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From u..@apache.org
Subject [15/39] flink-web git commit: Revert "Rebuild website"
Date Wed, 18 Jan 2017 14:01:51 GMT
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-      <h1>Introducing Gelly: Graph Processing with Apache Flink</h1>
-
-      <article>
-        <p>24 Aug 2015</p>
-
-<p>This blog post introduces <strong>Gelly</strong>, Apache Flink’s <em>graph-processing API and library</em>. Flink’s native support
-for iterations makes it a suitable platform for large-scale graph analytics.
-By leveraging delta iterations, Gelly is able to map various graph processing models such as
-vertex-centric or gather-sum-apply to Flink dataflows.</p>
-
-<p>Gelly allows Flink users to perform end-to-end data analysis in a single system.
-Gelly can be seamlessly used with Flink’s DataSet API,
-which means that pre-processing, graph creation, analysis, and post-processing can be done
-in the same application. At the end of this post, we will go through a step-by-step example
-in order to demonstrate that loading, transformation, filtering, graph creation, and analysis
-can be performed in a single Flink program.</p>
-
-<p><strong>Overview</strong></p>
-
-<ol>
-  <li><a href="#what-is-gelly">What is Gelly?</a></li>
-  <li><a href="#graph-representation-and-creation">Graph Representation and Creation</a></li>
-  <li><a href="#transformations-and-utilities">Transformations and Utilities</a></li>
-  <li><a href="#iterative-graph-processing">Iterative Graph Processing</a></li>
-  <li><a href="#library-of-graph-algorithms">Library of Graph Algorithms</a></li>
-  <li><a href="#use-case-music-profiles">Use-Case: Music Profiles</a></li>
-  <li><a href="#ongoing-and-future-work">Ongoing and Future Work</a></li>
-</ol>
-
-<p><a href="#top"></a></p>
-
-<h2 id="what-is-gelly">What is Gelly?</h2>
-
-<p>Gelly is a Graph API for Flink. It is currently supported in both Java and Scala.
-The Scala methods are implemented as wrappers on top of the basic Java operations.
-The API contains a set of utility functions for graph analysis, supports iterative graph
-processing and introduces a library of graph algorithms.</p>
-
-<center>
-<img src="/img/blog/flink-stack.png" style="width:90%;margin:15px" />
-</center>
-
-<p><a href="#top">Back to top</a></p>
-
-<h2 id="graph-representation-and-creation">Graph Representation and Creation</h2>
-
-<p>In Gelly, a graph is represented by a DataSet of vertices and a DataSet of edges.
-A vertex is defined by its unique ID and a value, whereas an edge is defined by its source ID,
-target ID, and value. A vertex or edge for which a value is not specified will simply have the
-value type set to <code>NullValue</code>.</p>
-
-<p>A graph can be created from:</p>
-
-<ol>
-  <li><strong>DataSet of edges</strong> and an optional <strong>DataSet of vertices</strong> using <code>Graph.fromDataSet()</code></li>
-  <li><strong>DataSet of Tuple3</strong> and an optional <strong>DataSet of Tuple2</strong> using <code>Graph.fromTupleDataSet()</code></li>
-  <li><strong>Collection of edges</strong> and an optional <strong>Collection of vertices</strong> using <code>Graph.fromCollection()</code></li>
-</ol>
-
-<p>In all three cases, if the vertices are not provided,
-Gelly will automatically produce the vertex IDs from the edge source and target IDs.</p>
-
-<p><a href="#top">Back to top</a></p>
-
-<h2 id="transformations-and-utilities">Transformations and Utilities</h2>
-
-<p>These are methods of the Graph class and include common graph metrics, transformations
-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>
-
-<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>
-
-<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>
-</ol>
-
-<h4 id="neighborhood-aggregations">Neighborhood Aggregations</h4>
-
-<p>Neighborhood methods allow vertices to perform an aggregation on their first-hop neighborhood.
-This provides a vertex-centric view, where each vertex can access its neighboring edges and neighbor values.</p>
-
-<p><code>reduceOnEdges()</code> provides access to the neighboring edges of a vertex,
-i.e. the edge value and the vertex ID of the edge endpoint. In order to also access the
-neighboring vertices’ values, one should call the <code>reduceOnNeighbors()</code> function.
-The scope of the neighborhood is defined by the EdgeDirection parameter, which can be IN, OUT or ALL,
-to gather in-coming, out-going or all edges (neighbors) of a vertex.</p>
-
-<p>The two neighborhood
-functions mentioned above can only be used when the aggregation function is associative and commutative.
-In case the function does not comply with these restrictions or if it is desirable to return zero,
-one or more values per vertex, the more general  <code>groupReduceOnEdges()</code> and 
-<code>groupReduceOnNeighbors()</code> functions must be called.</p>
-
-<p>Consider the following graph, for instance:</p>
-
-<center>
-<img src="/img/blog/neighborhood.png" style="width:60%;margin:15px" />
-</center>
-
-<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 neighbors’ 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="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>
-
-<center>
-<img src="/img/blog/reduce-on-neighbors.png" style="width:90%;margin:15px" />
-</center>
-
-<p><a href="#top">Back to top</a></p>
-
-<h2 id="iterative-graph-processing">Iterative Graph Processing</h2>
-
-<p>During the past few years, many different programming models for distributed graph processing
-have been introduced: <a href="http://delivery.acm.org/10.1145/2490000/2484843/a22-salihoglu.pdf?ip=141.23.53.206&amp;id=2484843&amp;acc=ACTIVE%20SERVICE&amp;key=2BA2C432AB83DA15.0F42380CB8DD3307.4D4702B0C3E38B35.4D4702B0C3E38B35&amp;CFID=706313474&amp;CFTOKEN=60107876&amp;__acm__=1440408958_b131e035942130653e5782409b5c0cde">vertex-centric</a>,
-<a href="http://researcher.ibm.com/researcher/files/us-ytian/giraph++.pdf">partition-centric</a>, <a href="http://www.eecs.harvard.edu/cs261/notes/gonzalez-2012.htm">gather-apply-scatter</a>,
-<a href="http://infoscience.epfl.ch/record/188535/files/paper.pdf">edge-centric</a>, <a href="http://www.vldb.org/pvldb/vol7/p1673-quamar.pdf">neighborhood-centric</a>.
-Each one of these models targets a specific class of graph applications and each corresponding
-system implementation optimizes the runtime respectively. In Gelly, we would like to exploit the
-flexible dataflow model and the efficient iterations of Flink, to support multiple distributed
-graph processing models on top of the same system.</p>
-
-<p>Currently, Gelly has methods for writing vertex-centric programs and provides support for programs
-implemented using the gather-sum(accumulate)-apply model. We are also considering to offer support
-for the partition-centric computation model, using Fink’s <code>mapPartition()</code> operator.
-This model exposes the partition structure to the user and allows local graph structure exploitation
-inside a partition to avoid unnecessary communication.</p>
-
-<h4 id="vertex-centric">Vertex-centric</h4>
-
-<p>Gelly wraps Flink’s <a href="https://ci.apache.org/projects/flink/flink-docs-release-0.8/spargel_guide.html">Spargel APi</a> to 
-support the vertex-centric, Pregel-like programming model. Gelly’s <code>runVertexCentricIteration</code> method accepts two user-defined functions:</p>
-
-<ol>
-  <li><strong>MessagingFunction:</strong> defines what messages a vertex sends out for the next superstep.</li>
-  <li><strong>VertexUpdateFunction:</strong>* defines how a vertex will update its value based on the received messages.</li>
-</ol>
-
-<p>The method will execute the vertex-centric iteration on the input Graph and return a new Graph, with updated vertex values.</p>
-
-<p>Gelly’s vertex-centric programming model exploits Flink’s efficient delta iteration operators.
-Many iterative graph algorithms expose non-uniform behavior, where some vertices converge to
-their final value faster than others. In such cases, the number of vertices that need to be
-recomputed during an iteration decreases as the algorithm moves towards convergence.</p>
-
-<p>For example, consider a Single Source Shortest Paths problem on the following graph, where S
-is the source node, i is the iteration counter and the edge values represent distances between nodes:</p>
-
-<center>
-<img src="/img/blog/sssp.png" style="width:90%;margin:15px" />
-</center>
-
-<p>In each iteration, a vertex receives distances from its neighbors and adopts the minimum of
-these distances and its current distance as the new value. Then, it  propagates its new value
-to its neighbors. If a vertex does not change value during an iteration, there is no need for
-it to propagate its old distance to its neighbors; as they have already taken it into account.</p>
-
-<p>Flink’s <code>IterateDelta</code> operator permits exploitation of this property as well as the
-execution of computations solely on the active parts of the graph. The operator receives two inputs:</p>
-
-<ol>
-  <li>the <strong>Solution Set</strong>, which represents the current state of the input and</li>
-  <li>the <strong>Workset</strong>, which determines which parts of the graph will be recomputed in the next iteration.</li>
-</ol>
-
-<p>In the SSSP example above, the Workset contains the vertices which update their distances.
-The user-defined iterative function is applied on these inputs to produce state updates.
-These updates are efficiently applied on the state, which is kept in memory.</p>
-
-<center>
-<img src="/img/blog/iteration.png" style="width:60%;margin:15px" />
-</center>
-
-<p>Internally, a vertex-centric iteration is a Flink delta iteration, where the initial Solution Set
-is the vertex set of the input graph and the Workset is created by selecting the active vertices,
-i.e. the ones that updated their value in the previous iteration. The messaging and vertex-update
-functions are user-defined functions wrapped inside coGroup operators. In each superstep,
-the active vertices (Workset) are coGrouped with the edges to generate the neighborhoods for
-each vertex. The messaging function is then applied on each neighborhood. Next, the result of the
-messaging function is coGrouped with the current vertex values (Solution Set) and the user-defined
-vertex-update function is applied on the result. The output of this coGroup operator is finally
-used to update the Solution Set and create the Workset input for the next iteration.</p>
-
-<center>
-<img src="/img/blog/vertex-centric-plan.png" style="width:40%;margin:15px" />
-</center>
-
-<h4 id="gather-sum-apply">Gather-Sum-Apply</h4>
-
-<p>Gelly supports a variation of the popular Gather-Sum-Apply-Scatter  computation model,
-introduced by PowerGraph. In GSA, a vertex pulls information from its neighbors as opposed to the
-vertex-centric approach where the updates are pushed from the incoming neighbors.
-The <code>runGatherSumApplyIteration()</code> accepts three user-defined functions:</p>
-
-<ol>
-  <li><strong>GatherFunction:</strong> gathers neighboring partial values along in-edges.</li>
-  <li><strong>SumFunction:</strong> accumulates/reduces the values into a single one.</li>
-  <li><strong>ApplyFunction:</strong> uses the result computed in the sum phase to update the current vertex’s value.</li>
-</ol>
-
-<p>Similarly to vertex-centric, GSA leverages Flink’s delta iteration operators as, in many cases,
-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>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
-an iteration, it no longer propagates its distance.</li>
-</ol>
-
-<p>Internally, a Gather-Sum-Apply Iteration is a Flink delta iteration where the initial solution
-set is the vertex input set and the workset is created by selecting the active vertices.</p>
-
-<p>The three functions: gather, sum and apply are user-defined functions wrapped in map, reduce
-and join operators respectively. In each superstep, the active vertices are joined with the
-edges in order to create neighborhoods for each vertex. The gather function is then applied on
-the neighborhood values via a map function. Afterwards, the result is grouped by the vertex ID
-and reduced using the sum function. Finally, the outcome of the sum phase is joined with the
-current vertex values (solution set), the values are updated, thus creating a new workset that
-serves as input for the next iteration.</p>
-
-<center>
-<img src="/img/blog/GSA-plan.png" style="width:40%;margin:15px" />
-</center>
-
-<p><a href="#top">Back to top</a></p>
-
-<h2 id="library-of-graph-algorithms">Library of Graph Algorithms</h2>
-
-<p>We are building a library of graph algorithms in Gelly, to easily analyze large-scale graphs.
-These algorithms extend the <code>GraphAlgorithm</code> interface and can be simply executed on
-the input graph by calling a <code>run()</code> method.</p>
-
-<p>We currently have implementations of the following algorithms:</p>
-
-<ol>
-  <li>PageRank</li>
-  <li>Single-Source-Shortest-Paths</li>
-  <li>Label Propagation</li>
-  <li>Community Detection (based on <a href="http://arxiv.org/pdf/0808.2633.pdf">this paper</a>)</li>
-  <li>Connected Components</li>
-  <li>GSA Connected Components</li>
-  <li>GSA PageRank</li>
-  <li>GSA Single-Source-Shortest-Paths</li>
-</ol>
-
-<p>Gelly also offers implementations of common graph algorithms through <a href="https://github.com/apache/flink/tree/master/flink-staging/flink-gelly/src/main/java/org/apache/flink/graph/example">examples</a>.
-Among them, one can find graph weighting schemes, like Jaccard Similarity and Euclidean Distance Weighting, 
-as well as computation of common graph metrics.</p>
-
-<p><a href="#top">Back to top</a></p>
-
-<h2 id="use-case-music-profiles">Use-Case: Music Profiles</h2>
-
-<p>In the following section, we go through a use-case scenario that combines the Flink DataSet API
-with Gelly in order to process users’ music preferences to suggest additions to their playlist.</p>
-
-<p>First, we read a user’s music profile which is in the form of user-id, song-id and the number of
-plays that each song has. We then filter out the list of songs the users do not wish to see in their
-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>
-
-<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
-can be found <a href="http://labrosa.ee.columbia.edu/millionsong/tasteprofile">here</a>. This data set contains <strong>48,373,586</strong> real user-id, song-id and
-play-count triplets.</p>
-
-<p><strong>Note:</strong> The code snippets in this post try to reduce verbosity by skipping type parameters of generic functions. Please have a look at <a href="https://github.com/apache/flink/blob/master/flink-staging/flink-gelly/src/main/java/org/apache/flink/graph/example/MusicProfiles.java">the full example</a> for the correct and complete code.</p>
-
-<h4 id="filtering-out-bad-records">Filtering out Bad Records</h4>
-
-<p>After reading the <code>(user-id, song-id, play-count)</code> triplets from a CSV file and after parsing a
-text file in order to retrieve the list of songs that a user would not want to include in a
-playlist, we use a coGroup function to filter out the mismatches.</p>
-
-<div class="highlight"><pre><code class="language-java"><span class="c1">// read the user-song-play triplets.</span>
-<span class="n">DataSet</span><span class="o">&lt;</span><span class="n">Tuple3</span><span class="o">&lt;</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">&gt;&gt;</span> <span class="n">triplets</span> <span class="o">=</span>
-    <span class="n">getUserSongTripletsData</span><span class="o">(</span><span class="n">env</span><span class="o">);</span>
-
-<span class="c1">// read the mismatches dataset and extract the songIDs</span>
-<span class="n">DataSet</span><span class="o">&lt;</span><span class="n">Tuple3</span><span class="o">&lt;</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">&gt;&gt;</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="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>
-                                <span class="n">out</span><span class="o">.</span><span class="na">collect</span><span class="o">(</span><span class="n">triplet</span><span class="o">);</span>
-                            <span class="o">}</span>
-                        <span class="o">}</span>
-                    <span class="o">}</span>
-                <span class="o">}</span></code></pre></div>
-
-<p>The coGroup simply takes the triplets whose song-id (second field) matches the song-id from the
-mismatches list (first field) and if the iterator was empty for a certain triplet, meaning that
-there were no mismatches found, the triplet associated with that song is collected.</p>
-
-<h4 id="compute-the-top-songs-per-user">Compute the Top Songs per User</h4>
-
-<p>As a next step, we would like to see which songs a user played more often. To this end, we
-build a user-song weighted, bipartite graph in which edge source vertices are users, edge target
-vertices are songs and where the weight represents the number of times the user listened to that
-certain song.</p>
-
-<center>
-<img src="/img/blog/user-song-graph.png" style="width:90%;margin:15px" />
-</center>
-
-<div class="highlight"><pre><code class="language-java"><span class="c1">// create a user -&gt; song weighted bipartite graph where the edge weights</span>
-<span class="c1">// correspond to play counts</span>
-<span class="n">Graph</span><span class="o">&lt;</span><span class="n">String</span><span class="o">,</span> <span class="n">NullValue</span><span class="o">,</span> <span class="n">Integer</span><span class="o">&gt;</span> <span class="n">userSongGraph</span> <span class="o">=</span> <span class="n">Graph</span><span class="o">.</span><span class="na">fromTupleDataSet</span><span class="o">(</span><span class="n">validTriplets</span><span class="o">,</span> <span class="n">env</span><span class="o">);</span></code></pre></div>
-
-<p>Consult the <a href="https://ci.apache.org/projects/flink/flink-docs-master/libs/gelly_guide.html">Gelly guide</a> for guidelines 
-on how to create a graph from a given DataSet of edges or from a collection.</p>
-
-<p>To retrieve the top songs per user, we call the groupReduceOnEdges function as it perform an
-aggregation over the first hop neighborhood taking just the edges into consideration. We will
-basically iterate through the edge value and collect the target (song) of the maximum weight edge.</p>
-
-<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">&lt;</span><span class="n">Tuple2</span><span class="o">&gt;</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="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">&lt;</span><span class="n">Edge</span><span class="o">&gt;</span> <span class="n">edges</span><span class="o">)</span> <span class="o">{</span>
-        <span class="kt">int</span> <span class="n">maxPlaycount</span> <span class="o">=</span> <span class="mi">0</span><span class="o">;</span>
-        <span class="n">String</span> <span class="n">topSong</span> <span class="o">=</span> <span class="s">&quot;&quot;</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="k">if</span> <span class="o">(</span><span class="n">edge</span><span class="o">.</span><span class="na">getValue</span><span class="o">()</span> <span class="o">&gt;</span> <span class="n">maxPlaycount</span><span class="o">)</span> <span class="o">{</span>
-                <span class="n">maxPlaycount</span> <span class="o">=</span> <span class="n">edge</span><span class="o">.</span><span class="na">getValue</span><span class="o">();</span>
-                <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="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>
-
-<h4 id="creating-a-user-user-similarity-graph">Creating a User-User Similarity Graph</h4>
-
-<p>Clustering users based on common interests, in this case, common top songs, could prove to be
-very useful for advertisements or for recommending new musical compilations. In a user-user graph,
-two users who listen to the same song will simply be linked together through an edge as depicted
-in the figure below.</p>
-
-<center>
-<img src="/img/blog/user-song-to-user-user.png" style="width:90%;margin:15px" />
-</center>
-
-<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>
-
-<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>
-
-<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
-straightforward as a call to the <code>Graph.fromDataSet()</code> method.</p>
-
-<div class="highlight"><pre><code class="language-java"><span class="c1">// create a user-user similarity graph:</span>
-<span class="c1">// two users that listen to the same song are connected</span>
-<span class="n">DataSet</span><span class="o">&lt;</span><span class="n">Edge</span><span class="o">&gt;</span> <span class="n">similarUsers</span> <span class="o">=</span> <span class="n">userSongGraph</span><span class="o">.</span><span class="na">getEdges</span><span class="o">()</span>
-        <span class="c1">// filter out user-song edges that are below the playcount threshold</span>
-        <span class="o">.</span><span class="na">filter</span><span class="o">(</span><span class="k">new</span> <span class="n">FilterFunction</span><span class="o">&lt;</span><span class="n">Edge</span><span class="o">&lt;</span><span class="n">String</span><span class="o">,</span> <span class="n">Integer</span><span class="o">&gt;&gt;()</span> <span class="o">{</span>
-            	<span class="kd">public</span> <span class="kt">boolean</span> <span class="nf">filter</span><span class="o">(</span><span class="n">Edge</span><span class="o">&lt;</span><span class="n">String</span><span class="o">,</span> <span class="n">Integer</span><span class="o">&gt;</span> <span class="n">edge</span><span class="o">)</span> <span class="o">{</span>
-                    <span class="k">return</span> <span class="o">(</span><span class="n">edge</span><span class="o">.</span><span class="na">getValue</span><span class="o">()</span> <span class="o">&gt;</span> <span class="n">playcountThreshold</span><span class="o">);</span>
-                <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="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">&lt;</span><span class="n">Edge</span><span class="o">&gt;</span> <span class="n">edges</span><span class="o">,</span> <span class="n">Collector</span><span class="o">&lt;</span><span class="n">Edge</span><span class="o">&gt;</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="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">&lt;</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">&lt;</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="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>
-                <span class="o">}</span>
-        <span class="o">})</span>
-        <span class="o">.</span><span class="na">distinct</span><span class="o">();</span>
-
-<span class="n">Graph</span> <span class="n">similarUsersGraph</span> <span class="o">=</span> <span class="n">Graph</span><span class="o">.</span><span class="na">fromDataSet</span><span class="o">(</span><span class="n">similarUsers</span><span class="o">).</span><span class="na">getUndirected</span><span class="o">();</span></code></pre></div>
-
-<p>After having created a user-user graph, it would make sense to detect the various communities
-formed. To do so, we first initialize each vertex with a numeric label using the
-<code>joinWithVertices()</code> function that takes a data set of Tuple2 as a parameter and joins
-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>
-
-<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>
-<span class="n">DataSet</span><span class="o">&lt;</span><span class="n">Tuple2</span><span class="o">&lt;</span><span class="n">String</span><span class="o">,</span> <span class="n">Long</span><span class="o">&gt;&gt;</span> <span class="n">idsWithInitialLabels</span> <span class="o">=</span> <span class="n">DataSetUtils</span>
-        <span class="o">.</span><span class="na">zipWithUniqueId</span><span class="o">(</span><span class="n">similarUsersGraph</span><span class="o">.</span><span class="na">getVertexIds</span><span class="o">())</span>
-        <span class="o">.</span><span class="na">map</span><span class="o">(</span><span class="k">new</span> <span class="n">MapFunction</span><span class="o">&lt;</span><span class="n">Tuple2</span><span class="o">&lt;</span><span class="n">Long</span><span class="o">,</span> <span class="n">String</span><span class="o">&gt;,</span> <span class="n">Tuple2</span><span class="o">&lt;</span><span class="n">String</span><span class="o">,</span> <span class="n">Long</span><span class="o">&gt;&gt;()</span> <span class="o">{</span>
-                <span class="nd">@Override</span>
-                <span class="kd">public</span> <span class="n">Tuple2</span><span class="o">&lt;</span><span class="n">String</span><span class="o">,</span> <span class="n">Long</span><span class="o">&gt;</span> <span class="nf">map</span><span class="o">(</span><span class="n">Tuple2</span><span class="o">&lt;</span><span class="n">Long</span><span class="o">,</span> <span class="n">String</span><span class="o">&gt;</span> <span class="n">tuple2</span><span class="o">)</span> <span class="kd">throws</span> <span class="n">Exception</span> <span class="o">{</span>
-                    <span class="k">return</span> <span class="k">new</span> <span class="n">Tuple2</span><span class="o">&lt;</span><span class="n">String</span><span class="o">,</span> <span class="n">Long</span><span class="o">&gt;(</span><span class="n">tuple2</span><span class="o">.</span><span class="na">f1</span><span class="o">,</span> <span class="n">tuple2</span><span class="o">.</span><span class="na">f0</span><span class="o">);</span>
-                <span class="o">}</span>
-        <span class="o">});</span>
-
-<span class="c1">// update the vertex values and run the label propagation algorithm</span>
-<span class="n">DataSet</span><span class="o">&lt;</span><span class="n">Vertex</span><span class="o">&gt;</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="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="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>
-
-<h2 id="ongoing-and-future-work">Ongoing and Future Work</h2>
-
-<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>
-
-<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>
-
-<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>
-
-<p>Curious? Read more about our plans for Gelly in the <a href="https://cwiki.apache.org/confluence/display/FLINK/Flink+Gelly">roadmap</a>.</p>
-
-<p><a href="#top">Back to top</a></p>
-
-<h2 id="links">Links</h2>
-<p><a href="https://ci.apache.org/projects/flink/flink-docs-master/libs/gelly_guide.html">Gelly Documentation</a></p>
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-                <span class="caret"></span></a>
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-                  <li><a href="http://ci.apache.org/projects/flink/flink-docs-release-1.2" target="_blank">1.2 (Snapshot) <small><span class="glyphicon glyphicon-new-window"></span></small></a></li>
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-      <h1>Apache Flink 0.9.1 available</h1>
-
-      <article>
-        <p>01 Sep 2015</p>
-
-<p>The Flink community is happy to announce that Flink 0.9.1 is now available.</p>
-
-<p>0.9.1 is a maintenance release, which includes a lot of minor fixes across
-several parts of the system. We suggest all users of Flink to work with this
-latest stable version.</p>
-
-<p><a href="/downloads.html">Download the release</a> and <a href="http://ci.apache.org/projects/flink/flink-docs-release-1.1">check out the
-documentation</a>. Feedback through the Flink mailing lists
-is, as always, very welcome!</p>
-
-<p>The following <a href="https://issues.apache.org/jira/issues/?jql=project%20%3D%20FLINK%20AND%20status%20in%20(Resolved%2C%20Closed)%20AND%20fixVersion%20%3D%200.9.1">issues were fixed</a>
-for this release:</p>
-
-<ul>
-  <li><a href="https://issues.apache.org/jira/browse/FLINK-1916">FLINK-1916</a> EOFException when running delta-iteration job</li>
-  <li><a href="https://issues.apache.org/jira/browse/FLINK-2089">FLINK-2089</a> “Buffer recycled” IllegalStateException during cancelling</li>
-  <li><a href="https://issues.apache.org/jira/browse/FLINK-2189">FLINK-2189</a> NullPointerException in MutableHashTable</li>
-  <li><a href="https://issues.apache.org/jira/browse/FLINK-2205">FLINK-2205</a> Confusing entries in JM Webfrontend Job Configuration section</li>
-  <li><a href="https://issues.apache.org/jira/browse/FLINK-2229">FLINK-2229</a> Data sets involving non-primitive arrays cannot be unioned</li>
-  <li><a href="https://issues.apache.org/jira/browse/FLINK-2238">FLINK-2238</a> Scala ExecutionEnvironment.fromCollection does not work with Sets</li>
-  <li><a href="https://issues.apache.org/jira/browse/FLINK-2248">FLINK-2248</a> Allow disabling of sdtout logging output</li>
-  <li><a href="https://issues.apache.org/jira/browse/FLINK-2257">FLINK-2257</a> Open and close of RichWindowFunctions is not called</li>
-  <li><a href="https://issues.apache.org/jira/browse/FLINK-2262">FLINK-2262</a> ParameterTool API misnamed function</li>
-  <li><a href="https://issues.apache.org/jira/browse/FLINK-2280">FLINK-2280</a> GenericTypeComparator.compare() does not respect ascending flag</li>
-  <li><a href="https://issues.apache.org/jira/browse/FLINK-2285">FLINK-2285</a> Active policy emits elements of the last window twice</li>
-  <li><a href="https://issues.apache.org/jira/browse/FLINK-2286">FLINK-2286</a> Window ParallelMerge sometimes swallows elements of the last window</li>
-  <li><a href="https://issues.apache.org/jira/browse/FLINK-2293">FLINK-2293</a> Division by Zero Exception</li>
-  <li><a href="https://issues.apache.org/jira/browse/FLINK-2298">FLINK-2298</a> Allow setting custom YARN application names through the CLI</li>
-  <li><a href="https://issues.apache.org/jira/browse/FLINK-2347">FLINK-2347</a> Rendering problem with Documentation website</li>
-  <li><a href="https://issues.apache.org/jira/browse/FLINK-2353">FLINK-2353</a> Hadoop mapred IOFormat wrappers do not respect JobConfigurable interface</li>
-  <li><a href="https://issues.apache.org/jira/browse/FLINK-2356">FLINK-2356</a> Resource leak in checkpoint coordinator</li>
-  <li><a href="https://issues.apache.org/jira/browse/FLINK-2361">FLINK-2361</a> CompactingHashTable loses entries</li>
-  <li><a href="https://issues.apache.org/jira/browse/FLINK-2362">FLINK-2362</a> distinct is missing in DataSet API documentation</li>
-  <li><a href="https://issues.apache.org/jira/browse/FLINK-2381">FLINK-2381</a> Possible class not found Exception on failed partition producer</li>
-  <li><a href="https://issues.apache.org/jira/browse/FLINK-2384">FLINK-2384</a> Deadlock during partition spilling</li>
-  <li><a href="https://issues.apache.org/jira/browse/FLINK-2386">FLINK-2386</a> Implement Kafka connector using the new Kafka Consumer API</li>
-  <li><a href="https://issues.apache.org/jira/browse/FLINK-2394">FLINK-2394</a> HadoopOutFormat OutputCommitter is default to FileOutputCommiter</li>
-  <li><a href="https://issues.apache.org/jira/browse/FLINK-2412">FLINK-2412</a> Race leading to IndexOutOfBoundsException when querying for buffer while releasing SpillablePartition</li>
-  <li><a href="https://issues.apache.org/jira/browse/FLINK-2422">FLINK-2422</a> Web client is showing a blank page if “Meta refresh” is disabled in browser</li>
-  <li><a href="https://issues.apache.org/jira/browse/FLINK-2424">FLINK-2424</a> InstantiationUtil.serializeObject(Object) does not close output stream</li>
-  <li><a href="https://issues.apache.org/jira/browse/FLINK-2437">FLINK-2437</a> TypeExtractor.analyzePojo has some problems around the default constructor detection</li>
-  <li><a href="https://issues.apache.org/jira/browse/FLINK-2442">FLINK-2442</a> PojoType fields not supported by field position keys</li>
-  <li><a href="https://issues.apache.org/jira/browse/FLINK-2447">FLINK-2447</a> TypeExtractor returns wrong type info when a Tuple has two fields of the same POJO type</li>
-  <li><a href="https://issues.apache.org/jira/browse/FLINK-2450">FLINK-2450</a> IndexOutOfBoundsException in KryoSerializer</li>
-  <li><a href="https://issues.apache.org/jira/browse/FLINK-2460">FLINK-2460</a> ReduceOnNeighborsWithExceptionITCase failure</li>
-  <li><a href="https://issues.apache.org/jira/browse/FLINK-2527">FLINK-2527</a> If a VertexUpdateFunction calls setNewVertexValue more than once, the MessagingFunction will only see the first value set</li>
-  <li><a href="https://issues.apache.org/jira/browse/FLINK-2540">FLINK-2540</a> LocalBufferPool.requestBuffer gets into infinite loop</li>
-  <li><a href="https://issues.apache.org/jira/browse/FLINK-2542">FLINK-2542</a> It should be documented that it is required from a join key to override hashCode(), when it is not a POJO</li>
-  <li><a href="https://issues.apache.org/jira/browse/FLINK-2555">FLINK-2555</a> Hadoop Input/Output Formats are unable to access secured HDFS clusters</li>
-  <li><a href="https://issues.apache.org/jira/browse/FLINK-2560">FLINK-2560</a> Flink-Avro Plugin cannot be handled by Eclipse</li>
-  <li><a href="https://issues.apache.org/jira/browse/FLINK-2572">FLINK-2572</a> Resolve base path of symlinked executable</li>
-  <li><a href="https://issues.apache.org/jira/browse/FLINK-2584">FLINK-2584</a> ASM dependency is not shaded away</li>
-</ul>
-
-      </article>
-    </div>
-
-    <div class="row">
-      <div id="disqus_thread"></div>
-      <script type="text/javascript">
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-      </div>
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-      <div class="footer text-center col-sm-12">
-        <p>Copyright © 2014-2016 <a href="http://apache.org">The Apache Software Foundation</a>. All Rights Reserved.</p>
-        <p>Apache Flink, Apache, and the Apache feather logo are either registered trademarks or trademarks of The Apache Software Foundation.</p>
-        <p><a href="/privacy-policy.html">Privacy Policy</a> &middot; <a href="/blog/feed.xml">RSS feed</a></p>
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http://git-wip-us.apache.org/repos/asf/flink-web/blob/16a92b0c/content/news/2015/09/03/flink-forward.html
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-<!DOCTYPE html>
-<html lang="en">
-  <head>
-    <meta charset="utf-8">
-    <meta http-equiv="X-UA-Compatible" content="IE=edge">
-    <meta name="viewport" content="width=device-width, initial-scale=1">
-    <!-- The above 3 meta tags *must* come first in the head; any other head content must come *after* these tags -->
-    <title>Apache Flink: Announcing Flink Forward 2015</title>
-    <link rel="shortcut icon" href="/favicon.ico" type="image/x-icon">
-    <link rel="icon" href="/favicon.ico" type="image/x-icon">
-
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-              <img alt="Apache Flink" src="/img/navbar-brand-logo.png" width="147px" height="73px">
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-        <div class="collapse navbar-collapse" id="bs-example-navbar-collapse-1">
-          <ul class="nav navbar-nav navbar-main">
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-            <li class=""><a class="btn btn-info" href="/downloads.html">Download Flink</a></li>
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-
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-
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-            <li class=" active hidden-md hidden-sm"><a href="/blog/"><b>Flink Blog</b></a></li>
-
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-
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-            <!-- Documentation -->
-            <!-- <li>
-              <a href="http://ci.apache.org/projects/flink/flink-docs-release-1.1" target="_blank">Documentation <small><span class="glyphicon glyphicon-new-window"></span></small></a>
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-            <li class="dropdown">
-              <a class="dropdown-toggle" data-toggle="dropdown" href="#">Documentation
-                <span class="caret"></span></a>
-                <ul class="dropdown-menu">
-                  <li><a href="http://ci.apache.org/projects/flink/flink-docs-release-1.1" target="_blank">1.1 (Latest stable release) <small><span class="glyphicon glyphicon-new-window"></span></small></a></li>
-                  <li><a href="http://ci.apache.org/projects/flink/flink-docs-release-1.2" target="_blank">1.2 (Snapshot) <small><span class="glyphicon glyphicon-new-window"></span></small></a></li>
-                </ul>
-              </li>
-
-            <!-- Quickstart -->
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-              <a href="http://ci.apache.org/projects/flink/flink-docs-release-1.1/quickstart/setup_quickstart.html" target="_blank">Quickstart <small><span class="glyphicon glyphicon-new-window"></span></small></a>
-            </li>
-
-            <!-- GitHub -->
-            <li>
-              <a href="https://github.com/apache/flink" target="_blank">Flink on GitHub <small><span class="glyphicon glyphicon-new-window"></span></small></a>
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-      <div class="col-sm-9">
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-  <div class="col-sm-12">
-    <div class="row">
-      <h1>Announcing Flink Forward 2015</h1>
-
-      <article>
-        <p>03 Sep 2015</p>
-
-<p><a href="http://2015.flink-forward.org/">Flink Forward 2015</a> is the first
-conference with Flink at its center that aims to bring together the
-Apache Flink community in a single place. The organizers are starting
-this conference in October 12 and 13 from Berlin, the place where
-Apache Flink started.</p>
-
-<center>
-<img src="/img/blog/flink-forward-banner.png" style="width:80%;margin:15px" />
-</center>
-
-<p>The <a href="http://2015.flink-forward.org/?post_type=day">conference program</a> has
-been announced by the organizers and a program committee consisting of
-Flink PMC members. The agenda contains talks from industry and
-academia as well as a dedicated session on hands-on Flink training.</p>
-
-<p>Some highlights of the talks include</p>
-
-<ul>
-  <li>
-    <p>A keynote by <a href="http://2015.flink-forward.org/?speaker=william-vambenepe">William
-Vambenepe</a>,
-lead of the product management team responsible for Big Data
-services on Google Cloud Platform (BigQuery, Dataflow, etc…) on
-data streaming, Google Cloud Dataflow, and Apache Flink.</p>
-  </li>
-  <li>
-    <p>Talks by several practitioners on how they are putting Flink to work
-in their projects, including ResearchGate, Bouygues Telecom,
-Amadeus, Telefonica, Capital One, Ericsson, and Otto Group.</p>
-  </li>
-  <li>
-    <p>Talks on how open source projects, including Apache Mahout, Apache
-SAMOA (incubating), Apache Zeppelin (incubating), Apache BigTop, and
-Apache Storm integrate with Apache Flink.</p>
-  </li>
-  <li>
-    <p>Talks by Flink committers on several aspects of the system, such as
-fault tolerance, the internal runtime architecture, and others.</p>
-  </li>
-</ul>
-
-<p>Check out the <a href="http://2015.flink-forward.org/?post_type=day">schedule</a> and
-register for the conference.</p>
-
-
-      </article>
-    </div>
-
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-      <div id="disqus_thread"></div>
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-      </div>
-    </div>
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-        <p>Apache Flink, Apache, and the Apache feather logo are either registered trademarks or trademarks of The Apache Software Foundation.</p>
-        <p><a href="/privacy-policy.html">Privacy Policy</a> &middot; <a href="/blog/feed.xml">RSS feed</a></p>
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