suresh Understanding the Difference Between Transformation and Action in Apache Spark
When working with Apache Spark, it's crucial to grasp the distinction between transformation and action operations. The focus keyword for this discussion is Apache Spark transformation and action.
Transformation in Apache Spark:
Transformations in Apache Spark are operations that are applied to RDDs (Resilient Distributed Datasets) to create a new RDD. These operations are lazily evaluated, meaning Spark will only compute them when an action is called. Examples of transformations include map, filter, and reduceByKey.
Action in Apache Spark:
Actions in Apache Spark are operations that trigger the execution of the computation on the RDDs and return the result to the driver program or write it to storage. Unlike transformations, actions are eagerly evaluated. Examples of actions include count, collect, and saveAsTextFile.
Therefore, the key difference lies in the timing of execution: transformations are deferred until an action is called, while actions are executed immediately to produce a result.
By understanding and effectively utilizing transformations and actions in Apache Spark, developers can optimize their Spark jobs for efficient processing and performance.
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