Part 2. Connecting vertices

GraphX has a large API with a number of built-in algorithms. But not all of the API is well-documented. Chapter 4 walks you through how to do basic operations in GraphX, including the cornerstone operations like pregel(), which is modeled after Google’s Pregel graph system.

Chapter 5 covers the core algorithms that GraphX provides, such as Page-Rank, and discusses how each can be used to solve a real-world problem.

After all that groundwork covered in the first five chapters, chapters 6 and 7 are where we finally really start putting GraphX into action. Chapter 6 shows how to implement some of the classical graph algorithms, such as Minimum Spanning Tree, in GraphX. You’ll also see an example of what Minimum Spanning Trees can be used for.

Machine learning has become pervasive. Although Spark’s MLlib is the primary way to do machine learning on Spark, graph-oriented machine learning can be done with GraphX. In the extensive chapter 7 you’ll learn the basics of machine learning on up through recommender systems (like those used by Netflix or Amazon) and document classification. Then you’ll see how to combine MLlib together with GraphX in a spam-detection example.