http://git-wip-us.apache.org/repos/asf/incubator-predictionio-site/blob/921d6861/docs/manual/build/datacollection/analytics-tableau/index.html ---------------------------------------------------------------------- diff --git a/docs/manual/build/datacollection/analytics-tableau/index.html b/docs/manual/build/datacollection/analytics-tableau/index.html new file mode 100644 index 0000000..dd0adea --- /dev/null +++ b/docs/manual/build/datacollection/analytics-tableau/index.html @@ -0,0 +1,91 @@ +<!DOCTYPE html><html><head><title>Machine Learning Analytics with Tableau</title><meta charset="utf-8"/><meta content="IE=edge,chrome=1" http-equiv="X-UA-Compatible"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><meta class="swiftype" name="title" data-type="string" content="Machine Learning Analytics with Tableau"/><link rel="canonical" href="https://docs.prediction.io/datacollection/analytics-tableau/"/><link href="/images/favicon/normal-b330020a.png" rel="shortcut 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href="/community/contribute-code/"><span>Contribute Code</span></a></li><li class="level-2"><a class="final" href="/community/contribute-documentation/"><span>Contribute Documentation</span></a></li><li class="level-2"><a class="final" href="/community/contribute-sdk/"><span>Contribute a SDK</span></a></li><li class="level-2"><a class="final" href="/community/contribute-webhook/"><span>Contribute a Webhook</span></a></li><li class="level-2"><a class="final" href="/community/projects/"><span>Community Projects</span></a></li></ul></li><li class="level-1"><a class="expandible" href="#"><span>Gett ing Help</span></a><ul><li class="level-2"><a class="final" href="/resources/faq/"><span>FAQs</span></a></li><li class="level-2"><a class="final" href="/support/"><span>Support</span></a></li></ul></li><li class="level-1"><a class="expandible" href="#"><span>Resources</span></a><ul><li class="level-2"><a class="final" href="/resources/intellij/"><span>Developing Engines with IntelliJ IDEA</span></a></li><li class="level-2"><a class="final" href="/resources/upgrade/"><span>Upgrade Instructions</span></a></li><li class="level-2"><a class="final" href="/resources/glossary/"><span>Glossary</span></a></li></ul></li></ul></nav></div><div class="col-md-9 col-sm-12"><div class="content-header hidden-md hidden-lg"><div id="page-title"><h1>Machine Learning Analytics with Tableau</h1></div></div><div id="table-of-content-wrapper"><h5>On this page</h5><aside id="table-of-contents"><ul> <li> <a href="#prerequisites">Prerequisites</a> </li> <li> <a href="#creating-hive-tables">Creating Hive Table s</a> </li> <li> <a href="#launch-spark-sql-s-thrift-jdbc-odbc-server">Launch Spark SQL's Thrift JDBC/ODBC Server</a> </li> <li> <a href="#performing-analysis-with-tableau">Performing Analysis with Tableau</a> </li> </ul> </aside><hr/><a id="edit-page-link" href="https://github.com/apache/incubator-predictionio/tree/livedoc/docs/manual/source/datacollection/analytics-tableau.html.md.erb";><img src="/images/icons/edit-pencil-d6c1bb3d.png"/>Edit this page</a></div><div class="content-header hidden-sm hidden-xs"><div id="page-title"><h1>Machine Learning Analytics with Tableau</h1></div></div><div class="content"><p>With Spark SQL, it is possible to connect Tableau to Apache PredictionIO (incubating) Event Server for interactive analysis of event data.</p><h2 id='prerequisites' class='header-anchors'>Prerequisites</h2> <ul> <li>Tableau Desktop 8.3+ with a proper license key that supports Spark SQL;</li> <li>Spark ODBC Driver from Databricks (<a href="https://databricks.com/spark/odbc-dri ver-download">https://databricks.com/spark/odbc-driver-download</a>);</li> <li>Apache Hadoop 2.4+</li> <li>Apache Hive 0.3.1+</li> </ul> <div class="alert-message info"><p>In this article, we will assume that you have a working HDFS, and that your environmental variable <code>HADOOP_HOME</code> has been properly set. This is essential for Apache Hive to function properly. In addition, <code>HADOOP_CONF_DIR</code> in <code>$PIO_HOME/conf/pio-env.sh</code> must also be properly set for the <code>pio export</code> command to write to HDFS instead of the local filesystem.</p></div><p><h2 id='export-events-to-apache-parquet' class='header-anchors'>Export Events to Apache Parquet</h2><p>PredictionIO supports exporting your events to <a href="http://parquet.incubator.apache.org/";>Apache Parquet</a>, a columnar storage format that allows you to query quickly.</p><p>Let's export the data we imported in <a href="/templates/recommendation/quickstart/#import-sample-data">Recommendation Engi ne Template Quick Start</a>, and assume the App ID is 1.</p><div class="highlight shell"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1</pre></td><td class="code"><pre><span class="gp">$ </span><span class="nv">$PIO_HOME</span>/bin/pio <span class="nb">export</span> --appid 1 --output /tmp/movies --format parquet +</pre></td></tr></tbody></table> </div> <p>After the command has finished successfully, you should see something similar to the following.</p><div class="highlight shell"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1 +2 +3 +4 +5 +6 +7 +8 +9 +10 +11</pre></td><td class="code"><pre>root + |-- creationTime: string <span class="o">(</span>nullable <span class="o">=</span> <span class="nb">true</span><span class="o">)</span> + |-- entityId: string <span class="o">(</span>nullable <span class="o">=</span> <span class="nb">true</span><span class="o">)</span> + |-- entityType: string <span class="o">(</span>nullable <span class="o">=</span> <span class="nb">true</span><span class="o">)</span> + |-- event: string <span class="o">(</span>nullable <span class="o">=</span> <span class="nb">true</span><span class="o">)</span> + |-- eventId: string <span class="o">(</span>nullable <span class="o">=</span> <span class="nb">true</span><span class="o">)</span> + |-- eventTime: string <span class="o">(</span>nullable <span class="o">=</span> <span class="nb">true</span><span class="o">)</span> + |-- properties: struct <span class="o">(</span>nullable <span class="o">=</span> <span class="nb">true</span><span class="o">)</span> + | |-- rating: double <span class="o">(</span>nullable <span class="o">=</span> <span class="nb">true</span><span class="o">)</span> + |-- targetEntityId: string <span class="o">(</span>nullable <span class="o">=</span> <span class="nb">true</span><span class="o">)</span> + |-- targetEntityType: string <span class="o">(</span>nullable <span class="o">=</span> <span class="nb">true</span><span class="o">)</span> +</pre></td></tr></tbody></table> </div></p><h2 id='creating-hive-tables' class='header-anchors'>Creating Hive Tables</h2><p>Before you can use Spark SQL's Thrift JDBC/ODBC Server, you will need to create the table schema in Hive first. Please make sure to replace <code>path_of_hive</code> with the real path.</p><div class="highlight shell"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1 +2 +3 +4</pre></td><td class="code"><pre><span class="gp">$ </span><span class="nb">cd </span>path_of_hive +<span class="gp">$ </span>bin/hive +<span class="gp">hive> </span>CREATE EXTERNAL TABLE events <span class="o">(</span>event STRING, entityType STRING, entityId STRING, targetEntityType STRING, targetEntityId STRING, properties STRUCT<rating:DOUBLE><span class="o">)</span> STORED AS parquet LOCATION <span class="s1">'/tmp/movies'</span>; +<span class="gp">hive> </span><span class="nb">exit</span>; +</pre></td></tr></tbody></table> </div> <h2 id='launch-spark-sql's-thrift-jdbc/odbc-server' class='header-anchors'>Launch Spark SQL's Thrift JDBC/ODBC Server</h2><p>Once you have created your Hive tables, create a Hive configuration in your Spark installation. If you have a custom <code>hive-site.xml</code>, simply copy or link it to <code>$SPARK_HOME/conf</code>. Otherwise, Hive would have created a local Derby database, and you will need to let Spark knows about it. Create <code>$SPARK_HOME/conf/hive-site.xml</code> from scratch with the following template.</p><div class="alert-message warning"><p>You must change <code>/opt/apache-hive-0.13.1-bin</code> below to a real Hive path.</p></div><div class="highlight xml"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1 +2 +3 +4 +5 +6 +7 +8</pre></td><td class="code"><pre><span class="cp"><?xml version="1.0" encoding="UTF-8" standalone="no"?></span> +<span class="cp"><?xml-stylesheet type="text/xsl" href="configuration.xsl"?></span> +<span class="nt"><configuration></span> + <span class="nt"><property></span> + <span class="nt"><name></span>javax.jdo.option.ConnectionURL<span class="nt"></name></span> + <span class="nt"><value></span>jdbc:derby:;databaseName=/opt/apache-hive-0.13.1-bin/metastore_db;create=true<span class="nt"></value></span> + <span class="nt"></property></span> +<span class="nt"></configuration></span> +</pre></td></tr></tbody></table> </div> <p>Launch Spark SQL's Thift JDBC/ODBC Server by</p><div class="highlight shell"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1</pre></td><td class="code"><pre><span class="gp">$ </span><span class="nv">$SPARK_HOME</span>/sbin/start-thriftserver.sh +</pre></td></tr></tbody></table> </div> <p>You can test the server using the included Beeline client.</p><div class="highlight shell"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1 +2 +3 +4 +5 +6 +7 +8 +9 +10 +11 +12 +13 +14 +15 +16 +17 +18 +19 +20</pre></td><td class="code"><pre><span class="gp">$ </span><span class="nv">$SPARK_HOME</span>/bin/beeline +<span class="gp">beeline> </span>!connect jdbc:hive2://localhost:10000 +<span class="o">(</span>Use empty username and password when prompted<span class="o">)</span> +0: jdbc:hive2://localhost:10000> <span class="k">select</span> <span class="k">*</span> from events limit 10; ++--------+-------------+-----------+-------------------+-----------------+------------------+ +| event | entitytype | entityid | targetentitytype | targetentityid | properties | ++--------+-------------+-----------+-------------------+-----------------+------------------+ +| buy | user | 3 | item | 0 | <span class="o">{</span><span class="s2">"rating"</span>:null<span class="o">}</span> | +| buy | user | 3 | item | 1 | <span class="o">{</span><span class="s2">"rating"</span>:null<span class="o">}</span> | +| rate | user | 3 | item | 2 | <span class="o">{</span><span class="s2">"rating"</span>:1.0<span class="o">}</span> | +| buy | user | 3 | item | 7 | <span class="o">{</span><span class="s2">"rating"</span>:null<span class="o">}</span> | +| buy | user | 3 | item | 8 | <span class="o">{</span><span class="s2">"rating"</span>:null<span class="o">}</span> | +| buy | user | 3 | item | 9 | <span class="o">{</span><span class="s2">"rating"</span>:null<span class="o">}</span> | +| rate | user | 3 | item | 14 | <span class="o">{</span><span class="s2">"rating"</span>:1.0<span class="o">}</span> | +| buy | user | 3 | item | 15 | <span class="o">{</span><span class="s2">"rating"</span>:null<span class="o">}</span> | +| buy | user | 3 | item | 16 | <span class="o">{</span><span class="s2">"rating"</span>:null<span class="o">}</span> | +| buy | user | 3 | item | 18 | <span class="o">{</span><span class="s2">"rating"</span>:null<span class="o">}</span> | ++--------+-------------+-----------+-------------------+-----------------+------------------+ +10 rows selected <span class="o">(</span>0.515 seconds<span class="o">)</span> +0: jdbc:hive2://localhost:10000> +</pre></td></tr></tbody></table> </div> <p>Now you are ready to use Tableau!</p><h2 id='performing-analysis-with-tableau' class='header-anchors'>Performing Analysis with Tableau</h2><p>Launch Tableau and Connect to Data. Click on <strong>Spark SQL (Beta)</strong> and enter Spark SQL's Thrift JDBC/ODBC Server information. Make sure to pick <strong>User Name</strong> as <strong>Authentication</strong>. Click <strong>Connect</strong>.</p><p><img alt="Tableau and Spark SQL" src="/images/datacollection/tableau-01-b5a23839.png"/></p><p>On the next page, pick <strong>default</strong> under <strong>Schema</strong>.</p><div class="alert-message info"><p>You may not see any choices when you click on Schema. Simply press Enter and Tableau will try to list all schemas.</p></div><p>Once you see a list of tables that includes <strong>events</strong>, click <strong>New Custom SQL</strong>, then enter the following.</p><div class="highlight sql"><table style="border-spacing: 0"><tbody><tr><td c lass="gutter gl" style="text-align: right"><pre class="lineno">1</pre></td><td class="code"><pre><span class="k">SELECT</span> <span class="n">event</span><span class="p">,</span> <span class="n">entityType</span><span class="p">,</span> <span class="n">entityId</span><span class="p">,</span> <span class="n">targetEntityType</span><span class="p">,</span> <span class="n">targetEntityId</span><span class="p">,</span> <span class="n">properties</span><span class="p">.</span><span class="n">rating</span> <span class="k">FROM</span> <span class="n">events</span> +</pre></td></tr></tbody></table> </div> <p>Click <strong>Update Now</strong>. You should see the following screen by now, indicating success in loading data. Using a custom SQL allows you to extract arbitrary fields from within properties.</p><p><img alt="Setting up Tableau" src="/images/datacollection/tableau-02-76e93443.png"/></p><p>Click <strong>Go to Worksheet</strong> and start analyzing. The following shows an example of breaking down different rating values.</p><p><img alt="Rating Values Breakdown" src="/images/datacollection/tableau-03-e389351e.png"/></p><p>The following shows a summary of interactions.</p><p><img alt="Interactions" src="/images/datacollection/tableau-04-c8c31bb7.png"/></p><p>Happy analyzing!</p></div></div></div></div><footer><div class="container"><div class="seperator"></div><div class="row"><div class="col-md-6 col-xs-6 footer-link-column"><div class="footer-link-column-row"><h4>Community</h4><ul><li><a href="//docs.prediction.io/install/" target="blank" >Download</a></li><li><a href="//docs.prediction.io/" >target="blank">Docs</a></li><li><a >href="//github.com/apache/incubator-predictionio" >target="blank">GitHub</a></li><li><a >href="mailto:[email protected]"; >target="blank">Subscribe to User Mailing List</a></li><li><a >href="//stackoverflow.com/questions/tagged/predictionio" >target="blank">Stackoverflow</a></li></ul></div></div><div class="col-md-6 >col-xs-6 footer-link-column"><div >class="footer-link-column-row"><h4>Contribute</h4><ul><li><a >href="//predictionio.incubator.apache.org/community/contribute-code/" >target="blank">Contribute</a></li><li><a >href="//github.com/apache/incubator-predictionio" target="blank">Source >Code</a></li><li><a href="//issues.apache.org/jira/browse/PIO" >target="blank">Bug Tracker</a></li><li><a >href="mailto:[email protected]"; >target="blank">Subscribe to Development Mailing >List</a></li></ul></div></div></div></div><div id="footer-bottom"><div c lass="container"><div class="row"><div class="col-md-12"><div id="footer-logo-wrapper"><img alt="PredictionIO" src="/images/logos/logo-white-d1e9c6e6.png"/></div><div id="social-icons-wrapper"><a class="github-button" href="https://github.com/apache/incubator-predictionio"; data-style="mega" data-count-href="/apache/incubator-predictionio/stargazers" data-count-api="/repos/apache/incubator-predictionio#stargazers_count" data-count-aria-label="# stargazers on GitHub" aria-label="Star apache/incubator-predictionio on GitHub">Star</a> <a class="github-button" href="https://github.com/apache/incubator-predictionio/fork"; data-icon="octicon-git-branch" data-style="mega" data-count-href="/apache/incubator-predictionio/network" data-count-api="/repos/apache/incubator-predictionio#forks_count" data-count-aria-label="# forks on GitHub" aria-label="Fork apache/incubator-predictionio on GitHub">Fork</a> <script id="github-bjs" async="" defer="" src="https://buttons.github.io/buttons.js";></script ><a href="//www.facebook.com/predictionio" target="blank"><img >alt="PredictionIO on Twitter" src="/images/icons/twitter-ea9dc152.png"/></a> ><a href="//twitter.com/predictionio" target="blank"><img alt="PredictionIO on >Facebook" src="/images/icons/facebook-5c57939c.png"/></a> ></div></div></div></div></div></footer></div><script>(function(w,d,t,u,n,s,e){w['SwiftypeObject']=n;w[n]=w[n]||function(){ +(w[n].q=w[n].q||[]).push(arguments);};s=d.createElement(t); +e=d.getElementsByTagName(t)[0];s.async=1;s.src=u;e.parentNode.insertBefore(s,e); +})(window,document,'script','//s.swiftypecdn.com/install/v1/st.js','_st'); + +_st('install','HaUfpXXV87xoB_zzCQ45');</script><script src="/javascripts/application-f819cf19.js"></script></body></html> \ No newline at end of file
http://git-wip-us.apache.org/repos/asf/incubator-predictionio-site/blob/921d6861/docs/manual/build/datacollection/analytics-tableau/index.html.gz ---------------------------------------------------------------------- diff --git a/docs/manual/build/datacollection/analytics-tableau/index.html.gz b/docs/manual/build/datacollection/analytics-tableau/index.html.gz new file mode 100644 index 0000000..28aea5e Binary files /dev/null and b/docs/manual/build/datacollection/analytics-tableau/index.html.gz differ http://git-wip-us.apache.org/repos/asf/incubator-predictionio-site/blob/921d6861/docs/manual/build/datacollection/analytics-zeppelin/index.html ---------------------------------------------------------------------- diff --git a/docs/manual/build/datacollection/analytics-zeppelin/index.html b/docs/manual/build/datacollection/analytics-zeppelin/index.html new file mode 100644 index 0000000..ad4f1ae --- /dev/null +++ b/docs/manual/build/datacollection/analytics-zeppelin/index.html @@ -0,0 +1,47 @@ +<!DOCTYPE html><html><head><title>Machine Learning Analytics with Zeppelin</title><meta charset="utf-8"/><meta content="IE=edge,chrome=1" http-equiv="X-UA-Compatible"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><meta class="swiftype" name="title" data-type="string" content="Machine Learning Analytics with Zeppelin"/><link rel="canonical" href="https://docs.prediction.io/datacollection/analytics-zeppelin/"/><link href="/images/favicon/normal-b330020a.png" rel="shortcut icon"/><link href="/images/favicon/apple-c0febcf2.png" rel="apple-touch-icon"/><link href="//fonts.googleapis.com/css?family=Open+Sans:300italic,400italic,600italic,700italic,800italic,400,300,600,700,800" rel="stylesheet"/><link href="//maxcdn.bootstrapcdn.com/font-awesome/4.2.0/css/font-awesome.min.css" rel="stylesheet"/><link href="/stylesheets/application-3a3867f7.css" rel="stylesheet" type="text/css"/><script 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href="/start/customize/"><span>Customizing the Engine</span></a></li></ul></li><li class="level-1"><a class="expan dible" href="#"><span>Integrating with Your App</span></a><ul><li class="level-2"><a class="final" href="/appintegration/"><span>App Integration Overview</span></a></li><li class="level-2"><a class="expandible" href="/sdk/"><span>List of SDKs</span></a><ul><li class="level-3"><a class="final" href="/sdk/java/"><span>Java & Android SDK</span></a></li><li class="level-3"><a class="final" href="/sdk/php/"><span>PHP SDK</span></a></li><li class="level-3"><a class="final" href="/sdk/python/"><span>Python SDK</span></a></li><li class="level-3"><a class="final" href="/sdk/ruby/"><span>Ruby SDK</span></a></li><li class="level-3"><a class="final" href="/sdk/community/"><span>Community Powered SDKs</span></a></li></ul></li></ul></li><li class="level-1"><a class="expandible" href="#"><span>Deploying an Engine</span></a><ul><li class="level-2"><a class="final" 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href="/api/current/#package"><span>Engine Scala APIs</span></a></li></ul></li><li class="level-1"><a class="expandible" href="#"><span>Collecting and Analyz ing Data</span></a><ul><li class="level-2"><a class="final" href="/datacollection/"><span>Event Server Overview</span></a></li><li class="level-2"><a class="final" href="/cli/#event-server-commands"><span>Event Server Command-line Interface</span></a></li><li class="level-2"><a class="final" href="/datacollection/eventapi/"><span>Collecting Data with REST/SDKs</span></a></li><li class="level-2"><a class="final" href="/datacollection/eventmodel/"><span>Events Modeling</span></a></li><li class="level-2"><a class="final" href="/datacollection/webhooks/"><span>Unifying Multichannel Data with Webhooks</span></a></li><li class="level-2"><a class="final" href="/datacollection/channel/"><span>Channel</span></a></li><li class="level-2"><a class="final" href="/datacollection/batchimport/"><span>Importing Data in Batch</span></a></li><li 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class="level-2"><a class="final" href="/community/projects/"><span>Community Projects</span></a></li></ul></li><li class="level-1"><a class="expandible" href="#"><span> Getting Help</span></a><ul><li class="level-2"><a class="final" href="/resources/faq/"><span>FAQs</span></a></li><li class="level-2"><a class="final" href="/support/"><span>Support</span></a></li></ul></li><li class="level-1"><a class="expandible" href="#"><span>Resources</span></a><ul><li class="level-2"><a class="final" href="/resources/intellij/"><span>Developing Engines with IntelliJ IDEA</span></a></li><li class="level-2"><a class="final" href="/resources/upgrade/"><span>Upgrade Instructions</span></a></li><li class="level-2"><a class="final" href="/resources/glossary/"><span>Glossary</span></a></li></ul></li></ul></nav></div><div class="col-md-9 col-sm-12"><div class="content-header hidden-md hidden-lg"><div id="page-title"><h1>Machine Learning Analytics with Zeppelin</h1></div></div><div id="table-of-content-wrapper"><h5>On this page</h5><aside id="table-of-contents"><ul> <li> <a href="#prerequisites">Prerequisites</a> </li> <li> <a href="#building-zeppelin-for-apache-spark-1 -2">Building Zeppelin for Apache Spark 1.2+</a> </li> <li> <a href="#preparing-zeppelin">Preparing Zeppelin</a> </li> <li> <a href="#performing-analysis-with-zeppelin">Performing Analysis with Zeppelin</a> </li> </ul> </aside><hr/><a id="edit-page-link" href="https://github.com/apache/incubator-predictionio/tree/livedoc/docs/manual/source/datacollection/analytics-zeppelin.html.md.erb";><img src="/images/icons/edit-pencil-d6c1bb3d.png"/>Edit this page</a></div><div class="content-header hidden-sm hidden-xs"><div id="page-title"><h1>Machine Learning Analytics with Zeppelin</h1></div></div><div class="content"><p><a href="http://zeppelin-project.org/";>Apache Zeppelin</a> is an interactive computational environment built on Apache Spark like the IPython Notebook. With <a href="http://predictionio.incubator.apache.org";>Apache PredictionIO (incubating)</a> and <a href="https://spark.apache.org/sql/";>Spark SQL</a>, you can easily analyze your collected events when you are developing or tuni ng your engine.</p><h2 id='prerequisites' class='header-anchors'>Prerequisites</h2><p>The following instructions assume that you have the command <code>sbt</code> accessible in your shell's search path. Alternatively, you can use the <code>sbt</code> command that comes with Apache PredictionIO (incubating) at <code>$PIO_HOME/sbt/sbt</code>.</p><p><h2 id='export-events-to-apache-parquet' class='header-anchors'>Export Events to Apache Parquet</h2><p>PredictionIO supports exporting your events to <a href="http://parquet.incubator.apache.org/";>Apache Parquet</a>, a columnar storage format that allows you to query quickly.</p><p>Let's export the data we imported in <a href="/templates/recommendation/quickstart/#import-sample-data">Recommendation Engine Template Quick Start</a>, and assume the App ID is 1.</p><div class="highlight shell"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1</pre></td><td class="code"><pre ><span class="gp">$ </span><span class="nv">$PIO_HOME</span>/bin/pio <span >class="nb">export</span> --appid 1 --output /tmp/movies --format parquet +</pre></td></tr></tbody></table> </div> <p>After the command has finished successfully, you should see something similar to the following.</p><div class="highlight shell"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1 +2 +3 +4 +5 +6 +7 +8 +9 +10 +11</pre></td><td class="code"><pre>root + |-- creationTime: string <span class="o">(</span>nullable <span class="o">=</span> <span class="nb">true</span><span class="o">)</span> + |-- entityId: string <span class="o">(</span>nullable <span class="o">=</span> <span class="nb">true</span><span class="o">)</span> + |-- entityType: string <span class="o">(</span>nullable <span class="o">=</span> <span class="nb">true</span><span class="o">)</span> + |-- event: string <span class="o">(</span>nullable <span class="o">=</span> <span class="nb">true</span><span class="o">)</span> + |-- eventId: string <span class="o">(</span>nullable <span class="o">=</span> <span class="nb">true</span><span class="o">)</span> + |-- eventTime: string <span class="o">(</span>nullable <span class="o">=</span> <span class="nb">true</span><span class="o">)</span> + |-- properties: struct <span class="o">(</span>nullable <span class="o">=</span> <span class="nb">true</span><span class="o">)</span> + | |-- rating: double <span class="o">(</span>nullable <span class="o">=</span> <span class="nb">true</span><span class="o">)</span> + |-- targetEntityId: string <span class="o">(</span>nullable <span class="o">=</span> <span class="nb">true</span><span class="o">)</span> + |-- targetEntityType: string <span class="o">(</span>nullable <span class="o">=</span> <span class="nb">true</span><span class="o">)</span> +</pre></td></tr></tbody></table> </div></p><h2 id='building-zeppelin-for-apache-spark-1.2+' class='header-anchors'>Building Zeppelin for Apache Spark 1.2+</h2><p>Start by cloning Zeppelin.</p><div class="highlight shell"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1</pre></td><td class="code"><pre><span class="gp">$ </span>git clone https://github.com/apache/incubator-zeppelin.git +</pre></td></tr></tbody></table> </div> <p>Build Zeppelin with Hadoop 2.4 and Spark 1.2 profiles.</p><div class="highlight shell"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1 +2</pre></td><td class="code"><pre><span class="gp">$ </span><span class="nb">cd </span>zeppelin +<span class="gp">$ </span>mvn clean package -Pspark-1.2 -Dhadoop.version<span class="o">=</span>2.4.0 -Phadoop-2.4 -DskipTests +</pre></td></tr></tbody></table> </div> <p>Now you should have working Zeppelin binaries.</p><h2 id='preparing-zeppelin' class='header-anchors'>Preparing Zeppelin</h2><p>First, start Zeppelin.</p><div class="highlight shell"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1</pre></td><td class="code"><pre><span class="gp">$ </span>bin/zeppelin-daemon.sh start +</pre></td></tr></tbody></table> </div> <p>By default, you should be able to access Zeppelin via web browser at <a href="http://localhost:8080";>http://localhost:8080</a>. Create a new notebook and put the following in the first cell.</p><div class="highlight scala"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1</pre></td><td class="code"><pre><span class="n">sqlc</span><span class="o">.</span><span class="n">parquetFile</span><span class="o">(</span><span class="s">"/tmp/movies"</span><span class="o">).</span><span class="n">registerTempTable</span><span class="o">(</span><span class="s">"events"</span><span class="o">)</span> +</pre></td></tr></tbody></table> </div> <p><img alt="Preparing Zeppelin" src="/images/datacollection/zeppelin-01-1c476081.png"/></p><h2 id='performing-analysis-with-zeppelin' class='header-anchors'>Performing Analysis with Zeppelin</h2><p>If all steps above ran successfully, you should have a ready-to-use analytics environment by now. Let's try a few examples to see if everything is functional.</p><p>In the second cell, put in this piece of code and run it.</p><div class="highlight shell"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1 +2 +3</pre></td><td class="code"><pre>%sql +SELECT entityType, event, targetEntityType, COUNT<span class="o">(</span><span class="k">*</span><span class="o">)</span> AS c FROM events +GROUP BY entityType, event, targetEntityType +</pre></td></tr></tbody></table> </div> <p><img alt="Summary of Events" src="/images/datacollection/zeppelin-02-b3687a33.png"/></p><p>We can also easily plot a pie chart.</p><div class="highlight shell"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1 +2</pre></td><td class="code"><pre>%sql +SELECT event, COUNT<span class="o">(</span><span class="k">*</span><span class="o">)</span> AS c FROM events GROUP BY event +</pre></td></tr></tbody></table> </div> <p><img alt="Summary of Event in Pie Chart" src="/images/datacollection/zeppelin-03-15dcefee.png"/></p><p>And see a breakdown of rating values.</p><div class="highlight shell"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1 +2 +3</pre></td><td class="code"><pre>%sql +SELECT properties.rating AS r, COUNT<span class="o">(</span><span class="k">*</span><span class="o">)</span> AS c FROM events +WHERE properties.rating IS NOT NULL GROUP BY properties.rating ORDER BY r +</pre></td></tr></tbody></table> </div> <p><img alt="Breakdown of Rating Values" src="/images/datacollection/zeppelin-04-d646c299.png"/></p><p>Happy analyzing!</p></div></div></div></div><footer><div class="container"><div class="seperator"></div><div class="row"><div class="col-md-6 col-xs-6 footer-link-column"><div class="footer-link-column-row"><h4>Community</h4><ul><li><a href="//docs.prediction.io/install/" 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