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Posted to commits@flink.apache.org by fh...@apache.org on 2015/10/08 13:03:28 UTC
[2/4] flink-web git commit: Build website
http://git-wip-us.apache.org/repos/asf/flink-web/blob/415ee891/content/news/2014/11/18/hadoop-compatibility.html
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diff --git a/content/news/2014/11/18/hadoop-compatibility.html b/content/news/2014/11/18/hadoop-compatibility.html
index ef5eb15..7786593 100644
--- a/content/news/2014/11/18/hadoop-compatibility.html
+++ b/content/news/2014/11/18/hadoop-compatibility.html
@@ -112,7 +112,9 @@
<li class="divider"></li>
<li role="presentation" class="dropdown-header"><strong>Contribute</strong></li>
<li><a href="/how-to-contribute.html">How to Contribute</a></li>
- <li><a href="/coding-guidelines.html">Coding Guidelines</a></li>
+ <li><a href="/contribute-code.html">Contribute Code</a></li>
+ <li><a href="/contribute-documentation.html">Contribute Documentation</a></li>
+ <li><a href="/improve-website.html">Improve the Website</a></li>
</ul>
</li>
@@ -153,7 +155,7 @@
<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 Hadoop’s 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>To close this gap, Flink provides a Hadoop Compatibility package to wrap functions implemented against Hadoop’s 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>
@@ -166,7 +168,7 @@
<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>
+<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>
http://git-wip-us.apache.org/repos/asf/flink-web/blob/415ee891/content/news/2015/01/06/december-in-flink.html
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diff --git a/content/news/2015/01/06/december-in-flink.html b/content/news/2015/01/06/december-in-flink.html
index 71663da..0fd101f 100644
--- a/content/news/2015/01/06/december-in-flink.html
+++ b/content/news/2015/01/06/december-in-flink.html
@@ -112,7 +112,9 @@
<li class="divider"></li>
<li role="presentation" class="dropdown-header"><strong>Contribute</strong></li>
<li><a href="/how-to-contribute.html">How to Contribute</a></li>
- <li><a href="/coding-guidelines.html">Coding Guidelines</a></li>
+ <li><a href="/contribute-code.html">Contribute Code</a></li>
+ <li><a href="/contribute-documentation.html">Contribute Documentation</a></li>
+ <li><a href="/improve-website.html">Improve the Website</a></li>
</ul>
</li>
http://git-wip-us.apache.org/repos/asf/flink-web/blob/415ee891/content/news/2015/01/21/release-0.8.html
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diff --git a/content/news/2015/01/21/release-0.8.html b/content/news/2015/01/21/release-0.8.html
index 65a8e70..933606c 100644
--- a/content/news/2015/01/21/release-0.8.html
+++ b/content/news/2015/01/21/release-0.8.html
@@ -112,7 +112,9 @@
<li class="divider"></li>
<li role="presentation" class="dropdown-header"><strong>Contribute</strong></li>
<li><a href="/how-to-contribute.html">How to Contribute</a></li>
- <li><a href="/coding-guidelines.html">Coding Guidelines</a></li>
+ <li><a href="/contribute-code.html">Contribute Code</a></li>
+ <li><a href="/contribute-documentation.html">Contribute Documentation</a></li>
+ <li><a href="/improve-website.html">Improve the Website</a></li>
</ul>
</li>
@@ -196,7 +198,7 @@
<li>Stefan Bunk</li>
<li>Paris Carbone</li>
<li>Ufuk Celebi</li>
- <li>Nils Engelbach</li>
+ <li>Nils Engelbach </li>
<li>Stephan Ewen</li>
<li>Gyula Fora</li>
<li>Gabor Hermann</li>
http://git-wip-us.apache.org/repos/asf/flink-web/blob/415ee891/content/news/2015/02/04/january-in-flink.html
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diff --git a/content/news/2015/02/04/january-in-flink.html b/content/news/2015/02/04/january-in-flink.html
index 8c54920..74f3565 100644
--- a/content/news/2015/02/04/january-in-flink.html
+++ b/content/news/2015/02/04/january-in-flink.html
@@ -112,7 +112,9 @@
<li class="divider"></li>
<li role="presentation" class="dropdown-header"><strong>Contribute</strong></li>
<li><a href="/how-to-contribute.html">How to Contribute</a></li>
- <li><a href="/coding-guidelines.html">Coding Guidelines</a></li>
+ <li><a href="/contribute-code.html">Contribute Code</a></li>
+ <li><a href="/contribute-documentation.html">Contribute Documentation</a></li>
+ <li><a href="/improve-website.html">Improve the Website</a></li>
</ul>
</li>
@@ -177,7 +179,7 @@
<h3 id="using-off-heap-memoryhttpsgithubcomapacheflinkpull290"><a href="https://github.com/apache/flink/pull/290">Using off-heap memory</a></h3>
-<p>This pull request enables Flink to use off-heap memory for its internal memory uses (sort, hash, caching of intermediate data sets).</p>
+<p>This pull request enables Flink to use off-heap memory for its internal memory uses (sort, hash, caching of intermediate data sets). </p>
<h3 id="gelly-flinks-graph-apihttpsgithubcomapacheflinkpull335"><a href="https://github.com/apache/flink/pull/335">Gelly, Flink’s Graph API</a></h3>
http://git-wip-us.apache.org/repos/asf/flink-web/blob/415ee891/content/news/2015/02/09/streaming-example.html
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diff --git a/content/news/2015/02/09/streaming-example.html b/content/news/2015/02/09/streaming-example.html
index 70ac3c8..4cf0561 100644
--- a/content/news/2015/02/09/streaming-example.html
+++ b/content/news/2015/02/09/streaming-example.html
@@ -112,7 +112,9 @@
<li class="divider"></li>
<li role="presentation" class="dropdown-header"><strong>Contribute</strong></li>
<li><a href="/how-to-contribute.html">How to Contribute</a></li>
- <li><a href="/coding-guidelines.html">Coding Guidelines</a></li>
+ <li><a href="/contribute-code.html">Contribute Code</a></li>
+ <li><a href="/contribute-documentation.html">Contribute Documentation</a></li>
+ <li><a href="/improve-website.html">Improve the Website</a></li>
</ul>
</li>
@@ -181,7 +183,7 @@ found <a href="https://github.com/mbalassi/flink/blob/stockprices/flink-staging/
<li>Read a socket stream of stock prices</li>
<li>Parse the text in the stream to create a stream of <code>StockPrice</code> objects</li>
<li>Add four other sources tagged with the stock symbol.</li>
- <li>Finally, merge the streams to create a unified stream.</li>
+ <li>Finally, merge the streams to create a unified stream. </li>
</ol>
<p><img alt="Reading from multiple inputs" src="/img/blog/blog_multi_input.png" width="70%" class="img-responsive center-block" /></p>
@@ -653,7 +655,7 @@ number of mentions of a given stock in the Twitter stream. As both of
these data streams are potentially infinite, we apply the join on a
30-second window.</p>
-<p><img alt="Streaming joins" src="/img/blog/blog_stream_join.png" width="60%" class="img-responsive center-block" /></p>
+<p><img alt="Streaming joins" src="/img/blog/blog_stream_join.png" width="60%" class="img-responsive center-block" /> </p>
<div class="codetabs">
http://git-wip-us.apache.org/repos/asf/flink-web/blob/415ee891/content/news/2015/03/02/february-2015-in-flink.html
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diff --git a/content/news/2015/03/02/february-2015-in-flink.html b/content/news/2015/03/02/february-2015-in-flink.html
index 128dedf..f5305c7 100644
--- a/content/news/2015/03/02/february-2015-in-flink.html
+++ b/content/news/2015/03/02/february-2015-in-flink.html
@@ -112,7 +112,9 @@
<li class="divider"></li>
<li role="presentation" class="dropdown-header"><strong>Contribute</strong></li>
<li><a href="/how-to-contribute.html">How to Contribute</a></li>
- <li><a href="/coding-guidelines.html">Coding Guidelines</a></li>
+ <li><a href="/contribute-code.html">Contribute Code</a></li>
+ <li><a href="/contribute-documentation.html">Contribute Documentation</a></li>
+ <li><a href="/improve-website.html">Improve the Website</a></li>
</ul>
</li>
http://git-wip-us.apache.org/repos/asf/flink-web/blob/415ee891/content/news/2015/03/13/peeking-into-Apache-Flinks-Engine-Room.html
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diff --git a/content/news/2015/03/13/peeking-into-Apache-Flinks-Engine-Room.html b/content/news/2015/03/13/peeking-into-Apache-Flinks-Engine-Room.html
index 4486bee..3378ca6 100644
--- a/content/news/2015/03/13/peeking-into-Apache-Flinks-Engine-Room.html
+++ b/content/news/2015/03/13/peeking-into-Apache-Flinks-Engine-Room.html
@@ -112,7 +112,9 @@
<li class="divider"></li>
<li role="presentation" class="dropdown-header"><strong>Contribute</strong></li>
<li><a href="/how-to-contribute.html">How to Contribute</a></li>
- <li><a href="/coding-guidelines.html">Coding Guidelines</a></li>
+ <li><a href="/contribute-code.html">Contribute Code</a></li>
+ <li><a href="/contribute-documentation.html">Contribute Documentation</a></li>
+ <li><a href="/improve-website.html">Improve the Website</a></li>
</ul>
</li>
@@ -152,7 +154,7 @@
<p>In this blog post, we cut through Apache Flink’s layered architecture and take a look at its internals with a focus on how it handles joins. Specifically, I will</p>
<ul>
- <li>show how easy it is to join data sets using Flink’s fluent APIs,</li>
+ <li>show how easy it is to join data sets using Flink’s fluent APIs, </li>
<li>discuss basic distributed join strategies, Flink’s join implementations, and its memory management,</li>
<li>talk about Flink’s optimizer that automatically chooses join strategies,</li>
<li>show some performance numbers for joining data sets of different sizes, and finally</li>
@@ -163,7 +165,7 @@
<h3 id="how-do-i-join-with-flink">How do I join with Flink?</h3>
-<p>Flink provides fluent APIs in Java and Scala to write data flow programs. Flink’s APIs are centered around parallel data collections which are called data sets. data sets are processed by applying Transformations that compute new data sets. Flink’s transformations include Map and Reduce as known from MapReduce <a href="http://research.google.com/archive/mapreduce.html">[1]</a> but also operators for joining, co-grouping, and iterative processing. The documentation gives an overview of all available transformations <a href="http://ci.apache.org/projects/flink/flink-docs-release-0.8/dataset_transformations.html">[2]</a>.</p>
+<p>Flink provides fluent APIs in Java and Scala to write data flow programs. Flink’s APIs are centered around parallel data collections which are called data sets. data sets are processed by applying Transformations that compute new data sets. Flink’s transformations include Map and Reduce as known from MapReduce <a href="http://research.google.com/archive/mapreduce.html">[1]</a> but also operators for joining, co-grouping, and iterative processing. The documentation gives an overview of all available transformations <a href="http://ci.apache.org/projects/flink/flink-docs-release-0.8/dataset_transformations.html">[2]</a>. </p>
<p>Joining two Scala case class data sets is very easy as the following example shows:</p>
@@ -200,7 +202,7 @@
<ol>
<li>The data of both inputs is distributed across all parallel instances that participate in the join and</li>
- <li>each parallel instance performs a standard stand-alone join algorithm on its local partition of the overall data.</li>
+ <li>each parallel instance performs a standard stand-alone join algorithm on its local partition of the overall data. </li>
</ol>
<p>The distribution of data across parallel instances must ensure that each valid join pair can be locally built by exactly one instance. For both steps, there are multiple valid strategies that can be independently picked and which are favorable in different situations. In Flink terminology, the first phase is called Ship Strategy and the second phase Local Strategy. In the following I will describe Flink’s ship and local strategies to join two data sets <em>R</em> and <em>S</em>.</p>
@@ -219,7 +221,7 @@
<img src="/img/blog/joins-repartition.png" style="width:90%;margin:15px" />
</center>
-<p>The Broadcast-Forward strategy sends one complete data set (R) to each parallel instance that holds a partition of the other data set (S), i.e., each parallel instance receives the full data set R. Data set S remains local and is not shipped at all. The cost of the BF strategy depends on the size of R and the number of parallel instances it is shipped to. The size of S does not matter because S is not moved. The figure below illustrates how both ship strategies work.</p>
+<p>The Broadcast-Forward strategy sends one complete data set (R) to each parallel instance that holds a partition of the other data set (S), i.e., each parallel instance receives the full data set R. Data set S remains local and is not shipped at all. The cost of the BF strategy depends on the size of R and the number of parallel instances it is shipped to. The size of S does not matter because S is not moved. The figure below illustrates how both ship strategies work. </p>
<center>
<img src="/img/blog/joins-broadcast.png" style="width:90%;margin:15px" />
@@ -228,7 +230,7 @@
<p>The Repartition-Repartition and Broadcast-Forward ship strategies establish suitable data distributions to execute a distributed join. Depending on the operations that are applied before the join, one or even both inputs of a join are already distributed in a suitable way across parallel instances. In this case, Flink will reuse such distributions and only ship one or no input at all.</p>
<h4 id="flinks-memory-management">Flink’s Memory Management</h4>
-<p>Before delving into the details of Flink’s local join algorithms, I will briefly discuss Flink’s internal memory management. Data processing algorithms such as joining, grouping, and sorting need to hold portions of their input data in memory. While such algorithms perform best if there is enough memory available to hold all data, it is crucial to gracefully handle situations where the data size exceeds memory. Such situations are especially tricky in JVM-based systems such as Flink because the system needs to reliably recognize that it is short on memory. Failure to detect such situations can result in an <code>OutOfMemoryException</code> and kill the JVM.</p>
+<p>Before delving into the details of Flink’s local join algorithms, I will briefly discuss Flink’s internal memory management. Data processing algorithms such as joining, grouping, and sorting need to hold portions of their input data in memory. While such algorithms perform best if there is enough memory available to hold all data, it is crucial to gracefully handle situations where the data size exceeds memory. Such situations are especially tricky in JVM-based systems such as Flink because the system needs to reliably recognize that it is short on memory. Failure to detect such situations can result in an <code>OutOfMemoryException</code> and kill the JVM. </p>
<p>Flink handles this challenge by actively managing its memory. When a worker node (TaskManager) is started, it allocates a fixed portion (70% by default) of the JVM’s heap memory that is available after initialization as 32KB byte arrays. These byte arrays are distributed as working memory to all algorithms that need to hold significant portions of data in memory. The algorithms receive their input data as Java data objects and serialize them into their working memory.</p>
@@ -245,7 +247,7 @@
<p>After the data has been distributed across all parallel join instances using either a Repartition-Repartition or Broadcast-Forward ship strategy, each instance runs a local join algorithm to join the elements of its local partition. Flink’s runtime features two common join strategies to perform these local joins:</p>
<ul>
- <li>the <em>Sort-Merge-Join</em> strategy (SM) and</li>
+ <li>the <em>Sort-Merge-Join</em> strategy (SM) and </li>
<li>the <em>Hybrid-Hash-Join</em> strategy (HH).</li>
</ul>
@@ -290,13 +292,13 @@
<ul>
<li>1GB : 1000GB</li>
<li>10GB : 1000GB</li>
- <li>100GB : 1000GB</li>
+ <li>100GB : 1000GB </li>
<li>1000GB : 1000GB</li>
</ul>
<p>The Broadcast-Forward strategy is only executed for up to 10GB. Building a hash table from 100GB broadcasted data in 5GB working memory would result in spilling proximately 95GB (build input) + 950GB (probe input) in each parallel thread and require more than 8TB local disk storage on each machine.</p>
-<p>As in the single-core benchmark, we run 1:N joins, generate the data on-the-fly, and immediately discard the result after the join. We run the benchmark on 10 n1-highmem-8 Google Compute Engine instances. Each instance is equipped with 8 cores, 52GB RAM, 40GB of which are configured as working memory (5GB per core), and one local SSD for spilling to disk. All benchmarks are performed using the same configuration, i.e., no fine tuning for the respective data sizes is done. The programs are executed with a parallelism of 80.</p>
+<p>As in the single-core benchmark, we run 1:N joins, generate the data on-the-fly, and immediately discard the result after the join. We run the benchmark on 10 n1-highmem-8 Google Compute Engine instances. Each instance is equipped with 8 cores, 52GB RAM, 40GB of which are configured as working memory (5GB per core), and one local SSD for spilling to disk. All benchmarks are performed using the same configuration, i.e., no fine tuning for the respective data sizes is done. The programs are executed with a parallelism of 80. </p>
<center>
<img src="/img/blog/joins-dist-perf.png" style="width:70%;margin:15px" />
@@ -313,7 +315,7 @@
<ul>
<li>Flink’s fluent Scala and Java APIs make joins and other data transformations easy as cake.</li>
<li>The optimizer does the hard choices for you, but gives you control in case you know better.</li>
- <li>Flink’s join implementations perform very good in-memory and gracefully degrade when going to disk.</li>
+ <li>Flink’s join implementations perform very good in-memory and gracefully degrade when going to disk. </li>
<li>Due to Flink’s robust memory management, there is no need for job- or data-specific memory tuning to avoid a nasty <code>OutOfMemoryException</code>. It just runs out-of-the-box.</li>
</ul>
http://git-wip-us.apache.org/repos/asf/flink-web/blob/415ee891/content/news/2015/04/07/march-in-flink.html
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diff --git a/content/news/2015/04/07/march-in-flink.html b/content/news/2015/04/07/march-in-flink.html
index 3a5d4bc..dbb3544 100644
--- a/content/news/2015/04/07/march-in-flink.html
+++ b/content/news/2015/04/07/march-in-flink.html
@@ -112,7 +112,9 @@
<li class="divider"></li>
<li role="presentation" class="dropdown-header"><strong>Contribute</strong></li>
<li><a href="/how-to-contribute.html">How to Contribute</a></li>
- <li><a href="/coding-guidelines.html">Coding Guidelines</a></li>
+ <li><a href="/contribute-code.html">Contribute Code</a></li>
+ <li><a href="/contribute-documentation.html">Contribute Documentation</a></li>
+ <li><a href="/improve-website.html">Improve the Website</a></li>
</ul>
</li>
http://git-wip-us.apache.org/repos/asf/flink-web/blob/415ee891/content/news/2015/04/13/release-0.9.0-milestone1.html
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diff --git a/content/news/2015/04/13/release-0.9.0-milestone1.html b/content/news/2015/04/13/release-0.9.0-milestone1.html
index c9031ef..56cfd10 100644
--- a/content/news/2015/04/13/release-0.9.0-milestone1.html
+++ b/content/news/2015/04/13/release-0.9.0-milestone1.html
@@ -112,7 +112,9 @@
<li class="divider"></li>
<li role="presentation" class="dropdown-header"><strong>Contribute</strong></li>
<li><a href="/how-to-contribute.html">How to Contribute</a></li>
- <li><a href="/coding-guidelines.html">Coding Guidelines</a></li>
+ <li><a href="/contribute-code.html">Contribute Code</a></li>
+ <li><a href="/contribute-documentation.html">Contribute Documentation</a></li>
+ <li><a href="/improve-website.html">Improve the Website</a></li>
</ul>
</li>
http://git-wip-us.apache.org/repos/asf/flink-web/blob/415ee891/content/news/2015/05/11/Juggling-with-Bits-and-Bytes.html
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diff --git a/content/news/2015/05/11/Juggling-with-Bits-and-Bytes.html b/content/news/2015/05/11/Juggling-with-Bits-and-Bytes.html
index 1b1abaa..b02e679 100644
--- a/content/news/2015/05/11/Juggling-with-Bits-and-Bytes.html
+++ b/content/news/2015/05/11/Juggling-with-Bits-and-Bytes.html
@@ -112,7 +112,9 @@
<li class="divider"></li>
<li role="presentation" class="dropdown-header"><strong>Contribute</strong></li>
<li><a href="/how-to-contribute.html">How to Contribute</a></li>
- <li><a href="/coding-guidelines.html">Coding Guidelines</a></li>
+ <li><a href="/contribute-code.html">Contribute Code</a></li>
+ <li><a href="/contribute-documentation.html">Contribute Documentation</a></li>
+ <li><a href="/improve-website.html">Improve the Website</a></li>
</ul>
</li>
@@ -164,7 +166,7 @@ However, this approach has a few notable drawbacks. First of all it is not trivi
<img src="/img/blog/memory-mgmt.png" style="width:90%;margin:15px" />
</center>
-<p>Flink’s style of active memory management and operating on binary data has several benefits:</p>
+<p>Flink’s style of active memory management and operating on binary data has several benefits: </p>
<ol>
<li><strong>Memory-safe execution & efficient out-of-core algorithms.</strong> Due to the fixed amount of allocated memory segments, it is trivial to monitor remaining memory resources. In case of memory shortage, processing operators can efficiently write larger batches of memory segments to disk and later them read back. Consequently, <code>OutOfMemoryErrors</code> are effectively prevented.</li>
@@ -173,13 +175,13 @@ However, this approach has a few notable drawbacks. First of all it is not trivi
<li><strong>Efficient binary operations & cache sensitivity.</strong> Binary data can be efficiently compared and operated on given a suitable binary representation. Furthermore, the binary representations can put related values, as well as hash codes, keys, and pointers, adjacently into memory. This gives data structures with usually more cache efficient access patterns.</li>
</ol>
-<p>These properties of active memory management are very desirable in a data processing systems for large-scale data analytics but have a significant price tag attached. Active memory management and operating on binary data is not trivial to implement, i.e., using <code>java.util.HashMap</code> is much easier than implementing a spillable hash-table backed by byte arrays and a custom serialization stack. Of course Apache Flink is not the only JVM-based data processing system that operates on serialized binary data. Projects such as <a href="http://drill.apache.org/">Apache Drill</a>, <a href="http://ignite.incubator.apache.org/">Apache Ignite (incubating)</a> or <a href="http://projectgeode.org/">Apache Geode (incubating)</a> apply similar techniques and it was recently announced that also <a href="http://spark.apache.org/">Apache Spark</a> will evolve into this direction with <a href="https://databricks.com/blog/2015/04/28/project-tungsten-bringing-spark-closer-to-bare-metal.html">
Project Tungsten</a>.</p>
+<p>These properties of active memory management are very desirable in a data processing systems for large-scale data analytics but have a significant price tag attached. Active memory management and operating on binary data is not trivial to implement, i.e., using <code>java.util.HashMap</code> is much easier than implementing a spillable hash-table backed by byte arrays and a custom serialization stack. Of course Apache Flink is not the only JVM-based data processing system that operates on serialized binary data. Projects such as <a href="http://drill.apache.org/">Apache Drill</a>, <a href="http://ignite.incubator.apache.org/">Apache Ignite (incubating)</a> or <a href="http://projectgeode.org/">Apache Geode (incubating)</a> apply similar techniques and it was recently announced that also <a href="http://spark.apache.org/">Apache Spark</a> will evolve into this direction with <a href="https://databricks.com/blog/2015/04/28/project-tungsten-bringing-spark-closer-to-bare-metal.html">
Project Tungsten</a>. </p>
<p>In the following we discuss in detail how Flink allocates memory, de/serializes objects, and operates on binary data. We will also show some performance numbers comparing processing objects on the heap and operating on binary data.</p>
<h2 id="how-does-flink-allocate-memory">How does Flink allocate memory?</h2>
-<p>A Flink worker, called TaskManager, is composed of several internal components such as an actor system for coordination with the Flink master, an IOManager that takes care of spilling data to disk and reading it back, and a MemoryManager that coordinates memory usage. In the context of this blog post, the MemoryManager is of most interest.</p>
+<p>A Flink worker, called TaskManager, is composed of several internal components such as an actor system for coordination with the Flink master, an IOManager that takes care of spilling data to disk and reading it back, and a MemoryManager that coordinates memory usage. In the context of this blog post, the MemoryManager is of most interest. </p>
<p>The MemoryManager takes care of allocating, accounting, and distributing MemorySegments to data processing operators such as sort and join operators. A <a href="https://github.com/apache/flink/blob/release-0.9.0-milestone-1/flink-core/src/main/java/org/apache/flink/core/memory/MemorySegment.java">MemorySegment</a> is Flink’s distribution unit of memory and is backed by a regular Java byte array (size is 32 KB by default). A MemorySegment provides very efficient write and read access to its backed byte array using Java’s unsafe methods. You can think of a MemorySegment as a custom-tailored version of Java’s NIO ByteBuffer. In order to operate on multiple MemorySegments like on a larger chunk of consecutive memory, Flink uses logical views that implement Java’s <code>java.io.DataOutput</code> and <code>java.io.DataInput</code> interfaces.</p>
@@ -191,7 +193,7 @@ However, this approach has a few notable drawbacks. First of all it is not trivi
<h2 id="how-does-flink-serialize-objects">How does Flink serialize objects?</h2>
-<p>The Java ecosystem offers several libraries to convert objects into a binary representation and back. Common alternatives are standard Java serialization, <a href="https://github.com/EsotericSoftware/kryo">Kryo</a>, <a href="http://avro.apache.org/">Apache Avro</a>, <a href="http://thrift.apache.org/">Apache Thrift</a>, or Google’s <a href="https://github.com/google/protobuf">Protobuf</a>. Flink includes its own custom serialization framework in order to control the binary representation of data. This is important because operating on binary data such as comparing or even manipulating binary data requires exact knowledge of the serialization layout. Further, configuring the serialization layout with respect to operations that are performed on binary data can yield a significant performance boost. Flink’s serialization stack also leverages the fact, that the type of the objects which are going through de/serialization are exactly known before a program is executed.</p>
+<p>The Java ecosystem offers several libraries to convert objects into a binary representation and back. Common alternatives are standard Java serialization, <a href="https://github.com/EsotericSoftware/kryo">Kryo</a>, <a href="http://avro.apache.org/">Apache Avro</a>, <a href="http://thrift.apache.org/">Apache Thrift</a>, or Google’s <a href="https://github.com/google/protobuf">Protobuf</a>. Flink includes its own custom serialization framework in order to control the binary representation of data. This is important because operating on binary data such as comparing or even manipulating binary data requires exact knowledge of the serialization layout. Further, configuring the serialization layout with respect to operations that are performed on binary data can yield a significant performance boost. Flink’s serialization stack also leverages the fact, that the type of the objects which are going through de/serialization are exactly known before a program is executed. </p>
<p>Flink programs can process data represented as arbitrary Java or Scala objects. Before a program is optimized, the data types at each processing step of the program’s data flow need to be identified. For Java programs, Flink features a reflection-based type extraction component to analyze the return types of user-defined functions. Scala programs are analyzed with help of the Scala compiler. Flink represents each data type with a <a href="https://github.com/apache/flink/blob/release-0.9.0-milestone-1/flink-core/src/main/java/org/apache/flink/api/common/typeinfo/TypeInformation.java">TypeInformation</a>. Flink has TypeInformations for several kinds of data types, including:</p>
@@ -201,11 +203,11 @@ However, this approach has a few notable drawbacks. First of all it is not trivi
<li>WritableTypeInfo: Any implementation of Hadoop’s Writable interface.</li>
<li>TupleTypeInfo: Any Flink tuple (Tuple1 to Tuple25). Flink tuples are Java representations for fixed-length tuples with typed fields.</li>
<li>CaseClassTypeInfo: Any Scala CaseClass (including Scala tuples).</li>
- <li>PojoTypeInfo: Any POJO (Java or Scala), i.e., an object with all fields either being public or accessible through getters and setter that follow the common naming conventions.</li>
+ <li>PojoTypeInfo: Any POJO (Java or Scala), i.e., an object with all fields either being public or accessible through getters and setter that follow the common naming conventions. </li>
<li>GenericTypeInfo: Any data type that cannot be identified as another type.</li>
</ul>
-<p>Each TypeInformation provides a serializer for the data type it represents. For example, a BasicTypeInfo returns a serializer that writes the respective primitive type, the serializer of a WritableTypeInfo delegates de/serialization to the write() and readFields() methods of the object implementing Hadoop’s Writable interface, and a GenericTypeInfo returns a serializer that delegates serialization to Kryo. Object serialization to a DataOutput which is backed by Flink MemorySegments goes automatically through Java’s efficient unsafe operations. For data types that can be used as keys, i.e., compared and hashed, the TypeInformation provides TypeComparators. TypeComparators compare and hash objects and can - depending on the concrete data type - also efficiently compare binary representations and extract fixed-length binary key prefixes.</p>
+<p>Each TypeInformation provides a serializer for the data type it represents. For example, a BasicTypeInfo returns a serializer that writes the respective primitive type, the serializer of a WritableTypeInfo delegates de/serialization to the write() and readFields() methods of the object implementing Hadoop’s Writable interface, and a GenericTypeInfo returns a serializer that delegates serialization to Kryo. Object serialization to a DataOutput which is backed by Flink MemorySegments goes automatically through Java’s efficient unsafe operations. For data types that can be used as keys, i.e., compared and hashed, the TypeInformation provides TypeComparators. TypeComparators compare and hash objects and can - depending on the concrete data type - also efficiently compare binary representations and extract fixed-length binary key prefixes. </p>
<p>Tuple, Pojo, and CaseClass types are composite types, i.e., containers for one or more possibly nested data types. As such, their serializers and comparators are also composite and delegate the serialization and comparison of their member data types to the respective serializers and comparators. The following figure illustrates the serialization of a (nested) <code>Tuple3<Integer, Double, Person></code> object where <code>Person</code> is a POJO and defined as follows:</p>
@@ -218,13 +220,13 @@ However, this approach has a few notable drawbacks. First of all it is not trivi
<img src="/img/blog/data-serialization.png" style="width:80%;margin:15px" />
</center>
-<p>Flink’s type system can be easily extended by providing custom TypeInformations, Serializers, and Comparators to improve the performance of serializing and comparing custom data types.</p>
+<p>Flink’s type system can be easily extended by providing custom TypeInformations, Serializers, and Comparators to improve the performance of serializing and comparing custom data types. </p>
<h2 id="how-does-flink-operate-on-binary-data">How does Flink operate on binary data?</h2>
<p>Similar to many other data processing APIs (including SQL), Flink’s APIs provide transformations to group, sort, and join data sets. These transformations operate on potentially very large data sets. Relational database systems feature very efficient algorithms for these purposes since several decades including external merge-sort, merge-join, and hybrid hash-join. Flink builds on this technology, but generalizes it to handle arbitrary objects using its custom serialization and comparison stack. In the following, we show how Flink operates with binary data by the example of Flink’s in-memory sort algorithm.</p>
-<p>Flink assigns a memory budget to its data processing operators. Upon initialization, a sort algorithm requests its memory budget from the MemoryManager and receives a corresponding set of MemorySegments. The set of MemorySegments becomes the memory pool of a so-called sort buffer which collects the data that is be sorted. The following figure illustrates how data objects are serialized into the sort buffer.</p>
+<p>Flink assigns a memory budget to its data processing operators. Upon initialization, a sort algorithm requests its memory budget from the MemoryManager and receives a corresponding set of MemorySegments. The set of MemorySegments becomes the memory pool of a so-called sort buffer which collects the data that is be sorted. The following figure illustrates how data objects are serialized into the sort buffer. </p>
<center>
<img src="/img/blog/sorting-binary-data-1.png" style="width:90%;margin:15px" />
@@ -237,7 +239,7 @@ The following figure shows how two objects are compared.</p>
<img src="/img/blog/sorting-binary-data-2.png" style="width:80%;margin:15px" />
</center>
-<p>The sort buffer compares two elements by comparing their binary fix-length sort keys. The comparison is successful if either done on a full key (not a prefix key) or if the binary prefix keys are not equal. If the prefix keys are equal (or the sort key data type does not provide a binary prefix key), the sort buffer follows the pointers to the actual object data, deserializes both objects and compares the objects. Depending on the result of the comparison, the sort algorithm decides whether to swap the compared elements or not. The sort buffer swaps two elements by moving their fix-length keys and pointers. The actual data is not moved. Once the sort algorithm finishes, the pointers in the sort buffer are correctly ordered. The following figure shows how the sorted data is returned from the sort buffer.</p>
+<p>The sort buffer compares two elements by comparing their binary fix-length sort keys. The comparison is successful if either done on a full key (not a prefix key) or if the binary prefix keys are not equal. If the prefix keys are equal (or the sort key data type does not provide a binary prefix key), the sort buffer follows the pointers to the actual object data, deserializes both objects and compares the objects. Depending on the result of the comparison, the sort algorithm decides whether to swap the compared elements or not. The sort buffer swaps two elements by moving their fix-length keys and pointers. The actual data is not moved. Once the sort algorithm finishes, the pointers in the sort buffer are correctly ordered. The following figure shows how the sorted data is returned from the sort buffer. </p>
<center>
<img src="/img/blog/sorting-binary-data-3.png" style="width:80%;margin:15px" />
@@ -255,7 +257,7 @@ The following figure shows how two objects are compared.</p>
<li><strong>Kryo-serialized.</strong> The tuple fields are serialized into a sort buffer of 600 MB size using Kryo serialization and sorted without binary sort keys. This means that each pair-wise comparison requires two object to be deserialized.</li>
</ol>
-<p>All sort methods are implemented using a single thread. The reported times are averaged over ten runs. After each run, we call <code>System.gc()</code> to request a garbage collection run which does not go into measured execution time. The following figure shows the time to store the input data in memory, sort it, and read it back as objects.</p>
+<p>All sort methods are implemented using a single thread. The reported times are averaged over ten runs. After each run, we call <code>System.gc()</code> to request a garbage collection run which does not go into measured execution time. The following figure shows the time to store the input data in memory, sort it, and read it back as objects. </p>
<center>
<img src="/img/blog/sort-benchmark.png" style="width:90%;margin:15px" />
@@ -313,13 +315,13 @@ The following figure shows how two objects are compared.</p>
<p><br /></p>
-<p>To summarize, the experiments verify the previously stated benefits of operating on binary data.</p>
+<p>To summarize, the experiments verify the previously stated benefits of operating on binary data. </p>
<h2 id="were-not-done-yet">We’re not done yet!</h2>
-<p>Apache Flink features quite a bit of advanced techniques to safely and efficiently process huge amounts of data with limited memory resources. However, there are a few points that could make Flink even more efficient. The Flink community is working on moving the managed memory to off-heap memory. This will allow for smaller JVMs, lower garbage collection overhead, and also easier system configuration. With Flink’s Table API, the semantics of all operations such as aggregations and projections are known (in contrast to black-box user-defined functions). Hence we can generate code for Table API operations that directly operates on binary data. Further improvements include serialization layouts which are tailored towards the operations that are applied on the binary data and code generation for serializers and comparators.</p>
+<p>Apache Flink features quite a bit of advanced techniques to safely and efficiently process huge amounts of data with limited memory resources. However, there are a few points that could make Flink even more efficient. The Flink community is working on moving the managed memory to off-heap memory. This will allow for smaller JVMs, lower garbage collection overhead, and also easier system configuration. With Flink’s Table API, the semantics of all operations such as aggregations and projections are known (in contrast to black-box user-defined functions). Hence we can generate code for Table API operations that directly operates on binary data. Further improvements include serialization layouts which are tailored towards the operations that are applied on the binary data and code generation for serializers and comparators. </p>
-<p>The groundwork (and a lot more) for operating on binary data is done but there is still some room for making Flink even better and faster. If you are crazy about performance and like to juggle with lot of bits and bytes, join the Flink community!</p>
+<p>The groundwork (and a lot more) for operating on binary data is done but there is still some room for making Flink even better and faster. If you are crazy about performance and like to juggle with lot of bits and bytes, join the Flink community! </p>
<h2 id="tldr-give-me-three-things-to-remember">TL;DR; Give me three things to remember!</h2>
http://git-wip-us.apache.org/repos/asf/flink-web/blob/415ee891/content/news/2015/05/14/Community-update-April.html
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diff --git a/content/news/2015/05/14/Community-update-April.html b/content/news/2015/05/14/Community-update-April.html
index e0edf16..ec9c204 100644
--- a/content/news/2015/05/14/Community-update-April.html
+++ b/content/news/2015/05/14/Community-update-April.html
@@ -112,7 +112,9 @@
<li class="divider"></li>
<li role="presentation" class="dropdown-header"><strong>Contribute</strong></li>
<li><a href="/how-to-contribute.html">How to Contribute</a></li>
- <li><a href="/coding-guidelines.html">Coding Guidelines</a></li>
+ <li><a href="/contribute-code.html">Contribute Code</a></li>
+ <li><a href="/contribute-documentation.html">Contribute Documentation</a></li>
+ <li><a href="/improve-website.html">Improve the Website</a></li>
</ul>
</li>
@@ -145,7 +147,7 @@
<article>
<p>14 May 2015 by Kostas Tzoumas (<a href="https://twitter.com/kostas_tzoumas">@kostas_tzoumas</a>)</p>
-<p>April was an packed month for Apache Flink.</p>
+<p>April was an packed month for Apache Flink. </p>
<h2 id="flink-090-milestone1-release">Flink 0.9.0-milestone1 release</h2>
@@ -161,7 +163,7 @@
<h2 id="flink-on-the-web">Flink on the web</h2>
-<p>Fabian Hueske gave an <a href="http://www.infoq.com/news/2015/04/hueske-apache-flink?utm_campaign=infoq_content&utm_source=infoq&utm_medium=feed&utm_term=global">interview at InfoQ</a> on Apache Flink.</p>
+<p>Fabian Hueske gave an <a href="http://www.infoq.com/news/2015/04/hueske-apache-flink?utm_campaign=infoq_content&utm_source=infoq&utm_medium=feed&utm_term=global">interview at InfoQ</a> on Apache Flink. </p>
<h2 id="upcoming-events">Upcoming events</h2>
http://git-wip-us.apache.org/repos/asf/flink-web/blob/415ee891/content/news/2015/06/24/announcing-apache-flink-0.9.0-release.html
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diff --git a/content/news/2015/06/24/announcing-apache-flink-0.9.0-release.html b/content/news/2015/06/24/announcing-apache-flink-0.9.0-release.html
index 3a97291..6cc3e35 100644
--- a/content/news/2015/06/24/announcing-apache-flink-0.9.0-release.html
+++ b/content/news/2015/06/24/announcing-apache-flink-0.9.0-release.html
@@ -112,7 +112,9 @@
<li class="divider"></li>
<li role="presentation" class="dropdown-header"><strong>Contribute</strong></li>
<li><a href="/how-to-contribute.html">How to Contribute</a></li>
- <li><a href="/coding-guidelines.html">Coding Guidelines</a></li>
+ <li><a href="/contribute-code.html">Contribute Code</a></li>
+ <li><a href="/contribute-documentation.html">Contribute Documentation</a></li>
+ <li><a href="/improve-website.html">Improve the Website</a></li>
</ul>
</li>
http://git-wip-us.apache.org/repos/asf/flink-web/blob/415ee891/content/news/2015/08/24/introducing-flink-gelly.html
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diff --git a/content/news/2015/08/24/introducing-flink-gelly.html b/content/news/2015/08/24/introducing-flink-gelly.html
index afc61ec..41a5f35 100644
--- a/content/news/2015/08/24/introducing-flink-gelly.html
+++ b/content/news/2015/08/24/introducing-flink-gelly.html
@@ -112,7 +112,9 @@
<li class="divider"></li>
<li role="presentation" class="dropdown-header"><strong>Contribute</strong></li>
<li><a href="/how-to-contribute.html">How to Contribute</a></li>
- <li><a href="/coding-guidelines.html">Coding Guidelines</a></li>
+ <li><a href="/contribute-code.html">Contribute Code</a></li>
+ <li><a href="/contribute-documentation.html">Contribute Documentation</a></li>
+ <li><a href="/improve-website.html">Improve the Website</a></li>
</ul>
</li>
@@ -211,21 +213,21 @@ and mutations as well as neighborhood aggregations.</p>
<h4 id="common-graph-metrics">Common Graph Metrics</h4>
<p>These methods can be used to retrieve several graph metrics and properties, such as the number
-of vertices, edges and the node degrees.</p>
+of vertices, edges and the node degrees. </p>
<h4 id="transformations">Transformations</h4>
<p>The transformation methods enable several Graph operations, using high-level functions similar to
the ones provided by the batch processing API. These transformations can be applied one after the
-other, yielding a new Graph after each step, in a fashion similar to operators on DataSets:</p>
+other, yielding a new Graph after each step, in a fashion similar to operators on DataSets: </p>
<div class="highlight"><pre><code class="language-java"><span class="n">inputGraph</span><span class="o">.</span><span class="na">getUndirected</span><span class="o">().</span><span class="na">mapEdges</span><span class="o">(</span><span class="k">new</span> <span class="nf">CustomEdgeMapper</span><span class="o">());</span></code></pre></div>
<p>Transformations can be applied on:</p>
<ol>
- <li><strong>Vertices</strong>: <code>mapVertices</code>, <code>joinWithVertices</code>, <code>filterOnVertices</code>, <code>addVertex</code>, …</li>
- <li><strong>Edges</strong>: <code>mapEdges</code>, <code>filterOnEdges</code>, <code>removeEdge</code>, …</li>
- <li><strong>Triplets</strong> (source vertex, target vertex, edge): <code>getTriplets</code></li>
+ <li><strong>Vertices</strong>: <code>mapVertices</code>, <code>joinWithVertices</code>, <code>filterOnVertices</code>, <code>addVertex</code>, … </li>
+ <li><strong>Edges</strong>: <code>mapEdges</code>, <code>filterOnEdges</code>, <code>removeEdge</code>, … </li>
+ <li><strong>Triplets</strong> (source vertex, target vertex, edge): <code>getTriplets</code> </li>
</ol>
<h4 id="neighborhood-aggregations">Neighborhood Aggregations</h4>
@@ -359,7 +361,7 @@ vertex values do not need to be recomputed during an iteration.</p>
<p>Let us reconsider the Single Source Shortest Paths algorithm. In each iteration, a vertex:</p>
<ol>
- <li><strong>Gather</strong> retrieves distances from its neighbors summed up with the corresponding edge values;</li>
+ <li><strong>Gather</strong> retrieves distances from its neighbors summed up with the corresponding edge values; </li>
<li><strong>Sum</strong> compares the newly obtained distances in order to extract the minimum;</li>
<li><strong>Apply</strong> and finally adopts the minimum distance computed in the sum step,
provided that it is lower than its current value. If a vertex’s value does not change during
@@ -418,7 +420,7 @@ plays that each song has. We then filter out the list of songs the users do not
playlist. Then we compute the top songs per user (i.e. the songs a user listened to the most).
Finally, as a separate use-case on the same data set, we create a user-user similarity graph based
on the common songs and use this resulting graph to detect communities by calling Gelly’s Label Propagation
-library method.</p>
+library method. </p>
<p>For running the example implementation, please use the 0.10-SNAPSHOT version of Flink as a
dependency. The full example code base can be found <a href="https://github.com/apache/flink/blob/master/flink-staging/flink-gelly/src/main/java/org/apache/flink/graph/example/MusicProfiles.java">here</a>. The public data set used for testing
@@ -508,10 +510,10 @@ in the figure below.</p>
<p>To form the user-user graph in Flink, we will simply take the edges from the user-song graph
(left-hand side of the image), group them by song-id, and then add all the users (source vertex ids)
-to an ArrayList.</p>
+to an ArrayList. </p>
<p>We then match users who listened to the same song two by two, creating a new edge to mark their
-common interest (right-hand side of the image).</p>
+common interest (right-hand side of the image). </p>
<p>Afterwards, we perform a <code>distinct()</code> operation to avoid creation of duplicate data.
Considering that we now have the DataSet of edges which present interest, creating a graph is as
@@ -550,7 +552,7 @@ formed. To do so, we first initialize each vertex with a numeric label using the
the id of a vertex with the first element of the tuple, afterwards applying a map function.
Finally, we call the <code>run()</code> method with the LabelPropagation library method passed
as a parameter. In the end, the vertices will be updated to contain the most frequent label
-among their neighbors.</p>
+among their neighbors. </p>
<div class="highlight"><pre><code class="language-java"><span class="c1">// detect user communities using label propagation</span>
<span class="c1">// initialize each vertex with a unique numeric label</span>
@@ -580,10 +582,10 @@ among their neighbors.</p>
<p>Currently, Gelly matches the basic functionalities provided by most state-of-the-art graph
processing systems. Our vision is to turn Gelly into more than “yet another library for running
PageRank-like algorithms” by supporting generic iterations, implementing graph partitioning,
-providing bipartite graph support and by offering numerous other features.</p>
+providing bipartite graph support and by offering numerous other features. </p>
<p>We are also enriching Flink Gelly with a set of operators suitable for highly skewed graphs
-as well as a Graph API built on Flink Streaming.</p>
+as well as a Graph API built on Flink Streaming. </p>
<p>In the near future, we would like to see how Gelly can be integrated with graph visualization
tools, graph database systems and sampling techniques.</p>
http://git-wip-us.apache.org/repos/asf/flink-web/blob/415ee891/content/news/2015/09/01/release-0.9.1.html
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diff --git a/content/news/2015/09/01/release-0.9.1.html b/content/news/2015/09/01/release-0.9.1.html
index 989615c..f8246b9 100644
--- a/content/news/2015/09/01/release-0.9.1.html
+++ b/content/news/2015/09/01/release-0.9.1.html
@@ -112,7 +112,9 @@
<li class="divider"></li>
<li role="presentation" class="dropdown-header"><strong>Contribute</strong></li>
<li><a href="/how-to-contribute.html">How to Contribute</a></li>
- <li><a href="/coding-guidelines.html">Coding Guidelines</a></li>
+ <li><a href="/contribute-code.html">Contribute Code</a></li>
+ <li><a href="/contribute-documentation.html">Contribute Documentation</a></li>
+ <li><a href="/improve-website.html">Improve the Website</a></li>
</ul>
</li>
http://git-wip-us.apache.org/repos/asf/flink-web/blob/415ee891/content/news/2015/09/03/flink-forward.html
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diff --git a/content/news/2015/09/03/flink-forward.html b/content/news/2015/09/03/flink-forward.html
index 9fdd3df..c4424c3 100644
--- a/content/news/2015/09/03/flink-forward.html
+++ b/content/news/2015/09/03/flink-forward.html
@@ -112,7 +112,9 @@
<li class="divider"></li>
<li role="presentation" class="dropdown-header"><strong>Contribute</strong></li>
<li><a href="/how-to-contribute.html">How to Contribute</a></li>
- <li><a href="/coding-guidelines.html">Coding Guidelines</a></li>
+ <li><a href="/contribute-code.html">Contribute Code</a></li>
+ <li><a href="/contribute-documentation.html">Contribute Documentation</a></li>
+ <li><a href="/improve-website.html">Improve the Website</a></li>
</ul>
</li>
http://git-wip-us.apache.org/repos/asf/flink-web/blob/415ee891/content/news/2015/09/16/off-heap-memory.html
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diff --git a/content/news/2015/09/16/off-heap-memory.html b/content/news/2015/09/16/off-heap-memory.html
index 5f15468..557f9ef 100644
--- a/content/news/2015/09/16/off-heap-memory.html
+++ b/content/news/2015/09/16/off-heap-memory.html
@@ -112,7 +112,9 @@
<li class="divider"></li>
<li role="presentation" class="dropdown-header"><strong>Contribute</strong></li>
<li><a href="/how-to-contribute.html">How to Contribute</a></li>
- <li><a href="/coding-guidelines.html">Coding Guidelines</a></li>
+ <li><a href="/contribute-code.html">Contribute Code</a></li>
+ <li><a href="/contribute-documentation.html">Contribute Documentation</a></li>
+ <li><a href="/improve-website.html">Improve the Website</a></li>
</ul>
</li>
http://git-wip-us.apache.org/repos/asf/flink-web/blob/415ee891/content/privacy-policy.html
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diff --git a/content/privacy-policy.html b/content/privacy-policy.html
index 26e018a..752de53 100644
--- a/content/privacy-policy.html
+++ b/content/privacy-policy.html
@@ -112,7 +112,9 @@
<li class="divider"></li>
<li role="presentation" class="dropdown-header"><strong>Contribute</strong></li>
<li><a href="/how-to-contribute.html">How to Contribute</a></li>
- <li><a href="/coding-guidelines.html">Coding Guidelines</a></li>
+ <li><a href="/contribute-code.html">Contribute Code</a></li>
+ <li><a href="/contribute-documentation.html">Contribute Documentation</a></li>
+ <li><a href="/improve-website.html">Improve the Website</a></li>
</ul>
</li>
http://git-wip-us.apache.org/repos/asf/flink-web/blob/415ee891/content/project.html
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diff --git a/content/project.html b/content/project.html
index c85264d..c22c6ed 100644
--- a/content/project.html
+++ b/content/project.html
@@ -112,7 +112,9 @@
<li class="divider"></li>
<li role="presentation" class="dropdown-header"><strong>Contribute</strong></li>
<li><a href="/how-to-contribute.html">How to Contribute</a></li>
- <li><a href="/coding-guidelines.html">Coding Guidelines</a></li>
+ <li><a href="/contribute-code.html">Contribute Code</a></li>
+ <li><a href="/contribute-documentation.html">Contribute Documentation</a></li>
+ <li><a href="/improve-website.html">Improve the Website</a></li>
</ul>
</li>