What is the difference between transformations and actions in Apache Spark?

What is the difference between transformations and actions in Apache Spark?

Transformations and actions are two important concepts in Apache Spark that are used to process data in a distributed computing environment. The main difference between transformations and actions is how they are executed and when the actual computation takes place.

Transformations:

Transformations in Apache Spark are lazy operations that are not executed immediately. Instead, when a transformation is applied to an RDD (Resilient Distributed Dataset), Spark creates a new RDD representing the result of the transformation without computing it right away. Examples of transformations include map, filter, and reduceByKey.

Actions:

Actions in Apache Spark are operations that trigger the actual computation on the RDD. When an action is called on an RDD, Spark starts the execution of all transformations and actions that lead to that action. Examples of actions include count, collect, and saveAsTextFile.

In summary, transformations are used to build a series of data processing steps in Spark, while actions are executed to trigger the computation and produce a result. Understanding the difference between transformations and actions is crucial for designing efficient and effective Spark programs.