Decoding Spark’s Power Duo: Magic of RDD and DAG in Distributed Data Processing

Let’s discuss about the two main abstractions in Apache Spark: DAG (Directed Acyclic Graph) and RDD (Resilient Distributed Dataset).

RDD (Resilient Distributed Dataset):

• An RDD is like a specific collection of books in our library. It represents a distributed, immutable dataset that can be processed in parallel across a cluster of computers (librarians).
• Example: If we have an RDD of mystery books, it means we’ve organised a specific set of mystery books that can be analysed or transformed by the librarians (computers) in parallel.

DAG (Directed Acyclic Graph):

• A DAG is like a flowchart that shows the sequence of tasks the librarians (computers) need to perform. It’s a directed graph because it indicates the order in which tasks should be executed, and it’s acyclic because there are no loops or repetitions in the task sequence.
• Example: If we want to categorize mystery books by author and then count the books for each author, the DAG would represent the steps: first, categorize by author, and second, count the books for each author. Each step is a node in the graph, and the arrows show the order of execution.

So, in our library of distributed data processing, RDD are like specific collections of books, and DAG are like organised plans or flowcharts that guide the librarians (computers) on how to process these collections efficiently.