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<p>即将去内蒙古农业大学读林业工程，不清楚未来会是什么样，也无法确定未来走向，可以确定的是，我对代码还是挺喜爱的。</p>
<p>故重新拾起博客，花时间学点自己认为有成就感的事情。Just do it.</p>
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<h1 id="redis-geo-性能分析及对问题的思考"><a href="#redis-geo-性能分析及对问题的思考" class="headerlink" title="redis geo 性能分析及对问题的思考"></a>redis geo 性能分析及对问题的思考</h1><p>[toc]</p>
<h2 id="对-redis-进行压力测试"><a href="#对-redis-进行压力测试" class="headerlink" title="对 redis 进行压力测试"></a>对 redis 进行压力测试</h2><p>将数据存入 redis 后（共 85318 条有效数据），通过压测工具<code>redis-benchmark</code>对 redis 进行压测。</p>
<figure class="highlight angelscript"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">redis-benchmark -h <span class="number">127.0</span><span class="number">.0</span><span class="number">.1</span> -p <span class="number">6379</span> -c <span class="number">50</span> -n <span class="number">1000</span> -q georadius outlet <span class="number">112</span> <span class="number">-7</span> <span class="number">200</span> km withdist</span><br></pre></td></tr></table></figure>
<p>并发数为 50，每个并发发送 1000 个请求，结果如下：</p>
<p><img src="/images/image-20210726174917453.png" alt="image-20210726174917453"></p>
<p><strong>注：</strong>本文所用电脑为 MacBook Pro (16-inch, 2019)，cpu 为：Intel(R) Core(TM) i7-9750H CPU @ 2.60GHz，内存为 16 g。</p>
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<h1 id="PostgreSQL-gis-Redis-geo-BuntDB-性能比拼"><a href="#PostgreSQL-gis-Redis-geo-BuntDB-性能比拼" class="headerlink" title="PostgreSQL-gis Redis-geo BuntDB 性能比拼"></a>PostgreSQL-gis Redis-geo BuntDB 性能比拼</h1><p>[toc]</p>
<p>听说 postgresql 中的 postGIS 在处理经纬度方面速率很快（gist 内部使用的是搜索树，geo 内部使用的是 geohash），所以比较一下，看看 8w 数据量的情况下，哪个快一点。</p>
<h2 id="PostgreSQL（pg）搭建"><a href="#PostgreSQL（pg）搭建" class="headerlink" title="PostgreSQL（pg）搭建"></a>PostgreSQL（pg）搭建</h2><p>pg 添加 postGIS 扩展（postGIS 扩展很强大，建议深入学习）：</p>
<figure class="highlight n1ql"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">create</span> extension postgis;</span><br></pre></td></tr></table></figure>
<p>附：Mac 安装 PostGIS 流程：<a href="https://morphocode.com/how-to-install-postgis-on-mac-os-x/" target="_blank" rel="noopener">link</a></p>
<p>pg 创建测试表：</p>
<figure class="highlight pgsql"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">postgres=# <span class="keyword">create</span> <span class="keyword">table</span> gis_idx_test(id <span class="type">int</span>, <span class="keyword">info</span> <span class="type">text</span>, pos geometry) <span class="keyword">with</span> (autovacuum_enabled=<span class="keyword">off</span>,toast.autovacuum_enabled=<span class="keyword">off</span>);</span><br></pre></td></tr></table></figure>
<p>pg 创建索引（使用 gist 的原因是：gist 比 RTree 更强大，虽然插入数据慢，但是检索数据快，适合存储空间的数据）：</p>
<figure class="highlight n1ql"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">create</span> <span class="keyword">index</span> idx_gis_idx_test <span class="keyword">on</span> gis_idx_test <span class="keyword">using</span> gist (pos);</span><br></pre></td></tr></table></figure>
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<h2 id="概述"><a href="#概述" class="headerlink" title="概述"></a>概述</h2><p>本博客是讲解 nsq 中一些文件中的主要函数的作用，另外，我个人还针对 nsq v0.1.1 版本的相关代码实现做了一些注释笔记，感兴趣的可以自行阅读：<a href="https://github.com/ghorges/nsq-0.1.1-ghorges" target="_blank" rel="noopener">nsq-0.1.1-ghorges</a></p>
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<h1 id="nsq-源码分析-1"><a href="#nsq-源码分析-1" class="headerlink" title="nsq 源码分析-1"></a>nsq 源码分析-1</h1><h2 id="前言"><a href="#前言" class="headerlink" title="前言"></a>前言</h2><p>前几周在学习 raft，但由于 raft 需要耗费的周期比较长，打算等到大四了再好好学一学论文和啃一啃 raft 源码（其实我已经把 raft 的 log 层、log 存储、process 等源码啃的差不多了，剩下最难啃的 raft 层和 node 层了。。。），最近不搞这个了，先认真备战秋招。</p>
<p>这两周学习 nsq，是因为之前在公司用到消息队列的场景还挺多的（客户端打点，日志等之类的都会用到），而我基本没学过 java，看不了 kafka，所以把用 go 编写的 nsq 拿来啃一啃。</p>
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<h2 id="Mysql-锁学习记录"><a href="#Mysql-锁学习记录" class="headerlink" title="Mysql 锁学习记录"></a>Mysql 锁学习记录</h2><p>Mysql 锁设计的初衷是处理并发问题。根据加锁的范围，MySQL 里面的锁大致可以分成全局锁、表级锁和行锁三类。</p>
<p>由于自己基本没有使用过锁，对锁的理解不够深刻，大都仅限于概念，所以需要多多实践。</p>
<h3 id="全局锁"><a href="#全局锁" class="headerlink" title="全局锁"></a>全局锁</h3><p>全局锁就是对整个数据库实例加锁。MySQL 提供了一个加全局读锁的方法，命令是：</p>
<figure class="highlight sql"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">Flush</span> <span class="keyword">tables</span> <span class="keyword">with</span> <span class="keyword">read</span> <span class="keyword">lock</span>;</span><br></pre></td></tr></table></figure>
<p>当你需要让整个库处于只读状态的时候，可以使用这个命令，之后其他线程的以下语句会被阻塞：数据更新语句（数据的增删改）、数据定义语句（包括建表、修改表结构等）和更新类事务的提交语句。</p>
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<p>着实睡不着，看一看并记录记录股票的知识吧。</p>
<h2 id="长白线"><a href="#长白线" class="headerlink" title="长白线"></a>长白线</h2><p>长白线是指<strong>交易时段的开盘价位于最低价附近，收盘价位于最高价附近。</strong></p>
<p>根据某些阴阳线专家的看法，一根具有明显意义的长白线，其实体的长度至少需要是前一天实体长度的三倍。</p>
<p>意思就是说，长白线要尽量的足够长，太短的没有啥意义。</p>
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<p>为什么需要索引？</p>
<p>一句话：索引的目的就是提高效率。</p>
<h2 id="索引的常见模型"><a href="#索引的常见模型" class="headerlink" title="索引的常见模型"></a>索引的常见模型</h2><h3 id="哈希"><a href="#哈希" class="headerlink" title="哈希"></a>哈希</h3><p>哈希速度很快，但是有一些缺点：</p>
<ul>
<li>只适用于等值查询，不适用于范围查询。</li>
<li>如果数据量特别多，冲突多，哈希速度会显著下降。</li>
<li>不能利用索引排序（order by）。</li>
<li>不支持多列联合索引的最左匹配规则。</li>
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<p>所以哈希适用于等值查询，Nosql 等等，但是不适合 mysql。</p>
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<p>立个 flag，早早睡觉，昨天一天就是因为头疼导致学习的时候超容易走神，今天为什么现在还没睡！！！<br>我太难了，待会出去喝碗胡辣汤就睡。。。</p>
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<p>其实最近还有一个小问题，大多时间用在学 Mysql 上了，看 Raft 时间少了。<br>这个之后再说吧。只要在进步就行，少写点 if else 就行|･ω･｀)~<br>现在感觉这时光真美好，没有 oncall、没有产品给你提需求，想学什么就学什么，想几点睡就几点睡？？？</p>
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<h2 id="前置知识"><a href="#前置知识" class="headerlink" title="前置知识"></a>前置知识</h2><h3 id="并发事务处理带来的问题"><a href="#并发事务处理带来的问题" class="headerlink" title="并发事务处理带来的问题"></a>并发事务处理带来的问题</h3><ul>
<li>脏读（Dirty reads）：一个事务处理一条数据，在这个事务还没有 commit 的时候，另一个事务来读取这个记录，并且用这个记录做一些工作。</li>
<li>不可重复读（Non-repeatable Reads）：一个事务在读取某些数据已经发生了改变、或某些记录已经被删除了。举个通俗点的例子：事务A第一次查询得到一行记录 row1，事务 B 提交修改后，事务 A 第二次查询得到 row1，但列内容发生了变化（相当于读到了 B 提交修改后的内容）。</li>
<li>幻读（Phantom Reads）：一个事务按相同的查询条件重新读取以前检索过的数据，却发现其他事务插入了满足其查询条件的新数据，这种现象就称为 “幻读” 。</li>
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