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404
\ No newline at end of file diff --git a/_headers b/_headers new file mode 100644 index 0000000..9079d85 --- /dev/null +++ b/_headers @@ -0,0 +1,3 @@ +/assets/* + cache-control: max-age=31536000 + cache-control: immutable diff --git a/about.html b/about.html new file mode 100644 index 0000000..76888ab --- /dev/null +++ b/about.html @@ -0,0 +1 @@ +如果没有你，何必要有我 - 苏

如果没有你，何必要有我

本文最后更新于1 分钟前，文中所描述的信息可能已发生改变。

Blog ​

Hi, guys. 这里是苏💕。

从小就有点想成为一名作家，为此甚至在高中想要选文科，最终变成了一名程序员。

在大学也曾百般折腾 WorkPress，最终因为忘记续费学业繁重不了了之。

希望可以在这里简单记录一下自己的成长轨迹，包括但不限于代码、生活碎片和对大自然一切现象的感悟。给自己的作家梦一个小小的交代。

感谢所有点进来的同学，大家有什么想法可以在留言里各抒己见，我都会认真看的^ ^.

关于自己的其他事情：

列表
  • 爱好：代码规范和文档规范（是的）、鼓捣一些花里胡哨的东西
  • 喜欢：西瓜🍉 、夏天🌞 、冰块🧊、喝酒🍸（甜且度数不高的）、诗📖 、电子鸦片（FPS 爱好者，MOBA 类精通）
  • 讨厌：不努力的自己、不真诚的朋友
  • 运动：羽毛球（希望有一天能快乐的跟自己爱的人打羽毛球，顺便帮她锻炼一下身体）
  • 目标：成为厉害的后端工程师（也想学一些前端）（最好能加入微软！）、很会解决问题的人、老师
  • 梦想：财富自由，专职写作✍
工作就输了

小破站的 History Version ​

TODO ​

  • 迁移至 Valxy 和 valxy-theme-yun（等待@云游君完善）
  • CDN 加速，国内访问加速（方便没有魔法的同学）
  • 已有文章主页展示格式美化（好看真的很重要）
  • 音乐插件（待定，感觉加了会很吵闹，但是可以试一下）

2024年5月3日 ​

  • 迁移至 Valxy 和 valxy-theme-yun，新的引擎真的很酷诶 🏃

2024年2月21日 ​

  • 修复了评论系统，可以继续快乐玩耍！

2022年10月31日 ​

  • 配置了评论系统，可以在评论区快乐玩耍啦🎈
  • 配置了多边形网格蒙版🎨
  • 配置了网站统计数据（并没有什么用）🔍

2022年11月4日 ​

  • 配置字数统计👀
  • 优化了评论系统使用体验(づ￣3￣)づ╭❤～

致谢 ​

首先致谢炒鸡优秀的开源作者 云游君，提供了功能强大且文档齐全的博客模板 hexo-theme-yun 。世界之大，容得下一方书桌。

致谢所有帮助过我，爱过我的人们。

LeetCode record 2024-05
© 2022 - 2024
苏
由 Valaxy v0.18.6 驱动 | 主题 - Yun v0.18.6
\ No newline at end of file diff --git a/about/site.html b/about/site.html new file mode 100644 index 0000000..55fe34f --- /dev/null +++ b/about/site.html @@ -0,0 +1 @@ +关于站点 - 苏

关于站点

本文最后更新于1 分钟前，文中所描述的信息可能已发生改变。

Valaxy + valaxy-theme-yun Demo Site

LeetCode record 2024-05
© 2022 - 2024
苏
由 Valaxy v0.18.6 驱动 | 主题 - Yun v0.18.6
\ No newline at end of file diff --git a/archives.html b/archives.html new file mode 100644 index 0000000..850561d --- /dev/null +++ b/archives.html @@ -0,0 +1 @@ +苏

归档

共计 17 篇日志
© 2022 - 2024
苏
由 Valaxy v0.18.6 驱动 | 主题 - Yun v0.18.6
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3-3]")],-1),v=l("blockquote",null,[l("p",null,"所示。段被写入后永远不会被修改，所以合并的段被写入一个新的文件。冻结段的合并和压缩可以在后台线程中完成，这个过程进行的同时，我们仍然可以继续使用旧的段文件来正常提供读写请求。合并过程完成后，我们将读取请求转换为使用新合并的段而不是旧的段 —— 然后旧的段文件就可以简单地删除掉了。")],-1),y=l("p",null,"不难看出这是一种非常好的处理思路。通过「压缩」这种方式处理旧数据，维护有限占用空间里的一份最新数据。这里可以想到多线程处理，主线程负责数据的更新，即写操作；其他线程将之前生成的「日志」文件读取并压缩，并视情况做其他处理。",-1),M=l("p",null,"BTW，作者在后面提到了一些实现过程中的细节，越来越像 Redis 等缓存系统了。",-1),I=l("hr",null,null,-1),w=l("blockquote",null,[l("p",null,"但是，散列表索引也有其局限性："),l("ul",null,[l("li",null,"散列表必须能放进内存。如果你有非常多的键，那真是倒霉。原则上可以在硬盘上维护一个散列映射，不幸的是硬盘散列映射很难表现优秀。它需要大量的随机访问 I/O，而后者耗尽时想要再扩充是很昂贵的，并且需要很烦琐的逻辑去解决散列冲突【5】。"),l("li",null,"范围查询效率不高。例如，你无法轻松扫描 kitty00000 和 kitty99999 之间的所有键 —— 你必须在散列映射中单独查找每个键。")])],-1),P=l("p",null,"我刚刚提到的硬盘上做散列映射的想法，应该是由于 IO 效率问题很难被实际应用。",-1),$=l("p",null,"不知道 SSD 上的表现会不会好一些。",-1),A=l("hr",null,null,-1),F=l("blockquote",null,[l("p",null,"现在我们可以对段文件的格式做一个简单的改变：要求键值对的序列按键排序。乍一看，这个要求似乎打破了我们使用顺序写入的能力，我们将稍后再回到这个问题。"),l("p",null,[n("我们把这个格式称为 "),l("strong",null,"排序字符串表（Sorted String Table）"),n("，简称 SSTable。我们还要求每个键只在每个合并的段文件中出现一次（压缩过程已经保证）。与使用散列索引的日志段相比，SSTable 有几个大的优势：")])],-1),V=l("p",null,"篇幅限制不引用具体优势了。这让我有了一些思考：",-1),H=l("ol",null,[l("li",null,[n("索引结构是不是最好是基于 "),l("strong",null,"已排序"),n(" 数据上的，或者索引结构本身包含 "),l("strong",null,"已排序"),n(" 这个特性。")]),l("li",null,[n("基于 "),l("strong",null,"已排序"),n(" 这个特性，二分查找似乎已经是效率最高的，普适性最强的查找算法了。树这种数据结构就很适合使用二分查找。作者循循善诱，给出了现代数据系统技术选型的来龙去脉。")])],-1),C=l("hr",null,null,-1),N=l("blockquote",null,[l("h4",{id:"构建和维护sstables",tabindex:"-1"},[n("构建和维护SSTables "),l("a",{class:"header-anchor",href:"#构建和维护sstables","aria-label":'Permalink to "构建和维护SSTables"'},"​")]),l("p",null,"到目前为止还不错，但是如何让你的数据能够预先排好序呢？毕竟我们接收到的写入请求可能以任何顺序发生。"),l("p",null,"虽然在硬盘上维护有序结构也是可能的（请参阅 “[B 树]”），但在内存保存则要容易得多。有许多可以使用的众所周知的树形数据结构，例如红黑树或 AVL 树【2】。使用这些数据结构，你可以按任何顺序插入键，并按排序顺序读取它们。")],-1),E=l("p",null,"这里可以看出来，其实树这种数据结构，在应用的时候除了本身的特性，还要参考 IO 速率。在硬盘和内存上是两种差距很大的应用场景。",-1),K=l("hr",null,null,-1),R=l("blockquote",null,[l("p",null,[n("Lucene，是一种全文搜索的索引引擎，在 Elasticsearch 和 Solr 被使用，它使用类似的方法来存储它的关键词词典【12,13】。全文索引比键值索引复杂得多，但是基于类似的想法：在搜索查询中，由一个给定的单词，找到提及单词的所有文档（网页，产品描述等）。这也是通过键值结构实现的：其中键是 "),l("strong",null,"单词（term）"),n("，值是所有包含该单词的文档的 ID 列表（"),l("strong",null,"postings list"),n("）。在 Lucene 中，从词语到记录列表的这种映射保存在类似于 SSTable 的有序文件中，并根据需要在后台执行合并【14】。")])],-1),W=l("p",null,"提到了 Elasticsearch，后面学习的时候可以注意一下啊。",-1),Q=l("p",null,[n("以 "),l("strong",null,"单词"),n(" 为键，用大量空间（但是使用压缩和树结构进行优化）换取时间。当把其中一项做到极致，比如特别快或者占用磁盘空间特别小时，也是一种成功。或者这时候应用范围会更广——相比于特点中庸的应用。")],-1),U=l("hr",null,null,-1),Z=l("blockquote",null,[l("h3",{id:"b树",tabindex:"-1"},[n("B树 "),l("a",{class:"header-anchor",href:"#b树","aria-label":'Permalink to "B树"'},"​")]),l("p",null,"前面讨论的日志结构索引看起来已经相当可用了，但它们却不是最常见的索引类型。使用最广泛的索引结构和日志结构索引相当不同，它就是我们接下来要讨论的 B 树。"),l("p",null,"从 1970 年被引入【17】，仅不到 10 年后就变得 “无处不在”【18】，B 树很好地经受了时间的考验。在几乎所有的关系数据库中，它们仍然是标准的索引实现，许多非关系数据库也会使用到 B 树。"),l("p",null,"像 SSTables 一样，B 树保持按键排序的键值对，这允许高效的键值查找和范围查询。但这也就是仅有的相似之处了：B 树有着非常不同的设计理念。")],-1),j=l("p",null,[n("终于到 B 树了！这里提到 B 树的设计理念与上文提到的 "),l("strong",null,"日志结构索引"),n(" 不同。先独立思考一下。")],-1),J=l("p",null,"B 树也是有序的树形结构，每一层都存储数据，有序且进行了分区。一直维护最新的数据。",-1),z=l("hr",null,null,-1),G=l("blockquote",null,[l("p",null,[n("我们前面看到的日志结构索引将数据库分解为可变大小的段，通常是几兆字节或更大的大小，并且总是按顺序写入段。相比之下，B 树将数据库分解成固定大小的 "),l("strong",null,"块（block）"),n(" 或 "),l("strong",null,"分页（page）"),n("，传统上大小为 4KB（有时会更大），并且一次只能读取或写入一个页面。这种设计更接近于底层硬件，因为硬盘空间也是按固定大小的块来组织的。")]),l("p",null,"每个页面都可以使用地址或位置来标识，这允许一个页面引用另一个页面 —— 类似于指针，但在硬盘而不是在内存中。我们可以使用这些页面引用来构建一个页面树，如 [图 3-6]")],-1),X=l("blockquote",null,[l("p",null,"所示。")],-1),Y=l("p",null,[n("果然自己思考没有想到重点，作者想说的重点是， B 树使用了 "),l("strong",null,"更接近底层硬件的设计"),n("，使用固定大小的块（block）或者分页（page）来组织数据。")],-1),ll=l("p",null,[n("BTW，在上文中曾经提过在保存数据时是保存 "),l("strong",null,"副本"),n(" 还是 "),l("strong",null,"引用"),n("，在「时间」这个指标相对来说更为重要的阶段，通常都会使用大量的「空间」，而保存 "),l("strong",null,"副本"),n(" 所占用的「空间」是不可估量的，保存引用可能是唯一可行的出路。")],-1),nl=l("hr",null,null,-1),tl=l("blockquote",null,[l("p",null,[n("在 B 树的一个页面中对子页面的引用的数量称为 "),l("strong",null,"分支因子（branching 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n)")])])]),l("span",{class:"katex-html","aria-hidden":"true"},[l("span",{class:"base"},[l("span",{class:"strut",style:{height:"1em","vertical-align":"-0.25em"}}),l("span",{class:"mord mathnormal",style:{"margin-right":"0.02778em"}},"O"),l("span",{class:"mopen"},"("),l("span",{class:"mord mathnormal",style:{"margin-right":"0.01968em"}},"l"),l("span",{class:"mord mathnormal"},"o"),l("span",{class:"mord mathnormal",style:{"margin-right":"0.03588em"}},"g"),l("span",{class:"mord mathnormal"},"n"),l("span",{class:"mclose"},")")])])]),n(" 的深度。大多数数据库可以放入一个三到四层的 B 树，所以你不需要追踪多个页面引用来找到你正在查找的页面（分支因子为 500 的 4KB 页面的四层树可以存储多达 256TB 的数据）。")])],-1),ul=l("p",null,"为什么要设计成「矮胖」形式而不是「瘦高」形式呢？我想，这里面还是跟「B 树使用更接近底层硬件的设计」有关，载入一页（一层）之后，在页内进行检索无需进行 IO 操作，时空间开销更小。",-1),rl=l("p",null,[n("同时 "),l("span",{class:"katex"},[l("span",{class:"katex-mathml"},[l("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[l("semantics",null,[l("mrow",null,[l("mi",null,"O"),l("mo",{stretchy:"false"},"("),l("mi",null,"l"),l("mi",null,"o"),l("mi",null,"g"),l("mi",null,"n"),l("mo",{stretchy:"false"},")")]),l("annotation",{encoding:"application/x-tex"},"O (log n)")])])]),l("span",{class:"katex-html","aria-hidden":"true"},[l("span",{class:"base"},[l("span",{class:"strut",style:{height:"1em","vertical-align":"-0.25em"}}),l("span",{class:"mord mathnormal",style:{"margin-right":"0.02778em"}},"O"),l("span",{class:"mopen"},"("),l("span",{class:"mord mathnormal",style:{"margin-right":"0.01968em"}},"l"),l("span",{class:"mord mathnormal"},"o"),l("span",{class:"mord mathnormal",style:{"margin-right":"0.03588em"}},"g"),l("span",{class:"mord mathnormal"},"n"),l("span",{class:"mclose"},")")])])]),n(" 的复杂度使每一层的 "),l("strong",null,"分支因子"),n(" 数量级可以稍微大一些。")],-1),cl=l("hr",null,null,-1),hl=l("blockquote",null,[l("h4",{id:"让b树更可靠",tabindex:"-1"},[n("让B树更可靠 "),l("a",{class:"header-anchor",href:"#让b树更可靠","aria-label":'Permalink to "让B树更可靠"'},"​")]),l("p",null,"B 树的基本底层写操作是用新数据覆写硬盘上的页面，并假定覆写不改变页面的位置：即，当页面被覆写时，对该页面的所有引用保持完整。这与日志结构索引（如 LSM 树）形成鲜明对比，后者只追加到文件（并最终删除过时的文件），但从不修改文件中已有的内容。"),l("p",null,"你可以把覆写硬盘上的页面对应为实际的硬件操作。在磁性硬盘驱动器上，这意味着将磁头移动到正确的位置，等待旋转盘上的正确位置出现，然后用新的数据覆写适当的扇区。在固态硬盘上，由于 SSD 必须一次擦除和重写相当大的存储芯片块，所以会发生更复杂的事情【19】。"),l("p",null,"而且，一些操作需要覆写几个不同的页面。例如，如果因为插入导致页面过满而拆分页面，则需要写入新拆分的两个页面，并覆写其父页面以更新对两个子页面的引用。这是一个危险的操作，因为如果数据库在系列操作进行到一半时崩溃，那么最终将导致一个损坏的索引（例如，可能有一个孤儿页面没有被任何页面引用） 。"),l("p",null,[n("为了使数据库能处理异常崩溃的场景，B 树实现通常会带有一个额外的硬盘数据结构："),l("strong",null,"预写式日志"),n("（WAL，即 write-ahead log，也称为 "),l("strong",null,"重做日志"),n("，即 redo log）。这是一个仅追加的文件，每个 B 树的修改在其能被应用到树本身的页面之前都必须先写入到该文件。当数据库在崩溃后恢复时，这个日志将被用来使 B 树恢复到一致的状态【5,20】。")])],-1),pl=l("p",null,"内容有点多，简而言之。",-1),dl=l("ol",null,[l("li",null,"B 树的查找效率可能很高，但是在面对插入删除操作时显得有些力不从心。不但有风险，开销还不小。"),l("li",null,[l("strong",null,"redo log"),n(" 是附着在 B 树这种数据结构上的一种额外的硬盘数据结构，使用追加的方式写入对 B 树的修改语句。用以应对数据库异常崩溃时数据丢失或索引损坏的情况。")])],-1),ml=l("p",null,"看到这里，感慨颇多。",-1),_l=l("p",null,"以前对于知识的态度是——欣赏能讲得出来龙去脉深入浅出的，也很爱直接把知识摆在桌子上自己啃的。但是现在看来，循循善诱的讲法还是更胜一筹。这可能也是习惯于填鸭式教育的一种觉醒吧。",-1),gl=l("hr",null,null,-1),bl=l("blockquote",null,[l("p",null,[n("由于反复压缩和合并 SSTables，日志结构索引也会多次重写数据。这种影响 —— 在数据库的生命周期中每笔数据导致对硬盘的多次写入 —— 被称为 "),l("strong",null,"写入放大（write amplification）"),n("。使用固态硬盘的机器需要额外关注这点，固态硬盘的闪存寿命在覆写有限次数后就会耗尽。")])],-1),fl=l("p",null,"作者会频繁提到有关硬件的细节，应该重视起来。",-1),kl=l("hr",null,null,-1),Bl=l("blockquote",null,[l("p",null,"在许多固态硬盘上，固件内部使用了日志结构化算法，以将随机写入转变为顺序写入底层存储芯片，因此存储引擎写入模式的影响不太明显【19】。但是，较低的写入放大率和减少的碎片仍然对固态硬盘更有利：更紧凑地表示数据允许在可用的 I/O 带宽内处理更多的读取和写入请求。")],-1),Sl=l("p",null,"note.",-1),Dl=l("hr",null,null,-1),ql=l("blockquote",null,[l("h4",{id:"lsm树的缺点",tabindex:"-1"},[n("LSM树的缺点 "),l("a",{class:"header-anchor",href:"#lsm树的缺点","aria-label":'Permalink to "LSM树的缺点"'},"​")]),l("p",null,"日志结构存储的缺点是压缩过程有时会干扰正在进行的读写操作。尽管存储引擎尝试增量地执行压缩以尽量不影响并发访问，但是硬盘资源有限，所以很容易发生某个请求需要等待硬盘先完成昂贵的压缩操作。对吞吐量和平均响应时间的影响通常很小，但是日志结构化存储引擎在更高百分位的响应时间（请参阅 “[描述性能]”）有时会相当长，而 B 树的行为则相对更具有可预测性【28】。"),l("p",null,"压缩的另一个问题出现在高写入吞吐量时：硬盘的有限写入带宽需要在初始写入（记录日志和刷新内存表到硬盘）和在后台运行的压缩线程之间共享。写入空数据库时，可以使用全硬盘带宽进行初始写入，但数据库越大，压缩所需的硬盘带宽就越多。"),l("p",null,"如果写入吞吐量很高，并且压缩没有仔细配置好，有可能导致压缩跟不上写入速率。在这种情况下，硬盘上未合并段的数量不断增加，直到硬盘空间用完，读取速度也会减慢，因为它们需要检查更多的段文件。通常情况下，即使压缩无法跟上，基于 SSTable 的存储引擎也不会限制传入写入的速率，所以你需要进行明确的监控来检测这种情况【29,30】。")],-1),Tl=l("p",null,"LSM 树的效率随着使用时间的增长和数据量的增加而下降。需要提供一个监测指标来防止这种情况的发生和恶化。",-1),xl=l("hr",null,null,-1),Ol=l("blockquote",null,[l("p",null,"反直觉的是，内存数据库的性能优势并不是因为它们不需要从硬盘读取的事实。只要有足够的内存即使是基于硬盘的存储引擎也可能永远不需要从硬盘读取，因为操作系统在内存中缓存了最近使用的硬盘块。相反，它们更快的原因在于省去了将内存数据结构编码为硬盘数据结构的开销【44】。")],-1),Ll=l("p",null,"note：Redis 相对于硬盘上的数据系统为什么快？",-1),vl=l("hr",null,null,-1),yl=l("blockquote",null,[l("p",null,[n("术语 "),l("strong",null,"交易 / 事务（transaction）"),n(" 仍留了下来，用于指代一组读写操作构成的逻辑单元。")])],-1),Ml=l("p",null,"note。",-1),Il=l("hr",null,null,-1),wl=l("blockquote",null,[l("h3",{id:"数据仓库",tabindex:"-1"},[n("数据仓库 "),l("a",{class:"header-anchor",href:"#数据仓库","aria-label":'Permalink to "数据仓库"'},"​")]),l("p",null,"一个企业可能有几十个不同的交易处理系统：面向终端客户的网站，控制实体商店的收银系统，仓库库存跟踪，车辆路线规划，供应链管理，员工管理等。这些系统中每一个都很复杂，需要专人维护，所以最终这些系统互相之间都是独立运行的。"),l("p",null,[n("这些 OLTP 系统往往对业务运作至关重要，因而通常会要求 "),l("strong",null,"高可用"),n(" 与 "),l("strong",null,"低延迟"),n("。所以 DBA 会密切关注他们的 OLTP 数据库，他们通常不愿意让业务分析人员在 OLTP 数据库上运行临时的分析查询，因为这些查询通常开销巨大，会扫描大部分数据集，这会损害同时在执行的事务的性能。")])],-1),Pl=l("p",null,"除去 SQL 语句本身的复杂性，在大规模的数据集上进行操作时，可能简单的 SQL 也会带来巨大的性能开销。",-1),$l=l("hr",null,null,-1),Al=l("blockquote",null,[l("p",null,[n("相比之下，数据仓库是一个独立的数据库，分析人员可以查询他们想要的内容而不影响 OLTP 操作【48】。数据仓库包含公司各种 OLTP 系统中所有的只读数据副本。从 OLTP 数据库中提取数据（使用定期的数据转储或连续的更新流），转换成适合分析的模式，清理并加载到数据仓库中。将数据存入仓库的过程称为 “"),l("strong",null,"抽取 - 转换 - 加载（ETL）"),n("”，如 [图 3-8]")])],-1),Fl=l("blockquote",null,[l("p",null,"所示。")],-1),Vl=l("p",null,"ETL 好像是现在大数据提到的概念来着。之前在微软的时候也看到了 spark 和 Kafka 这种用以处理大规模数据的系统。",-1),Hl=l("hr",null,null,-1),Cl=l("blockquote",null,[l("p",null,"对这个想法，有一个巧妙的扩展被 C-Store 发现，并在商业数据仓库 Vertica 中被采用【61,62】：既然不同的查询受益于不同的排序顺序，为什么不以几种不同的方式来存储相同的数据呢？反正数据都需要做备份，以防单点故障时丢失数据。因此你可以用不同排序方式来存储冗余数据，以便在处理查询时，调用最适合查询模式的版本。n")],-1),Nl=l("p",null,"note，涨知识。",-1),El=l("hr",null,null,-1),Kl=l("blockquote",null,[l("p",null,"关于 OLTP，我们最后还介绍了一些更复杂的索引结构，以及针对所有数据都放在内存里而优化的数据库。"),l("p",null,"然后，我们暂时放下了存储引擎的内部细节，查看了典型数据仓库的高级架构，并说明了为什么分析工作负载与 OLTP 差别很大：当你的查询需要在大量行中顺序扫描时，索引的重要性就会降低很多。相反，非常紧凑地编码数据变得非常重要，以最大限度地减少查询需要从硬盘读取的数据量。我们讨论了列式存储如何帮助实现这一目标。"),l("p",null,"作为一名应用程序开发人员，如果你掌握了有关存储引擎内部的知识，那么你就能更好地了解哪种工具最适合你的特定应用程序。当你调整数据库的优化参数时，这种理解让你能够设想增减某个值会产生怎样的效果。"),l("p",null,"尽管本章不能让你成为一个特定存储引擎的调参专家，但它至少大概率使你有了足够的概念与词汇储备去读懂你所选择的数据库的文档。")],-1),Rl=l("p",null,"第三章内容很多，但是大部分都是在介绍时下流行的一些数据系统，包括大型的公司是怎么处理海量数据的，做了哪些优化，深层次的原因是什么样的。",-1),Wl=l("p",null,"还介绍了索引和实现索引使用的各种数据结构，包括 B 树和 B 树的变种（我们常常说的 InnoDB 使用的 B+ 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"),l("span",{class:"katex"},[l("span",{class:"katex-mathml"},[l("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[l("semantics",null,[l("mrow",null,[l("msup",null,[l("mn",null,"2"),l("mn",null,"53")])]),l("annotation",{encoding:"application/x-tex"},"2^{53}")])])]),l("span",{class:"katex-html","aria-hidden":"true"},[l("span",{class:"base"},[l("span",{class:"strut",style:{height:"0.8141em"}}),l("span",{class:"mord"},[l("span",{class:"mord"},"2"),l("span",{class:"msupsub"},[l("span",{class:"vlist-t"},[l("span",{class:"vlist-r"},[l("span",{class:"vlist",style:{height:"0.8141em"}},[l("span",{style:{top:"-3.063em","margin-right":"0.05em"}},[l("span",{class:"pstrut",style:{height:"2.7em"}}),l("span",{class:"sizing reset-size6 size3 mtight"},[l("span",{class:"mord mtight"},[l("span",{class:"mord mtight"},"53")])])])])])])])])])])]),n(" 的问题（请参阅 “[JSON、XML 和二进制变体]”）。用单一语言编写的单个进程中不存在此问题。")])]),l("p",null,"所有这些因素意味着尝试使远程服务看起来像编程语言中的本地对象一样毫无意义，因为这是一个根本不同的事情。REST 的部分吸引力在于，它并不试图隐藏它是一个网络协议的事实（尽管这似乎并没有阻止人们在 REST 之上构建 RPC 库）。")],-1),f=l("p",null,[n("总体来说问题是因为由于「"),l("strong",null,"网络问题"),n("」因素的增加，导致 RPC 调用存在不可控的问题。")],-1),T=l("p",null,[n("重点强调了 "),l("strong",null,"幂等性"),n("（idempotence）和较大对象的传输问题。")],-1),C=l("p",null,"强调了语言之间翻译可能导致的「丑陋」。",-1),S=l("hr",null,null,-1),D=l("blockquote",null,[l("p",null,"对于可演化性，重要的是可以独立更改和部署 RPC 客户端和服务器。与通过数据库流动的数据相比（如上一节所述），我们可以在通过服务进行数据流的情况下做一个简化的假设：**假定所有的服务器都会先更新，其次是所有的客户端。**因此，你只需要在请求上具有向后兼容性，并且对响应具有向前兼容性。")],-1),E=l("p",null,[l("strong",null,"对请求具有向后兼容性，可以使更新前后的服务器都能应对客户端的请求；对响应具有向前兼容性，可以使尚未迭代的客户端应对已更新的服务器的响应。")],-1),b=l("hr",null,null,-1),v=l("blockquote",null,[l("p",null,"RESTful API 通常使用 JSON（没有正式指定的模式）用于响应，以及用于请求的 JSON 或 URI 编码 / 表单编码的请求参数。添加可选的请求参数并向响应对象添加新的字段通常被认为是保持兼容性的改变。")],-1),k=l("p",null,"JSON 有时候确实还挺好用的。",-1),A=l("hr",null,null,-1),I=l("p",null,[l("strong",null,"You"),n("：")],-1),$=l("blockquote",null,[l("p",null,"REST 和 RPC的数据传输方式有什么区别")],-1),O=l("p",null,[l("strong",null,"ChatGPT"),n("：")],-1),x=l("blockquote",null,[l("p",null,"REST（Representational State Transfer）和 RPC（Remote Procedure Call）是两种不同的通信模式，它们在数据传输方式上有一些区别。"),l("ol",null,[l("li",null,[l("strong",null,"REST（Representational State Transfer）："),l("ul",null,[l("li",null,"REST 是一种基于网络的软件架构风格，它使用标准的 HTTP 协议进行通信。"),l("li",null,"在 REST 中，资源以 URL 的形式暴露，通过 HTTP 方法（如 GET、POST、PUT、DELETE）对资源进行操作。"),l("li",null,"REST 的通信是无状态的，即每个请求都包含足够的信息来处理该请求，服务器不需要保存客户端的状态信息。"),l("li",null,"REST 通常使用 JSON 或 XML 格式来传输数据。")])]),l("li",null,[l("strong",null,"RPC（Remote Procedure Call）："),l("ul",null,[l("li",null,"RPC 是一种远程过程调用的机制，允许一个程序在另一个地址空间（通常在远程计算机上）上执行一个子程序。"),l("li",null,"在 RPC 中，客户端调用服务端的函数或过程，就像调用本地函数一样，但实际上是在不同的地址空间中执行。"),l("li",null,"RPC 通常使用自定义协议来进行通信，通常会在底层使用 TCP 或 UDP 协议。")])])]),l("p",null,"区别："),l("ul",null,[l("li",null,[l("strong",null,"协议和通信方式："),n(" REST 使用标准的 HTTP 协议，通过 URL 对资源进行操作；而 RPC 使用自定义的协议，在客户端和服务端之间进行函数调用。")]),l("li",null,[l("strong",null,"语义和操作："),n(" 在 REST 中，操作通常是面向资源的，例如 GET 获取资源、POST 创建资源等；而在 RPC 中，操作是面向函数或过程的，客户端调用服务端的函数。")]),l("li",null,[l("strong",null,"通信状态："),n(" REST 是无状态的，每个请求都包含足够的信息来处理该请求；而在 RPC 中，客户端和服务端之间可能需要维护一些状态信息。")])]),l("p",null,"总的来说，REST 更适合于资源导向的系统设计，而 RPC 更适合于函数调用和远程服务调用。选择 REST 还是 RPC 取决于你的应用程序的需求和设计。")],-1),J=l("hr",null,null,-1),M=l("blockquote",null,[l("h3",{id:"消息传递中的数据流",tabindex:"-1"},[n("消息传递中的数据流 "),l("a",{class:"header-anchor",href:"#消息传递中的数据流","aria-label":'Permalink to "消息传递中的数据流"'},"​")]),l("p",null,"我们一直在研究从一个过程到另一个过程的编码数据流的不同方式。到目前为止，我们已经讨论了 REST 和 RPC（其中一个进程通过网络向另一个进程发送请求并期望尽可能快的响应）以及数据库（一个进程写入编码数据，另一个进程在将来再次读取）。"),l("p",null,"在最后一节中，我们将简要介绍一下 RPC 和数据库之间的异步消息传递系统。它们与 RPC 类似，因为客户端的请求（通常称为消息）以低延迟传送到另一个进程。它们与数据库类似，不是通过直接的网络连接发送消息，而是通过称为消息代理（也称为消息队列或面向消息的中间件）的中介来临时存储消息。"),l("p",null,"与直接 RPC 相比，使用消息代理有几个优点："),l("ul",null,[l("li",null,"如果收件人不可用或过载，可以充当缓冲区，从而提高系统的可靠性。"),l("li",null,"它可以自动将消息重新发送到已经崩溃的进程，从而防止消息丢失。"),l("li",null,"避免发件人需要知道收件人的 IP 地址和端口号（这在虚拟机经常出入的云部署中特别有用）。"),l("li",null,"它允许将一条消息发送给多个收件人。"),l("li",null,"将发件人与收件人逻辑分离（发件人只是发布邮件，不关心使用者）。")]),l("p",null,"然而，与 RPC 相比，差异在于消息传递通信通常是单向的：发送者通常不期望收到其消息的回复。一个进程可能发送一个响应，但这通常是在一个单独的通道上完成的。这种通信模式是异步的：发送者不会等待消息被传递，而只是发送它，然后忘记它。")],-1),q=l("p",null,"虽然翻译不太国内，但是我还是读出了这就是消！息！队！列！",-1),y=l("hr",null,null,-1),w=l("blockquote",null,[l("p",null,"详细的交付语义因实现和配置而异，但通常情况下，消息代理的使用方式如下：一个进程将消息发送到指定的队列或主题，代理确保将消息传递给那个队列或主题的一个或多个消费者或订阅者。在同一主题上可以有许多生产者和许多消费者。")],-1),N=l("p",null,"ChatGPT：",-1),L=l("blockquote",null,[l("p",null,"这里的“交付语义”可以更清晰地表达为消息传递的可靠性保证和语义含义。可以将这部分重新表达如下："),l("p",null,"详细的消息传递语义因不同的消息代理实现和配置而有所不同，但通常情况下，消息代理确保消息的可靠传递，并提供了不同的交付语义以满足不同的业务需求。交付语义包括以下几种常见的模式："),l("ol",null,[l("li",null,[l("strong",null,"至少一次（At Least Once）："),n(" 消息代理保证消息至少会被传递一次给消费者，确保消息不会丢失，但可能会导致消息重复传递。")]),l("li",null,[l("strong",null,"至多一次（At Most 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的介绍中所讨论的那样，我们希望能复制数据，可能出于各种各样的原因："),l("ul",null,[l("li",null,"使得数据与用户在地理上接近（从而减少延迟）"),l("li",null,"即使系统的一部分出现故障，系统也能继续工作（从而提高可用性）"),l("li",null,"伸缩可以接受读请求的机器数量（从而提高读取吞吐量）")])],-1),D=l("p",null,"继续提到「副本」的概念，回忆「副本」和「引用」的区别，把这个概念应用到沟通中，力求精准表达。",-1),q=l("hr",null,null,-1),y=l("blockquote",null,[l("p",null,[n("如果复制中的数据不会随时间而改变，那复制就很简单：将数据复制到每个节点一次就万事大吉。复制的困难之处在于处理复制数据的 "),l("strong",null,"变更（change）"),n("，这就是本章所要讲的。我们将讨论三种流行的变更复制算法："),l("strong",null,"单领导者（single leader，单主）"),n("，"),l("strong",null,"多领导者（multi leader，多主）"),n(" 和 "),l("strong",null,"无领导者（leaderless，无主）"),n("。几乎所有分布式数据库都使用这三种方法之一。")])],-1),v=l("p",null,"高屋建瓴。",-1),M=l("hr",null,null,-1),x=l("blockquote",null,[l("p",null,[n("存储了数据库拷贝的每个节点被称为 "),l("strong",null,"副本（replica）"),n(" 。当存在多个副本时，会不可避免的出现一个问题：如何确保所有数据都落在了所有的副本上？")]),l("p",null,[n("每一次向数据库的写入操作都需要传播到所有副本上，否则副本就会包含不一样的数据。最常见的解决方案被称为 "),l("strong",null,"基于领导者的复制（leader-based replication）"),n(" （也称 "),l("strong",null,"主动/被动（active/passive）"),n(" 复制或 "),l("strong",null,"主/从（master/slave）"),n(" 复制），如 [图 5-1] 所示。它的工作原理如下：")]),l("ol",null,[l("li",null,[n("其中一个副本被指定为 "),l("strong",null,"领导者（leader）"),n("，也称为 "),l("strong",null,"主库（master|primary）"),n(" 。当客户端要向数据库写入时，它必须将请求发送给该 "),l("strong",null,"领导者"),n("，其会将新数据写入其本地存储。")]),l("li",null,[n("其他副本被称为 "),l("strong",null,"追随者（followers）"),n("，亦称为 "),l("strong",null,"只读副本（read replicas）"),n("、"),l("strong",null,"从库（slaves）"),n("、"),l("strong",null,"备库（ secondaries）"),n(" 或 "),l("strong",null,"热备（hot-standby）"),n("。每当领导者将新数据写入本地存储时，它也会将数据变更发送给所有的追随者，称之为 "),l("strong",null,"复制日志（replication log）"),n(" 或 "),l("strong",null,"变更流（change stream）"),n("。每个跟随者从领导者拉取日志，并相应更新其本地数据库副本，方法是按照与领导者相同的处理顺序来进行所有写入。")]),l("li",null,"当客户想要从数据库中读取数据时，它可以向领导者或任一追随者进行查询。但只有领导者才能接受写入操作（从客户端的角度来看从库都是只读的）。")])],-1),P=l("blockquote",null,[l("p",null,[l("strong",null,"图 5-1 基于领导者的（主/从）复制")]),l("p",null,"这种复制模式是许多关系数据库的内置功能，如 PostgreSQL（从 9.0 版本开始）、MySQL、Oracle Data Guard【2】和 SQL Server 的 AlwaysOn 可用性组【3】。它也被用于一些非关系数据库，包括 MongoDB、RethinkDB 和 Espresso【4】。最后，基于领导者的复制并不仅限于数据库：像 Kafka【5】和 RabbitMQ 高可用队列【6】这样的分布式消息代理也使用它。某些网络文件系统，例如 DRBD 这样的块复制设备也与之类似。")],-1),C=l("hr",null,null,-1),F=l("blockquote",null,[l("h3",{id:"同步复制与异步复制",tabindex:"-1"},[n("同步复制与异步复制 "),l("a",{class:"header-anchor",href:"#同步复制与异步复制","aria-label":'Permalink to "同步复制与异步复制"'},"​")]),l("p",null,[n("复制系统的一个重要细节是：复制是 "),l("strong",null,"同步（synchronously）"),n(" 发生的还是 "),l("strong",null,"异步（asynchronously）"),n(" 发生的。（在关系型数据库中这通常是一个配置项，其他系统则通常硬编码为其中一个）。")])],-1),w=l("blockquote",null,[l("p",null,"同步复制的优点是，从库能保证有与主库一致的最新数据副本。如果主库突然失效，我们可以确信这些数据仍然能在从库上找到。缺点是，如果同步从库没有响应（比如它已经崩溃，或者出现网络故障，或其它任何原因），主库就无法处理写入操作。主库必须阻止所有写入，并等待同步副本再次可用。"),l("p",null,[n("因此，将所有从库都设置为同步的是不切实际的：任何一个节点的中断都会导致整个系统停滞不前。实际上，如果在数据库上启用同步复制，通常意味着其中 "),l("strong",null,"一个"),n(" 从库是同步的，而其他的从库则是异步的。如果该同步从库变得不可用或缓慢，则将一个异步从库改为同步运行。这保证你至少在两个节点上拥有最新的数据副本：主库和同步从库。这种配置有时也被称为 "),l("strong",null,"半同步（semi-synchronous）"),n("【7】。")])],-1),A=l("blockquote",null,[l("h3",{id:"设置新从库",tabindex:"-1"},[n("设置新从库 "),l("a",{class:"header-anchor",href:"#设置新从库","aria-label":'Permalink to "设置新从库"'},"​")]),l("p",null,"有时候需要设置一个新的从库：也许是为了增加副本的数量，或替换失败的节点。如何确保新的从库拥有主库数据的精确副本？"),l("p",null,"简单地将数据文件从一个节点复制到另一个节点通常是不够的：客户端不断向数据库写入数据，数据总是在不断地变化，标准的文件复制会看到数据库的不同部分在不同的时间点的内容，其结果可能没有任何意义。"),l("p",null,"可以通过锁定数据库（使其不可用于写入）来使磁盘上的文件保持一致，但是这会违背高可用的目标。幸运的是，设置新从库通常并不需要停机。从概念上讲，其过程如下所示："),l("ol",null,[l("li",null,"在某个时刻获取主库的一致性快照（如果可能，不必锁定整个数据库）。大多数数据库都具有这个功能，因为它是备份必需的。对于某些场景，可能需要第三方工具，例如用于 MySQL 的 innobackupex【12】。"),l("li",null,"将快照复制到新的从库节点。"),l("li",null,[n("从库连接到主库，并拉取快照之后发生的所有数据变更。这要求快照与主库复制日志中的位置精确关联。该位置有不同的名称，例如 PostgreSQL 将其称为 "),l("strong",null,"日志序列号（log sequence number，LSN）"),n("，MySQL 将其称为 "),l("strong",null,"二进制日志坐标（binlog coordinates）"),n("。")]),l("li",null,[n("当从库处理完快照之后积累的数据变更，我们就说它 "),l("strong",null,"赶上（caught up）"),n(" 了主库，现在它可以继续及时处理主库产生的数据变化了。")])]),l("p",null,"建立从库的实际步骤因数据库而异。在某些系统中，这个过程是完全自动化的，而在另外一些系统中，它可能是一个需要由管理员手动执行的、有点神秘的多步骤工作流。")],-1),H=l("hr",null,null,-1),S=l("blockquote",null,[l("h4",{id:"主库失效-故障切换",tabindex:"-1"},[n("主库失效：故障切换 "),l("a",{class:"header-anchor",href:"#主库失效-故障切换","aria-label":'Permalink to "主库失效：故障切换"'},"​")]),l("p",null,[n("主库失效处理起来相当棘手：其中一个从库需要被提升为新的主库，需要重新配置客户端，以将它们的写操作发送给新的主库，其他从库需要开始拉取来自新主库的数据变更。这个过程被称为 "),l("strong",null,"故障切换（failover）"),n("。")])],-1),$=l("p",null,"所以给主库一个接口，比如 zookeeper？",-1),I=l("hr",null,null,-1),N=l("blockquote",null,[l("h3",{id:"复制日志的实现",tabindex:"-1"},[n("复制日志的实现 "),l("a",{class:"header-anchor",href:"#复制日志的实现","aria-label":'Permalink to "复制日志的实现"'},"​")]),l("p",null,"基于领导者的复制在底层是如何工作的？实践中有好几种不同的复制方式，所以先简要地看一下。"),l("h4",{id:"基于语句的复制",tabindex:"-1"},[n("基于语句的复制 "),l("a",{class:"header-anchor",href:"#基于语句的复制","aria-label":'Permalink to "基于语句的复制"'},"​")]),l("p",null,[n("在最简单的情况下，主库记录下它执行的每个写入请求（"),l("strong",null,"语句"),n("，即 statement）并将该语句日志发送给从库。对于关系数据库来说，这意味着每个 "),l("code",null,"INSERT"),n("、"),l("code",null,"UPDATE"),n(" 或 "),l("code",null,"DELETE"),n(" 语句都被转发给每个从库，每个从库解析并执行该 SQL 语句，就像直接从客户端收到一样。")]),l("p",null,"虽然听上去很合理，但有很多问题会搞砸这种复制方式："),l("ul",null,[l("li",null,[n("任何调用 "),l("strong",null,"非确定性函数（nondeterministic）"),n(" 的语句，可能会在每个副本上生成不同的值。例如，使用 "),l("code",null,"NOW()"),n(" 获取当前日期时间，或使用 "),l("code",null,"RAND()"),n(" 获取一个随机数。")]),l("li",null,[n("如果语句使用了 "),l("strong",null,"自增列（auto increment）"),n("，或者依赖于数据库中的现有数据（例如，"),l("code",null,"UPDATE ... WHERE <某些条件>"),n("），则必须在每个副本上按照完全相同的顺序执行它们，否则可能会产生不同的效果。当有多个并发执行的事务时，这可能成为一个限制。")]),l("li",null,"有副作用的语句（例如：触发器、存储过程、用户定义的函数）可能会在每个副本上产生不同的副作用，除非副作用是绝对确定性的。")])],-1),L=l("blockquote",null,[l("p",null,"的确有办法绕开这些问题 —— 例如，当语句被记录时，主库可以用固定的返回值替换掉任何不确定的函数调用，以便所有从库都能获得相同的值。但是由于边缘情况实在太多了，现在通常会选择其他的复制方法。"),l("p",null,"基于语句的复制在 5.1 版本前的 MySQL 中被使用到。因为它相当紧凑，现在有时候也还在用。但现在在默认情况下，如果语句中存在任何不确定性，MySQL 会切换到基于行的复制（稍后讨论）。VoltDB 使用了基于语句的复制，但要求事务必须是确定性的，以此来保证安全【15】。")],-1),V=l("hr",null,null,-1),O=l("blockquote",null,[l("p",null,[n("不幸的是，当应用程序从异步从库读取时，如果从库落后，它可能会看到过时的信息。这会导致数据库中出现明显的不一致：同时对主库和从库执行相同的查询，可能得到不同的结果，因为并非所有的写入都反映在从库中。这种不一致只是一个暂时的状态 —— 如果停止写入数据库并等待一段时间，从库最终会赶上并与主库保持一致。出于这个原因，这种效应被称为 "),l("strong",null,"最终一致性（eventual consistency）"),n("【22,23】。")]),l("p",null,[n("最终一致性中的 “最终” 一词有意进行了模糊化：总的来说，副本落后的程度是没有限制的。在正常的操作中，"),l("strong",null,"复制延迟（replication lag）"),n("，即写入主库到反映至从库之间的延迟，可能仅仅是几分之一秒，在实践中并不显眼。但如果系统在接近极限的情况下运行，或网络中存在问题时，延迟可以轻而易举地超过几秒，甚至达到几分钟。")]),l("p",null,"因为滞后时间太长引入的不一致性，不仅仅是一个理论问题，更是应用设计中会遇到的真实问题。本节将重点介绍三个在复制延迟时可能发生的问题实例，并简述解决这些问题的一些方法。")],-1),Q=l("p",null,"这里的「最终」只承诺会在未来某一时刻追赶上主库，但是并没有时间限制。",-1),E=l("hr",null,null,-1),B=l("blockquote",null,[l("h3",{id:"读己之写",tabindex:"-1"},[n("读己之写 "),l("a",{class:"header-anchor",href:"#读己之写","aria-label":'Permalink to "读己之写"'},"​")]),l("p",null,"许多应用让用户提交一些数据，然后查看他们提交的内容。可能是用户数据库中的记录，也可能是对讨论主题的评论，或其他类似的内容。提交新数据时，必须将其发送给主库，但是当用户查看数据时，可以通过从库进行读取。如果数据经常被查看，但只是偶尔写入，这是非常合适的。"),l("p",null,"但对于异步复制，问题就来了。如 [图 5-3]")],-1),R=l("blockquote",null,[l("p",null,"所示：如果用户在写入后马上就查看数据，则新数据可能尚未到达副本。对用户而言，看起来好像是刚提交的数据丢失了，所以他们不高兴是可以理解的。")],-1),T=l("blockquote",null,[l("p",null,[l("strong",null,"图 5-3 用户写入后从旧副本中读取数据。需要写后读 (read-after-write) 的一致性来防止这种异常")]),l("p",null,[n("在这种情况下，我们需要 "),l("strong",null,"写后读一致性（read-after-write consistency）"),n("，也称为 "),l("strong",null,"读己之写一致性（read-your-writes consistency）"),n("【24】。这是一个保证，如果用户重新加载页面，他们总会看到他们自己提交的任何更新。它不会对其他用户的写入做出承诺：其他用户的更新可能稍等才会看到。它保证用户自己的输入已被正确保存。")])],-1),U=l("blockquote",null,[l("p",null,"如何在基于领导者的复制系统中实现写后读一致性？有各种可能的技术，这里说一些："),l("ul",null,[l("li",null,[n("对于用户 "),l("strong",null,"可能修改过"),n(" 的内容，总是从主库读取；这就要求得有办法不通过实际的查询就可以知道用户是否修改了某些东西。举个例子，社交网络上的用户个人资料信息通常只能由用户本人编辑，而不能由其他人编辑。因此一个简单的规则就是：总是从主库读取用户自己的档案，如果要读取其他用户的档案就去从库。")]),l("li",null,"如果应用中的大部分内容都可能被用户编辑，那这种方法就没用了，因为大部分内容都必须从主库读取（读伸缩就没效果了）。在这种情况下可以使用其他标准来决定是否从主库读取。例如可以跟踪上次更新的时间，在上次更新后的一分钟内，从主库读。还可以监控从库的复制延迟，防止向任何滞后主库超过一分钟的从库发出查询。"),l("li",null,"客户端可以记住最近一次写入的时间戳，系统需要确保从库在处理该用户的读取请求时，该时间戳前的变更都已经传播到了本从库中。如果当前从库不够新，则可以从另一个从库读取，或者等待从库追赶上来。这里的时间戳可以是逻辑时间戳（表示写入顺序的东西，例如日志序列号）或实际的系统时钟（在这种情况下，时钟同步变得至关重要，请参阅 “[不可靠的时钟]”）。"),l("li",null,"如果你的副本分布在多个数据中心（为了在地理上接近用户或者出于可用性目的），还会有额外的复杂性。任何需要由主库提供服务的请求都必须路由到包含该主库的数据中心。")]),l("p",null,"另一种复杂的情况发生在同一位用户从多个设备（例如桌面浏览器和移动 APP）请求服务的时候。这种情况下可能就需要提供跨设备的写后读一致性：如果用户在一个设备上输入了一些信息，然后在另一个设备上查看，则应该看到他们刚输入的信息。"),l("p",null,"在这种情况下，还有一些需要考虑的问题："),l("ul",null,[l("li",null,"记住用户上次更新时间戳的方法变得更加困难，因为一个设备上运行的程序不知道另一个设备上发生了什么。需要对这些元数据进行中心化的存储。"),l("li",null,"如果副本分布在不同的数据中心，很难保证来自不同设备的连接会路由到同一数据中心。（例如，用户的台式计算机使用家庭宽带连接，而移动设备使用蜂窝数据网络，则设备的网络路由可能完全不同）。如果你的方法需要读主库，可能首先需要把来自该用户所有设备的请求都路由到同一个数据中心。")])],-1),W=l("ol",null,[l("li",null,"部分请求强制读主库"),l("li",null,"监控复制延迟，强制读已同步库"),l("li",null,"时间戳"),l("li",null,"路由请求，中心化存储元数据")],-1),Z=l("blockquote",null,[l("h3",{id:"单调读",tabindex:"-1"},[n("单调读 "),l("a",{class:"header-anchor",href:"#单调读","aria-label":'Permalink to "单调读"'},"​")]),l("p",null,[n("在从异步从库读取时可能发生的异常的第二个例子是用户可能会遇到 "),l("strong",null,"时光倒流（moving backward in time）"),n("。")]),l("p",null,"如果用户从不同从库进行多次读取，就可能发生这种情况。例如，[图 5-4]")],-1),j=l("blockquote",null,[l("p",null,"显示了用户 2345 两次进行相同的查询，首先查询了一个延迟很小的从库，然后是一个延迟较大的从库（如果用户刷新网页时每个请求都被路由到一个随机的服务器，这种情况就很有可能发生）。第一个查询返回了最近由用户 1234 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reads）"),n("【23】。这个保证的意思是说：如果一系列写入按某个顺序发生，那么任何人读取这些写入时，也会看见它们以同样的顺序出现。")]),l("p",null,[n("这是 "),l("strong",null,"分区（partitioned）"),n(" 或 "),l("strong",null,"分片（sharded）"),n(" 数据库中的一个特殊问题，我们将在 [第六章] 中讨论分区数据库。如果数据库总是以相同的顺序应用写入，而读取总是看到一致的前缀，那么这种异常不会发生。但是在许多分布式数据库中，不同的分区独立运行，因此不存在 "),l("strong",null,"全局的写入顺序"),n("：当用户从数据库中读取数据时，可能会看到数据库的某些部分处于较旧的状态，而某些则处于较新的状态。")]),l("p",null,"一种解决方案是，确保任何因果相关的写入都写入相同的分区，但在一些应用中可能无法高效地完成这种操作。还有一些显式跟踪因果依赖关系的算法，我们将在 “[“此前发生” 的关系和并发]” 一节中回到这个话题。")],-1),ll=l("p",null,[l("strong",null,"场景复现非常成功，虽然有点类似时光回溯，但是这是发生在分布式数据库中的问题，没有主库的存在。")],-1),nl=l("p",null,"但是关于解决方案会有些复杂，比如跟踪因果，首先要定义什么是因果，然后要分析根据哪个或哪些指标来分析数据间是否存在因果关系。",-1),tl=l("p",null,"除此之外，分析数据的行为想必必须在入库操作之前进行，这就要求算法不能太复杂，又是一个负担。",-1),ol=l("p",null,"等下看看作者给出的解决方案是什么吧。",-1),sl=l("hr",null,null,-1),el=l("blockquote",null,[l("p",null,[n("如果应用程序开发人员不必担心微妙的复制问题，并可以信赖他们的数据库 “做了正确的事情”，那该多好呀。这就是 "),l("strong",null,"事务（transaction）"),n(" 存在的原因："),l("strong",null,"数据库通过事务提供强大的保证"),n("，所以应用程序可以更加简单。")])],-1),al=l("p",null,"事务的强大！",-1),il=l("hr",null,null,-1),ul=l("blockquote",null,[l("p",null,"尽管多主复制有这些优势，但也有一个很大的缺点：两个不同的数据中心可能会同时修改相同的数据，写冲突是必须解决的（如 [图 5-6]")],-1),rl=l("blockquote",null,[l("p",null,"中的 “冲突解决（conflict resolution）”）。本书将在 “[处理写入冲突]” 中详细讨论这个问题。"),l("p",null,"由于多主复制在许多数据库中都属于改装的功能，所以常常存在微妙的配置缺陷，且经常与其他数据库功能之间出现意外的反应。比如自增主键、触发器、完整性约束等都可能会有麻烦。因此，多主复制往往被认为是危险的领域，应尽可能避免【28】。")],-1),cl=l("p",null,"避免多主复制的架构。",-1),pl=l("hr",null,null,-1),dl=l("blockquote",null,[l("h4",{id:"需要离线操作的客户端",tabindex:"-1"},[n("需要离线操作的客户端 "),l("a",{class:"header-anchor",href:"#需要离线操作的客户端","aria-label":'Permalink to 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"),l("a",{href:"https://github.com/Vonng/ddia/blob/master/img/fig5-10.png",target:"_blank",rel:"noreferrer"},"图 5-10"),n(" 中，用户 2345 获得了来自副本 3 的版本 6 值和来自副本 1 和 2 的版本 7 值。客户端发现副本 3 具有陈旧值，并将新值写回到该副本。这种方法适用于读频繁的值。")])]),l("li",null,[l("p",null,"反熵过程（Anti-entropy process）"),l("p",null,"此外，一些数据存储具有后台进程，该进程不断查找副本之间的数据差异，并将任何缺少的数据从一个副本复制到另一个副本。与基于领导者的复制中的复制日志不同，此反熵过程不会以任何特定的顺序复制写入，并且在复制数据之前可能会有显著的延迟。")])]),l("p",null,"并不是所有的系统都实现了这两种机制，例如，Voldemort 目前没有反熵过程。请注意，如果没有反熵过程，很少被读取的值可能会从某些副本中丢失，从而降低了持久性，因为只有在应用程序读取值时才执行读修复。")],-1),bl=l("p",null,"可能需要用多线程来写。",-1),_l=l("hr",null,null,-1),fl=l("blockquote",null,[l("p",null,[l("strong",null,[n("图 5-11 如果 "),l("span",{class:"katex"},[l("span",{class:"katex-mathml"},[l("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[l("semantics",null,[l("mrow",null,[l("mi",null,"w"),l("mo",null,"+"),l("mi",null,"r"),l("mo",null,">"),l("mi",null,"n")]),l("annotation",{encoding:"application/x-tex"},"w + r > 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a/assets/YunPageHeader.vue_vue_type_script_setup_true_lang-Bn3v0445.js b/assets/YunPageHeader.vue_vue_type_script_setup_true_lang-Bn3v0445.js new file mode 100644 index 0000000..a03aa6f --- /dev/null +++ b/assets/YunPageHeader.vue_vue_type_script_setup_true_lang-Bn3v0445.js @@ -0,0 +1 @@ +import{d as n,o as s,e as o,g as t,s as a,q as l,t as r,n as i}from"./app-Bc1rgBbU.js";const c={class:"post-header",m:"t-16 sm:t-6"},p={"inline-flex":"",class:"leading-none"},_=n({__name:"YunPageHeader",props:{color:{},icon:{},title:{}},setup(m){return(e,d)=>(s(),o("header",c,[t("h1",{class:"post-title flex-center",p:"2",text:"2xl center",font:"serif black",style:i(`color:${e.color}`)},[e.icon?(s(),o("div",{key:0,class:a(["icon",e.icon]),m:"r-1 t-1px","inline-flex":""},null,2)):l("v-if",!0),t("span",p,r(e.title),1)],4)]))}});export{_}; diff --git a/assets/YunPostCollapse-DzZAROS7.css b/assets/YunPostCollapse-DzZAROS7.css new file mode 100644 index 0000000..0d62391 --- /dev/null +++ b/assets/YunPostCollapse-DzZAROS7.css @@ -0,0 +1 @@ +.post-collapse .collection-title{border-bottom:2px solid rgba(var(--va-c-primary-rgb),.6)}.post-collapse .collection-title:before{content:"";position:absolute;top:50%;width:2px;height:50%;background:rgba(var(--va-c-primary-rgb),.3)}.post-collapse .collection-title .archive-year{color:var(--va-c-primary);margin:0 1.5rem}.post-collapse .collection-title .archive-year:before{content:"";position:absolute;left:0;top:35%;margin-left:-11px;margin-top:-4px;width:1.5rem;height:1.5rem;background:var(--va-c-primary);border-radius:50%}.post-collapse .post-item:before{content:"";position:absolute;width:2px;height:100%;background:rgba(var(--va-c-primary-rgb),.3)}.post-collapse .post-header{border-bottom:1px solid rgba(var(--va-c-primary-rgb),.3)}.post-collapse .post-header:before{content:"";position:absolute;left:0;width:10px;height:10px;margin-left:-4px;border-radius:50%;border:1px solid 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var(--va-c-primary-lighter);--page-btn-active-bg-color: var(--va-c-primary-light)}.dark{--page-btn-bg-color: var(--va-c-bg-light)}.pagination{display:flex;justify-content:center;align-items:center}.pagination .prev,.pagination .next,.pagination .page-number,.pagination .space{display:inline-flex;justify-content:center;align-items:center;width:2rem;height:2rem;margin:0;transition:background-color var(--va-transition-duration) ease}.pagination .prev,.pagination .next,.pagination .page-number{cursor:pointer;color:var(--va-c-text);text-decoration:none;background-color:var(--page-btn-bg-color)}.pagination .prev:hover,.pagination .next:hover,.pagination .page-number:hover{color:var(--va-c-bg);background:var(--page-btn-hover-bg-color)}.pagination .prev:active,.pagination .next:active,.pagination .page-number:active{color:var(--va-c-bg);background:var(--page-btn-active-bg-color)}.pagination .prev.active,.pagination .next.active,.pagination 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Data-Intensive Applications 读书笔记 0x05",name:"/posts/Designing Data-Intensive Applications 读书笔记 0x05",component:()=>be(()=>import("./Designing Data-Intensive Applications 读书笔记 0x05-CGvrw8t5.js"),__vite__mapDeps([26,9,10,2,11,12,13])),meta:{frontmatter:{time_warning:!0,title:"《Designing Data-Intensive Applications》 读书笔记 | 0x05",date:"2024-05-09T17:16:59.000Z",tags:["DDIA","System Design"],categories:["读书笔记","DDIA"],excerpt:"来看看作者如何从另一种角度描述分布式系统。",author:"苏",color:"palevioletred",updated:"2024-05-12T15:49:55.496Z",wordCount:179,readingTime:1},layout:"post",excerpt:`
    +

    我们必须跳出电脑指令序列的窠臼。叙述定义、描述元数据、梳理关系，而不是编写过程。

    +

    —— Grace Murray Hopper，未来的计算机及其管理（1962）

    +
    +`}},{path:"hello-valaxy",name:"/posts/hello-valaxy",component:()=>be(()=>import("./hello-valaxy-BHbDsZI-.js"),__vite__mapDeps([27,9,10,2,11,12,13])),meta:{frontmatter:{time_warning:!0,title:"Hello, Valaxy!",date:"2022-04-01T00:00:00.000Z",updated:"2022-04-01T00:00:00.000Z",categories:"Valaxy 笔记",tags:["valaxy","笔记"],wordCount:42,readingTime:1},layout:"post",excerpt:""}},{path:"hello-world",name:"/posts/hello-world",component:()=>be(()=>import("./hello-world-BHJGp-mh.js"),__vite__mapDeps([28,9,10,2,11,12,13])),meta:{frontmatter:{time_warning:!0,title:"Hello World",tags:["Hello","World"],date:"2022-11-02T18:03:34.000Z",categories:["测试","盘古开天辟地"],updated:"2024-05-12T15:49:55.496Z",wordCount:145,readingTime:1},layout:"post",excerpt:""}},{path:"Java Lock Notes",name:"/posts/Java Lock Notes",component:()=>be(()=>import("./Java Lock Notes-DDc1QzY_.js"),__vite__mapDeps([29,9,10,2,11,12,13])),meta:{frontmatter:{time_warning:!0,title:"Java Lock Notes",date:"2024-03-04T15:40:59.000Z",tags:["锁","JDK"],categories:["笔记","Java"],author:"苏",readmore:!0,updated:"2024-05-12T15:49:55.496Z",wordCount:"1.5k",readingTime:6},layout:"post",excerpt:""}},{path:"LeetCode record 2024-04",name:"/posts/LeetCode record 2024-04",component:()=>be(()=>import("./LeetCode record 2024-04-Dx0e42yv.js"),__vite__mapDeps([30,9,10,2,11,12,13])),meta:{frontmatter:{time_warning:!0,title:"LeetCode record 2024-04",date:"2024-04-29T15:37:59.000Z",tags:["LeetCode","算法"],categories:["LeetCode"],excerpt:"LeetCode 2024 年 4 月 刷题记录。",author:"苏",toc:!0,medium_zoom:!0,updated:"2024-05-12T15:49:55.496Z",wordCount:"2k",readingTime:9},layout:"post",excerpt:""}},{path:"LeetCode record 2024-05",name:"/posts/LeetCode record 2024-05",component:()=>be(()=>import("./LeetCode record 2024-05-BuEne_x4.js"),__vite__mapDeps([31,9,10,2,11,12,13])),meta:{frontmatter:{time_warning:!0,title:"LeetCode record 2024-05",tags:["LeetCode","算法"],categories:["LeetCode"],author:"苏",toc:!0,codeHeightLimit:300,medium_zoom:!0,date:"2024-05-12T15:49:55.496Z",updated:"2024-05-12T15:49:55.496Z",wordCount:"5.2k",readingTime:20},layout:"post",excerpt:`

    如果有一天没有打卡，会不会辜负踌躇满志的那个自己。

    +`}},{path:"LeetCode-SQL-notes",name:"/posts/LeetCode-SQL-notes",component:()=>be(()=>import("./LeetCode-SQL-notes-DI_Sacz4.js"),__vite__mapDeps([32,9,10,2,11,12,13])),meta:{frontmatter:{time_warning:!0,title:"LeetCode 数据库刷题笔记",author:"苏",date:"2022-11-02T18:04:34.000Z",tags:["LeetCode","数据库"],categories:["LeetCode","数据库"],readmore:!0,excerpt:"记录在 LeetCode SQL 相关的题目。调皮的小知识点有点多，用笔记📔把它们一网打尽！",updated:"2024-05-12T15:49:55.496Z",wordCount:628,readingTime:3},layout:"post",excerpt:""}},{path:"Oracle-Mybatis-批量更新返回值问题",name:"/posts/Oracle-Mybatis-批量更新返回值问题",component:()=>be(()=>import("./Oracle-Mybatis-批量更新返回值问题-ddNoz7cH.js"),__vite__mapDeps([33,9,10,2,11,12,13])),meta:{frontmatter:{time_warning:!0,title:"Oracle & Mybatis 批量更新返回值问题",date:"2022-11-04T09:29:59.000Z",tags:["数据库","Oracle","Mybatis","Java"],categories:["奇奇怪怪的项目里的问题"],author:"苏",readmore:!0,excerpt:"Mybatis 在使用（批量）更新语句的时候，应该在 SQL 语句正常执行完毕后返回「受影响行数」，但在连接的数据库是 Oracle 的时候却一直返回 -1，无法对 SQL 的执行状态进行判断，为了解决这个问题，我做了一些尝试……",updated:"2024-05-12T15:49:55.496Z",wordCount:"1.4k",readingTime:6},layout:"post",excerpt:""}},{path:"关于-Java-8-中函数式编程的粗浅见解",name:"/posts/关于-Java-8-中函数式编程的粗浅见解",component:()=>be(()=>import("./关于-Java-8-中函数式编程的粗浅见解-BlAXY4Wx.js"),__vite__mapDeps([34,9,10,2,11,12,13])),meta:{frontmatter:{time_warning:!0,title:"关于 Java 8 中函数式编程的粗浅见解",date:"2024-02-21T16:08:45.000Z",tags:null,updated:"2024-05-12T15:49:55.496Z",wordCount:27,readingTime:1},layout:"post",excerpt:`

    占坑~

    +`}},{path:"关于代码规范的一些粗浅想法",name:"/posts/关于代码规范的一些粗浅想法",component:()=>be(()=>import("./关于代码规范的一些粗浅想法-JwtRt2cx.js"),__vite__mapDeps([35,9,10,2,11,12,13])),meta:{frontmatter:{time_warning:!0,title:"关于代码规范的一些粗浅想法",date:"2023-01-05T15:07:18.000Z",tags:["代码规范"],categories:["奇奇怪怪的思考"],author:"苏",excerpt:"衡量代码质量的标准有很多——鲁棒性、可读性、复用性、是否符合各种设计原则等等。在我看来，代码规范即衡量代码质量标准的集合。其中可读性可能是代码规范最关注的一部分。",readmore:!0,updated:"2024-05-12T15:49:55.496Z",wordCount:"1.8k",readingTime:7},layout:"post",excerpt:""}},{path:"占坑 Java 多线程题目",name:"/posts/占坑 Java 多线程题目",component:()=>be(()=>import("./占坑 Java 多线程题目-CL6BOvFP.js"),__vite__mapDeps([36,9,10,2,11,12,13])),meta:{frontmatter:{time_warning:!0,title:"Java 多线程题目",tags:["Java","多线程","面试题"],categories:["代码","多线程代码片段"],author:"苏",type:"yuque",hide:!0,date:"2024-05-12T15:49:55.496Z",updated:"2024-05-12T15:49:55.496Z",wordCount:41,readingTime:1},layout:"post",excerpt:""}},{path:"常用代码片段",name:"/posts/常用代码片段",component:()=>be(()=>import("./常用代码片段-Krl59bYI.js"),__vite__mapDeps([37,9,10,2,11,12,13])),meta:{frontmatter:{time_warning:!0,title:"常用代码片段",date:"2023-02-06T13:06:59.000Z",tags:["代码","算法"],categories:["代码","算法代码片段"],author:"苏",excerpt:"每次都忘记快排怎么写💔，得想个办法。",readmore:!0,updated:"2024-05-12T15:49:55.496Z",wordCount:274,readingTime:2},layout:"post",excerpt:""}},{path:"记一次从 Hexo 到 Valaxy 的迁移，及 GitHub Actions 和 Github Pages 的配合",name:"/posts/记一次从 Hexo 到 Valaxy 的迁移，及 GitHub Actions 和 Github Pages 的配合",component:()=>be(()=>import("./记一次从 Hexo 到 Valaxy 的迁移，及 GitHub Actions 和 Github Pages 的配合-Ct4CtTv8.js"),__vite__mapDeps([38,9,10,2,11,12,13])),meta:{frontmatter:{time_warning:!0,title:"记一次从 Hexo 到 Valaxy 的迁移，GitHub Actions 部署 Github Pages",tags:["LeetCode","算法"],categories:["LeetCode"],author:"苏",type:"yuque",date:"2024-05-12T15:49:55.496Z",updated:"2024-05-12T15:49:55.496Z",wordCount:"1.2k",readingTime:5},layout:"post",excerpt:`

    仿佛只有有一个自己的博客，才像个理想中的程序员。

    +

    从买一台云主机，部署 WordPress，到 Hexo，再到前几天被 云游君 安利的（也就是现在在使用的）Valaxy，也经历了蛮久。

    +

    但是记录和折腾这么久，自己又真正学到了什么东西呢？

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[^(]*\((.*):(.+):(.+)\)$/gi,m=/@([^@]*):(\d+):(\d+)\s*$/gi,b=h.exec(A.stack)||m.exec(A.stack),x=b&&b[1]||!1,_=b&&b[2]||!1,E=document.location.href.replace(document.location.hash,""),k=document.getElementsByTagName("script");x===E&&(g=document.documentElement.outerHTML,u=new RegExp("(?:[^\\n]+?\\n){0,"+(_-2)+"}[^<]* \ No newline at end of file diff --git a/favicon.svg b/favicon.svg new file mode 100644 index 0000000..d7e032f --- /dev/null +++ b/favicon.svg @@ -0,0 +1,33 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/feed.json b/feed.json new file mode 100644 index 0000000..c3a543c --- /dev/null +++ b/feed.json @@ -0,0 +1,218 @@ +{ + "version": "https://jsonfeed.org/version/1", + "title": "苏", + "home_page_url": "https://674019130.github.io/", + "feed_url": "https://674019130.github.io/feed.json", + "description": "I was 26 years old and didn't know what I could do other than write some simple code.", + "icon": "https://gravatar.com/userimage/135478784/07954c26f630b810ec9709a8907dab5f.jpeg", + "author": { + "name": "苏", + "url": "https://valaxy.site" + }, + "items": [ + { + "id": "", + "content_html": "

    第一章：可靠性、可伸缩性和可维护性

    \n
    要变强，要变成更好的人！
    LeetCode record 2024-05
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    如果有一天没有打卡，会不会辜负踌躇满志的那个自己。

    LeetCode
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    记一次从 Hexo 到 Valaxy 的迁移，GitHub Actions 部署 Github Pages
    1.2k    -

    仿佛只有有一个自己的博客，才像个理想中的程序员。

    从买一台云主机，部署 WordPress，到 Hexo，再到前几天被 云游君 安利的（也就是现在在使用的）Valaxy，也经历了蛮久。

    但是记录和折腾这么久，自己又真正学到了什么东西呢？

    LeetCode
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    关于 Java 8 中函数式编程的粗浅见解
    -
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    占坑~

    © 2022 - 2024
    苏
    由 Valaxy v0.18.6 驱动 | 主题 - Yun v0.18.6
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    我的小伙伴们

    本文最后更新于1 分钟前，文中所描述的信息可能已发生改变。
    LeetCode record 2024-05
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    苏
    由 Valaxy v0.18.6 驱动 | 主题 - Yun v0.18.6
    \ No newline at end of file diff --git a/page.html b/page.html new file mode 100644 index 0000000..9a8f98a --- /dev/null +++ b/page.html @@ -0,0 +1 @@ +苏
    © 2022 - 2024
    苏
    由 Valaxy v0.18.6 驱动 | 主题 - Yun v0.18.6
    \ No newline at end of file diff --git a/posts.html b/posts.html new file mode 100644 index 0000000..4463a20 --- /dev/null +++ b/posts.html @@ -0,0 +1 @@ +苏
    404
    \ No newline at end of file diff --git a/posts/%E5%85%B3%E4%BA%8E%E4%BB%A3%E7%A0%81%E8%A7%84%E8%8C%83%E7%9A%84%E4%B8%80%E4%BA%9B%E7%B2%97%E6%B5%85%E6%83%B3%E6%B3%95.html b/posts/%E5%85%B3%E4%BA%8E%E4%BB%A3%E7%A0%81%E8%A7%84%E8%8C%83%E7%9A%84%E4%B8%80%E4%BA%9B%E7%B2%97%E6%B5%85%E6%83%B3%E6%B3%95.html new file mode 100644 index 0000000..ff329d3 --- /dev/null +++ b/posts/%E5%85%B3%E4%BA%8E%E4%BB%A3%E7%A0%81%E8%A7%84%E8%8C%83%E7%9A%84%E4%B8%80%E4%BA%9B%E7%B2%97%E6%B5%85%E6%83%B3%E6%B3%95.html @@ -0,0 +1,5 @@ +关于代码规范的一些粗浅想法 - 苏

    关于代码规范的一些粗浅想法

    -
    1.8k    -
    奇奇怪怪的思考
    代码规范
    本文最后更新于1 分钟前，文中所描述的信息可能已发生改变。

    什么是代码规范？ ​

    “衡量代码质量的唯一标准是阅读该代码时说脏话的次数”。

    ——Robert C. Martin

    衡量代码质量的标准有很多——鲁棒性、可读性、复用性、是否符合各种设计原则等等。在我看来，代码规范即衡量代码质量标准的集合。其中可读性可能是代码规范最关注的一部分。

    （当然，上图这种解释或许更容易理解🐶.

    代码规范的「技能」和「内功」 ​

    在我看来，代码规范不是一条条规定，而是属于对业务和技术的思考。很想把自己接触代码以来的所有思考有逻辑地、完整地讲出来，无奈才疏学浅，只能从几个不同的维度独立地对代码规范进行分析。

    「技能」 ​

    「技能」比「内功」更像是一些明文规定的条条框框，更「肉眼可见」。比如模仿英文书写的标点符号后加一个半角空格，条件分支和循环语句的关键字后面加一个半角空格等等。

    在我刚开始入门学习 C/C++ 语言的时候，一个简单的 for循环的规范使我困惑了许久。

    C
    for (int i = 0; i < length; ++i)
+{
+    doSomething();
+}

    为什么for关键字后要加一个空格；为什么运算符两边要加空格；为什么大括号要独占一行……还是个新手的我考虑不通这些问题，只能硬记下来，在敲每一个for循环时，都手动地添加这一个个不明所以的空格。

    很明显，在不明白这种硬性规定背后原因的情况下，身为新手的我也能写出从简单的「空格规范」角度来说算是「规范」的代码，这使我的代码的可读性大大提高——相比于其他同学「粘成一块」的代码。

    当然，「技能」远远不止这些，在我们的认知里，几乎所有的代码规范都是「技能」，是一些让代码看起来「好看」的硬性规定。在实际工作中，遵守与不遵守它的人，写出来的东西好像都一样可以运行，这无疑让人有点挫败。于是我开始思考为什么这么做。

    「内功」 ​

    我们每个人都能谈一谈空格、换行、变量命名……但所谓的「内功」将不再在这样简单的维度对代码规范进行考量，它包罗万象、因人而异而又有一个统一的标准。什么是内功？

    当我读《设计模式之禅》这本书的时候，里面提到了许多面向对象设计原则：

    • 开闭原则 OCP (The Open-Close Principle)
    • 单一职责原则 SRP (Single Responsibility Principle)
    • 依赖倒置原则 DIP (Dependence Inversion Principle)
    • 最少知识原则 LKP (Least Knowledge Principle)) / 迪米特法则 (Law Of Demeter)
    • 里氏替换原则 LSP (Liskov Substitution Principle)
    • 接口隔离原则 ISP (Interface Segregation Principle)
    • 组合/聚合复用原则 CARP (Composite/Aggregate Reuse Principle)

    面对这些既抽象又合理的原则，我意识到这可能才是衡量代码优劣的更高标准。这使得我们写出的代码可以尽可能符合高内聚低耦合的标准。

    这也是一种代码规范吗？如果你也在追求代码质量，或者你也有代码洁癖，那么毫无疑问，这是一份堪称圭臬的宝藏。

    除此之外，对「技能」的一些规定的长久的思考，也让我对一些问题有了答案。许多问题都可以用「便于区分」、「模块化」、「尽量避免你所使用的开发语言所拥有的缺陷」等来回答。

    还是上面的for关键字后加空格的问题，为什么我们在调用一个**函数（方法）**时不需要加空格呢？正是为了区分两者。

    为什么在类的公共方法中，调用类的成员方法要添加this.前缀，明明不添加也一样可以正常调用。正是为了区分类内方法和其他方法。

    ……

    「内功」对「技能」背后的原因做出了解释，也对其他「技能」所管辖不到的地方提供了**高自由度但「事实一致」**的规范。包括但不限于变量的命名、注释的使用、函数（方法）的职责、类内对象的暴露与封闭……

    小结 ​

    开发工作本身对抽象能力的高要求和工作中业务逻辑的复杂性，对代码规范的落地实施设置了许多障碍。在日常工作中，在阅读精炼的源码的时候，甚至在书写相关文档的时候，我们都不应该停止对产出高质量内容的追求。或许有人还是对这些「虚无缥缈」的东西不以为意，认为代码和人有一个能跑就行。

    Whatever，我们只要一直做酷的事情就好了。

    写的内容并不多，我尝试着总结，尝试一言以蔽之，但是代码规范的意义和内容远不止于此。其实，只要在写代码的过程中，保持思考和对技术的追求，对这些东西的理解自然会逐渐提升，自然养成好的习惯。

    精益求精，才是我们应该做的。

    一些常见的、易被忽略的、易于执行的简单规范 ​

    有则改之无则加勉。

    • 不滥用注释

    • 代码不应被注释，如实有必要，应写明注释原因（考虑注释的语义）

    • 单行注释不应与代码混在一起，且注释应开头空一格

      bad case:

      this.someMethod(); //some comments

      good case:

      java
      // some comments
+this.someMethod();

    • if, for, while, return等关键字后空一格，运算符两边各空一格

    • 变量命名尽可能使用英文，尽可能简单但意义明确

    • 容器类的变量命名包含其语义

      bad case:

      Map<String, String> score = new HashMap<>();

      good case:

      ArrayList<String> fileNameList = new ArrayList<>();

    • 在 Java 代码中尽可能避免传递和返回空指针

    • 一个代码块应该是一段完整的逻辑，使用换行将代码逻辑模块化

    • 一行代码不宜过长

    • 尽可能避免出现魔法数，使用常量进行代替，并在注释中写明用途

    • 方法参数尽可能少

    《Designing Data-Intensive Applications》 读书笔记 | 0x00
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    关于 Java 8 中函数式编程的粗浅见解

    -
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    本文最后更新于1 分钟前，文中所描述的信息可能已发生改变。

    占坑~

    《Designing Data-Intensive Applications》 读书笔记 | 0x04
    《Designing Data-Intensive Applications》 读书笔记 | 0x03
    © 2022 - 2024
    苏
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    Java 多线程题目

    41    -
    代码 > 多线程代码片段
    Java多线程面试题
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    常用代码片段

    -
    274    -
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    代码算法
    本文最后更新于1 分钟前，文中所描述的信息可能已发生改变。

    快速排序（递归版） ​

    java
    import java.util.Arrays;
+
+public class QuickSort {
+
+  public static void sort(int[] arr, int low, int high) {
+    if (arr == null || arr.length == 0)
+      return;
+
+    if (low >= high)
+      return;
+
+    // pick the pivot
+    int middle = low + (high - low) / 2;
+    int pivot = arr[middle];
+
+    // make left < pivot and right > pivot
+    int i = low, j = high;
+    while (i <= j) {
+      while (arr[i] < pivot) {
+        i++;
+      }
+
+      while (arr[j] > pivot) {
+        j--;
+      }
+
+      if (i <= j) {
+        int temp = arr[i];
+        arr[i] = arr[j];
+        arr[j] = temp;
+        i++;
+        j--;
+      }
+    }
+
+    // recursively sort two sub parts
+    if (low < j)
+      sort(arr, low, j);
+
+    if (high > i)
+      sort(arr, i, high);
+  }
+
+  public static void main(String[] args) {
+    int[] arr = { 3, 7, 8, 5, 2, 1, 9, 5, 4 };
+    QuickSort.sort(arr, 0, arr.length - 1);
+    System.out.println(Arrays.toString(arr));
+  }
+}

    二分查找 ​

    java
    public class BinarySearch {
+
+  public static int search(int[] arr, int target) {
+    if (arr == null || arr.length == 0)
+      return -1;
+
+    int left = 0;
+    int right = arr.length - 1;
+    while (left <= right) {
+      int mid = left + (right - left) / 2;
+      if (arr[mid] == target) {
+        return mid;
+      } else if (arr[mid] < target) {
+        left = mid + 1;
+      } else {
+        right = mid - 1;
+      }
+    }
+    return -1;
+  }
+
+  public static void main(String[] args) {
+    int[] arr = { 1, 2, 3, 4, 5, 6, 7, 8, 9 };
+    int target = 5;
+    System.out.println(BinarySearch.search(arr, target));
+  }
+}
    《Designing Data-Intensive Applications》 读书笔记 | 0x03
    《Designing Data-Intensive Applications》 读书笔记 | 0x02
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    苏
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    记一次从 Hexo 到 Valaxy 的迁移，GitHub Actions 部署 Github Pages

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    仿佛只有有一个自己的博客，才像个理想中的程序员。

    从买一台云主机，部署 WordPress，到 Hexo，再到前几天被 云游君 安利的（也就是现在在使用的）Valaxy，也经历了蛮久。

    但是记录和折腾这么久，自己又真正学到了什么东西呢？

    迁移 ​

    Migrate from Hexo to Valaxy https://valaxy.site/migration/hexo

    Valaxy 目前的文档还不太完善，最起码对一个没怎么搞过前端的人来说是这样的😢。

    先创建一个新的 Valaxy 项目，然后传到自己的 GitHub 仓库中。

    整个迁移分为两部分，迁移内容和迁移配置。

    内容上可以兼容 markdown 文件，只需要挪动位置即可。

    但是配置上存在较大的差异，通过读 TypeScript 写的引擎源码（其实是读接口定义的注释 😆 ）搞定了大部分配置，博客看起来勉强能用惹。

    接下来要搞定的就是部署的问题，这中间曾经有几个问题很困扰我，我根据迁移的进程一个个来说。

    从单纯的静态页面到一个 Vue 项目，GitHub Pages 如何识别该项目内容是一个静态页面的呢？ ​

    如果整个项目只有静态页面，是一个 HTML 和 JavaScript 组成的网站，那么入口是很好识别的，index.html。

    但是现在这个项目是一个 Vue 项目，里面是 .md 文件和一些样式和语法，那么怎么找到 index.html 呢？事实上，在 npm run build 之前，是没有 index.html 这个文件的。

    我按照指南里的部署手册，使用 GitHub Pages 的方式（这么说在我的理解里可能不太合适，感觉应该是用 GitHub Actions 来部署 GitHub Pages）。

    项目在初始化时就已内置文件.github/workflows/gh-pages.yml 以实现 GitHub Actions 的自动部署工作流。

    但是这个时候我还不太懂他们交互的方式和辅助部署的方式，尤其是前期我没搞好项目配置，导致思考的时候走了一些弯路。

    GitHub Actions 已经启动了工作流了，为什么没有部署到 username.github.io 呢？ ​

    这时候我有几个猜想：

    1. 仓库名称不对，不应该以 username.github.io 作为仓库名称，应是 Valaxy 项目代码一个仓库，username.github.io 一个仓库，避免占用地址之后导致识别不到真正的静态页面的内容。
    2. 仓库确实应该以 username.github.io 为名字，根据工作流确实也已经生成了 ./dist 里的文件，只是缺少部署这个动作。
    3. GitHub Pages 存在延迟，其实已经成功部署了，只是还没有刷新。

    多次验证之后，发现第二个猜想可能更接近真相。又去读了一遍指南，其中提到

    • 上传至 GitHub Repo，打开 Settings -> Pages，选择 gh-pages 分支。

    gh-pages 已由 .github/workflows/gh-pages.yml 自动部署。

    那么这个 gh-pages 分支是做什么用的呢？

    切换到这个分支，发现这就是打包生成的静态页面的内容，也就是说其实 GitHub Actions 做的事情是将打包后的文件发布到新的分支中，这样一个仓库里既有项目源代码，又有生成的文件。

    到这里答案就已经呼之欲出了，确实是 缺少将打包后的文件识别为网站入口 这一行为。

    于是尝试切换了 Pages 的部署方式，从 GitHub Actions 切换到 gh-pages 分支，并更新一次项目代码以触发 CI，之后根据设置，自动触发了 deployment。

    image-20240503120129238.pngimage-20240503120115816.png

    后记 ​

    首先再次对 云游君 表示感谢，迁移之后不但部署更方便，实时查看渲染效果的功能也非常方便，还有很多别的便利之处我还没完全搞懂（一定是因为使用手册还没写完的原因），但是真的炒鸡方便了！

    其实反思一下有什么收获，主要是在思考和猜测的过程中，在验证自己想法的过程中，不但获取了其他领域的知识，还加深了对 CI 流程的理解。正是因为 想破脑袋也得不出正确结论 这一行为的存在，才使获得的知识印象更加深刻吧。

    也为自己的行动力感到骄傲！✅

    LeetCode record 2024-05
    《Designing Data-Intensive Applications》 读书笔记 | 0x05
    © 2022 - 2024
    苏
    由 Valaxy v0.18.6 驱动 | 主题 - Yun v0.18.6
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    《Designing Data-Intensive Applications》 读书笔记 | 0x00

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    第一章：可靠性、可伸缩性和可维护性 ​

    将发布的推文 扇出 Fan-out 到其他关注者的 Timeline 缓存中。

    增加负载参数并保持系统资源（CPU、内存、网络带宽等）不变时，系统性能将受到什么影响？增加负载参数并希望保持性能不变时，需要增加多少系统资源？

    增加负载的时候考虑两个方面，性能 不变与 系统资源 不变。

    对于 Hadoop 这样的批处理系统，通常关心的是 吞吐量（throughput），即每秒可以处理的记录数量，或者在特定规模数据集上运行作业的总时间 。对于在线系统，通常更重要的是服务的 响应时间（response time），即客户端发送请求到接收响应之间的时间。

    分情况讨论，理解 批处理 和后面可能出现的 流处理。

    一个服务 100 请求的响应时间报表。

    值得注意的是，平均数（即算术平均值）在实际生产应用中的表现并不如其他指标，作者推荐使用的是百分位点，包括但不限于 p50, p90, p999。这些指标反映了 异常情况下 和 绝大多数 情况下对请求的响应表现。p99 等高百分位点（尾部延迟 tail lantencies）非常重要，直接影响了用户体验。

    这是因为请求响应最慢的客户往往也是数据最多的客户，也可以说是最有价值的客户 —— 因为他们掏钱了【19】。

    平均数会被「脏数据」污染，但表现不佳的情况是事实发生的，用户确确实实感知到了这次糟糕的体验。

    BTW，响应时间（response time）是客户端所见，包括 实际处理请求的时间、网络延迟和排队延迟。延迟（latency）是某个请求等待处理的 持续时长，在此期间它处于 休眠（latent） 状态，并等待服务。

    百分位点通常用于 服务级别目标（SLO, service level objectives） 和 服务级别协议（SLA, service level agreements），即定义服务预期性能和可用性的合同。

    涨知识。会在合同中使用这个指标做 量化 （在我的认知中，关于代码的很多东西其实是难以量化的。但是对客户来讲，最直观的体验可能就是响应时间，也是易于被量化的东西。这些甚至可能是工程师和其他岗位的人产生冲突的原因之一）。延伸一下，在指定相关指标时，必然需要对应用 / 服务做尽量全面的测试，模拟生产环境数据，压力测试，极端环境下系统的稳定性，预留安全冗余等。因为这些东西都关乎金钱。这似乎与沉迷于代码世界的工程师们的认知有偏差，但当思考的角度从「如何让这个系统更好」转换到「系统性能已经被确定，如何完全挖掘出其性能指标并提供给客户，保证收入」时，这些思考便水到渠成了。

    排队延迟（queueing delay） 通常占了高百分位点处响应时间的很大一部分。由于服务器只能并行处理少量的事务（如受其 CPU 核数的限制），所以只要有少量缓慢的请求就能阻碍后续请求的处理，这种效应有时被称为 头部阻塞（head-of-line blocking） 。

    在头部阻塞情况下，提交了其他请求的客户端感知可能是「少量的数据依然需要较高的处理时间，fuck」。

    适应某个级别负载的架构不太可能应付 10 倍于此的负载。如果你正在开发一个快速增长的服务，那么每次负载发生数量级的增长时，你可能都需要重新考虑架构 —— 或者更频繁。

    人们经常讨论 纵向伸缩（scaling up，也称为垂直伸缩，即 vertical scaling，转向更强大的机器）和 横向伸缩（scaling out，也称为水平伸缩，即 horizontal scaling，将负载分布到多台小机器上）之间的对立。跨多台机器分配负载也称为 “无共享（shared-nothing）” 架构。可以在单台机器上运行的系统通常更简单，但高端机器可能非常贵，所以非常密集的负载通常无法避免地需要横向伸缩。现实世界中的优秀架构需要将这两种方法务实地结合，因为使用几台足够强大的机器可能比使用大量的小型虚拟机更简单也更便宜。

    在讨论负载数量级和横纵向伸缩的时候，往往跟硬件条件关联起来，这似乎也是实践中的事实——升级硬件而不是做算法上的优化。

    跨多台机器部署 无状态服务（stateless services） 非常简单，但将带状态的数据系统从单节点变为分布式配置则可能引入许多额外复杂度。出于这个原因，常识告诉我们应该将数据库放在单个节点上（纵向伸缩），直到伸缩成本或可用性需求迫使其改为分布式。

    不要忘了数据库也是一种服务，而且往往是带状态的服务。这里的 状态 理解为后续请求的结果依赖于前置请求（当前数据库的数据状态）。

    一个良好适配应用的可伸缩架构，是围绕着 假设（assumption） 建立的：哪些操作是常见的？哪些操作是罕见的？这就是所谓负载参数。如果假设最终是错误的，那么为伸缩所做的工程投入就白费了，最糟糕的是适得其反。在早期创业公司或非正式产品中，通常支持产品快速迭代的能力，要比可伸缩至未来的假想负载要重要的多。

    假设，市场调研很重要，信息（情报）很重要。在务实的时候，信息不应该被忽视。

    负载，伸缩。可以具象化地想象为一块木板承载弹球。

    复杂度（complexity） 有各种可能的症状，例如：状态空间激增、模块间紧密耦合、纠结的依赖关系、不一致的命名和术语、解决性能问题的 Hack、需要绕开的特例等等，现在已经有很多关于这个话题的讨论【31,32,33】。

    用一些术语来描述复杂度提升的原因。试着用起来吧。

    简化系统并不一定意味着减少功能；它也可以意味着消除 额外的（accidental） 的复杂度。 Moseley 和 Marks【32】把 额外复杂度 定义为：由具体实现中涌现，而非（从用户视角看，系统所解决的）问题本身固有的复杂度。

    理解这句话，在开发过程中，由工具本身带来的局限性导致需要做一些额外的处理，这可以算作额外复杂度。

    用于消除 额外复杂度 的最好工具之一是 抽象（abstraction）。一个好的抽象可以将大量实现细节隐藏在一个干净，简单易懂的外观下面。一个好的抽象也可以广泛用于各类不同应用。比起重复造很多轮子，重用抽象不仅更有效率，而且有助于开发高质量的软件。抽象组件的质量改进将使所有使用它的应用受益。

    例如，高级编程语言是一种抽象，隐藏了机器码、CPU 寄存器和系统调用。 SQL 也是一种抽象，隐藏了复杂的磁盘 / 内存数据结构、来自其他客户端的并发请求、崩溃后的不一致性。当然在用高级语言编程时，我们仍然用到了机器码；只不过没有 直接（directly） 使用罢了，正是因为编程语言的抽象，我们才不必去考虑这些实现细节。

    在许多时候我们都在讨论 抽象，这意味着透过现象看到本质，将问题直截了当，但又不失优雅（这需要大量的经验）地用合适的标准化流程将其解决。这是一件非常困难的事情。

    直到现在我还没有熟悉各种常用的设计模式，long way to go.

    在组织流程方面， 敏捷（agile） 工作模式为适应变化提供了一个框架。敏捷社区还开发了对在频繁变化的环境中开发软件很有帮助的技术工具和模式，如 测试驱动开发（TDD, test-driven development） 和 重构（refactoring） 。

    需求一直在变。面对一直变化的需求，文中提到的解决方案在如今看来似乎已经司空见惯，小范围的重构和 TDD 也为将 抽象 应用于实践提供了便利。但本书将讨论更广义的提高敏捷性的办法。

    这有别于其他之前看过的技术书籍——屏蔽掉编码细节，只讨论数据系统和数据模型等，这也是一种 抽象。

    本章介绍了一些数据系统的评价指标，接下来将看到作者介绍其他数据系统用例。

    后记 ​

    在微软工作的那段时间，Galaxy team 所负责的 Galaxy 平台无疑就是一个数据流处理系统，当时在工作中没有对工作内容开放性地进行思考，当时隐约窥见了所有应用的本质其实都是对数据进行清洗、处理、转换等等。数据系统、数据管道（data pipeline）等概念在这个时候初入脑海。

    随后我阅读了《Kafka 权威指南》这本书。Kafka 作为顶级消息队列解决方案，在其设计和使用时处处透露出我们工作的本质——搬运数据。这本书更加验证了我的这个想法。

    跳出一直在做的 CRUD 这个圈子，从更高的角度思考我们所做的工作，可能更有利于提升自己，找到正确的方向。BTW，在微软的工作往往可以让我感觉我是真的在处理并解决问题，而非将数据库的数据搬运到接口中。

    无论如何，希望自己可以永远进步下去，保持对代码的热爱之心。

    《Designing Data-Intensive Applications》 读书笔记 | 0x01
    关于代码规范的一些粗浅想法
    © 2022 - 2024
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    由 Valaxy v0.18.6 驱动 | 主题 - Yun v0.18.6
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    《Designing Data-Intensive Applications》 读书笔记 | 0x01

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    第二章：数据模型与查询语言 ​

    多数应用使用层层叠加的数据模型构建。对于每层数据模型的关键问题是：它是如何用低一层数据模型来 表示 的？例如：

    1. 作为一名应用开发人员，你观察现实世界（里面有人员、组织、货物、行为、资金流向、传感器等），并采用对象或数据结构，以及操控那些数据结构的 API 来进行建模。那些结构通常是特定于应用程序的。
    2. 当要存储那些数据结构时，你可以利用通用数据模型来表示它们，如 JSON 或 XML 文档、关系数据库中的表或图模型。
    3. 数据库软件的工程师选定如何以内存、磁盘或网络上的字节来表示 JSON / XML/ 关系 / 图数据。这类表示形式使数据有可能以各种方式来查询，搜索，操纵和处理。
    4. 在更低的层次上，硬件工程师已经想出了使用电流、光脉冲、磁场或者其他东西来表示字节的方法。

    由上往下，作者分别从应用层、传输层、持久化（IO 数据传输）、硬件四个层次对数据模型的表现形式做了举例。

    值得注意的是，应用层的数据模型在设计的时候往往是 面向对象 思想比较直观的表现，这种「直观」表现中必然包含 抽象 。

    更底层一些的数据模型显然依赖于应用层表现出的 抽象 行为。

    一个复杂的应用程序可能会有更多的中间层次，比如基于 API 的 API，不过基本思想仍然是一样的：每个层都通过提供一个明确的数据模型来隐藏更低层次中的复杂性。这些抽象允许不同的人群有效地协作（例如数据库厂商的工程师和使用数据库的应用程序开发人员）。

    作者在这里也提到了 抽象 的概念。不同的是，作者从硬件自底向上地描述 抽象 行为对 层次之间协作 的重要性。

    掌握一个数据模型需要花费很多精力（想想关系数据建模有多少本书）。即便只使用一个数据模型，不用操心其内部工作机制，构建软件也是非常困难的。然而，因为数据模型对上层软件的功能（能做什么，不能做什么）有着至深的影响，所以选择一个适合的数据模型是非常重要的。

    我在考虑从一开始建立数据模型的时候是从 应用层 开始思考，作者是从底层数据模型对上层软件功能的影响开始思考。

    这让我想到，我的思维模式还停留在 应用层 开发，根据应用所需去选取合适的数据模型（包括面向对象设计和数据库的选取）。但是面对 数据密集型应用 ，可能从数据模型的选取开始思考才是更合适的。

    这是一种思维习惯和经验带来的差异，理应学会作者的这种思维方式。

    目前大多数应用程序开发都使用面向对象的编程语言来开发，这导致了对 SQL 数据模型的普遍批评：如果数据存储在关系表中，那么需要一个笨拙的转换层，处于应用程序代码中的对象和表，行，列的数据库模型之间。模型之间的不连贯有时被称为 阻抗不匹配（impedance mismatch）

    关系表 和 面向对象编程 之间确实存在一种反技术直觉的阻抗。就像本应如电流般顺畅的数据传输受到了阻力，需要添加一个臃肿、难以使用而又经常出错的转换器。

    ORM 框架减少了转换所需的工作量，但是问题依然客观存在。

    JSON 表示比 [图 2-1]

    中的多表模式具有更好的 局部性（locality）。如果在前面的关系型示例中获取简介，那需要执行多个查询（通过 user_id 查询每个表），或者在 User 表与其下属表之间混乱地执行多路连接。而在 JSON 表示中，所有相关信息都在同一个地方，一个查询就足够了。

    从用户简介文件到用户职位，教育历史和联系信息，这种一对多关系隐含了数据中的一个树状结构，而 JSON 表示使得这个树状结构变得明确（见 [图 2-2]

    JSON 相比于关系数据模型，优势在于能自由组织不同的相关信息，并在任一层级将整个数据单元独立出来。这就是作者所说的 局部性。

    在上一节的 [例 2-1] 中，region_id 和 industry_id 是以 ID，而不是纯字符串 “Greater Seattle Area” 和 “Philanthropy” 的形式给出的。为什么？

    如果用户界面用一个自由文本字段来输入区域和行业，那么将他们存储为纯文本字符串是合理的。另一方式是给出地理区域和行业的标准化的列表，并让用户从下拉列表或自动填充器中进行选择，其优势如下：

    • 各个简介之间样式和拼写统一
    • 避免歧义（例如，如果有几个同名的城市）
    • 易于更新 —— 名称只存储在一个地方，如果需要更改（例如，由于政治事件而改变城市名称），很容易进行全面更新。
    • 本地化支持 —— 当网站翻译成其他语言时，标准化的列表可以被本地化，使得地区和行业可以使用用户的语言来显示
    • 更好的搜索 —— 例如，搜索华盛顿州的慈善家就会匹配这份简介，因为地区列表可以编码记录西雅图在华盛顿这一事实（从 “Greater Seattle Area” 这个字符串中看不出来）

    存储 ID 还是文本字符串，这是个 副本（duplication） 问题。当使用 ID 时，对人类有意义的信息（比如单词：Philanthropy）只存储在一处，所有引用它的地方使用 ID（ID 只在数据库中有意义）。当直接存储文本时，对人类有意义的信息会复制在每处使用记录中。

    使用 ID 的好处是，ID 对人类没有任何意义，因而永远不需要改变：ID 可以保持不变，即使它标识的信息发生变化。任何对人类有意义的东西都可能需要在将来某个时候改变 —— 如果这些信息被复制，所有的冗余副本都需要更新。这会导致写入开销，也存在不一致的风险（一些副本被更新了，还有些副本没有被更新）。去除此类重复是数据库 规范化（normalization） 的关键思想。

    这一段将我工作中曾经捕风捉影观测到的问题，用高度精炼的语言概括了出来。关键词是 副本，规范化（标准化）。

    我的看法是，在需要使用 ID 的地方，往往我们是需要 ID 带来的 一致性，即 副本不可变性 的。这有时候是一种优势，辨证来看，这也增加了模块之间的 耦合。

    使用 字符串 副本相应的可以做 解耦，但是也导致了不一致的问题。

    应该根据场景灵活选择。

    不幸的是，对这些数据进行规范化需要多对一的关系（许多人生活在一个特定的地区，许多人在一个特定的行业工作），这与文档模型不太吻合。在关系数据库中，通过 ID 来引用其他表中的行是正常的，因为连接很容易。在文档数据库中，一对多树结构没有必要用连接，对连接的支持通常很弱 。

    : 在撰写本文时，RethinkDB 支持连接，MongoDB 不支持连接，而 CouchDB 只支持预先声明的视图。

    在我的计算机的学习过程中常常发现，在不变更底层算法的情况下，往往是用一种资源来换取另一种资源，最常见的就是空间换时间或时间换空间。

    使用文档数据库时，数据组织相对松散，可能带来的是轻便和易用（例如使用 JSON 时）。但在需要 连接 的地方显得有些力不从心。这也是一种取舍。

    这往往让我想起「没有银弹」。

    后记 ​

    这一章主要介绍了多种数据模型，除了 SQL 和 NoSQL，还有图模型和其他旧式但依然很有意义的模型。

    拓宽眼界，先知道有这个东西，适用于什么情景，这样在做选型的时候才能更得心应手。

    《Designing Data-Intensive Applications》 读书笔记 | 0x02
    《Designing Data-Intensive Applications》 读书笔记 | 0x00
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    《Designing Data-Intensive Applications》 读书笔记 | 0x02

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    本文最后更新于1 分钟前，文中所描述的信息可能已发生改变。

    第三章：存储与检索 ​

    为了高效查找数据库中特定键的值，我们需要一个数据结构：索引（index）。本章将介绍一系列的索引结构，并在它们之间进行比较。索引背后的大致思想是通过保存一些额外的元数据作为路标来帮助你找到想要的数据。如果你想以几种不同的方式搜索同一份数据，那么你也许需要在数据的不同部分上建立多个索引。

    没有废话，提到了 元数据 这个概念，可以在后面多思考一下，索引 里为什么一定要加这些元数据，这些元数据是怎样让搜索加速的。

    让我们从 键值数据（key-value Data） 的索引开始。这不是你可以索引的唯一数据类型，但键值数据是很常见的。在引入更复杂的索引之前，它是重要的第一步。

    键值存储与在大多数编程语言中可以找到的 字典（dictionary） 类型非常相似，通常字典都是用 散列映射（hash map） 或 散列表（hash table） 实现的。散列映射在许多算法教科书中都有描述【1,2】，所以这里我们不会讨论它的工作细节。既然我们已经可以用散列映射来表示 内存中 的数据结构，为什么不使用它来索引 硬盘上 的数据呢？

    内存上的散列索引，是不是可以理解为应用里类似 HashMap<K, V>的变量。之所以能表示内存中的数据结构，是因为编程语言为程序封装了寻址、调用等操作。

    而作者既然提到了也可以用来索引 硬盘上 的数据，那么必然也要实现相应的操作。

    像 Bitcask 这样的存储引擎非常适合每个键的值经常更新的情况。例如，键可能是某个猫咪视频的网址（URL），而值可能是该视频被播放的次数（每次有人点击播放按钮时递增）。在这种类型的工作负载中，有很多写操作，但是没有太多不同的键 —— 每个键有很多的写操作，但是将所有键保存在内存中是可行的。

    到目前为止，我们只是在追加写入一个文件 —— 所以如何避免最终用完硬盘空间？一种好的解决方案是，将日志分为特定大小的 段（segment），当日志增长到特定尺寸时关闭当前段文件，并开始写入一个新的段文件。然后，我们就可以对这些段进行 压缩（compaction），如 [图 3-2]

    所示。这里的压缩意味着在日志中丢弃重复的键，只保留每个键的最近更新。

    图 3-2 键值更新日志（统计猫咪视频的播放次数）的压缩，只保留每个键的最近值

    而且，由于压缩经常会使得段变得很小（假设在一个段内键被平均重写了好几次），我们也可以在执行压缩的同时将多个段合并在一起，如 [图 3-3]

    所示。段被写入后永远不会被修改，所以合并的段被写入一个新的文件。冻结段的合并和压缩可以在后台线程中完成，这个过程进行的同时，我们仍然可以继续使用旧的段文件来正常提供读写请求。合并过程完成后，我们将读取请求转换为使用新合并的段而不是旧的段 —— 然后旧的段文件就可以简单地删除掉了。

    不难看出这是一种非常好的处理思路。通过「压缩」这种方式处理旧数据，维护有限占用空间里的一份最新数据。这里可以想到多线程处理，主线程负责数据的更新，即写操作；其他线程将之前生成的「日志」文件读取并压缩，并视情况做其他处理。

    BTW，作者在后面提到了一些实现过程中的细节，越来越像 Redis 等缓存系统了。

    但是，散列表索引也有其局限性：

    • 散列表必须能放进内存。如果你有非常多的键，那真是倒霉。原则上可以在硬盘上维护一个散列映射，不幸的是硬盘散列映射很难表现优秀。它需要大量的随机访问 I/O，而后者耗尽时想要再扩充是很昂贵的，并且需要很烦琐的逻辑去解决散列冲突【5】。
    • 范围查询效率不高。例如，你无法轻松扫描 kitty00000 和 kitty99999 之间的所有键 —— 你必须在散列映射中单独查找每个键。

    我刚刚提到的硬盘上做散列映射的想法，应该是由于 IO 效率问题很难被实际应用。

    不知道 SSD 上的表现会不会好一些。

    现在我们可以对段文件的格式做一个简单的改变：要求键值对的序列按键排序。乍一看，这个要求似乎打破了我们使用顺序写入的能力，我们将稍后再回到这个问题。

    我们把这个格式称为 排序字符串表（Sorted String Table），简称 SSTable。我们还要求每个键只在每个合并的段文件中出现一次（压缩过程已经保证）。与使用散列索引的日志段相比，SSTable 有几个大的优势：

    篇幅限制不引用具体优势了。这让我有了一些思考：

    1. 索引结构是不是最好是基于 已排序 数据上的，或者索引结构本身包含 已排序 这个特性。
    2. 基于 已排序 这个特性，二分查找似乎已经是效率最高的，普适性最强的查找算法了。树这种数据结构就很适合使用二分查找。作者循循善诱，给出了现代数据系统技术选型的来龙去脉。

    构建和维护SSTables ​

    到目前为止还不错，但是如何让你的数据能够预先排好序呢？毕竟我们接收到的写入请求可能以任何顺序发生。

    虽然在硬盘上维护有序结构也是可能的（请参阅 “[B 树]”），但在内存保存则要容易得多。有许多可以使用的众所周知的树形数据结构，例如红黑树或 AVL 树【2】。使用这些数据结构，你可以按任何顺序插入键，并按排序顺序读取它们。

    这里可以看出来，其实树这种数据结构，在应用的时候除了本身的特性，还要参考 IO 速率。在硬盘和内存上是两种差距很大的应用场景。

    Lucene，是一种全文搜索的索引引擎，在 Elasticsearch 和 Solr 被使用，它使用类似的方法来存储它的关键词词典【12,13】。全文索引比键值索引复杂得多，但是基于类似的想法：在搜索查询中，由一个给定的单词，找到提及单词的所有文档（网页，产品描述等）。这也是通过键值结构实现的：其中键是 单词（term），值是所有包含该单词的文档的 ID 列表（postings list）。在 Lucene 中，从词语到记录列表的这种映射保存在类似于 SSTable 的有序文件中，并根据需要在后台执行合并【14】。

    提到了 Elasticsearch，后面学习的时候可以注意一下啊。

    以 单词 为键，用大量空间（但是使用压缩和树结构进行优化）换取时间。当把其中一项做到极致，比如特别快或者占用磁盘空间特别小时，也是一种成功。或者这时候应用范围会更广——相比于特点中庸的应用。

    B树 ​

    前面讨论的日志结构索引看起来已经相当可用了，但它们却不是最常见的索引类型。使用最广泛的索引结构和日志结构索引相当不同，它就是我们接下来要讨论的 B 树。

    从 1970 年被引入【17】，仅不到 10 年后就变得 “无处不在”【18】，B 树很好地经受了时间的考验。在几乎所有的关系数据库中，它们仍然是标准的索引实现，许多非关系数据库也会使用到 B 树。

    像 SSTables 一样，B 树保持按键排序的键值对，这允许高效的键值查找和范围查询。但这也就是仅有的相似之处了：B 树有着非常不同的设计理念。

    终于到 B 树了！这里提到 B 树的设计理念与上文提到的 日志结构索引 不同。先独立思考一下。

    B 树也是有序的树形结构，每一层都存储数据，有序且进行了分区。一直维护最新的数据。

    我们前面看到的日志结构索引将数据库分解为可变大小的段，通常是几兆字节或更大的大小，并且总是按顺序写入段。相比之下，B 树将数据库分解成固定大小的 块（block） 或 分页（page），传统上大小为 4KB（有时会更大），并且一次只能读取或写入一个页面。这种设计更接近于底层硬件，因为硬盘空间也是按固定大小的块来组织的。

    每个页面都可以使用地址或位置来标识，这允许一个页面引用另一个页面 —— 类似于指针，但在硬盘而不是在内存中。我们可以使用这些页面引用来构建一个页面树，如 [图 3-6]

    所示。

    果然自己思考没有想到重点，作者想说的重点是， B 树使用了 更接近底层硬件的设计，使用固定大小的块（block）或者分页（page）来组织数据。

    BTW，在上文中曾经提过在保存数据时是保存 副本 还是 引用，在「时间」这个指标相对来说更为重要的阶段，通常都会使用大量的「空间」，而保存 副本 所占用的「空间」是不可估量的，保存引用可能是唯一可行的出路。

    在 B 树的一个页面中对子页面的引用的数量称为 分支因子（branching factor）。例如，在 [图 3-6]

    中，分支因子是 6。在实践中，分支因子的大小取决于存储页面引用和范围边界所需的空间，但这个值通常是几百。

    分支因子 的数量级也显示出 B 树在应对大数据量时依然游刃有余，容量很大。

    与之相对的，B 树的 层高 应该不会很大。

    图 3-7 通过分割页面来生长 B 树

    [^ii]: 向 B 树中插入一个新的键是相当符合直觉的，但删除一个键（同时保持树平衡）就会牵扯很多其他东西了【2】。

    这个算法可以确保树保持平衡：具有 n 个键的 B 树总是具有 O(logn)O (log n) 的深度。大多数数据库可以放入一个三到四层的 B 树，所以你不需要追踪多个页面引用来找到你正在查找的页面（分支因子为 500 的 4KB 页面的四层树可以存储多达 256TB 的数据）。

    为什么要设计成「矮胖」形式而不是「瘦高」形式呢？我想，这里面还是跟「B 树使用更接近底层硬件的设计」有关，载入一页（一层）之后，在页内进行检索无需进行 IO 操作，时空间开销更小。

    同时 O(logn)O (log n) 的复杂度使每一层的 分支因子 数量级可以稍微大一些。

    让B树更可靠 ​

    B 树的基本底层写操作是用新数据覆写硬盘上的页面，并假定覆写不改变页面的位置：即，当页面被覆写时，对该页面的所有引用保持完整。这与日志结构索引（如 LSM 树）形成鲜明对比，后者只追加到文件（并最终删除过时的文件），但从不修改文件中已有的内容。

    你可以把覆写硬盘上的页面对应为实际的硬件操作。在磁性硬盘驱动器上，这意味着将磁头移动到正确的位置，等待旋转盘上的正确位置出现，然后用新的数据覆写适当的扇区。在固态硬盘上，由于 SSD 必须一次擦除和重写相当大的存储芯片块，所以会发生更复杂的事情【19】。

    而且，一些操作需要覆写几个不同的页面。例如，如果因为插入导致页面过满而拆分页面，则需要写入新拆分的两个页面，并覆写其父页面以更新对两个子页面的引用。这是一个危险的操作，因为如果数据库在系列操作进行到一半时崩溃，那么最终将导致一个损坏的索引（例如，可能有一个孤儿页面没有被任何页面引用） 。

    为了使数据库能处理异常崩溃的场景，B 树实现通常会带有一个额外的硬盘数据结构：预写式日志（WAL，即 write-ahead log，也称为 重做日志，即 redo log）。这是一个仅追加的文件，每个 B 树的修改在其能被应用到树本身的页面之前都必须先写入到该文件。当数据库在崩溃后恢复时，这个日志将被用来使 B 树恢复到一致的状态【5,20】。

    内容有点多，简而言之。

    1. B 树的查找效率可能很高，但是在面对插入删除操作时显得有些力不从心。不但有风险，开销还不小。
    2. redo log 是附着在 B 树这种数据结构上的一种额外的硬盘数据结构，使用追加的方式写入对 B 树的修改语句。用以应对数据库异常崩溃时数据丢失或索引损坏的情况。

    看到这里，感慨颇多。

    以前对于知识的态度是——欣赏能讲得出来龙去脉深入浅出的，也很爱直接把知识摆在桌子上自己啃的。但是现在看来，循循善诱的讲法还是更胜一筹。这可能也是习惯于填鸭式教育的一种觉醒吧。

    由于反复压缩和合并 SSTables，日志结构索引也会多次重写数据。这种影响 —— 在数据库的生命周期中每笔数据导致对硬盘的多次写入 —— 被称为 写入放大（write amplification）。使用固态硬盘的机器需要额外关注这点，固态硬盘的闪存寿命在覆写有限次数后就会耗尽。

    作者会频繁提到有关硬件的细节，应该重视起来。

    在许多固态硬盘上，固件内部使用了日志结构化算法，以将随机写入转变为顺序写入底层存储芯片，因此存储引擎写入模式的影响不太明显【19】。但是，较低的写入放大率和减少的碎片仍然对固态硬盘更有利：更紧凑地表示数据允许在可用的 I/O 带宽内处理更多的读取和写入请求。

    note.

    LSM树的缺点 ​

    日志结构存储的缺点是压缩过程有时会干扰正在进行的读写操作。尽管存储引擎尝试增量地执行压缩以尽量不影响并发访问，但是硬盘资源有限，所以很容易发生某个请求需要等待硬盘先完成昂贵的压缩操作。对吞吐量和平均响应时间的影响通常很小，但是日志结构化存储引擎在更高百分位的响应时间（请参阅 “[描述性能]”）有时会相当长，而 B 树的行为则相对更具有可预测性【28】。

    压缩的另一个问题出现在高写入吞吐量时：硬盘的有限写入带宽需要在初始写入（记录日志和刷新内存表到硬盘）和在后台运行的压缩线程之间共享。写入空数据库时，可以使用全硬盘带宽进行初始写入，但数据库越大，压缩所需的硬盘带宽就越多。

    如果写入吞吐量很高，并且压缩没有仔细配置好，有可能导致压缩跟不上写入速率。在这种情况下，硬盘上未合并段的数量不断增加，直到硬盘空间用完，读取速度也会减慢，因为它们需要检查更多的段文件。通常情况下，即使压缩无法跟上，基于 SSTable 的存储引擎也不会限制传入写入的速率，所以你需要进行明确的监控来检测这种情况【29,30】。

    LSM 树的效率随着使用时间的增长和数据量的增加而下降。需要提供一个监测指标来防止这种情况的发生和恶化。

    反直觉的是，内存数据库的性能优势并不是因为它们不需要从硬盘读取的事实。只要有足够的内存即使是基于硬盘的存储引擎也可能永远不需要从硬盘读取，因为操作系统在内存中缓存了最近使用的硬盘块。相反，它们更快的原因在于省去了将内存数据结构编码为硬盘数据结构的开销【44】。

    note：Redis 相对于硬盘上的数据系统为什么快？

    术语 交易 / 事务（transaction） 仍留了下来，用于指代一组读写操作构成的逻辑单元。

    note。

    数据仓库 ​

    一个企业可能有几十个不同的交易处理系统：面向终端客户的网站，控制实体商店的收银系统，仓库库存跟踪，车辆路线规划，供应链管理，员工管理等。这些系统中每一个都很复杂，需要专人维护，所以最终这些系统互相之间都是独立运行的。

    这些 OLTP 系统往往对业务运作至关重要，因而通常会要求 高可用 与 低延迟。所以 DBA 会密切关注他们的 OLTP 数据库，他们通常不愿意让业务分析人员在 OLTP 数据库上运行临时的分析查询，因为这些查询通常开销巨大，会扫描大部分数据集，这会损害同时在执行的事务的性能。

    除去 SQL 语句本身的复杂性，在大规模的数据集上进行操作时，可能简单的 SQL 也会带来巨大的性能开销。

    相比之下，数据仓库是一个独立的数据库，分析人员可以查询他们想要的内容而不影响 OLTP 操作【48】。数据仓库包含公司各种 OLTP 系统中所有的只读数据副本。从 OLTP 数据库中提取数据（使用定期的数据转储或连续的更新流），转换成适合分析的模式，清理并加载到数据仓库中。将数据存入仓库的过程称为 “抽取 - 转换 - 加载（ETL）”，如 [图 3-8]

    所示。

    ETL 好像是现在大数据提到的概念来着。之前在微软的时候也看到了 spark 和 Kafka 这种用以处理大规模数据的系统。

    对这个想法，有一个巧妙的扩展被 C-Store 发现，并在商业数据仓库 Vertica 中被采用【61,62】：既然不同的查询受益于不同的排序顺序，为什么不以几种不同的方式来存储相同的数据呢？反正数据都需要做备份，以防单点故障时丢失数据。因此你可以用不同排序方式来存储冗余数据，以便在处理查询时，调用最适合查询模式的版本。n

    note，涨知识。

    关于 OLTP，我们最后还介绍了一些更复杂的索引结构，以及针对所有数据都放在内存里而优化的数据库。

    然后，我们暂时放下了存储引擎的内部细节，查看了典型数据仓库的高级架构，并说明了为什么分析工作负载与 OLTP 差别很大：当你的查询需要在大量行中顺序扫描时，索引的重要性就会降低很多。相反，非常紧凑地编码数据变得非常重要，以最大限度地减少查询需要从硬盘读取的数据量。我们讨论了列式存储如何帮助实现这一目标。

    作为一名应用程序开发人员，如果你掌握了有关存储引擎内部的知识，那么你就能更好地了解哪种工具最适合你的特定应用程序。当你调整数据库的优化参数时，这种理解让你能够设想增减某个值会产生怎样的效果。

    尽管本章不能让你成为一个特定存储引擎的调参专家，但它至少大概率使你有了足够的概念与词汇储备去读懂你所选择的数据库的文档。

    第三章内容很多，但是大部分都是在介绍时下流行的一些数据系统，包括大型的公司是怎么处理海量数据的，做了哪些优化，深层次的原因是什么样的。

    还介绍了索引和实现索引使用的各种数据结构，包括 B 树和 B 树的变种（我们常常说的 InnoDB 使用的 B+ 树），讲了这些数据结构的来龙去脉。

    开拓了视野，也能为阅读文档打下基础。

    也让我在讨论相关内容的时候有话可说，有根据可判断。

    BTW，第四章稍微搁置一下，准备回归学习一下具体的应用。

    常用代码片段
    《Designing Data-Intensive Applications》 读书笔记 | 0x01
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    《Designing Data-Intensive Applications》 读书笔记 | 0x03

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    唯变所适

    —— 以弗所的赫拉克利特，为柏拉图所引（公元前 360 年）

    远程过程调用（RPC）的问题 ​

    Web 服务仅仅是通过网络进行 API 请求的一系列技术的最新版本，其中许多技术受到了大量的炒作，但是存在严重的问题。Enterprise JavaBeans（EJB）和 Java 的 远程方法调用（RMI） 仅限于 Java。分布式组件对象模型（DCOM） 仅限于 Microsoft 平台。公共对象请求代理体系结构（CORBA） 过于复杂，不提供向后或向前兼容性【41】。

    所有这些都是基于 远程过程调用（RPC） 的思想，该过程调用自 20 世纪 70 年代以来一直存在【42】。RPC 模型试图向远程网络服务发出请求，看起来与在同一进程中调用编程语言中的函数或方法相同（这种抽象称为位置透明）。尽管 RPC 起初看起来很方便，但这种方法根本上是有缺陷的【43,44】。网络请求与本地函数调用非常不同：

    • 本地函数调用是可预测的，并且成功或失败仅取决于受你控制的参数。网络请求是不可预测的：请求或响应可能由于网络问题会丢失，或者远程计算机可能很慢或不可用，这些问题完全不在你的控制范围之内。网络问题很常见，因此必须有所准备，例如重试失败的请求。
    • 本地函数调用要么返回结果，要么抛出异常，或者永远不返回（因为进入无限循环或进程崩溃）。网络请求有另一个可能的结果：由于超时，它返回时可能没有结果。在这种情况下，你根本不知道发生了什么：如果你没有得到来自远程服务的响应，你无法知道请求是否通过（我们将在 [第八章] 更详细地讨论这个问题）。
    • 如果你重试失败的网络请求，可能会发生请求实际上已经完成，只是响应丢失的情况。在这种情况下，重试将导致该操作被执行多次，除非你在协议中建立数据去重机制（幂等性，即 idempotence）。本地函数调用时没有这样的问题。（在 [第十一章] 更详细地讨论幂等性）
    • 每次调用本地函数时，通常需要大致相同的时间来执行。网络请求比函数调用要慢得多，而且其延迟也是非常可变的：好的时候它可能会在不到一毫秒的时间内完成，但是当网络拥塞或者远程服务超载时，可能需要几秒钟的时间才能完成相同的操作。
    • 调用本地函数时，可以高效地将引用（指针）传递给本地内存中的对象。当你发出一个网络请求时，所有这些参数都需要被编码成可以通过网络发送的一系列字节。如果参数是像数字或字符串这样的基本类型倒是没关系，但是对于较大的对象很快就会出现问题。
    • 客户端和服务可以用不同的编程语言实现，所以 RPC 框架必须将数据类型从一种语言翻译成另一种语言。这可能会变得很丑陋，因为不是所有的语言都具有相同的类型 —— 例如回想一下 JavaScript 的数字大于 2532^{53} 的问题（请参阅 “[JSON、XML 和二进制变体]”）。用单一语言编写的单个进程中不存在此问题。

    所有这些因素意味着尝试使远程服务看起来像编程语言中的本地对象一样毫无意义，因为这是一个根本不同的事情。REST 的部分吸引力在于，它并不试图隐藏它是一个网络协议的事实（尽管这似乎并没有阻止人们在 REST 之上构建 RPC 库）。

    总体来说问题是因为由于「网络问题」因素的增加，导致 RPC 调用存在不可控的问题。

    重点强调了 幂等性（idempotence）和较大对象的传输问题。

    强调了语言之间翻译可能导致的「丑陋」。

    对于可演化性，重要的是可以独立更改和部署 RPC 客户端和服务器。与通过数据库流动的数据相比（如上一节所述），我们可以在通过服务进行数据流的情况下做一个简化的假设：**假定所有的服务器都会先更新，其次是所有的客户端。**因此，你只需要在请求上具有向后兼容性，并且对响应具有向前兼容性。

    对请求具有向后兼容性，可以使更新前后的服务器都能应对客户端的请求；对响应具有向前兼容性，可以使尚未迭代的客户端应对已更新的服务器的响应。

    RESTful API 通常使用 JSON（没有正式指定的模式）用于响应，以及用于请求的 JSON 或 URI 编码 / 表单编码的请求参数。添加可选的请求参数并向响应对象添加新的字段通常被认为是保持兼容性的改变。

    JSON 有时候确实还挺好用的。

    You：

    REST 和 RPC的数据传输方式有什么区别

    ChatGPT：

    REST（Representational State Transfer）和 RPC（Remote Procedure Call）是两种不同的通信模式，它们在数据传输方式上有一些区别。

    1. REST（Representational State Transfer）：
      • REST 是一种基于网络的软件架构风格，它使用标准的 HTTP 协议进行通信。
      • 在 REST 中，资源以 URL 的形式暴露，通过 HTTP 方法（如 GET、POST、PUT、DELETE）对资源进行操作。
      • REST 的通信是无状态的，即每个请求都包含足够的信息来处理该请求，服务器不需要保存客户端的状态信息。
      • REST 通常使用 JSON 或 XML 格式来传输数据。
    2. RPC（Remote Procedure Call）：
      • RPC 是一种远程过程调用的机制，允许一个程序在另一个地址空间（通常在远程计算机上）上执行一个子程序。
      • 在 RPC 中，客户端调用服务端的函数或过程，就像调用本地函数一样，但实际上是在不同的地址空间中执行。
      • RPC 通常使用自定义协议来进行通信，通常会在底层使用 TCP 或 UDP 协议。

    区别：

    • 协议和通信方式： REST 使用标准的 HTTP 协议，通过 URL 对资源进行操作；而 RPC 使用自定义的协议，在客户端和服务端之间进行函数调用。
    • 语义和操作： 在 REST 中，操作通常是面向资源的，例如 GET 获取资源、POST 创建资源等；而在 RPC 中，操作是面向函数或过程的，客户端调用服务端的函数。
    • 通信状态： REST 是无状态的，每个请求都包含足够的信息来处理该请求；而在 RPC 中，客户端和服务端之间可能需要维护一些状态信息。

    总的来说，REST 更适合于资源导向的系统设计，而 RPC 更适合于函数调用和远程服务调用。选择 REST 还是 RPC 取决于你的应用程序的需求和设计。

    消息传递中的数据流 ​

    我们一直在研究从一个过程到另一个过程的编码数据流的不同方式。到目前为止，我们已经讨论了 REST 和 RPC（其中一个进程通过网络向另一个进程发送请求并期望尽可能快的响应）以及数据库（一个进程写入编码数据，另一个进程在将来再次读取）。

    在最后一节中，我们将简要介绍一下 RPC 和数据库之间的异步消息传递系统。它们与 RPC 类似，因为客户端的请求（通常称为消息）以低延迟传送到另一个进程。它们与数据库类似，不是通过直接的网络连接发送消息，而是通过称为消息代理（也称为消息队列或面向消息的中间件）的中介来临时存储消息。

    与直接 RPC 相比，使用消息代理有几个优点：

    • 如果收件人不可用或过载，可以充当缓冲区，从而提高系统的可靠性。
    • 它可以自动将消息重新发送到已经崩溃的进程，从而防止消息丢失。
    • 避免发件人需要知道收件人的 IP 地址和端口号（这在虚拟机经常出入的云部署中特别有用）。
    • 它允许将一条消息发送给多个收件人。
    • 将发件人与收件人逻辑分离（发件人只是发布邮件，不关心使用者）。

    然而，与 RPC 相比，差异在于消息传递通信通常是单向的：发送者通常不期望收到其消息的回复。一个进程可能发送一个响应，但这通常是在一个单独的通道上完成的。这种通信模式是异步的：发送者不会等待消息被传递，而只是发送它，然后忘记它。

    虽然翻译不太国内，但是我还是读出了这就是消！息！队！列！

    详细的交付语义因实现和配置而异，但通常情况下，消息代理的使用方式如下：一个进程将消息发送到指定的队列或主题，代理确保将消息传递给那个队列或主题的一个或多个消费者或订阅者。在同一主题上可以有许多生产者和许多消费者。

    ChatGPT：

    这里的“交付语义”可以更清晰地表达为消息传递的可靠性保证和语义含义。可以将这部分重新表达如下：

    详细的消息传递语义因不同的消息代理实现和配置而有所不同，但通常情况下，消息代理确保消息的可靠传递，并提供了不同的交付语义以满足不同的业务需求。交付语义包括以下几种常见的模式：

    1. 至少一次（At Least Once）： 消息代理保证消息至少会被传递一次给消费者，确保消息不会丢失，但可能会导致消息重复传递。
    2. 至多一次（At Most Once）： 消息代理保证消息最多被传递一次给消费者，确保消费者不会接收到重复的消息，但可能会导致消息丢失。
    3. 正好一次（Exactly Once）： 消息代理保证每条消息都会被传递且仅传递一次给消费者，既不会丢失消息，也不会导致消息重复传递，是最强的消息传递保证模式。

    这些交付语义定义了消息代理在消息传递过程中的行为规范，帮助开发人员选择适当的模式以满足应用程序的需求。

    关于 Java 8 中函数式编程的粗浅见解
    常用代码片段
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    《Designing Data-Intensive Applications》 读书笔记 | 0x04

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    与可能出错的东西比，“不可能”出错的东西最显著的特点就是：一旦真的出错，通常就彻底玩完了。

    —— 道格拉斯・亚当斯（1992）

    复制意味着在通过网络连接的多台机器上保留相同数据的副本。正如在 [第二部分] 的介绍中所讨论的那样，我们希望能复制数据，可能出于各种各样的原因：

    • 使得数据与用户在地理上接近（从而减少延迟）
    • 即使系统的一部分出现故障，系统也能继续工作（从而提高可用性）
    • 伸缩可以接受读请求的机器数量（从而提高读取吞吐量）

    继续提到「副本」的概念，回忆「副本」和「引用」的区别，把这个概念应用到沟通中，力求精准表达。

    如果复制中的数据不会随时间而改变，那复制就很简单：将数据复制到每个节点一次就万事大吉。复制的困难之处在于处理复制数据的 变更（change），这就是本章所要讲的。我们将讨论三种流行的变更复制算法：单领导者（single leader，单主），多领导者（multi leader，多主） 和 无领导者（leaderless，无主）。几乎所有分布式数据库都使用这三种方法之一。

    高屋建瓴。

    存储了数据库拷贝的每个节点被称为 副本（replica） 。当存在多个副本时，会不可避免的出现一个问题：如何确保所有数据都落在了所有的副本上？

    每一次向数据库的写入操作都需要传播到所有副本上，否则副本就会包含不一样的数据。最常见的解决方案被称为 基于领导者的复制（leader-based replication） （也称 主动/被动（active/passive） 复制或 主/从（master/slave） 复制），如 [图 5-1] 所示。它的工作原理如下：

    1. 其中一个副本被指定为 领导者（leader），也称为 主库（master|primary） 。当客户端要向数据库写入时，它必须将请求发送给该 领导者，其会将新数据写入其本地存储。
    2. 其他副本被称为 追随者（followers），亦称为 只读副本（read replicas）、从库（slaves）、备库（ secondaries） 或 热备（hot-standby）。每当领导者将新数据写入本地存储时，它也会将数据变更发送给所有的追随者，称之为 复制日志（replication log） 或 变更流（change stream）。每个跟随者从领导者拉取日志，并相应更新其本地数据库副本，方法是按照与领导者相同的处理顺序来进行所有写入。
    3. 当客户想要从数据库中读取数据时，它可以向领导者或任一追随者进行查询。但只有领导者才能接受写入操作（从客户端的角度来看从库都是只读的）。

    图 5-1 基于领导者的（主/从）复制

    这种复制模式是许多关系数据库的内置功能，如 PostgreSQL（从 9.0 版本开始）、MySQL、Oracle Data Guard【2】和 SQL Server 的 AlwaysOn 可用性组【3】。它也被用于一些非关系数据库，包括 MongoDB、RethinkDB 和 Espresso【4】。最后，基于领导者的复制并不仅限于数据库：像 Kafka【5】和 RabbitMQ 高可用队列【6】这样的分布式消息代理也使用它。某些网络文件系统，例如 DRBD 这样的块复制设备也与之类似。

    同步复制与异步复制 ​

    复制系统的一个重要细节是：复制是 同步（synchronously） 发生的还是 异步（asynchronously） 发生的。（在关系型数据库中这通常是一个配置项，其他系统则通常硬编码为其中一个）。

    同步复制的优点是，从库能保证有与主库一致的最新数据副本。如果主库突然失效，我们可以确信这些数据仍然能在从库上找到。缺点是，如果同步从库没有响应（比如它已经崩溃，或者出现网络故障，或其它任何原因），主库就无法处理写入操作。主库必须阻止所有写入，并等待同步副本再次可用。

    因此，将所有从库都设置为同步的是不切实际的：任何一个节点的中断都会导致整个系统停滞不前。实际上，如果在数据库上启用同步复制，通常意味着其中 一个 从库是同步的，而其他的从库则是异步的。如果该同步从库变得不可用或缓慢，则将一个异步从库改为同步运行。这保证你至少在两个节点上拥有最新的数据副本：主库和同步从库。这种配置有时也被称为 半同步（semi-synchronous）【7】。

    设置新从库 ​

    有时候需要设置一个新的从库：也许是为了增加副本的数量，或替换失败的节点。如何确保新的从库拥有主库数据的精确副本？

    简单地将数据文件从一个节点复制到另一个节点通常是不够的：客户端不断向数据库写入数据，数据总是在不断地变化，标准的文件复制会看到数据库的不同部分在不同的时间点的内容，其结果可能没有任何意义。

    可以通过锁定数据库（使其不可用于写入）来使磁盘上的文件保持一致，但是这会违背高可用的目标。幸运的是，设置新从库通常并不需要停机。从概念上讲，其过程如下所示：

    1. 在某个时刻获取主库的一致性快照（如果可能，不必锁定整个数据库）。大多数数据库都具有这个功能，因为它是备份必需的。对于某些场景，可能需要第三方工具，例如用于 MySQL 的 innobackupex【12】。
    2. 将快照复制到新的从库节点。
    3. 从库连接到主库，并拉取快照之后发生的所有数据变更。这要求快照与主库复制日志中的位置精确关联。该位置有不同的名称，例如 PostgreSQL 将其称为 日志序列号（log sequence number，LSN），MySQL 将其称为 二进制日志坐标（binlog coordinates）。
    4. 当从库处理完快照之后积累的数据变更，我们就说它 赶上（caught up） 了主库，现在它可以继续及时处理主库产生的数据变化了。

    建立从库的实际步骤因数据库而异。在某些系统中，这个过程是完全自动化的，而在另外一些系统中，它可能是一个需要由管理员手动执行的、有点神秘的多步骤工作流。

    主库失效：故障切换 ​

    主库失效处理起来相当棘手：其中一个从库需要被提升为新的主库，需要重新配置客户端，以将它们的写操作发送给新的主库，其他从库需要开始拉取来自新主库的数据变更。这个过程被称为 故障切换（failover）。

    所以给主库一个接口，比如 zookeeper？

    复制日志的实现 ​

    基于领导者的复制在底层是如何工作的？实践中有好几种不同的复制方式，所以先简要地看一下。

    基于语句的复制 ​

    在最简单的情况下，主库记录下它执行的每个写入请求（语句，即 statement）并将该语句日志发送给从库。对于关系数据库来说，这意味着每个 INSERT、UPDATE 或 DELETE 语句都被转发给每个从库，每个从库解析并执行该 SQL 语句，就像直接从客户端收到一样。

    虽然听上去很合理，但有很多问题会搞砸这种复制方式：

    • 任何调用 非确定性函数（nondeterministic） 的语句，可能会在每个副本上生成不同的值。例如，使用 NOW() 获取当前日期时间，或使用 RAND() 获取一个随机数。
    • 如果语句使用了 自增列（auto increment），或者依赖于数据库中的现有数据（例如，UPDATE ... WHERE <某些条件>），则必须在每个副本上按照完全相同的顺序执行它们，否则可能会产生不同的效果。当有多个并发执行的事务时，这可能成为一个限制。
    • 有副作用的语句（例如：触发器、存储过程、用户定义的函数）可能会在每个副本上产生不同的副作用，除非副作用是绝对确定性的。

    的确有办法绕开这些问题 —— 例如，当语句被记录时，主库可以用固定的返回值替换掉任何不确定的函数调用，以便所有从库都能获得相同的值。但是由于边缘情况实在太多了，现在通常会选择其他的复制方法。

    基于语句的复制在 5.1 版本前的 MySQL 中被使用到。因为它相当紧凑，现在有时候也还在用。但现在在默认情况下，如果语句中存在任何不确定性，MySQL 会切换到基于行的复制（稍后讨论）。VoltDB 使用了基于语句的复制，但要求事务必须是确定性的，以此来保证安全【15】。

    不幸的是，当应用程序从异步从库读取时，如果从库落后，它可能会看到过时的信息。这会导致数据库中出现明显的不一致：同时对主库和从库执行相同的查询，可能得到不同的结果，因为并非所有的写入都反映在从库中。这种不一致只是一个暂时的状态 —— 如果停止写入数据库并等待一段时间，从库最终会赶上并与主库保持一致。出于这个原因，这种效应被称为 最终一致性（eventual consistency）【22,23】。

    最终一致性中的 “最终” 一词有意进行了模糊化：总的来说，副本落后的程度是没有限制的。在正常的操作中，复制延迟（replication lag），即写入主库到反映至从库之间的延迟，可能仅仅是几分之一秒，在实践中并不显眼。但如果系统在接近极限的情况下运行，或网络中存在问题时，延迟可以轻而易举地超过几秒，甚至达到几分钟。

    因为滞后时间太长引入的不一致性，不仅仅是一个理论问题，更是应用设计中会遇到的真实问题。本节将重点介绍三个在复制延迟时可能发生的问题实例，并简述解决这些问题的一些方法。

    这里的「最终」只承诺会在未来某一时刻追赶上主库，但是并没有时间限制。

    读己之写 ​

    许多应用让用户提交一些数据，然后查看他们提交的内容。可能是用户数据库中的记录，也可能是对讨论主题的评论，或其他类似的内容。提交新数据时，必须将其发送给主库，但是当用户查看数据时，可以通过从库进行读取。如果数据经常被查看，但只是偶尔写入，这是非常合适的。

    但对于异步复制，问题就来了。如 [图 5-3]

    所示：如果用户在写入后马上就查看数据，则新数据可能尚未到达副本。对用户而言，看起来好像是刚提交的数据丢失了，所以他们不高兴是可以理解的。

    图 5-3 用户写入后从旧副本中读取数据。需要写后读 (read-after-write) 的一致性来防止这种异常

    在这种情况下，我们需要 写后读一致性（read-after-write consistency），也称为 读己之写一致性（read-your-writes consistency）【24】。这是一个保证，如果用户重新加载页面，他们总会看到他们自己提交的任何更新。它不会对其他用户的写入做出承诺：其他用户的更新可能稍等才会看到。它保证用户自己的输入已被正确保存。

    如何在基于领导者的复制系统中实现写后读一致性？有各种可能的技术，这里说一些：

    • 对于用户 可能修改过 的内容，总是从主库读取；这就要求得有办法不通过实际的查询就可以知道用户是否修改了某些东西。举个例子，社交网络上的用户个人资料信息通常只能由用户本人编辑，而不能由其他人编辑。因此一个简单的规则就是：总是从主库读取用户自己的档案，如果要读取其他用户的档案就去从库。
    • 如果应用中的大部分内容都可能被用户编辑，那这种方法就没用了，因为大部分内容都必须从主库读取（读伸缩就没效果了）。在这种情况下可以使用其他标准来决定是否从主库读取。例如可以跟踪上次更新的时间，在上次更新后的一分钟内，从主库读。还可以监控从库的复制延迟，防止向任何滞后主库超过一分钟的从库发出查询。
    • 客户端可以记住最近一次写入的时间戳，系统需要确保从库在处理该用户的读取请求时，该时间戳前的变更都已经传播到了本从库中。如果当前从库不够新，则可以从另一个从库读取，或者等待从库追赶上来。这里的时间戳可以是逻辑时间戳（表示写入顺序的东西，例如日志序列号）或实际的系统时钟（在这种情况下，时钟同步变得至关重要，请参阅 “[不可靠的时钟]”）。
    • 如果你的副本分布在多个数据中心（为了在地理上接近用户或者出于可用性目的），还会有额外的复杂性。任何需要由主库提供服务的请求都必须路由到包含该主库的数据中心。

    另一种复杂的情况发生在同一位用户从多个设备（例如桌面浏览器和移动 APP）请求服务的时候。这种情况下可能就需要提供跨设备的写后读一致性：如果用户在一个设备上输入了一些信息，然后在另一个设备上查看，则应该看到他们刚输入的信息。

    在这种情况下，还有一些需要考虑的问题：

    • 记住用户上次更新时间戳的方法变得更加困难，因为一个设备上运行的程序不知道另一个设备上发生了什么。需要对这些元数据进行中心化的存储。
    • 如果副本分布在不同的数据中心，很难保证来自不同设备的连接会路由到同一数据中心。（例如，用户的台式计算机使用家庭宽带连接，而移动设备使用蜂窝数据网络，则设备的网络路由可能完全不同）。如果你的方法需要读主库，可能首先需要把来自该用户所有设备的请求都路由到同一个数据中心。
    1. 部分请求强制读主库
    2. 监控复制延迟，强制读已同步库
    3. 时间戳
    4. 路由请求，中心化存储元数据

    单调读 ​

    在从异步从库读取时可能发生的异常的第二个例子是用户可能会遇到 时光倒流（moving backward in time）。

    如果用户从不同从库进行多次读取，就可能发生这种情况。例如，[图 5-4]

    显示了用户 2345 两次进行相同的查询，首先查询了一个延迟很小的从库，然后是一个延迟较大的从库（如果用户刷新网页时每个请求都被路由到一个随机的服务器，这种情况就很有可能发生）。第一个查询返回了最近由用户 1234 添加的评论，但是第二个查询不返回任何东西，因为滞后的从库还没有拉取到该写入内容。实际上可以认为第二个查询是在比第一个查询更早的时间点上观察系统。如果第一个查询没有返回任何内容，那问题并不大，因为用户 2345 可能不知道用户 1234 最近添加了评论。但如果用户 2345 先看见用户 1234 的评论，然后又看到它消失，这就会让人觉得非常困惑了。

    图 5-4 用户首先从新副本读取，然后从旧副本读取。时间看上去回退了。为了防止这种异常，我们需要单调的读取。

    单调读（monotonic reads）【23】可以保证这种异常不会发生。这是一个比 强一致性（strong consistency） 更弱，但比 最终一致性（eventual consistency） 更强的保证。当读取数据时，你可能会看到一个旧值；单调读仅意味着如果一个用户顺序地进行多次读取，则他们不会看到时间回退，也就是说，如果已经读取到较新的数据，后续的读取不会得到更旧的数据。

    实现单调读的一种方式是确保每个用户总是从同一个副本进行读取（不同的用户可以从不同的副本读取）。例如，可以基于用户 ID 的散列来选择副本，而不是随机选择副本。但是，如果该副本出现故障，用户的查询将需要重新路由到另一个副本。

    实现单调性的方式居然是哈希用户 ID，用以保证不会发生「时间回退」。但是仔细想一下，这其实保证了 每个请求观察系统的时间都是单调的，是合理的。

    一致前缀读 ​

    第三个复制延迟异常的例子违反了因果律。想象一下 Poons 先生和 Cake 夫人之间的以下简短对话：

    Mr. Poons

    Mrs. Cake，你能看到多远的未来？

    Mrs. Cake

    通常约十秒钟，Mr. Poons.

    这两句话之间有因果关系：Cake 夫人听到了 Poons 先生的问题并回答了这个问题。

    现在，想象第三个人正在通过从库来听这个对话。Cake 夫人说的内容是从一个延迟很低的从库读取的，但 Poons 先生所说的内容，从库的延迟要大的多（见 [图 5-5]

    ）。于是，这个观察者会听到以下内容：

    Mrs. Cake

    通常约十秒钟，Mr. Poons.

    Mr. Poons

    Mrs. Cake，你能看到多远的未来？

    对于观察者来说，看起来好像 Cake 夫人在 Poons 先生提问前就回答了这个问题。这种超能力让人印象深刻，但也会把人搞糊涂。【25】。

    图 5-5 如果某些分区的复制速度慢于其他分区，那么观察者可能会在看到问题之前先看到答案。

    要防止这种异常，需要另一种类型的保证：一致前缀读（consistent prefix reads）【23】。这个保证的意思是说：如果一系列写入按某个顺序发生，那么任何人读取这些写入时，也会看见它们以同样的顺序出现。

    这是 分区（partitioned） 或 分片（sharded） 数据库中的一个特殊问题，我们将在 [第六章] 中讨论分区数据库。如果数据库总是以相同的顺序应用写入，而读取总是看到一致的前缀，那么这种异常不会发生。但是在许多分布式数据库中，不同的分区独立运行，因此不存在 全局的写入顺序：当用户从数据库中读取数据时，可能会看到数据库的某些部分处于较旧的状态，而某些则处于较新的状态。

    一种解决方案是，确保任何因果相关的写入都写入相同的分区，但在一些应用中可能无法高效地完成这种操作。还有一些显式跟踪因果依赖关系的算法，我们将在 “[“此前发生” 的关系和并发]” 一节中回到这个话题。

    场景复现非常成功，虽然有点类似时光回溯，但是这是发生在分布式数据库中的问题，没有主库的存在。

    但是关于解决方案会有些复杂，比如跟踪因果，首先要定义什么是因果，然后要分析根据哪个或哪些指标来分析数据间是否存在因果关系。

    除此之外，分析数据的行为想必必须在入库操作之前进行，这就要求算法不能太复杂，又是一个负担。

    等下看看作者给出的解决方案是什么吧。

    如果应用程序开发人员不必担心微妙的复制问题，并可以信赖他们的数据库 “做了正确的事情”，那该多好呀。这就是 事务（transaction） 存在的原因：数据库通过事务提供强大的保证，所以应用程序可以更加简单。

    事务的强大！

    尽管多主复制有这些优势，但也有一个很大的缺点：两个不同的数据中心可能会同时修改相同的数据，写冲突是必须解决的（如 [图 5-6]

    中的 “冲突解决（conflict resolution）”）。本书将在 “[处理写入冲突]” 中详细讨论这个问题。

    由于多主复制在许多数据库中都属于改装的功能，所以常常存在微妙的配置缺陷，且经常与其他数据库功能之间出现意外的反应。比如自增主键、触发器、完整性约束等都可能会有麻烦。因此，多主复制往往被认为是危险的领域，应尽可能避免【28】。

    避免多主复制的架构。

    需要离线操作的客户端 ​

    多主复制的另一种适用场景是：应用程序在断网之后仍然需要继续工作。

    例如，考虑手机，笔记本电脑和其他设备上的日历应用。无论设备目前是否有互联网连接，你需要能随时查看你的会议（发出读取请求），输入新的会议（发出写入请求）。如果在离线状态下进行任何更改，则设备下次上线时，需要与服务器和其他设备同步。

    在这种情况下，每个设备都有一个充当主库的本地数据库（它接受写请求），并且在所有设备上的日历副本之间同步时，存在异步的多主复制过程。复制延迟可能是几小时甚至几天，具体取决于何时可以访问互联网。

    从架构的角度来看，这种设置实际上与数据中心之间的多主复制类似，每个设备都是一个 “数据中心”，而它们之间的网络连接是极度不可靠的。从历史上各类日历同步功能的破烂实现可以看出，想把多主复制用好是多么困难的一件事。

    有一些工具旨在使这种多主配置更容易。例如，CouchDB 就是为这种操作模式而设计的【29】。

    其实有时候，所谓的经验，就是业界对于这件事情的执行现状。

    读修复和反熵 ​

    复制方案应确保最终将所有数据复制到每个副本。在一个不可用的节点重新联机之后，它如何赶上它错过的写入？

    在 Dynamo 风格的数据存储中经常使用两种机制：

    • 读修复（Read repair）

      当客户端并行读取多个节点时，它可以检测到任何陈旧的响应。例如，在 图 5-10 中，用户 2345 获得了来自副本 3 的版本 6 值和来自副本 1 和 2 的版本 7 值。客户端发现副本 3 具有陈旧值，并将新值写回到该副本。这种方法适用于读频繁的值。

    • 反熵过程（Anti-entropy process）

      此外，一些数据存储具有后台进程，该进程不断查找副本之间的数据差异，并将任何缺少的数据从一个副本复制到另一个副本。与基于领导者的复制中的复制日志不同，此反熵过程不会以任何特定的顺序复制写入，并且在复制数据之前可能会有显著的延迟。

    并不是所有的系统都实现了这两种机制，例如，Voldemort 目前没有反熵过程。请注意，如果没有反熵过程，很少被读取的值可能会从某些副本中丢失，从而降低了持久性，因为只有在应用程序读取值时才执行读修复。

    可能需要用多线程来写。

    图 5-11 如果 w+r>nw + r > n，读取 r 个副本，至少有一个副本必然包含了最近的成功写入。

    如果可用的节点少于所需的 w 或 r，则写入或读取将返回错误。节点可能由于多种原因而不可用，比如：节点关闭（异常崩溃，电源关闭）、操作执行过程中的错误（由于磁盘已满而无法写入）、客户端和服务器节点之间的网络中断或任何其他原因。我们只需要关心节点是否返回了成功的响应，而不需要区分不同类型的错误。

    有点像抽屉原理。

    Java Lock Notes
    关于 Java 8 中函数式编程的粗浅见解
    © 2022 - 2024
    苏
    由 Valaxy v0.18.6 驱动 | 主题 - Yun v0.18.6
    \ No newline at end of file diff --git a/posts/Designing%20Data-Intensive%20Applications%20%E8%AF%BB%E4%B9%A6%E7%AC%94%E8%AE%B0%200x05.html b/posts/Designing%20Data-Intensive%20Applications%20%E8%AF%BB%E4%B9%A6%E7%AC%94%E8%AE%B0%200x05.html new file mode 100644 index 0000000..d91c083 --- /dev/null +++ b/posts/Designing%20Data-Intensive%20Applications%20%E8%AF%BB%E4%B9%A6%E7%AC%94%E8%AE%B0%200x05.html @@ -0,0 +1 @@ +《Designing Data-Intensive Applications》 读书笔记 | 0x05 - 苏

    《Designing Data-Intensive Applications》 读书笔记 | 0x05

    -
    179    -
    读书笔记 > DDIA
    DDIASystem Design
    本文最后更新于1 分钟前，文中所描述的信息可能已发生改变。

    我们必须跳出电脑指令序列的窠臼。叙述定义、描述元数据、梳理关系，而不是编写过程。

    —— Grace Murray Hopper，未来的计算机及其管理（1962）

    在 第五章 中，我们讨论了复制 —— 即数据在不同节点上的副本，对于非常大的数据集，或非常高的吞吐量，仅仅进行复制是不够的：我们需要将数据进行 分区（partitions），也称为 分片（sharding）。

    记一次从 Hexo 到 Valaxy 的迁移，GitHub Actions 部署 Github Pages
    LeetCode record 2024-04
    © 2022 - 2024
    苏
    由 Valaxy v0.18.6 驱动 | 主题 - Yun v0.18.6
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    Java Lock Notes

    -
    1.5k    -
    笔记 > Java
    锁JDK
    本文最后更新于1 分钟前，文中所描述的信息可能已发生改变。

    https://tech.meituan.com/2018/11/15/java-lock.html

    美团这篇写的很好，非常适合拿来复习。

    img

    1. 乐观锁 VS 悲观锁 ​

    乐观锁与悲观锁是一种广义上的概念，体现了看待线程同步的不同角度。在Java和数据库中都有此概念对应的实际应用。

    先说概念。对于同一个数据的并发操作，悲观锁认为自己在使用数据的时候一定有别的线程来修改数据，因此在获取数据的时候会先加锁，确保数据不会被别的线程修改。Java中，synchronized关键字和Lock的实现类都是悲观锁。

    而乐观锁认为自己在使用数据时不会有别的线程修改数据，所以不会添加锁，只是在更新数据的时候去判断之前有没有别的线程更新了这个数据。如果这个数据没有被更新，当前线程将自己修改的数据成功写入。如果数据已经被其他线程更新，则根据不同的实现方式执行不同的操作（例如报错或者自动重试）。

    乐观锁在Java中是通过使用无锁编程来实现，最常采用的是CAS算法，Java原子类中的递增操作就通过CAS自旋实现的。

    • 悲观锁适合写操作多的场景，先加锁可以保证写操作时数据正确。
    • 乐观锁适合读操作多的场景，不加锁的特点能够使其读操作的性能大幅提升。
    java
    public volatile AtomicInteger size = new AtomicInteger(0);
+
+void test() {
+    size.incrementAndGet();
+}

    乐观锁且线程安全的方法test()。

    在JNI里是借助于一个CPU指令完成的，属于原子操作，可以保证多个线程都能够看到同一个变量的修改值。

    后续JDK通过CPU的 cmpxchg 指令，去比较寄存器中的 A 和 内存中的值 V。

    CAS虽然很高效，但是它也存在三大问题，这里也简单说一下：

    1. ABA问题

      。CAS需要在操作值的时候检查内存值是否发生变化，没有发生变化才会更新内存值。但是如果内存值原来是A，后来变成了B，然后又变成了A，那么CAS进行检查时会发现值没有发生变化，但是实际上是有变化的。ABA问题的解决思路就是在变量前面添加版本号，每次变量更新的时候都把版本号加一，这样变化过程就从“A－B－A”变成了“1A－2B－3A”。

      • JDK从1.5开始提供了AtomicStampedReference类来解决ABA问题，具体操作封装在compareAndSet()中。compareAndSet()首先检查当前引用和当前标志与预期引用和预期标志是否相等，如果都相等，则以原子方式将引用值和标志的值设置为给定的更新值。

    2. 循环时间长开销大。CAS操作如果长时间不成功，会导致其一直自旋，给CPU带来非常大的开销。

    3. 只能保证一个共享变量的原子操作。

      对一个共享变量执行操作时，CAS能够保证原子操作，但是对多个共享变量操作时，CAS是无法保证操作的原子性的。

      java
      public volatile AtomicInteger size = new AtomicInteger(0);
+public volatile AtomicInteger size2 = new AtomicInteger(0);
+
+void test() {
+    size.incrementAndGet();
+    size2.incrementAndGet();
+}

      乐观锁但非线程安全的方法test()。

      • Java从1.5开始JDK提供了AtomicReference类来保证引用对象之间的原子性，可以把多个变量放在一个对象里来进行CAS操作。

    2. 自旋锁 VS 适应性自旋锁 ​

    在介绍自旋锁前，我们需要介绍一些前提知识来帮助大家明白自旋锁的概念。

    阻塞或唤醒一个Java线程需要操作系统切换CPU状态来完成，这种状态转换需要耗费处理器时间。如果同步代码块中的内容过于简单，状态转换消耗的时间有可能比用户代码执行的时间还要长。

    在许多场景中，同步资源的锁定时间很短，为了这一小段时间去切换线程，线程挂起和恢复现场的花费可能会让系统得不偿失。如果物理机器有多个处理器，能够让两个或以上的线程同时并行执行，我们就可以让后面那个请求锁的线程不放弃CPU的执行时间，看看持有锁的线程是否很快就会释放锁。

    而为了让当前线程“稍等一下”，我们需让当前线程进行自旋，如果在自旋完成后前面锁定同步资源的线程已经释放了锁，那么当前线程就可以不必阻塞而是直接获取同步资源，从而避免切换线程的开销。这就是自旋锁。

    3. 无锁 VS 偏向锁 VS 轻量级锁 VS 重量级锁 ​

    这四种锁是指锁的状态，专门针对synchronized的。在介绍这四种锁状态之前还需要介绍一些额外的知识。

    首先为什么Synchronized能实现线程同步？

    在回答这个问题之前我们需要了解两个重要的概念：“Java对象头”、“Monitor”。

    锁一共有4种状态，级别从低到高依次是：无锁、偏向锁、轻量级锁和重量级锁。锁状态只能升级不能降级。

    锁状态存储内容存储内容
    无锁对象的hashCode、对象分代年龄、是否是偏向锁（0）01
    偏向锁偏向线程ID、偏向时间戳、对象分代年龄、是否是偏向锁（1）01
    轻量级锁指向栈中锁记录的指针00
    重量级锁指向互斥量（重量级锁）的指针10
    LeetCode record 2024-04
    《Designing Data-Intensive Applications》 读书笔记 | 0x04
    © 2022 - 2024
    苏
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    \ No newline at end of file diff --git a/posts/LeetCode%20record%202024-04.html b/posts/LeetCode%20record%202024-04.html new file mode 100644 index 0000000..796f65e --- /dev/null +++ b/posts/LeetCode%20record%202024-04.html @@ -0,0 +1,194 @@ +LeetCode record 2024-04 - 苏

    LeetCode record 2024-04

    -
    2k    -
    LeetCode
    LeetCode算法
    本文最后更新于1 分钟前，文中所描述的信息可能已发生改变。

    1017. 负二进制转换 中等 ​

    给你一个整数 n ，以二进制字符串的形式返回该整数的 **负二进制（base -2）**表示。

    **注意，**除非字符串就是 "0"，否则返回的字符串中不能含有前导零。

    示例 1：

    输入：n = 2
+输出："110"
+解释：(-2)2 + (-2)1 = 2

    示例 2：

    输入：n = 3
+输出："111"
+解释：(-2)2 + (-2)1 + (-2)0 = 3

    示例 3：

    输入：n = 4
+输出："100"
+解释：(-2)2 = 4

    提示：

    • 0 <= n <= 109

    思路： ​

    第一时间没想到短除法（忘了T_T），先写了个 demo 算了下表示 n 的最大位数，然后打算在这个范围内穷举来着，但是太暴力了感觉写了可能也要 TLE，遂放弃。

    然后找规律，从 0 写到了 7，没什么很好的规律。然后搜二进制，想起了短除法，先写了普通二进制的 demo，对于负二进制想必同理，但是余数存在负数的情况，思路卡住，遂看题解。

    但是由于是负数，我们在取余的时候，可能会出现负数。但是二进制是没有负数的，因此我们要将余数为负数修正为余数为正数，即 -1 修正为 1，对应的商就应该增加 1。https://leetcode.cn/problems/convert-to-base-2/solutions/2759014/javapython3cmo-ni-shu-xue-jin-zhi-zhuan-9j7na/

    调整代码，AC。

    java
    package su.github.leetcode;
+
+public class m04d28 {
+    class Solution {
+        public String baseNeg2(int n) {
+
+            if (n == 0) {
+                return "0";
+            }
+
+            StringBuilder sb = new StringBuilder();
+            while (n != 0) {
+                int mo = n % -2;
+                n /= (-2);
+                if (mo < 0) {
+                    n += 1;
+                    mo = -mo;
+                }
+                sb.append(mo);
+            }
+
+            return sb.reverse().toString();
+        }
+    }
+}

    1329. 将矩阵按对角线排序 中等 ​

    矩阵对角线 是一条从矩阵最上面行或者最左侧列中的某个元素开始的对角线，沿右下方向一直到矩阵末尾的元素。例如，矩阵 mat 有 6 行 3 列，从 mat[2][0] 开始的 矩阵对角线 将会经过 mat[2][0]、mat[3][1] 和 mat[4][2] 。

    给你一个 m * n 的整数矩阵 mat ，请你将同一条 矩阵对角线 上的元素按升序排序后，返回排好序的矩阵。

    示例 1：

    img
    输入：mat = [[3,3,1,1],[2,2,1,2],[1,1,1,2]]
+输出：[[1,1,1,1],[1,2,2,2],[1,2,3,3]]

    示例 2：

    输入：mat = [[11,25,66,1,69,7],[23,55,17,45,15,52],[75,31,36,44,58,8],[22,27,33,25,68,4],[84,28,14,11,5,50]]
+输出：[[5,17,4,1,52,7],[11,11,25,45,8,69],[14,23,25,44,58,15],[22,27,31,36,50,66],[84,28,75,33,55,68]]

    提示：

    • m == mat.length
    • n == mat[i].length
    • 1 <= m, n <= 100
    • 1 <= mat[i][j] <= 100

    思路 ​

    模拟题且数据量不大，考虑暴力模拟，按对角线存储数据并排序，后按照遍历顺序放回原位。

    代码有点粗糙，应该还有优化空间，比如取完一条对角线后就排序并放回原位，应该会降低一下常数时间复杂度。不管怎么说，没看题解顺利 AC ，开心捏o(￣▽￣)ブ！

    后查看题解，提到 i−j+mi - j + m 的写法，对于数组int[n][m]，根据同一对角线上的元素的下标之差相等，考虑最差情况下

    i−j=0−(m−1)=1−mi - j = 0 - (m - 1) = 1 - m

    ，右平移 mm 即可将下标平移至以 1 开头的数组中（其实还是存在一个小疑问，为什么是 1 开头不是 0 开头。经测试，0 开头也是没问题的，可能是为了写起来简单吧）。附官方题解代码。

    java
    class Solution {
+    public int[][] diagonalSort(int[][] mat) {
+        int n = mat.length, m = mat[0].length;
+        List<List<Integer>> diag = new ArrayList<>(m + n);
+        for (int i = 0; i < m + n; i++) {
+            diag.add(new ArrayList<>());
+        }
+        for (int i = 0; i < n; i++) {
+            for (int j = 0; j < m; j++) {
+                // i - j + m - 1 也能 AC
+                diag.get(i - j + m).add(mat[i][j]);
+            }
+        }
+        for (List<Integer> d : diag) {
+            Collections.sort(d, Collections.reverseOrder());
+        }
+        for (int i = 0; i < n; i++) {
+            for (int j = 0; j < m; j++) {
+                mat[i][j] = diag.get(i - j + m).removeLast();
+            }
+        }
+        return mat;
+    }
+}
+
+作者：力扣官方题解
+链接：https://leetcode.cn/problems/sort-the-matrix-diagonally/solutions/2754949/jiang-ju-zhen-an-dui-jiao-xian-pai-xu-by-fsf0/
+来源：力扣（LeetCode）
+著作权归作者所有。商业转载请联系作者获得授权，非商业转载请注明出处。
    java
    class Solution {
+    public int[][] diagonalSort(int[][] mat) {
+        List<List<Integer>> diagonalArray = new ArrayList<>();
+
+        // 右上角开始的对角线 mat[3][4]
+        for (int j = mat[0].length - 1; j > 0; --j) {
+            List<Integer> temp = new ArrayList<>();
+            for (int i = 0, jj = j; i <= mat.length - 1 && jj <= mat[i].length - 1; i++, jj++) {
+                temp.add(mat[i][jj]);
+            }
+
+            diagonalArray.add(temp);
+        }
+
+        // [0][0]开始的对角线
+        {
+            int i = 0, j = 0;
+            List<Integer> temp = new ArrayList<>();
+            while (i <= mat.length - 1 && j <= mat[i].length - 1) {
+                temp.add(mat[i][j]);
+                i++;
+                j++;
+            }
+            diagonalArray.add(temp);
+        }
+
+        // 左下角开始的对角线
+        for (int i = mat.length - 1; i > 0; --i) {
+            List<Integer> temp = new ArrayList<>();
+            for (int j = 0, ii = i; j <= mat[i].length - 1 && ii <= mat.length - 1; ++j, ++ii) {
+                temp.add(mat[ii][j]);
+            }
+            diagonalArray.add(temp);
+        }
+
+        System.out.println(diagonalArray);
+
+        List<List<Integer>> sorted = diagonalArray.stream()
+                .map(s -> s.stream().sorted().toList())
+                .toList();
+
+        int[][] ret = new int[mat.length][mat[0].length];
+        int index = 0;
+        // 右上角开始的对角线 mat[3][4]
+        for (int j = mat[0].length - 1; j > 0; --j) {
+            List<Integer> temp = sorted.get(index++);
+            int indexx = 0;
+
+            for (int i = 0, jj = j; i <= mat.length - 1 && jj <= mat[i].length - 1; i++, jj++) {
+                ret[i][jj] = temp.get(indexx++);
+            }
+
+            diagonalArray.add(temp);
+        }
+
+        // [0][0]开始的对角线
+        {
+            int i = 0, j = 0;
+
+            int indexx = 0;
+            List<Integer> temp = sorted.get(index++);
+            while (i <= mat.length - 1 && j <= mat[i].length - 1) {
+                ret[i][j] = temp.get(indexx++);
+                i++;
+                j++;
+            }
+        }
+
+        // 左下角开始的对角线
+        for (int i = mat.length - 1; i > 0; --i) {
+            List<Integer> temp = sorted.get(index++);
+            int indexx = 0;
+
+            for (int j = 0, ii = i; j <= mat[i].length - 1 && ii <= mat.length - 1; ++j, ++ii) {
+                ret[ii][j] = temp.get(indexx++);
+            }
+            diagonalArray.add(temp);
+        }
+
+        return ret;
+    }
+}

    2. 两数相加 中等 ​

    给你两个 非空 的链表，表示两个非负的整数。它们每位数字都是按照 逆序 的方式存储的，并且每个节点只能存储 一位 数字。

    请你将两个数相加，并以相同形式返回一个表示和的链表。

    你可以假设除了数字 0 之外，这两个数都不会以 0 开头。

    示例 1：

    img
    输入：l1 = [2,4,3], l2 = [5,6,4]
+输出：[7,0,8]
+解释：342 + 465 = 807.

    示例 2：

    输入：l1 = [0], l2 = [0]
+输出：[0]

    示例 3：

    输入：l1 = [9,9,9,9,9,9,9], l2 = [9,9,9,9]
+输出：[8,9,9,9,0,0,0,1]

    提示：

    • 每个链表中的节点数在范围 [1, 100] 内
    • 0 <= Node.val <= 9
    • 题目数据保证列表表示的数字不含前导零

    思路 ​

    当日的每日一题太水了，半分钟 A 了之后从题库开头找了个 medium 题做。

    看题意感觉是简化后的链表加法模拟，直接上手做，可能是自己写法的问题，循环退出条件使用了.next != null，但是实际上使用当前节点不为空会更好写一点，有很多情况不用再特殊处理了。别的没什么好说的，主要是要注意细节，上 AC 代码。

    后看了一下官方题解，官方是两条链表并行遍历到都为null，中间的写法更鲁棒一些，代码行数更短。无所谓孰优孰劣吧，我的写法应该胜在分段逻辑清晰（？）。

    java
    public ListNode addTwoNumbers(ListNode l1, ListNode l2) {
+
+    ListNode ans = new ListNode(0);
+    ListNode head = ans;
+    boolean greaterThan10 = false;
+
+    while (l1.next != null && l2.next != null) {
+        // calc logic
+        ans.val = ((l1.val + l2.val) + (greaterThan10 ? 1 : 0)) % 10;
+        greaterThan10 = ((l1.val + l2.val) + (greaterThan10 ? 1 : 0)) >= 10;
+
+        // node move forward
+        ans.next = new ListNode(0);
+        ans = ans.next;
+        l1 = l1.next;
+        l2 = l2.next;
+    }
+
+    ans.val = ((l1.val + l2.val) + (greaterThan10 ? 1 : 0)) % 10;
+    greaterThan10 = ((l1.val + l2.val) + (greaterThan10 ? 1 : 0)) >= 10;
+
+    ListNode unfinished = l1.next != null ? l1.next : l2.next != null ? l2.next : null;
+    if (unfinished != null) {
+        ans.next = unfinished;
+        ans = ans.next;
+
+        while (greaterThan10 && ans.next != null) {
+            ans.val += greaterThan10 ? 1 : 0;
+            greaterThan10 = ans.val >= 10;
+            ans.val %= 10;
+            ans = ans.next;
+        }
+
+        ans.val += greaterThan10 ? 1 : 0;
+        greaterThan10 = ans.val >= 10;
+        ans.val %= 10;
+
+        if (greaterThan10) {
+            ans.next = new ListNode(1);
+            greaterThan10 = false;
+        }
+    }
+
+    if (greaterThan10) {
+        ans.next = new ListNode(1);
+    }
+
+    return head;
+}
    《Designing Data-Intensive Applications》 读书笔记 | 0x05
    Java Lock Notes
    © 2022 - 2024
    苏
    由 Valaxy v0.18.6 驱动 | 主题 - Yun v0.18.6
    \ No newline at end of file diff --git a/posts/LeetCode%20record%202024-05.html b/posts/LeetCode%20record%202024-05.html new file mode 100644 index 0000000..3768220 --- /dev/null +++ b/posts/LeetCode%20record%202024-05.html @@ -0,0 +1,326 @@ +LeetCode record 2024-05 - 苏

    LeetCode record 2024-05

    5.2k    -
    LeetCode
    LeetCode算法
    本文最后更新于1 分钟前，文中所描述的信息可能已发生改变。

    如果有一天没有打卡，会不会辜负踌躇满志的那个自己。

    2462. 雇佣 K 位工人的总代价 中等 ​

    给你一个下标从 0 开始的整数数组 costs ，其中 costs[i] 是雇佣第 i 位工人的代价。

    同时给你两个整数 k 和 candidates 。我们想根据以下规则恰好雇佣 k 位工人：

    • 总共进行 k 轮雇佣，且每一轮恰好雇佣一位工人。

    • 在每一轮雇佣中，从最前面 candidates 和最后面 candidates 人中选出代价最小的一位工人，如果有多位代价相同且最小的工人，选择下标更小的一位工人。

      • 比方说，costs = [3,2,7,7,1,2] 且 candidates = 2 ，第一轮雇佣中，我们选择第 4 位工人，因为他的代价最小 [*3,2*,7,7,***1**,2*] 。
      • 第二轮雇佣，我们选择第 1 位工人，因为他们的代价与第 4 位工人一样都是最小代价，而且下标更小，[*3,**2***,7,*7,2*] 。注意每一轮雇佣后，剩余工人的下标可能会发生变化。

    • 如果剩余员工数目不足 candidates 人，那么下一轮雇佣他们中代价最小的一人，如果有多位代价相同且最小的工人，选择下标更小的一位工人。

    • 一位工人只能被选择一次。

    返回雇佣恰好 k 位工人的总代价。

    示例 1：

    输入：costs = [17,12,10,2,7,2,11,20,8], k = 3, candidates = 4
+输出：11
+解释：我们总共雇佣 3 位工人。总代价一开始为 0 。
+- 第一轮雇佣，我们从 [17,12,10,2,7,2,11,20,8] 中选择。最小代价是 2 ，有两位工人，我们选择下标更小的一位工人，即第 3 位工人。总代价是 0 + 2 = 2 。
+- 第二轮雇佣，我们从 [17,12,10,7,2,11,20,8] 中选择。最小代价是 2 ，下标为 4 ，总代价是 2 + 2 = 4 。
+- 第三轮雇佣，我们从 [17,12,10,7,11,20,8] 中选择，最小代价是 7 ，下标为 3 ，总代价是 4 + 7 = 11 。注意下标为 3 的工人同时在最前面和最后面 4 位工人中。
+总雇佣代价是 11 。

    示例 2：

    输入：costs = [1,2,4,1], k = 3, candidates = 3
+输出：4
+解释：我们总共雇佣 3 位工人。总代价一开始为 0 。
+- 第一轮雇佣，我们从 [1,2,4,1] 中选择。最小代价为 1 ，有两位工人，我们选择下标更小的一位工人，即第 0 位工人，总代价是 0 + 1 = 1 。注意，下标为 1 和 2 的工人同时在最前面和最后面 3 位工人中。
+- 第二轮雇佣，我们从 [2,4,1] 中选择。最小代价为 1 ，下标为 2 ，总代价是 1 + 1 = 2 。
+- 第三轮雇佣，少于 3 位工人，我们从剩余工人 [2,4] 中选择。最小代价是 2 ，下标为 0 。总代价为 2 + 2 = 4 。
+总雇佣代价是 4 。

    提示：

    • 1 <= costs.length <= 105
    • 1 <= costs[i] <= 105
    • 1 <= k, candidates <= costs.length

    思路 ​

    第一时间想到的还是暴力模拟，没有细琢磨数据量下时间复杂度的问题，先是因为打印太多 Debug 的东西 OLE，改到过了前几个样例之后 TLE 了。

    问题主要出在每次选值都要获取两个子集，然后取其各自的最小值，时间复杂度大概在

    O(k∗candidates)≈O(n2)O ( k * candidates) \approx O(n ^ 2)

    对于 10510^5 的数据量来说暴力 TLE 也不奇怪了。（除去复制元素和开辟空间带来的时间开销，还有没有记录下标位置，还要再遍历去删除元素，再就是没有对 2∗candidates≥cost.size2 * candidates \ge cost.size 这种情况做剪枝处理。）

    想办法复用每次排序的结果，最简单的就是上优先队列，先改了一版出来，然后在数个 corner case 的折磨下终于 AC 了。

    最近大量依赖打印的日志 Debug，感觉应该逐渐减少这种依赖，最终目标是一次 AC。

    java
    public long totalCostTLE(int[] costs, int k, int candidates) {
+    long ans = 0;
+
+    List<Integer> costList = new java.util.ArrayList<>(Arrays.stream(costs)
+                                                       .boxed()
+                                                       .toList());
+
+    while (k-- > 0) {
+        //                System.out.println(costList);
+
+        // 前闭后开区间
+        List<Integer> pre = costList.subList(0, Math.min(candidates, costList.size()));
+        List<Integer> rear = costList.subList(Math.max(0, costList.size() - candidates), costList.size());
+
+        //                System.out.println(pre);
+        //                System.out.println(rear);
+
+        Integer minpre = pre.stream().min(Comparator.comparingInt(s -> s)).get();
+        Integer minRear = rear.stream().min(Comparator.comparingInt(s -> s)).get();
+
+        //                System.out.println("两个最小值：" + minpre + " " + minRear);
+
+        Integer minCost = Math.min(minpre, minRear);
+        int index = -1;
+        for (int i = 0; i < Math.min(candidates, costList.size()); ++i) {
+            if (Objects.equals(costList.get(i), minCost)) {
+                index = i;
+                break;
+            }
+        }
+
+        if (index == -1) {
+            for (int i = Math.max(0, costList.size() - candidates); i < costList.size(); ++i) {
+                if (Objects.equals(costList.get(i), minCost)) {
+                    index = i;
+                    break;
+                }
+            }
+        }
+
+        //                System.out.println("整个列表的实际最小值为：" + costList.stream().min(Comparator.comparingInt(s -> s)).get());
+        //                System.out.println("下标为" + index + "的被移除，其值为" + minCost);
+        costList.remove(index);
+
+        ans += minCost;
+    }
+
+    return ans;
+}
    java
    public long totalCost(int[] costs, int k, int candidates) {
+    Queue<Integer> pre = new PriorityQueue<>();
+    Queue<Integer> rear = new PriorityQueue<>();
+
+    // 左指针右移添加元素
+    int lp = 0;
+    // 右指针左移添加元素
+    int rp = costs.length - 1;
+    long ans = 0;
+
+    if (candidates * 2 >= costs.length) {
+        Arrays.sort(costs);
+        for (int i = 0; i < k; ++i) {
+            ans += costs[i];
+        }
+        return ans;
+    }
+
+    while (k > 0) {
+        while (pre.size() < candidates && lp <= rp) {
+            pre.add(costs[lp++]);
+        }
+        while (rear.size() < candidates && lp <= rp) {
+            rear.add(costs[rp--]);
+        }
+
+        k--;
+        int p = pre.isEmpty() ? Integer.MAX_VALUE : pre.peek();
+        int r = rear.isEmpty() ? Integer.MAX_VALUE : rear.peek();
+        if (p <= r) {
+            System.out.println("pre: " + pre.peek());
+            ans += pre.poll();
+        } else {
+            System.out.println("rear: " + rear.peek());
+            ans += rear.poll();
+        }
+    }
+
+    return ans;
+}

    857. 雇佣 K 名工人的最低成本 困难 ​

    算术评级: 8第 90 场周赛Q4

    2260

    有 n 名工人。 给定两个数组 quality 和 wage ，其中，quality[i] 表示第 i 名工人的工作质量，其最低期望工资为 wage[i] 。

    现在我们想雇佣 k 名工人组成一个*工资组。*在雇佣 一组 k 名工人时，我们必须按照下述规则向他们支付工资：

    1. 对工资组中的每名工人，应当按其工作质量与同组其他工人的工作质量的比例来支付工资。
    2. 工资组中的每名工人至少应当得到他们的最低期望工资。

    给定整数 k ，返回 组成满足上述条件的付费群体所需的最小金额 。在实际答案的 10-5 以内的答案将被接受。。

    示例 1：

    输入： quality = [10,20,5], wage = [70,50,30], k = 2
+输出： 105.00000
+解释： 我们向 0 号工人支付 70，向 2 号工人支付 35。

    示例 2：

    输入： quality = [3,1,10,10,1], wage = [4,8,2,2,7], k = 3
+输出： 30.66667
+解释： 我们向 0 号工人支付 4，向 2 号和 3 号分别支付 13.33333。

    提示：

    • n == quality.length == wage.length
    • 1 <= k <= n <= 104
    • 1 <= quality[i], wage[i] <= 104

    思路 ​

    hard 题，一开始没读懂题意，看了半天样例。quality 可以看作工时，第一条要求是同工同酬。

    第一想法贪心暴力，先算出时薪，对这个时薪的要求是 这个时薪 * 每个人的工时须 >= wage[i]。

    写了一版常数还挺大的 O(n2)O(n^2)​ 的，在第 41 个样例就 TLE 了。😢

    可能对于 medium 题来说这样的写法再优化一下常数已经可以 AC 了，但是对于 hard 来说显然需要更深层次的优化。

    考虑了很多剪枝，但是左右不大，几乎没有什么优化的效果。

    看了一眼提示，最重要的一句话出现了：所有期望时薪比当前时薪低的工人，都可以雇佣，所有期望时薪比当前时薪高的工人，都不能雇佣

    基于此结论，可以不必再遍历整个数组来维护 当前时薪下前 kk 个最小工时 了。因为要想有资格进入这个优先队列，首先当前时薪要符合这个人的要求。

    ans=qualitySum∗hourlyWageans = qualitySum * hourlyWage

    维护一个最大堆，里面放在按升序遍历 hourlyWagehourlyWage 时当前时薪下的最小工时，如果 curQualitycurQuality 小于堆顶元素，则触发 qualitySumqualitySum 更小的条件，则有可能有更小的 ansans。

    另，在按升序遍历 hourlyWagehourlyWage 的情况下，要想新的 ansans 更小，则必须有更小的 curQualitycurQuality，所以更新时不必思考 curQualitycurQuality 没有更小但是在当前时薪下 ansans 更小的情况。

    一开始是用 ans += qualityQueue.stream().map(s -> curHW * s).reduce(Double::sum).get();算的，结果在最后一个样例 TLE 了，于是改成维护一个当前总和，不用再每次都相加计算了。

    最后一个样例 k=6807k = 6807​，怪不得 TLE 了 QAQ

    一共做了接近三个半小时，有刚上大学时候通宵搞 ACM 的感觉了，忆往昔，而今迈步从头越。

    java
    public double mincostToHireWorkers(int[] quality, int[] wage, int k) {
+
+    class Pair<L, R> {
+        private L key;
+        private R value;
+
+        public Pair(L key, R value) {
+            this.key = key;
+            this.value = value;
+        }
+
+        public L getKey() {
+            return key;
+        }
+
+        public R getValue() {
+            return value;
+        }
+
+        @Override
+        public String toString() {
+            return "\nPair{" +
+                "key=" + key +
+                ", value=" + value +
+                '}';
+        }
+    }
+
+    // 1. 先算时薪
+    // 2. 这个时薪 * 每个人的工时须 >= wage[i]
+    // 思路：贪心，从最低时薪开始算
+    // 暴力算出当前时薪下每个人需要支付的薪水，取前 K 小个值
+
+    // 考虑如何剪枝
+    // 考虑一个推测，如果使用的当前时薪超出了半数人的时薪，那么每个人当前时薪下需要支付的薪水之和必然不是最低的，即符合要求的最低时薪一定在前 (quality.length / 2) + 1 中。
+    // 暂时想不到如何证明。(错误的。 T_T）
+
+    // 所有期望时薪比当前时薪低的工人，都可以雇佣，所有期望时薪比当前时薪高的工人，都不能雇佣
+    // ans = qualitySum * hourlyWage;
+    // 在按升序遍历 hourlyWage 的情况下，要想新的 ans 更小，则必须有更小的 curQuality，所以更新时不必思考 curQuality 没有更小但是在当前时薪下 ans 更小的情况。
+
+    List<Pair<Integer, Double>> hwList = new ArrayList<>();
+
+    for (int i = 0; i < quality.length; ++i) {
+        double hw = (double) wage[i] / quality[i];
+        hwList.add(new Pair<>(i, hw));
+    }
+
+    hwList.sort(Comparator.comparingDouble(Pair::getValue));
+
+    double minAns = Double.MAX_VALUE;
+
+    Queue<Integer> qualityQueue = new PriorityQueue<>(k, Comparator.comparingInt(s -> -s));
+    int curSumQuality = 0;
+
+    for (Pair<Integer, Double> pair : hwList) {
+        double curHW = pair.getValue();
+        int index = pair.key;
+        double ans = 0;
+
+        boolean include = false;
+        if (qualityQueue.size() < k) {
+            qualityQueue.add(quality[index]);
+            curSumQuality += quality[index];
+            include = true;
+        } else if (quality[index] < qualityQueue.peek() && qualityQueue.size() == k) {
+            curSumQuality -= qualityQueue.poll();
+            curSumQuality += quality[index];
+            qualityQueue.add(quality[index]);
+            include = true;
+        }
+
+        ans +=  curSumQuality * curHW;
+        if (qualityQueue.size() == k && include) {
+            minAns = Math.min(minAns, ans);
+        }
+    }
+
+    //        while (!hourlyWageQueue.isEmpty()) {
+    //            double ans = 0;
+    //
+    //            Pair<Integer, Double> poll = hourlyWageQueue.poll();
+    //            double curHW = poll.getValue();
+    //            ans += wage[poll.key];
+    //
+    //            // 指定容量，剪枝大数据量下的堆维护开销
+    //            Queue<Double> wageQueue = new PriorityQueue<>(k - 1 > 0 ? k - 1 : 1);
+    //
+    //            // 求当前时薪下前 k 小个满足工人要求的薪水
+    //            // 1. 考虑按时薪升序开始算，满 k 个就退出循环剪枝。但是有可能这个人的工时很高，只是时薪低，这时候这个人的薪水也会巨高，不能这样剪
+    //            // 2. 考虑按期望薪资升序开始算，满 k 个就退出循环剪枝。但是这个人可能时薪巨高，只是期望薪资低，工时其实更远小于其他人，这时候这个人的薪水也会巨高，感觉也不能这样剪
+    //            int curIndex = 0;
+    //            for (int i = 0; i < hwList.size(); ++i) {
+    //                if (hwList.get(i).getValue() == curHW) {
+    //                    curIndex = i;
+    //                }
+    //            }
+    //
+    //            for (int i = 0; i < curIndex; ++i) {
+    //                wageQueue.add(quality[hwList.get(i).getKey()] * curHW);
+    //            }
+    //
+    //            // 剪枝
+    //            if (wageQueue.size() >= k - 1) {
+    //                int kCopy = k;
+    //                while (--kCopy > 0) {
+    //                    ans += wageQueue.poll();
+    //                }
+    //
+    //                minAns = Math.min(ans, minAns);
+    //            }
+    //        }
+
+    return minAns;
+}

    1235. 规划兼职工作 困难 ​

    你打算利用空闲时间来做兼职工作赚些零花钱。

    这里有 n 份兼职工作，每份工作预计从 startTime[i] 开始到 endTime[i] 结束，报酬为 profit[i]。

    给你一份兼职工作表，包含开始时间 startTime，结束时间 endTime 和预计报酬 profit 三个数组，请你计算并返回可以获得的最大报酬。

    注意，时间上出现重叠的 2 份工作不能同时进行。

    如果你选择的工作在时间 X 结束，那么你可以立刻进行在时间 X 开始的下一份工作。

    示例 1：

    img

    输入：startTime = [1,2,3,3], endTime = [3,4,5,6], profit = [50,10,40,70]
+输出：120
+解释：
+我们选出第 1 份和第 4 份工作，
+时间范围是 [1-3]+[3-6]，共获得报酬 120 = 50 + 70。

    示例 2：

    img

    输入：startTime = [1,2,3,4,6], endTime = [3,5,10,6,9], profit = [20,20,100,70,60]
+输出：150
+解释：
+我们选择第 1，4，5 份工作。
+共获得报酬 150 = 20 + 70 + 60。

    示例 3：

    img

    输入：startTime = [1,1,1], endTime = [2,3,4], profit = [5,6,4]
+输出：6

    提示：

    • 1 <= startTime.length == endTime.length == profit.length <= 5 * 10^4
    • 1 <= startTime[i] < endTime[i] <= 10^9
    • 1 <= profit[i] <= 10^4

    思路 ​

    一眼 DP，但是动态规划一直都是我的弱项，之前学的时候只学会了完全背包多重背包那几个，但是还是试着推了一下递推公式。

    先按照开始时间升序排序，遍历工作。（现在想一下，可能应该遍历时间点，但是这样应该更复杂，不考虑做法了）

    不难发现要求的结果是 [0,endTimemax][0, endTime_{max}] 时间里的最大值，于是一开始定义 dp[i]dp[i] 为时间点 ii 能获取的最大报酬，递推公式：

    dp[Job[i].endTime]=max(dp[i−1],dp[Job[i].startTime]+Job[i].profit)dp[Job[i].endTime] = max(dp[i-1], \quad dp[Job[i].startTime] + Job[i].profit)

    写了一下发现过不去样例（写的也很简单，输出了一下 dp[]的值发现根本不连续，没有做到把问题拆分成子问题解决，递推公式里，dp[i] 和 dp[i-1] 之间没有明确的转移关系，这应该就是问题所在。

    既然这样写递推公式没法联系 dp[i] 和 dp[i-1] ，那么就要考虑改变 dp 数组的定义。

    看了一眼题解，感觉有点像模拟当前这个工作做与不做的模式，即定义 dp[i]dp[i] 为按照结束时间升序排序时前 ii 份工作能获取的最大报酬。

    对于当前工作 Job[i]Job[i]，有两种选择：

    • 不选当前工作，则 dp[i]=dp[i−1]dp[i] = dp[i - 1]
    • 选择当前工作，则 dp[i]=dp[j]+Job[i].profitdp[i] = dp[j] + Job[i].profit，其中 jj 为最接近在这份工作开始时间的工作的下标，通过这个下标可以获得在能开始这份工作之前能获取的最大报酬。

    递推公式：

    dp[i]=max(dp[i−1],dp[j]+Job[i].profit)dp[i] = max(dp[i - 1], \quad dp[j] + Job[i].profit)

    实现细节上，由于当 i=0i=0 时 i−1=−1i-1 = -1，会产生负数，所以下标都加一变成正数。

    jj 的寻找用了二分，都知道只有有序数组才能用二分，因为这里是按照结束时间升序排序的，所以查的也是结束时间，用二分是可以的。（这里曾经困扰了我一段时间，后来才想到找的是时间，只要在时间数组里做查找就可以了）

    总结一下，首先对于dp数组的定义有问题，傻傻的按照样例给的图例的时间轴进行的定义，但是实际上影响结果变化的是一个个工作，这里可能比较难想到的是按结束时间排序，题解里没说为什么这样排，感觉是为了用二分（？）。

    其次是对于状态转移方程的认识不够，到底应该怎么定义才是对的，可能要看有没有把问题拆分成子问题，并且使他们与对dp数组的定义联系起来，感觉还是挺难的。

    但是不管怎么说，这题还是思考之后理解并做出来了，是感觉到一点进步的。可能自己太笨，之前大学时候搞 ACM 对于这种题是一点点思路都没有，也可能是有畏难情绪在吧，现在长大一点了，反而有能力去思考了 T_T。

    五一就要过去了，一切都会好的！

    java
    public int jobScheduling(int[] startTime, int[] endTime, int[] profit) {
+    // 其中 dp[i] 表示前 i 份工作能获得的最大报酬
+    int[] dp = new int[endTime.length + 1];
+
+    class Job {
+        public int st;
+        public int ed;
+        public int pro;
+
+        public Job(int st, int ed, int pro) {
+            this.st = st;
+            this.ed = ed;
+            this.pro = pro;
+        }
+
+        @Override
+        public String toString() {
+            return "\nJob{" +
+                    "st=" + st +
+                    ", ed=" + ed +
+                    ", pro=" + pro +
+                    '}';
+        }
+
+        public static int bs(List<Job> jobs, int high, int target) {
+            int low = -1;
+            while (low + 1 < high) {
+                int mid = (high - low) / 2 + low;
+                if (jobs.get(mid).ed > target) {
+                    high = mid;
+                } else {
+                    low = mid;
+                }
+            }
+
+            return low;
+        }
+    }
+
+    List<Job> jobs = new ArrayList<>();
+
+    for (int i = 0; i < profit.length; ++i) {
+        jobs.add(new Job(startTime[i], endTime[i], profit[i]));
+    }
+
+    jobs.sort((a, b) -> b.ed - a.ed);
+
+    System.out.println(jobs);
+
+    for (int i = 0; i < jobs.size(); ++i) {
+        int j = Job.bs(jobs, i, jobs.get(i).st);
+        dp[i + 1] = Math.max(dp[i], dp[j + 1] + jobs.get(i).pro);
+    }
+
+    for (int i : dp) {
+        System.out.println(i);
+    }
+
+    return dp[profit.length];
+}

    3. 无重复字符的最长子串 中等 ​

    已解答

    算术评级: 5

    给定一个字符串 s ，请你找出其中不含有重复字符的 最长 子串 的长度。

    示例 1:

    输入: s = "abcabcbb"
+输出: 3
+解释: 因为无重复字符的最长子串是 "abc"，所以其长度为 3。

    示例 2:

    输入: s = "bbbbb"
+输出: 1
+解释: 因为无重复字符的最长子串是 "b"，所以其长度为 1。

    示例 3:

    输入: s = "pwwkew"
+输出: 3
+解释: 因为无重复字符的最长子串是 "wke"，所以其长度为 3。
+     请注意，你的答案必须是 子串 的长度，"pwke" 是一个子序列，不是子串。

    提示：

    • 0 <= s.length <= 5 * 104
    • s 由英文字母、数字、符号和空格组成

    思路 ​

    前两天的每日一题是在我看来不可做的 DP，直接放弃。今天来 A 一道经典题目，稍微思考一下就知道滑动窗口可做，5分钟速A。

    java
    public int lengthOfLongestSubstring(String s) {
+    int ans = 0;
+
+    for (int i = 0; i < s.length(); ++i) {
+        Set<Character> chars = new HashSet<>();
+        chars.add(s.charAt(i));
+
+        for (int j = i + 1; j < s.length(); ++j) {
+            if (!chars.contains(s.charAt(j))) {
+                chars.add(s.charAt(j));
+            } else {
+                break;
+            }
+        }
+        ans = Math.max(ans, chars.size());
+    }
+
+    return ans;
+}

    1553. 吃掉 N 个橘子的最少天数 困难 ​

    厨房里总共有 n 个橘子，你决定每一天选择如下方式之一吃这些橘子：

    • 吃掉一个橘子。
    • 如果剩余橘子数 n 能被 2 整除，那么你可以吃掉 n/2 个橘子。
    • 如果剩余橘子数 n 能被 3 整除，那么你可以吃掉 2*(n/3) 个橘子。

    每天你只能从以上 3 种方案中选择一种方案。

    请你返回吃掉所有 n 个橘子的最少天数。

    示例 1：

    输入：n = 10
+输出：4
+解释：你总共有 10 个橘子。
+第 1 天：吃 1 个橘子，剩余橘子数 10 - 1 = 9。
+第 2 天：吃 6 个橘子，剩余橘子数 9 - 2*(9/3) = 9 - 6 = 3。（9 可以被 3 整除）
+第 3 天：吃 2 个橘子，剩余橘子数 3 - 2*(3/3) = 3 - 2 = 1。
+第 4 天：吃掉最后 1 个橘子，剩余橘子数 1 - 1 = 0。
+你需要至少 4 天吃掉 10 个橘子。

    示例 2：

    输入：n = 6
+输出：3
+解释：你总共有 6 个橘子。
+第 1 天：吃 3 个橘子，剩余橘子数 6 - 6/2 = 6 - 3 = 3。（6 可以被 2 整除）
+第 2 天：吃 2 个橘子，剩余橘子数 3 - 2*(3/3) = 3 - 2 = 1。（3 可以被 3 整除）
+第 3 天：吃掉剩余 1 个橘子，剩余橘子数 1 - 1 = 0。
+你至少需要 3 天吃掉 6 个橘子。

    示例 3：

    输入：n = 1
+输出：1

    示例 4：

    输入：n = 56
+输出：6

    提示：

    • 1 <= n <= 2*10^9

    思路 ​

    一眼 DFS，一分钟写了一个，果然 TLE 了。

    考虑剪枝和记忆化，先存一下每次走到当前数目时是否比之前走过时需要的时间更短，如果已经更久，那么剪枝。

    考虑贪心，如果能被 3 整除，那么可以一次吃掉 2n3\frac{2n}{3} 个橘子，显然比 n2\frac{n}{2} 要大，优先走这一步，争取走出最终结果的函数调用足够靠前，这样能 break 后续的所有调用，也算是剪枝。

    但是还是在 n=166188n=166188 的时候 TLE 了。

    再改，初始化时将 Map<Integer, Integer> memo 的容量初始化为 nn，降低扩容次数

    记一次从 Hexo 到 Valaxy 的迁移，GitHub Actions 部署 Github Pages
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    LeetCode 数据库刷题笔记

    -
    628    -
    LeetCode > 数据库
    LeetCode数据库
    本文最后更新于1 分钟前，文中所描述的信息可能已发生改变。

    LeetCode 数据库刷题笔记 ​

    只做重点的记录。

    181. 超过经理收入的员工 ​

    183. 部门工资最高的员工 ​

    mysql
    输入：
+Employee 表:
++----+-------+--------+--------------+
+| id | name  | salary | departmentId |
++----+-------+--------+--------------+
+| 1  | Joe   | 70000  | 1            |
+| 2  | Jim   | 90000  | 1            |
+| 3  | Henry | 80000  | 2            |
+| 4  | Sam   | 60000  | 2            |
+| 5  | Max   | 90000  | 1            |
++----+-------+--------+--------------+
+Department 表:
++----+-------+
+| id | name  |
++----+-------+
+| 1  | IT    |
+| 2  | Sales |
++----+-------+
+输出：
++------------+----------+--------+
+| Department | Employee | Salary |
++------------+----------+--------+
+| IT         | Jim      | 90000  |
+| Sales      | Henry    | 80000  |
+| IT         | Max      | 90000  |
++------------+----------+--------+
+解释：Max 和 Jim 在 IT 部门的工资都是最高的，Henry 在销售部的工资最高。

    双字段使用in

    select d.name as Department, e.name as Employee, e.Salary as Salary
+from Employee e , Department d
+where e.Departmentid = d.id
+and
+(e.DepartmentId, Salary)
+in
+(select Departmentid, max(Salary) from Employee GROUP BY DepartmentId )

    180. 连续出现的数字 ​

    mysql
    输入：
+Logs 表：
++----+-----+
+| Id | Num |
++----+-----+
+| 1  | 1   |
+| 2  | 1   |
+| 3  | 1   |
+| 4  | 2   |
+| 5  | 1   |
+| 6  | 2   |
+| 7  | 2   |
++----+-----+
+输出：
+Result 表：
++-----------------+
+| ConsecutiveNums |
++-----------------+
+| 1               |
++-----------------+
+解释：1 是唯一连续出现至少三次的数字。

    对一张表重复关联3次，横向比较。

    mysql
    select distinct l1.num as ConsecutiveNums
+from logs l1, logs l2, logs l3
+where l1.num = l2.num and l2.num = l3.num and l1.id = l2.id - 1 and l2.id = l3.id - 1

    178. 分数排名 ​

    MySql8.x 版本以上支持rank()开窗函数。

    Oracle 和 SqlServer 也支持，但是没有查具体版本。

    https://blog.csdn.net/u013317445/article/details/100514974

    MySql 之 rank() over(order by)、rank() over(partition by order by)

    在版本不支持开窗函数的情况下，使用语义分析，**rank **即为前面有多少比自己「大」的数据，根据排名规则（如并列排名，顺序排名等）进行去重等操作，一样可以实现添加排名的功能。

    mysql
    select a.Score as Score,
+(select count(distinct b.Score) from Scores b where b.Score >= a.Score) as Rank
+from Scores a
+order by a.Score DESC

    626. 换座位 ​

    mysql
    输入:
+Seat 表:
++----+---------+
+| id | student |
++----+---------+
+| 1  | Abbot   |
+| 2  | Doris   |
+| 3  | Emerson |
+| 4  | Green   |
+| 5  | Jeames  |
++----+---------+
+输出:
++----+---------+
+| id | student |
++----+---------+
+| 1  | Doris   |
+| 2  | Abbot   |
+| 3  | Green   |
+| 4  | Emerson |
+| 5  | Jeames  |
++----+---------+
+解释:
+请注意，如果学生人数为奇数，则不需要更换最后一名学生的座位。

    解法一：

    mysql
    select (
+    case
+        when mod(id, 2) = 1 and id != counts.counts then id + 1
+        when mod(id, 2) = 1 and id = counts.counts then id
+        else id - 1
+        end) as id, student
+from seat,
+    (select count(1) as counts
+    from seat) counts
+order by id asc

    解法二：

    mysql
    select s1.id, coalesce(s2.student, s1.student) as student
+from seat s1
+left join seat s2
+on (s1.id+1)^1-1 = s2.id

    位运算实现相邻两数互换位置。

    Oracle & Mybatis 批量更新返回值问题
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    Oracle & Mybatis 批量更新返回值问题

    -
    1.4k    -
    奇奇怪怪的项目里的问题
    数据库OracleMybatisJava
    本文最后更新于1 分钟前，文中所描述的信息可能已发生改变。

    场景 ​

    ORM：Mybatis

    数据库：Oracle

    组里新人写的一个接口。

    传给 Mapper 层的是两个元素数量相等的 List，根据某两个字段是否与 List 中的元素匹配，对 status 字段做更新。

    问题 ​

    假如传入 3 条数据，对应数据库中，应该更新三行的 status，如果能够做到全部更新成功，则继续后续业务的执行。但是无论受影响行数是 0 或其他任何非负整数，方法的返回值都是 -1。无法获取受影响行数。

    解决方案 ​

    这个项目用的是 xml 文件写 mapper，为了实现批量查询，需要在 foreach 标签里添加一些东西。

    <foreach collection="list" item="item" index="index" open="begin" close=";end;" separator=";">

    解决了批量更新的问题，返回值还是 -1，开始查网上的资料。无奈 Mybatis & Oracle 的相关资料非常少，包括外网也是。

    当数据库是 MySQL 的时候，可以通过修改 JDBC 的 URL 参数，添加”affectedRows=true“来实现返回受影响行数的功能（我自己在 MySQL 数据库开发的时候并没有遇到需要额外配置这个参数的情况）。于是我去搜了 Oracle JDBC 的官方文档，官方给出的 URL 参数里并没有效果类似”affectedRows“的，这条路走不通。

    JDBC 连接上走不通，看看能不能从 Mybatis 入手。继续搜别的资料。

    搜到有解释说，返回值是 -1 是由于：

    是由于defaultExecutorType的引起的，defaultExecutorType有三个执行器SIMPLE、REUSE和BATCH。其中BATCH可以批量更新操作缓存SQL以提高性能，但是有个缺陷就是无法获取update、delete返回的行数。defaultExecutorType的默认执行器是SIMPLE。

    名称描述
    SIMPLE执行器执行其它语句
    REUSE可能重复使用prepared statements 语句
    BATCH可以重复执行语句和批量更新

    由于项目配置中启用了BATCH执行器，UPDATE和DELETE返回的行数就丢失了，把执行器改为SIMPLE即可。

    但是该项目的 Mybatis 配置是这样的：

    yaml
    mybatis:
+    configuration:
+        default-executor-type: simple

    所以这条路也走不通，继续找原因。

    在 StackOverflow 找到有个人说用 BEGIN + END + ; 实现的并不是 「a batch operation」，这里引出了 statement batch 的类型。

    image-20221104103007804

    但是实际测试的时候发现getUpdateCounts()返回的数组还是都是 -2。

    无奈已经下班一个小时了，只好先用 in代替了where条件中的=，抛弃了批量更新，修改为了一条 SQL 语句。这样可以正常获取返回值。

    第二天早上来了就在查还有没有相关的资料。查到一个人读了更多的 Oracle JDBC 的文档，解决了我的问题。

    后来，在 Oracle 的文档说明中，发现的原因：

    https://docs.oracle.com/cd/E11882_01/java.112/e16548/oraperf.htm#JJDBC28777

    其中，有一段说明：

    Update Counts in the Oracle Implementation of Standard Batching

    If a statement batch is processed successfully, then the integer array, or update counts array, returned by the statement executeBatch call will always have one element for each operation in the batch. In the Oracle implementation of standard update batching, the values of the array elements are as follows:

    • For a prepared statement batch, it is not possible to know the number of rows affected in the database by each individual statement in the batch. Therefore, all array elements have a value of -2. According to the JDBC 2.0 specification, a value of -2 indicates that the operation was successful but the number of rows affected is unknown.
    • For a generic statement batch, the array contains the actual update counts indicating the number of rows affected by each operation. The actual update counts can be provided only in the case of generic statements in the Oracle implementation of standard batching.
    • For a callable statement batch, the server always returns the value 1 as the update count, irrespective of the number rows affected by each operation.

    In your code, upon successful processing of a batch, you should be prepared to handle either -2, 1, or true update counts in the array elements. For a successful batch processing, the array contains either all -2, 1, or all positive integers.

    针对三种不同的「statement batch」，Oracle 给出的返回值是不同的，也代表了不同的意义，文档里说的很明白。

    • parpared statement：-2 表示执行成功，但无法返回实际成功行数；

    • generic statement：返回实际成功行数；

    • callable statement：永远返回 1；

    最终的解决方案，抛弃 Mybatis：

    java
    private int updateList(List < MyObject > myList) {
+    int size = myList.size();
+    int batchSize = 100;
+    int index = 0;
+    Long affectedRows = 0 L;
+
+    SqlSession sqlSession = sqlSessionFactory.openSession(ExecutorType.BATCH);
+    Connection conn = sqlSession.getConnection();
+    Statement statement = null;
+    try {
+        statement = conn.createStatement();
+        conn.setAutoCommit(false);
+
+        for (MyObject obj: myList) {
+            index++;
+
+            String sql = "update tb_test set status = " + obj.getStatus + " where id = " + obj.getId();
+            statement.addBatch(sql);
+
+            if (index % batchSize == 0 || index == size) {
+                int[] ints = statement.executeBatch();
+                affectedRows += IntStream.of(ints).sum();
+                statement.clearBatch();
+            }
+        }
+        conn.commit();
+        conn.setAutoCommit(true);
+
+    } catch (SQLException throwables) {
+        throwables.printStackTrace();
+    } finally {
+        try {
+            if (statement != null && !statement.isClosed()) {
+                statement.close();
+            }
+        } catch (SQLException throwables) {
+            throwables.printStackTrace();
+        }
+        try {
+            if (conn != null && !conn.isClosed()) {
+                conn.close();
+            }
+        } catch (SQLException throwables) {
+            throwables.printStackTrace();
+        }
+    }
+
+    return affectedRows.intValue();
+}

    吐槽 ​

    • Oracle 给我一种很难用的感觉。

    • 网络上（尤其是 CSDN & 博客园）的资料基本都是复制来复制去，毫无营养。

    • 新人手里那个项目的代码质量一言难尽，主要是注释和魔法数问题。

    • 三种 statement 的区分是什么，后面找机会再了解下。（懒狗一条🐶

    关于代码规范的一些粗浅想法
    LeetCode 数据库刷题笔记
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    Hello, Valaxy!

    42    -
    Valaxy 笔记
    valaxy笔记
    本文最后更新于2 年前，文中所描述的信息可能已发生改变。

    Valaxy ​

    Next Generation Static Blog Framework.

    Write your first post!

    testa2test a_2

    Usage ​

    Modify valaxy.config.ts to custom your blog.

    Hello World
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    Hello World

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    测试 > 盘古开天辟地
    HelloWorld
    本文最后更新于1 分钟前，文中所描述的信息可能已发生改变。

    Welcome to Hexo! This is your very first post. Check documentation for more info. If you get any problems when using Hexo, you can find the answer in troubleshooting or you can ask me on GitHub.

    Quick Start ​

    Create a new post ​

    bash
    $ hexo new "My New Post"

    More info: Writing

    Run server ​

    bash
    $ hexo server

    More info: Server

    Generate static files ​

    bash
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    More info: Generating

    Deploy to remote sites ​

    bash
    $ hexo deploy

    More info: Deployment

    LeetCode 数据库刷题笔记
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    Hi, {{username}}

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    + 您在 + 苏 的评论收到了新的回复。 +

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    + 本邮件为系统自动发送，不必回复～ +
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