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| gitHub |
| <svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" x="0px" y="0px" viewBox="0 0 100 100" width="15" height="15" class="icon outbound"><path fill="currentColor" d="M18.8,85.1h56l0,0c2.2,0,4-1.8,4-4v-32h-8v28h-48v-48h28v-8h-32l0,0c-2.2,0-4,1.8-4,4v56C14.8,83.3,16.6,85.1,18.8,85.1z"></path> <polygon fill="currentColor" points="45.7,48.7 51.3,54.3 77.2,28.5 77.2,37.2 85.2,37.2 85.2,14.9 62.8,14.9 62.8,22.9 71.5,22.9"></polygon></svg></a></nav> <ul class="sidebar-links"><li><section class="sidebar-group depth-0"><p class="sidebar-heading"><span>IoTDB用户手册 (In progress)</span> <!----></p> <!----></section></li><li><section class="sidebar-group collapsable depth-0"><p class="sidebar-heading"><span>开始</span> <span class="arrow right"></span></p> <!----></section></li><li><section class="sidebar-group collapsable depth-0"><p class="sidebar-heading"><span>概述</span> <span class="arrow right"></span></p> <!----></section></li><li><section class="sidebar-group collapsable depth-0"><p class="sidebar-heading"><span>概念</span> <span class="arrow right"></span></p> <!----></section></li><li><section class="sidebar-group collapsable depth-0"><p class="sidebar-heading"><span>服务器端</span> <span class="arrow right"></span></p> <!----></section></li><li><section class="sidebar-group collapsable depth-0"><p class="sidebar-heading"><span>客户端</span> <span class="arrow right"></span></p> <!----></section></li><li><section class="sidebar-group collapsable depth-0"><p class="sidebar-heading"><span>操作指南</span> <span class="arrow right"></span></p> <!----></section></li><li><section class="sidebar-group collapsable depth-0"><p class="sidebar-heading"><span>系统工具</span> <span class="arrow right"></span></p> <!----></section></li><li><section class="sidebar-group collapsable depth-0"><p class="sidebar-heading open"><span>生态集成</span> <span class="arrow down"></span></p> <ul class="sidebar-links sidebar-group-items"><li><a href="/zh/UserGuide/Master/Ecosystem Integration/Grafana.html" class="sidebar-link">Grafana</a></li><li><a href="/zh/UserGuide/Master/Ecosystem Integration/MapReduce TsFile.html" class="active sidebar-link">MapReduce TsFile</a></li><li><a href="/zh/UserGuide/Master/Ecosystem Integration/Spark TsFile.html" class="sidebar-link">Spark TsFile</a></li><li><a href="/zh/UserGuide/Master/Ecosystem Integration/Spark IoTDB.html" class="sidebar-link">Spark IoTDB</a></li><li><a href="/zh/UserGuide/Master/Ecosystem Integration/Hive TsFile.html" class="sidebar-link">Hive TsFile</a></li></ul></section></li><li><section class="sidebar-group collapsable depth-0"><p class="sidebar-heading"><span>系统设计</span> <span class="arrow right"></span></p> <!----></section></li></ul> </aside> <main class="page"> <div class="content content__default"><h1 id="mapreduce-tsfile"><a href="#mapreduce-tsfile" class="header-anchor">#</a> MapReduce TsFile</h1> <h2 id="概要"><a href="#概要" class="header-anchor">#</a> 概要</h2> <ul><li>TsFile的Hadoop连接器使用手册 |
| <ul><li>什么是TsFile的Hadoop连接器</li> <li>系统环境要求</li> <li>数据类型对应关系</li> <li>关于TSFInputFormat的说明</li> <li>使用示例 |
| <ul><li>读示例: 求和</li> <li>写示例: 计算平均数并写入Tsfile中</li></ul></li></ul></li></ul> <h1 id="tsfile的hadoop连接器使用手册"><a href="#tsfile的hadoop连接器使用手册" class="header-anchor">#</a> TsFile的Hadoop连接器使用手册</h1> <h2 id="什么是tsfile的hadoop连接器"><a href="#什么是tsfile的hadoop连接器" class="header-anchor">#</a> 什么是TsFile的Hadoop连接器</h2> <p>TsFile的Hadoop连接器实现了对Hadoop读取外部Tsfile类型的文件格式的支持。让用户可以使用Hadoop的map、reduce等操作对Tsfile文件进行读取、写入和查询。</p> <p>有了这个连接器，用户可以</p> <ul><li>将单个Tsfile文件加载进Hadoop，不论文件是存储在本地文件系统或者是HDFS中</li> <li>将某个特定目录下的所有文件加载进Hadoop，不论文件是存储在本地文件系统或者是HDFS中</li> <li>将Hadoop处理完后的结果以Tsfile的格式保存</li></ul> <h2 id="系统环境要求"><a href="#系统环境要求" class="header-anchor">#</a> 系统环境要求</h2> <table><thead><tr><th>Hadoop 版本</th> <th>Java 版本</th> <th>TsFile 版本</th></tr></thead> <tbody><tr><td><code>2.7.3</code></td> <td><code>1.8</code></td> <td><code>0.10.0</code></td></tr></tbody></table> <blockquote><p>注意：关于如何下载和使用Tsfile, 请参考以下链接: https://github.com/apache/incubator-iotdb/tree/master/tsfile.</p></blockquote> <h2 id="数据类型对应关系"><a href="#数据类型对应关系" class="header-anchor">#</a> 数据类型对应关系</h2> <table><thead><tr><th>TsFile 数据类型</th> <th>Hadoop writable</th></tr></thead> <tbody><tr><td>BOOLEAN</td> <td>BooleanWritable</td></tr> <tr><td>INT32</td> <td>IntWritable</td></tr> <tr><td>INT64</td> <td>LongWritable</td></tr> <tr><td>FLOAT</td> <td>FloatWritable</td></tr> <tr><td>DOUBLE</td> <td>DoubleWritable</td></tr> <tr><td>TEXT</td> <td>Text</td></tr></tbody></table> <h2 id="关于tsfinputformat的说明"><a href="#关于tsfinputformat的说明" class="header-anchor">#</a> 关于TSFInputFormat的说明</h2> <p>TSFInputFormat继承了Hadoop中FileInputFormat类，重写了其中切片的方法。</p> <p>目前的切片方法是根据每个ChunkGroup的中点的offset是否属于Hadoop所切片的startOffset和endOffset之间，来判断是否将该ChunkGroup放入此切片。</p> <p>TSFInputFormat将tsfile中的数据以多个<code>MapWritable</code>记录的形式返回给用户。</p> <p>假设我们想要从Tsfile中获得名为<code>d1</code>的设备的数据，该设备有三个传感器，名称分别为<code>s1</code>, <code>s2</code>, <code>s3</code>。</p> <p><code>s1</code>的类型是<code>BOOLEAN</code>, <code>s2</code>的类型是 <code>DOUBLE</code>, <code>s3</code>的类型是<code>TEXT</code>.</p> <p><code>MapWritable</code>的结构如下所示：</p> <div class="language- line-numbers-mode"><pre class="language-text"><code>{ |
| "time_stamp": 10000000, |
| "device_id": d1, |
| "s1": true, |
| "s2": 3.14, |
| "s3": "middle" |
| } |
| </code></pre> <div class="line-numbers-wrapper"><span class="line-number">1</span><br><span class="line-number">2</span><br><span class="line-number">3</span><br><span class="line-number">4</span><br><span class="line-number">5</span><br><span class="line-number">6</span><br><span class="line-number">7</span><br></div></div><p>在Hadoop的Map job中，你可以采用如下方法获得你想要的任何值</p> <p><code>mapwritable.get(new Text("s1"))</code></p> <blockquote><p>注意: <code>MapWritable</code>中所有的键值类型都是<code>Text</code>。</p></blockquote> <h2 id="使用示例"><a href="#使用示例" class="header-anchor">#</a> 使用示例</h2> <h3 id="读示例-求和"><a href="#读示例-求和" class="header-anchor">#</a> 读示例: 求和</h3> <p>首先，我们需要在TSFInputFormat中配置我们需要哪些数据</p> <div class="language- line-numbers-mode"><pre class="language-text"><code> // configure reading time enable |
| TSFInputFormat.setReadTime(job, true); |
| // configure reading deviceId enable |
| TSFInputFormat.setReadDeviceId(job, true); |
| // configure reading which deltaObjectIds |
| String[] deviceIds = {"device_1"}; |
| TSFInputFormat.setReadDeviceIds(job, deltaObjectIds); |
| // configure reading which measurementIds |
| String[] measurementIds = {"sensor_1", "sensor_2", "sensor_3"}; |
| TSFInputFormat.setReadMeasurementIds(job, measurementIds); |
| </code></pre> <div class="line-numbers-wrapper"><span class="line-number">1</span><br><span class="line-number">2</span><br><span class="line-number">3</span><br><span class="line-number">4</span><br><span class="line-number">5</span><br><span class="line-number">6</span><br><span class="line-number">7</span><br><span class="line-number">8</span><br><span class="line-number">9</span><br><span class="line-number">10</span><br></div></div><p>然后，必须指定mapper和reducer输出的键和值类型</p> <div class="language- line-numbers-mode"><pre class="language-text"><code> // set inputformat and outputformat |
| job.setInputFormatClass(TSFInputFormat.class); |
| // set mapper output key and value |
| job.setMapOutputKeyClass(Text.class); |
| job.setMapOutputValueClass(DoubleWritable.class); |
| // set reducer output key and value |
| job.setOutputKeyClass(Text.class); |
| job.setOutputValueClass(DoubleWritable.class); |
| </code></pre> <div class="line-numbers-wrapper"><span class="line-number">1</span><br><span class="line-number">2</span><br><span class="line-number">3</span><br><span class="line-number">4</span><br><span class="line-number">5</span><br><span class="line-number">6</span><br><span class="line-number">7</span><br><span class="line-number">8</span><br></div></div><p>接着，就可以编写包含具体的处理数据逻辑的<code>mapper</code>和<code>reducer</code>类了。</p> <div class="language- line-numbers-mode"><pre class="language-text"><code> public static class TSMapper extends Mapper<NullWritable, MapWritable, Text, DoubleWritable> { |
| |
| @Override |
| protected void map(NullWritable key, MapWritable value, |
| Mapper<NullWritable, MapWritable, Text, DoubleWritable>.Context context) |
| throws IOException, InterruptedException { |
| |
| Text deltaObjectId = (Text) value.get(new Text("device_id")); |
| context.write(deltaObjectId, (DoubleWritable) value.get(new Text("sensor_3"))); |
| } |
| } |
| |
| public static class TSReducer extends Reducer<Text, DoubleWritable, Text, DoubleWritable> { |
| |
| @Override |
| protected void reduce(Text key, Iterable<DoubleWritable> values, |
| Reducer<Text, DoubleWritable, Text, DoubleWritable>.Context context) |
| throws IOException, InterruptedException { |
| |
| double sum = 0; |
| for (DoubleWritable value : values) { |
| sum = sum + value.get(); |
| } |
| context.write(key, new DoubleWritable(sum)); |
| } |
| } |
| </code></pre> <div class="line-numbers-wrapper"><span class="line-number">1</span><br><span class="line-number">2</span><br><span class="line-number">3</span><br><span class="line-number">4</span><br><span class="line-number">5</span><br><span class="line-number">6</span><br><span class="line-number">7</span><br><span class="line-number">8</span><br><span class="line-number">9</span><br><span class="line-number">10</span><br><span class="line-number">11</span><br><span class="line-number">12</span><br><span class="line-number">13</span><br><span class="line-number">14</span><br><span class="line-number">15</span><br><span class="line-number">16</span><br><span class="line-number">17</span><br><span class="line-number">18</span><br><span class="line-number">19</span><br><span class="line-number">20</span><br><span class="line-number">21</span><br><span class="line-number">22</span><br><span class="line-number">23</span><br><span class="line-number">24</span><br><span class="line-number">25</span><br><span class="line-number">26</span><br></div></div><blockquote><p>注意: 完整的代码示例可以在如下链接中找到：https://github.com/apache/incubator-iotdb/blob/master/example/hadoop/src/main/java/org/apache/iotdb/hadoop/tsfile/TSFMRReadExample.java</p></blockquote> <h3 id="写示例-计算平均数并写入tsfile中"><a href="#写示例-计算平均数并写入tsfile中" class="header-anchor">#</a> 写示例: 计算平均数并写入Tsfile中</h3> <p>除了<code>OutputFormatClass</code>，剩下的配置代码跟上面的读示例是一样的</p> <div class="language- line-numbers-mode"><pre class="language-text"><code> job.setOutputFormatClass(TSFOutputFormat.class); |
| // set reducer output key and value |
| job.setOutputKeyClass(NullWritable.class); |
| job.setOutputValueClass(HDFSTSRecord.class); |
| </code></pre> <div class="line-numbers-wrapper"><span class="line-number">1</span><br><span class="line-number">2</span><br><span class="line-number">3</span><br><span class="line-number">4</span><br></div></div><p>然后，是包含具体的处理数据逻辑的<code>mapper</code>和<code>reducer</code>类。</p> <div class="language- line-numbers-mode"><pre class="language-text"><code> public static class TSMapper extends Mapper<NullWritable, MapWritable, Text, MapWritable> { |
| |
| @Override |
| protected void map(NullWritable key, MapWritable value, |
| Mapper<NullWritable, MapWritable, Text, MapWritable>.Context context) |
| throws IOException, InterruptedException { |
| |
| Text deltaObjectId = (Text) value.get(new Text("device_id")); |
| long timestamp = ((LongWritable)value.get(new Text("timestamp"))).get(); |
| if (timestamp % 100000 == 0) { |
| context.write(deltaObjectId, new MapWritable(value)); |
| } |
| } |
| } |
| |
| /** |
| * This reducer calculate the average value. |
| */ |
| public static class TSReducer extends Reducer<Text, MapWritable, NullWritable, HDFSTSRecord> { |
| |
| @Override |
| protected void reduce(Text key, Iterable<MapWritable> values, |
| Reducer<Text, MapWritable, NullWritable, HDFSTSRecord>.Context context) throws IOException, InterruptedException { |
| long sensor1_value_sum = 0; |
| long sensor2_value_sum = 0; |
| double sensor3_value_sum = 0; |
| long num = 0; |
| for (MapWritable value : values) { |
| num++; |
| sensor1_value_sum += ((LongWritable)value.get(new Text("sensor_1"))).get(); |
| sensor2_value_sum += ((LongWritable)value.get(new Text("sensor_2"))).get(); |
| sensor3_value_sum += ((DoubleWritable)value.get(new Text("sensor_3"))).get(); |
| } |
| HDFSTSRecord tsRecord = new HDFSTSRecord(1L, key.toString()); |
| DataPoint dPoint1 = new LongDataPoint("sensor_1", sensor1_value_sum / num); |
| DataPoint dPoint2 = new LongDataPoint("sensor_2", sensor2_value_sum / num); |
| DataPoint dPoint3 = new DoubleDataPoint("sensor_3", sensor3_value_sum / num); |
| tsRecord.addTuple(dPoint1); |
| tsRecord.addTuple(dPoint2); |
| tsRecord.addTuple(dPoint3); |
| context.write(NullWritable.get(), tsRecord); |
| } |
| } |
| </code></pre> <div class="line-numbers-wrapper"><span class="line-number">1</span><br><span class="line-number">2</span><br><span class="line-number">3</span><br><span class="line-number">4</span><br><span class="line-number">5</span><br><span class="line-number">6</span><br><span class="line-number">7</span><br><span class="line-number">8</span><br><span class="line-number">9</span><br><span class="line-number">10</span><br><span class="line-number">11</span><br><span class="line-number">12</span><br><span class="line-number">13</span><br><span class="line-number">14</span><br><span class="line-number">15</span><br><span class="line-number">16</span><br><span class="line-number">17</span><br><span class="line-number">18</span><br><span class="line-number">19</span><br><span class="line-number">20</span><br><span class="line-number">21</span><br><span class="line-number">22</span><br><span class="line-number">23</span><br><span class="line-number">24</span><br><span class="line-number">25</span><br><span class="line-number">26</span><br><span class="line-number">27</span><br><span class="line-number">28</span><br><span class="line-number">29</span><br><span class="line-number">30</span><br><span class="line-number">31</span><br><span class="line-number">32</span><br><span class="line-number">33</span><br><span class="line-number">34</span><br><span class="line-number">35</span><br><span class="line-number">36</span><br><span class="line-number">37</span><br><span class="line-number">38</span><br><span class="line-number">39</span><br><span class="line-number">40</span><br><span class="line-number">41</span><br><span class="line-number">42</span><br><span class="line-number">43</span><br></div></div><blockquote><p>注意: 完整的代码示例可以在如下链接中找到：https://github.com/apache/incubator-iotdb/blob/master/example/hadoop/src/main/java/org/apache/iotdb/hadoop/tsfile/TSMRWriteExample.java</p></blockquote></div> <div class="page-nav"><p class="inner"><span class="prev"> |
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| Apache and the Apache feather logo are trademarks of The Apache Software Foundation</p> <p style="text-align:justify!important;paddingLeft:10px;paddingRight:10px;"> |
| Disclaimer: Apache IoTDB (incubating) is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator. Incubation is required of all newly accepted projects until a further review indicates that the infrastructure, communications, and decision making process have stabilized in a manner consistent with other successful ASF projects. While incubation status is not necessarily a reflection of the completeness or stability of the code, it does indicate that the project has yet to be fully endorsed by the ASF. |
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