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淘客返利app后端系统架构设计：从数据一致性到高可用方案

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一、淘客返利系统的核心架构设计

淘客返利APP后端系统需处理商品推广、订单跟踪、佣金结算等核心业务，其架构设计需兼顾数据一致性与高可用性。聚娃科技省赚客APP采用"微服务集群+分布式存储"架构，核心模块包括：

• 推广服务：管理推广链接生成与渠道追踪
• 订单服务：对接电商平台API，同步订单状态
• 佣金服务：计算返利金额并处理提现请求
• 账户服务：管理用户资产与佣金流水

基础架构采用Spring Cloud Alibaba生态，服务间通过Dubbo实现RPC通信，数据层采用MySQL+Redis+MongoDB的混合存储方案。

二、数据一致性方案设计

2.1 分布式事务处理

佣金结算场景需保证订单状态与佣金记录的一致性，采用Seata的TCC模式实现：

package cn.juwatech.commission.service;@Service
public class CommissionTccService implements TccAction {@Autowiredprivate CommissionMapper commissionMapper;// Try阶段：预扣减佣金额度@Override@TwoPhaseBusinessAction(name = "commissionTccAction", commitMethod = "commit", rollbackMethod = "rollback")public boolean prepare(@BusinessActionContextParameter(paramName = "commissionDTO") CommissionDTO dto) {BusinessActionContext context = BusinessActionContextHolder.getContext();context.setActionContext("commissionId", dto.getOrderId());// 检查用户可提现余额BigDecimal available = accountService.getAvailableBalance(dto.getUserId());if (available.compareTo(dto.getAmount()) < 0) {throw new InsufficientBalanceException("可用佣金不足");}// 冻结佣金return commissionMapper.freezeCommission(dto.getUserId(), dto.getAmount(), dto.getOrderId()) > 0;}// Confirm阶段：确认扣减@Overridepublic boolean commit(BusinessActionContext context) {String orderId = context.getActionContext("commissionId").toString();return commissionMapper.confirmFrozenCommission(orderId) > 0;}// Cancel阶段：取消冻结@Overridepublic boolean rollback(BusinessActionContext context) {String orderId = context.getActionContext("commissionId").toString();return commissionMapper.cancelFrozenCommission(orderId) > 0;}
}

2.2 订单状态最终一致性

通过本地消息表实现跨系统订单状态同步：

package cn.juwatech.order.service;@Service
public class OrderSyncService {@Autowiredprivate OrderMapper orderMapper;@Autowiredprivate LocalMessageMapper messageMapper;@Autowiredprivate RocketMQTemplate rocketMQTemplate;@Transactionalpublic void updateOrderStatus(Long orderId, OrderStatus newStatus) {// 1. 更新本地订单状态OrderDO order = new OrderDO();order.setId(orderId);order.setStatus(newStatus);orderMapper.updateById(order);// 2. 插入本地消息表LocalMessageDO message = new LocalMessageDO();message.setBizType("ORDER_STATUS_CHANGE");message.setBizId(orderId.toString());message.setContent(JSON.toJSONString(order));message.setStatus(MessageStatus.PENDING);messageMapper.insert(message);// 3. 发送消息try {rocketMQTemplate.send("order-status-topic", MessageBuilder.withPayload(message).build());// 4. 标记消息为已发送messageMapper.updateStatus(message.getId(), MessageStatus.SENT);} catch (Exception e) {log.error("发送订单状态消息失败", e);// 消息发送失败由定时任务重试}}
}

三、高可用架构实践

3.1 服务容错设计

采用Sentinel实现服务熔断与限流：

package cn.juwatech.product.service;@Service
public class ProductServiceImpl implements ProductService {@Autowiredprivate TaobaoProductClient taobaoClient;@SentinelResource(value = "queryProductDetail", fallback = "queryProductFallback",blockHandler = "queryProductBlocked")@Overridepublic ProductDTO queryProductDetail(String itemId) {return taobaoClient.getItemDetail(itemId);}// 降级处理public ProductDTO queryProductFallback(String itemId, Throwable e) {log.warn("查询商品详情降级", e);// 返回缓存的基础商品信息return productCacheService.getCachedProduct(itemId);}// 限流处理public ProductDTO queryProductBlocked(String itemId, BlockException e) {log.warn("查询商品详情被限流", e);return new ProductDTO().setId(itemId).setName("商品信息加载中...");}
}

3.2 多级缓存架构

实现"本地缓存+Redis+数据库"三级缓存：

package cn.juwatech.cache.config;@Configuration
public class CacheConfig {// Caffeine本地缓存配置@Beanpublic Cache<String, Object> localCache() {return Caffeine.newBuilder().maximumSize(10_000).expireAfterWrite(5, TimeUnit.MINUTES).recordStats().build();}// Redis缓存配置@Beanpublic RedisCacheManager cacheManager(RedisConnectionFactory factory) {RedisCacheConfiguration config = RedisCacheConfiguration.defaultCacheConfig().entryTtl(Duration.ofHours(1)).serializeKeysWith(RedisSerializationContext.SerializationPair.fromSerializer(new StringRedisSerializer())).serializeValuesWith(RedisSerializationContext.SerializationPair.fromSerializer(new GenericJackson2JsonRedisSerializer()));// 不同缓存设置不同过期时间Map<String, RedisCacheConfiguration> configMap = new HashMap<>();configMap.put("productCache", config.entryTtl(Duration.ofHours(6)));configMap.put("userCache", config.entryTtl(Duration.ofHours(24)));return RedisCacheManager.builder(factory).cacheDefaults(config).withInitialCacheConfigurations(configMap).build();}
}

3.3 数据库高可用

采用主从复制+读写分离架构，使用Sharding-JDBC配置：

spring:shardingsphere:datasource:names: master,slave1,slave2master:type: com.zaxxer.hikari.HikariDataSourcedriver-class-name: com.mysql.cj.jdbc.Driverjdbc-url: jdbc:mysql://master:3306/taoke_dbusername: rootpassword: rootslave1:# 从库1配置slave2:# 从库2配置rules:readwrite-splitting:data-sources:taoke-db:type: Staticprops:write-data-source-name: masterread-data-source-names: slave1,slave2load-balancer-name: round_robinprops:sql-show: false

四、监控与容灾设计

4.1 全链路监控

集成SkyWalking实现分布式追踪：

package cn.juwatech.trace.config;@Configuration
public class TraceConfig {@Beanpublic FilterRegistrationBean<TraceFilter> traceFilter() {FilterRegistrationBean<TraceFilter> registration = new FilterRegistrationBean<>();registration.setFilter(new TraceFilter());registration.addUrlPatterns("/*");registration.setName("traceFilter");registration.setOrder(Ordered.HIGHEST_PRECEDENCE);return registration;}public static class TraceFilter implements Filter {@Overridepublic void doFilter(ServletRequest request, ServletResponse response, FilterChain chain)throws IOException, ServletException {String traceId = MDC.get("traceId");if (StringUtils.isEmpty(traceId)) {traceId = IdUtil.fastSimpleUUID();MDC.put("traceId", traceId);}try {chain.doFilter(request, response);} finally {MDC.remove("traceId");}}}
}

4.2 服务自动扩缩容

基于K8s HPA实现弹性伸缩：

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:name: order-service-hpa
spec:scaleTargetRef:apiVersion: apps/v1kind: Deploymentname: order-serviceminReplicas: 2maxReplicas: 10metrics:- type: Resourceresource:name: cputarget:type: UtilizationaverageUtilization: 60- type: Resourceresource:name: memorytarget:type: UtilizationaverageUtilization: 70

本文著作权归聚娃科技省赚客app开发者团队，转载请注明出处！