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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>Â </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>Flinkâs 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/";>Azureâs
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 weâve 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 Flinkâs
<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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