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 ---
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+---
+title: "Gelly: Flink Graph API"
+---
+<!--
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+Unless required by applicable law or agreed to in writing,
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+
+* 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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