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[48/68] [abbrv] [partial] flink-web git commit: Revert "[hotfix]
Manual build of docs"
http://git-wip-us.apache.org/repos/asf/flink-web/blob/f0ac0cdb/content/docs/0.9/apis/example_connectors.html
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- <h1>Connecting to other systems</h1>
-
-
-
-<h2 id="reading-from-filesystems">Reading from filesystems.</h2>
-
-<p>Flink has build-in support for the following file systems:</p>
-
-<table>
- <thead>
- <tr>
- <th>Filesystem</th>
- <th>Since</th>
- <th>Scheme</th>
- <th>Notes</th>
- </tr>
- </thead>
- <tbody>
- <tr>
- <td>Hadoop Distributed File System (HDFS)</td>
- <td>0.2</td>
- <td><code>hdfs://</code></td>
- <td>All HDFS versions are supported</td>
- </tr>
- <tr>
- <td>Amazon S3</td>
- <td>0.2</td>
- <td><code>s3://</code></td>
- <td> </td>
- </tr>
- <tr>
- <td>MapR file system</td>
- <td>0.7-incubating</td>
- <td><code>maprfs://</code></td>
- <td>The user has to manually place the required jar files in the <code>lib/</code> dir</td>
- </tr>
- <tr>
- <td>Tachyon</td>
- <td>0.9</td>
- <td><code>tachyon://</code></td>
- <td>Support through Hadoop file system implementation (see below)</td>
- </tr>
- </tbody>
-</table>
-
-<h3 id="using-hadoop-file-systems-with-apache-flink">Using Hadoop file systems with Apache Flink</h3>
-
-<p>Apache Flink allows users to use any file system implementing the <code>org.apache.hadoop.fs.FileSystem</code>
-interface. Hadoop ships adapters for FTP, <a href="http://hadoop.apache.org/docs/r1.2.1/hftp.html">Hftp</a>, and others.</p>
-
-<p>Flink has integrated testcases to validate the integration with <a href="http://tachyon-project.org/">Tachyon</a>.
-Other file systems we tested the integration is the
-<a href="https://cloud.google.com/hadoop/google-cloud-storage-connector">Google Cloud Storage Connector for Hadoop</a> and <a href="http://www.xtreemfs.org/">XtreemFS</a>.</p>
-
-<p>In order to use a Hadoop file system with Flink, make sure that the <code>flink-conf.yaml</code> has set the
-<code>fs.hdfs.hadoopconf</code> property set to the Hadoop configuration directory.
-In addition to that, the Hadoop configuration (in that directory) needs to have an entry for each supported file system.
-For example for tachyon support, there must be the following entry in the <code>core-site.xml</code> file:</p>
-
-<div class="highlight"><pre><code class="language-xml"><span class="nt"><property></span>
- <span class="nt"><name></span>fs.tachyon.impl<span class="nt"></name></span>
- <span class="nt"><value></span>tachyon.hadoop.TFS<span class="nt"></value></span>
-<span class="nt"></property></span></code></pre></div>
-
-<p>Also, the required classes for using the file system need to be placed in the <code>lib/</code> folder of the Flink installation (on all machines running Flink). If putting the files into the directory is not possible, Flink is also respecting the <code>HADOOP_CLASSPATH</code> environment variable to add Hadoop jar files to the classpath.</p>
-
-<h2 id="connecting-to-other-systems-using-input--output-format-wrappers-for-hadoop">Connecting to other systems using Input / Output Format wrappers for Hadoop</h2>
-
-<p>Apache Flink allows users to access many different systems as data sources or sinks.
-The system is designed for very easy extensibility. Similar to Apache Hadoop, Flink has the concept
-of so called <code>InputFormat</code>s and <code>OutputFormat</code>s.</p>
-
-<p>One implementation of these <code>InputFormat</code>s is the <code>HadoopInputFormat</code>. This is a wrapper that allows
-users to use all existing Hadoop input formats with Flink.</p>
-
-<p>This section shows some examples for connecting Flink to other systems.
-<a href="hadoop_compatibility.html">Read more about Hadoop compatibility in Flink</a>.</p>
-
-<h2 id="avro-support-in-flink">Avro support in Flink</h2>
-
-<p>Flink has extensive build-in support for <a href="http://avro.apache.org/">Apache Avro</a>. This allows to easily read from Avro files with Flink.
-Also, the serialization framework of Flink is able to handle classes generated from Avro schemas.</p>
-
-<p>In order to read data from an Avro file, you have to specify an <code>AvroInputFormat</code>.</p>
-
-<p><strong>Example</strong>:</p>
-
-<div class="highlight"><pre><code class="language-java"><span class="n">AvroInputFormat</span><span class="o"><</span><span class="n">User</span><span class="o">></span> <span class="n">users</span> <span class="o">=</span> <span class="k">new</span> <span class="n">AvroInputFormat</span><span class="o"><</span><span class="n">User</span><span class="o">>(</span><span class="n">in</span><span class="o">,</span> <span class="n">User</span><span class="o">.</span><span class="na">class</span><span class="o">);</span>
-<span class="n">DataSet</span><span class="o"><</span><span class="n">User</span><span class="o">></span> <span class="n">usersDS</span> <span class="o">=</span> <span class="n">env</span><span class="o">.</span><span class="na">createInput</span><span class="o">(</span><span class="n">users</span><span class="o">);</span></code></pre></div>
-
-<p>Note that <code>User</code> is a POJO generated by Avro. Flink also allows to perform string-based key selection of these POJOs. For example:</p>
-
-<div class="highlight"><pre><code class="language-java"><span class="n">usersDS</span><span class="o">.</span><span class="na">groupBy</span><span class="o">(</span><span class="s">"name"</span><span class="o">)</span></code></pre></div>
-
-<p>Note that using the <code>GenericData.Record</code> type is possible with Flink, but not recommended. Since the record contains the full schema, its very data intensive and thus probably slow to use.</p>
-
-<p>Flink’s POJO field selection also works with POJOs generated from Avro. However, the usage is only possible if the field types are written correctly to the generated class. If a field is of type <code>Object</code> you can not use the field as a join or grouping key.
-Specifying a field in Avro like this <code>{"name": "type_double_test", "type": "double"},</code> works fine, however specifying it as a UNION-type with only one field (<code>{"name": "type_double_test", "type": ["double"]},</code>) will generate a field of type <code>Object</code>. Note that specifying nullable types (<code>{"name": "type_double_test", "type": ["null", "double"]},</code>) is possible!</p>
-
-<h3 id="access-microsoft-azure-table-storage">Access Microsoft Azure Table Storage</h3>
-
-<p><em>Note: This example works starting from Flink 0.6-incubating</em></p>
-
-<p>This example is using the <code>HadoopInputFormat</code> wrapper to use an existing Hadoop input format implementation for accessing <a href="https://azure.microsoft.com/en-us/documentation/articles/storage-introduction/">Azure’s Table Storage</a>.</p>
-
-<ol>
- <li>
- <p>Download and compile the <code>azure-tables-hadoop</code> project. The input format developed by the project is not yet available in Maven Central, therefore, we have to build the project ourselves.
-Execute the following commands:</p>
-
- <div class="highlight"><pre><code class="language-bash">git clone https://github.com/mooso/azure-tables-hadoop.git
-<span class="nb">cd </span>azure-tables-hadoop
-mvn clean install</code></pre></div>
- </li>
- <li>
- <p>Setup a new Flink project using the quickstarts:</p>
-
- <div class="highlight"><pre><code class="language-bash">curl http://flink.apache.org/q/quickstart.sh <span class="p">|</span> bash</code></pre></div>
- </li>
- <li>
- <p>Add the following dependencies (in the <code><dependencies></code> section) to your <code>pom.xml</code> file:</p>
-
- <div class="highlight"><pre><code class="language-xml"><span class="nt"><dependency></span>
- <span class="nt"><groupId></span>org.apache.flink<span class="nt"></groupId></span>
- <span class="nt"><artifactId></span>flink-hadoop-compatibility<span class="nt"></artifactId></span>
- <span class="nt"><version></span>0.9.0<span class="nt"></version></span>
-<span class="nt"></dependency></span>
-<span class="nt"><dependency></span>
- <span class="nt"><groupId></span>com.microsoft.hadoop<span class="nt"></groupId></span>
- <span class="nt"><artifactId></span>microsoft-hadoop-azure<span class="nt"></artifactId></span>
- <span class="nt"><version></span>0.0.4<span class="nt"></version></span>
-<span class="nt"></dependency></span></code></pre></div>
-
- <p><code>flink-hadoop-compatibility</code> is a Flink package that provides the Hadoop input format wrappers.
-<code>microsoft-hadoop-azure</code> is adding the project we’ve build before to our project.</p>
- </li>
-</ol>
-
-<p>The project is now prepared for starting to code. We recommend to import the project into an IDE, such as Eclipse or IntelliJ. (Import as a Maven project!).
-Browse to the code of the <code>Job.java</code> file. Its an empty skeleton for a Flink job.</p>
-
-<p>Paste the following code into it:</p>
-
-<div class="highlight"><pre><code class="language-java"><span class="kn">import</span> <span class="nn">java.util.Map</span><span class="o">;</span>
-<span class="kn">import</span> <span class="nn">org.apache.flink.api.common.functions.MapFunction</span><span class="o">;</span>
-<span class="kn">import</span> <span class="nn">org.apache.flink.api.java.DataSet</span><span class="o">;</span>
-<span class="kn">import</span> <span class="nn">org.apache.flink.api.java.ExecutionEnvironment</span><span class="o">;</span>
-<span class="kn">import</span> <span class="nn">org.apache.flink.api.java.tuple.Tuple2</span><span class="o">;</span>
-<span class="kn">import</span> <span class="nn">org.apache.flink.hadoopcompatibility.mapreduce.HadoopInputFormat</span><span class="o">;</span>
-<span class="kn">import</span> <span class="nn">org.apache.hadoop.io.Text</span><span class="o">;</span>
-<span class="kn">import</span> <span class="nn">org.apache.hadoop.mapreduce.Job</span><span class="o">;</span>
-<span class="kn">import</span> <span class="nn">com.microsoft.hadoop.azure.AzureTableConfiguration</span><span class="o">;</span>
-<span class="kn">import</span> <span class="nn">com.microsoft.hadoop.azure.AzureTableInputFormat</span><span class="o">;</span>
-<span class="kn">import</span> <span class="nn">com.microsoft.hadoop.azure.WritableEntity</span><span class="o">;</span>
-<span class="kn">import</span> <span class="nn">com.microsoft.windowsazure.storage.table.EntityProperty</span><span class="o">;</span>
-
-<span class="kd">public</span> <span class="kd">class</span> <span class="nc">AzureTableExample</span> <span class="o">{</span>
-
- <span class="kd">public</span> <span class="kd">static</span> <span class="kt">void</span> <span class="nf">main</span><span class="o">(</span><span class="n">String</span><span class="o">[]</span> <span class="n">args</span><span class="o">)</span> <span class="kd">throws</span> <span class="n">Exception</span> <span class="o">{</span>
- <span class="c1">// set up the execution environment</span>
- <span class="kd">final</span> <span class="n">ExecutionEnvironment</span> <span class="n">env</span> <span class="o">=</span> <span class="n">ExecutionEnvironment</span><span class="o">.</span><span class="na">getExecutionEnvironment</span><span class="o">();</span>
-
- <span class="c1">// create a AzureTableInputFormat, using a Hadoop input format wrapper</span>
- <span class="n">HadoopInputFormat</span><span class="o"><</span><span class="n">Text</span><span class="o">,</span> <span class="n">WritableEntity</span><span class="o">></span> <span class="n">hdIf</span> <span class="o">=</span> <span class="k">new</span> <span class="n">HadoopInputFormat</span><span class="o"><</span><span class="n">Text</span><span class="o">,</span> <span class="n">WritableEntity</span><span class="o">>(</span><span class="k">new</span> <span class="nf">AzureTableInputFormat</span><span class="o">(),</span> <span class="n">Text</span><span class="o">.</span><span class="na">class</span><span class="o">,</span> <span class="n">WritableEntity</span><span class="o">.</span><span class="na">class</span><span class="o">,</span> <span class="k">new</span> <span class="nf">Job</span><span class="o">());</span>
-
- <span class="c1">// set the Account URI, something like: https://apacheflink.table.core.windows.net</span>
- <span class="n">hdIf</span><span class="o">.</span><span class="na">getConfiguration</span><span class="o">().</span><span class="na">set</span><span class="o">(</span><span class="n">AzureTableConfiguration</span><span class="o">.</span><span class="na">Keys</span><span class="o">.</span><span class="na">ACCOUNT_URI</span><span class="o">.</span><span class="na">getKey</span><span class="o">(),</span> <span class="s">"TODO"</span><span class="o">);</span>
- <span class="c1">// set the secret storage key here</span>
- <span class="n">hdIf</span><span class="o">.</span><span class="na">getConfiguration</span><span class="o">().</span><span class="na">set</span><span class="o">(</span><span class="n">AzureTableConfiguration</span><span class="o">.</span><span class="na">Keys</span><span class="o">.</span><span class="na">STORAGE_KEY</span><span class="o">.</span><span class="na">getKey</span><span class="o">(),</span> <span class="s">"TODO"</span><span class="o">);</span>
- <span class="c1">// set the table name here</span>
- <span class="n">hdIf</span><span class="o">.</span><span class="na">getConfiguration</span><span class="o">().</span><span class="na">set</span><span class="o">(</span><span class="n">AzureTableConfiguration</span><span class="o">.</span><span class="na">Keys</span><span class="o">.</span><span class="na">TABLE_NAME</span><span class="o">.</span><span class="na">getKey</span><span class="o">(),</span> <span class="s">"TODO"</span><span class="o">);</span>
-
- <span class="n">DataSet</span><span class="o"><</span><span class="n">Tuple2</span><span class="o"><</span><span class="n">Text</span><span class="o">,</span> <span class="n">WritableEntity</span><span class="o">>></span> <span class="n">input</span> <span class="o">=</span> <span class="n">env</span><span class="o">.</span><span class="na">createInput</span><span class="o">(</span><span class="n">hdIf</span><span class="o">);</span>
- <span class="c1">// a little example how to use the data in a mapper.</span>
- <span class="n">DataSet</span><span class="o"><</span><span class="n">String</span><span class="o">></span> <span class="n">fin</span> <span class="o">=</span> <span class="n">input</span><span class="o">.</span><span class="na">map</span><span class="o">(</span><span class="k">new</span> <span class="n">MapFunction</span><span class="o"><</span><span class="n">Tuple2</span><span class="o"><</span><span class="n">Text</span><span class="o">,</span><span class="n">WritableEntity</span><span class="o">>,</span> <span class="n">String</span><span class="o">>()</span> <span class="o">{</span>
- <span class="nd">@Override</span>
- <span class="kd">public</span> <span class="n">String</span> <span class="nf">map</span><span class="o">(</span><span class="n">Tuple2</span><span class="o"><</span><span class="n">Text</span><span class="o">,</span> <span class="n">WritableEntity</span><span class="o">></span> <span class="n">arg0</span><span class="o">)</span> <span class="kd">throws</span> <span class="n">Exception</span> <span class="o">{</span>
- <span class="n">System</span><span class="o">.</span><span class="na">err</span><span class="o">.</span><span class="na">println</span><span class="o">(</span><span class="s">"--------------------------------\nKey = "</span><span class="o">+</span><span class="n">arg0</span><span class="o">.</span><span class="na">f0</span><span class="o">);</span>
- <span class="n">WritableEntity</span> <span class="n">we</span> <span class="o">=</span> <span class="n">arg0</span><span class="o">.</span><span class="na">f1</span><span class="o">;</span>
-
- <span class="k">for</span><span class="o">(</span><span class="n">Map</span><span class="o">.</span><span class="na">Entry</span><span class="o"><</span><span class="n">String</span><span class="o">,</span> <span class="n">EntityProperty</span><span class="o">></span> <span class="n">prop</span> <span class="o">:</span> <span class="n">we</span><span class="o">.</span><span class="na">getProperties</span><span class="o">().</span><span class="na">entrySet</span><span class="o">())</span> <span class="o">{</span>
- <span class="n">System</span><span class="o">.</span><span class="na">err</span><span class="o">.</span><span class="na">println</span><span class="o">(</span><span class="s">"key="</span><span class="o">+</span><span class="n">prop</span><span class="o">.</span><span class="na">getKey</span><span class="o">()</span> <span class="o">+</span> <span class="s">" ; value (asString)="</span><span class="o">+</span><span class="n">prop</span><span class="o">.</span><span class="na">getValue</span><span class="o">().</span><span class="na">getValueAsString</span><span class="o">());</span>
- <span class="o">}</span>
-
- <span class="k">return</span> <span class="n">arg0</span><span class="o">.</span><span class="na">f0</span><span class="o">.</span><span class="na">toString</span><span class="o">();</span>
- <span class="o">}</span>
- <span class="o">});</span>
-
- <span class="c1">// emit result (this works only locally)</span>
- <span class="n">fin</span><span class="o">.</span><span class="na">print</span><span class="o">();</span>
-
- <span class="c1">// execute program</span>
- <span class="n">env</span><span class="o">.</span><span class="na">execute</span><span class="o">(</span><span class="s">"Azure Example"</span><span class="o">);</span>
- <span class="o">}</span>
-<span class="o">}</span></code></pre></div>
-
-<p>The example shows how to access an Azure table and turn data into Flink’s <code>DataSet</code> (more specifically, the type of the set is <code>DataSet<Tuple2<Text, WritableEntity>></code>). With the <code>DataSet</code>, you can apply all known transformations to the DataSet.</p>
-
-<h2 id="access-mongodb">Access MongoDB</h2>
-
-<p>This <a href="https://github.com/okkam-it/flink-mongodb-test">GitHub repository documents how to use MongoDB with Apache Flink (starting from 0.7-incubating)</a>.</p>
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