[GitHub] flink pull request: [FLINK-1462] Gelly documentation

[vasia <gi...@git.apache.org>]

GitHub user vasia opened a pull request:

    https://github.com/apache/flink/pull/430

    [FLINK-1462] Gelly documentation

    Hi,
    here's the first draft of the gelly-guide. I have also included the changes introduced by #402.
    Let me know what you think, whether everything is clear, whether I should add more examples and figures.
    Thanks!
    -V.

You can merge this pull request into a Git repository by running:

    $ git pull https://github.com/vasia/flink gelly-guide

Alternatively you can review and apply these changes as the patch at:

    https://github.com/apache/flink/pull/430.patch

To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:

    This closes #430
    
----
commit 8db66cefc0810f8621e2042dbf073768db591284
Author: vasia <va...@gmail.com>
Date:   2015-01-29T15:46:51Z

    [FLINK-1462][gelly][docs] added gelly guide

----


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[GitHub] flink pull request: [FLINK-1462] Gelly documentation

[vasia <gi...@git.apache.org>]

Github user vasia commented on the pull request:

    https://github.com/apache/flink/pull/430#issuecomment-76223366
  
    Nothing prevent us really, I just need to address your comments! I'll try to do this later tonight and then I'll merge ;)


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[GitHub] flink pull request: [FLINK-1462] Gelly documentation

[vasia <gi...@git.apache.org>]

Github user vasia commented on the pull request:

    https://github.com/apache/flink/pull/430#issuecomment-76155327
  
    Awesome! Thanks a lot for the careful reading @tillrohrmann ^^


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[GitHub] flink pull request: [FLINK-1462] Gelly documentation

[tillrohrmann <gi...@git.apache.org>]

Github user tillrohrmann commented on a diff in the pull request:

    https://github.com/apache/flink/pull/430#discussion_r25416192
  
    --- Diff: docs/gelly_guide.md ---
    @@ -0,0 +1,425 @@
    +---
    +title: "Gelly: Flink Graph API"
    +---
    +<!--
    +Licensed to the Apache Software Foundation (ASF) under one
    +or more contributor license agreements.  See the NOTICE file
    +distributed with this work for additional information
    +regarding copyright ownership.  The ASF licenses this file
    +to you under the Apache License, Version 2.0 (the
    +"License"); you may not use this file except in compliance
    +with the License.  You may obtain a copy of the License at
    +
    +  http://www.apache.org/licenses/LICENSE-2.0
    +
    +Unless required by applicable law or agreed to in writing,
    +software distributed under the License is distributed on an
    +"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
    +KIND, either express or implied.  See the License for the
    +specific language governing permissions and limitations
    +under the License.
    +-->
    +
    +* This will be replaced by the TOC
    +{:toc}
    +
    +<a href="#top"></a>
    +
    +Introduction
    +------------
    +
    +Gelly is a Java Graph API for Flink. It contains a set of methods and utilities which aim to simplify the development of graph analysis applications in Flink. In Gelly, graphs can be transformed and modified using high-level functions similar to the ones provided by the batch processing API. Gelly provides methods to create, transform and modify graphs, as well as a library of graph algorithms.
    +
    +Using Gelly
    +-----------
    +
    +Gelly is currently part of the *staging* Maven project. All relevant classes are located in the *org.apache.flink.graph* package.
    +
    +Add the following dependency to your `pom.xml` to use Gelly.
    +
    +~~~xml
    +<dependency>
    +    <groupId>org.apache.flink</groupId>
    +    <artifactId>flink-gelly</artifactId>
    +    <version>{{site.FLINK_VERSION_SHORT}}</version>
    +</dependency>
    +~~~
    +
    +The remaining sections provide a description of available methods and present several examples of how to use Gelly and how to mix it with the Flink Java API. After reading this guide, you might also want to check the {% gh_link /flink-staging/flink-gelly/src/main/java/org/apache/flink/graph/example/ "Gelly examples" %}.
    +
    +Graph Representation
    +-----------
    +
    +In Gelly, a `Graph` is represented by a `DataSet` of vertices and a `DataSet` of edges.
    +
    +The `Graph` nodes are represented by the `Vertex` type. A `Vertex` is defined by a unique ID and a value. `Vertex` IDs should implement the `Comparable` interface. Vertices without value can be represented by setting the value type to `NullValue`.
    +
    +{% highlight java %}
    +// create a new vertex with a Long ID and a String value
    +Vertex<Long, String> v = new Vertex<Long, String>(1L, "foo");
    +
    +// create a new vertex with a Long ID and no value
    +Vertex<Long, NullValue> v = new Vertex<Long, NullValue>(1L, NullValue.getInstance());
    +{% endhighlight %}
    +
    +The graph edges are represented by the `Edge` type. An `Edge` is defined by a source ID (the ID of the source `Vertex`), a target ID (the ID of the target `Vertex`) and an optional value. The source and target IDs should be of the same type as the `Vertex` IDs. Edges with no value have a `NullValue` value type.
    +
    +{% highlight java %}
    +Edge<Long, Double> e = new Edge<Long, Double>(1L, 2L, 0.5);
    +
    +// reverse the source and target of this edge
    +Edge<Long, Double> reversed = e.reverse();
    +
    +Double weight = e.getValue(); // weight = 0.5
    +{% endhighlight %}
    +
    +[Back to top](#top)
    +
    +Graph Creation
    +-----------
    +
    +You can create a `Graph` in the following ways:
    +
    +* from a `DataSet` of edges and an optional `DataSet` of vertices:
    +
    +{% highlight java %}
    +ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
    +
    +DataSet<Vertex<String, Long>> vertices = ...
    +
    +DataSet<Edge<String, Double>> edges = ...
    +
    +Graph<String, Long, Double> graph = Graph.fromDataSet(vertices, edges, env);
    +{% endhighlight %}
    +
    +* from a `DataSet` of `Tuple3` and an optional `DataSet` of `Tuple2`. In this case, Gelly will convert each `Tuple3` to an `Edge`, where the first field will be the source ID, the second field will be the target ID and the third field will be the edge value. Equivalently, each `Tuple2` will be converted to a `Vertex`, where the first field will be the vertex ID and the second field will be the vertex value:
    +
    +{% highlight java %}
    +ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
    +
    +DataSet<Tuple2<String, Long>> vertexTuples = env.readCsvFile("path/to/vertex/input");
    +
    +DataSet<Tuple3<String, String, Double>> edgeTuples = env.readCsvFile("path/to/edge/input");
    +
    +Graph<String, Long, Double> graph = Graph.fromTupleDataSet(vertexTuples, edgeTuples, env);
    +{% endhighlight %}
    +
    +* from a `Collection` of edges and an optional `Collection` of vertices:
    +
    +{% highlight java %}
    +ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
    +
    +List<Vertex<Long, Long>> vertexList = new ArrayList...
    +
    +List<Edge<Long, String>> edgeList = new ArrayList...
    +
    +Graph<Long, Long, String> graph = Graph.fromCollection(vertexList, edgeList, env);
    +{% endhighlight %}
    +
    +If no vertex input is provided during Graph creation, Gelly will automatically produce the `Vertex` `DataSet` from the edge input. In this case, the created vertices will have no values. Alternatively, you can provide a `MapFunction` as an argument to the creation method, in order to initialize the `Vertex` values:
    +
    +{% highlight java %}
    +ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
    +
    +// initialize the vertex value to be equal to the vertex ID
    +Graph<Long, Long, String> graph = Graph.fromCollection(edges, 
    +				new MapFunction<Long, Long>() {
    +					public Long map(Long value) { 
    +						return value; 
    +					} 
    +				}, env);
    +{% endhighlight %}
    +
    +[Back to top](#top)
    +
    +Graph Properties
    +------------
    +
    +Gelly includes the following methods for retrieving various Graph properties and metrics:
    +
    +{% highlight java %}
    +// get the Vertex DataSet
    +DataSet<Vertex<K, VV>> getVertices()
    +
    +// get the Edge DataSet
    +DataSet<Edge<K, EV>> getEdges()
    +
    +// get the IDs of the vertices as a DataSet
    +DataSet<K> getVertexIds()
    +
    +// get the source-target pairs of the edge IDs as a DataSet
    +DataSet<Tuple2<K, K>> getEdgeIds() 
    +
    +// get a DataSet of <vertex ID, in-degree> pairs for all vertices
    +DataSet<Tuple2<K, Long>> inDegrees() 
    +
    +// get a DataSet of <vertex ID, out-degree> pairs for all vertices
    +DataSet<Tuple2<K, Long>> outDegrees()
    +
    +// get a DataSet of <vertex ID, degree> pairs for all vertices, where degree is the sum of in- and out- degrees
    +DataSet<Tuple2<K, Long>> getDegrees()
    +
    +// get the number of vertices
    +DataSet<Integer> numberOfVertices()
    +
    +// get the number of edges
    +DataSet<Integer> numberOfEdges()
    +
    +{% endhighlight %}
    +
    +[Back to top](#top)
    +
    +Graph Transformations
    +-----------------
    +
    +* <strong>Map</strong>: Gelly provides specialized methods for applying a map transformation on the vertex values or edge values. `mapVertices` and `mapEdges` return a new `Graph`, where the IDs of the vertices (or edges) remain unchanged, while the values are transformed according to the provided user-defined map function. The map functions also allow changing the type of the vertex or edge values.
    +
    +{% highlight java %}
    +ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
    +Graph<Long, Long, Long> graph = Graph.fromDataSet(vertices, edges, env);
    +
    +// increment each vertex value by one
    +Graph<Long, Long, Long> updatedGraph = graph.mapVertices(
    +				new MapFunction<Vertex<Long, Long>, Long>() {
    +					public Long map(Vertex<Long, Long> value) {
    +						return value.getValue() + 1;
    +					}
    +				});
    +{% endhighlight %}
    +
    +* <strong>Filter</strong>: A filter transformation applies a user-defined filter function on the vertices or edges of the `Graph`. `filterOnEdges` will create a sub-graph of the original graph, keeping only the edges that satisfy the provided predicate. Note that the vertex dataset will not be modified. Respectively, `filterOnVertices` applies a filter on the vertices of the graph. Edges whose source and/or target do not satisfy the vertex predicate are removed from the resulting edge dataset. The `subgraph` method can be used to apply a filter function to the vertices and the edges at the same time.
    +
    +{% highlight java %}
    +Graph<Long, Long, Long> graph = ...
    +
    +graph.subgraph(
    +		new FilterFunction<Vertex<Long, Long>>() {
    +			   	public boolean filter(Vertex<Long, Long> vertex) {
    +					// keep only vertices with positive values
    +					return (vertex.getValue() > 0);
    +			   }
    +		   },
    +		new FilterFunction<Edge<Long, Long>>() {
    +				public boolean filter(Edge<Long, Long> edge) {
    +					// keep only edges with negative values
    +					return (edge.getValue() < 0);
    +				}
    +		})
    +{% endhighlight %}
    +
    +<p class="text-center">
    +    <img alt="Filter Transformations" width="80%" src="img/gelly-filter.png"/>
    +</p>
    +
    +* <strong>Join</strong>: Gelly provides specialized methods for joining the vertex and edge datasets with other input datasets. `joinWithVertices` joins the vertices with a `Tuple2` input data set. The join is performed using the vertex ID and the first field of the `Tuple2` input as the join keys. The method returns a new `Graph` where the vertex values have been updated according to the provided a user-defined map function.
    +Similarly, an input dataset can be joined with the edges, using one of three methods. `joinWithEdges` expects an input `DataSet` of `Tuple3` and joins on the composite key of both source and target vertex IDs. `joinWithEdgesOnSource` expects a `DataSet` of `Tuple2` and joins on the source key of the edges and the first attribute of the input dataset and `joinWithEdgesOnTarget` expects a `DataSet` of `Tuple2` and joins on the target key of the edges and the first attribute of the input dataset. All three methods apply a map function on the edge and the input data set values.
    +Note that if the input dataset contains a key multiple times, all Gelly join methods will only consider the first value encountered.
    +
    +{% highlight java %}
    +Graph<Long, Double, Double> network = ...
    +
    +DataSet<Tuple2<Long, Long>> vertexOutDegrees = network.outDegrees();
    +
    +// assign the transition probabilities as the edge weights
    +Graph<Long, Double, Double> networkWithWeights = network.joinWithEdgesOnSource(vertexOutDegrees,
    +				new MapFunction<Tuple2<Double, Long>, Double>() {
    +					public Double map(Tuple2<Double, Long> value) {
    +						return value.f0 / value.f1;
    +					}
    +				});
    +{% endhighlight %}
    +
    +* <strong>Reverse</strong>: the `reverse()` method returns a new `Graph` where the direction of all edges has been reversed.
    +
    +* <strong>Undirected</strong>: In Gelly, a `Graph` is always directed. Undirected graphs can be represented by adding all opposite-direction edges to a graph. For this purpose, Gelly provides the `getUndirected()` method.
    +
    +* <strong>Union</strong>: Gelly's `union()` method performs a union on the vertex and edges sets of the input graphs. Duplicate vertices are removed from the resulting `Graph`, while if duplicate edges exists, these will be maintained.
    +
    +<p class="text-center">
    +    <img alt="Union Transformation" width="50%" src="img/gelly-union.png"/>
    +</p>
    +
    +[Back to top](#top)
    +
    +Graph Mutations
    +-----------
    +
    +Gelly includes the following methods for adding and removing vertices and edges from an input `Graph`:
    +
    +{% highlight java %}
    +// adds a Vertex and the given edges to the Graph. If the Vertex already exists, it will not be added again, but the given edges will.
    +Graph<K, VV, EV> addVertex(final Vertex<K, VV> vertex, List<Edge<K, EV>> edges)
    +
    +// adds an Edge to the Graph. If the source and target vertices do not exist in the graph, they will also be added.
    +Graph<K, VV, EV> addEdge(Vertex<K, VV> source, Vertex<K, VV> target, EV edgeValue)
    +
    +// removes the given Vertex and its edges from the Graph.
    +Graph<K, VV, EV> removeVertex(Vertex<K, VV> vertex)
    +
    +// removes *all* edges that match the given Edge from the Graph.
    +Graph<K, VV, EV> removeEdge(Edge<K, EV> edge)
    +{% endhighlight %}
    +
    +Neighborhood Methods
    +-----------
    +
    +Neighborhood methods allow vertices to perform an aggregation on their first-hop neighborhood.
    +
    +`reduceOnEdges()` can be used to compute an aggregation on the neighboring edges of a vertex, while `reduceOnNeighbors()` has access on both the neighboring edges and vertices. The neighborhood scope is defined by the `EdgeDirection` parameter, which takes the values `IN`, `OUT` or `ALL`. `IN` will gather all in-coming edges (neighbors) of a vertex, `OUT` will gather all out-going edges (neighbors), while `ALL` will gather all edges (neighbors).
    +
    +For example, assume that you want to select the minimum weight of all out-edges for each vertex in the following graph:
    +
    +<p class="text-center">
    +    <img alt="reduceOnEdges Example" width="50%" src="img/gelly-example-graph.png"/>
    +</p>
    +
    +The following code will collect the out-edges for each vertex and apply the `SelectMinWeight()` user-defined function on each of the resulting neighborhoods:
    +
    +{% highlight java %}
    +Graph<Long, Long, Double> graph = ...
    +
    +DataSet<Tuple2<Long, Double>> minWeights = graph.reduceOnEdges(
    +				new SelectMinWeight(), EdgeDirection.OUT);
    +
    +// user-defined function to select the minimum weight
    +static final class SelectMinWeight implements EdgesFunction<Long, Double, Tuple2<Long, Double>> {
    +
    +    public Tuple2<Long, Double> iterateEdges(Iterable<Tuple2<Long, Edge<Long, Double>>> edges) {
    +
    +        long minWeight = Double.MAX_VALUE;
    +        long vertexId = -1;
    +
    +        for (Tuple2<Long, Edge<Long, Double>> edge: edges) {
    +            if (edge.f1.getValue() < weight) {
    +            weight = edge.f1.getValue();
    +            vertexId = edge.f0;
    +        }
    +        return new Tuple2<Long, Double>(vertexId, minWeight);
    +    }
    +}
    +{% endhighlight %}
    +
    +<p class="text-center">
    +    <img alt="reduceOnEdges Example" width="50%" src="img/gelly-reduceOnEdges.png"/>
    +</p>
    +
    +Similarly, assume that you would like to compute the sum of the values of all in-coming neighbors, for every vertex. The following code will collect the in-coming neighbors for each vertex and apply the `SumValues()` user-defined function on each neighborhood:
    +
    +{% highlight java %}
    +Graph<Long, Long, Double> graph = ...
    +
    +DataSet<Tuple2<Long, Long>> verticesWithSum = graph.reduceOnNeighbors(
    +				new SumValues(), EdgeDirection.IN);
    +
    +// user-defined function to sum the neighbor values
    +static final class SumValues implements NeighborsFunction<Long, Long, Long, Tuple2<Long, Long>> {
    +		
    +	public Tuple2<Long, Long> iterateNeighbors(Iterable<Tuple3<Long, Edge<Long, Long>, 
    +		Vertex<Long, Long>>> neighbors) {
    +		
    +		long sum = 0;
    +		long vertexId = -1;
    +
    +		for (Tuple3<Long, Edge<Long, Long>, Vertex<Long, Long>> neighbor : neighbors) {
    +			vertexId = neighbor.f0;
    +			sum += neighbor.f2.getValue();
    +		}
    +		return new Tuple2<Long, Long>(vertexId, sum);
    +	}
    +}
    +{% endhighlight %}
    +
    +<p class="text-center">
    +    <img alt="reduseOnNeighbors Example" width="70%" src="img/gelly-reduceOnNeighbors.png"/>
    +</p>
    +
    +When the aggregation computation does not require access to the vertex value, it is advised to use the more efficient `EdgesFunction` and `NeighborsFunction` for the user-defined functions. When access to the vertex value is required, one should use `EdgesFunctionWithVertexValue` and `NeighborsFunctionWithVertexValue` instead. 
    +
    +[Back to top](#top)
    +
    +Vertex-centric Iterations
    +-----------
    +
    +Gelly wraps Flink's [Spargel API](spargel_guide.html) to provide methods for vertex-centric iterations.
    +Like in Spargel, the user only needs to implement two functions: a `VertexUpdateFunction`, which defines how a vertex will update its value
    +based on the received messages and a `MessagingFunction`, which allows a vertex to send out messages for the next superstep.
    +These functions are given as parameters to Gelly's `createVertexCentricIteration`, which returns a `VertexCentricIteration`. 
    +The user can configure this iteration (set the name, the degree of parallelism, aggregators, etc.) and then run the computation, using the `runVertexCentricIteration` method:
    +
    +{% highlight java %}
    +Graph<Long, Double, Double> graph = ...
    +
    +// create the vertex-centric iteration
    +VertexCentricIteration<Long, Double, Double, Double> iteration = 
    +			graph.createVertexCentricIteration(
    +			new VertexDistanceUpdater(), new MinDistanceMessenger(), maxIterations);
    +
    +// set the iteration name
    +iteration.setName("Single Source Shortest Paths");
    +
    +// set the degree of parallelism
    +iteration.setDegreeOfParallelism(16);
    +
    +// run the computation
    +graph.runVertexCentricIteration(iteration);
    +
    +// user-defined functions
    +public static final class VertexDistanceUpdater {...}
    +public static final class MinDistanceMessenger {...}
    +
    +{% endhighlight %}
    +
    +[Back to top](#top)
    +
    +Graph Validation
    +-----------
    +
    +Gelly provides a simple utility for performing validation checks on input graphs. Depending on the application context, a graph may or may not be valid according to cerating criteria. For example, a user might need to validate whether their graph contains duplicate edges or whether its structure is bipartite. In order to validate a graph, one can define a custom `GraphValidator` and implement its `validate()` method. `InvalidVertexIdsValidator` is Gelly's pre-defined validator. It checks that the edge set contains valid vertex IDs, i.e. that all edge IDs
    --- End diff --
    
    typo: cerating, 
    probably: certain


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[GitHub] flink pull request: [FLINK-1462] Gelly documentation

[tillrohrmann <gi...@git.apache.org>]

Github user tillrohrmann commented on the pull request:

    https://github.com/apache/flink/pull/430#issuecomment-76210625
  
    What prevents us from merging this PR? Martin Neumann would like to use gelly and has asked for documentation.


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[GitHub] flink pull request: [FLINK-1462] Gelly documentation

[tillrohrmann <gi...@git.apache.org>]

Github user tillrohrmann commented on a diff in the pull request:

    https://github.com/apache/flink/pull/430#discussion_r25416065
  
    --- Diff: docs/gelly_guide.md ---
    @@ -0,0 +1,425 @@
    +---
    +title: "Gelly: Flink Graph API"
    +---
    +<!--
    +Licensed to the Apache Software Foundation (ASF) under one
    +or more contributor license agreements.  See the NOTICE file
    +distributed with this work for additional information
    +regarding copyright ownership.  The ASF licenses this file
    +to you under the Apache License, Version 2.0 (the
    +"License"); you may not use this file except in compliance
    +with the License.  You may obtain a copy of the License at
    +
    +  http://www.apache.org/licenses/LICENSE-2.0
    +
    +Unless required by applicable law or agreed to in writing,
    +software distributed under the License is distributed on an
    +"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
    +KIND, either express or implied.  See the License for the
    +specific language governing permissions and limitations
    +under the License.
    +-->
    +
    +* This will be replaced by the TOC
    +{:toc}
    +
    +<a href="#top"></a>
    +
    +Introduction
    +------------
    +
    +Gelly is a Java Graph API for Flink. It contains a set of methods and utilities which aim to simplify the development of graph analysis applications in Flink. In Gelly, graphs can be transformed and modified using high-level functions similar to the ones provided by the batch processing API. Gelly provides methods to create, transform and modify graphs, as well as a library of graph algorithms.
    +
    +Using Gelly
    +-----------
    +
    +Gelly is currently part of the *staging* Maven project. All relevant classes are located in the *org.apache.flink.graph* package.
    +
    +Add the following dependency to your `pom.xml` to use Gelly.
    +
    +~~~xml
    +<dependency>
    +    <groupId>org.apache.flink</groupId>
    +    <artifactId>flink-gelly</artifactId>
    +    <version>{{site.FLINK_VERSION_SHORT}}</version>
    +</dependency>
    +~~~
    +
    +The remaining sections provide a description of available methods and present several examples of how to use Gelly and how to mix it with the Flink Java API. After reading this guide, you might also want to check the {% gh_link /flink-staging/flink-gelly/src/main/java/org/apache/flink/graph/example/ "Gelly examples" %}.
    +
    +Graph Representation
    +-----------
    +
    +In Gelly, a `Graph` is represented by a `DataSet` of vertices and a `DataSet` of edges.
    +
    +The `Graph` nodes are represented by the `Vertex` type. A `Vertex` is defined by a unique ID and a value. `Vertex` IDs should implement the `Comparable` interface. Vertices without value can be represented by setting the value type to `NullValue`.
    +
    +{% highlight java %}
    +// create a new vertex with a Long ID and a String value
    +Vertex<Long, String> v = new Vertex<Long, String>(1L, "foo");
    +
    +// create a new vertex with a Long ID and no value
    +Vertex<Long, NullValue> v = new Vertex<Long, NullValue>(1L, NullValue.getInstance());
    +{% endhighlight %}
    +
    +The graph edges are represented by the `Edge` type. An `Edge` is defined by a source ID (the ID of the source `Vertex`), a target ID (the ID of the target `Vertex`) and an optional value. The source and target IDs should be of the same type as the `Vertex` IDs. Edges with no value have a `NullValue` value type.
    +
    +{% highlight java %}
    +Edge<Long, Double> e = new Edge<Long, Double>(1L, 2L, 0.5);
    +
    +// reverse the source and target of this edge
    +Edge<Long, Double> reversed = e.reverse();
    +
    +Double weight = e.getValue(); // weight = 0.5
    +{% endhighlight %}
    +
    +[Back to top](#top)
    +
    +Graph Creation
    +-----------
    +
    +You can create a `Graph` in the following ways:
    +
    +* from a `DataSet` of edges and an optional `DataSet` of vertices:
    +
    +{% highlight java %}
    +ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
    +
    +DataSet<Vertex<String, Long>> vertices = ...
    +
    +DataSet<Edge<String, Double>> edges = ...
    +
    +Graph<String, Long, Double> graph = Graph.fromDataSet(vertices, edges, env);
    +{% endhighlight %}
    +
    +* from a `DataSet` of `Tuple3` and an optional `DataSet` of `Tuple2`. In this case, Gelly will convert each `Tuple3` to an `Edge`, where the first field will be the source ID, the second field will be the target ID and the third field will be the edge value. Equivalently, each `Tuple2` will be converted to a `Vertex`, where the first field will be the vertex ID and the second field will be the vertex value:
    +
    +{% highlight java %}
    +ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
    +
    +DataSet<Tuple2<String, Long>> vertexTuples = env.readCsvFile("path/to/vertex/input");
    +
    +DataSet<Tuple3<String, String, Double>> edgeTuples = env.readCsvFile("path/to/edge/input");
    +
    +Graph<String, Long, Double> graph = Graph.fromTupleDataSet(vertexTuples, edgeTuples, env);
    +{% endhighlight %}
    +
    +* from a `Collection` of edges and an optional `Collection` of vertices:
    +
    +{% highlight java %}
    +ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
    +
    +List<Vertex<Long, Long>> vertexList = new ArrayList...
    +
    +List<Edge<Long, String>> edgeList = new ArrayList...
    +
    +Graph<Long, Long, String> graph = Graph.fromCollection(vertexList, edgeList, env);
    +{% endhighlight %}
    +
    +If no vertex input is provided during Graph creation, Gelly will automatically produce the `Vertex` `DataSet` from the edge input. In this case, the created vertices will have no values. Alternatively, you can provide a `MapFunction` as an argument to the creation method, in order to initialize the `Vertex` values:
    +
    +{% highlight java %}
    +ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
    +
    +// initialize the vertex value to be equal to the vertex ID
    +Graph<Long, Long, String> graph = Graph.fromCollection(edges, 
    +				new MapFunction<Long, Long>() {
    +					public Long map(Long value) { 
    +						return value; 
    +					} 
    +				}, env);
    +{% endhighlight %}
    +
    +[Back to top](#top)
    +
    +Graph Properties
    +------------
    +
    +Gelly includes the following methods for retrieving various Graph properties and metrics:
    +
    +{% highlight java %}
    +// get the Vertex DataSet
    +DataSet<Vertex<K, VV>> getVertices()
    +
    +// get the Edge DataSet
    +DataSet<Edge<K, EV>> getEdges()
    +
    +// get the IDs of the vertices as a DataSet
    +DataSet<K> getVertexIds()
    +
    +// get the source-target pairs of the edge IDs as a DataSet
    +DataSet<Tuple2<K, K>> getEdgeIds() 
    +
    +// get a DataSet of <vertex ID, in-degree> pairs for all vertices
    +DataSet<Tuple2<K, Long>> inDegrees() 
    +
    +// get a DataSet of <vertex ID, out-degree> pairs for all vertices
    +DataSet<Tuple2<K, Long>> outDegrees()
    +
    +// get a DataSet of <vertex ID, degree> pairs for all vertices, where degree is the sum of in- and out- degrees
    +DataSet<Tuple2<K, Long>> getDegrees()
    +
    +// get the number of vertices
    +DataSet<Integer> numberOfVertices()
    +
    +// get the number of edges
    +DataSet<Integer> numberOfEdges()
    +
    +{% endhighlight %}
    +
    +[Back to top](#top)
    +
    +Graph Transformations
    +-----------------
    +
    +* <strong>Map</strong>: Gelly provides specialized methods for applying a map transformation on the vertex values or edge values. `mapVertices` and `mapEdges` return a new `Graph`, where the IDs of the vertices (or edges) remain unchanged, while the values are transformed according to the provided user-defined map function. The map functions also allow changing the type of the vertex or edge values.
    +
    +{% highlight java %}
    +ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
    +Graph<Long, Long, Long> graph = Graph.fromDataSet(vertices, edges, env);
    +
    +// increment each vertex value by one
    +Graph<Long, Long, Long> updatedGraph = graph.mapVertices(
    +				new MapFunction<Vertex<Long, Long>, Long>() {
    +					public Long map(Vertex<Long, Long> value) {
    +						return value.getValue() + 1;
    +					}
    +				});
    +{% endhighlight %}
    +
    +* <strong>Filter</strong>: A filter transformation applies a user-defined filter function on the vertices or edges of the `Graph`. `filterOnEdges` will create a sub-graph of the original graph, keeping only the edges that satisfy the provided predicate. Note that the vertex dataset will not be modified. Respectively, `filterOnVertices` applies a filter on the vertices of the graph. Edges whose source and/or target do not satisfy the vertex predicate are removed from the resulting edge dataset. The `subgraph` method can be used to apply a filter function to the vertices and the edges at the same time.
    +
    +{% highlight java %}
    +Graph<Long, Long, Long> graph = ...
    +
    +graph.subgraph(
    +		new FilterFunction<Vertex<Long, Long>>() {
    +			   	public boolean filter(Vertex<Long, Long> vertex) {
    +					// keep only vertices with positive values
    +					return (vertex.getValue() > 0);
    +			   }
    +		   },
    +		new FilterFunction<Edge<Long, Long>>() {
    +				public boolean filter(Edge<Long, Long> edge) {
    +					// keep only edges with negative values
    +					return (edge.getValue() < 0);
    +				}
    +		})
    +{% endhighlight %}
    +
    +<p class="text-center">
    +    <img alt="Filter Transformations" width="80%" src="img/gelly-filter.png"/>
    +</p>
    +
    +* <strong>Join</strong>: Gelly provides specialized methods for joining the vertex and edge datasets with other input datasets. `joinWithVertices` joins the vertices with a `Tuple2` input data set. The join is performed using the vertex ID and the first field of the `Tuple2` input as the join keys. The method returns a new `Graph` where the vertex values have been updated according to the provided a user-defined map function.
    +Similarly, an input dataset can be joined with the edges, using one of three methods. `joinWithEdges` expects an input `DataSet` of `Tuple3` and joins on the composite key of both source and target vertex IDs. `joinWithEdgesOnSource` expects a `DataSet` of `Tuple2` and joins on the source key of the edges and the first attribute of the input dataset and `joinWithEdgesOnTarget` expects a `DataSet` of `Tuple2` and joins on the target key of the edges and the first attribute of the input dataset. All three methods apply a map function on the edge and the input data set values.
    +Note that if the input dataset contains a key multiple times, all Gelly join methods will only consider the first value encountered.
    +
    +{% highlight java %}
    +Graph<Long, Double, Double> network = ...
    +
    +DataSet<Tuple2<Long, Long>> vertexOutDegrees = network.outDegrees();
    +
    +// assign the transition probabilities as the edge weights
    +Graph<Long, Double, Double> networkWithWeights = network.joinWithEdgesOnSource(vertexOutDegrees,
    +				new MapFunction<Tuple2<Double, Long>, Double>() {
    +					public Double map(Tuple2<Double, Long> value) {
    +						return value.f0 / value.f1;
    +					}
    +				});
    +{% endhighlight %}
    +
    +* <strong>Reverse</strong>: the `reverse()` method returns a new `Graph` where the direction of all edges has been reversed.
    +
    +* <strong>Undirected</strong>: In Gelly, a `Graph` is always directed. Undirected graphs can be represented by adding all opposite-direction edges to a graph. For this purpose, Gelly provides the `getUndirected()` method.
    +
    +* <strong>Union</strong>: Gelly's `union()` method performs a union on the vertex and edges sets of the input graphs. Duplicate vertices are removed from the resulting `Graph`, while if duplicate edges exists, these will be maintained.
    +
    +<p class="text-center">
    +    <img alt="Union Transformation" width="50%" src="img/gelly-union.png"/>
    +</p>
    +
    +[Back to top](#top)
    +
    +Graph Mutations
    +-----------
    +
    +Gelly includes the following methods for adding and removing vertices and edges from an input `Graph`:
    +
    +{% highlight java %}
    +// adds a Vertex and the given edges to the Graph. If the Vertex already exists, it will not be added again, but the given edges will.
    +Graph<K, VV, EV> addVertex(final Vertex<K, VV> vertex, List<Edge<K, EV>> edges)
    +
    +// adds an Edge to the Graph. If the source and target vertices do not exist in the graph, they will also be added.
    +Graph<K, VV, EV> addEdge(Vertex<K, VV> source, Vertex<K, VV> target, EV edgeValue)
    +
    +// removes the given Vertex and its edges from the Graph.
    +Graph<K, VV, EV> removeVertex(Vertex<K, VV> vertex)
    +
    +// removes *all* edges that match the given Edge from the Graph.
    +Graph<K, VV, EV> removeEdge(Edge<K, EV> edge)
    +{% endhighlight %}
    +
    +Neighborhood Methods
    +-----------
    +
    +Neighborhood methods allow vertices to perform an aggregation on their first-hop neighborhood.
    +
    +`reduceOnEdges()` can be used to compute an aggregation on the neighboring edges of a vertex, while `reduceOnNeighbors()` has access on both the neighboring edges and vertices. The neighborhood scope is defined by the `EdgeDirection` parameter, which takes the values `IN`, `OUT` or `ALL`. `IN` will gather all in-coming edges (neighbors) of a vertex, `OUT` will gather all out-going edges (neighbors), while `ALL` will gather all edges (neighbors).
    +
    +For example, assume that you want to select the minimum weight of all out-edges for each vertex in the following graph:
    +
    +<p class="text-center">
    +    <img alt="reduceOnEdges Example" width="50%" src="img/gelly-example-graph.png"/>
    +</p>
    +
    +The following code will collect the out-edges for each vertex and apply the `SelectMinWeight()` user-defined function on each of the resulting neighborhoods:
    +
    +{% highlight java %}
    +Graph<Long, Long, Double> graph = ...
    +
    +DataSet<Tuple2<Long, Double>> minWeights = graph.reduceOnEdges(
    +				new SelectMinWeight(), EdgeDirection.OUT);
    +
    +// user-defined function to select the minimum weight
    +static final class SelectMinWeight implements EdgesFunction<Long, Double, Tuple2<Long, Double>> {
    +
    +    public Tuple2<Long, Double> iterateEdges(Iterable<Tuple2<Long, Edge<Long, Double>>> edges) {
    +
    +        long minWeight = Double.MAX_VALUE;
    +        long vertexId = -1;
    +
    +        for (Tuple2<Long, Edge<Long, Double>> edge: edges) {
    +            if (edge.f1.getValue() < weight) {
    +            weight = edge.f1.getValue();
    +            vertexId = edge.f0;
    +        }
    +        return new Tuple2<Long, Double>(vertexId, minWeight);
    +    }
    +}
    +{% endhighlight %}
    +
    +<p class="text-center">
    +    <img alt="reduceOnEdges Example" width="50%" src="img/gelly-reduceOnEdges.png"/>
    +</p>
    +
    +Similarly, assume that you would like to compute the sum of the values of all in-coming neighbors, for every vertex. The following code will collect the in-coming neighbors for each vertex and apply the `SumValues()` user-defined function on each neighborhood:
    +
    +{% highlight java %}
    +Graph<Long, Long, Double> graph = ...
    +
    +DataSet<Tuple2<Long, Long>> verticesWithSum = graph.reduceOnNeighbors(
    +				new SumValues(), EdgeDirection.IN);
    +
    +// user-defined function to sum the neighbor values
    +static final class SumValues implements NeighborsFunction<Long, Long, Long, Tuple2<Long, Long>> {
    +		
    +	public Tuple2<Long, Long> iterateNeighbors(Iterable<Tuple3<Long, Edge<Long, Long>, 
    +		Vertex<Long, Long>>> neighbors) {
    +		
    +		long sum = 0;
    +		long vertexId = -1;
    +
    +		for (Tuple3<Long, Edge<Long, Long>, Vertex<Long, Long>> neighbor : neighbors) {
    +			vertexId = neighbor.f0;
    +			sum += neighbor.f2.getValue();
    +		}
    +		return new Tuple2<Long, Long>(vertexId, sum);
    +	}
    +}
    +{% endhighlight %}
    +
    +<p class="text-center">
    +    <img alt="reduseOnNeighbors Example" width="70%" src="img/gelly-reduceOnNeighbors.png"/>
    +</p>
    +
    +When the aggregation computation does not require access to the vertex value, it is advised to use the more efficient `EdgesFunction` and `NeighborsFunction` for the user-defined functions. When access to the vertex value is required, one should use `EdgesFunctionWithVertexValue` and `NeighborsFunctionWithVertexValue` instead. 
    --- End diff --
    
    I was a little bit confused which vertex value you meant, because in the example above we are accessing the vertex values of the neighbours. Maybe we could emphasize that we're talking here about the vertex for which the aggregation is performed.


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[GitHub] flink pull request: [FLINK-1462] Gelly documentation

[StephanEwen <gi...@git.apache.org>]

Github user StephanEwen commented on the pull request:

    https://github.com/apache/flink/pull/430#issuecomment-75513013
  
    Looks very good, Vasia!
    
    Big +1


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[GitHub] flink pull request: [FLINK-1462] Gelly documentation

[asfgit <gi...@git.apache.org>]

Github user asfgit closed the pull request at:

    https://github.com/apache/flink/pull/430


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[GitHub] flink pull request: [FLINK-1462] Gelly documentation

[tillrohrmann <gi...@git.apache.org>]

Github user tillrohrmann commented on a diff in the pull request:

    https://github.com/apache/flink/pull/430#discussion_r25416336
  
    --- Diff: docs/gelly_guide.md ---
    @@ -0,0 +1,425 @@
    +---
    +title: "Gelly: Flink Graph API"
    +---
    +<!--
    +Licensed to the Apache Software Foundation (ASF) under one
    +or more contributor license agreements.  See the NOTICE file
    +distributed with this work for additional information
    +regarding copyright ownership.  The ASF licenses this file
    +to you under the Apache License, Version 2.0 (the
    +"License"); you may not use this file except in compliance
    +with the License.  You may obtain a copy of the License at
    +
    +  http://www.apache.org/licenses/LICENSE-2.0
    +
    +Unless required by applicable law or agreed to in writing,
    +software distributed under the License is distributed on an
    +"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
    +KIND, either express or implied.  See the License for the
    +specific language governing permissions and limitations
    +under the License.
    +-->
    +
    +* This will be replaced by the TOC
    +{:toc}
    +
    +<a href="#top"></a>
    +
    +Introduction
    +------------
    +
    +Gelly is a Java Graph API for Flink. It contains a set of methods and utilities which aim to simplify the development of graph analysis applications in Flink. In Gelly, graphs can be transformed and modified using high-level functions similar to the ones provided by the batch processing API. Gelly provides methods to create, transform and modify graphs, as well as a library of graph algorithms.
    +
    +Using Gelly
    +-----------
    +
    +Gelly is currently part of the *staging* Maven project. All relevant classes are located in the *org.apache.flink.graph* package.
    +
    +Add the following dependency to your `pom.xml` to use Gelly.
    +
    +~~~xml
    +<dependency>
    +    <groupId>org.apache.flink</groupId>
    +    <artifactId>flink-gelly</artifactId>
    +    <version>{{site.FLINK_VERSION_SHORT}}</version>
    +</dependency>
    +~~~
    +
    +The remaining sections provide a description of available methods and present several examples of how to use Gelly and how to mix it with the Flink Java API. After reading this guide, you might also want to check the {% gh_link /flink-staging/flink-gelly/src/main/java/org/apache/flink/graph/example/ "Gelly examples" %}.
    +
    +Graph Representation
    +-----------
    +
    +In Gelly, a `Graph` is represented by a `DataSet` of vertices and a `DataSet` of edges.
    +
    +The `Graph` nodes are represented by the `Vertex` type. A `Vertex` is defined by a unique ID and a value. `Vertex` IDs should implement the `Comparable` interface. Vertices without value can be represented by setting the value type to `NullValue`.
    +
    +{% highlight java %}
    +// create a new vertex with a Long ID and a String value
    +Vertex<Long, String> v = new Vertex<Long, String>(1L, "foo");
    +
    +// create a new vertex with a Long ID and no value
    +Vertex<Long, NullValue> v = new Vertex<Long, NullValue>(1L, NullValue.getInstance());
    +{% endhighlight %}
    +
    +The graph edges are represented by the `Edge` type. An `Edge` is defined by a source ID (the ID of the source `Vertex`), a target ID (the ID of the target `Vertex`) and an optional value. The source and target IDs should be of the same type as the `Vertex` IDs. Edges with no value have a `NullValue` value type.
    +
    +{% highlight java %}
    +Edge<Long, Double> e = new Edge<Long, Double>(1L, 2L, 0.5);
    +
    +// reverse the source and target of this edge
    +Edge<Long, Double> reversed = e.reverse();
    +
    +Double weight = e.getValue(); // weight = 0.5
    +{% endhighlight %}
    +
    +[Back to top](#top)
    +
    +Graph Creation
    +-----------
    +
    +You can create a `Graph` in the following ways:
    +
    +* from a `DataSet` of edges and an optional `DataSet` of vertices:
    +
    +{% highlight java %}
    +ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
    +
    +DataSet<Vertex<String, Long>> vertices = ...
    +
    +DataSet<Edge<String, Double>> edges = ...
    +
    +Graph<String, Long, Double> graph = Graph.fromDataSet(vertices, edges, env);
    +{% endhighlight %}
    +
    +* from a `DataSet` of `Tuple3` and an optional `DataSet` of `Tuple2`. In this case, Gelly will convert each `Tuple3` to an `Edge`, where the first field will be the source ID, the second field will be the target ID and the third field will be the edge value. Equivalently, each `Tuple2` will be converted to a `Vertex`, where the first field will be the vertex ID and the second field will be the vertex value:
    +
    +{% highlight java %}
    +ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
    +
    +DataSet<Tuple2<String, Long>> vertexTuples = env.readCsvFile("path/to/vertex/input");
    +
    +DataSet<Tuple3<String, String, Double>> edgeTuples = env.readCsvFile("path/to/edge/input");
    +
    +Graph<String, Long, Double> graph = Graph.fromTupleDataSet(vertexTuples, edgeTuples, env);
    +{% endhighlight %}
    +
    +* from a `Collection` of edges and an optional `Collection` of vertices:
    +
    +{% highlight java %}
    +ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
    +
    +List<Vertex<Long, Long>> vertexList = new ArrayList...
    +
    +List<Edge<Long, String>> edgeList = new ArrayList...
    +
    +Graph<Long, Long, String> graph = Graph.fromCollection(vertexList, edgeList, env);
    +{% endhighlight %}
    +
    +If no vertex input is provided during Graph creation, Gelly will automatically produce the `Vertex` `DataSet` from the edge input. In this case, the created vertices will have no values. Alternatively, you can provide a `MapFunction` as an argument to the creation method, in order to initialize the `Vertex` values:
    +
    +{% highlight java %}
    +ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
    +
    +// initialize the vertex value to be equal to the vertex ID
    +Graph<Long, Long, String> graph = Graph.fromCollection(edges, 
    +				new MapFunction<Long, Long>() {
    +					public Long map(Long value) { 
    +						return value; 
    +					} 
    +				}, env);
    +{% endhighlight %}
    +
    +[Back to top](#top)
    +
    +Graph Properties
    +------------
    +
    +Gelly includes the following methods for retrieving various Graph properties and metrics:
    +
    +{% highlight java %}
    +// get the Vertex DataSet
    +DataSet<Vertex<K, VV>> getVertices()
    +
    +// get the Edge DataSet
    +DataSet<Edge<K, EV>> getEdges()
    +
    +// get the IDs of the vertices as a DataSet
    +DataSet<K> getVertexIds()
    +
    +// get the source-target pairs of the edge IDs as a DataSet
    +DataSet<Tuple2<K, K>> getEdgeIds() 
    +
    +// get a DataSet of <vertex ID, in-degree> pairs for all vertices
    +DataSet<Tuple2<K, Long>> inDegrees() 
    +
    +// get a DataSet of <vertex ID, out-degree> pairs for all vertices
    +DataSet<Tuple2<K, Long>> outDegrees()
    +
    +// get a DataSet of <vertex ID, degree> pairs for all vertices, where degree is the sum of in- and out- degrees
    +DataSet<Tuple2<K, Long>> getDegrees()
    +
    +// get the number of vertices
    +DataSet<Integer> numberOfVertices()
    +
    +// get the number of edges
    +DataSet<Integer> numberOfEdges()
    +
    +{% endhighlight %}
    +
    +[Back to top](#top)
    +
    +Graph Transformations
    +-----------------
    +
    +* <strong>Map</strong>: Gelly provides specialized methods for applying a map transformation on the vertex values or edge values. `mapVertices` and `mapEdges` return a new `Graph`, where the IDs of the vertices (or edges) remain unchanged, while the values are transformed according to the provided user-defined map function. The map functions also allow changing the type of the vertex or edge values.
    +
    +{% highlight java %}
    +ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
    +Graph<Long, Long, Long> graph = Graph.fromDataSet(vertices, edges, env);
    +
    +// increment each vertex value by one
    +Graph<Long, Long, Long> updatedGraph = graph.mapVertices(
    +				new MapFunction<Vertex<Long, Long>, Long>() {
    +					public Long map(Vertex<Long, Long> value) {
    +						return value.getValue() + 1;
    +					}
    +				});
    +{% endhighlight %}
    +
    +* <strong>Filter</strong>: A filter transformation applies a user-defined filter function on the vertices or edges of the `Graph`. `filterOnEdges` will create a sub-graph of the original graph, keeping only the edges that satisfy the provided predicate. Note that the vertex dataset will not be modified. Respectively, `filterOnVertices` applies a filter on the vertices of the graph. Edges whose source and/or target do not satisfy the vertex predicate are removed from the resulting edge dataset. The `subgraph` method can be used to apply a filter function to the vertices and the edges at the same time.
    +
    +{% highlight java %}
    +Graph<Long, Long, Long> graph = ...
    +
    +graph.subgraph(
    +		new FilterFunction<Vertex<Long, Long>>() {
    +			   	public boolean filter(Vertex<Long, Long> vertex) {
    +					// keep only vertices with positive values
    +					return (vertex.getValue() > 0);
    +			   }
    +		   },
    +		new FilterFunction<Edge<Long, Long>>() {
    +				public boolean filter(Edge<Long, Long> edge) {
    +					// keep only edges with negative values
    +					return (edge.getValue() < 0);
    +				}
    +		})
    +{% endhighlight %}
    +
    +<p class="text-center">
    +    <img alt="Filter Transformations" width="80%" src="img/gelly-filter.png"/>
    +</p>
    +
    +* <strong>Join</strong>: Gelly provides specialized methods for joining the vertex and edge datasets with other input datasets. `joinWithVertices` joins the vertices with a `Tuple2` input data set. The join is performed using the vertex ID and the first field of the `Tuple2` input as the join keys. The method returns a new `Graph` where the vertex values have been updated according to the provided a user-defined map function.
    +Similarly, an input dataset can be joined with the edges, using one of three methods. `joinWithEdges` expects an input `DataSet` of `Tuple3` and joins on the composite key of both source and target vertex IDs. `joinWithEdgesOnSource` expects a `DataSet` of `Tuple2` and joins on the source key of the edges and the first attribute of the input dataset and `joinWithEdgesOnTarget` expects a `DataSet` of `Tuple2` and joins on the target key of the edges and the first attribute of the input dataset. All three methods apply a map function on the edge and the input data set values.
    +Note that if the input dataset contains a key multiple times, all Gelly join methods will only consider the first value encountered.
    +
    +{% highlight java %}
    +Graph<Long, Double, Double> network = ...
    +
    +DataSet<Tuple2<Long, Long>> vertexOutDegrees = network.outDegrees();
    +
    +// assign the transition probabilities as the edge weights
    +Graph<Long, Double, Double> networkWithWeights = network.joinWithEdgesOnSource(vertexOutDegrees,
    +				new MapFunction<Tuple2<Double, Long>, Double>() {
    +					public Double map(Tuple2<Double, Long> value) {
    +						return value.f0 / value.f1;
    +					}
    +				});
    +{% endhighlight %}
    +
    +* <strong>Reverse</strong>: the `reverse()` method returns a new `Graph` where the direction of all edges has been reversed.
    +
    +* <strong>Undirected</strong>: In Gelly, a `Graph` is always directed. Undirected graphs can be represented by adding all opposite-direction edges to a graph. For this purpose, Gelly provides the `getUndirected()` method.
    +
    +* <strong>Union</strong>: Gelly's `union()` method performs a union on the vertex and edges sets of the input graphs. Duplicate vertices are removed from the resulting `Graph`, while if duplicate edges exists, these will be maintained.
    +
    +<p class="text-center">
    +    <img alt="Union Transformation" width="50%" src="img/gelly-union.png"/>
    +</p>
    +
    +[Back to top](#top)
    +
    +Graph Mutations
    +-----------
    +
    +Gelly includes the following methods for adding and removing vertices and edges from an input `Graph`:
    +
    +{% highlight java %}
    +// adds a Vertex and the given edges to the Graph. If the Vertex already exists, it will not be added again, but the given edges will.
    +Graph<K, VV, EV> addVertex(final Vertex<K, VV> vertex, List<Edge<K, EV>> edges)
    +
    +// adds an Edge to the Graph. If the source and target vertices do not exist in the graph, they will also be added.
    +Graph<K, VV, EV> addEdge(Vertex<K, VV> source, Vertex<K, VV> target, EV edgeValue)
    +
    +// removes the given Vertex and its edges from the Graph.
    +Graph<K, VV, EV> removeVertex(Vertex<K, VV> vertex)
    +
    +// removes *all* edges that match the given Edge from the Graph.
    +Graph<K, VV, EV> removeEdge(Edge<K, EV> edge)
    +{% endhighlight %}
    +
    +Neighborhood Methods
    +-----------
    +
    +Neighborhood methods allow vertices to perform an aggregation on their first-hop neighborhood.
    +
    +`reduceOnEdges()` can be used to compute an aggregation on the neighboring edges of a vertex, while `reduceOnNeighbors()` has access on both the neighboring edges and vertices. The neighborhood scope is defined by the `EdgeDirection` parameter, which takes the values `IN`, `OUT` or `ALL`. `IN` will gather all in-coming edges (neighbors) of a vertex, `OUT` will gather all out-going edges (neighbors), while `ALL` will gather all edges (neighbors).
    +
    +For example, assume that you want to select the minimum weight of all out-edges for each vertex in the following graph:
    +
    +<p class="text-center">
    +    <img alt="reduceOnEdges Example" width="50%" src="img/gelly-example-graph.png"/>
    +</p>
    +
    +The following code will collect the out-edges for each vertex and apply the `SelectMinWeight()` user-defined function on each of the resulting neighborhoods:
    +
    +{% highlight java %}
    +Graph<Long, Long, Double> graph = ...
    +
    +DataSet<Tuple2<Long, Double>> minWeights = graph.reduceOnEdges(
    +				new SelectMinWeight(), EdgeDirection.OUT);
    +
    +// user-defined function to select the minimum weight
    +static final class SelectMinWeight implements EdgesFunction<Long, Double, Tuple2<Long, Double>> {
    +
    +    public Tuple2<Long, Double> iterateEdges(Iterable<Tuple2<Long, Edge<Long, Double>>> edges) {
    +
    +        long minWeight = Double.MAX_VALUE;
    +        long vertexId = -1;
    +
    +        for (Tuple2<Long, Edge<Long, Double>> edge: edges) {
    +            if (edge.f1.getValue() < weight) {
    +            weight = edge.f1.getValue();
    +            vertexId = edge.f0;
    +        }
    +        return new Tuple2<Long, Double>(vertexId, minWeight);
    +    }
    +}
    +{% endhighlight %}
    +
    +<p class="text-center">
    +    <img alt="reduceOnEdges Example" width="50%" src="img/gelly-reduceOnEdges.png"/>
    +</p>
    +
    +Similarly, assume that you would like to compute the sum of the values of all in-coming neighbors, for every vertex. The following code will collect the in-coming neighbors for each vertex and apply the `SumValues()` user-defined function on each neighborhood:
    +
    +{% highlight java %}
    +Graph<Long, Long, Double> graph = ...
    +
    +DataSet<Tuple2<Long, Long>> verticesWithSum = graph.reduceOnNeighbors(
    +				new SumValues(), EdgeDirection.IN);
    +
    +// user-defined function to sum the neighbor values
    +static final class SumValues implements NeighborsFunction<Long, Long, Long, Tuple2<Long, Long>> {
    +		
    +	public Tuple2<Long, Long> iterateNeighbors(Iterable<Tuple3<Long, Edge<Long, Long>, 
    +		Vertex<Long, Long>>> neighbors) {
    +		
    +		long sum = 0;
    +		long vertexId = -1;
    +
    +		for (Tuple3<Long, Edge<Long, Long>, Vertex<Long, Long>> neighbor : neighbors) {
    +			vertexId = neighbor.f0;
    +			sum += neighbor.f2.getValue();
    +		}
    +		return new Tuple2<Long, Long>(vertexId, sum);
    +	}
    +}
    +{% endhighlight %}
    +
    +<p class="text-center">
    +    <img alt="reduseOnNeighbors Example" width="70%" src="img/gelly-reduceOnNeighbors.png"/>
    +</p>
    +
    +When the aggregation computation does not require access to the vertex value, it is advised to use the more efficient `EdgesFunction` and `NeighborsFunction` for the user-defined functions. When access to the vertex value is required, one should use `EdgesFunctionWithVertexValue` and `NeighborsFunctionWithVertexValue` instead. 
    +
    +[Back to top](#top)
    +
    +Vertex-centric Iterations
    +-----------
    +
    +Gelly wraps Flink's [Spargel API](spargel_guide.html) to provide methods for vertex-centric iterations.
    +Like in Spargel, the user only needs to implement two functions: a `VertexUpdateFunction`, which defines how a vertex will update its value
    +based on the received messages and a `MessagingFunction`, which allows a vertex to send out messages for the next superstep.
    +These functions are given as parameters to Gelly's `createVertexCentricIteration`, which returns a `VertexCentricIteration`. 
    +The user can configure this iteration (set the name, the degree of parallelism, aggregators, etc.) and then run the computation, using the `runVertexCentricIteration` method:
    +
    +{% highlight java %}
    +Graph<Long, Double, Double> graph = ...
    +
    +// create the vertex-centric iteration
    +VertexCentricIteration<Long, Double, Double, Double> iteration = 
    +			graph.createVertexCentricIteration(
    +			new VertexDistanceUpdater(), new MinDistanceMessenger(), maxIterations);
    +
    +// set the iteration name
    +iteration.setName("Single Source Shortest Paths");
    +
    +// set the degree of parallelism
    +iteration.setDegreeOfParallelism(16);
    +
    +// run the computation
    +graph.runVertexCentricIteration(iteration);
    +
    +// user-defined functions
    +public static final class VertexDistanceUpdater {...}
    +public static final class MinDistanceMessenger {...}
    +
    +{% endhighlight %}
    +
    +[Back to top](#top)
    +
    +Graph Validation
    +-----------
    +
    +Gelly provides a simple utility for performing validation checks on input graphs. Depending on the application context, a graph may or may not be valid according to cerating criteria. For example, a user might need to validate whether their graph contains duplicate edges or whether its structure is bipartite. In order to validate a graph, one can define a custom `GraphValidator` and implement its `validate()` method. `InvalidVertexIdsValidator` is Gelly's pre-defined validator. It checks that the edge set contains valid vertex IDs, i.e. that all edge IDs
    +also exist in the vertex IDs set.
    +
    +{% highlight java %}
    +ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
    +
    +// create a list of vertices with IDs = {1, 2, 3, 4, 5}
    +List<Vertex<Long, Long>> vertices = ...
    +
    +// create a list of edges with IDs = {(1, 2) (1, 3), (2, 4), (5, 6)}
    +List<Edge<Long, Long>> edges = ...
    +
    +Graph<Long, Long, Long> graph = Graph.fromcollection(vertices, edges, env);
    +
    +// will return false: 6 is an invalid ID
    +graph.validate(new InvalidVertexIdsValidator<Long, Long, Long>()); 
    +
    +{% endhighlight %}
    +
    +[Back to top](#top)
    +
    +Library Methods
    +-----------
    +Gelly has a growing collection of graph algorithms for easily analyzing large-scale Graphs. So far, the following library methods are implemented:
    +
    +* PageRank
    +* Single-Source Shortest Paths
    +* Label Propagation
    +
    +Gelly's library methods can be used by simply calling the `run()` method on the input graph:
    +
    +{% highlight java %}
    +ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
    +
    +Graph<Long, Long, NullValue> graph = ...
    +
    +// run Label Propagation for 30 iterations to detect communitites on the input graph
    --- End diff --
    
    typo: communitites
    probably: communities


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[GitHub] flink pull request: [FLINK-1462] Gelly documentation

[tillrohrmann <gi...@git.apache.org>]

Github user tillrohrmann commented on a diff in the pull request:

    https://github.com/apache/flink/pull/430#discussion_r25415172
  
    --- Diff: docs/gelly_guide.md ---
    @@ -0,0 +1,425 @@
    +---
    +title: "Gelly: Flink Graph API"
    +---
    +<!--
    +Licensed to the Apache Software Foundation (ASF) under one
    +or more contributor license agreements.  See the NOTICE file
    +distributed with this work for additional information
    +regarding copyright ownership.  The ASF licenses this file
    +to you under the Apache License, Version 2.0 (the
    +"License"); you may not use this file except in compliance
    +with the License.  You may obtain a copy of the License at
    +
    +  http://www.apache.org/licenses/LICENSE-2.0
    +
    +Unless required by applicable law or agreed to in writing,
    +software distributed under the License is distributed on an
    +"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
    +KIND, either express or implied.  See the License for the
    +specific language governing permissions and limitations
    +under the License.
    +-->
    +
    +* This will be replaced by the TOC
    +{:toc}
    +
    +<a href="#top"></a>
    +
    +Introduction
    +------------
    +
    +Gelly is a Java Graph API for Flink. It contains a set of methods and utilities which aim to simplify the development of graph analysis applications in Flink. In Gelly, graphs can be transformed and modified using high-level functions similar to the ones provided by the batch processing API. Gelly provides methods to create, transform and modify graphs, as well as a library of graph algorithms.
    +
    +Using Gelly
    +-----------
    +
    +Gelly is currently part of the *staging* Maven project. All relevant classes are located in the *org.apache.flink.graph* package.
    +
    +Add the following dependency to your `pom.xml` to use Gelly.
    +
    +~~~xml
    +<dependency>
    +    <groupId>org.apache.flink</groupId>
    +    <artifactId>flink-gelly</artifactId>
    +    <version>{{site.FLINK_VERSION_SHORT}}</version>
    +</dependency>
    +~~~
    +
    +The remaining sections provide a description of available methods and present several examples of how to use Gelly and how to mix it with the Flink Java API. After reading this guide, you might also want to check the {% gh_link /flink-staging/flink-gelly/src/main/java/org/apache/flink/graph/example/ "Gelly examples" %}.
    +
    +Graph Representation
    +-----------
    +
    +In Gelly, a `Graph` is represented by a `DataSet` of vertices and a `DataSet` of edges.
    +
    +The `Graph` nodes are represented by the `Vertex` type. A `Vertex` is defined by a unique ID and a value. `Vertex` IDs should implement the `Comparable` interface. Vertices without value can be represented by setting the value type to `NullValue`.
    +
    +{% highlight java %}
    +// create a new vertex with a Long ID and a String value
    +Vertex<Long, String> v = new Vertex<Long, String>(1L, "foo");
    +
    +// create a new vertex with a Long ID and no value
    +Vertex<Long, NullValue> v = new Vertex<Long, NullValue>(1L, NullValue.getInstance());
    +{% endhighlight %}
    +
    +The graph edges are represented by the `Edge` type. An `Edge` is defined by a source ID (the ID of the source `Vertex`), a target ID (the ID of the target `Vertex`) and an optional value. The source and target IDs should be of the same type as the `Vertex` IDs. Edges with no value have a `NullValue` value type.
    +
    +{% highlight java %}
    +Edge<Long, Double> e = new Edge<Long, Double>(1L, 2L, 0.5);
    +
    +// reverse the source and target of this edge
    +Edge<Long, Double> reversed = e.reverse();
    +
    +Double weight = e.getValue(); // weight = 0.5
    +{% endhighlight %}
    +
    +[Back to top](#top)
    +
    +Graph Creation
    +-----------
    +
    +You can create a `Graph` in the following ways:
    +
    +* from a `DataSet` of edges and an optional `DataSet` of vertices:
    +
    +{% highlight java %}
    +ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
    +
    +DataSet<Vertex<String, Long>> vertices = ...
    +
    +DataSet<Edge<String, Double>> edges = ...
    +
    +Graph<String, Long, Double> graph = Graph.fromDataSet(vertices, edges, env);
    +{% endhighlight %}
    +
    +* from a `DataSet` of `Tuple3` and an optional `DataSet` of `Tuple2`. In this case, Gelly will convert each `Tuple3` to an `Edge`, where the first field will be the source ID, the second field will be the target ID and the third field will be the edge value. Equivalently, each `Tuple2` will be converted to a `Vertex`, where the first field will be the vertex ID and the second field will be the vertex value:
    +
    +{% highlight java %}
    +ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
    +
    +DataSet<Tuple2<String, Long>> vertexTuples = env.readCsvFile("path/to/vertex/input");
    +
    +DataSet<Tuple3<String, String, Double>> edgeTuples = env.readCsvFile("path/to/edge/input");
    +
    +Graph<String, Long, Double> graph = Graph.fromTupleDataSet(vertexTuples, edgeTuples, env);
    +{% endhighlight %}
    +
    +* from a `Collection` of edges and an optional `Collection` of vertices:
    +
    +{% highlight java %}
    +ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
    +
    +List<Vertex<Long, Long>> vertexList = new ArrayList...
    +
    +List<Edge<Long, String>> edgeList = new ArrayList...
    +
    +Graph<Long, Long, String> graph = Graph.fromCollection(vertexList, edgeList, env);
    +{% endhighlight %}
    +
    +If no vertex input is provided during Graph creation, Gelly will automatically produce the `Vertex` `DataSet` from the edge input. In this case, the created vertices will have no values. Alternatively, you can provide a `MapFunction` as an argument to the creation method, in order to initialize the `Vertex` values:
    +
    +{% highlight java %}
    +ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
    +
    +// initialize the vertex value to be equal to the vertex ID
    +Graph<Long, Long, String> graph = Graph.fromCollection(edges, 
    +				new MapFunction<Long, Long>() {
    +					public Long map(Long value) { 
    +						return value; 
    +					} 
    +				}, env);
    +{% endhighlight %}
    +
    +[Back to top](#top)
    +
    +Graph Properties
    +------------
    +
    +Gelly includes the following methods for retrieving various Graph properties and metrics:
    +
    +{% highlight java %}
    +// get the Vertex DataSet
    +DataSet<Vertex<K, VV>> getVertices()
    +
    +// get the Edge DataSet
    +DataSet<Edge<K, EV>> getEdges()
    +
    +// get the IDs of the vertices as a DataSet
    +DataSet<K> getVertexIds()
    +
    +// get the source-target pairs of the edge IDs as a DataSet
    +DataSet<Tuple2<K, K>> getEdgeIds() 
    +
    +// get a DataSet of <vertex ID, in-degree> pairs for all vertices
    +DataSet<Tuple2<K, Long>> inDegrees() 
    +
    +// get a DataSet of <vertex ID, out-degree> pairs for all vertices
    +DataSet<Tuple2<K, Long>> outDegrees()
    +
    +// get a DataSet of <vertex ID, degree> pairs for all vertices, where degree is the sum of in- and out- degrees
    +DataSet<Tuple2<K, Long>> getDegrees()
    +
    +// get the number of vertices
    +DataSet<Integer> numberOfVertices()
    +
    +// get the number of edges
    +DataSet<Integer> numberOfEdges()
    +
    +{% endhighlight %}
    +
    +[Back to top](#top)
    +
    +Graph Transformations
    +-----------------
    +
    +* <strong>Map</strong>: Gelly provides specialized methods for applying a map transformation on the vertex values or edge values. `mapVertices` and `mapEdges` return a new `Graph`, where the IDs of the vertices (or edges) remain unchanged, while the values are transformed according to the provided user-defined map function. The map functions also allow changing the type of the vertex or edge values.
    +
    +{% highlight java %}
    +ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
    +Graph<Long, Long, Long> graph = Graph.fromDataSet(vertices, edges, env);
    +
    +// increment each vertex value by one
    +Graph<Long, Long, Long> updatedGraph = graph.mapVertices(
    +				new MapFunction<Vertex<Long, Long>, Long>() {
    +					public Long map(Vertex<Long, Long> value) {
    +						return value.getValue() + 1;
    +					}
    +				});
    +{% endhighlight %}
    +
    +* <strong>Filter</strong>: A filter transformation applies a user-defined filter function on the vertices or edges of the `Graph`. `filterOnEdges` will create a sub-graph of the original graph, keeping only the edges that satisfy the provided predicate. Note that the vertex dataset will not be modified. Respectively, `filterOnVertices` applies a filter on the vertices of the graph. Edges whose source and/or target do not satisfy the vertex predicate are removed from the resulting edge dataset. The `subgraph` method can be used to apply a filter function to the vertices and the edges at the same time.
    +
    +{% highlight java %}
    +Graph<Long, Long, Long> graph = ...
    +
    +graph.subgraph(
    +		new FilterFunction<Vertex<Long, Long>>() {
    +			   	public boolean filter(Vertex<Long, Long> vertex) {
    +					// keep only vertices with positive values
    +					return (vertex.getValue() > 0);
    +			   }
    +		   },
    +		new FilterFunction<Edge<Long, Long>>() {
    +				public boolean filter(Edge<Long, Long> edge) {
    +					// keep only edges with negative values
    +					return (edge.getValue() < 0);
    +				}
    +		})
    +{% endhighlight %}
    +
    +<p class="text-center">
    +    <img alt="Filter Transformations" width="80%" src="img/gelly-filter.png"/>
    +</p>
    +
    +* <strong>Join</strong>: Gelly provides specialized methods for joining the vertex and edge datasets with other input datasets. `joinWithVertices` joins the vertices with a `Tuple2` input data set. The join is performed using the vertex ID and the first field of the `Tuple2` input as the join keys. The method returns a new `Graph` where the vertex values have been updated according to the provided a user-defined map function.
    --- End diff --
    
    "...updated according to a provided user-defined map function"


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[GitHub] flink pull request: [FLINK-1462] Gelly documentation

[rmetzger <gi...@git.apache.org>]

Github user rmetzger commented on the pull request:

    https://github.com/apache/flink/pull/430#issuecomment-75388691
  
    +1 for merging. Very well written.
    I now have a rough idea what the Graph API is doing ;)


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[GitHub] flink pull request: [FLINK-1462] Gelly documentation

[tillrohrmann <gi...@git.apache.org>]

Github user tillrohrmann commented on a diff in the pull request:

    https://github.com/apache/flink/pull/430#discussion_r25415850
  
    --- Diff: docs/gelly_guide.md ---
    @@ -0,0 +1,425 @@
    +---
    +title: "Gelly: Flink Graph API"
    +---
    +<!--
    +Licensed to the Apache Software Foundation (ASF) under one
    +or more contributor license agreements.  See the NOTICE file
    +distributed with this work for additional information
    +regarding copyright ownership.  The ASF licenses this file
    +to you under the Apache License, Version 2.0 (the
    +"License"); you may not use this file except in compliance
    +with the License.  You may obtain a copy of the License at
    +
    +  http://www.apache.org/licenses/LICENSE-2.0
    +
    +Unless required by applicable law or agreed to in writing,
    +software distributed under the License is distributed on an
    +"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
    +KIND, either express or implied.  See the License for the
    +specific language governing permissions and limitations
    +under the License.
    +-->
    +
    +* This will be replaced by the TOC
    +{:toc}
    +
    +<a href="#top"></a>
    +
    +Introduction
    +------------
    +
    +Gelly is a Java Graph API for Flink. It contains a set of methods and utilities which aim to simplify the development of graph analysis applications in Flink. In Gelly, graphs can be transformed and modified using high-level functions similar to the ones provided by the batch processing API. Gelly provides methods to create, transform and modify graphs, as well as a library of graph algorithms.
    +
    +Using Gelly
    +-----------
    +
    +Gelly is currently part of the *staging* Maven project. All relevant classes are located in the *org.apache.flink.graph* package.
    +
    +Add the following dependency to your `pom.xml` to use Gelly.
    +
    +~~~xml
    +<dependency>
    +    <groupId>org.apache.flink</groupId>
    +    <artifactId>flink-gelly</artifactId>
    +    <version>{{site.FLINK_VERSION_SHORT}}</version>
    +</dependency>
    +~~~
    +
    +The remaining sections provide a description of available methods and present several examples of how to use Gelly and how to mix it with the Flink Java API. After reading this guide, you might also want to check the {% gh_link /flink-staging/flink-gelly/src/main/java/org/apache/flink/graph/example/ "Gelly examples" %}.
    +
    +Graph Representation
    +-----------
    +
    +In Gelly, a `Graph` is represented by a `DataSet` of vertices and a `DataSet` of edges.
    +
    +The `Graph` nodes are represented by the `Vertex` type. A `Vertex` is defined by a unique ID and a value. `Vertex` IDs should implement the `Comparable` interface. Vertices without value can be represented by setting the value type to `NullValue`.
    +
    +{% highlight java %}
    +// create a new vertex with a Long ID and a String value
    +Vertex<Long, String> v = new Vertex<Long, String>(1L, "foo");
    +
    +// create a new vertex with a Long ID and no value
    +Vertex<Long, NullValue> v = new Vertex<Long, NullValue>(1L, NullValue.getInstance());
    +{% endhighlight %}
    +
    +The graph edges are represented by the `Edge` type. An `Edge` is defined by a source ID (the ID of the source `Vertex`), a target ID (the ID of the target `Vertex`) and an optional value. The source and target IDs should be of the same type as the `Vertex` IDs. Edges with no value have a `NullValue` value type.
    +
    +{% highlight java %}
    +Edge<Long, Double> e = new Edge<Long, Double>(1L, 2L, 0.5);
    +
    +// reverse the source and target of this edge
    +Edge<Long, Double> reversed = e.reverse();
    +
    +Double weight = e.getValue(); // weight = 0.5
    +{% endhighlight %}
    +
    +[Back to top](#top)
    +
    +Graph Creation
    +-----------
    +
    +You can create a `Graph` in the following ways:
    +
    +* from a `DataSet` of edges and an optional `DataSet` of vertices:
    +
    +{% highlight java %}
    +ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
    +
    +DataSet<Vertex<String, Long>> vertices = ...
    +
    +DataSet<Edge<String, Double>> edges = ...
    +
    +Graph<String, Long, Double> graph = Graph.fromDataSet(vertices, edges, env);
    +{% endhighlight %}
    +
    +* from a `DataSet` of `Tuple3` and an optional `DataSet` of `Tuple2`. In this case, Gelly will convert each `Tuple3` to an `Edge`, where the first field will be the source ID, the second field will be the target ID and the third field will be the edge value. Equivalently, each `Tuple2` will be converted to a `Vertex`, where the first field will be the vertex ID and the second field will be the vertex value:
    +
    +{% highlight java %}
    +ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
    +
    +DataSet<Tuple2<String, Long>> vertexTuples = env.readCsvFile("path/to/vertex/input");
    +
    +DataSet<Tuple3<String, String, Double>> edgeTuples = env.readCsvFile("path/to/edge/input");
    +
    +Graph<String, Long, Double> graph = Graph.fromTupleDataSet(vertexTuples, edgeTuples, env);
    +{% endhighlight %}
    +
    +* from a `Collection` of edges and an optional `Collection` of vertices:
    +
    +{% highlight java %}
    +ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
    +
    +List<Vertex<Long, Long>> vertexList = new ArrayList...
    +
    +List<Edge<Long, String>> edgeList = new ArrayList...
    +
    +Graph<Long, Long, String> graph = Graph.fromCollection(vertexList, edgeList, env);
    +{% endhighlight %}
    +
    +If no vertex input is provided during Graph creation, Gelly will automatically produce the `Vertex` `DataSet` from the edge input. In this case, the created vertices will have no values. Alternatively, you can provide a `MapFunction` as an argument to the creation method, in order to initialize the `Vertex` values:
    +
    +{% highlight java %}
    +ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
    +
    +// initialize the vertex value to be equal to the vertex ID
    +Graph<Long, Long, String> graph = Graph.fromCollection(edges, 
    +				new MapFunction<Long, Long>() {
    +					public Long map(Long value) { 
    +						return value; 
    +					} 
    +				}, env);
    +{% endhighlight %}
    +
    +[Back to top](#top)
    +
    +Graph Properties
    +------------
    +
    +Gelly includes the following methods for retrieving various Graph properties and metrics:
    +
    +{% highlight java %}
    +// get the Vertex DataSet
    +DataSet<Vertex<K, VV>> getVertices()
    +
    +// get the Edge DataSet
    +DataSet<Edge<K, EV>> getEdges()
    +
    +// get the IDs of the vertices as a DataSet
    +DataSet<K> getVertexIds()
    +
    +// get the source-target pairs of the edge IDs as a DataSet
    +DataSet<Tuple2<K, K>> getEdgeIds() 
    +
    +// get a DataSet of <vertex ID, in-degree> pairs for all vertices
    +DataSet<Tuple2<K, Long>> inDegrees() 
    +
    +// get a DataSet of <vertex ID, out-degree> pairs for all vertices
    +DataSet<Tuple2<K, Long>> outDegrees()
    +
    +// get a DataSet of <vertex ID, degree> pairs for all vertices, where degree is the sum of in- and out- degrees
    +DataSet<Tuple2<K, Long>> getDegrees()
    +
    +// get the number of vertices
    +DataSet<Integer> numberOfVertices()
    +
    +// get the number of edges
    +DataSet<Integer> numberOfEdges()
    +
    +{% endhighlight %}
    +
    +[Back to top](#top)
    +
    +Graph Transformations
    +-----------------
    +
    +* <strong>Map</strong>: Gelly provides specialized methods for applying a map transformation on the vertex values or edge values. `mapVertices` and `mapEdges` return a new `Graph`, where the IDs of the vertices (or edges) remain unchanged, while the values are transformed according to the provided user-defined map function. The map functions also allow changing the type of the vertex or edge values.
    +
    +{% highlight java %}
    +ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
    +Graph<Long, Long, Long> graph = Graph.fromDataSet(vertices, edges, env);
    +
    +// increment each vertex value by one
    +Graph<Long, Long, Long> updatedGraph = graph.mapVertices(
    +				new MapFunction<Vertex<Long, Long>, Long>() {
    +					public Long map(Vertex<Long, Long> value) {
    +						return value.getValue() + 1;
    +					}
    +				});
    +{% endhighlight %}
    +
    +* <strong>Filter</strong>: A filter transformation applies a user-defined filter function on the vertices or edges of the `Graph`. `filterOnEdges` will create a sub-graph of the original graph, keeping only the edges that satisfy the provided predicate. Note that the vertex dataset will not be modified. Respectively, `filterOnVertices` applies a filter on the vertices of the graph. Edges whose source and/or target do not satisfy the vertex predicate are removed from the resulting edge dataset. The `subgraph` method can be used to apply a filter function to the vertices and the edges at the same time.
    +
    +{% highlight java %}
    +Graph<Long, Long, Long> graph = ...
    +
    +graph.subgraph(
    +		new FilterFunction<Vertex<Long, Long>>() {
    +			   	public boolean filter(Vertex<Long, Long> vertex) {
    +					// keep only vertices with positive values
    +					return (vertex.getValue() > 0);
    +			   }
    +		   },
    +		new FilterFunction<Edge<Long, Long>>() {
    +				public boolean filter(Edge<Long, Long> edge) {
    +					// keep only edges with negative values
    +					return (edge.getValue() < 0);
    +				}
    +		})
    +{% endhighlight %}
    +
    +<p class="text-center">
    +    <img alt="Filter Transformations" width="80%" src="img/gelly-filter.png"/>
    +</p>
    +
    +* <strong>Join</strong>: Gelly provides specialized methods for joining the vertex and edge datasets with other input datasets. `joinWithVertices` joins the vertices with a `Tuple2` input data set. The join is performed using the vertex ID and the first field of the `Tuple2` input as the join keys. The method returns a new `Graph` where the vertex values have been updated according to the provided a user-defined map function.
    +Similarly, an input dataset can be joined with the edges, using one of three methods. `joinWithEdges` expects an input `DataSet` of `Tuple3` and joins on the composite key of both source and target vertex IDs. `joinWithEdgesOnSource` expects a `DataSet` of `Tuple2` and joins on the source key of the edges and the first attribute of the input dataset and `joinWithEdgesOnTarget` expects a `DataSet` of `Tuple2` and joins on the target key of the edges and the first attribute of the input dataset. All three methods apply a map function on the edge and the input data set values.
    +Note that if the input dataset contains a key multiple times, all Gelly join methods will only consider the first value encountered.
    +
    +{% highlight java %}
    +Graph<Long, Double, Double> network = ...
    +
    +DataSet<Tuple2<Long, Long>> vertexOutDegrees = network.outDegrees();
    +
    +// assign the transition probabilities as the edge weights
    +Graph<Long, Double, Double> networkWithWeights = network.joinWithEdgesOnSource(vertexOutDegrees,
    +				new MapFunction<Tuple2<Double, Long>, Double>() {
    +					public Double map(Tuple2<Double, Long> value) {
    +						return value.f0 / value.f1;
    +					}
    +				});
    +{% endhighlight %}
    +
    +* <strong>Reverse</strong>: the `reverse()` method returns a new `Graph` where the direction of all edges has been reversed.
    +
    +* <strong>Undirected</strong>: In Gelly, a `Graph` is always directed. Undirected graphs can be represented by adding all opposite-direction edges to a graph. For this purpose, Gelly provides the `getUndirected()` method.
    +
    +* <strong>Union</strong>: Gelly's `union()` method performs a union on the vertex and edges sets of the input graphs. Duplicate vertices are removed from the resulting `Graph`, while if duplicate edges exists, these will be maintained.
    +
    +<p class="text-center">
    +    <img alt="Union Transformation" width="50%" src="img/gelly-union.png"/>
    +</p>
    +
    +[Back to top](#top)
    +
    +Graph Mutations
    +-----------
    +
    +Gelly includes the following methods for adding and removing vertices and edges from an input `Graph`:
    +
    +{% highlight java %}
    +// adds a Vertex and the given edges to the Graph. If the Vertex already exists, it will not be added again, but the given edges will.
    +Graph<K, VV, EV> addVertex(final Vertex<K, VV> vertex, List<Edge<K, EV>> edges)
    +
    +// adds an Edge to the Graph. If the source and target vertices do not exist in the graph, they will also be added.
    +Graph<K, VV, EV> addEdge(Vertex<K, VV> source, Vertex<K, VV> target, EV edgeValue)
    +
    +// removes the given Vertex and its edges from the Graph.
    +Graph<K, VV, EV> removeVertex(Vertex<K, VV> vertex)
    +
    +// removes *all* edges that match the given Edge from the Graph.
    +Graph<K, VV, EV> removeEdge(Edge<K, EV> edge)
    +{% endhighlight %}
    +
    +Neighborhood Methods
    +-----------
    +
    +Neighborhood methods allow vertices to perform an aggregation on their first-hop neighborhood.
    +
    +`reduceOnEdges()` can be used to compute an aggregation on the neighboring edges of a vertex, while `reduceOnNeighbors()` has access on both the neighboring edges and vertices. The neighborhood scope is defined by the `EdgeDirection` parameter, which takes the values `IN`, `OUT` or `ALL`. `IN` will gather all in-coming edges (neighbors) of a vertex, `OUT` will gather all out-going edges (neighbors), while `ALL` will gather all edges (neighbors).
    +
    +For example, assume that you want to select the minimum weight of all out-edges for each vertex in the following graph:
    +
    +<p class="text-center">
    +    <img alt="reduceOnEdges Example" width="50%" src="img/gelly-example-graph.png"/>
    +</p>
    +
    +The following code will collect the out-edges for each vertex and apply the `SelectMinWeight()` user-defined function on each of the resulting neighborhoods:
    +
    +{% highlight java %}
    +Graph<Long, Long, Double> graph = ...
    +
    +DataSet<Tuple2<Long, Double>> minWeights = graph.reduceOnEdges(
    +				new SelectMinWeight(), EdgeDirection.OUT);
    +
    +// user-defined function to select the minimum weight
    +static final class SelectMinWeight implements EdgesFunction<Long, Double, Tuple2<Long, Double>> {
    +
    +    public Tuple2<Long, Double> iterateEdges(Iterable<Tuple2<Long, Edge<Long, Double>>> edges) {
    +
    +        long minWeight = Double.MAX_VALUE;
    +        long vertexId = -1;
    +
    +        for (Tuple2<Long, Edge<Long, Double>> edge: edges) {
    +            if (edge.f1.getValue() < weight) {
    +            weight = edge.f1.getValue();
    +            vertexId = edge.f0;
    +        }
    +        return new Tuple2<Long, Double>(vertexId, minWeight);
    +    }
    +}
    +{% endhighlight %}
    +
    +<p class="text-center">
    +    <img alt="reduceOnEdges Example" width="50%" src="img/gelly-reduceOnEdges.png"/>
    +</p>
    +
    +Similarly, assume that you would like to compute the sum of the values of all in-coming neighbors, for every vertex. The following code will collect the in-coming neighbors for each vertex and apply the `SumValues()` user-defined function on each neighborhood:
    +
    +{% highlight java %}
    +Graph<Long, Long, Double> graph = ...
    +
    +DataSet<Tuple2<Long, Long>> verticesWithSum = graph.reduceOnNeighbors(
    +				new SumValues(), EdgeDirection.IN);
    +
    +// user-defined function to sum the neighbor values
    +static final class SumValues implements NeighborsFunction<Long, Long, Long, Tuple2<Long, Long>> {
    --- End diff --
    
    Shouldn't the third type parameter be a ```Double``` because the edge values are of this type?


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[GitHub] flink pull request: [FLINK-1462] Gelly documentation

[tillrohrmann <gi...@git.apache.org>]

Github user tillrohrmann commented on the pull request:

    https://github.com/apache/flink/pull/430#issuecomment-76153180
  
    Really good documentation.


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[GitHub] flink pull request: [FLINK-1462] Gelly documentation

[ktzoumas <gi...@git.apache.org>]

Github user ktzoumas commented on the pull request:

    https://github.com/apache/flink/pull/430#issuecomment-75775383
  
    Reads very very well. Big +1 as well


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