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This is the -first release of the system inside the Apache Incubator and under the -name Flink. Releases up to 0.5 were under the name Stratosphere, the -academic and open source project that Flink originates from.</p> - -<h2 id="what-is-flink">What is Flink?</h2> - -<p>Apache Flink is a general-purpose data processing engine for -clusters. It runs on YARN clusters on top of data stored in Hadoop, as -well as stand-alone. Flink currently has programming APIs in Java and -Scala. Jobs are executed via Flinkâs own runtime engine. Flink -features:</p> - -<p><strong>Robust in-memory and out-of-core processing:</strong> once read, data stays - in memory as much as possible, and is gracefully de-staged to disk in - the presence of memory pressure from limited memory or other - applications. The runtime is designed to perform very well both in - setups with abundant memory and in setups where memory is scarce.</p> - -<p><strong>POJO-based APIs:</strong> when programming, you do not have to pack your - data into key-value pairs or some other framework-specific data - model. Rather, you can use arbitrary Java and Scala types to model - your data.</p> - -<p><strong>Efficient iterative processing:</strong> Flink contains explicit âiterateâ operators - that enable very efficient loops over data sets, e.g., for machine - learning and graph applications.</p> - -<p><strong>A modular system stack:</strong> Flink is not a direct implementation of its - APIs but a layered system. All programming APIs are translated to an - intermediate program representation that is compiled and optimized - via a cost-based optimizer. Lower-level layers of Flink also expose - programming APIs for extending the system.</p> - -<p><strong>Data pipelining/streaming:</strong> Flinkâs runtime is designed as a - pipelined data processing engine rather than a batch processing - engine. Operators do not wait for their predecessors to finish in - order to start processing data. This results to very efficient - handling of large data sets.</p> - -<h2 id="release-06">Release 0.6</h2> - -<p>Flink 0.6 builds on the latest Stratosphere 0.5 release. It includes -many bug fixes and improvements that make the system more stable and -robust, as well as breaking API changes.</p> - -<p>The full release notes are available <a href="https://issues.apache.org/jira/secure/ReleaseNote.jspa?projectId=12315522&version=12327101";>here</a>.</p> - -<p>Download the release <a href="http://flink.incubator.apache.org/downloads.html";>here</a>.</p> - -<h2 id="contributors">Contributors</h2> - -<ul> - <li>Wilson Cao</li> - <li>Ufuk Celebi</li> - <li>Stephan Ewen</li> - <li>Jonathan Hasenburg</li> - <li>Markus Holzemer</li> - <li>Fabian Hueske</li> - <li>Sebastian Kunert</li> - <li>Vikhyat Korrapati</li> - <li>Aljoscha Krettek</li> - <li>Sebastian Kruse</li> - <li>Raymond Liu</li> - <li>Robert Metzger</li> - <li>Mingliang Qi</li> - <li>Till Rohrmann</li> - <li>Henry Saputra</li> - <li>Chesnay Schepler</li> - <li>Kostas Tzoumas</li> - <li>Robert Waury</li> - <li>Timo Walther</li> - <li>Daniel Warneke</li> - <li>Tobias Wiens</li> -</ul> - - </article> - </div> - - <div class="row"> - <div id="disqus_thread"></div> - <script type="text/javascript"> - /* * * CONFIGURATION VARIABLES: EDIT BEFORE PASTING INTO YOUR WEBPAGE * * */ - var disqus_shortname = 'stratosphere-eu'; // required: replace example with your forum shortname - - /* * * DON'T EDIT BELOW THIS LINE * * */ - (function() { - var dsq = document.createElement('script'); dsq.type = 'text/javascript'; dsq.async = true; - dsq.src = '//' + disqus_shortname + '.disqus.com/embed.js'; - (document.getElementsByTagName('head')[0] || document.getElementsByTagName('body')[0]).appendChild(dsq); - })(); - </script> - </div> - </div> -</div> - - <hr /> - <div class="footer text-center"> - <p>Copyright © 2014-2016 <a href="http://apache.org";>The Apache Software Foundation</a>. 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Starting with a <strong>Flink hackathon in Stockholm</strong> (Oct 8-9) and a talk about Flink at the <strong>Stockholm Hadoop User Group</strong> (Oct 8). This is followed by the very first <strong>Flink Meetup in Berlin</strong> (Oct 15). In the US, there will be two Flink Meetup talks: the first one at the <strong>Pasadena Big Data User Group</strong> (Oct 29) and the second one at <strong>Silicon Valley Hands On Programming Events</strong> (Nov 4).</p> - -<p>We are looking forward to seeing you at any of these events. The following is an overview of each event and links to the respective Meetup pages.</p> - -<h3 id="flink-hackathon-stockholm-oct-8-9">Flink Hackathon, Stockholm (Oct 8-9)</h3> - -<p>The hackathon will take place at KTH/SICS from Oct 8th-9th. You can sign up here: https://docs.google.com/spreadsheet/viewform?formkey=dDZnMlRtZHJ3Z0hVTlFZVjU2MWtoX0E6MA.</p> - -<p>Here is a rough agenda and a list of topics to work upon or look into. Suggestions and more topics are welcome.</p> - -<h4 id="wednesday-8th">Wednesday (8th)</h4> - -<p>9:00 - 10:00 Introduction to Apache Flink, System overview, and Dev -environment (by Stephan)</p> - -<p>10:15 - 11:00 Introduction to the topics (Streaming API and system by Gyula -& Marton), (Graphs by Vasia / Martin / Stephan)</p> - -<p>11:00 - 12:30 Happy hacking (part 1)</p> - -<p>12:30 - Lunch (Food will be provided by KTH / SICS. A big thank you to them -and also to Paris, for organizing that)</p> - -<p>13:xx - Happy hacking (part 2)</p> - -<h4 id="thursday-9th">Thursday (9th)</h4> - -<p>Happy hacking (continued)</p> - -<h4 id="suggestions-for-topics">Suggestions for topics</h4> - -<h5 id="streaming">Streaming</h5> - -<ul> - <li> - <p>Sample streaming applications (e.g. continuous heavy hitters and topics -on the twitter stream)</p> - </li> - <li> - <p>Implement a simple SQL to Streaming program parser. Possibly using -Apache Calcite (http://optiq.incubator.apache.org/)</p> - </li> - <li> - <p>Implement different windowing methods (count-based, time-based, â¦)</p> - </li> - <li> - <p>Implement different windowed operations (windowed-stream-join, -windowed-stream-co-group)</p> - </li> - <li> - <p>Streaming state, and interaction with other programs (that access state -of a stream program)</p> - </li> -</ul> - -<h5 id="graph-analysis">Graph Analysis</h5> - -<ul> - <li> - <p>Prototype a Graph DSL (simple graph building, filters, graph -properties, some algorithms)</p> - </li> - <li> - <p>Prototype abstractions different Graph processing paradigms -(vertex-centric, partition-centric).</p> - </li> - <li> - <p>Generalize the delta iterations, allow flexible state access.</p> - </li> -</ul> - -<h3 id="meetup-hadoop-user-group-talk-stockholm-oct-8">Meetup: Hadoop User Group Talk, Stockholm (Oct 8)</h3> - -<p>Hosted by Spotify, opens at 6 PM.</p> - -<p>http://www.meetup.com/stockholm-hug/events/207323222/</p> - -<h3 id="st-flink-meetup-berlin-oct-15">1st Flink Meetup, Berlin (Oct 15)</h3> - -<p>We are happy to announce the first Flink meetup in Berlin. You are very welcome to to sign up and attend. The event will be held in Betahaus Cafe.</p> - -<p>http://www.meetup.com/Apache-Flink-Meetup/events/208227422/</p> - -<h3 id="meetup-pasadena-big-data-user-group-oct-29">Meetup: Pasadena Big Data User Group (Oct 29)</h3> - -<p>http://www.meetup.com/Pasadena-Big-Data-Users-Group/</p> - -<h3 id="meetup-silicon-valley-hands-on-programming-events-nov-4">Meetup: Silicon Valley Hands On Programming Events (Nov 4)</h3> - -<p>http://www.meetup.com/HandsOnProgrammingEvents/events/210504392/</p> - - - </article> - </div> - - <div class="row"> - <div id="disqus_thread"></div> - <script type="text/javascript"> - /* * * CONFIGURATION VARIABLES: EDIT BEFORE PASTING INTO YOUR WEBPAGE * * */ - var disqus_shortname = 'stratosphere-eu'; // required: replace example with your forum shortname - - /* * * DON'T EDIT BELOW THIS LINE * * */ - (function() { - var dsq = document.createElement('script'); dsq.type = 'text/javascript'; dsq.async = true; - dsq.src = '//' + disqus_shortname + '.disqus.com/embed.js'; - (document.getElementsByTagName('head')[0] || document.getElementsByTagName('body')[0]).appendChild(dsq); - })(); - </script> - </div> - </div> -</div> - - <hr /> - <div class="footer text-center"> - <p>Copyright © 2014-2016 <a href="http://apache.org";>The Apache Software Foundation</a>. 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This release includes new user-facing features as well as performance and bug fixes, brings the Scala and Java APIs in sync, and introduces Flink Streaming. A total of 34 people have contributed to this release, a big thanks to all of them!</p> - -<p>Download Flink 0.7.0 <a href="http://flink.incubator.apache.org/downloads.html";>here</a></p> - -<p>See the release changelog <a href="https://issues.apache.org/jira/secure/ReleaseNote.jspa?projectId=12315522&version=12327648";>here</a></p> - -<h2 id="overview-of-major-new-features">Overview of major new features</h2> - -<p><strong>Flink Streaming:</strong> The gem of the 0.7.0 release is undoubtedly Flink Streaming. Available currently in alpha, Flink Streaming provides a Java API on top of Apache Flink that can consume streaming data sources (e.g., from Apache Kafka, Apache Flume, and others) and process them in real time. A dedicated blog post on Flink Streaming and its performance is coming up here soon. You can check out the Streaming programming guide <a href="http://ci.apache.org/projects/flink/flink-docs-release-0.7/streaming_guide.html";>here</a>.</p> - -<p><strong>New Scala API:</strong> The Scala API has been completely rewritten. The Java and Scala APIs have now the same syntax and transformations and will be kept from now on in sync in every future release. See the new Scala API <a href="http://ci.apache.org/projects/flink/flink-docs-release-0.7/programming_guide.html";>here</a>.</p> - -<p><strong>Logical key expressions:</strong> You can now specify grouping and joining keys with logical names for member variables of POJO data types. For example, you can join two data sets as <code>persons.join(cities).where(âzipâ).equalTo(âzipcodeâ)</code>. Read more <a href="http://ci.apache.org/projects/flink/flink-docs-release-0.7/programming_guide.html#specifying-keys";>here</a>.</p> - -<p><strong>Hadoop MapReduce compatibility:</strong> You can run unmodified Hadoop Mappers and Reducers (mapred API) in Flink, use all Hadoop data types, and read data with all Hadoop InputFormats.</p> - -<p><strong>Collection-based execution backend:</strong> The collection-based execution backend enables you to execute a Flink job as a simple Java collections program, bypassing completely the Flink runtime and optimizer. This feature is extremely useful for prototyping, and embedding Flink jobs in projects in a very lightweight manner.</p> - -<p><strong>Record API deprecated:</strong> The (old) Stratosphere Record API has been marked as deprecated and is planned for removal in the 0.9.0 release.</p> - -<p><strong>BLOB service:</strong> This release contains a new service to distribute jar files and other binary data among the JobManager, TaskManagers and the client.</p> - -<p><strong>Intermediate data sets:</strong> A major rewrite of the system internals introduces intermediate data sets as first class citizens. The internal state machine that tracks the distributed tasks has also been completely rewritten for scalability. While this is not visible as a user-facing feature yet, it is the foundation for several upcoming exciting features.</p> - -<p><strong>Note:</strong> Currently, there is limited support for Java 8 lambdas when compiling and running from an IDE. The problem is due to type erasure and whether Java compilers retain type information. We are currently working with the Eclipse and OpenJDK communities to resolve this.</p> - -<h2 id="contributors">Contributors</h2> - -<ul> - <li>Tamas Ambrus</li> - <li>Mariem Ayadi</li> - <li>Marton Balassi</li> - <li>Daniel Bali</li> - <li>Ufuk Celebi</li> - <li>Hung Chang</li> - <li>David Eszes</li> - <li>Stephan Ewen</li> - <li>Judit Feher</li> - <li>Gyula Fora</li> - <li>Gabor Hermann</li> - <li>Fabian Hueske</li> - <li>Vasiliki Kalavri</li> - <li>Kristof Kovacs</li> - <li>Aljoscha Krettek</li> - <li>Sebastian Kruse</li> - <li>Sebastian Kunert</li> - <li>Matyas Manninger</li> - <li>Robert Metzger</li> - <li>Mingliang Qi</li> - <li>Till Rohrmann</li> - <li>Henry Saputra</li> - <li>Chesnay Schelper</li> - <li>Moritz Schubotz</li> - <li>Hung Sendoh Chang</li> - <li>Peter Szabo</li> - <li>Jonas Traub</li> - <li>Fabian Tschirschnitz</li> - <li>Artem Tsikiridis</li> - <li>Kostas Tzoumas</li> - <li>Timo Walther</li> - <li>Daniel Warneke</li> - <li>Tobias Wiens</li> - <li>Yingjun Wu</li> -</ul> - - </article> - </div> - - <div class="row"> - <div id="disqus_thread"></div> - <script type="text/javascript"> - /* * * CONFIGURATION VARIABLES: EDIT BEFORE PASTING INTO YOUR WEBPAGE * * */ - var disqus_shortname = 'stratosphere-eu'; // required: replace example with your forum shortname - - /* * * DON'T EDIT BELOW THIS LINE * * */ - (function() { - var dsq = document.createElement('script'); dsq.type = 'text/javascript'; dsq.async = true; - dsq.src = '//' + disqus_shortname + '.disqus.com/embed.js'; - (document.getElementsByTagName('head')[0] || document.getElementsByTagName('body')[0]).appendChild(dsq); - })(); - </script> - </div> - </div> -</div> - - <hr /> - <div class="footer text-center"> - <p>Copyright © 2014-2016 <a href="http://apache.org";>The Apache Software Foundation</a>. 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Many data analysis applications have been implemented as Hadoop MapReduce jobs and run in clusters around the world. Apache Flink can be an alternative to MapReduce and improves it in many dimensions. Among other features, Flink provides much better performance and offers APIs in Java and Scala, which are very easy to use. Similar to Hadoop, Flinkâs APIs provide interfaces for Mapper and Reducer functions, as well as Input- and OutputFormats along with many more operators. While being conceptually equivalent, Hadoopâs MapReduce and Flinkâs interfaces for these functions are unfortunately not source compatible.</p> - -<h2 id="flinks-hadoop-compatibility-package">Flinkâs Hadoop Compatibility Package</h2> - -<center> -<img src="/img/blog/hcompat-logos.png" style="width:30%;margin:15px" /> -</center> - -<p>To close this gap, Flink provides a Hadoop Compatibility package to wrap functions implemented against Hadoopâs MapReduce interfaces and embed them in Flink programs. This package was developed as part of a <a href="https://developers.google.com/open-source/soc/";>Google Summer of Code</a> 2014 project.</p> - -<p>With the Hadoop Compatibility package, you can reuse all your Hadoop</p> - -<ul> - <li><code>InputFormats</code> (mapred and mapreduce APIs)</li> - <li><code>OutputFormats</code> (mapred and mapreduce APIs)</li> - <li><code>Mappers</code> (mapred API)</li> - <li><code>Reducers</code> (mapred API)</li> -</ul> - -<p>in Flink programs without changing a line of code. Moreover, Flink also natively supports all Hadoop data types (<code>Writables</code> and <code>WritableComparable</code>).</p> - -<p>The following code snippet shows a simple Flink WordCount program that solely uses Hadoop data types, InputFormat, OutputFormat, Mapper, and Reducer functions.</p> - -<div class="highlight"><pre><code class="language-java"><span class="c1">// Definition of Hadoop Mapper function</span> -<span class="kd">public</span> <span class="kd">class</span> <span class="nc">Tokenizer</span> <span class="kd">implements</span> <span class="n">Mapper</span><span class="o"><</span><span class="n">LongWritable</span><span class="o">,</span> <span class="n">Text</span><span class="o">,</span> <span class="n">Text</span><span class="o">,</span> <span class="n">LongWritable</span><span class="o">></span> <span class="o">{</span> <span class="o">...</span> <span class="o">}</span> -<span class="c1">// Definition of Hadoop Reducer function</span> -<span class="kd">public</span> <span class="kd">class</span> <span class="nc">Counter</span> <span class="kd">implements</span> <span class="n">Reducer</span><span class="o"><</span><span class="n">Text</span><span class="o">,</span> <span class="n">LongWritable</span><span class="o">,</span> <span class="n">Text</span><span class="o">,</span> <span class="n">LongWritable</span><span class="o">></span> <span class="o">{</span> <span class="o">...</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="o">{</span> - <span class="kd">final</span> <span class="n">String</span> <span class="n">inputPath</span> <span class="o">=</span> <span class="n">args</span><span class="o">[</span><span class="mi">0</span><span class="o">];</span> - <span class="kd">final</span> <span class="n">String</span> <span class="n">outputPath</span> <span class="o">=</span> <span class="n">args</span><span class="o">[</span><span class="mi">1</span><span class="o">];</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">// Setup Hadoopâs TextInputFormat</span> - <span class="n">HadoopInputFormat</span><span class="o"><</span><span class="n">LongWritable</span><span class="o">,</span> <span class="n">Text</span><span class="o">></span> <span class="n">hadoopInputFormat</span> <span class="o">=</span> - <span class="k">new</span> <span class="n">HadoopInputFormat</span><span class="o"><</span><span class="n">LongWritable</span><span class="o">,</span> <span class="n">Text</span><span class="o">>(</span> - <span class="k">new</span> <span class="nf">TextInputFormat</span><span class="o">(),</span> <span class="n">LongWritable</span><span class="o">.</span><span class="na">class</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="k">new</span> <span class="nf">JobConf</span><span class="o">());</span> - <span class="n">TextInputFormat</span><span class="o">.</span><span class="na">addInputPath</span><span class="o">(</span><span class="n">hadoopInputFormat</span><span class="o">.</span><span class="na">getJobConf</span><span class="o">(),</span> <span class="k">new</span> <span class="nf">Path</span><span class="o">(</span><span class="n">inputPath</span><span class="o">));</span> - - <span class="c1">// Read a DataSet with the Hadoop InputFormat</span> - <span class="n">DataSet</span><span class="o"><</span><span class="n">Tuple2</span><span class="o"><</span><span class="n">LongWritable</span><span class="o">,</span> <span class="n">Text</span><span class="o">>></span> <span class="n">text</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">hadoopInputFormat</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">LongWritable</span><span class="o">>></span> <span class="n">words</span> <span class="o">=</span> <span class="n">text</span> - <span class="c1">// Wrap Tokenizer Mapper function</span> - <span class="o">.</span><span class="na">flatMap</span><span class="o">(</span><span class="k">new</span> <span class="n">HadoopMapFunction</span><span class="o"><</span><span class="n">LongWritable</span><span class="o">,</span> <span class="n">Text</span><span class="o">,</span> <span class="n">Text</span><span class="o">,</span> <span class="n">LongWritable</span><span class="o">>(</span><span class="k">new</span> <span class="nf">Tokenizer</span><span class="o">()))</span> - <span class="o">.</span><span class="na">groupBy</span><span class="o">(</span><span class="mi">0</span><span class="o">)</span> - <span class="c1">// Wrap Counter Reducer function (used as Reducer and Combiner)</span> - <span class="o">.</span><span class="na">reduceGroup</span><span class="o">(</span><span class="k">new</span> <span class="n">HadoopReduceCombineFunction</span><span class="o"><</span><span class="n">Text</span><span class="o">,</span> <span class="n">LongWritable</span><span class="o">,</span> <span class="n">Text</span><span class="o">,</span> <span class="n">LongWritable</span><span class="o">>(</span> - <span class="k">new</span> <span class="nf">Counter</span><span class="o">(),</span> <span class="k">new</span> <span class="nf">Counter</span><span class="o">()));</span> - - <span class="c1">// Setup Hadoopâs TextOutputFormat</span> - <span class="n">HadoopOutputFormat</span><span class="o"><</span><span class="n">Text</span><span class="o">,</span> <span class="n">LongWritable</span><span class="o">></span> <span class="n">hadoopOutputFormat</span> <span class="o">=</span> - <span class="k">new</span> <span class="n">HadoopOutputFormat</span><span class="o"><</span><span class="n">Text</span><span class="o">,</span> <span class="n">LongWritable</span><span class="o">>(</span> - <span class="k">new</span> <span class="n">TextOutputFormat</span><span class="o"><</span><span class="n">Text</span><span class="o">,</span> <span class="n">LongWritable</span><span class="o">>(),</span> <span class="k">new</span> <span class="nf">JobConf</span><span class="o">());</span> - <span class="n">hadoopOutputFormat</span><span class="o">.</span><span class="na">getJobConf</span><span class="o">().</span><span class="na">set</span><span class="o">(</span><span class="s">"mapred.textoutputformat.separator"</span><span class="o">,</span> <span class="s">" "</span><span class="o">);</span> - <span class="n">TextOutputFormat</span><span class="o">.</span><span class="na">setOutputPath</span><span class="o">(</span><span class="n">hadoopOutputFormat</span><span class="o">.</span><span class="na">getJobConf</span><span class="o">(),</span> <span class="k">new</span> <span class="nf">Path</span><span class="o">(</span><span class="n">outputPath</span><span class="o">));</span> - - <span class="c1">// Output & Execute</span> - <span class="n">words</span><span class="o">.</span><span class="na">output</span><span class="o">(</span><span class="n">hadoopOutputFormat</span><span class="o">);</span> - <span class="n">env</span><span class="o">.</span><span class="na">execute</span><span class="o">(</span><span class="s">"Hadoop Compat WordCount"</span><span class="o">);</span> -<span class="o">}</span></code></pre></div> - -<p>As you can see, Flink represents Hadoop key-value pairs as <code>Tuple2<key, value></code> tuples. Note, that the program uses Flinkâs <code>groupBy()</code> transformation to group data on the key field (field 0 of the <code>Tuple2<key, value></code>) before it is given to the Reducer function. At the moment, the compatibility package does not evaluate custom Hadoop partitioners, sorting comparators, or grouping comparators.</p> - -<p>Hadoop functions can be used at any position within a Flink program and of course also be mixed with native Flink functions. This means that instead of assembling a workflow of Hadoop jobs in an external driver method or using a workflow scheduler such as <a href="http://oozie.apache.org";>Apache Oozie</a>, you can implement an arbitrary complex Flink program consisting of multiple Hadoop Input- and OutputFormats, Mapper and Reducer functions. When executing such a Flink program, data will be pipelined between your Hadoop functions and will not be written to HDFS just for the purpose of data exchange.</p> - -<center> -<img src="/img/blog/hcompat-flow.png" style="width:100%;margin:15px" /> -</center> - -<h2 id="what-comes-next">What comes next?</h2> - -<p>While the Hadoop compatibility package is already very useful, we are currently working on a dedicated Hadoop Job operation to embed and execute Hadoop jobs as a whole in Flink programs, including their custom partitioning, sorting, and grouping code. With this feature, you will be able to chain multiple Hadoop jobs, mix them with Flink functions, and other operations such as <a href="http://ci.apache.org/projects/flink/flink-docs-release-0.7/spargel_guide.html";>Spargel</a> operations (Pregel/Giraph-style jobs).</p> - -<h2 id="summary">Summary</h2> - -<p>Flink lets you reuse a lot of the code you wrote for Hadoop MapReduce, including all data types, all Input- and OutputFormats, and Mapper and Reducers of the mapred-API. Hadoop functions can be used within Flink programs and mixed with all other Flink functions. Due to Flinkâs pipelined execution, Hadoop functions can arbitrarily be assembled without data exchange via HDFS. Moreover, the Flink community is currently working on a dedicated Hadoop Job operation to supporting the execution of Hadoop jobs as a whole.</p> - -<p>If you want to use Flinkâs Hadoop compatibility package checkout our <a href="https://ci.apache.org/projects/flink/flink-docs-master/apis/batch/hadoop_compatibility.html";>documentation</a>.</p> - - </article> - </div> - - <div class="row"> - <div id="disqus_thread"></div> - <script type="text/javascript"> - /* * * CONFIGURATION VARIABLES: EDIT BEFORE PASTING INTO YOUR WEBPAGE * * */ - var disqus_shortname = 'stratosphere-eu'; // required: replace example with your forum shortname - - /* * * DON'T EDIT BELOW THIS LINE * * */ - (function() { - var dsq = document.createElement('script'); dsq.type = 'text/javascript'; dsq.async = true; - dsq.src = '//' + disqus_shortname + '.disqus.com/embed.js'; - (document.getElementsByTagName('head')[0] || document.getElementsByTagName('body')[0]).appendChild(dsq); - })(); - </script> - </div> - </div> -</div> - - <hr /> - <div class="footer text-center"> - <p>Copyright © 2014-2016 <a href="http://apache.org";>The Apache Software Foundation</a>. 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