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我的新课**《C2C 电商系统微服务架构120天实战训练营》在公众号儒猿技术窝**上线了，感兴趣的同学，可以点击下方链接了解详情：
目录
一、前情提示
二、什么是数据一致性？
三、一个数据计算链路的梳理
四、数据计算链路的bug
五、电商库存数据的不一致问题
六、大型系统的数据不一致排查有多困难
七、下篇预告
一、前情提示
这篇文章，咱们继续来聊聊之前的亿级流量架构的演进，之前对这个系列的文章已经更新到了可扩展架构的设计，如果有不太清楚的同学，建议一定先回看一下之前的文章：
1、****亿级流量系统架构之如何支撑百亿级数据的存储与计算
6、亿级流量系统架构之如何在上万并发场景下设计可扩展架构（上）？
7、亿级流量系统架构之如何在上万并发场景下设计可扩展架构（中）？
8、亿级流量系统架构之如何在上万并发场景下设计可扩展架构（下）？
老规矩！我们首先看一下这个复杂的系统架构演进到当前阶段，整体的架构图是什么样子的。
笔者再次友情提醒，如果各位小伙伴对下面这个复杂的架构图还有什么不理解的地方，一定要先回看之前的文章，因为系列文必须对上下文有清晰的理解和认识。
接着文本我们来聊聊一个核心系统每天承载百亿流量的背景下，应该如何来保证复杂系统中的数据一致性？
二、什么是数据一致性？
简单来说，在一个复杂的系统中一定会对一些数据做出非常复杂的处理，而且可能是多个不同的子系统，甚至是多个服务。
对一个数据按照一定的顺序依次做出复杂的业务逻辑的执行，最终可能就会生产出一份宝贵的系统核心数据，落地到存储里去，比如说在数据库里存储。
给大家来一张手绘彩图，感受下这个现场的氛围：
从上图中我们就可以看到，多个系统如何对一个数据依次进行处理，最终拿到一份核心数据，并落地到存储里去。
那么在这个过程中，就可能会产生所谓的数据不一致的问题。
什么意思呢？给大家举一个最简单的例子，我们本来期望数据的变化过程是：数据1 -> 数据2 -> 数据3 -> 数据4。
那么最后落地到数据库里的应该是数据4，对不对？
结果呢？不知道为啥，经过上面那个复杂的分布式系统中的各个子系统，或者是各个服务的协作处理，最后居然搞出来一个数据87。
搞了半天，搞了一个跟数据4风马牛不相及的一个东西，最后落地到了数据库里。
然后啊，这套系统的最终用户，可能通过前台的界面看到了一个莫名其妙的数据87。
这就尴尬了，用户明显会觉得这个数据有错误，就会反馈给公司的客服，此时就会上报bug到工程师团队，大家就开始吭哧吭哧的找问题。
上面说的这个场景，其实就是一种数据不一致的问题，也是我们接下来几篇文章要讨论的一个问题。
实际上，在任何一个大规模分布式系统里，都会存在类似的问题。无论是电商，O2O，还是本文举例的数据平台系统，都一样。
三、一个数据计算链路的梳理
那么既然已经明确了问题，接下来就来看看在数据平台这个系统里，到底是什么问题可能会导致一个最终落地存储的数据的异常呢？
要明白这个问题，咱们先回过头看看，在之前提过的数据平台这个项目里，一个最终落地的数据的计算链路是什么样的？
大家看看下面的图：
如上图所示，其实从最简单的一个角度来说，这个数据计算的链路大概也就是上面的那个样子。
- 首先，通过MySQL binlog采集中间件获取到数据，转发给数据接入层。
- 然后，数据接入层会把原始数据落地到kv存储里去
- 接着，是实时计算平台会从kv存储里提取数据进行计算
- 最后，会将计算结果写入到数据库+缓存的集群里。数据查询平台会从数据库 + 缓存的集群里提取数据，提供用户来进行查询
看起来很简单，对吧？
但是哪怕是这个系统里，数据计算链路，也绝对不是这么简单的。
如果大家看过之前的系列文章的话，就应该知道，这个系统为了支撑高并发、高可用、高性能等场景，引入了大量的复杂机制。
所以实际上一条原始数据进入到系统，一直到最后落地到存储里，计算链路还会包含下面的东西：
- 接入层的限流处理
- 实时计算层的失败重试
- 实时计算层的本地内存存储的降级机制
- 数据分片的聚合与计算，单条数据在这里可能会进入一个数据分片里
- 数据查询层的多级缓存机制
上面只不过是随便列举了几条。然而哪怕只是上述几条，都可以把一个数据的计算链路变得复杂很多倍了。
四、数据计算链路的bug
既然大家已经明白了，在一个复杂系统里，一份核心数据可能是经过一个极为复杂的计算链路的处理，中间百转千回，任何可能的情况都会发生。
那么就可以理解在大型分布式系统中，数据不一致的问题是如何产生的了。
其实原因非常的简单，说白了，就是数据计算链路的bug。
也就是说，在数据的计算过程中，某个子系统出现了bug，并没有按照我们预期的行为去处理，导致最终产出去的数据变得错误了。
那么，为什么会在数据计算链路中出现这种bug呢？
原因很简单，如果大家曾经参与过上百人协作的大型分布式系统，或者是主导过上百人协作开发的大型分布式系统的架构设计，应该对核心数据的异常和错误非常熟悉，并且会感到头疼不已。
大规模分布式系统中，动辄上百人协作开发。很可能某个子系统或者是某个服务的负责人，对数据的处理逻辑理解偏差了，代码里写了一个隐藏的bug。
而这个bug，轻易不会触发，并且在QA测试环境还没测出来，结果带着一颗定时炸弹，系统上线。
最后在线上某种特殊的场景下，触发了这个bug，导致最终的数据出现问题。
五、电商库存数据的不一致问题
接触过电商的同学，可能此时脑子里就可以快速的想到一个类似的经典场景：电商中的库存。
在大规模的电商系统中，库存数据绝对是核心中的核心。但是实际上，在一个分布式系统中，很多系统可能都会采用一定的逻辑来更新库存。
这就可能导致跟上述说的场景类似的问题，就是多个系统都更新库存，但就是某个系统对库存的更新出现了bug。
这可能是因为那个系统的负责人没理解到底应该如何更新库存，也或者是他更新的时候采用的逻辑，没有考虑到一些特殊情况。
这样导致的结果就是，系统里的库存和仓库中实际的库存，死活对不上。但就是不知道到底哪个环节出了问题，导致库存数据出错。
这个，其实就是一个典型的数据不一致的问题。
六、大型系统的数据不一致排查有多困难
当面对一个大型分布式系统时，如果你之前压根儿没考虑过数据不一致的问题，那么我敢打赌，当你负责的系统在线上被客服反馈有某个核心数据不一致的时候，你绝对会一脸蒙圈。
因为一个核心数据的处理，少则涉及几个系统的协作处理，多则涉及十个以上的系统的协作处理。
如果你没有留存任何日志、或者仅仅就是有部分日志，然后基本就只能所有人干瞪眼，大家大眼对小眼，都盯着自己的代码看。
大家根据一个数据最后的错误结果，比如数据87。10多个人对着自己的代码，反复的思考，冥思苦想。
然后每个人都在大脑中疯狂的模拟自己代码的运行，但是就是想不明白，为什么本来应该是数据4的，结果出来了一个数据87？
所以现实问题就是这样，这种数据不一致的问题，大概有以下几个痛点：
- 自己基本无法主动提前感知到数据问题，要被动等待用户发现，反馈给客服，这很可能导致你的产品被大量投诉，老板很生气，后果很严重。
- 即使客服告诉了你数据错了，但是你们没法还原现场，没有留存证据，基本就是一群工程师对着代码想象，猜测。
- 即使你解决了一次数据不一致的问题，但是以后也许还有下一次，这样搞下去，会导致团队里好几个能干的小伙儿时间都搭在这种破事儿上。
七、下篇预告
所以针对本文描述的大型分布式系统数据不一致的问题，下篇文章我们将给出：在百亿流量的场景下，一套复杂系统我们是如何构建整套核心数据保证方案的。
敬请期待：
- 亿级流量系统架构之如何保证百亿流量下的数据一致性（中）？
- 亿级流量系统架构之如何保证百亿流量下的数据一致性（下）？
end
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> 36、[亿级流量架构第二弹：你的系统真的无懈可击吗？](https://juejin.cn/post/6844903752030240782)**
作者：石杉的架构笔记
链接：juejin.cn/post/684490…
来源：掘金
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