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From rmetz...@apache.org
Subject [1/2] flink-web git commit: Publish April community update
Date Sun, 17 May 2015 08:35:17 GMT
Repository: flink-web
Updated Branches:
  refs/heads/asf-site 5e1410e56 -> 643628857


http://git-wip-us.apache.org/repos/asf/flink-web/blob/64362885/content/news/2014/01/13/stratosphere-release-0.4.html
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diff --git a/content/news/2014/01/13/stratosphere-release-0.4.html b/content/news/2014/01/13/stratosphere-release-0.4.html
index 9bb35cb..5c4a874 100644
--- a/content/news/2014/01/13/stratosphere-release-0.4.html
+++ b/content/news/2014/01/13/stratosphere-release-0.4.html
@@ -133,7 +133,7 @@
       <article>
         <p>13 Jan 2014</p>
 
-<p>We are pleased to announce that version 0.4 of the Stratosphere system has been released.</p>
+<p>We are pleased to announce that version 0.4 of the Stratosphere system has been released. </p>
 
 <p>Our team has been working hard during the last few months to create an improved and stable Stratosphere version. The new version comes with many new features, usability and performance improvements in all levels, including a new Scala API for the concise specification of programs, a Pregel-like API, support for Yarn clusters, and major performance improvements. The system features now first-class support for iterative programs and thus covers traditional analytical use cases as well as data mining and graph processing use cases with great performance.</p>
 
@@ -161,7 +161,7 @@ Follow <a href="/docs/0.4/setup/yarn.html">our guide</a> on how to start a Strat
 <p>The high-level language Meteor now natively serializes JSON trees for greater performance and offers additional operators and file formats. We greatly empowered the user to write crispier scripts by adding second-order functions, multi-output operators, and other syntactical sugar. For developers of Meteor packages, the API is much more comprehensive and allows to define custom data types that can be easily embedded in JSON trees through ad-hoc byte code generation.</p>
 
 <h3 id="spargel-pregel-inspired-graph-processing">Spargel: Pregel Inspired Graph Processing</h3>
-<p>Spargel is a vertex-centric API similar to the interface proposed in Google’s Pregel paper and implemented in Apache Giraph. Spargel is implemented in 500 lines of code (including comments) on top of Stratosphere’s delta iterations feature. This confirms the flexibility of Stratosphere’s architecture.</p>
+<p>Spargel is a vertex-centric API similar to the interface proposed in Google’s Pregel paper and implemented in Apache Giraph. Spargel is implemented in 500 lines of code (including comments) on top of Stratosphere’s delta iterations feature. This confirms the flexibility of Stratosphere’s architecture. </p>
 
 <h3 id="web-frontend">Web Frontend</h3>
 <p>Using the new web frontend, you can monitor the progress of Stratosphere jobs. For finished jobs, the frontend shows a breakdown of the execution times for each operator. The webclient also visualizes the execution strategies chosen by the optimizer.</p>
@@ -189,7 +189,7 @@ Follow <a href="/docs/0.4/setup/yarn.html">our guide</a> on how to start a Strat
 </ul>
 
 <h3 id="download-and-get-started-with-stratosphere-v04">Download and get started with Stratosphere v0.4</h3>
-<p>There are several options for getting started with Stratosphere.</p>
+<p>There are several options for getting started with Stratosphere. </p>
 
 <ul>
   <li>Download it on the <a href="/downloads">download page</a></li>

http://git-wip-us.apache.org/repos/asf/flink-web/blob/64362885/content/news/2014/02/18/amazon-elastic-mapreduce-cloud-yarn.html
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diff --git a/content/news/2014/02/18/amazon-elastic-mapreduce-cloud-yarn.html b/content/news/2014/02/18/amazon-elastic-mapreduce-cloud-yarn.html
index da3b30f..4aeea80 100644
--- a/content/news/2014/02/18/amazon-elastic-mapreduce-cloud-yarn.html
+++ b/content/news/2014/02/18/amazon-elastic-mapreduce-cloud-yarn.html
@@ -203,7 +203,7 @@
 ssh hadoop@ec2-54-213-61-105.us-west-2.compute.amazonaws.com -i ~/Downloads/work-laptop.pem</code></pre></div>
 
 <p>(Windows users have to follow <a href="http://docs.aws.amazon.com/ElasticMapReduce/latest/DeveloperGuide/emr-connect-master-node-ssh.html">these instructions</a> to SSH into the machine running the master.) &lt;/br&gt;&lt;/br&gt;
-Once connected to the master, download and start Stratosphere for YARN:</p>
+Once connected to the master, download and start Stratosphere for YARN: </p>
 <ul>
 	<li>Download and extract Stratosphere-YARN</li>
 
@@ -226,11 +226,11 @@ The arguments have the following meaning
 	</ul>
 </ul>
 
-<p>Once the output has changed from</p>
+<p>Once the output has changed from </p>
 
 <div class="highlight"><pre><code class="language-bash" data-lang="bash">JobManager is now running on N/A:6123</code></pre></div>
 
-<p>to</p>
+<p>to </p>
 
 <div class="highlight"><pre><code class="language-bash" data-lang="bash">JobManager is now running on ip-172-31-13-68.us-west-2.compute.internal:6123</code></pre></div>
 

http://git-wip-us.apache.org/repos/asf/flink-web/blob/64362885/content/news/2014/11/04/release-0.7.0.html
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diff --git a/content/news/2014/11/04/release-0.7.0.html b/content/news/2014/11/04/release-0.7.0.html
index a3f8fc8..98b29ff 100644
--- a/content/news/2014/11/04/release-0.7.0.html
+++ b/content/news/2014/11/04/release-0.7.0.html
@@ -153,7 +153,7 @@
 
 <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>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>
 

http://git-wip-us.apache.org/repos/asf/flink-web/blob/64362885/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 df24619..ff421dd 100644
--- a/content/news/2014/11/18/hadoop-compatibility.html
+++ b/content/news/2014/11/18/hadoop-compatibility.html
@@ -141,7 +141,7 @@
 <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>
 
@@ -154,7 +154,7 @@
 
 <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">&lt;</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">&gt;</span> <span class="o">{</span> <span class="o">...</span> <span class="o">}</span>

http://git-wip-us.apache.org/repos/asf/flink-web/blob/64362885/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 c3ae95f..6295134 100644
--- a/content/news/2015/01/21/release-0.8.html
+++ b/content/news/2015/01/21/release-0.8.html
@@ -184,7 +184,7 @@
   <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/64362885/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 7a339f3..6172f4a 100644
--- a/content/news/2015/02/04/january-in-flink.html
+++ b/content/news/2015/02/04/january-in-flink.html
@@ -165,7 +165,7 @@
 
 <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/64362885/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 5428a77..4b22f34 100644
--- a/content/news/2015/02/09/streaming-example.html
+++ b/content/news/2015/02/09/streaming-example.html
@@ -169,7 +169,7 @@ found <a href="https://github.com/apache/flink/blob/master/flink-staging/flink-s
   <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>
@@ -641,7 +641,7 @@ 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/64362885/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 e1112fb..be21a53 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
@@ -140,7 +140,7 @@
 <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>
@@ -151,7 +151,7 @@
 
 <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>
 
@@ -188,7 +188,7 @@
 
 <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>
@@ -207,7 +207,7 @@
 <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" />
@@ -216,7 +216,7 @@
 <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>
 
@@ -233,7 +233,7 @@
 <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>
 
@@ -278,13 +278,13 @@
 <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" />
@@ -301,7 +301,7 @@
 <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/64362885/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 86f2fbd..c64b147 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
@@ -152,7 +152,7 @@ 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 &amp; 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>
@@ -161,13 +161,13 @@ However, this approach has a few notable drawbacks. First of all it is not trivi
   <li><strong>Efficient binary operations &amp; 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>
 
@@ -179,7 +179,7 @@ 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>
 
@@ -189,11 +189,11 @@ 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&lt;Integer, Double, Person&gt;</code> object where <code>Person</code> is a POJO and defined as follows:</p>
 
@@ -206,13 +206,13 @@ 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" />
@@ -225,7 +225,7 @@ 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" />
@@ -243,7 +243,7 @@ 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" />
@@ -301,13 +301,13 @@ 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/64362885/content/news/2015/05/14/Community-update-April.html
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+      <h1>April 2015 in the Flink community</h1>
+
+      <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>
+
+<h2 id="flink-090-milestone1-release">Flink 0.9.0-milestone1 release</h2>
+
+<p>The highlight of April was of course the availability of <a href="/news/2015/04/13/release-0.9.0-milestone1.html">Flink 0.9-milestone1</a>. This was a release packed with new features, including, a Python DataSet API, the new SQL-like Table API, FlinkML, a machine learning library on Flink, Gelly, FLink’s Graph API, as well as a mode to run Flink on YARN leveraging Tez. In case you missed it, check out the <a href="/news/2015/04/13/release-0.9.0-milestone1.html">release announcement blog post</a> for details</p>
+
+<h2 id="conferences-and-meetups">Conferences and meetups</h2>
+
+<p>April kicked off the conference season. Apache Flink was presented at ApacheCon in Texas (<a href="http://www.slideshare.net/fhueske/apache-flink">slides</a>), the Hadoop Summit in Brussels featured two talks on Flink (see slides <a href="http://www.slideshare.net/AljoschaKrettek/data-analysis-with-apache-flink-hadoop-summit-2015">here</a> and <a href="http://www.slideshare.net/GyulaFra/flink-streaming-hadoopsummit">here</a>), as well as at the Hadoop User Groups of the Netherlands (<a href="http://www.slideshare.net/stephanewen1/apache-flink-overview-and-use-cases-at-prehadoop-summit-meetups">slides</a>) and Stockholm. The brand new <a href="http://www.meetup.com/Apache-Flink-Stockholm/">Apache Flink meetup Stockholm</a> was also established.</p>
+
+<h2 id="google-summer-of-code">Google Summer of Code</h2>
+
+<p>Three students will work on Flink during Google’s <a href="https://www.google-melange.com/gsoc/homepage/google/gsoc2015">Summer of Code program</a> on distributed pattern matching, exact and approximate statistics for data streams and windows, as well as asynchronous iterations and updates.</p>
+
+<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&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=global">interview at InfoQ</a> on Apache Flink. </p>
+
+<h2 id="upcoming-events">Upcoming events</h2>
+
+<p>Stay tuned for a wealth of upcoming events! Two Flink talsk will be presented at <a href="http://berlinbuzzwords.de/15/sessions">Berlin Buzzwords</a>, Flink will be presented at the <a href="http://2015.hadoopsummit.org/san-jose/">Hadoop Summit in San Jose</a>. A <a href="http://www.meetup.com/Apache-Flink-Meetup/events/220557545/">training workshop on Apache Flink</a> is being organized in Berlin. Finally, <a href="http://flink-forward.org">Flink Forward</a>, the first conference to bring together the whole Flink community is taking place in Berlin in October 2015.</p>
+
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