In Apache Spark, transformations and actions are two fundamental concepts that play crucial roles in defining and executing Spark jobs. Understanding the difference between transformations and actions is essential for effectively designing and optimizing Spark applications.
⏩What are Transformations in Spark?
👉Transformations in Spark are operations that are applied to RDDs (Resilient Distributed Datasets) to create a new RDD.
👉When a transformation is applied to an RDD, it does not compute the result immediately. Instead, it creates a new RDD representing the transformed data but keeps track of the lineage (dependencies) between the original RDD and the transformed RDD.
👉Transformations are lazy evaluated, meaning Spark delays the actual computation until an action is triggered.
👉Examples of transformations include map(), filter(), flatMap(), groupByKey(), reduceByKey(), sortByKey(), etc.
⏩What are Actions in Spark?
👉Actions in Spark are operations that trigger the computation of a result from an RDD and return a non-RDD value.
👉When an action is invoked on an RDD, Spark calculates the result of all transformations leading to that RDD based on its lineage and executes the computation.
👉Actions are eager evaluated, meaning they kick off the actual computation in Spark.
👉Examples of actions include collect(), count(), reduce(), saveAsTextFile(), foreach(), take(), first(), etc.
