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1. Introduction

Graph processing is useful for many applications from social networks to advertisements. Inside a big data scenario, we need a tool to distribute that processing load.

In this tutorial, we’ll load and explore graph possibilities using Apache Spark in Java. To avoid complex structures, we’ll be using an easy and high-level Apache Spark graph API: the GraphFrames API.

2. Graphs

First of all, let’s define a graph and its components. A graph is a data structure having edges and vertices. The edges carry information that represents relationships between the vertices.

The vertices are points in an n-dimensional space, and edges connect the vertices according to their relationships:

Graph Example 1

In the image above, we have a social network example. We can see the vertices represented by letters and the edges carrying which kind of relationship is between the vertices.

3. Maven Setup

Now, let’s start the project by setting up the Maven configuration.

Let’s add spark-graphx 2.11, graphframes, and spark-sql 2.11:


These artifact versions support Scala 2.11.

Also, it so happens that GraphFrames is not in Maven Central. So, let’s add the needed Maven repository, too:


4. Spark Configuration

In order to work with GraphFrames, we’ll need to download Hadoop and define the HADOOP_HOME environment variable.

In the case of Windows as the operating system, we’ll also download the appropriate winutils.exe to the HADOOP_HOME/bin folder.

Next, let’s begin our code by creating the basic configuration:

SparkConf sparkConf = new SparkConf()
JavaSparkContext javaSparkContext = new JavaSparkContext(sparkConf);

We’ll also need to create a SparkSession:

SparkSession session = SparkSession.builder()
  .config("spark.sql.warehouse.dir", "/file:C:/temp")

5. Graph Construction

Now, we’re all set to start with our main code. So, let’s define the entities for our vertices and edges, and create the GraphFrame instance.

We’ll work on the relationships between users from a hypothetical social network.

5.1. Data

First, for this example, let’s define both entities as User and Relationship:

public class User {
    private Long id;
    private String name;
    // constructor, getters and setters
public class Relationship implements Serializable {
    private String type;
    private String src;
    private String dst;
    private UUID id;

    public Relationship(String type, String src, String dst) {
        this.type = type;
        this.src = src;
        this.dst = dst; = UUID.randomUUID();
    // getters and setters

Next, let’s define some User and Relationship instances:

List<User> users = new ArrayList<>();
users.add(new User(1L, "John"));
users.add(new User(2L, "Martin"));
users.add(new User(3L, "Peter"));
users.add(new User(4L, "Alicia"));

List<Relationship> relationships = new ArrayList<>();
relationships.add(new Relationship("Friend", "1", "2"));
relationships.add(new Relationship("Following", "1", "4"));
relationships.add(new Relationship("Friend", "2", "4"));
relationships.add(new Relationship("Relative", "3", "1"));
relationships.add(new Relationship("Relative", "3", "4"));

5.2. GraphFrame Instance

Now, in order to create and manipulate our graph of relationships, we’ll create an instance of GraphFrame. The GraphFrame constructor expects two Dataset<Row> instances, the first representing the vertices and the second, the edges:

Dataset<Row> userDataset = session.createDataFrame(users, User.class);
Dataset<Row> relationshipDataset = session.createDataFrame(relationships, Relation.class);

GraphFrame graph = new GraphFrame(userDataframe, relationshipDataframe);

At last, we’ll log our vertices and edges in the console to see how it looks:

| id|  name|
|  1|  John|
|  2|Martin|
|  3| Peter|
|  4|Alicia|

|dst|                  id|src|     type|
|  2|622da83f-fb18-484...|  1|   Friend|
|  4|c6dde409-c89d-490...|  1|Following|
|  4|360d06e1-4e9b-4ec...|  2|   Friend|
|  1|de5e738e-c958-4e0...|  3| Relative|
|  4|d96b045a-6320-4a6...|  3| Relative|

6. Graph Operators

Now that we have a GraphFrame instance, let’s see what we can do with it.

6.1. Filter

GraphFrames allows us to filter edges and vertices by a query.

Next, then, let’s filter the vertices by the name property on User:

graph.vertices().filter("name = 'Martin'").show();

At the console, we can see the result:

| id|  name|
|  2|Martin|

Also, we can directly filter on the graph by calling filterEdges or filterVertices:

graph.filterEdges("type = 'Friend'")

Now, since we filtered the edges, we might still have some isolated vertices. So, we’ll call dropIsolatedVertices(). 

As a result, we have a subgraph, still a GraphFrame instance, with just the relationships that have “Friend” status:

| id|  name|
|  1|  John|
|  2|Martin|
|  4|Alicia|

6.2. Degrees

Another interesting feature set is the degrees set of operations. These operations return the number of edges incident on each vertex.

The degrees operation just returns the count of all edges of each vertex. On the other hand, inDegrees counts only incoming edges, and outDegrees counts only outgoing edges.

Let’s count the incoming degrees of all vertices in our graph:


As a result, we have a GraphFrame that shows the number of incoming edges to each vertex, excluding those with none:

| id|inDegree|
|  1|       1|
|  4|       3|
|  2|       1|

7. Graph Algorithms

GraphFrames also provides popular algorithms ready to use — let’s take a look at some of them.

7.1. Page Rank

The Page Rank algorithm weighs the incoming edges to a vertex and transforms it into a score.

The idea is that each incoming edge represents an endorsement and makes the vertex more relevant in the given graph.

For example, in a social network, if a person is followed by various people, he or she will be ranked highly.

Running the page rank algorithm is quite straightforward:


To configure this algorithm, we just need to provide:

  • maxIter – the number of iterations of page rank to run – 20 is recommended, too few will decrease the quality, and too many will degrade the performance
  • resetProbability – the random reset probability (alpha) – the lower it is, the bigger the score spread between the winners and losers will be – valid ranges are from 0 to 1. Usually, 0.15 is a good score

The response is a similar GraphFrame, though this time we see an additional column giving the page rank of each vertex:

| id|  name|          pagerank|
|  4|Alicia|1.9393230468864597|
|  3| Peter|0.4848822786454427|
|  1|  John|0.7272991738542318|
|  2|Martin| 0.848495500613866|

In our graph, Alicia is the most relevant vertex, followed by Martin and John.

7.2. Connected Components

The connected components algorithm finds isolated clusters or isolated sub-graphs. These clusters are sets of connected vertices in a graph where each vertex is reachable from any other vertex in the same set.

We can call the algorithm without any parameters via the connectedComponents() method:


The algorithm returns a GraphFrame containing each vertex and the component to which each is connected:

| id|  name|   component|
|  1|  John|154618822656|
|  2|Martin|154618822656|
|  3| Peter|154618822656|
|  4|Alicia|154618822656|

Our graph has only one component — this means that we do not have isolated sub-graphs. The component has an auto-generated id, which is 154618822656, in our case.

Although we have one more column here – the component id – our graph is still the same.

7.3. Triangle Counting

Triangle counting is commonly used as community detection and counting in a social network graph. A triangle is a set of three vertices, where each vertex has a relationship to the other two vertices in the triangle.

In a social network community, it’s easy to find a considerable number of triangles connected to each other.

We can easily perform a triangle counting directly from our GraphFrame instance:


The algorithm also returns a GraphFrame with the number of triangles passing through each vertex.

|count| id|  name|
|    1|  3| Peter|
|    2|  1|  John|
|    2|  4|Alicia|
|    1|  2|Martin|

8. Conclusion

Apache Spark is a great tool for computing a relevant amount of data in an optimized and distributed way. And, the GraphFrames library allows us to easily distribute graph operations over Spark.

As always, the complete source code for the example is available over on GitHub.

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