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From mj...@apache.org
Subject [1/8] flink-web git commit: fixed broken links and removed old stuff (prior Flink)
Date Mon, 11 Apr 2016 09:20:55 GMT
Repository: flink-web
Updated Branches:
  refs/heads/asf-site 029a30551 -> 24f3ba5a1


fixed broken links and removed old stuff (prior Flink)


Project: http://git-wip-us.apache.org/repos/asf/flink-web/repo
Commit: http://git-wip-us.apache.org/repos/asf/flink-web/commit/52d8fe19
Tree: http://git-wip-us.apache.org/repos/asf/flink-web/tree/52d8fe19
Diff: http://git-wip-us.apache.org/repos/asf/flink-web/diff/52d8fe19

Branch: refs/heads/asf-site
Commit: 52d8fe19d0a89104178f4c5d4b7564ffacc528da
Parents: 029a305
Author: mjsax <mjsax@apache.org>
Authored: Sun Apr 10 14:28:24 2016 +0200
Committer: mjsax <mjsax@apache.org>
Committed: Sun Apr 10 14:31:48 2016 +0200

----------------------------------------------------------------------
 _posts/2012-08-21-release02.html                |  19 --
 _posts/2012-10-15-icde2013.html                 |  18 --
 _posts/2012-11-12-btw2013demo.html              |  15 --
 _posts/2012-11-21-previewICDE2013.html          |  12 --
 _posts/2013-03-27-www-demo-paper.html           |  14 --
 _posts/2013-10-21-cikm2013-paper.md             |  49 -----
 .../2013-12-13-humboldt-innovation-award.html   |  20 --
 _posts/2014-01-10-stratosphere-hadoop-summit.md |  10 -
 _posts/2014-01-12-0.4-migration-guide.md        |  90 ---------
 _posts/2014-01-13-stratosphere-release-0.4.md   |  75 -------
 ...4-01-26-optimizer_plan_visualization_tool.md |  22 ---
 _posts/2014-01-28-querying_mongodb.md           | 110 -----------
 ...02-18-amazon-elastic-mapreduce-cloud-yarn.md | 194 -------------------
 ...4-stratosphere-google-summer-of-code-2014.md |  16 --
 ...-04-16-stratosphere-goes-apache-incubator.md |  11 --
 _posts/2014-05-31-release-0.5.md                |  94 ---------
 _posts/2014-11-18-hadoop-compatibility.md       |   2 +-
 _posts/2015-02-04-january-in-flink.md           |   8 +-
 _posts/2015-02-09-streaming-example.md          |  12 +-
 _posts/2015-03-02-february-2015-in-flink.md     |   6 +-
 ...13-peeking-into-Apache-Flinks-Engine-Room.md |   6 +-
 _posts/2015-04-07-march-in-flink.md             |  21 +-
 _posts/2015-04-13-release-0.9.0-milestone1.md   |   8 +-
 _posts/2015-05-14-Community-update-April.md     |  13 +-
 _posts/2015-09-03-flink-forward.md              |  11 +-
 _posts/2015-09-16-off-heap-memory.md            |   2 +-
 _posts/2015-12-11-storm-compatibility.md        |   2 +-
 _posts/2015-12-18-a-year-in-review.md           |   4 +-
 28 files changed, 45 insertions(+), 819 deletions(-)
----------------------------------------------------------------------


http://git-wip-us.apache.org/repos/asf/flink-web/blob/52d8fe19/_posts/2012-08-21-release02.html
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diff --git a/_posts/2012-08-21-release02.html b/_posts/2012-08-21-release02.html
deleted file mode 100644
index a7e3a77..0000000
--- a/_posts/2012-08-21-release02.html
+++ /dev/null
@@ -1,19 +0,0 @@
----
-layout: post
-title:  'Version 0.2 Released'
-date:   2012-08-21 14:57:18
-categories: news
----
-
-
-
-<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>
\ No newline at end of file

http://git-wip-us.apache.org/repos/asf/flink-web/blob/52d8fe19/_posts/2012-10-15-icde2013.html
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diff --git a/_posts/2012-10-15-icde2013.html b/_posts/2012-10-15-icde2013.html
deleted file mode 100644
index 76e2cf6..0000000
--- a/_posts/2012-10-15-icde2013.html
+++ /dev/null
@@ -1,18 +0,0 @@
----
-layout: post
-title:  'Stratosphere Demo Accepted for ICDE 2013'
-date:   2012-10-15 14:57:18
-categories: news
----
-
-
- <p>Our demo submission<br />
-<strong><cite>"Peeking into the Optimization of Data Flow Programs with MapReduce-style UDFs"</cite></strong><br />
-has been accepted for ICDE 2013 in Brisbane, Australia.<br />
-The demo illustrates the contributions of our VLDB 2012 paper <cite>"Opening the Black Boxes in Data Flow Optimization"</cite> <a href="{{ site.baseurl }}/assets/papers/optimizationOfDataFlowsWithUDFs_13.pdf">[PDF]</a> and <a href="{{ site.baseurl }}/assets/papers/optimizationOfDataFlowsWithUDFs_poster_13.pdf">[Poster PDF]</a>.</p>
-<p>Visit our poster, enjoy the demo, and talk to us if you are going to attend ICDE 2013.</p>
-<p><strong>Abstract:</strong><br />
-Data flows are a popular abstraction to define data-intensive processing tasks. In order to support a wide range of use cases, many data processing systems feature MapReduce-style user-defined functions (UDFs). In contrast to UDFs as known from relational DBMS, MapReduce-style UDFs have less strict templates. These templates do not alone provide all the information needed to decide whether they can be reordered with relational operators and other UDFs. However, it is well-known that reordering operators such as filters, joins, and aggregations can yield runtime improvements by orders of magnitude.<br />
-We demonstrate an optimizer for data flows that is able to reorder operators with MapReduce-style UDFs written in an imperative language. Our approach leverages static code analysis to extract information from UDFs which is used to reason about the reorderbility of UDF operators. This information is sufficient to enumerate a large fraction of the search space covered by conventional RDBMS optimizers including filter and aggregation push-down, bushy join orders, and choice of physical execution strategies based on interesting properties.<br />
-We demonstrate our optimizer and a job submission client that allows users to peek step-by-step into each phase of the optimization process: the static code analysis of UDFs, the enumeration of reordered candidate data flows, the generation of physical execution plans, and their parallel execution. For the demonstration, we provide a selection of relational and non-relational data flow programs which highlight the salient features of our approach.</p>
-

http://git-wip-us.apache.org/repos/asf/flink-web/blob/52d8fe19/_posts/2012-11-12-btw2013demo.html
----------------------------------------------------------------------
diff --git a/_posts/2012-11-12-btw2013demo.html b/_posts/2012-11-12-btw2013demo.html
deleted file mode 100644
index 92b35c9..0000000
--- a/_posts/2012-11-12-btw2013demo.html
+++ /dev/null
@@ -1,15 +0,0 @@
----
-layout: post
-title:  'Stratosphere Demo Paper Accepted for BTW 2013'
-date:   2012-11-12 14:57:18
-categories: news
----
-
- <p>Our demo submission<br />
-<strong><cite>"Applying Stratosphere for Big Data Analytics"</cite></strong><br />
-has been accepted for BTW 2013 in Magdeburg, Germany.<br />
-The demo focuses on Stratosphere's query language Meteor, which has been presented in our paper <cite>"Meteor/Sopremo: An Extensible Query Language and Operator Model"</cite> <a href="{{ site.baseurl }}/assets/papers/Sopremo_Meteor BigData.pdf">[pdf]</a> at the BigData workshop associated with VLDB 2012 in Istanbul.</p>
-<p>Visit our demo, and talk to us if you are going to attend BTW 2013.</p>
-<p><strong>Abstract:</strong><br />
-Analyzing big data sets as they occur in modern business and science applications requires query languages that allow for the specification of complex data processing tasks. Moreover, these ideally declarative query specifications have to be optimized, parallelized and scheduled for processing on massively parallel data processing platforms. This paper demonstrates the application of Stratosphere to different kinds of Big Data Analytics tasks. Using examples from different application domains, we show how to formulate analytical tasks as Meteor queries and execute them with Stratosphere. These examples include data cleansing and information extraction tasks, and a correlation analysis of microblogging and stock trade volume data that we describe in detail in this paper.</p>
-

http://git-wip-us.apache.org/repos/asf/flink-web/blob/52d8fe19/_posts/2012-11-21-previewICDE2013.html
----------------------------------------------------------------------
diff --git a/_posts/2012-11-21-previewICDE2013.html b/_posts/2012-11-21-previewICDE2013.html
deleted file mode 100644
index f839a1b..0000000
--- a/_posts/2012-11-21-previewICDE2013.html
+++ /dev/null
@@ -1,12 +0,0 @@
----
-layout: post
-title:  'ICDE 2013 Demo Preview'
-date:   2012-11-21 14:57:18
-categories: news
----
-
-
- <p>This is a preview of our demo that will be presented at ICDE 2013 in Brisbane.<br />
-The demo shows how static code analysis can be leveraged to reordered UDF operators in data flow programs.</p>
-<p>Detailed information can be found in our papers which are available on the <a href="{{ site.baseurl }}/publications">publication</a> page.</p>
-<p><iframe width="420" height="315" src="http://www.youtube.com/embed/ZYwCMgPXFVE" frameborder="0" allowfullscreen></iframe></p>
\ No newline at end of file

http://git-wip-us.apache.org/repos/asf/flink-web/blob/52d8fe19/_posts/2013-03-27-www-demo-paper.html
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diff --git a/_posts/2013-03-27-www-demo-paper.html b/_posts/2013-03-27-www-demo-paper.html
deleted file mode 100644
index 750bd58..0000000
--- a/_posts/2013-03-27-www-demo-paper.html
+++ /dev/null
@@ -1,14 +0,0 @@
----
-layout: post
-title:  'Demo Paper "Large-Scale Social-Media Analytics on Stratosphere" Accepted at WWW 2013'
-date:   2013-03-27 14:57:18
-categories: news
----
-
-   <p>Our demo submission<br />
-<strong><cite>"Large-Scale Social-Media Analytics on Stratosphere"</cite></strong><br />
-by Christoph Boden, Marcel Karnstedt, Miriam Fernandez and Volker Markl<br />
-has been accepted for WWW 2013 in Rio de Janeiro, Brazil.</p>
-<p>Visit our demo, and talk to us if you are attending WWW 2013.</p>
-<p><strong>Abstract:</strong><br />
-The importance of social-media platforms and online communities - in business as well as public context - is more and more acknowledged and appreciated by industry and researchers alike. Consequently, a wide range of analytics has been proposed to understand, steer, and exploit the mechanics and laws driving their functionality and creating the resulting benefits. However, analysts usually face significant problems in scaling existing and novel approaches to match the data volume and size of modern online communities. In this work, we propose and demonstrate the usage of the massively parallel data prossesing system Stratosphere, based on second order functions as an extended notion of the MapReduce paradigm, to provide a new level of scalability to such social-media analytics. Based on the popular example of role analysis, we present and illustrate how this massively parallel approach can be leveraged to scale out complex data-mining tasks, while providing a programming approach th
 at eases the formulation of complete analytical workflows.</p> 

http://git-wip-us.apache.org/repos/asf/flink-web/blob/52d8fe19/_posts/2013-10-21-cikm2013-paper.md
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diff --git a/_posts/2013-10-21-cikm2013-paper.md b/_posts/2013-10-21-cikm2013-paper.md
deleted file mode 100644
index 0fd506f..0000000
--- a/_posts/2013-10-21-cikm2013-paper.md
+++ /dev/null
@@ -1,49 +0,0 @@
----
-layout: post
-title:  'Paper "All Roads Lead to Rome: Optimistic Recovery for Distributed
-Iterative Data Processing" accepted at CIKM 2013'
-date:   2013-10-21 9:57:18
-categories: news
----
-
-Our paper "“All Roads Lead to Rome:” Optimistic Recovery for Distributed
-Iterative Data Processing" authored by Sebastian Schelter, Kostas
-Tzoumas, Stephan Ewen and Volker Markl has been accepted accepted at the
-ACM International Conference on Information and Knowledge Management
-(CIKM 2013) in San Francisco.
-
-**Abstract**
-
-Executing data-parallel iterative algorithms on large datasets is
-crucial for many advanced analytical applications in the fields of data
-mining and machine learning. Current systems for executing iterative
-tasks in large clusters typically achieve fault tolerance through
-rollback recovery. The principle behind this pessimistic approach is to
-periodically checkpoint the algorithm state. Upon failure, the system
-restores a consistent state from a previously written checkpoint and
-resumes execution from that point.
-
-We propose an optimistic recovery mechanism using algorithmic
-compensations. Our method leverages the robust, self-correcting nature
-of a large class of fixpoint algorithms used in data mining and machine
-learning, which converge to the correct solution from various
-intermediate consistent states. In the case of a failure, we apply a
-user-defined compensate function that algorithmically creates such a
-consistent state, instead of rolling back to a previous checkpointed
-state. Our optimistic recovery does not checkpoint any state and hence
-achieves optimal failure-free performance with respect to the overhead
-necessary for guaranteeing fault tolerance. We illustrate the
-applicability of this approach for three wide classes of problems.
-Furthermore, we show how to implement the proposed optimistic recovery
-mechanism in a data flow system. Similar to the Combine operator in
-MapReduce, our proposed functionality is optional and can be applied to
-increase performance without changing the semantics of programs. In an
-experimental evaluation on large datasets, we show that our proposed
-approach provides optimal failure-free performance. In the absence of
-failures our optimistic scheme is able to outperform a pessimistic
-approach by a factor of two to five. In presence of failures, our
-approach provides fast recovery and outperforms pessimistic approaches
-in the majority of cases.
-
-<a href="{{ site.baseurl }}/assets/papers/optimistic.pdf">Download the paper [PDF]</a>
-

http://git-wip-us.apache.org/repos/asf/flink-web/blob/52d8fe19/_posts/2013-12-13-humboldt-innovation-award.html
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diff --git a/_posts/2013-12-13-humboldt-innovation-award.html b/_posts/2013-12-13-humboldt-innovation-award.html
deleted file mode 100644
index 9b88257..0000000
--- a/_posts/2013-12-13-humboldt-innovation-award.html
+++ /dev/null
@@ -1,20 +0,0 @@
----
-layout: post
-title:  'Stratosphere wins award at Humboldt Innovation Competition "Big Data: Research meets Startups"'
-date:   2013-12-13 14:57:18
-categories: news
----
-
-
-    <p> Stratosphere won the second place in
-    the <a href="http://www.humboldt-innovation.de/de/newsdetail/News/View/Forum%2BJunge%2BSpitzenforscher%2BBIG%2BData%2B%2BResearch%2Bmeets%2BStartups-123.html">competition</a>
-    organized by Humboldt Innovation on "Big Data: Research meets
-    Startups," where several research projects were evaluated by a
-    panel of experts from the Berlin startup ecosystem. The award
-    includes a monetary prize of 10,000 euros.
-    </p>
-
-    <p>We are extremely excited about this award, as it further
-    showcases the relevance of the Stratosphere platform and Big Data
-    technology in general for the technology startup world.
-    </p>

http://git-wip-us.apache.org/repos/asf/flink-web/blob/52d8fe19/_posts/2014-01-10-stratosphere-hadoop-summit.md
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diff --git a/_posts/2014-01-10-stratosphere-hadoop-summit.md b/_posts/2014-01-10-stratosphere-hadoop-summit.md
deleted file mode 100644
index bf0c734..0000000
--- a/_posts/2014-01-10-stratosphere-hadoop-summit.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-layout: post
-title:  'Stratosphere got accepted to the Hadoop Summit Europe in Amsterdam'
-date:   2014-01-10 10:57:18
-categories: news
----
-
-
-The Stratosphere team is proud to announce that it is going to present at the [Hadoop Summit 2014 in Amsterdam](http://hadoopsummit.org/amsterdam/) on April 2-3. Our talk "Big Data looks tiny from Stratosphere" is part of the "Future of Hadoop" Track. The talk abstract already made it into the top 5 in the [Community Vote](https://hadoopsummit.uservoice.com/forums/196822-future-of-apache-hadoop/filters/top) that took place by the end of last year.
-

http://git-wip-us.apache.org/repos/asf/flink-web/blob/52d8fe19/_posts/2014-01-12-0.4-migration-guide.md
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diff --git a/_posts/2014-01-12-0.4-migration-guide.md b/_posts/2014-01-12-0.4-migration-guide.md
deleted file mode 100644
index 995fd5b..0000000
--- a/_posts/2014-01-12-0.4-migration-guide.md
+++ /dev/null
@@ -1,90 +0,0 @@
----
-layout: post
-title:  'Stratosphere Version 0.4 Migration Guide'
-date:   2014-01-12 19:57:18
-categories: news
----
-
-This guide is intended to help users of previous Stratosphere versions to migrate their programs to the new API of v0.4.
-
-Version `0.4-rc1`, `0.4` and all newer versions have the new API. If you want to have the most recent version before the code change, please set the version to `0.4-alpha.3-SNAPSHOT`. (Note that the `0.4-alpha` versions are only available in the snapshot repository).
-
-#### Maven Dependencies
-Since we also reorganized the Maven project structure, existing programs need to update the Maven dependencies to `stratosphere-java` (and `stratosphere-clients`, for examples and executors).
-
-The typical set of Maven dependencies for Stratosphere Java programs is:
-
-```diff
-       <groupId>eu.stratosphere</groupId>
--      <artifactId>pact-common</artifactId>
--      <version>0.4-SNAPSHOT</version>
-+      <artifactId>stratosphere-java</artifactId>
-+      <version>0.4</version>
-
--      <artifactId>pact-clients</artifactId>
--      <version>0.4-SNAPSHOT</version>
-+      <artifactId>stratosphere-clients</artifactId>
-+      <version>0.4</version>
-```
-
-
-#### Renamed classes
-
-We renamed many of the most commonly used classes to make their names more intuitive:
-
-<table class="table table-striped">
-  <thead>
-  	<tr>
-  	<th>Old Name (before <code>0.4</code>)</th>
-  	<th>New Name (<code>0.4</code> and after)</th>
-  </tr>
-  </thead>
- 	<tbody>
-	  <tr>
-	  	<td>Contract</td>
-	  	<td>Operator</td>
-	  </tr>
-	  <tr>
-	  	<td>MatchContract</td>
-	  	<td>JoinOperator</td>
-	  </tr>
-
-	  	  <tr>
-	  	<td>[Map, Reduce, ...]Stub</td>
-	  	<td>[Map, Reduce, ...]Function</td>
-	  </tr>
-	  	  <tr>
-	  	<td>MatchStub</td>
-	  	<td>JoinFunction</td>
-	  </tr>
-	  	  <tr>
-	  	<td>Pact[Integer, Double, ...]</td>
-	  	<td>IntValue, DoubleValue, ...</td>
-	  </tr>	  
-	  <tr>
-	  	<td>PactRecord</td>
-	  	<td>Record</td>
-	  </tr>
-	  	  <tr>
-	  	<td>PlanAssembler</td>
-	  	<td>Program</td>
-	  </tr>
-	  	  <tr>
-	  	<td>PlanAssemblerDescription</td>
-	  	<td>ProgramDescription</td>
-	  </tr>
-	  	  <tr>
-	  	<td>RecordOutputFormat</td>
-	  	<td>CsvOutputFormat</td>
-	  </tr>
-	</tbody>
-</table>
-
-
-Package names have been adapted as well.
-For a complete overview of the renamings, have a look at [issue #257 on GitHub](https://github.com/stratosphere/stratosphere/issues/257).
-
-
-We suggest for Eclipse user adjust the programs as follows: Delete all old Stratosphere imports, then rename the the classes (`PactRecord` to `Record` and so on). Finally, use the “Organize Imports” function (`CTRL+SHIFT+O`) to choose the right imports. The names should be unique so always pick the classes that are in the `eu.stratosphere` package.
-
-Please contact us in the comments below, on the mailing list or on GitHub if you have any issues migrating to the latest Stratosphere release.

http://git-wip-us.apache.org/repos/asf/flink-web/blob/52d8fe19/_posts/2014-01-13-stratosphere-release-0.4.md
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diff --git a/_posts/2014-01-13-stratosphere-release-0.4.md b/_posts/2014-01-13-stratosphere-release-0.4.md
deleted file mode 100644
index 22907c0..0000000
--- a/_posts/2014-01-13-stratosphere-release-0.4.md
+++ /dev/null
@@ -1,75 +0,0 @@
----
-layout: post
-title:  'Stratosphere 0.4 Released'
-date:   2014-01-13 20:57:18
-categories: news
----
-
-We are pleased to announce that version 0.4 of the Stratosphere system has been released. 
-
-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.
-
-In the course of the transition from v0.2 to v0.4 of the system, we have changed pre-existing APIs based on valuable user feedback. This means that, in the interest of easier programming, we have broken backwards compatibility and existing jobs must be adapted, as described in [the migration guide]({{ site.baseurl }}/blog/tutorial/2014/01/12/0.4-migration-guide.html).
-
-This article will guide you through the feature list of the new release.
-
-### Scala Programming Interface
-The new Stratosphere version comes with a new programming API in Scala that supports very fluent and efficient programs that can be expressed with very few lines of code. The API uses Scala's native type system (no special boxed data types) and supports grouping and joining on types beyond key/value pairs. We use code analysis and code generation to transform Scala's data model to the Stratosphere runtime. Stratosphere Scala programs are optimized before execution by Stratosphere's optimizer just like Stratosphere Java programs.
-
-Learn more about the Scala API at the [Scala Programming Guide]({{ site.baseurl }}/docs/0.4/programming_guides/scala.html)
-
-### Iterations
-Stratosphere v0.4 introduces deep support for iterative algorithms, required by a large class of advanced analysis algorithms. In contrast to most other systems, "looping over the data" is done inside the system's runtime, rather than in the client. Individual iterations (supersteps) can be as fast as sub-second times. Loop-invariant data is automatically cached in memory.
-
-We support a special form of iterations called “delta iterations” that selectively modify only some elements of intermediate solution in each iteration. These are applicable to a variety of applications, e.g., use cases of Apache Giraph. We have observed speedups of 70x when using delta iterations instead of regular iterations.
-
-Read more about the new iteration feature in [the documentation]({{ site.baseurl }}/docs/0.4/programming_guides/iterations.html)
-
-### Hadoop YARN Support
-YARN (Yet Another Resource Negotiator) is the major new feature of the recently announced [Hadoop 2.2](http://hadoop.apache.org/docs/r2.2.0/). It allows to share existing clusters with different runtimes. So you can run MapReduce alongside Storm and others. With the 0.4 release, Stratosphere supports YARN.
-Follow [our guide]({{ site.baseurl }}/docs/0.4/setup/yarn.html) on how to start a Stratosphere YARN session.
-
-### Improved Scripting Language Meteor
-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.
-
-### Spargel: Pregel Inspired Graph Processing
-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. 
-
-### Web Frontend
-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.
-
-### Accumulators
-Stratosphere's accumulators allow program developers to compute simple statistics, such as counts, sums, min/max values, or histograms, as a side effect of the processing functions. An example application would be to count the total number of records/tuples processed by a function. Stratosphere will not launch additional tasks (reducers), but will compute the number "on the fly" as a side-product of the functions application to the data. The concept is similar to Hadoop's counters, but supports more types of aggregation.
-
-### Refactored APIs
-Based on valuable user feedback, we refactored the Java programming interface to make it more intuitive and easier to use. The basic concepts are still the same, however the naming of most interfaces changed and the structure of the code was adapted. When updating to the 0.4 release you will need to adapt your jobs and dependencies. A previous blog post has a guide to the necessary changes to adapt programs to Stratosphere 0.4.
-
-### Local Debugging
-You can now test and debug Stratosphere jobs locally. The [LocalExecutor]({{ site.baseurl }}/docs/0.4/program_execution/local_executor.html) allows to execute Stratosphere Jobs from IDE's. The same code that runs on clusters also runs in a single JVM multi-threaded. The mode supports full debugging capabilities known from regular applications (placing breakpoints and stepping through the program's functions). An advanced mode supports simulating fully distributed operation locally.
-
-### Miscellaneous
-
-* The configuration of Stratosphere has been changed to YAML
-* HBase support
-* JDBC Input format
-* Improved Windows Compatibility: Batch-files to start Stratosphere on Windows and all unit tests passing on Windows.
-* Stratosphere is available in Maven Central and Sonatype Snapshot Repository
-* Improved build system that supports different Hadoop versions using Maven profiles
-* Maven Archetypes for Stratosphere Jobs.
-* Stability and Usability improvements with many bug fixes.
-
-
-### Download and get started with Stratosphere v0.4
-There are several options for getting started with Stratosphere. 
-
-* Download it on the [download page]({{ site.baseurl }}/downloads)
-* Start your program with the [Quick-start guides]({{ site.baseurl }}/quickstart/).
-* Complete [documentation and set-up guides]({{ site.baseurl }}/docs/0.4/)
-
-### Tell us what you think!
-Are you using, or planning to use Stratosphere? Sign up in our [mailing list](https://groups.google.com/forum/#!forum/stratosphere-dev) and drop us a line.
-
-Have you found a bug? [Post an issue](https://github.com/stratosphere/stratosphere) on GitHub.
-
-Follow us on [Twitter](https://twitter.com/stratosphere_eu) and [GitHub](https://github.com/stratosphere/stratosphere) to stay in touch with the latest news!
-

http://git-wip-us.apache.org/repos/asf/flink-web/blob/52d8fe19/_posts/2014-01-26-optimizer_plan_visualization_tool.md
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diff --git a/_posts/2014-01-26-optimizer_plan_visualization_tool.md b/_posts/2014-01-26-optimizer_plan_visualization_tool.md
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+++ /dev/null
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----
-layout: post
-title:  "Optimizer Plan Visualization Tool"
-date:   2014-01-26 9:00:00
-categories: news
----
-
-Stratosphere's hybrid approach combines **MapReduce** and **MPP database** techniques. One central part of this approach is to have a **separation between the programming (API) and the way programs are executed** *(execution plans)*. The **compiler/optimizer** 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).
-
-**If you want to know how exactly the system executes your program, you can find it out in two ways**:
-
-  1. The **browser-based webclient UI**, which takes programs packaged into JARs and draws the execution plan as a visual data flow (check out the [documentation](http://stratosphere.eu/docs/0.4/program_execution/web_interface.html) for details).
- 
-  2. For **programs using the [Local- ](http://stratosphere.eu/docs/0.4/program_execution/local_executor.html) or [Remote Executor] (http://stratosphere.eu/docs/0.4/program_execution/remote_executor.html)**, you can get the optimizer plan using the method `LocalExecutor.optimizerPlanAsJSON(plan)`. The **resulting JSON** string describes the execution strategies chosen by the optimizer. Naturally, you do not want to parse that yourself, especially for longer programs.
-
-  The builds *0.5-SNAPSHOT* and later come with a **tool that visualizes the JSON** string. It is a standalone version of the webclient's visualization, packed as an html document `tools/planVisualizer.html`.
-
-  If you open it in a browser (for example `chromium-browser tools/planVisualizer.html`) it shows a text area where you can paste the JSON string and it renders that string as a dataflow plan (assuming it was a valid JSON string and plan). The pictures below show how that looks for the [included sample program](https://github.com/stratosphere/stratosphere/blob/release-0.4/stratosphere-examples/stratosphere-java-examples/src/main/java/eu/stratosphere/example/java/record/connectedcomponents/WorksetConnectedComponents.java?source=cc) that uses delta iterations to compute the connected components of a graph.
-
-<img src="{{ site.baseurl }}/img/blog/plan_visualizer1.png" style="width:100%;">
-
-<img src="{{ site.baseurl }}/img/blog/plan_visualizer2.png" style="width:100%;">
\ No newline at end of file

http://git-wip-us.apache.org/repos/asf/flink-web/blob/52d8fe19/_posts/2014-01-28-querying_mongodb.md
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diff --git a/_posts/2014-01-28-querying_mongodb.md b/_posts/2014-01-28-querying_mongodb.md
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--- a/_posts/2014-01-28-querying_mongodb.md
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----
-layout: post
-title:  "Accessing Data Stored in MongoDB with Stratosphere"
-date:   2014-01-28 9:00:00
-author: "Robert Metzger"
-author-twitter: "rmetzger_"
-categories: news
----
-
-We recently merged a [pull request](https://github.com/stratosphere/stratosphere/pull/437) that allows you to use any existing Hadoop [InputFormat](http://developer.yahoo.com/hadoop/tutorial/module5.html#inputformat) with Stratosphere. So you can now (in the `0.5-SNAPSHOT` and upwards versions) define a Hadoop-based data source:
-
-```java
-HadoopDataSource source = new HadoopDataSource(new TextInputFormat(), new JobConf(), "Input Lines");
-TextInputFormat.addInputPath(source.getJobConf(), new Path(dataInput));
-```
-
-We describe in the following article how to access data stored in [MongoDB](http://www.mongodb.org/) with Stratosphere. This allows users to join data from multiple sources (e.g. MonogDB and HDFS) or perform machine learning with the documents stored in MongoDB.
-
-The approach here is to use the `MongoInputFormat` that was developed for Apache Hadoop but now also runs with Stratosphere.
-
-```java
-JobConf conf = new JobConf();
-conf.set("mongo.input.uri","mongodb://localhost:27017/enron_mail.messages");
-HadoopDataSource src = new HadoopDataSource(new MongoInputFormat(), conf, "Read from Mongodb", new WritableWrapperConverter());
-```
-
-### Example Program
-The example program reads data from the [enron dataset](http://www.cs.cmu.edu/~enron/) that contains about 500k internal e-mails. The data is stored in MongoDB and the Stratosphere program counts the number of e-mails per day.
-
-The complete code of this sample program is available on [GitHub](https://github.com/stratosphere/stratosphere-mongodb-example).
-
-#### Prepare MongoDB and the Data
-
- - Install MongoDB
- - Download the enron dataset from [their website](http://mongodb-enron-email.s3-website-us-east-1.amazonaws.com/).
- - Unpack and load it
-
- ```bash
- bunzip2 enron_mongo.tar.bz2
- tar xvf enron_mongo.tar
- mongorestore dump/enron_mail/messages.bson
- ```
-
-We used [Robomongo](http://robomongo.org/) to visually examine the dataset stored in MongoDB.
-
-<img src="{{ site.baseurl }}/img/blog/robomongo.png" style="width:90%;margin:15px">
-
-
-#### Build `MongoInputFormat`
-
-MongoDB offers an InputFormat for Hadoop on their [GitHub page](https://github.com/mongodb/mongo-hadoop). The code is not available in any Maven repository, so we have to build the jar file on our own.
-
-- Check out the repository
-
-```
-git clone https://github.com/mongodb/mongo-hadoop.git
-cd mongo-hadoop
-```
-
-- Set the appropriate Hadoop version in the `build.sbt`, we used `1.1`.
-
-```bash
-hadoopRelease in ThisBuild := "1.1"
-```
-- Build the input format
-
-```bash
-./sbt package
-```
-
-The jar-file is now located in `core/target`.
-
-#### The Stratosphere Program
-
-Now we have everything prepared to run the Stratosphere program. I only ran it on my local computer, out of Eclipse. To do that, check out the code ...
-
-```bash
-git clone https://github.com/stratosphere/stratosphere-mongodb-example.git
-```
-
-... and import it as a Maven project into your Eclipse. You have to manually add the previously built mongo-hadoop jar-file as a dependency.
-You can now press the "Run" button and see how Stratosphere executes the little program. It was running for about 8 seconds on the 1.5 GB dataset.
-
-
-The result (located in `/tmp/enronCountByDay`) now looks like this.
-
-```
-11,Fri Sep 26 10:00:00 CEST 1997
-154,Tue Jun 29 10:56:00 CEST 1999
-292,Tue Aug 10 12:11:00 CEST 1999
-185,Thu Aug 12 18:35:00 CEST 1999
-26,Fri Mar 19 12:33:00 CET 1999
-```
-
-There is one thing left I want to point out here. MongoDB represents objects stored in the database as JSON-documents. Since Stratosphere's standard types do not support JSON documents, I was using the `WritableWrapper` here. This wrapper allows to use any Hadoop datatype with Stratosphere.
-
-The following code example shows how the JSON-documents are accessed in Stratosphere.
-
-```java
-public void map(Record record, Collector<Record> out) throws Exception {
-	Writable valWr = record.getField(1, WritableWrapper.class).value();
-	BSONWritable value = (BSONWritable) valWr;
-	Object headers = value.getDoc().get("headers");
-	BasicDBObject headerOb = (BasicDBObject) headers;
-	String date = (String) headerOb.get("Date");
-	// further date processing
-}
-```
-
-Please use the comments if you have questions or if you want to showcase your own MongoDB-Stratosphere integration.
\ No newline at end of file

http://git-wip-us.apache.org/repos/asf/flink-web/blob/52d8fe19/_posts/2014-02-18-amazon-elastic-mapreduce-cloud-yarn.md
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diff --git a/_posts/2014-02-18-amazon-elastic-mapreduce-cloud-yarn.md b/_posts/2014-02-18-amazon-elastic-mapreduce-cloud-yarn.md
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----
-layout: post
-title:  'Use Stratosphere with Amazon Elastic MapReduce'
-date:   2014-02-18 19:57:18
-author: "Robert Metzger"
-author-twitter: "rmetzger_"
-categories: news
----
-
-<div class="lead">Get started with Stratosphere within 10 minutes using Amazon Elastic MapReduce.</div>
-
-This step-by-step tutorial will guide you through the setup of Stratosphere using Amazon Elastic MapReduce.
-
-### Background
-[Amazon Elastic MapReduce](http://aws.amazon.com/elasticmapreduce/) (Amazon EMR) is part of Amazon Web services. EMR allows to create Hadoop clusters that analyze data stored in Amazon S3 (AWS' cloud storage). Stratosphere runs on top of Hadoop using the [recently](http://hadoop.apache.org/docs/r2.2.0/hadoop-project-dist/hadoop-common/releasenotes.html) released cluster resource manager [YARN](http://hadoop.apache.org/docs/current2/hadoop-yarn/hadoop-yarn-site/YARN.html). YARN allows to use many different data analysis tools in your cluster side by side. Tools that run with YARN are, for example [Apache Giraph](https://giraph.apache.org/), [Spark](http://spark.incubator.apache.org/) or [HBase](http://hortonworks.com/blog/introducing-hoya-hbase-on-yarn/). Stratosphere also [runs on YARN]({{site.baseurl}}/docs/0.4/setup/yarn.html) and that's the approach for this tutorial.
-
-### 1. Step: Login to AWS and prepare secure access
-
-* Log in to the [AWS Console](https://console.aws.amazon.com/console/home)
-
-You need to have SSH keys to access the Hadoop master node. If you do not have keys for your computer, generate them:
-
-<div class="row" style="padding-top:15px">
-	<div class="col-md-6">
-<a data-lightbox="inputs" href="{{site.baseurl}}/img/blog/emr-security.png" data-lightbox="example-1"><img class="img-responsive" src="{{site.baseurl}}/img/blog/emr-security.png" /></a>
-	</div>
-	<div class="col-md-6">
-		<ul>
-			<li>Select <a href="https://console.aws.amazon.com/ec2/v2/home">EC2</a> and click on "Key Pairs" in the "NETWORK & SECURITY" section.</li>
-			<li>Click on "Create Key Pair" and give it a name</li>
-			<li>After pressing "Yes" it will download a .pem file.</li>
-			<li>Change the permissions of the .pem file</li>
-{% highlight bash %}
-chmod og-rwx ~/work-laptop.pem 
-{% endhighlight %}
-		</ul>
-	</div>
-</div>
-
-### 2. Step: Create your Hadoop Cluster in the cloud
-
-* Select [Elastic MapReduce](https://console.aws.amazon.com/elasticmapreduce/vnext/) from the AWS console
-* Click the blue "Create cluster" button.
-
-<div class="row" style="padding-top:15px">
-	<div class="col-md-6">
-<a data-lightbox="inputs" href="{{site.baseurl}}/img/blog/emr-hadoopversion.png" data-lightbox="example-1"><img class="img-responsive" src="{{site.baseurl}}/img/blog/emr-hadoopversion.png" /></a>
-	</div>
-	<div class="col-md-6">
-		<ul>
-			<li>Choose a Cluster name</li>
-			<li>You can let the other settings remain unchanged (termination protection, logging, debugging)</li>
-			<li>For the Hadoop distribution, it is very important to choose one with YARN support. We use <b>3.0.3 (Hadoop 2.2.0)</b> (the minor version might change over time)</li>
-			<li>Remove all applications to be installed (unless you want to use them)</li>
-			<li>Choose the instance types you want to start. Stratosphere runs fine with m1.large instances. Core and Task instances both run Stratosphere, but only core instances contain HDFS data nodes.</li>
-			<li>Choose the <b>EC2 key pair</b> you've created in the previous step!</li>
-		</ul>
-	</div>
-</div>
-
-* Thats it! You can now press the "Create cluster" button at the end of the form to boot it!
-
-### 3. Step: Launch Stratosphere
-
-You might need to wait a few minutes until Amazon started your cluster. (You can monitor the progress of the instances in EC2). Use the refresh button in the top right corner.
-
-You see that the master is up if the field <b>Master public DNS</b> contains a value (first line), connect to it using SSH.
-
-{% highlight bash %}
-ssh hadoop@<your master public DNS> -i <path to your .pem>
-# for my example, it looks like this:
-ssh hadoop@ec2-54-213-61-105.us-west-2.compute.amazonaws.com -i ~/Downloads/work-laptop.pem
-{% endhighlight %}
-
-
-(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.) </br></br>
-Once connected to the master, download and start Stratosphere for YARN: 
-<ul>
-	<li>Download and extract Stratosphere-YARN</li>
-{% highlight bash %}
-wget http://stratosphere-bin.s3-website-us-east-1.amazonaws.com/stratosphere-dist-0.5-SNAPSHOT-yarn.tar.gz
-# extract it
-tar xvzf stratosphere-dist-0.5-SNAPSHOT-yarn.tar.gz
-{% endhighlight %}
-	<li>Start Stratosphere in the cluster using Hadoop YARN</li>
-
-{% highlight bash %}
-cd stratosphere-yarn-0.5-SNAPSHOT/
-./bin/yarn-session.sh -n 4 -jm 1024 -tm 3000
-{% endhighlight %}
-
-The arguments have the following meaning
-	<ul>
-			<li><code>-n</code> number of TaskManagers (=workers). This number must not exeed the number of task instances</li>
-			<li><code>-jm</code> memory (heapspace) for the JobManager</li>
-			<li><code>-tm</code> memory for the TaskManagers</li>
-	</ul>
-</ul>
-
-Once the output has changed from 
-{% highlight bash %}
-JobManager is now running on N/A:6123
-{% endhighlight %}
-to 
-{% highlight bash %}
-JobManager is now running on ip-172-31-13-68.us-west-2.compute.internal:6123
-{% endhighlight %}
-Stratosphere has started the JobManager. It will take a few seconds until the TaskManagers (workers) have connected to the JobManager. To see how many TaskManagers connected, you have to access the JobManager's web interface. Follow the steps below to do that ...
-
-
-
-
-<h3> 4. Step: Launch a Stratosphere Job</h3>
-
-This step shows how to submit and monitor a Stratosphere Job in the Amazon Cloud.
-
-<ul>
-<li> Open an additional terminal and connect again to the master of your cluster. </li>
-
-We recommend to create a SOCKS-proxy with your SSH that allows you to easily connect into the cluster. (If you've already a VPN setup with EC2, you can probably use that as well.)
-
-{% highlight bash %}
-ssh -D localhost:2001 hadoop@<your master dns name> -i <your pem file>
-{% endhighlight %}
-
-Notice the <code>-D localhost:2001</code> argument: It opens a SOCKS proxy on your computer allowing any application to use it to communicate through the proxy via an SSH tunnel to the master node. This allows you to access all services in your EMR cluster, such as the HDFS NameNode or the YARN web interface.
-
-<li>Configure a browser to use the SOCKS proxy. Open a browser with SOCKS proxy support (such as Firefox). Ideally, do not use your primary browser for this, since ALL traffic will be routed through Amazon.</li>
-
-<div class="row" style="padding-top:15px">
-	<div class="col-md-6">
-<a data-lightbox="inputs" href="{{site.baseurl}}/img/blog/emr-firefoxsettings.png" data-lightbox="example-1"><img class="img-responsive" src="{{site.baseurl}}/img/blog/emr-firefoxsettings.png" /></a>
-	</div>
-	<div class="col-md-6">
-		<ul>
-			<li>To configure the SOCKS proxy with Firefox, click on "Edit", "Preferences", choose the "Advanced" tab and press the "Settings ..." button.</li>
-			<li>Enter the details of the SOCKS proxy <b>localhost:2001</b>. Choose SOCKS v4.</li>
-			<li>Close the settings, your browser is now talking to the master node of your cluster</li>
-		</ul>
-	</div>
-</div>
-
-</ul>
-
-Since you're connected to the master now, you can open several web interfaces: <br>
-<b>YARN Resource Manager</b>: <code>http://&lt;masterIPAddress>:9026/</code> <br>
-<b>HDFS NameNode</b>: <code>http://&lt;masterIPAddress>:9101/</code>
-
-You find the `masterIPAddress` by entering `ifconfig` into the terminal:
-{% highlight bash %}
-[hadoop@ip-172-31-38-95 ~]$ ifconfig
-eth0      Link encap:Ethernet  HWaddr 02:CF:8E:CB:28:B2  
-          inet addr:172.31.38.95  Bcast:172.31.47.255  Mask:255.255.240.0
-          inet6 addr: fe80::cf:8eff:fecb:28b2/64 Scope:Link
-          RX bytes:166314967 (158.6 MiB)  TX bytes:89319246 (85.1 MiB)
-{% endhighlight %}
-
-**Optional:** If you want to use the hostnames within your Firefox (that also makes the NameNode links work), you have to enable DNS resolution over the SOCKS proxy. Open the Firefox config `about:config` and set `network.proxy.socks_remote_dns` to `true`.
-
-The YARN ResourceManager also allows you to connect to <b>Stratosphere's JobManager web interface</b>. Click the <b>ApplicationMaster</b> link in the "Tracking UI" column.
-
-To run the Wordcount example, you have to upload some sample data.
-{% highlight bash %}
-# download a text
-wget http://www.gnu.org/licenses/gpl.txt
-# upload it to HDFS:
-hadoop fs -copyFromLocal gpl.txt /input
-{% endhighlight %}
-
-To run a Job, enter the following command into the master's command line:
-{% highlight bash %}
-# optional: go to the extracted directory
-cd stratosphere-yarn-0.5-SNAPSHOT/
-# run the wordcount example
-./bin/stratosphere run -w -j examples/stratosphere-java-examples-0.5-SNAPSHOT-WordCount.jar  -a 16 hdfs:///input hdfs:///output
-{% endhighlight %}
-
-Make sure that the number of TaskManager's have connected to the JobManager.
-
-Lets go through the command in detail:
-
-* `./bin/stratosphere` is the standard launcher for Stratosphere jobs from the command line
-* The `-w` flag stands for "wait". It is a very useful to track the progress of the job.
-* `-j examples/stratosphere-java-examples-0.5-SNAPSHOT-WordCount.jar` the `-j` command sets the jar file containing the job. If you have you own application, place your Jar-file here.
-* `-a 16 hdfs:///input hdfs:///output` the `-a` command specifies the Job-specific arguments. In this case, the wordcount expects the following input `<numSubStasks> <input> <output>`.
-
-You can monitor the progress of your job in the JobManager webinterface. Once the job has finished (which should be the case after less than 10 seconds), you can analyze it there.
-Inspect the result in HDFS using:
-
-{% highlight bash %}
-hadoop fs -tail /output
-{% endhighlight %}
-
-If you want to shut down the whole cluster in the cloud, use Amazon's webinterface and click on "Terminate cluster". If you just want to stop the YARN session, press CTRL+C in the terminal. The Stratosphere instances will be killed by YARN.
\ No newline at end of file

http://git-wip-us.apache.org/repos/asf/flink-web/blob/52d8fe19/_posts/2014-02-24-stratosphere-google-summer-of-code-2014.md
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diff --git a/_posts/2014-02-24-stratosphere-google-summer-of-code-2014.md b/_posts/2014-02-24-stratosphere-google-summer-of-code-2014.md
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----
-layout: post
-title:  'Stratosphere got accepted for Google Summer of Code 2014'
-date:   2014-02-24 20:57:18
-categories: news
----
-
-<div class="lead">Students: Apply now for exciting summer projects in the Big Data / Analytics field</div>
-
-We are pleased to announce that Stratosphere got accepted to [Google Summer of Code 2014](http://www.google-melange.com/gsoc/homepage/google/gsoc2014) as a mentoring organization. This means that we will host a bunch of students to conduct projects within Stratosphere over the summer. [Read more on the GSoC manual for students](http://en.flossmanuals.net/GSoCStudentGuide/) and the [official FAQ](http://www.google-melange.com/gsoc/document/show/gsoc_program/google/gsoc2014/help_page). Students can improve their coding skills, learn to work with open-source projects, improve their CV and get a nice paycheck from Google.
-
-If you are an interested student, check out our [idea list](https://github.com/stratosphere/stratosphere/wiki/Google-Summer-of-Code-2014) in the wiki. It contains different projects with varying ranges of difficulty and requirement profiles. Students can also suggest their own projects.
-
-We welcome students to sign up at our [developer mailing list](https://groups.google.com/forum/#!forum/stratosphere-dev) to discuss their ideas.
-Applying students can use our wiki (create a new page) to create a project proposal. We are happy to have a look at it.
-

http://git-wip-us.apache.org/repos/asf/flink-web/blob/52d8fe19/_posts/2014-04-16-stratosphere-goes-apache-incubator.md
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diff --git a/_posts/2014-04-16-stratosphere-goes-apache-incubator.md b/_posts/2014-04-16-stratosphere-goes-apache-incubator.md
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----
-layout: post
-title:  'Stratosphere accepted as Apache Incubator Project'
-date:   2014-04-16 10:00:00
-categories: news
----
-
-We are happy to announce that Stratosphere has been accepted as a project for the [Apache Incubator](https://incubator.apache.org/). The [proposal](https://wiki.apache.org/incubator/StratosphereProposal) has been accepted by the Incubator PMC members earlier this week. The Apache Incubator is the first step in the process of giving a project to the [Apache Software Foundation](http://apache.org). While under incubation, the project will move to the Apache infrastructure and adopt the community-driven development principles of the Apache Foundation. Projects can graduate from incubation to become top-level projects if they show activity, a healthy community dynamic, and releases.
-
-We are glad to have Alan Gates as champion on board, as well as a set of great mentors, including Sean Owen, Ted Dunning, Owen O'Malley, Henry Saputra, and Ashutosh Chauhan. We are confident that we will make this a great open source effort.
-

http://git-wip-us.apache.org/repos/asf/flink-web/blob/52d8fe19/_posts/2014-05-31-release-0.5.md
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diff --git a/_posts/2014-05-31-release-0.5.md b/_posts/2014-05-31-release-0.5.md
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----
-layout: post
-title:  'Stratosphere version 0.5 available'
-date:   2014-05-31 10:00:00
-categories: news
----
-
-We are happy to announce a new major Stratosphere release, version 0.5. This release adds many new features and improves the interoperability, stability, and performance of the system. The major theme of the release is the completely new Java API that makes it easy to write powerful distributed programs.
-
-The release can be downloaded from the [Stratosphere website] (http://stratosphere.eu/downloads/) and from [GitHub] (https://github.com/stratosphere/stratosphere/releases/tag/release-0.5). All components are available as Apache Maven dependencies, making it simple to include Stratosphere in other projects. The website provides [extensive documentation](http://stratosphere.eu/docs/0.5/) of the system and the new features.
-
-
-
-## Shortlist of new Features
-
-Below is a short list of the most important additions to the Stratosphere system.
-
-#### New Java API
-This release introduces a completely new **data set-centric Java API**. This programming model significantly eases the development of Stratosphere programs, supports flexible use of regular Java classes as data types, and adds many new built-in operators to simplify the writing of powerful programs. The result are programs that need less code, are more readable, interoperate better with existing code, and execute faster.
-
-Take a look at the [examples](http://stratosphere.eu/docs/0.5/programming_guides/examples_java.html)  to get a feel for the API.
-
-
-#### General API Improvements
-**Broadcast Variables:** Publish a data set to all instances of another operator. This is handy if the your operator depends on the result of a computation, e.g., filter all values smaller than the average.
-
-**Distributed Cache:** Make (local and HDFS) files locally available on each machine processing a task.
-
-**Iteration Termination Improvements** Iterative algorithms can now terminate based on intermediate data sets, not only through aggregated statistics.
-
-**Collection data sources and sinks:** Speed-up the development and testing of Stratosphere programs by reading data from regular Java collections and inserting back into them.
-
-**JDBC data sources and sinks:** Read data from and write data to relational databases using a JDBC driver.
-
-**Hadoop input format and output format support:** Read and write data with any Hadoop input or output format.
-
-**Support for Avro encoded data:** Read data that has been materialized using Avro.
-
-**Deflate Files:** Stratosphere now transparently reads `.deflate` compressed files.
-
-
-#### Runtime and Optimizer Improvements
-
-**DAG Runtime Streaming:** Detection and resolution of streaming data flow deadlocks in the data flow optimizer.
-
-**Intermediate results across iteration boundaries:** Intermediate results computed outside iterative parts can be used inside iterative parts of the program.
-
-**Stability fixes:** Various stability fixes in both optimizer and runtime.
-
-
-#### Setup & Tooling
-
-**Improved YARN support:** Many improvements based on user-feedback: Packaging, Permissions, Error handling.
-
-**Java 8 compatibility**
-
-
-## Contributors
-
-In total, 26 people have contributed to Stratosphere since the last release. Thank you for making this project possible!
-
-
-* Alexander Alexandrov
-* Jesus Camacho
-* Ufuk Celebi
-* Mikhail Erofeev
-* Stephan Ewen
-* Alexandr Ferodov
-* Filip Haase
-* Jonathan Hasenberg
-* Markus Holzemer
-* Fabian Hueske
-* Vasia Kalavri
-* Aljoscha Krettek
-* Rajika Kumarasiri
-* Sebastian Kunert
-* Aaron Lam
-* Robert Metzger
-* Faisal Moeen
-* Martin Neumann
-* Mingliang Qi
-* Till Rohrmann
-* Chesnay Schepler
-* Vyachislav Soludev
-* Tuan Trieu
-* Artem Tsikiridis
-* Timo Walther
-* Robert Waury
-
-
-## Stratosphere is going Apache
-
-The Stratosphere project has been accepted to the Apache Incubator and will continue its work under the umbrella of the Apache Software Foundation. Due to a name conflict, we are switching the name of the project. We will make future releases of Stratosphere through the Apache foundation under a new name.
-

http://git-wip-us.apache.org/repos/asf/flink-web/blob/52d8fe19/_posts/2014-11-18-hadoop-compatibility.md
----------------------------------------------------------------------
diff --git a/_posts/2014-11-18-hadoop-compatibility.md b/_posts/2014-11-18-hadoop-compatibility.md
index 154c92a..ac599ce 100644
--- a/_posts/2014-11-18-hadoop-compatibility.md
+++ b/_posts/2014-11-18-hadoop-compatibility.md
@@ -87,4 +87,4 @@ While the Hadoop compatibility package is already very useful, we are currently
 
 Flink lets you reuse a lot of the code you wrote for Hadoop MapReduce, including all data types, all Input- and OutputFormats, and Mapper and Reducers of the mapred-API. Hadoop functions can be used within Flink programs and mixed with all other Flink functions. Due to Flink’s pipelined execution, Hadoop functions can arbitrarily be assembled without data exchange via HDFS. Moreover, the Flink community is currently working on a dedicated Hadoop Job operation to supporting the execution of Hadoop jobs as a whole.
 
-If you want to use Flink’s Hadoop compatibility package checkout our [documentation](http://ci.apache.org/projects/flink/flink-docs-release-0.7/hadoop_compatibility.html).
\ No newline at end of file
+If you want to use Flink’s Hadoop compatibility package checkout our [documentation](https://ci.apache.org/projects/flink/flink-docs-master/apis/batch/hadoop_compatibility.html).

http://git-wip-us.apache.org/repos/asf/flink-web/blob/52d8fe19/_posts/2015-02-04-january-in-flink.md
----------------------------------------------------------------------
diff --git a/_posts/2015-02-04-january-in-flink.md b/_posts/2015-02-04-january-in-flink.md
index 6a5315a..9d638e6 100644
--- a/_posts/2015-02-04-january-in-flink.md
+++ b/_posts/2015-02-04-january-in-flink.md
@@ -15,17 +15,13 @@ Flink 0.8.0 was released. See [here](http://flink.apache.org/news/2015/01/21/rel
 
 The community has published a [roadmap for 2015](https://cwiki.apache.org/confluence/display/FLINK/Flink+Roadmap) on the Flink wiki. Check it out to see what is coming up in Flink, and pick up an issue to contribute!
 
-### Scaling ALS
-
-Flink committers employed at [data Artisans](http://data-artisans.com) published a [blog post](http://data-artisans.com/computing-recommendations-with-flink.html) on how they scaled matrix factorization with Flink and Google Compute Engine to matrices with 28 billion elements.
-
 ### Articles in the press
 
 The Apache Software Foundation [announced](https://blogs.apache.org/foundation/entry/the_apache_software_foundation_announces69) Flink as a Top-Level Project. The announcement was picked up by the media, e.g., [here](http://sdtimes.com/inside-apache-software-foundations-newest-top-level-project-apache-flink/?utm_content=11232092&utm_medium=social&utm_source=twitter), [here](http://www.datanami.com/2015/01/12/apache-flink-takes-route-distributed-data-processing/), and [here](http://i-programmer.info/news/197-data-mining/8176-flink-reaches-top-level-status.html).
 
 ### Hadoop Summit
 
-A submitted abstract on Flink Streaming [won the community](http://2015.hadoopsummit.org/amsterdam-blog/announcing-the-community-vote-session-winners-for-the-2015-hadoop-summit-europe/) vote at “The Future of Hadoop” track.
+A submitted abstract on Flink Streaming won the community vote at “The Future of Hadoop” track.
 
 ### Meetups and talks
 
@@ -49,4 +45,4 @@ Semantic annotations are a powerful mechanism to expose information about the be
 
 ### [New YARN client](https://github.com/apache/flink/pull/292)
 
-The improved YARN client of Flink now allows users to deploy Flink on YARN for executing a single job. Older versions only supported a long-running YARN session. The code of the YARN client has been refactored to provide an (internal) Java API for controlling YARN clusters more easily.
\ No newline at end of file
+The improved YARN client of Flink now allows users to deploy Flink on YARN for executing a single job. Older versions only supported a long-running YARN session. The code of the YARN client has been refactored to provide an (internal) Java API for controlling YARN clusters more easily.

http://git-wip-us.apache.org/repos/asf/flink-web/blob/52d8fe19/_posts/2015-02-09-streaming-example.md
----------------------------------------------------------------------
diff --git a/_posts/2015-02-09-streaming-example.md b/_posts/2015-02-09-streaming-example.md
index 1027272..7dfd6a0 100644
--- a/_posts/2015-02-09-streaming-example.md
+++ b/_posts/2015-02-09-streaming-example.md
@@ -15,7 +15,7 @@ In this post, we go through an example that uses the Flink Streaming
 API to compute statistics on stock market data that arrive
 continuously and combine the stock market data with Twitter streams.
 See the [Streaming Programming
-Guide](http://ci.apache.org/projects/flink/flink-docs-master/apis/streaming_guide.html) for a
+Guide](http://ci.apache.org/projects/flink/flink-docs-master/apis/streaming/index.html) for a
 detailed presentation of the Streaming API.
 
 First, we read a bunch of stock price streams and combine them into
@@ -115,11 +115,11 @@ public static void main(String[] args) throws Exception {
 </div>
 
 See
-[here](http://ci.apache.org/projects/flink/flink-docs-master/apis/streaming_guide.html#sources)
+[here](http://ci.apache.org/projects/flink/flink-docs-master/apis/streaming/index.html#data-sources)
 on how you can create streaming sources for Flink Streaming
 programs. Flink, of course, has support for reading in streams from
 [external
-sources](http://ci.apache.org/projects/flink/flink-docs-master/apis/streaming_guide.html#stream-connectors)
+sources](http://ci.apache.org/projects/flink/flink-docs-master/apis/streaming/connectors/index.html)
 such as Apache Kafka, Apache Flume, RabbitMQ, and others. For the sake
 of this example, the data streams are simply generated using the
 `generateStock` method:
@@ -230,7 +230,7 @@ Window aggregations
 ---------------
 
 We first compute aggregations on time-based windows of the
-data. Flink provides [flexible windowing semantics](http://ci.apache.org/projects/flink/flink-docs-master/apis/streaming_guide.html#window-operators) where windows can
+data. Flink provides [flexible windowing semantics](http://ci.apache.org/projects/flink/flink-docs-master/apis/streaming/windows.html) where windows can
 also be defined based on count of records or any custom user defined
 logic.
 
@@ -432,7 +432,7 @@ Combining with a Twitter stream
 
 Next, we will read a Twitter stream and correlate it with our stock
 price stream. Flink has support for connecting to [Twitter's
-API](http://ci.apache.org/projects/flink/flink-docs-master/apis/streaming_guide.html#twitter-streaming-api),
+API](https://ci.apache.org/projects/flink/flink-docs-master/apis/streaming/connectors/twitter.html)
 but for the sake of this example we generate dummy tweet data.
 
 <img alt="Social media analytics" src="{{ site.baseurl }}/img/blog/blog_social_media.png" width="100%" class="img-responsive center-block">
@@ -666,7 +666,7 @@ public static final class WindowCorrelation
 Other things to try
 ---------------
 
-For a full feature overview please check the [Streaming Guide](http://ci.apache.org/projects/flink/flink-docs-master/apis/streaming_guide.html), which describes all the available API features.
+For a full feature overview please check the [Streaming Guide](http://ci.apache.org/projects/flink/flink-docs-master/apis/streaming/index.html), which describes all the available API features.
 You are very welcome to try out our features for different use-cases we are looking forward to your experiences. Feel free to [contact us](http://flink.apache.org/community.html#mailing-lists).
 
 Upcoming for streaming

http://git-wip-us.apache.org/repos/asf/flink-web/blob/52d8fe19/_posts/2015-03-02-february-2015-in-flink.md
----------------------------------------------------------------------
diff --git a/_posts/2015-03-02-february-2015-in-flink.md b/_posts/2015-03-02-february-2015-in-flink.md
index a9fe325..5a85f9f 100644
--- a/_posts/2015-03-02-february-2015-in-flink.md
+++ b/_posts/2015-03-02-february-2015-in-flink.md
@@ -70,12 +70,12 @@ DataSet<Vertex<Long, Double>> singleSourceShortestPaths = graph
 {% endhighlight %}	   
 
 See more Gelly examples
-[here](https://github.com/apache/flink/tree/master/flink-staging/flink-gelly/src/main/java/org/apache/flink/graph/example).
+[here](https://github.com/apache/flink/tree/master/flink-libraries/flink-gelly-examples).
 
 ### Flink Expressions
 
 The newly merged
-[flink-table](https://github.com/apache/flink/tree/master/flink-staging/flink-table)
+[flink-table](https://github.com/apache/flink/tree/master/flink-libraries/flink-table)
 module is the first step in Flink’s roadmap towards logical queries
 and SQL support. Here’s a preview on how you can read two CSV file,
 assign a logical schema to, and apply transformations like filters and
@@ -99,7 +99,7 @@ val items =
 ### Access to HCatalog tables
 
 With the [flink-hcatalog
-module](https://github.com/apache/flink/tree/master/flink-staging/flink-hcatalog),
+module](https://github.com/apache/flink/tree/master/flink-batch-connectors/flink-hcatalog),
 you can now conveniently access HCatalog/Hive tables. The module
 supports projection (selection and order of fields) and partition
 filters.

http://git-wip-us.apache.org/repos/asf/flink-web/blob/52d8fe19/_posts/2015-03-13-peeking-into-Apache-Flinks-Engine-Room.md
----------------------------------------------------------------------
diff --git a/_posts/2015-03-13-peeking-into-Apache-Flinks-Engine-Room.md b/_posts/2015-03-13-peeking-into-Apache-Flinks-Engine-Room.md
index 8156780..0d136ed 100644
--- a/_posts/2015-03-13-peeking-into-Apache-Flinks-Engine-Room.md
+++ b/_posts/2015-03-13-peeking-into-Apache-Flinks-Engine-Room.md
@@ -122,7 +122,7 @@ The Hybrid-Hash-Join distinguishes its inputs as build-side and probe-side input
 
 Ship and local strategies do not depend on each other and can be independently chosen. Therefore, Flink can execute a join of two data sets R and S in nine different ways by combining any of the three ship strategies (RR, BF with R being broadcasted, BF with S being broadcasted) with any of the three local strategies (SM, HH with R being build-side, HH with S being build-side). Each of these strategy combinations results in different execution performance depending on the data sizes and the available amount of working memory. In case of a small data set R and a much larger data set S, broadcasting R and using it as build-side input of a Hybrid-Hash-Join is usually a good choice because the much larger data set S is not shipped and not materialized (given that the hash table completely fits into memory). If both data sets are rather large or the join is performed on many parallel instances, repartitioning both inputs is a robust choice.
 
-Flink features a cost-based optimizer which automatically chooses the execution strategies for all operators including joins. Without going into the details of cost-based optimization, this is done by computing cost estimates for execution plans with different strategies and picking the plan with the least estimated costs. Thereby, the optimizer estimates the amount of data which is shipped over the the network and written to disk. If no reliable size estimates for the input data can be obtained, the optimizer falls back to robust default choices. A key feature of the optimizer is to reason about existing data properties. For example, if the data of one input is already partitioned in a suitable way, the generated candidate plans will not repartition this input. Hence, the choice of a RR ship strategy becomes more likely. The same applies for previously sorted data and the Sort-Merge-Join strategy. Flink programs can help the optimizer to reason about existing data properties by pro
 viding semantic information about  user-defined functions [[4]](http://ci.apache.org/projects/flink/flink-docs-master/apis/programming_guide.html#semantic-annotations). While the optimizer is a killer feature of Flink, it can happen that a user knows better than the optimizer how to execute a specific join. Similar to relational database systems, Flink offers optimizer hints to tell the optimizer which join strategies to pick [[5]](http://ci.apache.org/projects/flink/flink-docs-master/apis/dataset_transformations.html#join-algorithm-hints).
+Flink features a cost-based optimizer which automatically chooses the execution strategies for all operators including joins. Without going into the details of cost-based optimization, this is done by computing cost estimates for execution plans with different strategies and picking the plan with the least estimated costs. Thereby, the optimizer estimates the amount of data which is shipped over the the network and written to disk. If no reliable size estimates for the input data can be obtained, the optimizer falls back to robust default choices. A key feature of the optimizer is to reason about existing data properties. For example, if the data of one input is already partitioned in a suitable way, the generated candidate plans will not repartition this input. Hence, the choice of a RR ship strategy becomes more likely. The same applies for previously sorted data and the Sort-Merge-Join strategy. Flink programs can help the optimizer to reason about existing data properties by pro
 viding semantic information about  user-defined functions [[4]](https://ci.apache.org/projects/flink/flink-docs-release-1.0/apis/batch/index.html#semantic-annotations). While the optimizer is a killer feature of Flink, it can happen that a user knows better than the optimizer how to execute a specific join. Similar to relational database systems, Flink offers optimizer hints to tell the optimizer which join strategies to pick [[5]](https://ci.apache.org/projects/flink/flink-docs-release-1.0/apis/batch/dataset_transformations.html#join-algorithm-hints).
 
 ### How is Flink’s join performance?
 
@@ -173,5 +173,5 @@ We have seen that off-the-shelf distributed joins work really well in Flink. But
 [1] [“MapReduce: Simplified data processing on large clusters”](), Dean, Ghemawat, 2004 <br>
 [2] [Flink 0.8.1 documentation: Data Transformations](http://ci.apache.org/projects/flink/flink-docs-release-0.8/dataset_transformations.html) <br>
 [3] [Flink 0.8.1 documentation: Joins](http://ci.apache.org/projects/flink/flink-docs-release-0.8/dataset_transformations.html#join) <br>
-[4] [Flink 0.9-SNAPSHOT documentation: Semantic annotations](http://ci.apache.org/projects/flink/flink-docs-master/apis/programming_guide.html#semantic-annotations) <br>
-[5] [Flink 0.9-SNAPSHOT documentation: Optimizer join hints](http://ci.apache.org/projects/flink/flink-docs-master/apis/dataset_transformations.html#join-algorithm-hints) <br>
+[4] [Flink 1.0 documentation: Semantic annotations](https://ci.apache.org/projects/flink/flink-docs-release-1.0/apis/batch/index.html#semantic-annotations) <br>
+[5] [Flink 1.0 documentation: Optimizer join hints](https://ci.apache.org/projects/flink/flink-docs-release-1.0/apis/batch/dataset_transformations.html#join-algorithm-hints) <br>

http://git-wip-us.apache.org/repos/asf/flink-web/blob/52d8fe19/_posts/2015-04-07-march-in-flink.md
----------------------------------------------------------------------
diff --git a/_posts/2015-04-07-march-in-flink.md b/_posts/2015-04-07-march-in-flink.md
index cf0c8e6..3a9044e 100644
--- a/_posts/2015-04-07-march-in-flink.md
+++ b/_posts/2015-04-07-march-in-flink.md
@@ -7,15 +7,9 @@ categories: news
 
 March has been a busy month in the Flink community.
 
-### Flink runner for Google Cloud Dataflow
+### Scaling ALS
 
-A Flink runner for Google Cloud Dataflow was announced. See the blog
-posts by [data Artisans](http://data-artisans.com/dataflow.html) and
-the [Google Cloud Platform Blog](http://googlecloudplatform.blogspot.de/2015/03/announcing-Google-Cloud-Dataflow-runner-for-Apache-Flink.html).
-Google Cloud Dataflow programs can be written using and open-source
-SDK and run in multiple backends, either as a managed service inside
-Google's infrastructure, or leveraging open source runners,
-including Apache Flink.
+Flink committers employed at [data Artisans](http://data-artisans.com) published a [blog post](http://data-artisans.com/how-to-factorize-a-700-gb-matrix-with-apache-flink/) on how they scaled matrix factorization with Flink and Google Compute Engine to matrices with 28 billion elements.
 
 ### Learn about the internals of Flink
 
@@ -48,14 +42,14 @@ future of Flink. The talk is available on
 ### Table API in Scala and Java
 
 The new [Table
-API](https://github.com/apache/flink/tree/master/flink-staging/flink-table)
+API](https://github.com/apache/flink/tree/master/flink-libraries/flink-table)
 in Flink is now available in both Java and Scala. Check out the
-examples [here (Java)](https://github.com/apache/flink/blob/master/flink-staging/flink-table/src/main/java/org/apache/flink/examples/java/JavaTableExample.java) and [here (Scala)](https://github.com/apache/flink/tree/master/flink-staging/flink-table/src/main/scala/org/apache/flink/examples/scala).
+examples [here (Java)](https://github.com/apache/flink/blob/master/flink-libraries/flink-table/src/main/java/org/apache/flink/examples/java/JavaTableExample.java) and [here (Scala)](https://github.com/apache/flink/tree/master/flink-libraries/flink-table/src/main/scala/org/apache/flink/examples/scala).
 
 ### Additions to the Machine Learning library
 
 Flink's [Machine Learning
-library](https://github.com/apache/flink/tree/master/flink-staging/flink-ml)
+library](https://github.com/apache/flink/tree/master/flink-libraries/flink-ml)
 is seeing quite a bit of traction. Recent additions include the [CoCoA
 algorithm](http://arxiv.org/abs/1409.1458) for distributed
 optimization.
@@ -70,8 +64,3 @@ checkpoints at failure recovery. This functionality is currently
 limited in that it does not yet handle large state and iterative
 programs.
 
-### Flink on Tez
-
-A new execution environment enables non-iterative Flink jobs to use
-Tez as an execution backend instead of Flink's own network stack. Learn more
-[here](http://ci.apache.org/projects/flink/flink-docs-master/setup/flink_on_tez.html).
\ No newline at end of file

http://git-wip-us.apache.org/repos/asf/flink-web/blob/52d8fe19/_posts/2015-04-13-release-0.9.0-milestone1.md
----------------------------------------------------------------------
diff --git a/_posts/2015-04-13-release-0.9.0-milestone1.md b/_posts/2015-04-13-release-0.9.0-milestone1.md
index f10823d..145c407 100644
--- a/_posts/2015-04-13-release-0.9.0-milestone1.md
+++ b/_posts/2015-04-13-release-0.9.0-milestone1.md
@@ -45,7 +45,7 @@ for Flink programs. Tables are available for both static and streaming
 data sources (DataSet and DataStream APIs).
 
 Check out the Table guide for Java and Scala
-[here](http://ci.apache.org/projects/flink/flink-docs-master/libs/table.html).
+[here](https://ci.apache.org/projects/flink/flink-docs-master/apis/batch/libs/table.html).
 
 ### Gelly Graph Processing API
 
@@ -60,13 +60,13 @@ algorithms, including PageRank, SSSP, label propagation, and community
 detection.
 
 Gelly internally builds on top of Flink’s [delta
-iterations](http://ci.apache.org/projects/flink/flink-docs-master/apis/iterations.html). Iterative
+iterations](https://ci.apache.org/projects/flink/flink-docs-master/apis/batch/iterations.html). Iterative
 graph algorithms are executed leveraging mutable state, achieving
 similar performance with specialized graph processing systems.
 
 Gelly will eventually subsume Spargel, Flink’s Pregel-like API. Check
 out the Gelly guide
-[here](http://ci.apache.org/projects/flink/flink-docs-master/libs/gelly_guide.html).
+[here](https://ci.apache.org/projects/flink/flink-docs-master/apis/batch/libs/gelly.html).
 
 ### Flink Machine Learning Library
 
@@ -227,4 +227,4 @@ automatically.  Users can also manually register serializers to Kryo
   socket sink
 
 * [FLINK-1436](https://issues.apache.org/jira/browse/FLINK-1436):
-  Improve usability of command line interface
\ No newline at end of file
+  Improve usability of command line interface

http://git-wip-us.apache.org/repos/asf/flink-web/blob/52d8fe19/_posts/2015-05-14-Community-update-April.md
----------------------------------------------------------------------
diff --git a/_posts/2015-05-14-Community-update-April.md b/_posts/2015-05-14-Community-update-April.md
index a524ec9..79bb258 100644
--- a/_posts/2015-05-14-Community-update-April.md
+++ b/_posts/2015-05-14-Community-update-April.md
@@ -11,6 +11,17 @@ excerpt: "<p>The monthly update from the Flink community. Including the availabi
 
 April was an packed month for Apache Flink. 
 
+### Flink runner for Google Cloud Dataflow
+
+A Flink runner for Google Cloud Dataflow was announced. See the blog
+posts by [data Artisans](http://data-artisans.com/announcing-google-cloud-dataflow-on-flink-and-easy-flink-deployment-on-google-cloud/) and
+the [Google Cloud Platform Blog](http://googlecloudplatform.blogspot.de/2015/03/announcing-Google-Cloud-Dataflow-runner-for-Apache-Flink.html).
+Google Cloud Dataflow programs can be written using and open-source
+SDK and run in multiple backends, either as a managed service inside
+Google's infrastructure, or leveraging open source runners,
+including 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
@@ -29,4 +40,4 @@ Fabian Hueske gave an [interview at InfoQ](http://www.infoq.com/news/2015/04/hue
 
 ## 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.
\ No newline at end of file
+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://2015.flink-forward.org/), the first conference to bring together the whole Flink community is taking place in Berlin in October 2015.

http://git-wip-us.apache.org/repos/asf/flink-web/blob/52d8fe19/_posts/2015-09-03-flink-forward.md
----------------------------------------------------------------------
diff --git a/_posts/2015-09-03-flink-forward.md b/_posts/2015-09-03-flink-forward.md
index 146f14d..cd35e13 100644
--- a/_posts/2015-09-03-flink-forward.md
+++ b/_posts/2015-09-03-flink-forward.md
@@ -5,7 +5,7 @@ date:   2015-09-03 08:00:00
 categories: news
 ---
 
-[Flink Forward 2015](http://flink-forward.org/) is the first
+[Flink Forward 2015](http://2015.flink-forward.org/) is the first
 conference with Flink at its center that aims to bring together the
 Apache Flink community in a single place. The organizers are starting
 this conference in October 12 and 13 from Berlin, the place where
@@ -15,7 +15,7 @@ Apache Flink started.
 <img src="{{ site.baseurl }}/img/blog/flink-forward-banner.png" style="width:80%;margin:15px">
 </center>
 
-The [conference program](http://flink-forward.org/?post_type=day) has
+The [conference program](http://2015.flink-forward.org/?post_type=day) has
 been announced by the organizers and a program committee consisting of
 Flink PMC members. The agenda contains talks from industry and
 academia as well as a dedicated session on hands-on Flink training.
@@ -23,7 +23,7 @@ academia as well as a dedicated session on hands-on Flink training.
 Some highlights of the talks include
 
 - A keynote by [William
-  Vambenepe](http://flink-forward.org/?speaker=william-vambenepe),
+  Vambenepe](http://2015.flink-forward.org/?speaker=william-vambenepe),
   lead of the product management team responsible for Big Data
   services on Google Cloud Platform (BigQuery, Dataflow, etc...) on
   data streaming, Google Cloud Dataflow, and Apache Flink.
@@ -39,7 +39,6 @@ Some highlights of the talks include
 - Talks by Flink committers on several aspects of the system, such as
   fault tolerance, the internal runtime architecture, and others.
 
-Check out the [schedule](http://flink-forward.org/?post_type=day) and
-[register](http://flink-forward.org/?page_id=96) for the
-conference.
+Check out the [schedule](http://2015.flink-forward.org/?post_type=day) and
+register for the conference.
 

http://git-wip-us.apache.org/repos/asf/flink-web/blob/52d8fe19/_posts/2015-09-16-off-heap-memory.md
----------------------------------------------------------------------
diff --git a/_posts/2015-09-16-off-heap-memory.md b/_posts/2015-09-16-off-heap-memory.md
index 1208406..0b75945 100644
--- a/_posts/2015-09-16-off-heap-memory.md
+++ b/_posts/2015-09-16-off-heap-memory.md
@@ -159,7 +159,7 @@ There is still a bit of mystery left, specifically why sometimes code is faster
 
 ## Appendix: Detailed Micro Benchmarks
 
-These microbenchmarks test the performance of the different memory segment implementations on various operation. The code is available [as part of the Flink project](https://github.com/apache/flink/blob/master/flink-core/src/test/java/org/apache/flink/core/memory/benchmarks/MemorySegmentSpeedBenchmark.java)
+These microbenchmarks test the performance of the different memory segment implementations on various operation.
 
 Each experiments tests the different implementations multiple times in different orders, to balance the advantage/disadvantage of the JIT compiler specializing towards certain code paths. All experiments were run 5x, discarding the fastest and slowest run, and then averaged. This compensated for delay before the JIT kicks in.
 

http://git-wip-us.apache.org/repos/asf/flink-web/blob/52d8fe19/_posts/2015-12-11-storm-compatibility.md
----------------------------------------------------------------------
diff --git a/_posts/2015-12-11-storm-compatibility.md b/_posts/2015-12-11-storm-compatibility.md
index 1dae5df..5fa39c3 100644
--- a/_posts/2015-12-11-storm-compatibility.md
+++ b/_posts/2015-12-11-storm-compatibility.md
@@ -148,7 +148,7 @@ Flink's compatibility package for Storm allows using unmodified Spouts and Bolts
 This enables you to even embed third-party Spouts and Bolts where the source code is not available.
 While you can embed Spouts/Bolts in a Flink program and mix-and-match them with Flink operators, running whole topologies is the easiest way to get started and can be achieved with almost no code changes.
 
-If you want to try out Flink's Storm compatibility package checkout our [Documentation](https://ci.apache.org/projects/flink/flink-docs-master/apis/storm_compatibility.html).
+If you want to try out Flink's Storm compatibility package checkout our [Documentation](https://ci.apache.org/projects/flink/flink-docs-master/apis/streaming/storm_compatibility.html).
 
 <hr />
 

http://git-wip-us.apache.org/repos/asf/flink-web/blob/52d8fe19/_posts/2015-12-18-a-year-in-review.md
----------------------------------------------------------------------
diff --git a/_posts/2015-12-18-a-year-in-review.md b/_posts/2015-12-18-a-year-in-review.md
index 538bc73..de516da 100644
--- a/_posts/2015-12-18-a-year-in-review.md
+++ b/_posts/2015-12-18-a-year-in-review.md
@@ -49,14 +49,14 @@ meetups around the globe](http://apache-flink.meetup.com/):
 # Flink Forward 2015
 
 One of the highlights of the year for Flink was undoubtedly the [Flink
-Forward](http://flink-forward.org/) conference, the first conference
+Forward](http://2015.flink-forward.org/) conference, the first conference
 on Apache Flink that was held in October in Berlin. More than 250
 participants (roughly half based outside Germany where the conference
 was held) attended more than 33 technical talks from organizations
 including Google, MongoDB, Bouygues Telecom, NFLabs, Euranova, RedHat,
 IBM, Huawei, Intel, Ericsson, Capital One, Zalando, Amadeus, the Otto
 Group, and ResearchGate. If you have not yet watched their talks,
-check out the [slides](http://flink-forward.org/?post_type=day) and
+check out the [slides](http://2015.flink-forward.org/?post_type=day) and
 [videos](https://www.youtube.com/playlist?list=PLDX4T_cnKjD31JeWR1aMOi9LXPRQ6nyHO)
 from Flink Forward.
 


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