespace: default spec: selector: app: postgres ports: - port: 5432 targetPort: 5432 EOF $ kubectl apply -f postgres-deployment.yaml 4. 验证PostgreSQL部署： $ kubectl get pods -l app=postgres NAME READY STATUS RESTARTS AGE postgres-6d6f58987b-7f5f8 1/1 Running 0 5m $ kubectl get services postgres NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE postgres ClusterIP 10.96.123.45
5. 连接PostgreSQL：
$ kubectl run -it –rm –image=postgres:14 postgres-client — psql -h postgres -U fgedu -d fgedudb
Password for user fgedu: fgedu123
psql (14.0 (Debian 14.0-1.pgdg110+1))
Type “help” for help.
fgedudb=> CREATE TABLE fgedu_users (，更多视频教程www.fgedu.net.cn。
fgedudb(> id SERIAL PRIMARY KEY,
fgedudb(> name VARCHAR(100),
fgedudb(> email VARCHAR(100),
fgedudb(> created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
fgedudb(> );
CREATE TABLE
fgedudb=> INSERT INTO fgedu_users (name, email) VALUES (‘Alice’, ‘alice@example.com’);
INSERT 0 1
fgedudb=> INSERT INTO fgedu_users (name, email) VALUES (‘Bob’, ‘bob@example.com’);
INSERT 0 1
fgedudb=> SELECT * FROM fgedu_users;
id | name | email | created_at
—-+——-+———————+—————————-
1 | Alice | alice@example.com | 2024-01-01 00:00:00.000000
2 | Bob | bob@example.com | 2024-01-01 00:00:00.000000
(2 rows)
fgedudb=> \q
# 案例：MySQL数据仓库部署
# 场景：在Kubernetes集群中部署MySQL作为数据仓库，用于存储和管理业务数据
# 问题：
– 需要在Kubernetes集群中部署MySQL数据仓库
– 需要确保数据持久化和高可用性
– 需要配置网络访问和安全管理
# 解决方案：
1. 创建StorageClass：
$ cat > storageclass.yaml << 'EOF' apiVersion: storage.k8s.io/v1 kind: StorageClass metadata: name: mysql-storage provisioner: kubernetes.io/aws-ebs parameters: type: gp2 iopsPerGB: "10" reclaimPolicy: Retain allowVolumeExpansion: true EOF $ kubectl apply -f storageclass.yaml 2. 创建PersistentVolumeClaim： $ cat > pvc.yaml << 'EOF' apiVersion: v1 kind: PersistentVolumeClaim metadata: name: mysql-pvc spec: storageClassName: mysql-storage accessModes: - ReadWriteOnce resources: requests: storage: 100Gi EOF $ kubectl apply -f pvc.yaml 3. 部署MySQL： $ cat > mysql-deployment.yaml << 'EOF' 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 ports: - containerPort: 3306 env: - name: MYSQL_ROOT_PASSWORD value: "fgedu123" - name: MYSQL_DATABASE value: "fgedudb" - name: MYSQL_USER value: "fgedu" - name: MYSQL_PASSWORD value: "fgedu123" volumeMounts: - name: mysql-data mountPath: /var/lib/mysql volumes: - name: mysql-data persistentVolumeClaim: claimName: mysql-pvc --- apiVersion: v1 kind: Service metadata: name: mysql namespace: default spec: selector: app: mysql ports: - port: 3306 targetPort: 3306 EOF $ kubectl apply -f mysql-deployment.yaml 4. 验证MySQL部署： $ kubectl get pods -l app=mysql NAME READY STATUS RESTARTS AGE mysql-6d6f58987b-7f5f8 1/1 Running 0 5m $ kubectl get services mysql NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE mysql ClusterIP 10.96.123.46
5. 连接MySQL：
$ kubectl run -it –rm –image=mysql:8.0 mysql-client — mysql -h mysql -u fgedu -pfgedu123 fgedudb
mysql: [Warning] Using a password on the command line interface can be insecure.
Welcome to the MySQL monitor. Commands end with ; or \g.
Your MySQL connection id is 10
Server version: 8.0.28 MySQL Community Server – GPL
Copyright (c) 2000, 2022, Oracle and/or its affiliates.
Oracle is a registered trademark of Oracle Corporation and/or its
affiliates. Other names may be trademarks of their respective
owners.
Type ‘help;’ or ‘\h’ for help. Type ‘\c’ to clear the current input statement.
mysql> CREATE TABLE fgedu_users (
-> id INT AUTO_INCREMENT PRIMARY KEY,
-> name VARCHAR(100),
-> email VARCHAR(100),
-> created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
-> );
Query OK, 0 rows affected (0.01 sec)
mysql> INSERT INTO fgedu_users (name, email) VALUES (‘Alice’, ‘alice@example.com’);
Query OK, 1 row affected (0.00 sec)
mysql> INSERT INTO fgedu_users (name, email) VALUES (‘Bob’, ‘bob@example.com’);
Query OK, 1 row affected (0.00 sec)
mysql> SELECT * FROM fgedu_users;
+—-+——-+———————+———————+
| id | name | email | created_at |
+—-+——-+———————+———————+
| 1 | Alice | alice@example.com | 2024-01-01 00:00:00 |
| 2 | Bob | bob@example.com | 2024-01-01 00:00:00 |
+—-+——-+———————+———————+
2 rows in set (0.00 sec)
mysql> exit
Bye
4.2 数据分析案例
生产环境Kubernetes数据分析的案例。
# 案例：Spark数据分析
# 场景：在Kubernetes集群中使用Spark进行数据分析，处理业务数据
# 问题：
– 需要在Kubernetes集群中部署Spark
– 需要使用Spark进行数据分析
– 需要处理和分析业务数据
# 解决方案：
1. 部署Spark Master：
$ cat > spark-master-deployment.yaml << 'EOF' apiVersion: apps/v1 kind: Deployment metadata: name: spark-master namespace: default spec: replicas: 1 selector: matchLabels: app: spark-master template: metadata: labels: app: spark-master spec: containers: - name: spark-master image: bitnami/spark:3.3.0 ports: - containerPort: 7077 - containerPort: 8080 env: - name: SPARK_MODE value: "master" --- apiVersion: v1 kind: Service metadata: name: spark-master namespace: default spec: selector: app: spark-master ports: - port: 7077 targetPort: 7077 - port: 8080 targetPort: 8080 EOF $ kubectl apply -f spark-master-deployment.yaml 2. 部署Spark Worker： $ cat > spark-worker-deployment.yaml << 'EOF' apiVersion: apps/v1 kind: Deployment metadata: name: spark-worker namespace: default spec: replicas: 2 selector: matchLabels: app: spark-worker template: metadata: labels: app: spark-worker spec: containers: - name: spark-worker image: bitnami/spark:3.3.0 ports: - containerPort: 8081 env: - name: SPARK_MODE value: "worker" - name: SPARK_MASTER_URL value: "spark://spark-master:7077" EOF $ kubectl apply -f spark-worker-deployment.yaml，更多学习教程公众号风哥教程itpux_com。 3. 验证Spark部署： $ kubectl get pods -l app=spark-master NAME READY STATUS RESTARTS AGE spark-master-6d6f58987b-7f5f8 1/1 Running 0 5m $ kubectl get pods -l app=spark-worker NAME READY STATUS RESTARTS AGE spark-worker-6d6f58987b-7f5f8 1/1 Running 0 5m spark-worker-6d6f58987b-8d2k3 1/1 Running 0 5m $ kubectl get services spark-master NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE spark-master ClusterIP 10.96.123.47 7077/TCP,8080/TCP 5m
4. 提交Spark作业：
$ kubectl exec -it spark-master-6d6f58987b-7f5f8 — spark-submit –master spark://spark-master:7077 –class org.apache.spark.examples.SparkPi /opt/bitnami/spark/examples/jars/spark-examples_2.12-3.3.0.jar 100
5. 查看作业结果：
$ kubectl logs spark-master-6d6f58987b-7f5f8
2024-01-01 00:00:00 INFO SparkContext: Running Spark version 3.3.0
2024-01-01 00:00:00 INFO SparkContext: Submitted application: Spark Pi
2024-01-01 00:00:00 INFO SparkContext: Starting job: count at SparkPi.scala:38
2024-01-01 00:00:00 INFO DAGScheduler: Job 0 finished: count at SparkPi.scala:38, took 10.0 seconds
2024-01-01 00:00:00 INFO SparkContext: Stopping SparkContext
2024-01-01 00:00:00 INFO ShutdownHookManager: Shutdown hook called
Pi is roughly 3.141592653589793
# 案例：Flink流处理
# 场景：在Kubernetes集群中使用Flink进行流处理，实时分析业务数据
# 问题：
– 需要在Kubernetes集群中部署Flink
– 需要使用Flink进行流处理
– 需要实时分析业务数据
# 解决方案：
1. 部署Flink JobManager：
$ cat > flink-jobmanager-deployment.yaml << 'EOF' apiVersion: apps/v1 kind: Deployment metadata: name: flink-jobmanager namespace: default spec: replicas: 1 selector: matchLabels: app: flink-jobmanager template: metadata: labels: app: flink-jobmanager spec: containers: - name: flink-jobmanager image: flink:1.15.0 ports: - containerPort: 8081 - containerPort: 6123 command: - /opt/flink/bin/jobmanager.sh args: - start-foreground --- apiVersion: v1 kind: Service metadata: name: flink-jobmanager namespace: default spec: selector: app: flink-jobmanager ports: - port: 8081 targetPort: 8081 - port: 6123 targetPort: 6123 EOF $ kubectl apply -f flink-jobmanager-deployment.yaml 2. 部署Flink TaskManager： $ cat > flink-taskmanager-deployment.yaml << 'EOF' apiVersion: apps/v1 kind: Deployment metadata: name: flink-taskmanager namespace: default spec: replicas: 2 selector: matchLabels: app: flink-taskmanager template: metadata: labels: app: flink-taskmanager spec: containers: - name: flink-taskmanager image: flink:1.15.0 ports: - containerPort: 6121 - containerPort: 6122 command: - /opt/flink/bin/taskmanager.sh args: - start-foreground env: - name: JOB_MANAGER_RPC_ADDRESS value: "flink-jobmanager" EOF $ kubectl apply -f flink-taskmanager-deployment.yaml 3. 验证Flink部署： $ kubectl get pods -l app=flink-jobmanager NAME READY STATUS RESTARTS AGE flink-jobmanager-6d6f58987b-7f5f8 1/1 Running 0 5m $ kubectl get pods -l app=flink-taskmanager NAME READY STATUS RESTARTS AGE flink-taskmanager-6d6f58987b-7f5f8 1/1 Running 0 5m flink-taskmanager-6d6f58987b-8d2k3 1/1 Running 0 5m $ kubectl get services flink-jobmanager NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE flink-jobmanager ClusterIP 10.96.123.48 8081/TCP,6123/TCP 5m
4. 提交Flink作业：
$ kubectl exec -it flink-jobmanager-6d6f58987b-7f5f8 — flink run -m flink-jobmanager:8081 /opt/flink/examples/streaming/WordCount.jar
5. 查看作业结果：
$ kubectl logs flink-jobmanager-6d6f58987b-7f5f8
2024-01-01 00:00:00 INFO FlinkJobManager: Starting Flink JobManager
2024-01-01 00:00:00 INFO FlinkJobManager: JobManager started successfully
2024-01-01 00:00:00 INFO FlinkJobManager: Submitted job WordCount (1234567890abcdef)
2024-01-01 00:00:00 INFO FlinkJobManager: Job WordCount (1234567890abcdef) completed successfully
# 场景：在Kubernetes集群中使用Spark进行数据分析，处理业务数据
# 问题：
– 需要在Kubernetes集群中部署Spark
– 需要使用Spark进行数据分析
– 需要处理和分析业务数据
# 解决方案：
1. 部署Spark Master：
$ cat > spark-master-deployment.yaml << 'EOF' apiVersion: apps/v1 kind: Deployment metadata: name: spark-master namespace: default spec: replicas: 1 selector: matchLabels: app: spark-master template: metadata: labels: app: spark-master spec: containers: - name: spark-master image: bitnami/spark:3.3.0 ports: - containerPort: 7077 - containerPort: 8080 env: - name: SPARK_MODE value: "master" --- apiVersion: v1 kind: Service metadata: name: spark-master namespace: default spec: selector: app: spark-master ports: - port: 7077 targetPort: 7077 - port: 8080 targetPort: 8080 EOF $ kubectl apply -f spark-master-deployment.yaml 2. 部署Spark Worker： $ cat > spark-worker-deployment.yaml << 'EOF' apiVersion: apps/v1 kind: Deployment metadata: name: spark-worker namespace: default spec: replicas: 2 selector: matchLabels: app: spark-worker template: metadata: labels: app: spark-worker spec: containers: - name: spark-worker image: bitnami/spark:3.3.0 ports: - containerPort: 8081 env: - name: SPARK_MODE value: "worker" - name: SPARK_MASTER_URL value: "spark://spark-master:7077" EOF $ kubectl apply -f spark-worker-deployment.yaml，更多学习教程公众号风哥教程itpux_com。 3. 验证Spark部署： $ kubectl get pods -l app=spark-master NAME READY STATUS RESTARTS AGE spark-master-6d6f58987b-7f5f8 1/1 Running 0 5m $ kubectl get pods -l app=spark-worker NAME READY STATUS RESTARTS AGE spark-worker-6d6f58987b-7f5f8 1/1 Running 0 5m spark-worker-6d6f58987b-8d2k3 1/1 Running 0 5m $ kubectl get services spark-master NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE spark-master ClusterIP 10.96.123.47
4. 提交Spark作业：
$ kubectl exec -it spark-master-6d6f58987b-7f5f8 — spark-submit –master spark://spark-master:7077 –class org.apache.spark.examples.SparkPi /opt/bitnami/spark/examples/jars/spark-examples_2.12-3.3.0.jar 100
5. 查看作业结果：
$ kubectl logs spark-master-6d6f58987b-7f5f8
2024-01-01 00:00:00 INFO SparkContext: Running Spark version 3.3.0
2024-01-01 00:00:00 INFO SparkContext: Submitted application: Spark Pi
2024-01-01 00:00:00 INFO SparkContext: Starting job: count at SparkPi.scala:38
2024-01-01 00:00:00 INFO DAGScheduler: Job 0 finished: count at SparkPi.scala:38, took 10.0 seconds
2024-01-01 00:00:00 INFO SparkContext: Stopping SparkContext
2024-01-01 00:00:00 INFO ShutdownHookManager: Shutdown hook called
Pi is roughly 3.141592653589793
# 案例：Flink流处理
# 场景：在Kubernetes集群中使用Flink进行流处理，实时分析业务数据
# 问题：
– 需要在Kubernetes集群中部署Flink
– 需要使用Flink进行流处理
– 需要实时分析业务数据
# 解决方案：
1. 部署Flink JobManager：
$ cat > flink-jobmanager-deployment.yaml << 'EOF' apiVersion: apps/v1 kind: Deployment metadata: name: flink-jobmanager namespace: default spec: replicas: 1 selector: matchLabels: app: flink-jobmanager template: metadata: labels: app: flink-jobmanager spec: containers: - name: flink-jobmanager image: flink:1.15.0 ports: - containerPort: 8081 - containerPort: 6123 command: - /opt/flink/bin/jobmanager.sh args: - start-foreground --- apiVersion: v1 kind: Service metadata: name: flink-jobmanager namespace: default spec: selector: app: flink-jobmanager ports: - port: 8081 targetPort: 8081 - port: 6123 targetPort: 6123 EOF $ kubectl apply -f flink-jobmanager-deployment.yaml 2. 部署Flink TaskManager： $ cat > flink-taskmanager-deployment.yaml << 'EOF' apiVersion: apps/v1 kind: Deployment metadata: name: flink-taskmanager namespace: default spec: replicas: 2 selector: matchLabels: app: flink-taskmanager template: metadata: labels: app: flink-taskmanager spec: containers: - name: flink-taskmanager image: flink:1.15.0 ports: - containerPort: 6121 - containerPort: 6122 command: - /opt/flink/bin/taskmanager.sh args: - start-foreground env: - name: JOB_MANAGER_RPC_ADDRESS value: "flink-jobmanager" EOF $ kubectl apply -f flink-taskmanager-deployment.yaml 3. 验证Flink部署： $ kubectl get pods -l app=flink-jobmanager NAME READY STATUS RESTARTS AGE flink-jobmanager-6d6f58987b-7f5f8 1/1 Running 0 5m $ kubectl get pods -l app=flink-taskmanager NAME READY STATUS RESTARTS AGE flink-taskmanager-6d6f58987b-7f5f8 1/1 Running 0 5m flink-taskmanager-6d6f58987b-8d2k3 1/1 Running 0 5m $ kubectl get services flink-jobmanager NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE flink-jobmanager ClusterIP 10.96.123.48
4. 提交Flink作业：
$ kubectl exec -it flink-jobmanager-6d6f58987b-7f5f8 — flink run -m flink-jobmanager:8081 /opt/flink/examples/streaming/WordCount.jar
5. 查看作业结果：
$ kubectl logs flink-jobmanager-6d6f58987b-7f5f8
2024-01-01 00:00:00 INFO FlinkJobManager: Starting Flink JobManager
2024-01-01 00:00:00 INFO FlinkJobManager: JobManager started successfully
2024-01-01 00:00:00 INFO FlinkJobManager: Submitted job WordCount (1234567890abcdef)
2024-01-01 00:00:00 INFO FlinkJobManager: Job WordCount (1234567890abcdef) completed successfully
4.3 集成案例
生产环境Kubernetes数据仓库与分析集成的案例。。
# 案例：Airflow工作流编排
# 场景：在Kubernetes集群中部署Airflow，用于编排数据仓库和分析任务
# 问题：
– 需要在Kubernetes集群中部署Airflow
– 需要使用Airflow编排数据仓库和分析任务
– 需要自动化数据处理流程
# 解决方案：
1. 创建StorageClass：
$ cat > storageclass.yaml << 'EOF' apiVersion: storage.k8s.io/v1 kind: StorageClass metadata: name: airflow-storage provisioner: kubernetes.io/aws-ebs parameters: type: gp2 iopsPerGB: "10" reclaimPolicy: Retain allowVolumeExpansion: true EOF $ kubectl apply -f storageclass.yaml 2. 创建PersistentVolumeClaim： $ cat > pvc.yaml << 'EOF' apiVersion: v1 kind: PersistentVolumeClaim metadata: name: airflow-pvc spec: storageClassName: airflow-storage accessModes: - ReadWriteOnce resources: requests: storage: 100Gi EOF $ kubectl apply -f pvc.yaml 3. 部署Airflow： $ cat > airflow-deployment.yaml << 'EOF' apiVersion: apps/v1 kind: Deployment metadata: name: airflow-webserver namespace: default spec: replicas: 1 selector: matchLabels: app: airflow-webserver template: metadata: labels: app: airflow-webserver spec: containers: - name: airflow-webserver image: apache/airflow:2.5.0 ports: - containerPort: 8080 env: - name: AIRFLOW__CORE__EXECUTOR value: "LocalExecutor" - name: AIRFLOW__DATABASE__SQL_ALCHEMY_CONN value: "postgresql+psycopg2://fgedu:fgedu123@postgres:5432/airflow" - name: AIRFLOW__WEBSERVER__SECRET_KEY value: "fgedu-secret-key" volumeMounts: - name: airflow-data mountPath: /opt/airflow/dags volumes: - name: airflow-data persistentVolumeClaim: claimName: airflow-pvc --- apiVersion: v1 kind: Service metadata: name: airflow-webserver namespace: default spec: selector: app: airflow-webserver ports: - port: 8080 targetPort: 8080 EOF $ kubectl apply -f airflow-deployment.yaml 4. 初始化Airflow数据库： $ kubectl exec -it $(kubectl get pods -l app=airflow-webserver -o jsonpath="{.items[0].metadata.name}") -- airflow db init $ kubectl exec -it $(kubectl get pods -l app=airflow-webserver -o jsonpath="{.items[0].metadata.name}") -- airflow users create --username admin --firstname Admin --lastname User --role Admin --email admin@example.com --password admin 5. 验证Airflow部署： $ kubectl get pods -l app=airflow-webserver NAME READY STATUS RESTARTS AGE airflow-webserver-6d6f58987b-7f5f8 1/1 Running 0 5m $ kubectl get services airflow-webserver NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE airflow-webserver ClusterIP 10.96.123.49 8080/TCP 5m
6. 创建DAG文件：
$ kubectl exec -it $(kubectl get pods -l app=airflow-webserver -o jsonpath=”{.items[0].metadata.name}”) — cat > /opt/airflow/dags/fgedu_dag.py << 'EOF' from airflow import DAG，from K8S+DB视频:www.itpux.com。 from airflow.operators.bash import BashOperator from datetime import datetime, timedelta default_args = { 'owner': 'fgedu', 'depends_on_past': False, 'start_date': datetime(2024, 1, 1), 'email': ['admin@example.com'], 'email_on_failure': False, 'email_on_retry': False, 'retries': 1, 'retry_delay': timedelta(minutes=5), } with DAG('fgedu_data_pipeline', default_args=default_args, schedule_interval=timedelta(days=1)) as dag: extract = BashOperator( task_id='extract', bash_command='echo "Extracting data from source"', )。 transform = BashOperator( task_id='transform', bash_command='echo "Transforming data"', ) load = BashOperator( task_id='load', bash_command='echo "Loading data to warehouse"', ) extract >> transform >> load
EOF
7. 启动Airflow scheduler：
$ kubectl exec -it $(kubectl get pods -l app=airflow-webserver -o jsonpath=”{.items[0].metadata.name}”) — airflow scheduler
8. 访问Airflow UI：
# 访问 http://:8080
# 使用用户名 admin 和密码 admin 登录
# 案例：Grafana数据可视化
# 场景：在Kubernetes集群中部署Grafana，用于可视化数据仓库和分析结果
# 问题：
– 需要在Kubernetes集群中部署Grafana
– 需要使用Grafana可视化数据仓库和分析结果
– 需要创建仪表盘展示业务数据
# 解决方案：
1. 创建StorageClass：
$ cat > storageclass.yaml << 'EOF' apiVersion: storage.k8s.io/v1 kind: StorageClass metadata: name: grafana-storage provisioner: kubernetes.io/aws-ebs parameters: type: gp2 iopsPerGB: "10" reclaimPolicy: Retain allowVolumeExpansion: true EOF $ kubectl apply -f storageclass.yaml 2. 创建PersistentVolumeClaim： $ cat > pvc.yaml << 'EOF' apiVersion: v1 kind: PersistentVolumeClaim metadata: name: grafana-pvc spec: storageClassName: grafana-storage accessModes: - ReadWriteOnce resources: requests: storage: 100Gi EOF $ kubectl apply -f pvc.yaml 3. 部署Grafana： $ cat > grafana-deployment.yaml << 'EOF' apiVersion: apps/v1 kind: Deployment metadata: name: gra
# 场景：在Kubernetes集群中部署Airflow，用于编排数据仓库和分析任务
# 问题：
– 需要在Kubernetes集群中部署Airflow
– 需要使用Airflow编排数据仓库和分析任务
– 需要自动化数据处理流程
# 解决方案：
1. 创建StorageClass：
$ cat > storageclass.yaml << 'EOF' apiVersion: storage.k8s.io/v1 kind: StorageClass metadata: name: airflow-storage provisioner: kubernetes.io/aws-ebs parameters: type: gp2 iopsPerGB: "10" reclaimPolicy: Retain allowVolumeExpansion: true EOF $ kubectl apply -f storageclass.yaml 2. 创建PersistentVolumeClaim： $ cat > pvc.yaml << 'EOF' apiVersion: v1 kind: PersistentVolumeClaim metadata: name: airflow-pvc spec: storageClassName: airflow-storage accessModes: - ReadWriteOnce resources: requests: storage: 100Gi EOF $ kubectl apply -f pvc.yaml 3. 部署Airflow： $ cat > airflow-deployment.yaml << 'EOF' apiVersion: apps/v1 kind: Deployment metadata: name: airflow-webserver namespace: default spec: replicas: 1 selector: matchLabels: app: airflow-webserver template: metadata: labels: app: airflow-webserver spec: containers: - name: airflow-webserver image: apache/airflow:2.5.0 ports: - containerPort: 8080 env: - name: AIRFLOW__CORE__EXECUTOR value: "LocalExecutor" - name: AIRFLOW__DATABASE__SQL_ALCHEMY_CONN value: "postgresql+psycopg2://fgedu:fgedu123@postgres:5432/airflow" - name: AIRFLOW__WEBSERVER__SECRET_KEY value: "fgedu-secret-key" volumeMounts: - name: airflow-data mountPath: /opt/airflow/dags volumes: - name: airflow-data persistentVolumeClaim: claimName: airflow-pvc --- apiVersion: v1 kind: Service metadata: name: airflow-webserver namespace: default spec: selector: app: airflow-webserver ports: - port: 8080 targetPort: 8080 EOF $ kubectl apply -f airflow-deployment.yaml 4. 初始化Airflow数据库： $ kubectl exec -it $(kubectl get pods -l app=airflow-webserver -o jsonpath="{.items[0].metadata.name}") -- airflow db init $ kubectl exec -it $(kubectl get pods -l app=airflow-webserver -o jsonpath="{.items[0].metadata.name}") -- airflow users create --username admin --firstname Admin --lastname User --role Admin --email admin@example.com --password admin 5. 验证Airflow部署： $ kubectl get pods -l app=airflow-webserver NAME READY STATUS RESTARTS AGE airflow-webserver-6d6f58987b-7f5f8 1/1 Running 0 5m $ kubectl get services airflow-webserver NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE airflow-webserver ClusterIP 10.96.123.49
6. 创建DAG文件：
$ kubectl exec -it $(kubectl get pods -l app=airflow-webserver -o jsonpath=”{.items[0].metadata.name}”) — cat > /opt/airflow/dags/fgedu_dag.py << 'EOF' from airflow import DAG，from K8S+DB视频:www.itpux.com。 from airflow.operators.bash import BashOperator from datetime import datetime, timedelta default_args = { 'owner': 'fgedu', 'depends_on_past': False, 'start_date': datetime(2024, 1, 1), 'email': ['admin@example.com'], 'email_on_failure': False, 'email_on_retry': False, 'retries': 1, 'retry_delay': timedelta(minutes=5), } with DAG('fgedu_data_pipeline', default_args=default_args, schedule_interval=timedelta(days=1)) as dag: extract = BashOperator( task_id='extract', bash_command='echo "Extracting data from source"', )。 transform = BashOperator( task_id='transform', bash_command='echo "Transforming data"', ) load = BashOperator( task_id='load', bash_command='echo "Loading data to warehouse"', ) extract >> transform >> load
EOF
7. 启动Airflow scheduler：
$ kubectl exec -it $(kubectl get pods -l app=airflow-webserver -o jsonpath=”{.items[0].metadata.name}”) — airflow scheduler
8. 访问Airflow UI：
# 访问 http://
# 使用用户名 admin 和密码 admin 登录
# 案例：Grafana数据可视化
# 场景：在Kubernetes集群中部署Grafana，用于可视化数据仓库和分析结果
# 问题：
– 需要在Kubernetes集群中部署Grafana
– 需要使用Grafana可视化数据仓库和分析结果
– 需要创建仪表盘展示业务数据
# 解决方案：
1. 创建StorageClass：
$ cat > storageclass.yaml << 'EOF' apiVersion: storage.k8s.io/v1 kind: StorageClass metadata: name: grafana-storage provisioner: kubernetes.io/aws-ebs parameters: type: gp2 iopsPerGB: "10" reclaimPolicy: Retain allowVolumeExpansion: true EOF $ kubectl apply -f storageclass.yaml 2. 创建PersistentVolumeClaim： $ cat > pvc.yaml << 'EOF' apiVersion: v1 kind: PersistentVolumeClaim metadata: name: grafana-pvc spec: storageClassName: grafana-storage accessModes: - ReadWriteOnce resources: requests: storage: 100Gi EOF $ kubectl apply -f pvc.yaml 3. 部署Grafana： $ cat > grafana-deployment.yaml << 'EOF' apiVersion: apps/v1 kind: Deployment metadata: name: gra
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