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From fhue...@apache.org
Subject [2/5] flink-web git commit: Rebuild website for Stream SQL blog post
Date Tue, 24 May 2016 15:02:31 GMT
http://git-wip-us.apache.org/repos/asf/flink-web/blob/7e1269c3/content/news/2015/08/24/introducing-flink-gelly.html
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diff --git a/content/news/2015/08/24/introducing-flink-gelly.html b/content/news/2015/08/24/introducing-flink-gelly.html
index f8a4fd7..726f15a 100644
--- a/content/news/2015/08/24/introducing-flink-gelly.html
+++ b/content/news/2015/08/24/introducing-flink-gelly.html
@@ -226,21 +226,21 @@ and mutations as well as neighborhood aggregations.</p>
 
 <h4 id="common-graph-metrics">Common Graph Metrics</h4>
 <p>These methods can be used to retrieve several graph metrics and properties, such as the number
-of vertices, edges and the node degrees.</p>
+of vertices, edges and the node degrees. </p>
 
 <h4 id="transformations">Transformations</h4>
 <p>The transformation methods enable several Graph operations, using high-level functions similar to
 the ones provided by the batch processing API. These transformations can be applied one after the
-other, yielding a new Graph after each step, in a fashion similar to operators on DataSets:</p>
+other, yielding a new Graph after each step, in a fashion similar to operators on DataSets: </p>
 
 <div class="highlight"><pre><code class="language-java"><span class="n">inputGraph</span><span class="o">.</span><span class="na">getUndirected</span><span class="o">().</span><span class="na">mapEdges</span><span class="o">(</span><span class="k">new</span> <span class="nf">CustomEdgeMapper</span><span class="o">());</span></code></pre></div>
 
 <p>Transformations can be applied on:</p>
 
 <ol>
-  <li><strong>Vertices</strong>: <code>mapVertices</code>, <code>joinWithVertices</code>, <code>filterOnVertices</code>, <code>addVertex</code>, …</li>
-  <li><strong>Edges</strong>: <code>mapEdges</code>, <code>filterOnEdges</code>, <code>removeEdge</code>, …</li>
-  <li><strong>Triplets</strong> (source vertex, target vertex, edge): <code>getTriplets</code></li>
+  <li><strong>Vertices</strong>: <code>mapVertices</code>, <code>joinWithVertices</code>, <code>filterOnVertices</code>, <code>addVertex</code>, …  </li>
+  <li><strong>Edges</strong>: <code>mapEdges</code>, <code>filterOnEdges</code>, <code>removeEdge</code>, …   </li>
+  <li><strong>Triplets</strong> (source vertex, target vertex, edge): <code>getTriplets</code>  </li>
 </ol>
 
 <h4 id="neighborhood-aggregations">Neighborhood Aggregations</h4>
@@ -374,7 +374,7 @@ vertex values do not need to be recomputed during an iteration.</p>
 <p>Let us reconsider the Single Source Shortest Paths algorithm. In each iteration, a vertex:</p>
 
 <ol>
-  <li><strong>Gather</strong> retrieves distances from its neighbors summed up with the corresponding edge values;</li>
+  <li><strong>Gather</strong> retrieves distances from its neighbors summed up with the corresponding edge values; </li>
   <li><strong>Sum</strong> compares the newly obtained distances in order to extract the minimum;</li>
   <li><strong>Apply</strong> and finally adopts the minimum distance computed in the sum step,
 provided that it is lower than its current value. If a vertex’s value does not change during
@@ -433,7 +433,7 @@ plays that each song has. We then filter out the list of songs the users do not
 playlist. Then we compute the top songs per user (i.e. the songs a user listened to the most).
 Finally, as a separate use-case on the same data set, we create a user-user similarity graph based
 on the common songs and use this resulting graph to detect communities by calling Gelly’s Label Propagation
-library method.</p>
+library method. </p>
 
 <p>For running the example implementation, please use the 0.10-SNAPSHOT version of Flink as a
 dependency. The full example code base can be found <a href="https://github.com/apache/flink/blob/master/flink-staging/flink-gelly/src/main/java/org/apache/flink/graph/example/MusicProfiles.java">here</a>. The public data set used for testing
@@ -523,10 +523,10 @@ in the figure below.</p>
 
 <p>To form the user-user graph in Flink, we will simply take the edges from the user-song graph
 (left-hand side of the image), group them by song-id, and then add all the users (source vertex ids)
-to an ArrayList.</p>
+to an ArrayList. </p>
 
 <p>We then match users who listened to the same song two by two, creating a new edge to mark their
-common interest (right-hand side of the image).</p>
+common interest (right-hand side of the image). </p>
 
 <p>Afterwards, we perform a <code>distinct()</code> operation to avoid creation of duplicate data.
 Considering that we now have the DataSet of edges which present interest, creating a graph is as
@@ -565,7 +565,7 @@ formed. To do so, we first initialize each vertex with a numeric label using the
 the id of a vertex with the first element of the tuple, afterwards applying a map function.
 Finally, we call the <code>run()</code> method with the LabelPropagation library method passed
 as a parameter. In the end, the vertices will be updated to contain the most frequent label
-among their neighbors.</p>
+among their neighbors. </p>
 
 <div class="highlight"><pre><code class="language-java"><span class="c1">// detect user communities using label propagation</span>
 <span class="c1">// initialize each vertex with a unique numeric label</span>
@@ -595,10 +595,10 @@ among their neighbors.</p>
 <p>Currently, Gelly matches the basic functionalities provided by most state-of-the-art graph
 processing systems. Our vision is to turn Gelly into more than “yet another library for running
 PageRank-like algorithms” by supporting generic iterations, implementing graph partitioning,
-providing bipartite graph support and by offering numerous other features.</p>
+providing bipartite graph support and by offering numerous other features. </p>
 
 <p>We are also enriching Flink Gelly with a set of operators suitable for highly skewed graphs
-as well as a Graph API built on Flink Streaming.</p>
+as well as a Graph API built on Flink Streaming. </p>
 
 <p>In the near future, we would like to see how Gelly can be integrated with graph visualization
 tools, graph database systems and sampling techniques.</p>

http://git-wip-us.apache.org/repos/asf/flink-web/blob/7e1269c3/content/news/2015/09/16/off-heap-memory.html
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diff --git a/content/news/2015/09/16/off-heap-memory.html b/content/news/2015/09/16/off-heap-memory.html
index 40db6ba..317e596 100644
--- a/content/news/2015/09/16/off-heap-memory.html
+++ b/content/news/2015/09/16/off-heap-memory.html
@@ -206,7 +206,7 @@
 
 <h2 id="the-off-heap-memory-implementation">The off-heap Memory Implementation</h2>
 
-<p>Given that all memory intensive internal algorithms are already implemented against the <code>MemorySegment</code>, our implementation to switch to off-heap memory is actually trivial. You can compare it to replacing all <code>ByteBuffer.allocate(numBytes)</code> calls with <code>ByteBuffer.allocateDirect(numBytes)</code>. In Flink’s case it meant that we made the <code>MemorySegment</code> abstract and added the <code>HeapMemorySegment</code> and <code>OffHeapMemorySegment</code> subclasses. The <code>OffHeapMemorySegment</code> takes the off-heap memory pointer from a <code>java.nio.DirectByteBuffer</code> and implements its specialized access methods using <code>sun.misc.Unsafe</code>. We also made a few adjustments to the startup scripts and the deployment code to make sure that the JVM is permitted enough off-heap memory (direct memory, <em>-XX:MaxDirectMemorySize</em>).</p>
+<p>Given that all memory intensive internal algorithms are already implemented against the <code>MemorySegment</code>, our implementation to switch to off-heap memory is actually trivial. You can compare it to replacing all <code>ByteBuffer.allocate(numBytes)</code> calls with <code>ByteBuffer.allocateDirect(numBytes)</code>. In Flink’s case it meant that we made the <code>MemorySegment</code> abstract and added the <code>HeapMemorySegment</code> and <code>OffHeapMemorySegment</code> subclasses. The <code>OffHeapMemorySegment</code> takes the off-heap memory pointer from a <code>java.nio.DirectByteBuffer</code> and implements its specialized access methods using <code>sun.misc.Unsafe</code>. We also made a few adjustments to the startup scripts and the deployment code to make sure that the JVM is permitted enough off-heap memory (direct memory, <em>-XX:MaxDirectMemorySize</em>). </p>
 
 <p>In practice we had to go one step further, to make the implementation perform well. While the <code>ByteBuffer</code> is used in I/O code paths to compose headers and move bulk memory into place, the MemorySegment is part of the innermost loops of many algorithms (sorting, hash tables, …). That means that the access methods have to be as fast as possible.</p>
 

http://git-wip-us.apache.org/repos/asf/flink-web/blob/7e1269c3/content/news/2015/11/16/release-0.10.0.html
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diff --git a/content/news/2015/11/16/release-0.10.0.html b/content/news/2015/11/16/release-0.10.0.html
index d3627b9..163f98e 100644
--- a/content/news/2015/11/16/release-0.10.0.html
+++ b/content/news/2015/11/16/release-0.10.0.html
@@ -162,7 +162,7 @@
 
 <p>The Apache Flink community is pleased to announce the availability of the 0.10.0 release. The community put significant effort into improving and extending Apache Flink since the last release, focusing on data stream processing and operational features. About 80 contributors provided bug fixes, improvements, and new features such that in total more than 400 JIRA issues could be resolved.</p>
 
-<p>For Flink 0.10.0, the focus of the community was to graduate the DataStream API from beta and to evolve Apache Flink into a production-ready stream data processor with a competitive feature set. These efforts resulted in support for event-time and out-of-order streams, exactly-once guarantees in the case of failures, a very flexible windowing mechanism, sophisticated operator state management, and a highly-available cluster operation mode. Flink 0.10.0 also brings a new monitoring dashboard with real-time system and job monitoring capabilities. Both batch and streaming modes of Flink benefit from the new high availability and improved monitoring features. Needless to say that Flink 0.10.0 includes many more features, improvements, and bug fixes.</p>
+<p>For Flink 0.10.0, the focus of the community was to graduate the DataStream API from beta and to evolve Apache Flink into a production-ready stream data processor with a competitive feature set. These efforts resulted in support for event-time and out-of-order streams, exactly-once guarantees in the case of failures, a very flexible windowing mechanism, sophisticated operator state management, and a highly-available cluster operation mode. Flink 0.10.0 also brings a new monitoring dashboard with real-time system and job monitoring capabilities. Both batch and streaming modes of Flink benefit from the new high availability and improved monitoring features. Needless to say that Flink 0.10.0 includes many more features, improvements, and bug fixes. </p>
 
 <p>We encourage everyone to <a href="/downloads.html">download the release</a> and <a href="https://ci.apache.org/projects/flink/flink-docs-release-0.10/">check out the documentation</a>. Feedback through the Flink <a href="/community.html#mailing-lists">mailing lists</a> is, as always, very welcome!</p>
 

http://git-wip-us.apache.org/repos/asf/flink-web/blob/7e1269c3/content/news/2015/12/04/Introducing-windows.html
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diff --git a/content/news/2015/12/04/Introducing-windows.html b/content/news/2015/12/04/Introducing-windows.html
index 715c0cb..1f9201c 100644
--- a/content/news/2015/12/04/Introducing-windows.html
+++ b/content/news/2015/12/04/Introducing-windows.html
@@ -160,7 +160,7 @@
       <article>
         <p>04 Dec 2015 by Fabian Hueske (<a href="https://twitter.com/fhueske">@fhueske</a>)</p>
 
-<p>The data analysis space is witnessing an evolution from batch to stream processing for many use cases. Although batch can be handled as a special case of stream processing, analyzing never-ending streaming data often requires a shift in the mindset and comes with its own terminology (for example, “windowing” and “at-least-once”/”exactly-once” processing). This shift and the new terminology can be quite confusing for people being new to the space of stream processing. Apache Flink is a production-ready stream processor with an easy-to-use yet very expressive API to define advanced stream analysis programs. Flink’s API features very flexible window definitions on data streams which let it stand out among other open source stream processors.</p>
+<p>The data analysis space is witnessing an evolution from batch to stream processing for many use cases. Although batch can be handled as a special case of stream processing, analyzing never-ending streaming data often requires a shift in the mindset and comes with its own terminology (for example, “windowing” and “at-least-once”/”exactly-once” processing). This shift and the new terminology can be quite confusing for people being new to the space of stream processing. Apache Flink is a production-ready stream processor with an easy-to-use yet very expressive API to define advanced stream analysis programs. Flink’s API features very flexible window definitions on data streams which let it stand out among other open source stream processors. </p>
 
 <p>In this blog post, we discuss the concept of windows for stream processing, present Flink’s built-in windows, and explain its support for custom windowing semantics.</p>
 
@@ -223,17 +223,17 @@
 
 <p>There is one aspect that we haven’t discussed yet, namely the exact meaning of “<em>collects elements for one minute</em>” which boils down to the question, “<em>How does the stream processor interpret time?</em>”.</p>
 
-<p>Apache Flink features three different notions of time, namely <em>processing time</em>, <em>event time</em>, and <em>ingestion time</em>.</p>
+<p>Apache Flink features three different notions of time, namely <em>processing time</em>, <em>event time</em>, and <em>ingestion time</em>. </p>
 
 <ol>
-  <li>In <strong>processing time</strong>, windows are defined with respect to the wall clock of the machine that builds and processes a window, i.e., a one minute processing time window collects elements for exactly one minute.</li>
-  <li>In <strong>event time</strong>, windows are defined with respect to timestamps that are attached to each event record. This is common for many types of events, such as log entries, sensor data, etc, where the timestamp usually represents the time at which the event occurred. Event time has several benefits over processing time. First of all, it decouples the program semantics from the actual serving speed of the source and the processing performance of system. Hence you can process historic data, which is served at maximum speed, and continuously produced data with the same program. It also prevents semantically incorrect results in case of backpressure or delays due to failure recovery. Second, event time windows compute correct results, even if events arrive out-of-order of their timestamp which is common if a data stream gathers events from distributed sources.</li>
+  <li>In <strong>processing time</strong>, windows are defined with respect to the wall clock of the machine that builds and processes a window, i.e., a one minute processing time window collects elements for exactly one minute. </li>
+  <li>In <strong>event time</strong>, windows are defined with respect to timestamps that are attached to each event record. This is common for many types of events, such as log entries, sensor data, etc, where the timestamp usually represents the time at which the event occurred. Event time has several benefits over processing time. First of all, it decouples the program semantics from the actual serving speed of the source and the processing performance of system. Hence you can process historic data, which is served at maximum speed, and continuously produced data with the same program. It also prevents semantically incorrect results in case of backpressure or delays due to failure recovery. Second, event time windows compute correct results, even if events arrive out-of-order of their timestamp which is common if a data stream gathers events from distributed sources. </li>
   <li><strong>Ingestion time</strong> is a hybrid of processing and event time. It assigns wall clock timestamps to records as soon as they arrive in the system (at the source) and continues processing with event time semantics based on the attached timestamps.</li>
 </ol>
 
 <h2 id="count-windows">Count Windows</h2>
 
-<p>Apache Flink also features count windows. A tumbling count window of 100 will collect 100 events in a window and evaluate the window when the 100th element has been added.</p>
+<p>Apache Flink also features count windows. A tumbling count window of 100 will collect 100 events in a window and evaluate the window when the 100th element has been added. </p>
 
 <p>In Flink’s DataStream API, tumbling and sliding count windows are defined as follows:</p>
 
@@ -256,7 +256,7 @@
 
 <h2 id="dissecting-flinks-windowing-mechanics">Dissecting Flink’s windowing mechanics</h2>
 
-<p>Flink’s built-in time and count windows cover a wide range of common window use cases. However, there are of course applications that require custom windowing logic that cannot be addressed by Flink’s built-in windows. In order to support also applications that need very specific windowing semantics, the DataStream API exposes interfaces for the internals of its windowing mechanics. These interfaces give very fine-grained control about the way that windows are built and evaluated.</p>
+<p>Flink’s built-in time and count windows cover a wide range of common window use cases. However, there are of course applications that require custom windowing logic that cannot be addressed by Flink’s built-in windows. In order to support also applications that need very specific windowing semantics, the DataStream API exposes interfaces for the internals of its windowing mechanics. These interfaces give very fine-grained control about the way that windows are built and evaluated. </p>
 
 <p>The following figure depicts Flink’s windowing mechanism and introduces the components being involved.</p>
 

http://git-wip-us.apache.org/repos/asf/flink-web/blob/7e1269c3/content/news/2016/03/08/release-1.0.0.html
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diff --git a/content/news/2016/03/08/release-1.0.0.html b/content/news/2016/03/08/release-1.0.0.html
index 6393e6e..8e428d8 100644
--- a/content/news/2016/03/08/release-1.0.0.html
+++ b/content/news/2016/03/08/release-1.0.0.html
@@ -160,7 +160,7 @@
       <article>
         <p>08 Mar 2016</p>
 
-<p>The Apache Flink community is pleased to announce the availability of the 1.0.0 release. The community put significant effort into improving and extending Apache Flink since the last release, focusing on improving the experience of writing and executing data stream processing pipelines in production.</p>
+<p>The Apache Flink community is pleased to announce the availability of the 1.0.0 release. The community put significant effort into improving and extending Apache Flink since the last release, focusing on improving the experience of writing and executing data stream processing pipelines in production. </p>
 
 <center>
 <img src="/img/blog/flink-1.0.png" style="height:200px;margin:15px" />
@@ -201,7 +201,7 @@ When using this backend, active state in streaming programs can grow well beyond
 
 <p>The checkpointing has been extended by a more fine-grained control mechanism: In previous versions, new checkpoints were triggered independent of the speed at which old checkpoints completed. This can lead to situations where new checkpoints are piling up, because they are triggered too frequently.</p>
 
-<p>The checkpoint coordinator now exposes statistics through our REST monitoring API and the web interface. Users can review the checkpoint size and duration on a per-operator basis and see the last completed checkpoints. This is helpful for identifying performance issues, such as processing slowdown by the checkpoints.</p>
+<p>The checkpoint coordinator now exposes statistics through our REST monitoring API and the web interface. Users can review the checkpoint size and duration on a per-operator basis and see the last completed checkpoints. This is helpful for identifying performance issues, such as processing slowdown by the checkpoints. </p>
 
 <h2 id="improved-kafka-connector-and-support-for-kafka-09">Improved Kafka connector and support for Kafka 0.9</h2>
 

http://git-wip-us.apache.org/repos/asf/flink-web/blob/7e1269c3/content/news/2016/05/24/stream-sql.html
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diff --git a/content/news/2016/05/24/stream-sql.html b/content/news/2016/05/24/stream-sql.html
new file mode 100644
index 0000000..a696301
--- /dev/null
+++ b/content/news/2016/05/24/stream-sql.html
@@ -0,0 +1,329 @@
+<!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: Stream Processing for Everyone with SQL and Apache Flink</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" />
+
+    <!-- jQuery (necessary for Bootstrap's JavaScript plugins) -->
+    <!-- We need to load Jquery in the header for custom google analytics event tracking-->
+    <script src="https://ajax.googleapis.com/ajax/libs/jquery/1.11.2/jquery.min.js"></script>
+
+    <!-- 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]-->
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+            <span class="icon-bar"></span>
+          </button>
+          <div class="navbar-logo">
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+              <img alt="Apache Flink" src="/img/navbar-brand-logo.jpg" width="78px" height="40px">
+            </a>
+          </div>
+        </div><!-- /.navbar-header -->
+
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+        <div class="collapse navbar-collapse" id="bs-example-navbar-collapse-1">
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+      <h1>Stream Processing for Everyone with SQL and Apache Flink</h1>
+
+      <article>
+        <p>24 May 2016 by Fabian Hueske (<a href="https://twitter.com/fhueske">@fhueske</a>)</p>
+
+<p>The capabilities of open source systems for distributed stream processing have evolved significantly over the last years. Initially, the first systems in the field (notably <a href="https://storm.apache.org">Apache Storm</a>) provided low latency processing, but were limited to at-least-once guarantees, processing-time semantics, and rather low-level APIs. Since then, several new systems emerged and pushed the state of the art of open source stream processing in several dimensions. Today, users of Apache Flink or <a href="https://beam.incubator.apache.org">Apache Beam</a> can use fluent Scala and Java APIs to implement stream processing jobs that operate in event-time with exactly-once semantics at high throughput and low latency. </p>
+
+<p>In the meantime, stream processing has taken off in the industry. We are witnessing a rapidly growing interest in stream processing which is reflected by prevalent deployments of streaming processing infrastructure such as <a href="https://kafka.apache.org">Apache Kafka</a> and Apache Flink. The increasing number of available data streams results in a demand for people that can analyze streaming data and turn it into real-time insights. However, stream data analysis requires a special skill set including knowledge of streaming concepts such as the characteristics of unbounded streams, windows, time, and state as well as the skills to implement stream analysis jobs usually against Java or Scala APIs. People with this skill set are rare and hard to find.</p>
+
+<p>About six months ago, the Apache Flink community started an effort to add a SQL interface for stream data analysis. SQL is <em>the</em> standard language to access and process data. Everybody who occasionally analyzes data is familiar with SQL. Consequently, a SQL interface for stream data processing will make this technology accessible to a much wider audience. Moreover, SQL support for streaming data will also enable new use cases such as interactive and ad-hoc stream analysis and significantly simplify many applications including stream ingestion and simple transformations. In this blog post, we report on the current status, architectural design, and future plans of the Apache Flink community to implement support for SQL as a language for analyzing data streams.</p>
+
+<h2 id="where-did-we-come-from">Where did we come from?</h2>
+
+<p>With the <a href="http://flink.apache.org/news/2015/04/13/release-0.9.0-milestone1.html">0.9.0-milestone1</a> release, Apache Flink added an API to process relational data with SQL-like expressions called the Table API. The central concept of this API is a Table, a structured data set or stream on which relational operations can be applied. The Table API is tightly integrated with the DataSet and DataStream API. A Table can be easily created from a DataSet or DataStream and can also be converted back into a DataSet or DataStream as the following example shows</p>
+
+<div class="highlight"><pre><code class="language-scala"><span class="k">val</span> <span class="n">execEnv</span> <span class="k">=</span> <span class="nc">ExecutionEnvironment</span><span class="o">.</span><span class="n">getExecutionEnvironment</span>
+<span class="k">val</span> <span class="n">tableEnv</span> <span class="k">=</span> <span class="nc">TableEnvironment</span><span class="o">.</span><span class="n">getTableEnvironment</span><span class="o">(</span><span class="n">execEnv</span><span class="o">)</span>
+
+<span class="c1">// obtain a DataSet from somewhere</span>
+<span class="k">val</span> <span class="n">tempData</span><span class="k">:</span> <span class="kt">DataSet</span><span class="o">[(</span><span class="kt">String</span>, <span class="kt">Long</span>, <span class="kt">Double</span><span class="o">)]</span> <span class="k">=</span>
+
+<span class="c1">// convert the DataSet to a Table</span>
+<span class="k">val</span> <span class="n">tempTable</span><span class="k">:</span> <span class="kt">Table</span> <span class="o">=</span> <span class="n">tempData</span><span class="o">.</span><span class="n">toTable</span><span class="o">(</span><span class="n">tableEnv</span><span class="o">,</span> <span class="-Symbol">&#39;location</span><span class="o">,</span> <span class="-Symbol">&#39;time</span><span class="o">,</span> <span class="-Symbol">&#39;tempF</span><span class="o">)</span>
+<span class="c1">// compute your result</span>
+<span class="k">val</span> <span class="n">avgTempCTable</span><span class="k">:</span> <span class="kt">Table</span> <span class="o">=</span> <span class="n">tempTable</span>
+ <span class="o">.</span><span class="n">where</span><span class="o">(</span><span class="-Symbol">&#39;location</span><span class="o">.</span><span class="n">like</span><span class="o">(</span><span class="s">&quot;room%&quot;</span><span class="o">))</span>
+ <span class="o">.</span><span class="n">select</span><span class="o">(</span>
+   <span class="o">(</span><span class="-Symbol">&#39;time</span> <span class="o">/</span> <span class="o">(</span><span class="mi">3600</span> <span class="o">*</span> <span class="mi">24</span><span class="o">))</span> <span class="n">as</span> <span class="-Symbol">&#39;day</span><span class="o">,</span> 
+   <span class="-Symbol">&#39;Location</span> <span class="n">as</span> <span class="-Symbol">&#39;room</span><span class="o">,</span> 
+   <span class="o">((</span><span class="-Symbol">&#39;tempF</span> <span class="o">-</span> <span class="mi">32</span><span class="o">)</span> <span class="o">*</span> <span class="mf">0.556</span><span class="o">)</span> <span class="n">as</span> <span class="-Symbol">&#39;tempC</span>
+  <span class="o">)</span>
+ <span class="o">.</span><span class="n">groupBy</span><span class="o">(</span><span class="-Symbol">&#39;day</span><span class="o">,</span> <span class="-Symbol">&#39;room</span><span class="o">)</span>
+ <span class="o">.</span><span class="n">select</span><span class="o">(</span><span class="-Symbol">&#39;day</span><span class="o">,</span> <span class="-Symbol">&#39;room</span><span class="o">,</span> <span class="-Symbol">&#39;tempC</span><span class="o">.</span><span class="n">avg</span> <span class="n">as</span> <span class="-Symbol">&#39;avgTempC</span><span class="o">)</span>
+<span class="c1">// convert result Table back into a DataSet and print it</span>
+<span class="n">avgTempCTable</span><span class="o">.</span><span class="n">toDataSet</span><span class="o">[</span><span class="kt">Row</span><span class="o">].</span><span class="n">print</span><span class="o">()</span></code></pre></div>
+
+<p>Although the example shows Scala code, there is also an equivalent Java version of the Table API. The following picture depicts the original architecture of the Table API.</p>
+
+<center>
+<img src="/img/blog/stream-sql/old-table-api.png" style="width:75%;margin:15px" />
+</center>
+
+<p>A Table is created from a DataSet or DataStream and transformed into a new Table by applying relational transformations such as <code>filter</code>, <code>join</code>, or <code>select</code> on them. Internally, a logical table operator tree is constructed from the applied Table transformations. When a Table is translated back into a DataSet or DataStream, the respective translator translates the logical operator tree into DataSet or DataStream operators. Expressions like <code>'location.like("room%")</code> are compiled into Flink functions via code generation.</p>
+
+<p>However, the original Table API had a few limitations. First of all, it could not stand alone. Table API queries had to be always embedded into a DataSet or DataStream program. Queries against batch Tables did not support outer joins, sorting, and many scalar functions which are commonly used in SQL queries. Queries against streaming tables only supported filters, union, and projections and no aggregations or joins. Also, the translation process did not leverage query optimization techniques except for the physical optimization that is applied to all DataSet programs.</p>
+
+<h2 id="table-api-joining-forces-with-sql">Table API joining forces with SQL</h2>
+
+<p>The discussion about adding support for SQL came up a few times in the Flink community. With Flink 0.9 and the availability of the Table API, code generation for relational expressions, and runtime operators, the foundation for such an extension seemed to be there and SQL support the next logical step. On the other hand, the community was also well aware of the multitude of dedicated “SQL-on-Hadoop” solutions in the open source landscape (<a href="https://hive.apache.org">Apache Hive</a>, <a href="https://drill.apache.org">Apache Drill</a>, <a href="http://impala.io">Apache Impala</a>, <a href="https://tajo.apache.org">Apache Tajo</a>, just to name a few). Given these alternatives, we figured that time would be better spent improving Flink in other ways than implementing yet another SQL-on-Hadoop solution. </p>
+
+<p>However, with the growing popularity of stream processing and the increasing adoption of Flink in this area, the Flink community saw the need for a simpler API to enable more users to analyze streaming data. About half a year ago, we decided to take the Table API to the next level, extend the stream processing capabilities of the Table API, and add support for SQL on streaming data. What we came up with was a revised architecture for a Table API that supports SQL (and Table API) queries on streaming and static data sources. We did not want to reinvent the wheel and decided to build the new Table API on top of <a href="https://calcite.apache.org">Apache Calcite</a>, a popular SQL parser and optimizer framework. Apache Calcite is used by many projects including Apache Hive, Apache Drill, Cascading, and many <a href="https://calcite.apache.org/docs/powered_by.html">more</a>. Moreover, the Calcite community put <a href="https://calcite.apache.org/docs/stream.html">SQL on streams</a> 
 on their roadmap which makes it a perfect fit for Flink’s SQL interface.</p>
+
+<p>Calcite is central in the new design as the following architecture sketch shows:</p>
+
+<center>
+<img src="/img/blog/stream-sql/new-table-api.png" style="width:75%;margin:15px" />
+</center>
+
+<p>The new architecture features two integrated APIs to specify relational queries, the Table API and SQL. Queries of both APIs are validated against a catalog of registered tables and converted into Calcite’s representation for logical plans. In this representation, stream and batch queries look exactly the same. Next, Calcite’s cost-based optimizer applies transformation rules and optimizes the logical plans. Depending on the nature of the sources (streaming or static) we use different rule sets. Finally, the optimized plan is translated into a regular Flink DataStream or DataSet program. This step involves again code generation to compile relational expressions into Flink functions.</p>
+
+<p>The new architecture of the Table API maintains the basic principles of the original Table API and improves it. It keeps a uniform interface for relational queries on streaming and static data. In addition, we take advantage of Calcite’s query optimization framework and SQL parser. The design builds upon Flink’s established APIs, i.e., the DataStream API that offers low-latency, high-throughput stream processing with exactly-once semantics and consistent results due to event-time processing, and the DataSet API with robust and efficient in-memory operators and pipelined data exchange. Any improvements to Flink’s core APIs and engine will automatically improve the execution of Table API and SQL queries.</p>
+
+<p>With this effort, we are adding SQL support for both streaming and static data to Flink. However, we do not want to see this as a competing solution to dedicated, high-performance SQL-on-Hadoop solutions, such as Impala, Drill, and Hive. Instead, we see the sweet spot of Flink’s SQL integration primarily in providing access to streaming analytics to a wider audience. In addition, it will facilitate integrated applications that use Flink’s API’s as well as SQL while being executed on a single runtime engine.</p>
+
+<h2 id="how-will-flinks-sql-on-streams-look-like">How will Flink’s SQL on streams look like?</h2>
+
+<p>So far we discussed the motivation for and architecture of Flink’s stream SQL interface, but how will it actually look like? The new SQL interface is integrated into the Table API. DataStreams, DataSets, and external data sources can be registered as tables at the <code>TableEnvironment</code> in order to make them queryable with SQL. The <code>TableEnvironment.sql()</code> method states a SQL query and returns its result as a Table. The following example shows a complete program that reads a streaming table from a JSON encoded Kafka topic, processes it with a SQL query and writes the resulting stream into another Kafka topic. Please note that the KafkaJsonSource and KafkaJsonSink are under development and not available yet. In the future, TableSources and TableSinks can be persisted to and loaded from files to ease reuse of source and sink definitions and to reduce boilerplate code.</p>
+
+<div class="highlight"><pre><code class="language-scala"><span class="c1">// get environments</span>
+<span class="k">val</span> <span class="n">execEnv</span> <span class="k">=</span> <span class="nc">StreamExecutionEnvironment</span><span class="o">.</span><span class="n">getExecutionEnvironment</span>
+<span class="k">val</span> <span class="n">tableEnv</span> <span class="k">=</span> <span class="nc">TableEnvironment</span><span class="o">.</span><span class="n">getTableEnvironment</span><span class="o">(</span><span class="n">execEnv</span><span class="o">)</span>
+
+<span class="c1">// configure Kafka connection</span>
+<span class="k">val</span> <span class="n">kafkaProps</span> <span class="k">=</span> <span class="o">...</span>
+<span class="c1">// define a JSON encoded Kafka topic as external table</span>
+<span class="k">val</span> <span class="n">sensorSource</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">KafkaJsonSource</span><span class="o">[(</span><span class="kt">String</span>, <span class="kt">Long</span>, <span class="kt">Double</span><span class="o">)](</span>
+    <span class="s">&quot;sensorTopic&quot;</span><span class="o">,</span>
+    <span class="n">kafkaProps</span><span class="o">,</span>
+    <span class="o">(</span><span class="s">&quot;location&quot;</span><span class="o">,</span> <span class="s">&quot;time&quot;</span><span class="o">,</span> <span class="s">&quot;tempF&quot;</span><span class="o">))</span>
+
+<span class="c1">// register external table</span>
+<span class="n">tableEnv</span><span class="o">.</span><span class="n">registerTableSource</span><span class="o">(</span><span class="s">&quot;sensorData&quot;</span><span class="o">,</span> <span class="n">sensorSource</span><span class="o">)</span>
+
+<span class="c1">// define query in external table</span>
+<span class="k">val</span> <span class="n">roomSensors</span><span class="k">:</span> <span class="kt">Table</span> <span class="o">=</span> <span class="n">tableEnv</span><span class="o">.</span><span class="n">sql</span><span class="o">(</span>
+    <span class="s">&quot;SELECT STREAM time, location AS room, (tempF - 32) * 0.556 AS tempC &quot;</span> <span class="o">+</span>
+    <span class="s">&quot;FROM sensorData &quot;</span> <span class="o">+</span>
+    <span class="s">&quot;WHERE location LIKE &#39;room%&#39;&quot;</span>
+  <span class="o">)</span>
+
+<span class="c1">// define a JSON encoded Kafka topic as external sink</span>
+<span class="k">val</span> <span class="n">roomSensorSink</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">KafkaJsonSink</span><span class="o">(...)</span>
+
+<span class="c1">// define sink for room sensor data and execute query</span>
+<span class="n">roomSensors</span><span class="o">.</span><span class="n">toSink</span><span class="o">(</span><span class="n">roomSensorSink</span><span class="o">)</span>
+<span class="n">execEnv</span><span class="o">.</span><span class="n">execute</span><span class="o">()</span></code></pre></div>
+
+<p>You might have noticed that this example left out the most interesting aspects of stream data processing: window aggregates and joins. How will these operations be expressed in SQL? Well, that is a very good question. The Apache Calcite community put out an excellent proposal that discusses the syntax and semantics of <a href="https://calcite.apache.org/docs/stream.html">SQL on streams</a>. It describes Calcite’s stream SQL as <em>“an extension to standard SQL, not another ‘SQL-like’ language”</em>. This has several benefits. First, people who are familiar with standard SQL will be able to analyze data streams without learning a new syntax. Queries on static tables and streams are (almost) identical and can be easily ported. Moreover it is possible to specify queries that reference static and streaming tables at the same time which goes well together with Flink’s vision to handle batch processing as a special case of stream processing, i.e., as processing finite strea
 ms. Finally, using standard SQL for stream data analysis means following a well established standard that is supported by many tools.</p>
+
+<p>Although we haven’t completely fleshed out the details of how windows will be defined in Flink’s SQL syntax and Table API, the following examples show how a tumbling window query could look like in SQL and the Table API.</p>
+
+<h3 id="sql-following-the-syntax-proposal-of-calcites-streaming-sql-document">SQL (following the syntax proposal of Calcite’s streaming SQL document)</h3>
+
+<div class="highlight"><pre><code class="language-sql"><span class="k">SELECT</span> <span class="n">STREAM</span> 
+  <span class="n">TUMBLE_END</span><span class="p">(</span><span class="n">time</span><span class="p">,</span> <span class="nb">INTERVAL</span> <span class="s1">&#39;1&#39;</span> <span class="k">DAY</span><span class="p">)</span> <span class="k">AS</span> <span class="k">day</span><span class="p">,</span> 
+  <span class="k">location</span> <span class="k">AS</span> <span class="n">room</span><span class="p">,</span> 
+  <span class="k">AVG</span><span class="p">((</span><span class="n">tempF</span> <span class="o">-</span> <span class="mi">32</span><span class="p">)</span> <span class="o">*</span> <span class="mi">0</span><span class="p">.</span><span class="mi">556</span><span class="p">)</span> <span class="k">AS</span> <span class="n">avgTempC</span>
+<span class="k">FROM</span> <span class="n">sensorData</span>
+<span class="k">WHERE</span> <span class="k">location</span> <span class="k">LIKE</span> <span class="s1">&#39;room%&#39;</span>
+<span class="k">GROUP</span> <span class="k">BY</span> <span class="n">TUMBLE</span><span class="p">(</span><span class="n">time</span><span class="p">,</span> <span class="nb">INTERVAL</span> <span class="s1">&#39;1&#39;</span> <span class="k">DAY</span><span class="p">),</span> <span class="k">location</span></code></pre></div>
+
+<h3 id="table-api">Table API</h3>
+
+<div class="highlight"><pre><code class="language-scala"><span class="k">val</span> <span class="n">avgRoomTemp</span><span class="k">:</span> <span class="kt">Table</span> <span class="o">=</span> <span class="n">tableEnv</span><span class="o">.</span><span class="n">ingest</span><span class="o">(</span><span class="s">&quot;sensorData&quot;</span><span class="o">)</span>
+  <span class="o">.</span><span class="n">where</span><span class="o">(</span><span class="-Symbol">&#39;location</span><span class="o">.</span><span class="n">like</span><span class="o">(</span><span class="s">&quot;room%&quot;</span><span class="o">))</span>
+  <span class="o">.</span><span class="n">partitionBy</span><span class="o">(</span><span class="-Symbol">&#39;location</span><span class="o">)</span>
+  <span class="o">.</span><span class="n">window</span><span class="o">(</span><span class="nc">Tumbling</span> <span class="n">every</span> <span class="nc">Days</span><span class="o">(</span><span class="mi">1</span><span class="o">)</span> <span class="n">on</span> <span class="-Symbol">&#39;time</span> <span class="n">as</span> <span class="-Symbol">&#39;w</span><span class="o">)</span>
+  <span class="o">.</span><span class="n">select</span><span class="o">(</span><span class="-Symbol">&#39;w</span><span class="o">.</span><span class="n">end</span><span class="o">,</span> <span class="-Symbol">&#39;location</span><span class="o">,</span> <span class="o">,</span> <span class="o">((</span><span class="-Symbol">&#39;tempF</span> <span class="o">-</span> <span class="mi">32</span><span class="o">)</span> <span class="o">*</span> <span class="mf">0.556</span><span class="o">).</span><span class="n">avg</span> <span class="n">as</span> <span class="-Symbol">&#39;avgTempCs</span><span class="o">)</span></code></pre></div>
+
+<h2 id="whats-up-next">What’s up next?</h2>
+
+<p>The Flink community is actively working on SQL support for the next minor version Flink 1.1.0. In the first version, SQL (and Table API) queries on streams will be limited to selection, filter, and union operators. Compared to Flink 1.0.0, the revised Table API will support many more scalar functions and be able to read tables from external sources and write them back to external sinks. A lot of work went into reworking the architecture of the Table API and integrating Apache Calcite. </p>
+
+<p>In Flink 1.2.0, the feature set of SQL on streams will be significantly extended. Among other things, we plan to support different types of window aggregates and maybe also streaming joins. For this effort, we want to closely collaborate with the Apache Calcite community and help extending Calcite’s support for relational operations on streaming data when necessary.</p>
+
+<p>If this post made you curious and you want to try out Flink’s SQL interface and the new Table API, we encourage you to do so! Simply clone the SNAPSHOT <a href="https://github.com/apache/flink/tree/master">master branch</a> and check out the <a href="https://ci.apache.org/projects/flink/flink-docs-master/apis/table.html">Table API documentation for the SNAPSHOT version</a>. Please note that the branch is under heavy development, and hence some code examples in this blog post might not work. We are looking forward to your feedback and welcome contributions.</p>
+
+      </article>
+    </div>
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http://git-wip-us.apache.org/repos/asf/flink-web/blob/7e1269c3/content/slides.html
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diff --git a/content/slides.html b/content/slides.html
index eee7aa6..8c61405 100644
--- a/content/slides.html
+++ b/content/slides.html
@@ -160,12 +160,12 @@
 
 <div class="page-toc">
 <ul id="markdown-toc">
-  <li><a href="#training" id="markdown-toc-training">Training</a></li>
-  <li><a href="#flink-forward" id="markdown-toc-flink-forward">Flink Forward</a></li>
-  <li><a href="#slides" id="markdown-toc-slides">Slides</a>    <ul>
-      <li><a href="#section" id="markdown-toc-section">2016</a></li>
-      <li><a href="#section-1" id="markdown-toc-section-1">2015</a></li>
-      <li><a href="#section-2" id="markdown-toc-section-2">2014</a></li>
+  <li><a href="#training">Training</a></li>
+  <li><a href="#flink-forward">Flink Forward</a></li>
+  <li><a href="#slides">Slides</a>    <ul>
+      <li><a href="#section">2016</a></li>
+      <li><a href="#section-1">2015</a></li>
+      <li><a href="#section-2">2014</a></li>
     </ul>
   </li>
 </ul>


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