http://git-wip-us.apache.org/repos/asf/flink-web/blob/48be7c6f/content/news/2015/08/24/introducing-flink-gelly.html ---------------------------------------------------------------------- diff --git a/content/news/2015/08/24/introducing-flink-gelly.html b/content/news/2015/08/24/introducing-flink-gelly.html index 355ac32..f8a4fd7 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="n">CustomEdgeMapper</span><span class="o">());</span></code></pre></div> +<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>, â¦</li> + <li><strong>Edges</strong>: <code>mapEdges</code>, <code>filterOnEdges</code>, <code>removeEdge</code>, â¦</li> + <li><strong>Triplets</strong> (source vertex, target vertex, edge): <code>getTriplets</code></li> </ol> <h4 id="neighborhood-aggregations">Neighborhood Aggregations</h4> @@ -269,7 +269,7 @@ one or more values per vertex, the more general <code>groupReduceOnEdges()</cod <p>Assume you would want to compute the sum of the values of all incoming neighbors for each vertex. We will call the <code>reduceOnNeighbors()</code> aggregation method since the sum is an associative and commutative operation and the neighborsâ values are needed:</p> -<div class="highlight"><pre><code class="language-java"><span class="n">graph</span><span class="o">.</span><span class="na">reduceOnNeighbors</span><span class="o">(</span><span class="k">new</span> <span class="n">SumValues</span><span class="o">(),</span> <span class="n">EdgeDirection</span><span class="o">.</span><span class="na">IN</span><span class="o">);</span></code></pre></div> +<div class="highlight"><pre><code class="language-java"><span class="n">graph</span><span class="o">.</span><span class="na">reduceOnNeighbors</span><span class="o">(</span><span class="k">new</span> <span class="nf">SumValues</span><span class="o">(),</span> <span class="n">EdgeDirection</span><span class="o">.</span><span class="na">IN</span><span class="o">);</span></code></pre></div> <p>The vertex with id 1 is the only node that has no incoming edges. The result is therefore:</p> @@ -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 vertexâs 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 Gellyâs 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 @@ -455,7 +455,7 @@ playlist, we use a coGroup function to filter out the mismatches.</p> <span class="c1">// read the mismatches dataset and extract the songIDs</span> <span class="n">DataSet</span><span class="o"><</span><span class="n">Tuple3</span><span class="o"><</span><span class="n">String</span><span class="o">,</span> <span class="n">String</span><span class="o">,</span> <span class="n">Integer</span><span class="o">>></span> <span class="n">validTriplets</span> <span class="o">=</span> <span class="n">triplets</span> <span class="o">.</span><span class="na">coGroup</span><span class="o">(</span><span class="n">mismatches</span><span class="o">).</span><span class="na">where</span><span class="o">(</span><span class="mi">1</span><span class="o">).</span><span class="na">equalTo</span><span class="o">(</span><span class="mi">0</span><span class="o">)</span> - <span class="o">.</span><span class="na">with</span><span class="o">(</span><span class="k">new</span> <span class="n">CoGroupFunction</span><span class="o">()</span> <span class="o">{</span> + <span class="o">.</span><span class="na">with</span><span class="o">(</span><span class="k">new</span> <span class="nf">CoGroupFunction</span><span class="o">()</span> <span class="o">{</span> <span class="kt">void</span> <span class="nf">coGroup</span><span class="o">(</span><span class="n">Iterable</span> <span class="n">triplets</span><span class="o">,</span> <span class="n">Iterable</span> <span class="n">invalidSongs</span><span class="o">,</span> <span class="n">Collector</span> <span class="n">out</span><span class="o">)</span> <span class="o">{</span> <span class="k">if</span> <span class="o">(!</span><span class="n">invalidSongs</span><span class="o">.</span><span class="na">iterator</span><span class="o">().</span><span class="na">hasNext</span><span class="o">())</span> <span class="o">{</span> <span class="k">for</span> <span class="o">(</span><span class="n">Tuple3</span> <span class="n">triplet</span> <span class="o">:</span> <span class="n">triplets</span><span class="o">)</span> <span class="o">{</span> <span class="c1">// valid triplet</span> @@ -493,7 +493,7 @@ basically iterate through the edge value and collect the target (song) of the ma <div class="highlight"><pre><code class="language-java"><span class="c1">//get the top track (most listened to) for each user</span> <span class="n">DataSet</span><span class="o"><</span><span class="n">Tuple2</span><span class="o">></span> <span class="n">usersWithTopTrack</span> <span class="o">=</span> <span class="n">userSongGraph</span> - <span class="o">.</span><span class="na">groupReduceOnEdges</span><span class="o">(</span><span class="k">new</span> <span class="n">GetTopSongPerUser</span><span class="o">(),</span> <span class="n">EdgeDirection</span><span class="o">.</span><span class="na">OUT</span><span class="o">);</span> + <span class="o">.</span><span class="na">groupReduceOnEdges</span><span class="o">(</span><span class="k">new</span> <span class="nf">GetTopSongPerUser</span><span class="o">(),</span> <span class="n">EdgeDirection</span><span class="o">.</span><span class="na">OUT</span><span class="o">);</span> <span class="kd">class</span> <span class="nc">GetTopSongPerUser</span> <span class="kd">implements</span> <span class="n">EdgesFunctionWithVertexValue</span> <span class="o">{</span> <span class="kt">void</span> <span class="nf">iterateEdges</span><span class="o">(</span><span class="n">Vertex</span> <span class="n">vertex</span><span class="o">,</span> <span class="n">Iterable</span><span class="o"><</span><span class="n">Edge</span><span class="o">></span> <span class="n">edges</span><span class="o">)</span> <span class="o">{</span> @@ -506,7 +506,7 @@ basically iterate through the edge value and collect the target (song) of the ma <span class="n">topSong</span> <span class="o">=</span> <span class="n">edge</span><span class="o">.</span><span class="na">getTarget</span><span class="o">();</span> <span class="o">}</span> <span class="o">}</span> - <span class="k">return</span> <span class="k">new</span> <span class="n">Tuple2</span><span class="o">(</span><span class="n">vertex</span><span class="o">.</span><span class="na">getId</span><span class="o">(),</span> <span class="n">topSong</span><span class="o">);</span> + <span class="k">return</span> <span class="k">new</span> <span class="nf">Tuple2</span><span class="o">(</span><span class="n">vertex</span><span class="o">.</span><span class="na">getId</span><span class="o">(),</span> <span class="n">topSong</span><span class="o">);</span> <span class="o">}</span> <span class="o">}</span></code></pre></div> @@ -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 @@ -542,14 +542,14 @@ straightforward as a call to the <code>Graph.fromDataSet()</code> method.</p> <span class="o">}</span> <span class="o">})</span> <span class="o">.</span><span class="na">groupBy</span><span class="o">(</span><span class="mi">1</span><span class="o">)</span> - <span class="o">.</span><span class="na">reduceGroup</span><span class="o">(</span><span class="k">new</span> <span class="n">GroupReduceFunction</span><span class="o">()</span> <span class="o">{</span> + <span class="o">.</span><span class="na">reduceGroup</span><span class="o">(</span><span class="k">new</span> <span class="nf">GroupReduceFunction</span><span class="o">()</span> <span class="o">{</span> <span class="kt">void</span> <span class="nf">reduce</span><span class="o">(</span><span class="n">Iterable</span><span class="o"><</span><span class="n">Edge</span><span class="o">></span> <span class="n">edges</span><span class="o">,</span> <span class="n">Collector</span><span class="o"><</span><span class="n">Edge</span><span class="o">></span> <span class="n">out</span><span class="o">)</span> <span class="o">{</span> - <span class="n">List</span> <span class="n">users</span> <span class="o">=</span> <span class="k">new</span> <span class="n">ArrayList</span><span class="o">();</span> + <span class="n">List</span> <span class="n">users</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">ArrayList</span><span class="o">();</span> <span class="k">for</span> <span class="o">(</span><span class="n">Edge</span> <span class="n">edge</span> <span class="o">:</span> <span class="n">edges</span><span class="o">)</span> <span class="o">{</span> <span class="n">users</span><span class="o">.</span><span class="na">add</span><span class="o">(</span><span class="n">edge</span><span class="o">.</span><span class="na">getSource</span><span class="o">());</span> <span class="k">for</span> <span class="o">(</span><span class="kt">int</span> <span class="n">i</span> <span class="o">=</span> <span class="mi">0</span><span class="o">;</span> <span class="n">i</span> <span class="o"><</span> <span class="n">users</span><span class="o">.</span><span class="na">size</span><span class="o">()</span> <span class="o">-</span> <span class="mi">1</span><span class="o">;</span> <span class="n">i</span><span class="o">++)</span> <span class="o">{</span> <span class="k">for</span> <span class="o">(</span><span class="kt">int</span> <span class="n">j</span> <span class="o">=</span> <span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="o">;</span> <span class="n">j</span> <span class="o"><</span> <span class="n">users</span><span class="o">.</span><span class="na">size</span><span class="o">()</span> <span class="o">-</span> <span class="mi">1</span><span class="o">;</span> <span class="n">j</span><span class="o">++)</span> <span class="o">{</span> - <span class="n">out</span><span class="o">.</span><span class="na">collect</span><span class="o">(</span><span class="k">new</span> <span class="n">Edge</span><span class="o">(</span><span class="n">users</span><span class="o">.</span><span class="na">get</span><span class="o">(</span><span class="n">i</span><span class="o">),</span> <span class="n">users</span><span class="o">.</span><span class="na">get</span><span class="o">(</span><span class="n">j</span><span class="o">)));</span> + <span class="n">out</span><span class="o">.</span><span class="na">collect</span><span class="o">(</span><span class="k">new</span> <span class="nf">Edge</span><span class="o">(</span><span class="n">users</span><span class="o">.</span><span class="na">get</span><span class="o">(</span><span class="n">i</span><span class="o">),</span> <span class="n">users</span><span class="o">.</span><span class="na">get</span><span class="o">(</span><span class="n">j</span><span class="o">)));</span> <span class="o">}</span> <span class="o">}</span> <span class="o">}</span> @@ -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> @@ -580,12 +580,12 @@ among their neighbors. </p> <span class="c1">// update the vertex values and run the label propagation algorithm</span> <span class="n">DataSet</span><span class="o"><</span><span class="n">Vertex</span><span class="o">></span> <span class="n">verticesWithCommunity</span> <span class="o">=</span> <span class="n">similarUsersGraph</span> - <span class="o">.</span><span class="na">joinWithVertices</span><span class="o">(</span><span class="n">idsWithlLabels</span><span class="o">,</span> <span class="k">new</span> <span class="n">MapFunction</span><span class="o">()</span> <span class="o">{</span> + <span class="o">.</span><span class="na">joinWithVertices</span><span class="o">(</span><span class="n">idsWithlLabels</span><span class="o">,</span> <span class="k">new</span> <span class="nf">MapFunction</span><span class="o">()</span> <span class="o">{</span> <span class="kd">public</span> <span class="n">Long</span> <span class="nf">map</span><span class="o">(</span><span class="n">Tuple2</span> <span class="n">idWithLabel</span><span class="o">)</span> <span class="o">{</span> <span class="k">return</span> <span class="n">idWithLabel</span><span class="o">.</span><span class="na">f1</span><span class="o">;</span> <span class="o">}</span> <span class="o">})</span> - <span class="o">.</span><span class="na">run</span><span class="o">(</span><span class="k">new</span> <span class="n">LabelPropagation</span><span class="o">(</span><span class="n">numIterations</span><span class="o">))</span> + <span class="o">.</span><span class="na">run</span><span class="o">(</span><span class="k">new</span> <span class="nf">LabelPropagation</span><span class="o">(</span><span class="n">numIterations</span><span class="o">))</span> <span class="o">.</span><span class="na">getVertices</span><span class="o">();</span></code></pre></div> <p><a href="#top">Back to top</a></p> @@ -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 âyet another library for running PageRank-like algorithmsâ 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/48be7c6f/content/news/2015/09/16/off-heap-memory.html ---------------------------------------------------------------------- diff --git a/content/news/2015/09/16/off-heap-memory.html b/content/news/2015/09/16/off-heap-memory.html index 317e596..40db6ba 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 Flinkâs 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 Flinkâs 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, â¦). 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/48be7c6f/content/news/2015/11/16/release-0.10.0.html ---------------------------------------------------------------------- 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 163f98e..d3627b9 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/48be7c6f/content/news/2015/12/04/Introducing-windows.html ---------------------------------------------------------------------- diff --git a/content/news/2015/12/04/Introducing-windows.html b/content/news/2015/12/04/Introducing-windows.html index 1f9201c..715c0cb 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, âwindowingâ and âat-least-onceâ/âexactly-onceâ 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. Flinkâs 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, âwindowingâ and âat-least-onceâ/âexactly-onceâ 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. Flinkâs 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 Flinkâs built-in windows, and explain its support for custom windowing semantics.</p> @@ -223,17 +223,17 @@ <p>There is one aspect that we havenât discussed yet, namely the exact meaning of â<em>collects elements for one minute</em>â which boils down to the question, â<em>How does the stream processor interpret time?</em>â.</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 Flinkâs DataStream API, tumbling and sliding count windows are defined as follows:</p> @@ -256,7 +256,7 @@ <h2 id="dissecting-flinks-windowing-mechanics">Dissecting Flinkâs windowing mechanics</h2> -<p>Flinkâs 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 Flinkâs 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>Flinkâs 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 Flinkâs 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 Flinkâs windowing mechanism and introduces the components being involved.</p> http://git-wip-us.apache.org/repos/asf/flink-web/blob/48be7c6f/content/news/2015/12/11/storm-compatibility.html ---------------------------------------------------------------------- diff --git a/content/news/2015/12/11/storm-compatibility.html b/content/news/2015/12/11/storm-compatibility.html index 95b4303..c2a139f 100644 --- a/content/news/2015/12/11/storm-compatibility.html +++ b/content/news/2015/12/11/storm-compatibility.html @@ -199,13 +199,13 @@ For this, you only need to replace the dependency <code>storm-core</code> by <co First, the program is assembled the Storm way without any code change to Spouts, Bolts, or the topology itself.</p> <div class="highlight"><pre><code class="language-java"><span class="c1">// assemble topology, the Storm way</span> -<span class="n">TopologyBuilder</span> <span class="n">builder</span> <span class="o">=</span> <span class="k">new</span> <span class="n">TopologyBuilder</span><span class="o">();</span> -<span class="n">builder</span><span class="o">.</span><span class="na">setSpout</span><span class="o">(</span><span class="s">"source"</span><span class="o">,</span> <span class="k">new</span> <span class="n">StormFileSpout</span><span class="o">(</span><span class="n">inputFilePath</span><span class="o">));</span> -<span class="n">builder</span><span class="o">.</span><span class="na">setBolt</span><span class="o">(</span><span class="s">"tokenizer"</span><span class="o">,</span> <span class="k">new</span> <span class="n">StormBoltTokenizer</span><span class="o">())</span> +<span class="n">TopologyBuilder</span> <span class="n">builder</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">TopologyBuilder</span><span class="o">();</span> +<span class="n">builder</span><span class="o">.</span><span class="na">setSpout</span><span class="o">(</span><span class="s">"source"</span><span class="o">,</span> <span class="k">new</span> <span class="nf">StormFileSpout</span><span class="o">(</span><span class="n">inputFilePath</span><span class="o">));</span> +<span class="n">builder</span><span class="o">.</span><span class="na">setBolt</span><span class="o">(</span><span class="s">"tokenizer"</span><span class="o">,</span> <span class="k">new</span> <span class="nf">StormBoltTokenizer</span><span class="o">())</span> <span class="o">.</span><span class="na">shuffleGrouping</span><span class="o">(</span><span class="s">"source"</span><span class="o">);</span> -<span class="n">builder</span><span class="o">.</span><span class="na">setBolt</span><span class="o">(</span><span class="s">"counter"</span><span class="o">,</span> <span class="k">new</span> <span class="n">StormBoltCounter</span><span class="o">())</span> - <span class="o">.</span><span class="na">fieldsGrouping</span><span class="o">(</span><span class="s">"tokenizer"</span><span class="o">,</span> <span class="k">new</span> <span class="n">Fields</span><span class="o">(</span><span class="s">"word"</span><span class="o">));</span> -<span class="n">builder</span><span class="o">.</span><span class="na">setBolt</span><span class="o">(</span><span class="s">"sink"</span><span class="o">,</span> <span class="k">new</span> <span class="n">StormBoltFileSink</span><span class="o">(</span><span class="n">outputFilePath</span><span class="o">))</span> +<span class="n">builder</span><span class="o">.</span><span class="na">setBolt</span><span class="o">(</span><span class="s">"counter"</span><span class="o">,</span> <span class="k">new</span> <span class="nf">StormBoltCounter</span><span class="o">())</span> + <span class="o">.</span><span class="na">fieldsGrouping</span><span class="o">(</span><span class="s">"tokenizer"</span><span class="o">,</span> <span class="k">new</span> <span class="nf">Fields</span><span class="o">(</span><span class="s">"word"</span><span class="o">));</span> +<span class="n">builder</span><span class="o">.</span><span class="na">setBolt</span><span class="o">(</span><span class="s">"sink"</span><span class="o">,</span> <span class="k">new</span> <span class="nf">StormBoltFileSink</span><span class="o">(</span><span class="n">outputFilePath</span><span class="o">))</span> <span class="o">.</span><span class="na">shuffleGrouping</span><span class="o">(</span><span class="s">"counter"</span><span class="o">);</span></code></pre></div> <p>In order to execute the topology, we need to translate it to a <code>FlinkTopology</code> and submit it to a local or remote Flink cluster, very similar to submitting the application to a Storm cluster.<sup><a href="#fn1" id="ref1">1</a></sup></p> @@ -214,7 +214,7 @@ First, the program is assembled the Storm way without any code change to Spouts, <span class="c1">// replaces: StormTopology topology = builder.createTopology();</span> <span class="n">FlinkTopology</span> <span class="n">topology</span> <span class="o">=</span> <span class="n">FlinkTopology</span><span class="o">.</span><span class="na">createTopology</span><span class="o">(</span><span class="n">builder</span><span class="o">);</span> -<span class="n">Config</span> <span class="n">conf</span> <span class="o">=</span> <span class="k">new</span> <span class="n">Config</span><span class="o">();</span> +<span class="n">Config</span> <span class="n">conf</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">Config</span><span class="o">();</span> <span class="k">if</span><span class="o">(</span><span class="n">runLocal</span><span class="o">)</span> <span class="o">{</span> <span class="c1">// use FlinkLocalCluster instead of LocalCluster</span> <span class="n">FlinkLocalCluster</span> <span class="n">cluster</span> <span class="o">=</span> <span class="n">FlinkLocalCluster</span><span class="o">.</span><span class="na">getLocalCluster</span><span class="o">();</span> @@ -254,14 +254,14 @@ As Storm is type agnostic, it is required to specify the output type of embedded <span class="c1">// use Spout as source</span> <span class="n">DataStream</span><span class="o"><</span><span class="n">Tuple1</span><span class="o"><</span><span class="n">String</span><span class="o">>></span> <span class="n">source</span> <span class="o">=</span> <span class="n">env</span><span class="o">.</span><span class="na">addSource</span><span class="o">(</span><span class="c1">// Flink provided wrapper including original Spout</span> - <span class="k">new</span> <span class="n">SpoutWrapper</span><span class="o"><</span><span class="n">String</span><span class="o">>(</span><span class="k">new</span> <span class="n">FileSpout</span><span class="o">(</span><span class="n">localFilePath</span><span class="o">)),</span> + <span class="k">new</span> <span class="n">SpoutWrapper</span><span class="o"><</span><span class="n">String</span><span class="o">>(</span><span class="k">new</span> <span class="nf">FileSpout</span><span class="o">(</span><span class="n">localFilePath</span><span class="o">)),</span> <span class="c1">// specify output type manually</span> <span class="n">TypeExtractor</span><span class="o">.</span><span class="na">getForObject</span><span class="o">(</span><span class="k">new</span> <span class="n">Tuple1</span><span class="o"><</span><span class="n">String</span><span class="o">>(</span><span class="s">""</span><span class="o">)));</span> <span class="c1">// FileSpout cannot be parallelized</span> <span class="n">DataStream</span><span class="o"><</span><span class="n">Tuple1</span><span class="o"><</span><span class="n">String</span><span class="o">>></span> <span class="n">text</span> <span class="o">=</span> <span class="n">source</span><span class="o">.</span><span class="na">setParallelism</span><span class="o">(</span><span class="mi">1</span><span class="o">);</span> <span class="c1">// further processing with Flink</span> -<span class="n">DataStream</span><span class="o"><</span><span class="n">Tuple2</span><span class="o"><</span><span class="n">String</span><span class="o">,</span><span class="n">Integer</span><span class="o">></span> <span class="n">tokens</span> <span class="o">=</span> <span class="n">text</span><span class="o">.</span><span class="na">flatMap</span><span class="o">(</span><span class="k">new</span> <span class="n">Tokenizer</span><span class="o">()).</span><span class="na">keyBy</span><span class="o">(</span><span class="mi">0</span><span class="o">);</span> +<span class="n">DataStream</span><span class="o"><</span><span class="n">Tuple2</span><span class="o"><</span><span class="n">String</span><span class="o">,</span><span class="n">Integer</span><span class="o">></span> <span class="n">tokens</span> <span class="o">=</span> <span class="n">text</span><span class="o">.</span><span class="na">flatMap</span><span class="o">(</span><span class="k">new</span> <span class="nf">Tokenizer</span><span class="o">()).</span><span class="na">keyBy</span><span class="o">(</span><span class="mi">0</span><span class="o">);</span> <span class="c1">// use Bolt for counting</span> <span class="n">DataStream</span><span class="o"><</span><span class="n">Tuple2</span><span class="o"><</span><span class="n">String</span><span class="o">,</span><span class="n">Integer</span><span class="o">></span> <span class="n">counts</span> <span class="o">=</span> @@ -269,7 +269,7 @@ As Storm is type agnostic, it is required to specify the output type of embedded <span class="c1">// specify output type manually</span> <span class="n">TypeExtractor</span><span class="o">.</span><span class="na">getForObject</span><span class="o">(</span><span class="k">new</span> <span class="n">Tuple2</span><span class="o"><</span><span class="n">String</span><span class="o">,</span><span class="n">Integer</span><span class="o">>(</span><span class="s">""</span><span class="o">,</span><span class="mi">0</span><span class="o">))</span> <span class="c1">// Flink provided wrapper including original Bolt</span> - <span class="k">new</span> <span class="n">BoltWrapper</span><span class="o"><</span><span class="n">String</span><span class="o">,</span><span class="n">Tuple2</span><span class="o"><</span><span class="n">String</span><span class="o">,</span><span class="n">Integer</span><span class="o">>>(</span><span class="k">new</span> <span class="n">BoltCounter</span><span class="o">()));</span> + <span class="k">new</span> <span class="n">BoltWrapper</span><span class="o"><</span><span class="n">String</span><span class="o">,</span><span class="n">Tuple2</span><span class="o"><</span><span class="n">String</span><span class="o">,</span><span class="n">Integer</span><span class="o">>>(</span><span class="k">new</span> <span class="nf">BoltCounter</span><span class="o">()));</span> <span class="c1">// write result to file via Flink sink</span> <span class="n">counts</span><span class="o">.</span><span class="na">writeAsText</span><span class="o">(</span><span class="n">outputPath</span><span class="o">);</span> http://git-wip-us.apache.org/repos/asf/flink-web/blob/48be7c6f/content/news/2016/03/08/release-1.0.0.html ---------------------------------------------------------------------- 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 8e428d8..6393e6e 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/48be7c6f/content/news/2016/04/06/cep-monitoring.html ---------------------------------------------------------------------- diff --git a/content/news/2016/04/06/cep-monitoring.html b/content/news/2016/04/06/cep-monitoring.html index f545a47..1be1758 100644 --- a/content/news/2016/04/06/cep-monitoring.html +++ b/content/news/2016/04/06/cep-monitoring.html @@ -280,7 +280,7 @@ Our pattern select function generates for each matching pattern a <code>Temperat <span class="n">TemperatureEvent</span> <span class="n">first</span> <span class="o">=</span> <span class="o">(</span><span class="n">TemperatureEvent</span><span class="o">)</span> <span class="n">pattern</span><span class="o">.</span><span class="na">get</span><span class="o">(</span><span class="s">"First Event"</span><span class="o">);</span> <span class="n">TemperatureEvent</span> <span class="n">second</span> <span class="o">=</span> <span class="o">(</span><span class="n">TemperatureEvent</span><span class="o">)</span> <span class="n">pattern</span><span class="o">.</span><span class="na">get</span><span class="o">(</span><span class="s">"Second Event"</span><span class="o">);</span> - <span class="k">return</span> <span class="k">new</span> <span class="n">TemperatureWarning</span><span class="o">(</span> + <span class="k">return</span> <span class="k">new</span> <span class="nf">TemperatureWarning</span><span class="o">(</span> <span class="n">first</span><span class="o">.</span><span class="na">getRackID</span><span class="o">(),</span> <span class="o">(</span><span class="n">first</span><span class="o">.</span><span class="na">getTemperature</span><span class="o">()</span> <span class="o">+</span> <span class="n">second</span><span class="o">.</span><span class="na">getTemperature</span><span class="o">())</span> <span class="o">/</span> <span class="mi">2</span><span class="o">);</span> <span class="o">}</span> @@ -322,7 +322,7 @@ Thus, we will only generate a <code>TemperatureAlert</code> if and only if the t <span class="n">TemperatureWarning</span> <span class="n">second</span> <span class="o">=</span> <span class="n">pattern</span><span class="o">.</span><span class="na">get</span><span class="o">(</span><span class="s">"Second Event"</span><span class="o">);</span> <span class="k">if</span> <span class="o">(</span><span class="n">first</span><span class="o">.</span><span class="na">getAverageTemperature</span><span class="o">()</span> <span class="o"><</span> <span class="n">second</span><span class="o">.</span><span class="na">getAverageTemperature</span><span class="o">())</span> <span class="o">{</span> - <span class="n">out</span><span class="o">.</span><span class="na">collect</span><span class="o">(</span><span class="k">new</span> <span class="n">TemperatureAlert</span><span class="o">(</span><span class="n">first</span><span class="o">.</span><span class="na">getRackID</span><span class="o">()));</span> + <span class="n">out</span><span class="o">.</span><span class="na">collect</span><span class="o">(</span><span class="k">new</span> <span class="nf">TemperatureAlert</span><span class="o">(</span><span class="n">first</span><span class="o">.</span><span class="na">getRackID</span><span class="o">()));</span> <span class="o">}</span> <span class="o">});</span></code></pre></div> http://git-wip-us.apache.org/repos/asf/flink-web/blob/48be7c6f/content/news/2016/04/14/flink-forward-announce.html ---------------------------------------------------------------------- diff --git a/content/news/2016/04/14/flink-forward-announce.html b/content/news/2016/04/14/flink-forward-announce.html new file mode 100644 index 0000000..2a1d7b9 --- /dev/null +++ b/content/news/2016/04/14/flink-forward-announce.html @@ -0,0 +1,213 @@ +<!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; 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The conference will take place September 12-14, 2016 in Berlin, Germany, bringing together the open source stream processing community. Most Apache Flink committers will attend the conference, making it the ideal venue to learn more about the project and its roadmap and connect with the community.</p> + +<p>The conference welcomes submissions on everything Flink-related, including experiences with using Flink, products based on Flink, technical talks on extending Flink, as well as connecting Flink with other open source or proprietary software.</p> + +<p>Read more <a href="http://flink-forward.org/";>here</a>.</p> + + </article> + </div> + + <div class="row"> + <div id="disqus_thread"></div> + <script type="text/javascript"> + /* * * CONFIGURATION VARIABLES: EDIT BEFORE PASTING INTO YOUR WEBPAGE * * */ + var disqus_shortname = 'stratosphere-eu'; // required: replace example with your forum shortname + + /* * * DON'T EDIT BELOW THIS LINE * * */ + (function() { + var dsq = document.createElement('script'); dsq.type = 'text/javascript'; dsq.async = true; + dsq.src = '//' + disqus_shortname + '.disqus.com/embed.js'; + (document.getElementsByTagName('head')[0] || document.getElementsByTagName('body')[0]).appendChild(dsq); + })(); + </script> + </div> + </div> +</div> + + <hr /> + <div class="footer text-center"> + <p>Copyright © 2014-2015 <a href="http://apache.org";>The Apache Software Foundation</a>. 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