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| <span><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" focusable="false" 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> <span class="sr-only">(opens new window)</span></span></a></nav> <ul class="sidebar-links"><li><section class="sidebar-group depth-0"><p class="sidebar-heading"><span>IoTDB User Guide (V0.11.x)</span> <!----></p> <!----></section></li><li><section class="sidebar-group collapsable depth-0"><p class="sidebar-heading"><span>Get Started</span> <span class="arrow right"></span></p> <!----></section></li><li><section class="sidebar-group collapsable depth-0"><p class="sidebar-heading"><span>Overview</span> <span class="arrow right"></span></p> <!----></section></li><li><section class="sidebar-group collapsable depth-0"><p class="sidebar-heading"><span>Concept</span> <span class="arrow right"></span></p> <!----></section></li><li><section class="sidebar-group collapsable depth-0"><p class="sidebar-heading"><span>Server</span> <span class="arrow right"></span></p> <!----></section></li><li><section class="sidebar-group collapsable depth-0"><p class="sidebar-heading"><span>Client</span> <span class="arrow right"></span></p> <!----></section></li><li><section class="sidebar-group collapsable depth-0"><p class="sidebar-heading"><span>Operation Manual</span> <span class="arrow right"></span></p> <!----></section></li><li><section class="sidebar-group collapsable depth-0"><p class="sidebar-heading"><span>System Tools</span> <span class="arrow right"></span></p> <!----></section></li><li><section class="sidebar-group collapsable depth-0"><p class="sidebar-heading open"><span>Ecosystem Integration</span> <span class="arrow down"></span></p> <ul class="sidebar-links sidebar-group-items"><li><a href="/UserGuide/V0.11.x/Ecosystem Integration/Grafana.html" class="sidebar-link">Grafana</a></li><li><a href="/UserGuide/V0.11.x/Ecosystem Integration/MapReduce TsFile.html" class="active sidebar-link">MapReduce TsFile</a></li><li><a href="/UserGuide/V0.11.x/Ecosystem Integration/Spark TsFile.html" class="sidebar-link">Spark TsFile</a></li><li><a href="/UserGuide/V0.11.x/Ecosystem Integration/Spark IoTDB.html" class="sidebar-link">Spark IoTDB</a></li><li><a href="/UserGuide/V0.11.x/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>Architecture</span> <span class="arrow right"></span></p> <!----></section></li><li><section class="sidebar-group collapsable depth-0"><p class="sidebar-heading"><span>Comparison with TSDBs</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="outline"><a href="#outline" class="header-anchor">#</a> Outline</h2> <ul><li>TsFile-Hadoop-Connector User Guide |
| <ul><li>About TsFile-Hadoop-Connector</li> <li>System Requirements</li> <li>Data Type Correspondence</li> <li>TSFInputFormat Explanation</li> <li>Examples |
| <ul><li>Read Example: calculate the sum</li> <li>Write Example: write the average into Tsfile</li></ul></li></ul></li></ul> <h1 id="tsfile-hadoop-connector-user-guide"><a href="#tsfile-hadoop-connector-user-guide" class="header-anchor">#</a> TsFile-Hadoop-Connector User Guide</h1> <h2 id="about-tsfile-hadoop-connector"><a href="#about-tsfile-hadoop-connector" class="header-anchor">#</a> About TsFile-Hadoop-Connector</h2> <p>TsFile-Hadoop-Connector implements the support of Hadoop for external data sources of Tsfile type. This enables users to read, write and query Tsfile by Hadoop.</p> <p>With this connector, you can</p> <ul><li>load a single TsFile, from either the local file system or hdfs, into Hadoop</li> <li>load all files in a specific directory, from either the local file system or hdfs, into hadoop</li> <li>write data from Hadoop into TsFile</li></ul> <h2 id="system-requirements"><a href="#system-requirements" class="header-anchor">#</a> System Requirements</h2> <table><thead><tr><th>Hadoop Version</th> <th>Java Version</th> <th>TsFile Version</th></tr></thead> <tbody><tr><td><code>2.7.3</code></td> <td><code>1.8</code></td> <td><code>0.11.1</code></td></tr></tbody></table> <blockquote><p>Note: For more information about how to download and use TsFile, please see the following link: https://github.com/apache/iotdb/tree/master/tsfile.</p></blockquote> <h2 id="data-type-correspondence"><a href="#data-type-correspondence" class="header-anchor">#</a> Data Type Correspondence</h2> <table><thead><tr><th>TsFile data type</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="tsfinputformat-explanation"><a href="#tsfinputformat-explanation" class="header-anchor">#</a> TSFInputFormat Explanation</h2> <p>TSFInputFormat extract data from tsfile and format them into records of <code>MapWritable</code>.</p> <p>Suppose that we want to extract data of the device named <code>d1</code> which has three sensors named <code>s1</code>, <code>s2</code>, <code>s3</code>.</p> <p><code>s1</code>'s type is <code>BOOLEAN</code>, <code>s2</code>'s type is <code>DOUBLE</code>, <code>s3</code>'s type is <code>TEXT</code>.</p> <p>The <code>MapWritable</code> struct will be like:</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>In the Map job of Hadoop, you can get any value you want by key as following:</p> <p><code>mapwritable.get(new Text("s1"))</code></p> <blockquote><p>Note: All keys in <code>MapWritable</code> are <code>Text</code> type.</p></blockquote> <h2 id="examples"><a href="#examples" class="header-anchor">#</a> Examples</h2> <h3 id="read-example-calculate-the-sum"><a href="#read-example-calculate-the-sum" class="header-anchor">#</a> Read Example: calculate the sum</h3> <p>First of all, we should tell InputFormat what kind of data we want from tsfile.</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>And then,the output key and value of mapper and reducer should be specified</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>Then, the <code>mapper</code> and <code>reducer</code> class is how you deal with the <code>MapWritable</code> produced by <code>TSFInputFormat</code> class.</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>Note: For the complete code, please see the following link: https://github.com/apache/iotdb/blob/master/example/hadoop/src/main/java/org/apache/iotdb//hadoop/tsfile/TSFMRReadExample.java</p></blockquote> <h3 id="write-example-write-the-average-into-tsfile"><a href="#write-example-write-the-average-into-tsfile" class="header-anchor">#</a> Write Example: write the average into Tsfile</h3> <p>Except for the <code>OutputFormatClass</code>, the rest of configuration code for hadoop map-reduce job is almost same as above.</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>Then, the <code>mapper</code> and <code>reducer</code> class is how you deal with the <code>MapWritable</code> produced by <code>TSFInputFormat</code> class.</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></div></div><blockquote><p>Note: For the complete code, please see the following link: https://github.com/apache/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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