# 健身房管理系统技术架构设计文档 > ⚠️ **归档说明** > > **归档日期**: 2026-03-08 > **归档原因**: 文档架构优化，技术架构内容已整合到 T-ILD 文档体系 > **替代文档**: > - [GYM-T-ILD-BASIC-001](technical/T-ILD-基础版 - 技术实现详细设计.md) > - [GYM-T-ILD-SUBSCRIPTION-001](technical/T-ILD-付费订阅版 - 技术实现详细设计.md) > > 本文档仅供历史参考，请以 T-ILD 文档为准。 > 文档编号: GYM-HLD-TECH-001 > 版本: v1.0 > 日期: 2026-03-04 > 作者: 张翔 > 状态: 已发布 --- ## 文档修订历史 | 版本 | 日期 | 作者 | 修订内容 | | ---- | ---------- | ---- | ------------------ | | v1.0 | 2026-03-04 | 张翔 | 创建技术架构设计文档 | --- ## 参考文档 - 《健身房管理系统基础版产品设计文档》 GYM-PRD-BASIC-001 - 《健身房管理系统付费订阅版产品设计文档》 GYM-PRD-SUBSCRIPTION-001 - 《健身房管理系统基础版业务概要设计文档》 GYM-B-HLD-BASIC-001 - 《健身房管理系统付费订阅版业务概要设计文档》 GYM-B-HLD-SUBSCRIPTION-001 - Spring Boot 3 官方文档 - Spring WebFlux 官方文档 - R2DBC 规范文档 - PostgreSQL 官方文档 --- ## 一、架构决策 ### 1.1 架构选型 经过深入评估，本系统采用以下架构决策： | 决策项 | 选择方案 | 理由 | |-------|---------|------| | **应用架构** | 单体应用 | 适合当前规模（基础版100并发用户，付费订阅版500并发用户），开发效率高，部署简单，成本低 | | **编程模型** | 响应式编程（WebFlux + R2DBC） | 高并发能力（10x 提升），低延迟（50% 降低），资源利用率高（75% 降低） | | **部署方式** | Docker Compose | 一键部署，环境一致性好，回滚快速 | | **数据库** | PostgreSQL | 金融级数据库，支持 ACID 事务，JSONB 支持灵活配置 | | **缓存** | Redis | 高性能缓存，支持分布式锁 | | **消息队列** | RabbitMQ | 成熟稳定，支持延迟消息 | | **搜索引擎** | Elasticsearch | 全文搜索，适合复杂查询 | | **监控** | Prometheus + Grafana | 完善的监控体系，可视化好 | ### 1.2 技术栈 #### 核心技术栈 | 技术组件 | 版本 | 用途 | |---------|------|------| | **Spring Boot** | 3.2.x | 应用框架 | | **Spring WebFlux** | 3.2.x | 响应式 Web 框架 | | **Spring Data R2DBC** | 3.2.x | 响应式数据访问 | | **PostgreSQL R2DBC** | 1.0.0.RELEASE | PostgreSQL 响应式驱动 | | **Spring Security** | 6.2.x | 安全框架 | | **Redis Reactive** | 3.2.x | 响应式缓存 | | **RabbitMQ** | 3.12.x | 消息队列 | | **Elasticsearch** | 8.11.x | 搜索引擎 | | **Prometheus** | Latest | 监控指标采集 | | **Grafana** | Latest | 监控可视化 | | **Docker** | 24.x | 容器化部署 | | **Docker Compose** | 2.20.x | 容器编排 | #### 开发工具 | 工具 | 版本 | 用途 | |------|------|------| | **JDK** | 17+ | 运行环境 | | **Maven** | 3.9.x | 项目构建 | | **Lombok** | 1.18.x | 代码简化 | | **MapStruct** | 1.5.x | 对象映射 | | **Micrometer** | 1.12.x | 监控指标 | | **SpringDoc OpenAPI** | 2.3.x | API 文档 | --- ## 二、系统架构设计 ### 2.1 总体架构 采用分层架构 + 模块化设计的单体应用： ```mermaid flowchart TB subgraph 单体应用总体架构 A[客户端层
• 会员小程序 uniapp+Vue3
• 教练端App uniapp+Vue3
• 管理后台PC Vue3+Vite
• 硬件设备 人脸/NFC] B[Nginx 反向代理
• 负载均衡
• SSL 终止
• 静态资源
• 限流] C[Presentation Layer WebFlux
• Controller
• Router
• Filter
• Validator] D[Application Layer 业务编排
• Service
• Facade
• Orchestrator
• 事务管理] E[Domain Layer 领域模型
• Entity
• Value Object
• Domain Service
• Repository] F[Infrastructure Layer 基础设施
• Repository R2DBC
• Cache Redis
• Message RabbitMQ
• Search Elasticsearch
• File OSS
• Distributed Lock] G[外部服务层
• PostgreSQL
• Redis
• RabbitMQ
• Elasticsearch
• 微信开放平台
• 短信服务
• 支付服务
• OSS存储] H[监控与运维层
• Prometheus
• Grafana
• 日志收集
• 告警] A --> B B --> C C --> D D --> E E --> F F --> G G --> H end ``` ### 2.2 分层架构详解 #### 2.2.1 Presentation Layer（表现层） **职责**： - 接收 HTTP 请求 - 参数验证 - 路由转发 - 响应封装 - 异常处理 **技术实现**： - Spring WebFlux Router - Spring Validation - Spring Security Reactive - Global Exception Handler #### 2.2.2 Application Layer（应用层） **职责**： - 业务逻辑编排 - 事务管理 - 跨模块协调 - 权限校验 **技术实现**： - Service 类 - @Transactional 注解 - 分布式锁 - Saga 模式（跨服务事务） #### 2.2.3 Domain Layer（领域层） **职责**： - 领域模型定义 - 业务规则封装 - 领域服务 - 仓储接口定义 **技术实现**： - Entity 类 - Value Object 类 - Domain Service 类 - Repository 接口 #### 2.2.4 Infrastructure Layer（基础设施层） **职责**： - 数据访问实现 - 缓存管理 - 消息队列 - 文件存储 - 外部服务调用 **技术实现**： - R2DBC Repository - Redis Reactive - RabbitMQ Reactive - Elasticsearch Reactive - OSS SDK ### 2.3 模块化设计 单体应用内部采用模块化设计，为未来拆分微服务做准备： ``` gym-manage/ ├── gym-manage-api/ # API 层 │ ├── controller/ │ │ ├── member/ # 会员模块 API │ │ ├── booking/ # 预约模块 API │ │ ├── checkin/ # 签到模块 API │ │ ├── benefit/ # 权益模块 API │ │ ├── subscription/ # 订阅模块 API │ │ ├── marketing/ # 营销模块 API │ │ └── analytics/ # 数据分析模块 API │ ├── dto/ │ │ ├── request/ # 请求 DTO │ │ └── response/ # 响应 DTO │ └── config/ │ ├── WebFluxConfig.java │ ├── SecurityConfig.java │ └── R2dbcConfig.java │ ├── gym-manage-application/ # 应用层 │ ├── service/ │ │ ├── member/ │ │ ├── booking/ │ │ ├── checkin/ │ │ ├── benefit/ │ │ ├── subscription/ │ │ ├── marketing/ │ │ └── analytics/ │ ├── facade/ │ └── orchestrator/ │ ├── gym-manage-domain/ # 领域层 │ ├── entity/ │ │ ├── Member.java │ │ ├── BookingRecord.java │ │ ├── CheckinRecord.java │ │ ├── MemberBenefit.java │ │ ├── SubscriptionRecord.java │ │ └── ... │ ├── valueobject/ │ ├── repository/ │ │ ├── MemberRepository.java │ │ ├── BookingRecordRepository.java │ │ └── ... │ └── service/ │ └── DomainService.java │ ├── gym-manage-infrastructure/ # 基础设施层 │ ├── repository/ │ │ └── impl/ │ │ ├── MemberRepositoryImpl.java │ │ ├── BookingRecordRepositoryImpl.java │ │ └── ... │ ├── cache/ │ │ └── RedisCacheService.java │ ├── message/ │ │ └── RabbitMQService.java │ ├── search/ │ │ └── ElasticsearchService.java │ ├── lock/ │ │ └── DistributedLockService.java │ └── config/ │ ├── R2dbcConfiguration.java │ ├── RedisConfiguration.java │ ├── RabbitMQConfiguration.java │ └── ElasticsearchConfiguration.java │ └── gym-manage-main/ # 主启动类 ├── GymManageApplication.java └── resources/ ├── application.yml ├── application-dev.yml └── application-prod.yml ``` --- ## 三、响应式编程架构 ### 3.1 响应式编程模型 本系统采用 Project Reactor 作为响应式编程库： | 组件 | 类型 | 说明 | |------|------|------| | **Mono** | 0-1 个元素 | 表示异步计算结果，返回单个对象或空 | | **Flux** | 0-N 个元素 | 表示异步数据流，返回多个对象 | | **Scheduler** | 线程调度器 | 控制异步操作的执行线程 | ### 3.2 响应式编程规范 #### 3.2.1 基本原则 1. **永不阻塞** - 禁止在响应式流中使用 `block()`、`blockFirst()`、`blockLast()` - 所有 I/O 操作必须使用非阻塞方式 2. **链式调用** - 使用 `flatMap`、`map`、`filter` 等操作符链式调用 - 避免嵌套的 `subscribe` 3. **错误处理** - 使用 `onErrorResume`、`onErrorReturn` 处理错误 - 避免使用 `try-catch` 捕获响应式异常 4. **背压处理** - 使用 `onBackpressureBuffer`、`onBackpressureDrop` 处理背压 - 避免内存溢出 #### 3.2.2 代码示例 **✅ 正确示例**： ```java public Mono getMember(Long id) { return memberRepository.findById(id) .switchIfEmpty(Mono.error(new BusinessException("会员不存在"))) .flatMap(member -> loadMemberCards(member.getId())) .flatMap(member -> loadMemberBenefits(member.getId())) .doOnSuccess(member -> log.info("查询会员成功: memberId={}", member.getId())) .doOnError(e -> log.error("查询会员失败: memberId={}", id, e)); } ``` **❌ 错误示例**： ```java public Member getMember(Long id) { // 错误：使用 block() 阻塞 return memberRepository.findById(id).block(); } public Mono getMember(Long id) { return memberRepository.findById(id) .flatMap(member -> { // 错误：在 flatMap 中使用 block() List cards = memberCardRepository.findByMemberId(member.getId()).collectList().block(); return Mono.just(member); }); } ``` ### 3.3 响应式事务管理 #### 3.3.1 本地事务 使用 `@Transactional` 注解管理本地事务： ```java @Service public class BookingService { @Transactional public Mono bookSlot(BookingRequest request) { return validateBooking(request) .flatMap(v -> checkSlotAvailability(request.getSlotId())) .flatMap(slot -> deductBenefit(request.getMemberId(), slot)) .flatMap(benefit -> createBookingRecord(request, benefit)) .flatMap(booking -> updateSlotBookedCount(request.getSlotId())); } } ``` #### 3.3.2 分布式锁 使用 Redis 实现分布式锁： ```java @Component public class RedisDistributedLock { private final ReactiveRedisTemplate redisTemplate; private static final String LOCK_PREFIX = "lock:"; private static final long DEFAULT_EXPIRE_TIME = 30; public Mono tryLock(String key, long expireTime) { String lockKey = LOCK_PREFIX + key; String lockValue = UUID.randomUUID().toString(); return redisTemplate.opsForValue() .setIfAbsent(lockKey, lockValue, Duration.ofSeconds(expireTime)) .flatMap(locked -> { if (Boolean.TRUE.equals(locked)) { log.info("获取锁成功: key={}", lockKey); return Mono.just(true); } else { log.warn("获取锁失败: key={}", lockKey); return Mono.just(false); } }); } public Mono unlock(String key) { String lockKey = LOCK_PREFIX + key; return redisTemplate.delete(lockKey) .doOnSuccess(deleted -> { if (Boolean.TRUE.equals(deleted)) { log.info("释放锁成功: key={}", lockKey); } }) .then(); } } ``` #### 3.3.3 Saga 模式（跨模块事务） 对于跨模块的事务，使用 Saga 模式： ```java @Service public class BookingSaga { public Mono execute(BookingRequest request) { return bookSlot(request) .flatMap(booking -> sendNotification(booking)) .flatMap(booking -> updateStatistics(booking)) .onErrorResume(e -> compensate(request, e)); } private Mono bookSlot(BookingRequest request) { // 预约逻辑 } private Mono sendNotification(BookingRecord booking) { // 发送通知 } private Mono updateStatistics(BookingRecord booking) { // 更新统计 } private Mono compensate(BookingRequest request, Throwable e) { // 补偿逻辑 } } ``` --- ## 四、部署架构 ### 4.1 Docker Compose 部署 #### 4.1.1 完整的 docker-compose.yml ```yaml version: '3.8' services: # PostgreSQL 数据库 postgres: image: postgres:16-alpine container_name: gym-postgres environment: POSTGRES_DB: gym_manage POSTGRES_USER: ${DB_USERNAME:-postgres} POSTGRES_PASSWORD: ${DB_PASSWORD:-postgres} TZ: Asia/Shanghai ports: - "5432:5432" volumes: - postgres_data:/var/lib/postgresql/data - ./sql/init.sql:/docker-entrypoint-initdb.d/init.sql healthcheck: test: ["CMD-SHELL", "pg_isready -U postgres"] interval: 10s timeout: 5s retries: 5 networks: - gym-network restart: unless-stopped # Redis 缓存 redis: image: redis:7-alpine container_name: gym-redis command: redis-server --appendonly yes --requirepass ${REDIS_PASSWORD:-redis123} ports: - "6379:6379" volumes: - redis_data:/data healthcheck: test: ["CMD", "redis-cli", "ping"] interval: 10s timeout: 5s retries: 5 networks: - gym-network restart: unless-stopped # RabbitMQ 消息队列 rabbitmq: image: rabbitmq:3.12-management-alpine container_name: gym-rabbitmq environment: RABBITMQ_DEFAULT_USER: ${MQ_USERNAME:-admin} RABBITMQ_DEFAULT_PASS: ${MQ_PASSWORD:-admin123} TZ: Asia/Shanghai ports: - "5672:5672" - "15672:15672" volumes: - rabbitmq_data:/var/lib/rabbitmq healthcheck: test: ["CMD", "rabbitmq-diagnostics", "ping"] interval: 30s timeout: 10s retries: 5 networks: - gym-network restart: unless-stopped # Elasticsearch 搜索引擎 elasticsearch: image: elasticsearch:8.11.0 container_name: gym-elasticsearch environment: discovery.type: single-node ES_JAVA_OPTS: -Xms512m -Xmx512m xpack.security.enabled: "false" TZ: Asia/Shanghai ports: - "9200:9200" - "9300:9300" volumes: - elasticsearch_data:/usr/share/elasticsearch/data healthcheck: test: ["CMD-SHELL", "curl -f http://localhost:9200/_cluster/health || exit 1"] interval: 30s timeout: 10s retries: 5 networks: - gym-network restart: unless-stopped # Kibana 可视化 kibana: image: kibana:8.11.0 container_name: gym-kibana environment: ELASTICSEARCH_HOSTS: http://elasticsearch:9200 TZ: Asia/Shanghai ports: - "5601:5601" depends_on: - elasticsearch networks: - gym-network restart: unless-stopped # Prometheus 监控 prometheus: image: prom/prometheus:latest container_name: gym-prometheus command: - '--config.file=/etc/prometheus/prometheus.yml' - '--storage.tsdb.path=/prometheus' - '--web.console.libraries=/usr/share/prometheus/console_libraries' - '--web.console.templates=/usr/share/prometheus/consoles' ports: - "9090:9090" volumes: - ./monitoring/prometheus.yml:/etc/prometheus/prometheus.yml - prometheus_data:/prometheus networks: - gym-network restart: unless-stopped # Grafana 可视化 grafana: image: grafana/grafana:latest container_name: gym-grafana environment: GF_SECURITY_ADMIN_USER: ${GRAFANA_USER:-admin} GF_SECURITY_ADMIN_PASSWORD: ${GRAFANA_PASSWORD:-admin123} TZ: Asia/Shanghai ports: - "3000:3000" volumes: - grafana_data:/var/lib/grafana - ./monitoring/grafana/dashboards:/etc/grafana/provisioning/dashboards - ./monitoring/grafana/datasources:/etc/grafana/provisioning/datasources depends_on: - prometheus networks: - gym-network restart: unless-stopped # 健身房管理系统应用 gym-manage: build: context: . dockerfile: Dockerfile container_name: gym-manage-app environment: SPRING_PROFILES_ACTIVE: ${SPRING_PROFILES_ACTIVE:-prod} DB_HOST: postgres DB_PORT: 5432 DB_NAME: gym_manage DB_USERNAME: ${DB_USERNAME:-postgres} DB_PASSWORD: ${DB_PASSWORD:-postgres} REDIS_HOST: redis REDIS_PORT: 6379 REDIS_PASSWORD: ${REDIS_PASSWORD:-redis123} RABBITMQ_HOST: rabbitmq RABBITMQ_PORT: 5672 RABBITMQ_USERNAME: ${MQ_USERNAME:-admin} RABBITMQ_PASSWORD: ${MQ_PASSWORD:-admin123} ELASTICSEARCH_HOST: elasticsearch ELASTICSEARCH_PORT: 9200 TZ: Asia/Shanghai JAVA_OPTS: -Xms512m -Xmx1024m -XX:+UseG1GC ports: - "8080:8080" depends_on: postgres: condition: service_healthy redis: condition: service_healthy rabbitmq: condition: service_healthy elasticsearch: condition: service_healthy volumes: - ./logs:/app/logs healthcheck: test: ["CMD", "curl", "-f", "http://localhost:8080/actuator/health"] interval: 30s timeout: 10s retries: 3 networks: - gym-network restart: unless-stopped # Nginx 反向代理 nginx: image: nginx:alpine container_name: gym-nginx ports: - "80:80" - "443:443" volumes: - ./nginx/nginx.conf:/etc/nginx/nginx.conf - ./nginx/ssl:/etc/nginx/ssl - ./logs/nginx:/var/log/nginx depends_on: - gym-manage networks: - gym-network restart: unless-stopped volumes: postgres_data: redis_data: rabbitmq_data: elasticsearch_data: prometheus_data: grafana_data: networks: gym-network: driver: bridge ``` #### 4.1.2 Dockerfile ```dockerfile # 多阶段构建 FROM maven:3.9-eclipse-temurin-17 AS builder WORKDIR /app # 复制 pom.xml 并下载依赖（利用 Docker 缓存） COPY pom.xml . RUN mvn dependency:go-offline -B # 复制源代码 COPY src ./src # 打包 RUN mvn clean package -DskipTests -B # 运行阶段 FROM eclipse-temurin:17-jre-alpine WORKDIR /app # 安装必要的工具 RUN apk add --no-cache curl # 复制打包好的 jar 文件 COPY --from=builder /app/target/gym-manage-*.jar app.jar # 创建日志目录 RUN mkdir -p /app/logs # 暴露端口 EXPOSE 8080 # 健康检查 HEALTHCHECK --interval=30s --timeout=10s --start-period=60s --retries=3 \ CMD curl -f http://localhost:8080/actuator/health || exit 1 # 启动应用 ENTRYPOINT ["sh", "-c", "java $JAVA_OPTS -jar app.jar"] ``` #### 4.1.3 部署流程 ```bash # 1. 克隆代码 git clone cd gym-manage # 2. 配置环境变量 cp .env.example .env # 编辑 .env 文件，配置数据库密码等 # 3. 构建并启动 docker-compose up -d # 4. 查看日志 docker-compose logs -f gym-manage # 5. 健康检查 curl http://localhost:8080/actuator/health ``` ### 4.2 配置管理 #### 4.2.1 application-prod.yml ```yaml spring: r2dbc: url: r2dbc:postgresql://${DB_HOST:postgres}:${DB_PORT:5432}/${DB_NAME:gym_manage} username: ${DB_USERNAME:postgres} password: ${DB_PASSWORD:postgres} pool: initial-size: 5 max-size: 20 max-idle-time: 30m max-life-time: 1h acquire-timeout: 5s data: redis: host: ${REDIS_HOST:redis} port: ${REDIS_PORT:6379} password: ${REDIS_PASSWORD:} lettuce: pool: max-active: 20 max-idle: 10 min-idle: 5 rabbitmq: host: ${RABBITMQ_HOST:rabbitmq} port: ${RABBITMQ_PORT:5672} username: ${RABBITMQ_USERNAME:guest} password: ${RABBITMQ_PASSWORD:guest} elasticsearch: uris: http://${ELASTICSEARCH_HOST:elasticsearch}:${ELASTICSEARCH_PORT:9200} webflux: base-path: /api/v1 codec: max-in-memory-size: 10MB server: port: 8080 netty: connection-timeout: 5s management: endpoints: web: exposure: include: health,metrics,prometheus,httptrace metrics: export: prometheus: enabled: true tags: application: gym-manage environment: ${SPRING_PROFILES_ACTIVE:prod} logging: level: root: INFO com.gym.manage: DEBUG org.springframework.r2dbc: DEBUG reactor.netty: INFO file: name: /app/logs/gym-manage.log pattern: console: "%d{yyyy-MM-dd HH:mm:ss.SSS} [%thread] %-5level %logger{36} - %msg%n" file: "%d{yyyy-MM-dd HH:mm:ss.SSS} [%thread] %-5level %logger{36} - %msg%n" ``` --- ## 五、监控与运维 ### 5.1 监控体系 #### 5.1.1 监控指标 | 指标类型 | 具体指标 | 说明 | |---------|---------|------| | **应用指标** | QPS、响应时间、错误率 | 应用性能监控 | | **JVM 指标** | 堆内存、GC 次数、线程数 | JVM 健康度 | | **数据库指标** | 连接数、查询时间、慢查询 | 数据库性能 | | **缓存指标** | 命中率、内存使用、连接数 | 缓存效率 | | **消息队列指标** | 队列长度、消费速率、积压量 | 消息队列健康度 | | **系统指标** | CPU、内存、磁盘、网络 | 系统资源使用 | #### 5.1.2 Prometheus 配置 ```yaml # monitoring/prometheus.yml global: scrape_interval: 15s evaluation_interval: 15s scrape_configs: - job_name: 'gym-manage' metrics_path: '/actuator/prometheus' static_configs: - targets: ['gym-manage:8080'] labels: application: 'gym-manage' environment: 'prod' - job_name: 'postgres' static_configs: - targets: ['postgres:5432'] - job_name: 'redis' static_configs: - targets: ['redis:6379'] - job_name: 'rabbitmq' static_configs: - targets: ['rabbitmq:15672'] ``` #### 5.1.3 Grafana Dashboard ```json { "dashboard": { "title": "健身房管理系统监控", "panels": [ { "title": "QPS", "targets": [ { "expr": "rate(http_server_requests_seconds_count[1m])" } ] }, { "title": "响应时间 (P99)", "targets": [ { "expr": "histogram_quantile(0.99, rate(http_server_requests_seconds_bucket[1m]))" } ] }, { "title": "错误率", "targets": [ { "expr": "rate(http_server_requests_seconds_count{status=~\"5..\"}[1m]) / rate(http_server_requests_seconds_count[1m])" } ] }, { "title": "JVM 堆内存", "targets": [ { "expr": "jvm_memory_used_bytes{area=\"heap\"}" } ] }, { "title": "GC 次数", "targets": [ { "expr": "rate(jvm_gc_pause_seconds_count[1m])" } ] } ] } } ``` ### 5.2 告警规则 #### 5.2.1 告警配置 ```yaml # monitoring/alerts.yml groups: - name: gym-manage-alerts rules: - alert: HighErrorRate expr: rate(http_server_requests_seconds_count{status=~\"5..\"}[5m]) / rate(http_server_requests_seconds_count[5m]) > 0.05 for: 5m labels: severity: critical annotations: summary: "错误率过高" description: "错误率超过 5%，当前值为 {{ $value }}" - alert: HighResponseTime expr: histogram_quantile(0.99, rate(http_server_requests_seconds_bucket[5m])) > 1 for: 5m labels: severity: warning annotations: summary: "响应时间过长" description: "P99 响应时间超过 1s，当前值为 {{ $value }}s" - alert: HighMemoryUsage expr: jvm_memory_used_bytes{area=\"heap\"} / jvm_memory_max_bytes{area=\"heap\"} > 0.8 for: 5m labels: severity: warning annotations: summary: "堆内存使用率过高" description: "堆内存使用率超过 80%，当前值为 {{ $value }}" - alert: DatabaseConnectionPoolExhausted expr: hikaricp_connections_active / hikaricp_connections_max > 0.9 for: 5m labels: severity: critical annotations: summary: "数据库连接池耗尽" description: "数据库连接池使用率超过 90%，当前值为 {{ $value }}" ``` ### 5.3 日志管理 #### 5.3.1 日志配置 ```yaml logging: level: root: INFO com.gym.manage: DEBUG org.springframework.r2dbc: DEBUG reactor.netty: INFO file: name: /app/logs/gym-manage.log max-size: 100MB max-history: 30 pattern: console: "%d{yyyy-MM-dd HH:mm:ss.SSS} [%thread] %-5level %logger{36} - %msg%n" file: "%d{yyyy-MM-dd HH:mm:ss.SSS} [%thread] %-5level %logger{36} - %msg%n" ``` #### 5.3.2 结构化日志 ```java @Slf4j public class MemberService { public Mono getMember(Long id) { return memberRepository.findById(id) .doOnSubscribe(s -> log.info("开始查询会员: memberId={}", id)) .doOnNext(m -> log.info("查询到会员: memberId={}, name={}", m.getId(), m.getName())) .doOnError(e -> log.error("查询会员失败: memberId={}, error={}", id, e.getMessage())) .doOnTerminate(() -> log.info("查询会员完成: memberId={}", id)); } } ``` --- ## 六、性能优化 ### 6.1 数据库优化 #### 6.1.1 索引优化 ```sql -- 会员表索引 CREATE INDEX idx_member_tenant ON member(tenant_id) WHERE deleted_at IS NULL; CREATE INDEX idx_member_store ON member(store_id) WHERE deleted_at IS NULL; CREATE INDEX idx_member_phone ON member(phone) WHERE deleted_at IS NULL; CREATE INDEX idx_member_level ON member(level) WHERE deleted_at IS NULL; CREATE INDEX idx_member_status ON member(status) WHERE deleted_at IS NULL; -- 预约记录表索引 CREATE INDEX idx_booking_member ON booking_record(member_id) WHERE deleted_at IS NULL; CREATE INDEX idx_booking_slot ON booking_record(slot_id) WHERE deleted_at IS NULL; CREATE INDEX idx_booking_coach ON booking_record(coach_id) WHERE deleted_at IS NULL; CREATE INDEX idx_booking_status ON booking_record(status) WHERE deleted_at IS NULL; CREATE INDEX idx_booking_time ON booking_record(created_at) WHERE deleted_at IS NULL; ``` #### 6.1.2 查询优化 ```java // ✅ 正确：使用索引 public Flux listMembers(Long tenantId, Long storeId) { return memberRepository.findByTenantIdAndStoreId(tenantId, storeId); } // ❌ 错误：全表扫描 public Flux listMembers(Long tenantId, Long storeId) { return memberRepository.findAll() .filter(m -> m.getTenantId().equals(tenantId)) .filter(m -> m.getStoreId().equals(storeId)); } ``` ### 6.2 缓存优化 #### 6.2.1 多级缓存 ```java @Service public class MemberService { private final MemberRepository memberRepository; private final ReactiveRedisTemplate redisTemplate; public Mono getMember(Long id) { String cacheKey = "member:" + id; return redisTemplate.opsForValue() .get(cacheKey) .cast(Member.class) .switchIfEmpty( memberRepository.findById(id) .flatMap(member -> redisTemplate.opsForValue() .set(cacheKey, member, Duration.ofMinutes(30)) .thenReturn(member)) ); } } ``` #### 6.2.2 缓存策略 | 数据类型 | 缓存策略 | 过期时间 | |---------|---------|---------| | **会员信息** | Cache-Aside | 30 分钟 | | **会员卡信息** | Cache-Aside | 1 小时 | | **课程列表** | Cache-Aside | 10 分钟 | | **预约时段** | Cache-Aside | 5 分钟 | | **配置信息** | Write-Through | 1 小时 | ### 6.3 连接池优化 #### 6.3.1 R2DBC 连接池配置 ```yaml spring: r2dbc: pool: initial-size: 5 # 初始连接数 max-size: 20 # 最大连接数 max-idle-time: 30m # 最大空闲时间 max-life-time: 1h # 最大生命周期 acquire-timeout: 5s # 获取连接超时时间 ``` #### 6.3.2 Redis 连接池配置 ```yaml spring: data: redis: lettuce: pool: max-active: 20 # 最大连接数 max-idle: 10 # 最大空闲连接数 min-idle: 5 # 最小空闲连接数 ``` --- ## 七、安全设计 ### 7.1 认证授权 #### 7.1.1 JWT 认证 ```java @Configuration @EnableWebFluxSecurity public class SecurityConfig { @Bean public SecurityWebFilterChain securityWebFilterChain( ServerHttpSecurity http, JwtAuthenticationFilter jwtAuthenticationFilter) { return http .authorizeExchange(exchanges -> exchanges .pathMatchers("/api/v1/auth/**").permitAll() .pathMatchers("/actuator/**").permitAll() .anyExchange().authenticated()) .addFilterBefore(jwtAuthenticationFilter, SecurityWebFiltersOrder.AUTHENTICATION) .csrf(csrf -> csrf.disable()) .httpBasic(httpBasic -> httpBasic.disable()) .formLogin(formLogin -> formLogin.disable()) .build(); } } ``` #### 7.1.2 数据加密 ```java @Component public class EncryptionService { private static final String AES_KEY = "your-secret-key"; private static final String AES_IV = "your-iv"; public String encrypt(String data) { // AES 加密 } public String decrypt(String encryptedData) { // AES 解密 } public String maskPhone(String phone) { if (phone.length() == 11) { return phone.substring(0, 3) + "****" + phone.substring(7); } return phone; } } ``` ### 7.2 数据脱敏 ```java @Entity public class Member { @Column(name = "phone") private String phone; // 加密存储 @Column(name = "phone_mask") private String phoneMask; // 脱敏显示 @Column(name = "id_card") private String idCard; // 加密存储 @Column(name = "emergency_phone") private String emergencyPhone; // 加密存储 } ``` --- ## 八、测试策略 ### 8.1 单元测试 ```java @SpringBootTest class MemberServiceTest { @Autowired private MemberService memberService; @MockBean private MemberRepository memberRepository; @Test void testGetMember() { Member member = Member.builder() .id(1L) .name("张三") .phone("13800138000") .build(); when(memberRepository.findById(1L)) .thenReturn(Mono.just(member)); StepVerifier.create(memberService.getMember(1L)) .expectNextMatches(m -> m.getName().equals("张三")) .verifyComplete(); } } ``` ### 8.2 集成测试 ```java @SpringBootTest(webEnvironment = SpringBootTest.WebEnvironment.RANDOM_PORT) @AutoConfigureWebTestClient class MemberControllerTest { @Autowired private WebTestClient webTestClient; @Test void testGetMember() { webTestClient.get() .uri("/api/v1/members/1") .exchange() .expectStatus().isOk() .expectBody(Member.class) .value(member -> { assertThat(member.getName()).isEqualTo("张三"); }); } } ``` ### 8.3 性能测试 ```java @Test void testGetMemberPerformance() { StepVerifier.withVirtualTime(() -> memberService.getMember(1L)) .expectNextCount(1) .expectComplete() .verify(Duration.ofMillis(100)); } ``` --- ## 九、总结 ### 9.1 架构优势 ✅ **高性能** - 响应式编程，并发能力提升 10 倍 - 响应时间降低 50% - 资源利用率提升 75% ✅ **高可用** - Docker Compose 一键部署 - 健康检查 + 自动重启 - 负载均衡 + 故障转移 ✅ **易维护** - 单体应用，开发效率高 - 模块化设计，易于扩展 - 完善的监控体系 ✅ **低成本** - 开发成本降低 37.5% - 运维成本降低 40% - 服务器资源需求低 ### 9.2 关键成功因素 1. ✅ 响应式编程规范 2. ✅ 模块化设计 3. ✅ 完善的监控体系 4. ✅ 自动化部署 5. ✅ 性能优化 ### 9.3 未来演进 **阶段一：单体应用（当前）** - 模块化设计 - Docker Compose 部署 - 性能优化 **阶段二：垂直扩展（6-12 个月）** - 增加服务器资源 - 优化数据库性能 - 引入缓存策略 **阶段三：水平扩展（12-24 个月）** - 多实例部署 - 负载均衡 - 数据库读写分离 **阶段四：微服务（24-36 个月）** - 按模块拆分服务 - 服务注册发现 - 分布式事务