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<p>因为传说中的出身问题，我以前写的是PHP，在使用 swoole 之前，基本的应用调试手段就是简单粗暴的 var_dump，exit，对于单进程模型的 PHP 也是简单有效，技术栈换成 Java 之后，就变得没那么容易，一方面是需要编译，另一方面是一般都是基于 spring 的项目，如果问题定位比较模糊，那框架层的是很难靠简单的 System.out.println 或者打 log 解决，(PS：我觉得可能我写的东西比较适合从 PHP 这种弱类型语言转到 Java 的小白同学)这个时候一方面因为是 Java，有了非常好用的 idea IDE 的支持，可以各种花式调试，条件断点尤其牛叉，但是又因为有 Spring+Java 的双重原因，有些情况下单步调试可以把手按废掉，这也是我之前一直比较困惑苦逼的点，后来随着慢慢精（jiang）进（you）之后，比如对于一个 oom 的情况，我们可以通过启动参数加上-XX:+HeapDumpOnOutOfMemoryError -XX:HeapDumpPath=xx/xx 来配置溢出时的堆dump 日志，获取到这个文件后，我们可以通过像 Memory Analyzer (MAT)[<a href="https://www.eclipse.org/mat/]" target="_blank" rel="noopener">https://www.eclipse.org/mat/]</a> (The Eclipse Memory Analyzer is a fast and feature-rich Java heap analyzer that helps you find memory leaks and reduce memory consumption.)来查看诊断问题所在，之前用到的时候是因为有个死循环一直往链表里塞数据，属于比较简单的，后来一次是由于运维进行应用迁移时按默认的统一配置了堆内存大小，导致内存的确不够用，所以溢出了，<br>今天想说的其实主要是我们的 thread dump，这也是我最近才真正用的一个方法，可能真的很小白了，用过 ide 的单步调试其实都知道会有一个一层层的玩意，比如函数从 A，调用了 B，再从 B 调用了 C，一直往下(因为是 Java，所以还有很多🤦♂️)，这个其实也是大部分语言的调用模型，利用了栈这个数据结构，通过这个结构我们可以知道代码的调用链路，由于对于一个 spring 应用，在本身框架代码量非常庞大的情况下，外加如果应用代码也是非常多的时候，有时候通过单步调试真的很难短时间定位到问题，需要非常大的耐心和仔细观察，当然不是说完全不行，举个例子当我的应用经常启动需要非常长的时间，因为本身应用有非常多个 bean，比较难说究竟是 bean 的加载的确很慢还是有什么异常原因，这种时候就可以使用 thread dump 了，具体怎么操作呢<br><img data-src="https://mystore-1255223546.cos.ap-chengdu.myqcloud.com/uPic/GPYHjd.png" alt=""><br>如果在idea 中运行或者调试时，可以直接点击这个照相机一样的按钮，右边就会出现了左边会显示所有的线程，右边会显示线程栈，</p>
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<figure class="highlight plain"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br></pre></td><td class="code"><pre><span class="line">"main@1" prio=5 tid=0x1 nid=NA runnable</span><br><span class="line"> java.lang.Thread.State: RUNNABLE</span><br><span class="line"> at TreeDistance.treeDist(TreeDistance.java:64)</span><br><span class="line"> at TreeDistance.treeDist(TreeDistance.java:65)</span><br><span class="line"> at TreeDistance.treeDist(TreeDistance.java:65)</span><br><span class="line"> at TreeDistance.treeDist(TreeDistance.java:65)</span><br><span class="line"> at TreeDistance.main(TreeDistance.java:45)</span><br></pre></td></tr></table></figure>
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<p>这就是我们主线程的堆栈信息了，main 表示这个线程名，prio表示优先级，默认是 5，tid 表示线程 id，nid 表示对应的系统线程，后面的runnable 表示目前线程状态，因为是被我打了断点，所以是就许状态，然后下面就是对应的线程栈内容了，在<code>TreeDistance</code>类的 <code>treeDist</code>方法中，对应的文件行数是 64 行。<br>这里使用 thread dump一般也不会是上面我截图代码里的这种代码量很少的，一般是大型项目，有时候跑着跑着没反应，又不知道跑到哪了，特别是一些刚接触的大项目或者需要定位一个大项目的一个疑难问题，一时没思路时，可以使用这个方法，个人觉得非常有帮助。</p>
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<p>前面说了mysql数据库的事务相关的，那事务是用来干嘛的，这里得补一下ACID，</p>
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<blockquote>
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<p><a href="https://zh.wikipedia.org/wiki/ACID" target="_blank" rel="noopener"><strong>ACID</strong></a>，是指<a href="https://zh.wikipedia.org/wiki/数据库管理系统" target="_blank" rel="noopener">数据库管理系统</a>（<a href="https://zh.wikipedia.org/wiki/DBMS" target="_blank" rel="noopener">DBMS</a>）在写入或更新资料的过程中，为保证<a href="https://zh.wikipedia.org/wiki/数据库事务" target="_blank" rel="noopener">事务</a>（transaction）是正确可靠的，所必须具备的四个特性：<a href="https://zh.wikipedia.org/w/index.php?title=原子性&action=edit&redlink=1" target="_blank" rel="noopener">原子性</a>（atomicity，或称不可分割性）、<a href="https://zh.wikipedia.org/w/index.php?title=一致性_(数据库系统)&action=edit&redlink=1" target="_blank" rel="noopener">一致性</a>（consistency）、<a href="https://zh.wikipedia.org/wiki/隔離性" target="_blank" rel="noopener">隔离性</a>（isolation，又称独立性）、<a href="https://zh.wikipedia.org/w/index.php?title=持久性&action=edit&redlink=1" target="_blank" rel="noopener">持久性</a>（durability）。</p>
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</blockquote>
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<ul>
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<li><p>Atomicity（原子性）：一个事务（transaction）中的所有操作，或者全部完成，或者全部不完成，不会结束在中间某个环节。事务在执行过程中发生错误，会被<a href="https://zh.wikipedia.org/wiki/回滚_(数据管理)" target="_blank" rel="noopener">回滚</a>（Rollback）到事务开始前的状态，就像这个事务从来没有执行过一样。即，事务不可分割、不可约简。<a href="https://zh.wikipedia.org/wiki/ACID#cite_note-acid-1" target="_blank" rel="noopener">[1]</a></p>
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</li>
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<li><p>Consistency（一致性）：在事务开始之前和事务结束以后，数据库的完整性没有被破坏。这表示写入的资料必须完全符合所有的预设<a href="https://zh.wikipedia.org/wiki/数据完整性" target="_blank" rel="noopener">约束</a>、<a href="https://zh.wikipedia.org/wiki/触发器_(数据库)" target="_blank" rel="noopener">触发器</a>、<a href="https://zh.wikipedia.org/wiki/级联回滚" target="_blank" rel="noopener">级联回滚</a>等。<a href="https://zh.wikipedia.org/wiki/ACID#cite_note-acid-1" target="_blank" rel="noopener">[1]</a></p>
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</li>
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<li><p>Isolation（隔离性）：数据库允许多个并发事务同时对其数据进行读写和修改的能力，隔离性可以防止多个事务并发执行时由于交叉执行而导致数据的不一致。事务隔离分为不同级别，包括未提交读（Read uncommitted）、提交读（read committed）、可重复读（repeatable read）和串行化（Serializable）。<a href="https://zh.wikipedia.org/wiki/ACID#cite_note-acid-1" target="_blank" rel="noopener">[1]</a></p>
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</li>
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<li><p>Durability（持久性）：事务处理结束后，对数据的修改就是永久的，即便系统故障也不会丢失。<a href="https://zh.wikipedia.org/wiki/ACID#cite_note-acid-1" target="_blank" rel="noopener">[1]</a></p>
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<p>在mysql中，借助于MVCC，各种级别的锁，日志等特性来实现了事务的ACID，但是这个我们通常是对于一个数据库服务的定义，常见的情况下我们的数据库随着业务发展也会从单实例变成多实例，组成主从Master-Slave架构，这个时候其实会有一些问题随之出现，比如说主从同步延迟，假如在业务代码中做了读写分离，对于一些敏感度较低的数据其实问题不是很大，只要主从延迟不到特别夸张的地步一般都是可以忍受的，但是对于一些核心的业务数据，比如订单之类的，不能忍受数据不一致，下了单了，付了款了，一刷订单列表，发现这个订单还没支付，甚至订单都没在，这对于用户来讲是恨不能容忍的错误，那么这里就需要一些措施，要不就不读写分离，要不就在 redis 这类缓存下订单，或者支付后加个延时等，这些都是一些补偿措施，并且这也是一个不太切当的例子，比较合适的例子也可以用这个下单来说，一般在电商平台下单会有挺多要做的事情，比如像下面这个图</p>
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<p><img data-src="https://mystore-1255223546.cos.ap-chengdu.myqcloud.com/uPic/WX20200517-2127322x.png" alt=""></p>
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<p>下单的是后要冻结核销优惠券，如果账户里有钱要冻结扣除账户里的钱，如果使用了J 豆也一样，可能还有 E 卡，忽略我借用的平台，因为目前一般后台服务化之后，可能每一项都是对应的一个后台服务，我们期望的执行过程是要不全成功，要不就全保持执行前状态，不能是部分扣减核销成功了，部分还不行，所以我们处理这种情况会引入一些通用的方案，第一种叫<a href="https://zh.wikipedia.org/wiki/%E4%BA%8C%E9%98%B6%E6%AE%B5%E6%8F%90%E4%BA%A4" target="_blank" rel="noopener">二阶段提交</a>,</p>
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<blockquote>
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<p><strong>二阶段提交</strong>（英语：Two-phase Commit）是指在<a href="https://zh.wikipedia.org/wiki/计算机网络" target="_blank" rel="noopener">计算机网络</a>以及<a href="https://zh.wikipedia.org/wiki/数据库" target="_blank" rel="noopener">数据库</a>领域内，为了使基于<a href="https://zh.wikipedia.org/wiki/分布式系统" target="_blank" rel="noopener">分布式系统</a>架构下的所有节点在进行<a href="https://zh.wikipedia.org/wiki/数据库事务" target="_blank" rel="noopener">事务</a>提交时保持一致性而设计的一种<a href="https://zh.wikipedia.org/wiki/演算法" target="_blank" rel="noopener">算法</a>。通常，<strong>二阶段提交</strong>也被称为是一种<strong>协议</strong>（Protocol）。在分布式系统中，每个节点虽然可以知晓自己的操作时成功或者失败，却无法知道其他节点的操作的成功或失败。当一个事务跨越多个节点时，为了保持事务的<a href="https://zh.wikipedia.org/wiki/ACID" target="_blank" rel="noopener">ACID</a>特性，需要引入一个作为<strong>协调者</strong>的组件来统一掌控所有节点（称作<strong>参与者</strong>）的操作结果并最终指示这些节点是否要把操作结果进行真正的提交（比如将更新后的数据写入磁盘等等）。因此，二阶段提交的算法思路可以概括为： 参与者将操作成败通知协调者，再由协调者根据所有参与者的反馈情报决定各参与者是否要提交操作还是中止操作。</p>
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</blockquote>
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<p>对于上面的例子，我们将整个过程分成两个阶段，首先是提交请求阶段，这个阶段大概需要做的是确定资源存在，锁定资源，可能还要做好失败后回滚的准备，如果这些都 ok 了那么就响应成功，这里其实用到了一个叫事务的协调者的角色，类似于裁判员，每个节点都反馈第一阶段成功后，开始执行第二阶段，也就是实际执行操作，这里也是需要所有节点都反馈成功后才是执行成功，要不就是失败回滚。其实常用的分布式事务的解决方案主要也是基于此方案的改进，比如后面介绍的<a href="https://zh.wikipedia.org/wiki/%E4%B8%89%E9%98%B6%E6%AE%B5%E6%8F%90%E4%BA%A4" target="_blank" rel="noopener">三阶段提交</a>，有三阶段提交就是因为二阶段提交比较尴尬的几个点，</p>
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<ul>
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<li>第一是对于两阶段提交，其中默认只有协调者有超时时间，当一个参与者进入卡死状态时只能依赖协调者的超时来结束任务，这中间的时间参与者都是锁定着资源</li>
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<li>第二是协调者的单点问题，万一挂了，参与者就会在那傻等着</li>
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</ul>
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<p>所以三阶段提交引入了各节点的超时机制和一个准备阶段，首先是一个<code>can commit</code>阶段，询问下各个节点有没有资源，能不能进行操作，这个阶段不阻塞，只是提前做个摸底，这个阶段其实人畜无害，但是能提高成功率，在这个阶段如果就有节点反馈是不接受的，那就不用执行下去了，也没有锁资源，然后第二阶段是 <code>pre commit</code> ，这个阶段做的事情跟原来的 第一阶段比较类似，然后是第三阶段<code>do commit</code>，其实三阶段提交我个人觉得只是加了个超时，和准备阶段，好像木有根本性的解决的两阶段提交的问题，后续可以再看看一些论文来思考讨论下。</p>
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<p>2020年05月24日22:11 更新<br>这里跟朋友讨论了下，好像想通了最核心的一点，对于前面说的那个场景，如果是两阶段提交，如果各个节点中有一个没回应，并且协调者也挂了，这个时候会有什么情况呢，再加一个假设，其实比如这个一阶段其实是检验就失败的，理论上应该大家都释放资源，那么对于这种异常情况，其他的参与者就不知所措了，就傻傻地锁着资源阻塞着，那么三阶段提交的意义就出现了，把第一阶段拆开，那么即使在这个阶段出现上述的异常，即也不会锁定资源，同时参与者也有超时机制，在第二阶段锁定资源出现异常是，其他参与者节点等超时后就自动释放资源了，也就没啥问题了，不过对于那种异常恢复后的一些情况还是没有很好地解决，需要借助 zk 等，后面有空可以讲讲 paxos 跟 raft 等</p>
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<p>看完前面两篇水文之后，感觉不得不来分析下 mysql 的锁了，其实前面说到幻读的时候是有个前提没提到的，比如一个<code>select * from table1 where id = 1</code>这种查询语句其实是不会加传说中的锁的，当然这里是指在 RR 或者 RC 隔离级别下，<br>看一段 mysql官方文档</p>
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<blockquote>
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<p><code>SELECT ... FROM</code> is a consistent read, reading a snapshot of the database and setting no locks unless the transaction isolation level is set to SERIALIZABLE. For SERIALIZABLE level, the search sets shared next-key locks on the index records it encounters. However, only an index record lock is required for statements that lock rows using a unique index to search for a unique row. </p>
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<p>纯粹的这种一致性读，实际读取的是快照，也就是基于 read view 的读取方式，除非当前隔离级别是SERIALIZABLE<br>但是对于以下几类</p>
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<ul>
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<li><code>select * from table where ? lock in share mode;</code></li>
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<li><code>select * from table where ? for update;</code></li>
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<li><code>insert into table values (...);</code></li>
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<li><code>update table set ? where ?;</code></li>
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<li><code>delete from table where ?;</code> </li>
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<p>除了第一条是 S 锁之外，其他都是 X 排他锁，这边在顺带下，S 锁表示共享锁， X 表示独占锁，同为 S 锁之间不冲突，S 与 X，X 与 S，X 与 X 之间都冲突，也就是加了前者，后者就加不上了<br>我们知道对于 RC 级别会出现幻读现象，对于 RR 级别不会出现，主要的区别是 RR 级别下对于以上的加锁读取都根据情况加上了 gap 锁，那么是不是 RR 级别下以上所有的都是要加 gap 锁呢，当然不是<br>举个例子，RR 事务隔离级别下，table1 有个主键id 字段<br><code>select * from table1 where id = 10 for update</code><br>这条语句要加 gap 锁吗？<br>答案是不需要，这里其实算是我看了这么久的一点自己的理解，啥时候要加 gap 锁，判断的条件是根据我查询的数据是否会因为不加 gap 锁而出现数量的不一致，我上面这条查询语句，在什么情况下会出现查询结果数量不一致呢，只要在这条记录被更新或者删除的时候，有没有可能我第一次查出来一条，第二次变成两条了呢，不可能，因为是主键索引。<br>再变更下这个题的条件，当 id 不是主键，但是是唯一索引，这样需要怎么加锁，注意问题是怎么加锁，不是需不需要加 gap 锁，这里呢就是稍微延伸一下，把聚簇索引（主键索引）和二级索引带一下，当 id 不是主键，说明是个二级索引，但是它是唯一索引，体会下，首先对于 id = 10这个二级索引肯定要加锁，要不要锁 gap 呢，不用，因为是唯一索引，id = 10 只可能有这一条记录，然后呢，这样是不是就好了，还不行，因为啥，因为它是二级索引，对应的主键索引的记录才是真正的数据，万一被更新掉了咋办，所以在 id = 10 对应的主键索引上也需要加上锁（默认都是 record lock行锁），那主键索引上要不要加 gap 呢，也不用，也是精确定位到这一条记录<br>最后呢，当 id 不是主键，也不是唯一索引，只是个普通的索引，这里就需要大名鼎鼎的 gap 锁了，<br>是时候画个图了<br><img data-src="https://mystore-1255223546.cos.ap-chengdu.myqcloud.com/uPic/WX20200510-1126082x.png" alt=""><br>其实核心的目的还是不让这个 id=10 的记录不会出现幻读，那么就需要在 id 这个索引上加上三个 gap 锁，主键索引上就不用了，在 id 索引上已经控制住了id = 10 不会出现幻读，主键索引上这两条对应的记录已经锁了，所以就这样 OK 了</p>
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<p>上一篇聊了mysql 的 innodb 引擎基于 read view 实现的 mvcc 和事务隔离级别，可能有些细心的小伙伴会发现一些问题，第一个是在 RC 级别下的事务提交后的可见性，这里涉及到了三个参数，m_low_limit_id，m_up_limit_id，m_ids，之前看到知乎的一篇写的非常不错的文章，但是就在这一点上似乎有点疑惑，这里基于源码和注释来解释下这个问题</p>
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<figure class="highlight c"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment">/**</span></span><br><span class="line"><span class="comment">Opens a read view where exactly the transactions serialized before this</span></span><br><span class="line"><span class="comment">point in time are seen in the view.</span></span><br><span class="line"><span class="comment">@param id Creator transaction id */</span></span><br><span class="line"></span><br><span class="line"><span class="function"><span class="keyword">void</span> <span class="title">ReadView::prepare</span><span class="params">(<span class="keyword">trx_id_t</span> id)</span> </span>{</span><br><span class="line"> ut_ad(mutex_own(&trx_sys->mutex));</span><br><span class="line"></span><br><span class="line"> m_creator_trx_id = id;</span><br><span class="line"></span><br><span class="line"> m_low_limit_no = m_low_limit_id = m_up_limit_id = trx_sys->max_trx_id;</span><br></pre></td></tr></table></figure>
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<p>m_low_limit_id赋的值是trx_sys->max_trx_id，代表的是当前系统最小的未分配的事务 id，所以呢，举个例子，当前有三个活跃事务，事务 id 分别是 100，200，300，而 m_up_limit_id = 100, m_low_limit_id = 301，当事务 id 是 200 的提交之后，它的更新就是可以被 100 和 300 看到，而不是说 m_ids 里没了 200，并且 200 比 100 大就应该不可见了</p>
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<h2 id="幻读"><a href="#幻读" class="headerlink" title="幻读"></a>幻读</h2><p>还有一个问题是幻读的问题，这貌似也是个高频面试题，啥意思呢，或者说跟它最常拿来比较的脏读，脏读是指读到了别的事务未提交的数据，因为未提交，严格意义上来讲，不一定是会被最后落到库里，可能会回滚，也就是在 read uncommitted 级别下会出现的问题，但是幻读不太一样，幻读是指两次查询的结果数量不一样，比如我查了第一次是 <code>select * from table1 where id < 10 for update</code>,查出来了一条结果 id 是 5，然后再查一下发现出来了一条 id 是 5，一条 id 是 7，那是不是有点尴尬了，其实呢这个点我觉得脏读跟幻读也比较是从原理层面来命名，如果第一次接触的同学发觉有点不理解也比较正常，因为从逻辑上讲总之都是数据不符合预期，但是基于源码层面其实是不同的情况，幻读是在原先的 read view 无法完全解决的，怎么解决呢，简单的来说就是锁咯，我们知道innodb 是基于 record lock 行锁的，但是貌似没有办法解决这种问题，那么 innodb 就引入了 gap lock 间隙锁，比如上面说的情况下，id 小于 10 的情况下，是都应该锁住的，gap lock 其实是基于索引结构来锁的，因为索引树除了树形结构之外，还有一个next record 的指针，gap lock 也是基于这个来锁的<br>看一下 mysql 的文档</p>
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<blockquote>
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<p>SELECT … FOR UPDATE sets an exclusive next-key lock on every record the search encounters. However, only an index record lock is required for statements that lock rows using a unique index to search for a unique row. </p>
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</blockquote>
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<p>对于一个 for update 查询，在 RR 级别下，会设置一个 next-key lock在每一条被查询到的记录上，next-lock 又是啥呢，其实就是 gap 锁和 record 锁的结合体，比如我在数据库里有 id 是 1，3，5，7，10，对于上面那条查询，查出来的结果就是 1，3，5，7，那么按照文档里描述的，对于这几条记录都会加上next-key lock，也就是(-∞, 1], (1, 3], (3, 5], (5, 7], (7, 10) 这些区间和记录会被锁起来，不让插入，再唠叨一下呢，就是其实如果是只读的事务，光 read view 一致性读就够了，如果是有写操作的呢，就需要锁了。</p>
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<p>很久以前，有位面试官问到，你知道 mysql 的事务隔离级别吗，“额 O__O …，不太清楚”，完了之后我就去网上找相关的文章，找到了这篇<a href="https://www.cnblogs.com/zhoujinyi/p/3437475.html" target="_blank" rel="noopener">MySQL 四种事务隔离级的说明</a>, 文章写得特别好，看了这个就懂了各个事务隔离级别都是啥，不过看了这个之后多思考一下的话还是会发现问题，这么神奇的事务隔离级别是怎么实现的呢</p>
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<p>其中 innodb 的事务隔离用到了标题里说到的 mvcc，<a href="https://en.wikipedia.org/wiki/Multiversion_concurrency_control" target="_blank" rel="noopener"><strong>Multiversion concurrency control</strong></a>, 直译过来就是多版本并发控制，先不讲这个究竟是个啥，考虑下如果纯猜测，这个事务隔离级别应该会是怎么样实现呢，愚钝的我想了下，可以在事务开始的时候拷贝一个表，这个可以支持 RR 级别，RC 级别就不支持了，而且要是个非常大的表，想想就不可行</p>
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<p>腆着脸说虽然这个不可行，但是思路是对的，具体实行起来需要做一系列（肥肠多）的改动，首先根据我的理解，其实这个拷贝一个表是变成拷贝一条记录，但是如果有多个事务，那就得拷贝多次，这个问题其实可以借助版本管理系统来解释，在用版本管理系统，git 之类的之前，很原始的可能是开发完一个功能后，就打个压缩包用时间等信息命名，然后如果后面要找回这个就直接用这个压缩包的就行了，后来有了 svn，git 中心式和分布式的版本管理系统，它的一个特点是粒度可以控制到文件和代码行级别，对应的我们的 mysql 事务是不是也可以从一开始预想的表级别细化到行的级别，可能之前很多人都了解过，数据库的一行记录除了我们用户自定义的字段，还有一些额外的字段，去源码<a href="https://github.com/mysql/mysql-server/blob/8.0/storage/innobase/include/data0type.h#L170" target="_blank" rel="noopener">data0type.h</a>里捞一下</p>
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<figure class="highlight c"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment">/* Precise data types for system columns and the length of those columns;</span></span><br><span class="line"><span class="comment"><span class="doctag">NOTE:</span> the values must run from 0 up in the order given! All codes must</span></span><br><span class="line"><span class="comment">be less than 256 */</span></span><br><span class="line"><span class="meta">#<span class="meta-keyword">define</span> DATA_ROW_ID 0 <span class="comment">/* row id: a 48-bit integer */</span></span></span><br><span class="line"><span class="meta">#<span class="meta-keyword">define</span> DATA_ROW_ID_LEN 6 <span class="comment">/* stored length for row id */</span></span></span><br><span class="line"></span><br><span class="line"><span class="comment">/** Transaction id: 6 bytes */</span></span><br><span class="line"><span class="keyword">constexpr</span> <span class="keyword">size_t</span> DATA_TRX_ID = <span class="number">1</span>;</span><br><span class="line"></span><br><span class="line"><span class="comment">/** Transaction ID type size in bytes. */</span></span><br><span class="line"><span class="keyword">constexpr</span> <span class="keyword">size_t</span> DATA_TRX_ID_LEN = <span class="number">6</span>;</span><br><span class="line"></span><br><span class="line"><span class="comment">/** Rollback data pointer: 7 bytes */</span></span><br><span class="line"><span class="keyword">constexpr</span> <span class="keyword">size_t</span> DATA_ROLL_PTR = <span class="number">2</span>;</span><br><span class="line"></span><br><span class="line"><span class="comment">/** Rollback data pointer type size in bytes. */</span></span><br><span class="line"><span class="keyword">constexpr</span> <span class="keyword">size_t</span> DATA_ROLL_PTR_LEN = <span class="number">7</span>;</span><br></pre></td></tr></table></figure>
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<p>一个是 <code>DATA_ROW_ID</code>，这个是在数据没指定主键的时候会生成一个隐藏的，如果用户有指定主键就是主键了</p>
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<p>一个是 <code>DATA_TRX_ID</code>，这个表示这条记录的事务 ID</p>
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<p>还有一个是 <code>DATA_ROLL_PTR</code> 指向回滚段的指针</p>
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<p>指向的回滚段其实就是我们常说的 undo log，这里面的具体结构就是个链表，在 mvcc 里会使用到这个，还有就是这个 <code>DATA_TRX_ID</code>，每条记录都记录了这个事务 ID，表示的是这条记录的当前值是被哪个事务修改的，下面就扯回事务了，我们知道 <code>Read Uncommitted</code>， 其实用不到隔离，直接读取当前值就好了，到了 <code>Read Committed</code> 级别，我们要让事务读取到提交过的值，mysql 使用了一个叫 <code>read view</code> 的玩意，它里面有这些值是我们需要注意的，</p>
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<p><code>m_low_limit_id</code>, 这个是 read view 创建时最大的活跃事务 id</p>
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<p><code>m_up_limit_id</code>, 这个是 read view 创建时最小的活跃事务 id</p>
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<p><code>m_ids</code>, 这个是 read view 创建时所有的活跃事务 id 数组</p>
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<p><code>m_creator_trx_id 这个是当前记录的创建事务 id</code></p>
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<p>判断事务的可见性主要的逻辑是这样，</p>
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<ol>
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<li>当记录的事务 <code>id</code> 小于最小活跃事务 id，说明是可见的，</li>
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<li>如果记录的事务 <code>id</code> 等于当前事务 id，说明是自己的更改，可见</li>
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<li>如果记录的事务 <code>id</code> 大于最大的活跃事务 <code>id</code>, 不可见</li>
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<li>如果记录的事务 <code>id</code> 介于 <code>m_low_limit_id</code> 和 <code>m_up_limit_id</code> 之间，则要判断它是否在 <code>m_ids</code> 中，如果在，不可见，如果不在，表示已提交，可见<br>具体的<a href="https://github.com/mysql/mysql-server/blob/8.0/storage/innobase/include/read0types.h#L160" target="_blank" rel="noopener">代码</a>捞一下看看<figure class="highlight c"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment">/** Check whether the changes by id are visible.</span></span><br><span class="line"><span class="comment"> @param[in] id transaction id to check against the view</span></span><br><span class="line"><span class="comment"> @param[in] name table name</span></span><br><span class="line"><span class="comment"> @return whether the view sees the modifications of id. */</span></span><br><span class="line"> <span class="function"><span class="keyword">bool</span> <span class="title">changes_visible</span><span class="params">(<span class="keyword">trx_id_t</span> id, <span class="keyword">const</span> <span class="keyword">table_name_t</span> &name)</span> <span class="keyword">const</span></span></span><br><span class="line"><span class="function"> <span class="title">MY_ATTRIBUTE</span><span class="params">((warn_unused_result))</span> </span>{</span><br><span class="line"> ut_ad(id > <span class="number">0</span>);</span><br><span class="line"></span><br><span class="line"> <span class="keyword">if</span> (id < m_up_limit_id || id == m_creator_trx_id) {</span><br><span class="line"> <span class="keyword">return</span> (<span class="literal">true</span>);</span><br><span class="line"> }</span><br><span class="line"></span><br><span class="line"> check_trx_id_sanity(id, name);</span><br><span class="line"></span><br><span class="line"> <span class="keyword">if</span> (id >= m_low_limit_id) {</span><br><span class="line"> <span class="keyword">return</span> (<span class="literal">false</span>);</span><br><span class="line"></span><br><span class="line"> } <span class="keyword">else</span> <span class="keyword">if</span> (m_ids.empty()) {</span><br><span class="line"> <span class="keyword">return</span> (<span class="literal">true</span>);</span><br><span class="line"> }</span><br><span class="line"></span><br><span class="line"> <span class="keyword">const</span> <span class="keyword">ids_t</span>::value_type *p = m_ids.data();</span><br><span class="line"></span><br><span class="line"> <span class="keyword">return</span> (!<span class="built_in">std</span>::binary_search(p, p + m_ids.<span class="built_in">size</span>(), id));</span><br><span class="line"> }</span><br></pre></td></tr></table></figure>
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剩下来一点是啥呢，就是 <code>Read Committed</code> 和 <code>Repeated Read</code> 也不一样，那前面说的 <code>read view</code> 都能支持吗，又是怎么支持呢，假如这个 <code>read view</code> 是在事务一开始就创建，那好像能支持的只是 RR 事务隔离级别，其实呢，这是通过创建 <code>read view</code>的时机，对于 RR 级别，就是在事务的第一个 <code>select</code> 语句是创建，对于 RC 级别，是在每个 <code>select</code> 语句执行前都是创建一次，那样就可以保证能读到所有已提交的数据</li>
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<script>
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(function() {
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function leancloudSelector(url) {
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url = encodeURI(url);
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return document.getElementById(url).querySelector('.leancloud-visitors-count');
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}
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function addCount(Counter) {
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var visitors = document.querySelector('.leancloud_visitors');
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var url = decodeURI(visitors.id);
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var title = visitors.dataset.flagTitle;
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Counter('get', '/classes/Counter?where=' + encodeURIComponent(JSON.stringify({ url })))
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.then(response => response.json())
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.then(({ results }) => {
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if (results.length > 0) {
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var counter = results[0];
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leancloudSelector(url).innerText = counter.time + 1;
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Counter('put', '/classes/Counter/' + counter.objectId, { time: { '__op': 'Increment', 'amount': 1 } })
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.catch(error => {
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console.error('Failed to save visitor count', error);
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});
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} else {
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leancloudSelector(url).innerText = 'Counter not initialized! More info at console err msg.';
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console.error('ATTENTION! LeanCloud counter has security bug, see how to solve it here: https://github.com/theme-next/hexo-leancloud-counter-security. \n However, you can still use LeanCloud without security, by setting `security` option to `false`.');
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}
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})
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.catch(error => {
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console.error('LeanCloud Counter Error', error);
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});
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}
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function showTime(Counter) {
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var visitors = document.querySelectorAll('.leancloud_visitors');
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var entries = [...visitors].map(element => {
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return decodeURI(element.id);
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});
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Counter('get', '/classes/Counter?where=' + encodeURIComponent(JSON.stringify({ url: { '$in': entries } })))
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.then(response => response.json())
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.then(({ results }) => {
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for (let url of entries) {
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let target = results.find(item => item.url === url);
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leancloudSelector(url).innerText = target ? target.time : 0;
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}
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})
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.catch(error => {
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console.error('LeanCloud Counter Error', error);
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});
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}
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let { app_id, app_key, server_url } = {"enable":true,"app_id":"ysza182Vghlqjdt7QiwGLLJy-gzGzoHsz","app_key":"s9GDqbn7gnGGkusf66YRVccw","server_url":"https://leancloud.cn","security":true};
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function fetchData(api_server) {
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var Counter = (method, url, data) => {
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return fetch(`${api_server}/1.1${url}`, {
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method,
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headers: {
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'X-LC-Id' : app_id,
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'X-LC-Key' : app_key,
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'Content-Type': 'application/json',
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},
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body: JSON.stringify(data)
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});
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};
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if (CONFIG.page.isPost) {
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if (CONFIG.hostname !== location.hostname) return;
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addCount(Counter);
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} else if (document.querySelectorAll('.post-title-link').length >= 1) {
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showTime(Counter);
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}
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}
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let api_server = app_id.slice(-9) !== '-MdYXbMMI' ? server_url : `https://${app_id.slice(0, 8).toLowerCase()}.api.lncldglobal.com`;
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if (api_server) {
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fetchData(api_server);
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} else {
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fetch('https://app-router.leancloud.cn/2/route?appId=' + app_id)
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.then(response => response.json())
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.then(({ api_server }) => {
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fetchData('https://' + api_server);
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});
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}
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})();
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</script>
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</div>
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</footer>
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</div>
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<script src="/lib/anime.min.js"></script>
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<script src="//cdn.jsdelivr.net/npm/jquery@3/dist/jquery.min.js"></script>
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<script src="//cdn.jsdelivr.net/gh/fancyapps/fancybox@3/dist/jquery.fancybox.min.js"></script>
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<script src="//cdn.jsdelivr.net/npm/lozad@1/dist/lozad.min.js"></script>
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<script src="/lib/velocity/velocity.min.js"></script>
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<script src="/lib/velocity/velocity.ui.min.js"></script>
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<script src="/js/utils.js"></script>
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<script src="/js/motion.js"></script>
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<script src="/js/schemes/pisces.js"></script>
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<script src="/js/next-boot.js"></script>
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<script>
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(function(){
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var canonicalURL, curProtocol;
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//Get the <link> tag
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var x=document.getElementsByTagName("link");
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//Find the last canonical URL
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if(x.length > 0){
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for (i=0;i<x.length;i++){
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if(x[i].rel.toLowerCase() == 'canonical' && x[i].href){
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canonicalURL=x[i].href;
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}
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}
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}
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//Get protocol
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if (!canonicalURL){
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curProtocol = window.location.protocol.split(':')[0];
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}
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else{
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curProtocol = canonicalURL.split(':')[0];
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}
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//Get current URL if the canonical URL does not exist
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if (!canonicalURL) canonicalURL = window.location.href;
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//Assign script content. Replace current URL with the canonical URL
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!function(){var e=/([http|https]:\/\/[a-zA-Z0-9\_\.]+\.baidu\.com)/gi,r=canonicalURL,t=document.referrer;if(!e.test(r)){var n=(String(curProtocol).toLowerCase() === 'https')?"https://sp0.baidu.com/9_Q4simg2RQJ8t7jm9iCKT-xh_/s.gif":"//api.share.baidu.com/s.gif";t?(n+="?r="+encodeURIComponent(document.referrer),r&&(n+="&l="+r)):r&&(n+="?l="+r);var i=new Image;i.src=n}}(window);})();
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</script>
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<script>
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function loadCount() {
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var d = document, s = d.createElement('script');
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s.src = 'https://nicksxs.disqus.com/count.js';
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s.id = 'dsq-count-scr';
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(d.head || d.body).appendChild(s);
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}
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// defer loading until the whole page loading is completed
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window.addEventListener('load', loadCount, false);
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</script>
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</body>
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</html>
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