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.
⏩⏩Key Differences between Transformation & Action:
💎Execution Trigger:
👉Transformations are lazily evaluated, and Spark waits until an action is called to execute them. They help build the RDD lineage.
👉Actions trigger the actual computation in Spark by evaluating the transformations and returning results to the driver program or writing data to external storage.
👉Transformations return new RDDs representing the transformed data but do not compute results immediately.
👉Actions return non-RDD values such as integers, lists, or write data to external storage (like files or databases).
💎Optimization Opportunities:
👉Spark optimizes transformations by applying optimizations like pipelining, lazy evaluation, and narrow transformations to reduce shuffling and improve performance.
👉Actions trigger the execution of transformations and provide opportunities for Spark to optimize the execution plan based on the actual computations required.
Understanding when to apply transformations and actions correctly is crucial for optimizing Spark jobs, reducing unnecessary computations, and improving overall performance.
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