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Posted to commits@flink.apache.org by rm...@apache.org on 2015/05/17 10:35:18 UTC
[2/2] flink-web git commit: Publish April community update
Publish April community update
Project: http://git-wip-us.apache.org/repos/asf/flink-web/repo
Commit: http://git-wip-us.apache.org/repos/asf/flink-web/commit/64362885
Tree: http://git-wip-us.apache.org/repos/asf/flink-web/tree/64362885
Diff: http://git-wip-us.apache.org/repos/asf/flink-web/diff/64362885
Branch: refs/heads/asf-site
Commit: 643628857ef0e3f72c5b789cd02507df1f929266
Parents: 5e1410e
Author: Robert Metzger <rm...@apache.org>
Authored: Sun May 17 10:35:08 2015 +0200
Committer: Robert Metzger <rm...@apache.org>
Committed: Sun May 17 10:35:08 2015 +0200
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content/community.html | 12 +-
content/downloads.html | 12 +-
content/faq.html | 4 +-
content/how-to-contribute.html | 20 +-
content/index.html | 8 +-
content/material.html | 10 +-
.../2014/01/13/stratosphere-release-0.4.html | 6 +-
.../18/amazon-elastic-mapreduce-cloud-yarn.html | 6 +-
content/news/2014/11/04/release-0.7.0.html | 2 +-
.../news/2014/11/18/hadoop-compatibility.html | 4 +-
content/news/2015/01/21/release-0.8.html | 2 +-
content/news/2015/02/04/january-in-flink.html | 2 +-
content/news/2015/02/09/streaming-example.html | 4 +-
.../peeking-into-Apache-Flinks-Engine-Room.html | 18 +-
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http://git-wip-us.apache.org/repos/asf/flink-web/blob/64362885/_posts/2015-05-14-Community-update-April.md
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diff --git a/_posts/2015-05-14-Community-update-April.md b/_posts/2015-05-14-Community-update-April.md
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@@ -0,0 +1,32 @@
+---
+layout: post
+title: "April 2015 in the Flink community"
+date: 2015-05-14 10:00:00
+categories: news
+author: "Kostas Tzoumas"
+author-twitter: "kostas_tzoumas"
+excerpt: "<p>The monthly update from the Flink community. Including the availability of a new preview release, lots of meetups and conference talks and a great interview about Flink.</p>"
+---
+
+
+April was an packed month for Apache Flink.
+
+## Flink 0.9.0-milestone1 release
+
+The highlight of April was of course the availability of [Flink 0.9-milestone1]({{site.baseurl}}/news/2015/04/13/release-0.9.0-milestone1.html). 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 [release announcement blog post]({{site.baseurl}}/news/2015/04/13/release-0.9.0-milestone1.html) for details
+
+## Conferences and meetups
+
+April kicked off the conference season. Apache Flink was presented at ApacheCon in Texas ([slides](http://www.slideshare.net/fhueske/apache-flink)), the Hadoop Summit in Brussels featured two talks on Flink (see slides [here](http://www.slideshare.net/AljoschaKrettek/data-analysis-with-apache-flink-hadoop-summit-2015) and [here](http://www.slideshare.net/GyulaFra/flink-streaming-hadoopsummit)), as well as at the Hadoop User Groups of the Netherlands ([slides](http://www.slideshare.net/stephanewen1/apache-flink-overview-and-use-cases-at-prehadoop-summit-meetups)) and Stockholm. The brand new [Apache Flink meetup Stockholm](http://www.meetup.com/Apache-Flink-Stockholm/) was also established.
+
+## Google Summer of Code
+
+Three students will work on Flink during Google's [Summer of Code program](https://www.google-melange.com/gsoc/homepage/google/gsoc2015) on distributed pattern matching, exact and approximate statistics for data streams and windows, as well as asynchronous iterations and updates.
+
+## Flink on the web
+
+Fabian Hueske gave an [interview at InfoQ](http://www.infoq.com/news/2015/04/hueske-apache-flink?utm_campaign=infoq_content&utm_source=infoq&utm_medium=feed&utm_term=global) on Apache Flink.
+
+## Upcoming events
+
+Stay tuned for a wealth of upcoming events! Two Flink talsk will be presented at [Berlin Buzzwords](http://berlinbuzzwords.de/15/sessions), Flink will be presented at the [Hadoop Summit in San Jose](http://2015.hadoopsummit.org/san-jose/). A [training workshop on Apache Flink](http://www.meetup.com/Apache-Flink-Meetup/events/220557545/) is being organized in Berlin. Finally, [Flink Forward](http://flink-forward.org), the first conference to bring together the whole Flink community is taking place in Berlin in October 2015.
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diff --git a/content/blog/feed.xml b/content/blog/feed.xml
index 0e5ad19..949445a 100644
--- a/content/blog/feed.xml
+++ b/content/blog/feed.xml
@@ -7,6 +7,35 @@
<atom:link href="http://flink.apache.org/blog/feed.xml" rel="self" type="application/rss+xml" />
<item>
+<title>April 2015 in the Flink community</title>
+<description><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>
+</description>
+<pubDate>Thu, 14 May 2015 12:00:00 +0200</pubDate>
+<link>http://flink.apache.org/news/2015/05/14/Community-update-April.html</link>
+<guid isPermaLink="true">/news/2015/05/14/Community-update-April.html</guid>
+</item>
+
+<item>
<title>Juggling with Bits and Bytes</title>
<description><h2 id="how-apache-flink-operates-on-binary-data">How Apache Flink operates on binary data</h2>
@@ -27,7 +56,7 @@ 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>
@@ -36,13 +65,13 @@ 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 &
lt;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 &
lt;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>
@@ -54,7 +83,7 @@ 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>
@@ -64,11 +93,11 @@ 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>
@@ -81,13 +110,13 @@ 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" />
@@ -100,7 +129,7 @@ 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" />
@@ -118,7 +147,7 @@ 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" />
@@ -176,13 +205,13 @@ 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>
@@ -537,7 +566,7 @@ Tez as an execution backend instead of Flink’s own network stack. Learn more
<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>
@@ -548,7 +577,7 @@ Tez as an execution backend instead of Flink’s own network stack. Learn more
<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>
@@ -585,7 +614,7 @@ Tez as an execution backend instead of Flink’s own network stack. Learn more
<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>
@@ -604,7 +633,7 @@ Tez as an execution backend instead of Flink’s own network stack. Learn more
<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" />
@@ -613,7 +642,7 @@ Tez as an execution backend instead of Flink’s own network stack. Learn more
<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>
@@ -630,7 +659,7 @@ Tez as an execution backend instead of Flink’s own network stack. Learn more
<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>
@@ -675,13 +704,13 @@ Tez as an execution backend instead of Flink’s own network stack. Learn more
<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" />
@@ -698,7 +727,7 @@ Tez as an execution backend instead of Flink’s own network stack. Learn more
<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>
@@ -872,7 +901,7 @@ found <a href="https://github.com/apache/flink/blob/master/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>
@@ -1344,7 +1373,7 @@ 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">
@@ -1513,7 +1542,7 @@ internally, fault tolerance, and performance measurements!</p>
<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>
@@ -1585,7 +1614,7 @@ internally, fault tolerance, and performance measurements!</p>
<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>
@@ -1690,7 +1719,7 @@ Flink serialization system improved a lot over time and by now surpasses the cap
<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>
@@ -1703,7 +1732,7 @@ Flink serialization system improved a lot over time and by now surpasses the cap
<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>
@@ -1789,7 +1818,7 @@ Flink serialization system improved a lot over time and by now surpasses the cap
<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>
@@ -2225,7 +2254,7 @@ Applying students can use our wiki (create a new page) to create a project propo
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>
@@ -2248,11 +2277,11 @@ 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>
@@ -2474,7 +2503,7 @@ You can now press the “Run” button and see how Stratosphere executes the lit
<item>
<title>Stratosphere 0.4 Released</title>
-<description><p>We are pleased to announce that version 0.4 of the Stratosphere system has been released.</p>
+<description><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>
@@ -2502,7 +2531,7 @@ Follow <a href="/docs/0.4/setup/yarn.html">our guide</a> o
<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>
@@ -2530,7 +2559,7 @@ Follow <a href="/docs/0.4/setup/yarn.html">our guide</a> o
</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/blog/index.html
----------------------------------------------------------------------
diff --git a/content/blog/index.html b/content/blog/index.html
index 3f3b5f1..3b86752 100644
--- a/content/blog/index.html
+++ b/content/blog/index.html
@@ -134,6 +134,17 @@
<!-- Blog posts -->
<article>
+ <h2 class="blog-title"><a href="/news/2015/05/14/Community-update-April.html">April 2015 in the Flink community</a></h2>
+ <p>14 May 2015 by Kostas Tzoumas (<a href="https://twitter.com/kostas_tzoumas">@kostas_tzoumas</a>)</p>
+
+ <p><p>The monthly update from the Flink community. Including the availability of a new preview release, lots of meetups and conference talks and a great interview about Flink.</p></p>
+
+ <p><a href="/news/2015/05/14/Community-update-April.html">Continue reading »</a></p>
+ </article>
+
+ <hr>
+
+ <article>
<h2 class="blog-title"><a href="/news/2015/05/11/Juggling-with-Bits-and-Bytes.html">Juggling with Bits and Bytes</a></h2>
<p>11 May 2015 by Fabian Hüske (<a href="https://twitter.com/fhueske">@fhueske</a>)</p>
@@ -259,19 +270,6 @@ and offers a new API including definition of flexible windows.</p>
<hr>
- <article>
- <h2 class="blog-title"><a href="/news/2014/11/18/hadoop-compatibility.html">Hadoop Compatibility in Flink</a></h2>
- <p>18 Nov 2014 by Fabian Hüske (<a href="https://twitter.com/fhueske">@fhueske</a>)</p>
-
- <p><p><a href="http://hadoop.apache.org">Apache Hadoop</a> is an industry standard for scalable analytical data processing. Many data analysis applications have been implemented as Hadoop MapReduce jobs and run in clusters around the world. Apache Flink can be an alternative to MapReduce and improves it in many dimensions. Among other features, Flink provides much better performance and offers APIs in Java and Scala, which are very easy to use. Similar to Hadoop, Flink’s APIs provide interfaces for Mapper and Reducer functions, as well as Input- and OutputFormats along with many more operators. While being conceptually equivalent, Hadoop’s MapReduce and Flink’s interfaces for these functions are unfortunately not source compatible.</p>
-
-</p>
-
- <p><a href="/news/2014/11/18/hadoop-compatibility.html">Continue reading »</a></p>
- </article>
-
- <hr>
-
<!-- Pagination links -->
@@ -282,7 +280,7 @@ and offers a new API including definition of flexible windows.</p>
</li>
<li>
- <span class="page_number ">Page: 1 of 3</span>
+ <span class="page_number ">Page: 1 of 4</span>
</li>
<li>
@@ -304,6 +302,16 @@ and offers a new API including definition of flexible windows.</p>
<ul id="markdown-toc">
+ <li><a href="/news/2015/05/14/Community-update-April.html">April 2015 in the Flink community</a></li>
+
+
+
+
+
+
+
+
+
<li><a href="/news/2015/05/11/Juggling-with-Bits-and-Bytes.html">Juggling with Bits and Bytes</a></li>
http://git-wip-us.apache.org/repos/asf/flink-web/blob/64362885/content/blog/page2/index.html
----------------------------------------------------------------------
diff --git a/content/blog/page2/index.html b/content/blog/page2/index.html
index ae86230..8cdd238 100644
--- a/content/blog/page2/index.html
+++ b/content/blog/page2/index.html
@@ -134,6 +134,19 @@
<!-- Blog posts -->
<article>
+ <h2 class="blog-title"><a href="/news/2014/11/18/hadoop-compatibility.html">Hadoop Compatibility in Flink</a></h2>
+ <p>18 Nov 2014 by Fabian Hüske (<a href="https://twitter.com/fhueske">@fhueske</a>)</p>
+
+ <p><p><a href="http://hadoop.apache.org">Apache Hadoop</a> is an industry standard for scalable analytical data processing. Many data analysis applications have been implemented as Hadoop MapReduce jobs and run in clusters around the world. Apache Flink can be an alternative to MapReduce and improves it in many dimensions. Among other features, Flink provides much better performance and offers APIs in Java and Scala, which are very easy to use. Similar to Hadoop, Flink’s APIs provide interfaces for Mapper and Reducer functions, as well as Input- and OutputFormats along with many more operators. While being conceptually equivalent, Hadoop’s MapReduce and Flink’s interfaces for these functions are unfortunately not source compatible.</p>
+
+</p>
+
+ <p><a href="/news/2014/11/18/hadoop-compatibility.html">Continue reading »</a></p>
+ </article>
+
+ <hr>
+
+ <article>
<h2 class="blog-title"><a href="/news/2014/11/04/release-0.7.0.html">Apache Flink 0.7.0 available</a></h2>
<p>04 Nov 2014</p>
@@ -253,19 +266,6 @@ academic and open source project that Flink originates from.</p>
<hr>
- <article>
- <h2 class="blog-title"><a href="/news/2014/01/26/optimizer_plan_visualization_tool.html">Optimizer Plan Visualization Tool</a></h2>
- <p>26 Jan 2014</p>
-
- <p><p>Stratosphere’s hybrid approach combines <strong>MapReduce</strong> and <strong>MPP database</strong> techniques. One central part of this approach is to have a <strong>separation between the programming (API) and the way programs are executed</strong> <em>(execution plans)</em>. The <strong>compiler/optimizer</strong> decides the details concerning caching or when to partition/broadcast with a holistic view of the program. The same program may actually be executed differently in different scenarios (input data of different sizes, different number of machines).</p>
-
-</p>
-
- <p><a href="/news/2014/01/26/optimizer_plan_visualization_tool.html">Continue reading »</a></p>
- </article>
-
- <hr>
-
<!-- Pagination links -->
@@ -276,7 +276,7 @@ academic and open source project that Flink originates from.</p>
</li>
<li>
- <span class="page_number ">Page: 2 of 3</span>
+ <span class="page_number ">Page: 2 of 4</span>
</li>
<li>
@@ -298,6 +298,16 @@ academic and open source project that Flink originates from.</p>
<ul id="markdown-toc">
+ <li><a href="/news/2015/05/14/Community-update-April.html">April 2015 in the Flink community</a></li>
+
+
+
+
+
+
+
+
+
<li><a href="/news/2015/05/11/Juggling-with-Bits-and-Bytes.html">Juggling with Bits and Bytes</a></li>
http://git-wip-us.apache.org/repos/asf/flink-web/blob/64362885/content/blog/page3/index.html
----------------------------------------------------------------------
diff --git a/content/blog/page3/index.html b/content/blog/page3/index.html
index ce1d055..2904b9c 100644
--- a/content/blog/page3/index.html
+++ b/content/blog/page3/index.html
@@ -134,10 +134,23 @@
<!-- Blog posts -->
<article>
+ <h2 class="blog-title"><a href="/news/2014/01/26/optimizer_plan_visualization_tool.html">Optimizer Plan Visualization Tool</a></h2>
+ <p>26 Jan 2014</p>
+
+ <p><p>Stratosphere’s hybrid approach combines <strong>MapReduce</strong> and <strong>MPP database</strong> techniques. One central part of this approach is to have a <strong>separation between the programming (API) and the way programs are executed</strong> <em>(execution plans)</em>. The <strong>compiler/optimizer</strong> decides the details concerning caching or when to partition/broadcast with a holistic view of the program. The same program may actually be executed differently in different scenarios (input data of different sizes, different number of machines).</p>
+
+</p>
+
+ <p><a href="/news/2014/01/26/optimizer_plan_visualization_tool.html">Continue reading »</a></p>
+ </article>
+
+ <hr>
+
+ <article>
<h2 class="blog-title"><a href="/news/2014/01/13/stratosphere-release-0.4.html">Stratosphere 0.4 Released</a></h2>
<p>13 Jan 2014</p>
- <p><p>We are pleased to announce that version 0.4 of the Stratosphere system has been released.</p>
+ <p><p>We are pleased to announce that version 0.4 of the Stratosphere system has been released. </p>
</p>
@@ -284,28 +297,6 @@ We demonstrate our optimizer and a job submission client that allows users to pe
<hr>
- <article>
- <h2 class="blog-title"><a href="/news/2012/08/21/release02.html">Version 0.2 Released</a></h2>
- <p>21 Aug 2012</p>
-
- <p><p>We are happy to announce that version 0.2 of the Stratosphere System has been released. It has a lot of performance improvements as well as a bunch of exciting new features like:</p>
-<ul>
-<li>The new Sopremo Algebra Layer and the Meteor Scripting Language</li>
-<li>The whole new tuple data model for the PACT API</li>
-<li>Fault tolerance through local checkpoints</li>
-<li>A ton of performance improvements on all layers</li>
-<li>Support for plug-ins on the data flow channel layer</li>
-<li>Many new library classes (for example new Input-/Output-Formats)</li>
-</ul>
-<p>For a complete list of new features, check out the <a href="https://stratosphere.eu/wiki/doku.php/wiki:changesrelease0.2">change log</a>.</p>
-
-</p>
-
- <p><a href="/news/2012/08/21/release02.html">Continue reading »</a></p>
- </article>
-
- <hr>
-
<!-- Pagination links -->
@@ -316,11 +307,11 @@ We demonstrate our optimizer and a job submission client that allows users to pe
</li>
<li>
- <span class="page_number ">Page: 3 of 3</span>
+ <span class="page_number ">Page: 3 of 4</span>
</li>
<li>
- <span>Next</span>
+ <a href="/blog/page4" class="next">Next</a>
</li>
</ul>
@@ -338,6 +329,16 @@ We demonstrate our optimizer and a job submission client that allows users to pe
<ul id="markdown-toc">
+ <li><a href="/news/2015/05/14/Community-update-April.html">April 2015 in the Flink community</a></li>
+
+
+
+
+
+
+
+
+
<li><a href="/news/2015/05/11/Juggling-with-Bits-and-Bytes.html">Juggling with Bits and Bytes</a></li>
http://git-wip-us.apache.org/repos/asf/flink-web/blob/64362885/content/blog/page4/index.html
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diff --git a/content/blog/page4/index.html b/content/blog/page4/index.html
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--- /dev/null
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+<!DOCTYPE html>
+<html lang="en">
+ <head>
+ <meta charset="utf-8">
+ <meta http-equiv="X-UA-Compatible" content="IE=edge">
+ <meta name="viewport" content="width=device-width, initial-scale=1">
+ <!-- The above 3 meta tags *must* come first in the head; any other head content must come *after* these tags -->
+ <title>Apache Flink: Blog</title>
+ <link rel="shortcut icon" href="/favicon.ico" type="image/x-icon">
+ <link rel="icon" href="/favicon.ico" type="image/x-icon">
+
+ <!-- Bootstrap -->
+ <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.4/css/bootstrap.min.css">
+ <link rel="stylesheet" href="/css/flink.css">
+ <link rel="stylesheet" href="/css/syntax.css">
+
+ <!-- Blog RSS feed -->
+ <link href="/blog/feed.xml" rel="alternate" type="application/rss+xml" title="Apache Flink Blog: RSS feed" />
+
+ <!-- HTML5 shim and Respond.js for IE8 support of HTML5 elements and media queries -->
+ <!-- WARNING: Respond.js doesn't work if you view the page via file:// -->
+ <!--[if lt IE 9]>
+ <script src="https://oss.maxcdn.com/html5shiv/3.7.2/html5shiv.min.js"></script>
+ <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
+ <![endif]-->
+ </head>
+ <body>
+
+
+ <!-- Top navbar. -->
+ <nav class="navbar navbar-default navbar-fixed-top">
+ <div class="container">
+ <!-- The logo. -->
+ <div class="navbar-header">
+ <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#bs-example-navbar-collapse-1">
+ <span class="icon-bar"></span>
+ <span class="icon-bar"></span>
+ <span class="icon-bar"></span>
+ </button>
+ <div class="navbar-logo">
+ <a href="/"><img alt="Apache Flink" src="/img/navbar-brand-logo.jpg" width="78px" height="40px"></a>
+ </div>
+ </div><!-- /.navbar-header -->
+
+ <!-- The navigation links. -->
+ <div class="collapse navbar-collapse" id="bs-example-navbar-collapse-1">
+ <ul class="nav navbar-nav">
+ <!-- Overview -->
+ <li><a href="/index.html">Overview</a></li>
+
+ <!-- Features -->
+ <li><a href="/features.html">Features</a></li>
+
+ <!-- Downloads -->
+ <li><a href="/downloads.html">Downloads</a></li>
+
+ <!-- Documentation -->
+ <li class="dropdown">
+ <a href="" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false">Documentation <span class="caret"></span></a>
+ <ul class="dropdown-menu" role="menu">
+ <!-- Latest stable release -->
+ <li role="presentation" class="dropdown-header"><strong>Latest Release</strong> (Stable)</li>
+ <li><a href="http://ci.apache.org/projects/flink/flink-docs-release-0.8">0.8.1 Documentation</a></li>
+ <li><a href="http://ci.apache.org/projects/flink/flink-docs-release-0.8/api/java" class="active">0.8.1 Javadocs</a></li>
+ <li><a href="http://ci.apache.org/projects/flink/flink-docs-release-0.8/api/scala/index.html" class="active">0.8.1 ScalaDocs</a></li>
+
+ <!-- Snapshot docs -->
+ <li class="divider"></li>
+ <li role="presentation" class="dropdown-header"><strong>Snapshot</strong> (Development)</li>
+ <li><a href="http://ci.apache.org/projects/flink/flink-docs-master">0.9 Documentation</a></li>
+ <li><a href="http://ci.apache.org/projects/flink/flink-docs-master/api/java" class="active">0.9 Javadocs</a></li>
+ <li><a href="http://ci.apache.org/projects/flink/flink-docs-master/api/scala/index.html" class="active">0.9 ScalaDocs</a></li>
+
+ <!-- Wiki -->
+ <li class="divider"></li>
+ <li><a href="https://cwiki.apache.org/confluence/display/FLINK/Apache+Flink+Home"><small><span class="glyphicon glyphicon-new-window"></span></small> Wiki</a></li>
+ </ul>
+ </li>
+
+ <!-- FAQ -->
+ <li><a href="/faq.html">FAQ</span></a></li>
+ </ul>
+
+ <ul class="nav navbar-nav navbar-right hidden-sm">
+ <!-- Blog -->
+ <li class="active"><a href="/blog/">Blog</a></li>
+
+ <li class="dropdown">
+ <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false">Community <span class="caret"></span></a>
+ <ul class="dropdown-menu" role="menu">
+ <!-- Community -->
+ <li role="presentation" class="dropdown-header"><strong>Community</strong></li>
+ <li><a href="/community.html#mailing-lists">Mailing Lists</a></li>
+ <li><a href="/community.html#irc">IRC</a></li>
+ <li><a href="/community.html#stack-overflow">Stack Overflow</a></li>
+ <li><a href="/community.html#issue-tracker">Issue Tracker</a></li>
+ <li><a href="/community.html#people">People</a></li>
+
+ <!-- Contribute -->
+ <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>
+ </ul>
+ </li>
+
+ <li class="dropdown">
+ <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false">Project <span class="caret"></span></a>
+ <ul class="dropdown-menu" role="menu">
+ <!-- Project -->
+ <li role="presentation" class="dropdown-header"><strong>Project</strong></li>
+ <li><a href="/material.html">Material</a></li>
+ <li><a href="https://twitter.com/apacheflink"><small><span class="glyphicon glyphicon-new-window"></span></small> Twitter</a></li>
+ <li><a href="https://github.com/apache/flink"><small><span class="glyphicon glyphicon-new-window"></span></small> GitHub</a></li>
+ <li><a href="https://cwiki.apache.org/confluence/display/FLINK/Apache+Flink+Home"><small><span class="glyphicon glyphicon-new-window"></span></small> Wiki</a></li>
+ </ul>
+ </li>
+ </ul>
+ </div><!-- /.navbar-collapse -->
+ </div><!-- /.container -->
+ </nav>
+
+
+ <!-- Main content. -->
+ <div class="container">
+
+
+<div class="row">
+ <div class="col-sm-12"><h1>Blog</h1></div>
+</div>
+
+<div class="row">
+ <div class="col-sm-8">
+ <!-- Blog posts -->
+
+ <article>
+ <h2 class="blog-title"><a href="/news/2012/08/21/release02.html">Version 0.2 Released</a></h2>
+ <p>21 Aug 2012</p>
+
+ <p><p>We are happy to announce that version 0.2 of the Stratosphere System has been released. It has a lot of performance improvements as well as a bunch of exciting new features like:</p>
+<ul>
+<li>The new Sopremo Algebra Layer and the Meteor Scripting Language</li>
+<li>The whole new tuple data model for the PACT API</li>
+<li>Fault tolerance through local checkpoints</li>
+<li>A ton of performance improvements on all layers</li>
+<li>Support for plug-ins on the data flow channel layer</li>
+<li>Many new library classes (for example new Input-/Output-Formats)</li>
+</ul>
+<p>For a complete list of new features, check out the <a href="https://stratosphere.eu/wiki/doku.php/wiki:changesrelease0.2">change log</a>.</p>
+
+</p>
+
+ <p><a href="/news/2012/08/21/release02.html">Continue reading »</a></p>
+ </article>
+
+ <hr>
+
+
+ <!-- Pagination links -->
+
+ <ul class="pager">
+ <li>
+
+ <a href="/blog/page3" class="previous">Previous</a>
+
+ </li>
+ <li>
+ <span class="page_number ">Page: 4 of 4</span>
+ </li>
+ <li>
+
+ <span>Next</span>
+
+ </li>
+ </ul>
+
+ </div>
+
+ <div class="col-sm-4" markdown="1">
+ <!-- Blog posts by YEAR -->
+
+
+
+
+
+ <h2>2015</h2>
+
+ <ul id="markdown-toc">
+
+ <li><a href="/news/2015/05/14/Community-update-April.html">April 2015 in the Flink community</a></li>
+
+
+
+
+
+
+
+
+
+ <li><a href="/news/2015/05/11/Juggling-with-Bits-and-Bytes.html">Juggling with Bits and Bytes</a></li>
+
+
+
+
+
+
+
+
+
+ <li><a href="/news/2015/04/13/release-0.9.0-milestone1.html">Announcing Flink 0.9.0-milestone1 preview release</a></li>
+
+
+
+
+
+
+
+
+
+ <li><a href="/news/2015/04/07/march-in-flink.html">March 2015 in the Flink community</a></li>
+
+
+
+
+
+
+
+
+
+ <li><a href="/news/2015/03/13/peeking-into-Apache-Flinks-Engine-Room.html">Peeking into Apache Flink's Engine Room</a></li>
+
+
+
+
+
+
+
+
+
+ <li><a href="/news/2015/03/02/february-2015-in-flink.html">February 2015 in the Flink community</a></li>
+
+
+
+
+
+
+
+
+
+ <li><a href="/news/2015/02/09/streaming-example.html">Introducing Flink Streaming</a></li>
+
+
+
+
+
+
+
+
+
+ <li><a href="/news/2015/02/04/january-in-flink.html">January 2015 in the Flink community</a></li>
+
+
+
+
+
+
+
+
+
+ <li><a href="/news/2015/01/21/release-0.8.html">Apache Flink 0.8.0 available</a></li>
+
+
+
+
+
+
+
+
+
+ <li><a href="/news/2015/01/06/december-in-flink.html">December 2014 in the Flink community</a></li>
+
+
+
+ </ul>
+ <hr>
+ <h2>2014</h2>
+ <ul id="markdown-toc">
+
+
+
+
+
+
+
+ <li><a href="/news/2014/11/18/hadoop-compatibility.html">Hadoop Compatibility in Flink</a></li>
+
+
+
+
+
+
+
+
+
+ <li><a href="/news/2014/11/04/release-0.7.0.html">Apache Flink 0.7.0 available</a></li>
+
+
+
+
+
+
+
+
+
+ <li><a href="/news/2014/10/03/upcoming_events.html">Upcoming Events</a></li>
+
+
+
+
+
+
+
+
+
+ <li><a href="/news/2014/09/26/release-0.6.1.html">Apache Flink 0.6.1 available</a></li>
+
+
+
+
+
+
+
+
+
+ <li><a href="/news/2014/08/26/release-0.6.html">Apache Flink 0.6 available</a></li>
+
+
+
+
+
+
+
+
+
+ <li><a href="/news/2014/05/31/release-0.5.html">Stratosphere version 0.5 available</a></li>
+
+
+
+
+
+
+
+
+
+ <li><a href="/news/2014/04/16/stratosphere-goes-apache-incubator.html">Stratosphere accepted as Apache Incubator Project</a></li>
+
+
+
+
+
+
+
+
+
+ <li><a href="/news/2014/02/24/stratosphere-google-summer-of-code-2014.html">Stratosphere got accepted for Google Summer of Code 2014</a></li>
+
+
+
+
+
+
+
+
+
+ <li><a href="/news/2014/02/18/amazon-elastic-mapreduce-cloud-yarn.html">Use Stratosphere with Amazon Elastic MapReduce</a></li>
+
+
+
+
+
+
+
+
+
+ <li><a href="/news/2014/01/28/querying_mongodb.html">Accessing Data Stored in MongoDB with Stratosphere</a></li>
+
+
+
+
+
+
+
+
+
+ <li><a href="/news/2014/01/26/optimizer_plan_visualization_tool.html">Optimizer Plan Visualization Tool</a></li>
+
+
+
+
+
+
+
+
+
+ <li><a href="/news/2014/01/13/stratosphere-release-0.4.html">Stratosphere 0.4 Released</a></li>
+
+
+
+
+
+
+
+
+
+ <li><a href="/news/2014/01/12/0.4-migration-guide.html">Stratosphere Version 0.4 Migration Guide</a></li>
+
+
+
+
+
+
+
+
+
+ <li><a href="/news/2014/01/10/stratosphere-hadoop-summit.html">Stratosphere got accepted to the Hadoop Summit Europe in Amsterdam</a></li>
+
+
+
+ </ul>
+ <hr>
+ <h2>2013</h2>
+ <ul id="markdown-toc">
+
+
+
+
+
+
+
+ <li><a href="/news/2013/12/13/humboldt-innovation-award.html">Stratosphere wins award at Humboldt Innovation Competition "Big Data: Research meets Startups"</a></li>
+
+
+
+
+
+
+
+
+
+ <li><a href="/news/2013/10/21/cikm2013-paper.html">Paper "All Roads Lead to Rome: Optimistic Recovery for Distributed Iterative Data Processing" accepted at CIKM 2013</a></li>
+
+
+
+
+
+
+
+
+
+ <li><a href="/news/2013/03/27/www-demo-paper.html">Demo Paper "Large-Scale Social-Media Analytics on Stratosphere" Accepted at WWW 2013</a></li>
+
+
+
+ </ul>
+ <hr>
+ <h2>2012</h2>
+ <ul id="markdown-toc">
+
+
+
+
+
+
+
+ <li><a href="/news/2012/11/21/previewICDE2013.html">ICDE 2013 Demo Preview</a></li>
+
+
+
+
+
+
+
+
+
+ <li><a href="/news/2012/11/12/btw2013demo.html">Stratosphere Demo Paper Accepted for BTW 2013</a></li>
+
+
+
+
+
+
+
+
+
+ <li><a href="/news/2012/10/15/icde2013.html">Stratosphere Demo Accepted for ICDE 2013</a></li>
+
+
+
+
+
+
+
+
+
+ <li><a href="/news/2012/08/21/release02.html">Version 0.2 Released</a></li>
+
+
+ </ul>
+
+
+ </div>
+</div>
+
+ <hr />
+ <div class="footer text-center">
+ <p>Copyright © 2014-2015 <a href="http://apache.org">The Apache Software Foundation</a>. All Rights Reserved.</p>
+ <p>Apache Flink, Apache, and the Apache feather logo are trademarks of The Apache Software Foundation.</p>
+ <p><a href="/privacy-policy.html">Privacy Policy</a> · <a href="/blog/feed.xml">RSS feed</a></p>
+ </div>
+
+ </div><!-- /.container -->
+
+ <!-- jQuery (necessary for Bootstrap's JavaScript plugins) -->
+ <script src="https://ajax.googleapis.com/ajax/libs/jquery/1.11.2/jquery.min.js"></script>
+ <!-- Include all compiled plugins (below), or include individual files as needed -->
+ <script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.4/js/bootstrap.min.js"></script>
+ <script src="/js/codetabs.js"></script>
+
+ <!-- Google Analytics -->
+ <script>
+ (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+ (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
+ m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
+ })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+
+ ga('create', 'UA-52545728-1', 'auto');
+ ga('send', 'pageview');
+ </script>
+ </body>
+</html>
http://git-wip-us.apache.org/repos/asf/flink-web/blob/64362885/content/community.html
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diff --git a/content/community.html b/content/community.html
index bfdda7a..c03ba0d 100644
--- a/content/community.html
+++ b/content/community.html
@@ -135,12 +135,12 @@
<div class="page-toc">
<ul id="markdown-toc">
- <li><a href="#mailing-lists" id="markdown-toc-mailing-lists">Mailing Lists</a></li>
- <li><a href="#irc" id="markdown-toc-irc">IRC</a></li>
- <li><a href="#stack-overflow" id="markdown-toc-stack-overflow">Stack Overflow</a></li>
- <li><a href="#issue-tracker" id="markdown-toc-issue-tracker">Issue Tracker</a></li>
- <li><a href="#people" id="markdown-toc-people">People</a></li>
- <li><a href="#former-mentors" id="markdown-toc-former-mentors">Former mentors</a></li>
+ <li><a href="#mailing-lists">Mailing Lists</a></li>
+ <li><a href="#irc">IRC</a></li>
+ <li><a href="#stack-overflow">Stack Overflow</a></li>
+ <li><a href="#issue-tracker">Issue Tracker</a></li>
+ <li><a href="#people">People</a></li>
+ <li><a href="#former-mentors">Former mentors</a></li>
</ul>
</div>
http://git-wip-us.apache.org/repos/asf/flink-web/blob/64362885/content/downloads.html
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diff --git a/content/downloads.html b/content/downloads.html
index 2457ce7..0525894 100644
--- a/content/downloads.html
+++ b/content/downloads.html
@@ -144,12 +144,12 @@ $( document ).ready(function() {
<div class="page-toc">
<ul id="markdown-toc">
- <li><a href="#latest-stable-release-v081" id="markdown-toc-latest-stable-release-v081">Latest stable release (v0.8.1)</a></li>
- <li><a href="#maven-dependencies" id="markdown-toc-maven-dependencies">Maven Dependencies</a></li>
- <li><a href="#preview-release" id="markdown-toc-preview-release">Preview release</a></li>
- <li><a href="#snapshot" id="markdown-toc-snapshot">Snapshot</a></li>
- <li><a href="#build-from-source" id="markdown-toc-build-from-source">Build from Source</a></li>
- <li><a href="#all-releases" id="markdown-toc-all-releases">All releases</a></li>
+ <li><a href="#latest-stable-release-v081">Latest stable release (v0.8.1)</a></li>
+ <li><a href="#maven-dependencies">Maven Dependencies</a></li>
+ <li><a href="#preview-release">Preview release</a></li>
+ <li><a href="#snapshot">Snapshot</a></li>
+ <li><a href="#build-from-source">Build from Source</a></li>
+ <li><a href="#all-releases">All releases</a></li>
</ul>
</div>
http://git-wip-us.apache.org/repos/asf/flink-web/blob/64362885/content/faq.html
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diff --git a/content/faq.html b/content/faq.html
index e2e8d80..63b7eb3 100644
--- a/content/faq.html
+++ b/content/faq.html
@@ -135,8 +135,8 @@
<div class="page-toc">
<ul id="markdown-toc">
- <li><a href="#is-flink-a-hadoop-project" id="markdown-toc-is-flink-a-hadoop-project">Is Flink a Hadoop Project?</a></li>
- <li><a href="#do-i-have-to-install-apache-hadoop-to-use-flink" id="markdown-toc-do-i-have-to-install-apache-hadoop-to-use-flink">Do I have to install Apache Hadoop to use Flink?</a></li>
+ <li><a href="#is-flink-a-hadoop-project">Is Flink a Hadoop Project?</a></li>
+ <li><a href="#do-i-have-to-install-apache-hadoop-to-use-flink">Do I have to install Apache Hadoop to use Flink?</a></li>
</ul>
</div>
http://git-wip-us.apache.org/repos/asf/flink-web/blob/64362885/content/how-to-contribute.html
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diff --git a/content/how-to-contribute.html b/content/how-to-contribute.html
index 8307561..48feeed 100644
--- a/content/how-to-contribute.html
+++ b/content/how-to-contribute.html
@@ -135,20 +135,20 @@
<div class="page-toc">
<ul id="markdown-toc">
- <li><a href="#easy-issues-for-starters" id="markdown-toc-easy-issues-for-starters">Easy Issues for Starters</a></li>
- <li><a href="#contributing-code--documentation" id="markdown-toc-contributing-code--documentation">Contributing Code & Documentation</a> <ul>
- <li><a href="#setting-up-the-infrastructure-and-creating-a-pull-request" id="markdown-toc-setting-up-the-infrastructure-and-creating-a-pull-request">Setting up the Infrastructure and Creating a Pull Request</a></li>
- <li><a href="#verifying-the-compliance-of-your-code" id="markdown-toc-verifying-the-compliance-of-your-code">Verifying the Compliance of your Code</a></li>
+ <li><a href="#easy-issues-for-starters">Easy Issues for Starters</a></li>
+ <li><a href="#contributing-code--documentation">Contributing Code & Documentation</a> <ul>
+ <li><a href="#setting-up-the-infrastructure-and-creating-a-pull-request">Setting up the Infrastructure and Creating a Pull Request</a></li>
+ <li><a href="#verifying-the-compliance-of-your-code">Verifying the Compliance of your Code</a></li>
</ul>
</li>
- <li><a href="#contribute-changes-to-the-website" id="markdown-toc-contribute-changes-to-the-website">Contribute changes to the Website</a> <ul>
- <li><a href="#files-and-directories-in-the-svn-repository" id="markdown-toc-files-and-directories-in-the-svn-repository">Files and Directories in the SVN repository</a></li>
- <li><a href="#the-buildsh-script" id="markdown-toc-the-buildsh-script">The <code>build.sh</code> script</a></li>
- <li><a href="#submit-a-patch" id="markdown-toc-submit-a-patch">Submit a patch</a></li>
+ <li><a href="#contribute-changes-to-the-website">Contribute changes to the Website</a> <ul>
+ <li><a href="#files-and-directories-in-the-svn-repository">Files and Directories in the SVN repository</a></li>
+ <li><a href="#the-buildsh-script">The <code>build.sh</code> script</a></li>
+ <li><a href="#submit-a-patch">Submit a patch</a></li>
</ul>
</li>
- <li><a href="#how-to-become-a-committer" id="markdown-toc-how-to-become-a-committer">How to become a committer</a> <ul>
- <li><a href="#how-to-use-git-as-a-committer" id="markdown-toc-how-to-use-git-as-a-committer">How to use git as a committer</a></li>
+ <li><a href="#how-to-become-a-committer">How to become a committer</a> <ul>
+ <li><a href="#how-to-use-git-as-a-committer">How to use git as a committer</a></li>
</ul>
</li>
</ul>
http://git-wip-us.apache.org/repos/asf/flink-web/blob/64362885/content/index.html
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--- a/content/index.html
+++ b/content/index.html
@@ -214,6 +214,10 @@
<ul class="list-group">
+ <li class="list-group-item"><span>14 May 2015</span> »
+ <a href="/news/2015/05/14/Community-update-April.html">April 2015 in the Flink community</a>
+ </li>
+
<li class="list-group-item"><span>11 May 2015</span> »
<a href="/news/2015/05/11/Juggling-with-Bits-and-Bytes.html">Juggling with Bits and Bytes</a>
</li>
@@ -229,10 +233,6 @@
<li class="list-group-item"><span>13 Mar 2015</span> »
<a href="/news/2015/03/13/peeking-into-Apache-Flinks-Engine-Room.html">Peeking into Apache Flink's Engine Room</a>
</li>
-
- <li class="list-group-item"><span>02 Mar 2015</span> »
- <a href="/news/2015/03/02/february-2015-in-flink.html">February 2015 in the Flink community</a>
- </li>
</ul>
http://git-wip-us.apache.org/repos/asf/flink-web/blob/64362885/content/material.html
----------------------------------------------------------------------
diff --git a/content/material.html b/content/material.html
index 0cead94..a61003a 100644
--- a/content/material.html
+++ b/content/material.html
@@ -133,13 +133,13 @@
<div class="page-toc">
<ul id="markdown-toc">
- <li><a href="#apache-flink-logos" id="markdown-toc-apache-flink-logos">Apache Flink Logos</a> <ul>
- <li><a href="#portable-network-graphics-png" id="markdown-toc-portable-network-graphics-png">Portable Network Graphics (PNG)</a></li>
- <li><a href="#scalable-vector-graphics-svg" id="markdown-toc-scalable-vector-graphics-svg">Scalable Vector Graphics (SVG)</a></li>
- <li><a href="#photoshop-psd" id="markdown-toc-photoshop-psd">Photoshop (PSD)</a></li>
+ <li><a href="#apache-flink-logos">Apache Flink Logos</a> <ul>
+ <li><a href="#portable-network-graphics-png">Portable Network Graphics (PNG)</a></li>
+ <li><a href="#scalable-vector-graphics-svg">Scalable Vector Graphics (SVG)</a></li>
+ <li><a href="#photoshop-psd">Photoshop (PSD)</a></li>
</ul>
</li>
- <li><a href="#slides" id="markdown-toc-slides">Slides</a></li>
+ <li><a href="#slides">Slides</a></li>
</ul>
</div>