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<h1 id="HDFS-–Hadoop-Distributed-File-system"><a href="#HDFS-–Hadoop-Distributed-File-system" class="headerlink" title="HDFS –Hadoop Distributed File system"></a>HDFS –Hadoop Distributed File system</h1><p>HDFS是为了解决当今数据量过大单机无法存储的问题。</p>
<p><img src="C:\Users\guoxiatong\AppData\Roaming\marktext\images\2022-02-13-17-00-18-image.png"></p>
<p><img src="C:\Users\guoxiatong\AppData\Roaming\marktext\images\2022-02-13-17-01-32-image.png"></p>
<p>其中 主节点为namenode，其他节点为datanode</p>
<p><img src="C:\Users\guoxiatong\AppData\Roaming\marktext\images\2022-02-13-17-11-54-image.png"></p>
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<h1 id="Hadoop理论2"><a href="#Hadoop理论2" class="headerlink" title="Hadoop理论2"></a>Hadoop理论2</h1><p><img src="C:\Users\guoxiatong\AppData\Roaming\marktext\images\2022-02-11-15-12-01-image.png"></p>
<p>其中Hive是Hadoop中的数据仓库，可以做离线批处理的数据分析</p>
<p>Pig是轻量级的流数据分析，其sql语句可以嵌入到其他的语言中</p>
<p>如何选择Linux</p>
<p><img src="C:\Users\guoxiatong\AppData\Roaming\marktext\images\2022-02-11-17-43-40-image.png"></p>
<p>关于Linux的基础知识</p>
<p><img src="C:\Users\guoxiatong\AppData\Roaming\marktext\images\2022-02-11-17-45-23-image.png"></p>
<p>三种方式：</p>
<p><img src="C:\Users\guoxiatong\AppData\Roaming\marktext\images\2022-02-11-17-46-16-image.png"></p>
<ol>
<li><p>单机模式：本地电脑运行</p>
</li>
<li><p>伪分布式： 模拟分布式，但是NameNode和DataNode在同一台机器</p>
</li>
<li><p>分布式：存在namenode和datanode多个节点。</p>
</li>
</ol>
<h5 id="SSH"><a href="#SSH" class="headerlink" title="SSH "></a>SSH <img src="C:\Users\guoxiatong\AppData\Roaming\marktext\images\2022-02-11-17-56-28-image.png"></h5><h5 id=""><a href="#" class="headerlink" title=""></a></h5><h3 id="NameNode和DataNode"><a href="#NameNode和DataNode" class="headerlink" title="NameNode和DataNode"></a>NameNode和DataNode</h3><p>Name Node和Data Node共同构成了HDFS。NameNode类似于索引，指出每个DataNode的位置</p>
<p><img src="C:\Users\guoxiatong\AppData\Roaming\marktext\images\2022-02-13-15-42-48-image.png"></p>
<p><img src="C:\Users\guoxiatong\AppData\Roaming\marktext\images\2022-02-13-15-44-42-image.png"></p>
<h5 id="-1"><a href="#-1" class="headerlink" title=""></a><img src="C:\Users\guoxiatong\AppData\Roaming\marktext\images\2022-02-13-16-48-10-image.png"></h5><h5 id="参考文章及视频"><a href="#参考文章及视频" class="headerlink" title="参考文章及视频"></a>参考文章及视频</h5><ol>
<li>文章内容参考林子雨教授的<a target="_blank" rel="noopener" href="http://dblab.xmu.edu.cn/blog/2630-2/">大数据软件安装和基础编程实践指南（2020年6月版）_厦大数据库实验室博客 (xmu.edu.cn)</a>文章</li>
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<h1 id="hadoop理论-1"><a href="#hadoop理论-1" class="headerlink" title="hadoop理论(1)"></a>hadoop理论(1)</h1><h2 id="Hadoop的两大核心"><a href="#Hadoop的两大核心" class="headerlink" title="Hadoop的两大核心"></a>Hadoop的两大核心</h2><ol>
<li><p>HDFS 分布式文件系统 实现了如何存储海量数据</p>
</li>
<li><p>Mapreduce 分布式并行框架 如何并行处理</p>
</li>
</ol>
<h2 id="Hadoop-的特性"><a href="#Hadoop-的特性" class="headerlink" title="Hadoop 的特性"></a>Hadoop 的特性</h2><ul>
<li><p>可靠性 ：分布式具体将各个任务分片 partiton，存在实现同一个任务的partition 叫做 冗余集，即即使一个分片出现错误，但是其他分片仍然可以正常的完成任务。</p>
</li>
<li><p>高效性</p>
</li>
<li><p>扩展性：可以增加节点</p>
</li>
<li><p>成本低</p>
</li>
<li><p>多语言</p>
</li>
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<h1 id="hadoop的前置内容"><a href="#hadoop的前置内容" class="headerlink" title="hadoop的前置内容"></a>hadoop的前置内容</h1><h4 id="问：什么是分布式，什么是单机式，什么是伪分布式"><a href="#问：什么是分布式，什么是单机式，什么是伪分布式" class="headerlink" title="问：什么是分布式，什么是单机式，什么是伪分布式"></a>问：什么是分布式，什么是单机式，什么是伪分布式</h4><h4 id="分布式：若干独立计算机的集合"><a href="#分布式：若干独立计算机的集合" class="headerlink" title="分布式：若干独立计算机的集合"></a>分布式：若干独立计算机的集合</h4><figure class="highlight plaintext"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br></pre></td><td class="code"><pre><span class="line">分布式相对于集中式而言。对于传统集中式而言处理应用用一台服务器中的资源来处理</span><br><span class="line">而分布式则是将应用细分成为各个模块，各个模块则有多个服务器的资源来解决。</span><br></pre></td></tr></table></figure>
<h4 id="单机式："><a href="#单机式：" class="headerlink" title="单机式："></a>单机式：</h4><figure class="highlight plaintext"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br></pre></td><td class="code"><pre><span class="line">单机式也称集中式，由一台服务器来解决所有问题。在目前中，无法处理较为复杂的问题：</span><br><span class="line">因为解决复杂问题所需要服务器的成本过高，不如通过分布式来分解问题解决。</span><br></pre></td></tr></table></figure>
<h4 id="伪分布式"><a href="#伪分布式" class="headerlink" title="伪分布式"></a>伪分布式</h4><figure class="highlight plaintext"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">伪分布式是在一台机器上模拟出了分布式的这个过程。</span><br></pre></td></tr></table></figure>
<h4 id="hadoop"><a href="#hadoop" class="headerlink" title="hadoop"></a>hadoop</h4><figure class="highlight plaintext"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br></pre></td><td class="code"><pre><span class="line">hadoop是一个可以对大量数据采用分布式处理的软件框架。</span><br><span class="line">hadoop是实现了Google的云计算的开源系统包括并行计算模型：Map/Reduce</span><br><span class="line">分布式文件系统HDFS以及分布式数据库Hbase。</span><br></pre></td></tr></table></figure>
<h4 id="关于分布式框架的区别"><a href="#关于分布式框架的区别" class="headerlink" title="关于分布式框架的区别"></a>关于分布式框架的区别</h4><figure class="highlight plaintext"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br></pre></td><td class="code"><pre><span class="line">hadoop用于分布式储存和map-reduce计算，Spark用于分布式机器学习，Hive是分布式数据库</span><br><span class="line">Hbaes是分布式kv系统。</span><br></pre></td></tr></table></figure>
<h4 id="按照操作需要在Ubantu创建一个专门为hadoop操作的用户"><a href="#按照操作需要在Ubantu创建一个专门为hadoop操作的用户" class="headerlink" title="按照操作需要在Ubantu创建一个专门为hadoop操作的用户"></a>按照操作需要在Ubantu创建一个专门为hadoop操作的用户</h4><figure class="highlight shell"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">sudo useradd -m hadoop -s /bin/bash</span><br></pre></td></tr></table></figure>
<p>上述操作实现了创建一个名为 hadoop的用户并且以 bin/bash 作为shell</p>
<figure class="highlight shell"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">sudo passwd hadoop</span><br></pre></td></tr></table></figure>
<p>为hadoop用户设置密码</p>
<figure class="highlight shell"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">sudo adduser hadoop sudo</span><br></pre></td></tr></table></figure>
<p>为我们的hadoop用户开放管理员权限</p>
<figure class="highlight shell"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">sudo apt-get install -n</span><br></pre></td></tr></table></figure>
<p>下载软件</p>
<h4 id="Vim操作"><a href="#Vim操作" class="headerlink" title="Vim操作"></a>Vim操作</h4><p>vim的模式有三种：正常模式，插入模式，可视模式</p>
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<li><p>正常模式：进入vim时就是正常模式。任何模式下按esc可进入正常模式</p>
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<li><p>编辑插入模式，按i键可进入编辑模式</p>
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<li><p>退出前记得保存修改：esc返回正常模式，输入:wq即可。</p>
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<h4 id="用ssh来登入"><a href="#用ssh来登入" class="headerlink" title="用ssh来登入"></a>用ssh来登入</h4><p>关于ssh</p>
<figure class="highlight plaintext"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br></pre></td><td class="code"><pre><span class="line">ssh是一种基于应用层的基础安全协议，全名是secure shell。是专门为远程登入会话和其他</span><br><span class="line">网络服务提供的。用ssh可以有效防止远程登入会话中的信息泄露。</span><br></pre></td></tr></table></figure>
<p>两种方式：1.基于口令的安全验证</p>
<figure class="highlight plaintext"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br></pre></td><td class="code"><pre><span class="line">只要你知道你的远程服务器的账号和口令就可以登入，但是会出现登入到其他冒充服务器即</span><br><span class="line">中间人(man in the middle)。</span><br></pre></td></tr></table></figure>
<p>2.基于密钥的验证</p>
<figure class="highlight plaintext"><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></pre></td><td class="code"><pre><span class="line">这种方式为，创建一对密钥，将公共密钥放入服务器中的特定位置，然后访问远程访问服务器时</span><br><span class="line">先获取到服务器中的公共密钥，获取后再将其与本地密钥质询，若与本地公共密钥相同则再使用</span><br><span class="line">私人密钥来解开公共密钥再发送。这种方法虽然登入速度相对于第一种较慢，但是相对安全，不会</span><br><span class="line">出现 man in the middle来攻击。</span><br></pre></td></tr></table></figure>
<p>以上内容总结图片</p>
<p><img src="C:\Users\guoxiatong\AppData\Roaming\marktext\images\2022-02-11-17-52-12-image.png"></p>
<h5 id="参考文章及视频"><a href="#参考文章及视频" class="headerlink" title="参考文章及视频"></a>参考文章及视频</h5><ol>
<li>文章内容参考林子雨教授的<a target="_blank" rel="noopener" href="http://dblab.xmu.edu.cn/blog/2630-2/">大数据软件安装和基础编程实践指南（2020年6月版）_厦大数据库实验室博客 (xmu.edu.cn)</a>文章</li>
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<h1 id="大数据基础学习"><a href="#大数据基础学习" class="headerlink" title="大数据基础学习"></a>大数据基础学习</h1><h2 id="大数据的关键技术"><a href="#大数据的关键技术" class="headerlink" title="大数据的关键技术"></a>大数据的关键技术</h2><ol>
<li><p>批处理计算</p>
<p>最典型的是 MapReduce :将大量数据拿来做批量处理，只要涉及批处理都无法满足秒级响应（即实时性）。Spark也是解决了MapReduce的缺点，可以用来做数据挖掘。</p>
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<li><p>流计算</p>
<p>流数据满足的是实时处理，给出实时响应，否则商业结果就会失去商业价值，不可以分批处理。</p>
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<li><p>图计算：专门处理图中信息的。</p>
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<li><p>查询分析计算</p>
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<h2 id="云计算"><a href="#云计算" class="headerlink" title="云计算"></a>云计算</h2><p>云计算的特征：虚拟化，用户多</p>
<p>云计算的概念：通过网络给用户提供廉价的IT资源</p>
<p>云计算的三种</p>
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<li><p>公共云：百度云</p>
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<li><p>私有云：企业内部</p>
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<li><p>混合云：即提供部分给用户，又提供部分给内部</p>
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