Summary
This chapter showed by example how Scala-based code can be used to call GraphX algorithms in Apache Spark. Scala has been used because it requires less code to develop the examples than Java, which saves time. Note that GraphX is not available for Python or R. A Scala-based shell can be used, and the code can be compiled into Spark applications.
The most common graph algorithms have been covered and you should have an idea now on how to solve any graph problem with GraphX. Especially since you've understood that a Graph in GraphX is still represented and backed by RDDs, so you are already familiar with using them. The configuration and code examples from this chapter will also be available for download with the book.
We hope that you found this chapter useful. The next chapter will delve into graph frames, which make use of DataFrames, Tungsten, and Catalyst for graph processing.
