Mastering Spark with R

Chapter 2. Getting Started

I always wanted to be a wizard.

—Samwell Tarly

After reading Chapter 1, you should now be familiar with the kinds of problems that Spark can help you solve. And it should be clear that Spark solves problems by making use of multiple computers when data does not fit in a single machine or when computation is too slow. If you are newer to R, it should also be clear that combining Spark with data science tools like ggplot2 for visualization and dplyr to perform data transformations brings a promising landscape for doing data science at scale. We also hope you are excited to become proficient in large-scale computing.

In this chapter, we take a tour of the tools you’ll need to become proficient in Spark. We encourage you to walk through the code in this chapter because it will force you to go through the motions of analyzing, modeling, reading, and writing data. In other words, you will need to do some wax-on, wax-off, repeat before you get fully immersed in the world of Spark.

In Chapter 3 we dive into analysis followed by modeling, which presents examples using a single-cluster machine: your personal computer. Subsequent chapters introduce cluster computing and the concepts and techniques that you’ll need to successfully run code across multiple machines.

Overview

From R, getting started with Spark using sparklyr and a local cluster is as easy as installing and loading the sparklyr package followed by installing Spark using sparklyr; however, we assume ...