你的代码“炸”了吗？深度剖析为何 TransactionProcessingService 必须加悲观锁！✨

技术文档

你的代码“炸”了吗？💣 深度剖析为何 `TransactionProcessingService` 必须加悲观锁！

大家好！今天我们不聊宽泛的理论，而是直接深入一段真实的代码——TransactionProcessingService。这段代码处理的是一个非常典型的业务场景：在一个原子操作中，既要核销用户的优惠券，又要抵扣积分。 这是一个涉及用户“真金白银”的核心流程，如果出现任何差错，后果不堪设想。😱

你可能会问：“我已经用了 @Transactional，为什么还不够？为什么非要加上那把看起来很‘重’的悲观锁？”

别急，这篇博客将带你一步步揭开谜底，让你彻底明白，在这段代码中，悲观锁不是“可选项”，而是“必需品”！🛡️

快速概览：为什么你的代码需要悲观锁？📝

核心操作 (Core Operation) 并发风险 (Concurrency Risk) 无锁的后果 (Consequence without Lock) 解决方案 (Solution) 使用优惠券 (UserCoupon) “读-改-写”冲突：多个请求同时读取到同一张“未使用”的优惠券，并各自将其状态更新为“已使用”。 优惠券超用：一张本应只能使用一次的优惠券，被核销了多次，导致公司资产损失。 悲观锁 (PESSIMISTIC_WRITE): 在读取 UserCoupon 时加锁，确保只有一个事务能操作它。 抵扣积分 (MemberUser) “读-改-写”冲突：多个请求同时读取用户当前的积分余额，然后各自计算并扣除，最后更新回数据库。 积分超扣/数据错乱：用户的积分可能被扣成负数，或者最终的积分余额与实际流水不符。 悲观锁 (PESSIMISTIC_WRITE): 在读取和更新 MemberUser 的积分时加锁，保证积分计算和更新的原子性。 整个交易流程 原子性问题：如果券用了，但扣积分时系统崩溃，会导致数据不一致。 数据不一致：用户资产状态混乱，难以追溯和修复。 数据库事务 (@Transactional): 将整个流程包裹在事务中，保证所有操作要么全部成功，要么全部失败回滚。

结论: @Transactional 保证了流程的原子性，但无法阻止并发事务同时读取旧数据。悲观锁正是为了解决这个“同时读取”的问题，它确保在“读-改-写”的整个周期内，数据行被独占。

代码剖析：并发的“魔鬼”藏在哪里？😈

让我们回顾一下 TransactionProcessingService 的简化逻辑：

@Transactionalpublic TransactionProcessResultDto processTransactionByAdmin(...) { // 1. 加载用户 MemberUser memberUser = memberUserRepository.findById(...); // 2. 处理优惠券 (如果提供了) if (payload.getUserCouponId() != null) { // --- 风险点 A: 读-改-写 UserCoupon --- // 读: 查询优惠券状态是否为“未使用” UserCoupon userCoupon = userCouponRepository.findById(...); validateUserCouponForUsage(userCoupon, ...); // 改: 计算优惠，更新应付金额 // 写: 将优惠券状态更新为“已使用” userCoupon.setStatus(USER_COUPON_STATUS_USED); userCouponRepository.save(userCoupon); } // 3. 处理积分抵扣 (如果提供了) if (payload.getPointsToUse() > 0) { // --- 风险点 B: 读-改-写 MemberUser --- // 读: 查询用户可用积分 MemberUser freshMemberUser = memberUserRepository.findById(...); long availablePoints = freshMemberUser.getTotalPoints() - freshMemberUser.getUsedPoints(); // ... 校验积分 ... // 改: 计算抵扣金额 // 写: 更新用户的 usedPoints freshMemberUser.setUsedPoints(...); memberUserRepository.save(freshMemberUser); // 写: 创建积分交易流水 pointTransactionRepository.save(...); } return result;}

风险点 A (优惠券超用) 和 风险点 B (积分超扣) 就是并发问题的重灾区。

可视化分析：无锁世界的混乱 🌪️

流程图：并发下的“读-改-写”冲突

这个流程图展示了两个事务（A和B）在没有锁的情况下，如何同时进入“读-改-写”的关键区域。

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(例如：库存=10)开始在内存中计算
(10 - 2 = 8)写入新值 \'8\' 到数据库结束读取数据
(例如：库存=10)开始在内存中计算
(10 - 1 = 9)写入新值 \'9\' 到数据库结束问题：事务A的更新被覆盖了！
最终库存是8，而不是7。
这就是\'丢失更新\'问题。

时序图：积分超扣的“完美犯罪”

这个时序图生动地展示了两个请求如何让用户的积分被错误地扣减。

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解决方案：悲观锁如何力挽狂澜 🦸

悲观锁通过在事务开始读取数据时就锁定它，强制其他事务等待，从而将并发操作串行化。

状态图：被锁定的数据行

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类图：实现悲观锁的 Repository 设计

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实体关系图 (ERD - Entity Relationship Diagram)

这个 ERD (Entity Relationship Diagram, 实体关系图) 展示了 TransactionProcessingService 操作的核心实体及其关系。

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思维导图总结 🧠

在这里插入图片描述

结语

在处理核心交易逻辑时，我们必须像悲观锁一样“悲观”思考，预见所有可能出错的并发场景。TransactionProcessingService 的例子完美地诠释了这一点：@Transactional 提供了宏观的保护，而悲观锁则在微观层面，为每一个关键的数据行站岗放哨。

你的代码“炸”了吗？深度剖析为何 TransactionProcessingService 必须加悲观锁！✨