Graph processing is another very important topic when it comes to data analysis. In fact, a majority of problems can be expressed as a graph.

A graph is basically a network of items and their relationships to each other. Items are called nodes and relationships are called edges. Relationships can be directed or undirected. Relationships, as well as items, can have properties. So a map, for example, can be represented as a graph as well. Each city is a node and the streets between the cities are edges. The distance between the cities can be assigned as properties on the edge.

The Apache Spark GraphX module allows Apache Spark to offer fast big data in-memory graph processing. This allows you to run graph algorithms at scale.

One of the most famous algorithms, for example, is the traveling salesman problem. Consider the graph representation of the map mentioned earlier. A salesman has to visit all cities of a region but wants to minimize the distance that he has to travel. As the distances between all the nodes are stored on the edges, a graph algorithm can actually tell you the optimal route. GraphX is able to create, manipulate, and analyze graphs using a variety of built-in algorithms.

It introduces two new data types to support graph processing in Spark--VertexRDD and EdgeRDD--to represent graph nodes and edges. It also introduces graph processing algorithms, such as PageRank and triangle processing. Many of these functions will be examined in Chapter 11, Apache Spark GraphX and Chapter 12, Apache Spark GraphFrames.