Spark in Action, Second Edition

4. Fundamentally lazy

This chapter covers

• Using Spark’s efficient laziness to your benefit
• Building a data application the traditional way vs. the Spark way
• Building great data-centric applications using Spark
• Learning more about transformations and actions
• Using Catalyst, Spark’s built-in optimizer
• Introducing directed acyclic graphs

This chapter is not only about celebrating laziness. It also teaches, through examples and experiments, the fundamental differences between building a data application the traditional way and building one with Spark.

There are at least two kinds of laziness: sleeping under the trees when you’ve committed to doing something else, and thinking ahead in order to do your job in the smartest possible way. Although, ...