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<article id="post-Hive解析任务" class="article article-type-post" itemscope itemprop="blogPost">
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<a class="article-title" href="/2019/03/12/Hive解析任务/">Hive解析任务-将json的多个属性拆分成多条记录</a>
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<a class="tag-link" href="/tags/Hive/">Hive</a>, <a class="tag-link" href="/tags/数仓/">数仓</a>
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<p></p><p>[TOC]</p>
<p>需求环境：</p>
<p>在hive表dwb.dwb_r_thrid_data中，data字段存放有json字符串</p>
<p><img src="http://img.gangtieguo.cn/006tKfTcgy1g10yhuqk6bj32jc0e4wfy.jpg" alt></p><p></p>
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<article id="post-mac与virtualbox共享文件夹，挂载到docker容器中文件目录权限解决" class="article article-type-post" itemscope itemprop="blogPost">
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<a class="article-title" href="/2019/03/12/mac与virtualbox共享文件夹，挂载到docker容器中文件目录权限解决/">mac与virtualbox共享文件夹，挂载到docker容器中文件目录权限解决</a>
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<p></p><p>[TOC]</p>
<h1 id="共享文件夹的权限问题"><a href="#共享文件夹的权限问题" class="headerlink" title="共享文件夹的权限问题"></a>共享文件夹的权限问题</h1><p>参考：<a href="http://yukai.space/2018/05/02/VirtualBox%E5%85%B1%E4%BA%AB%E6%96%87%E4%BB%B6%E5%A4%B9/" target="_blank" rel="noopener">Virtualbox设置共享文件夹</a></p>
<p>由于共享文件夹并不是虚拟机的本地目录，我们在虚拟机中可以配置共享文件夹的权限是有限的。</p>
<p>手动挂载或自动挂载的目录，所属用户默认为root，组为vboxsf，并且使用 <code>chmod chown</code> 等命令是无法改变的。<br></p>
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<article id="post-Docker-machine的搭建(与宿主机在同一ip段下)" class="article article-type-post" itemscope itemprop="blogPost">
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<a class="article-title" href="/2019/03/05/Docker-machine的搭建(与宿主机在同一ip段下)/">Docker-machine的创建，mac宿主机和docker容器网络互通Docker容器与宿主机在同一ip段下</a>
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<p></p><p><img src="https://www.github.com/yaosong5/tuchuang/raw/master/mdtc/2018/7/20/1532027033015.jpg" alt="enter description here"></p>
<p>此文纯属命令记录，后续更新原理解说</p><p></p>
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<article id="post-ELK流程小实例" class="article article-type-post" itemscope itemprop="blogPost">
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<a class="article-title" href="/2018/09/08/ELK流程小实例/">ELK-Logstash->kafka->es流程实例</a>
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<p></p><p>elk流程实例</p>
<p>[TOC]</p>
<p>前言：一般日志收集，我们通过logstash来接入，写入到kafka，再通过logstash将kafka的数据写入到es</p>
<p>那么就通过一个菜的抠脚的实例来看看</p>
<h1 id="LOGSTASH-gt-KAFKA-gt-ES"><a href="#LOGSTASH-gt-KAFKA-gt-ES" class="headerlink" title="LOGSTASH->KAFKA->ES"></a>LOGSTASH->KAFKA->ES</h1><h2 id="1-启动logstash将日志写入到kafka"><a href="#1-启动logstash将日志写入到kafka" class="headerlink" title="1.启动logstash将日志写入到kafka"></a>1.启动logstash将日志写入到kafka</h2><h3 id="①准备工作创建topic"><a href="#①准备工作创建topic" class="headerlink" title="①准备工作创建topic"></a>①准备工作创建topic</h3><p>先创建对应topic，（按照机器配置，集群情况配置，不然默认创建的话，不是最优效果）</p><p></p>
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<article id="post-DataFrame DataSet" class="article article-type-post" itemscope itemprop="blogPost">
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<a class="article-title" href="/2018/09/06/DataFrame DataSet/">DataStream DataSet介绍</a>
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<p></p><p>[TOC]</p>
<h1 id="什么是DataStream"><a href="#什么是DataStream" class="headerlink" title="什么是DataStream"></a>什么是DataStream</h1><p>Discretized Stream是Spark Streaming的基础抽象，代表持续性的数据流和经过各种Spark原语操作后的结果数据流。在内部实现上，DStream是一系列连续的RDD来表示。每个RDD含有一段时间间隔内的数据，如下图：<br><img src="http://img.gangtieguo.cn/15360627803806.jpg" alt><br></p>
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<a class="article-title" href="/2018/09/06/SparkSql的使用/">SparkSQL使用</a>
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<p></p><p>[TOC]</p>
<p>//1.读取数据，将每一行的数据使用列分隔符分割</p>
<p>val lineRDD = sc.textFile(“hdfs://bigdata1:9000/person.txt”, 1).map(_.split(“ “))<br></p>
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<article id="post-Spark-on-Yarn源码解析④Spark业务代码的执行及其任务分配调度stage划分" class="article article-type-post" itemscope itemprop="blogPost">
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<a class="article-title" href="/2018/09/04/Spark-on-Yarn源码解析④Spark业务代码的执行及其任务分配调度stage划分/">Spark-on-Yarn源码解析(四)Spark业务代码的执行及其任务分配调度stage划分</a>
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<p></p><p>spark-on-yarn系列</p>
<p><a href="http://www.gangtieguo.cn/2018/09/04/Spark-on-Yarn源码解析①Yarn任务解析/" target="_blank" rel="noopener">Spark-on-Yarn 源码解析①Yarn 任务解析</a><br><a href="http://www.gangtieguo.cn/2018/09/04/Spark-on-Yarn源码解析②Spark-Submit解析/" target="_blank" rel="noopener">Spark-on-Yarn 源码解析②Spark-Submit 解析</a><br><a href="http://www.gangtieguo.cn/2018/09/04/Spark-on-Yarn源码解析③client做的事情/" target="_blank" rel="noopener">Spark-on-Yarn 源码解析③client 做的事情</a><br><a href="http://www.gangtieguo.cn/2018/09/04/Spark-on-Yarn源码解析④Spark业务代码的执行及其任务分配调度stage划分/" target="_blank" rel="noopener">Spark-on-Yarn 源码解析④Spark 业务代码的执行及其任务分配调度 stage 划分</a></p>
<p>看看自定义的类</p>
<figure class="highlight scala"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br></pre></td><td class="code"><pre><span class="line"><span class="class"><span class="keyword">object</span> <span class="title">WordCount</span> </span>{</span><br><span class="line"></span><br><span class="line"> <span class="function"><span class="keyword">def</span> <span class="title">main</span></span>(args: <span class="type">Array</span>[<span class="type">String</span>]): <span class="type">Unit</span> = {</span><br><span class="line"> <span class="keyword">val</span> conf = <span class="keyword">new</span> <span class="type">SparkConf</span>().setAppName(<span class="string">"yaoWordCount"</span>).setMaster(<span class="string">"local[2]"</span>)</span><br><span class="line"> <span class="keyword">val</span> sc = <span class="keyword">new</span> <span class="type">SparkContext</span>(conf)</span><br><span class="line"> <span class="keyword">var</span> hadoopRDD: <span class="type">RDD</span>[<span class="type">String</span>] = sc.textFile(args(<span class="number">0</span>))</span><br><span class="line"> <span class="keyword">var</span> hdfsRDD: <span class="type">RDD</span>[<span class="type">String</span>] = hadoopRDD.flatMap(_.split(<span class="string">""</span>))</span><br><span class="line"> <span class="comment">//单词和出现的次数，构建RDD并且调用了他的Transformation</span></span><br><span class="line"> <span class="comment">//返回的是一个hadoopRDD</span></span><br><span class="line"> <span class="comment">//transFormation都是返回的RDD</span></span><br><span class="line"> <span class="keyword">var</span> wordAndCount: <span class="type">RDD</span>[(<span class="type">String</span>, <span class="type">Int</span>)] = hdfsRDD.map((_, <span class="number">1</span>))</span><br><span class="line"> <span class="comment">//创建RDD 这里面有两个RDD,一个是hadoopRDD，然后会生成一个paritionRDD</span></span><br><span class="line"> <span class="comment">//savaasTextfile还会产生一个RDD,因为会调用mapPartitons</span></span><br><span class="line"> <span class="comment">//调用RDD的action 开始真正提交任务</span></span><br><span class="line"> <span class="keyword">var</span> reducedRDD: <span class="type">RDD</span>[(<span class="type">String</span>, <span class="type">Int</span>)] = wordAndCount.reduceByKey(_ + _)</span><br><span class="line"> reducedRDD.saveAsTextFile(args(<span class="number">1</span>))</span><br><span class="line"> <span class="comment">//关闭saprkContext资源</span></span><br><span class="line"> sc.stop()</span><br><span class="line"> }</span><br><span class="line">}</span><br></pre></td></tr></table></figure><p></p>
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}
.bdshare_popup_bottom {
height: auto;
padding: 5px;
}
</style>