What is the difference between Apache Spark transformations and actions?

Apache Spark Transformations vs Actions

Apache Spark, a popular distributed computing framework, processes data through two main types of operations: transformations and actions. Understanding the difference between these two concepts is crucial for efficient data processing in Spark.

Transformations

Transformations in Apache Spark are lazy operations that are applied to RDDs (Resilient Distributed Datasets) to create a new RDD. These transformations do not compute results immediately but instead create a directed acyclic graph (DAG) of the computation that will be executed when an action is triggered. Examples of transformations include map, filter, reduceByKey, and join.

Actions

Actions in Apache Spark are operations that trigger the execution of the DAG created by transformations and return a result to the driver program or write data to an external storage system. Actions are eager operations that initiate the computation on RDDs. Examples of actions include collect, count, saveAsTextFile, and foreach.

Key Differences

• Transformations are lazy and create a DAG without executing any computation, while actions trigger the execution of the DAG.
• Transformations are used to build the computation DAG, while actions actually perform computation and return results.
• Transformations allow for optimization of the computation plan, while actions are necessary to obtain final results from the RDDs.

Understanding the distinction between transformations and actions is essential for writing efficient and optimized Apache Spark programs.