Data Abstractions in Distributed Processing: RDDs and PCollections
Modern data processing frameworks rely on powerful abstractions to manage data at scale. Two notable examples are Resilient Distributed Datasets (RDDs) in Apache Spark and Parallel Collections (PCollections) in Apache Beam. Understanding the design of these core data structures is important for grasping how these frameworks operate. Apache Spark’s RDDs An RDD is an immutable, distributed collection of elements that can be processed in parallel across a cluster. RDDs are important because they enable efficient, in-memory computations, making them well-suited for a wide range of workloads. Immutability, lazy evaluation, fault tolerance, and in-memory computation are the key features of Spark RDDs. RDD was presented in the paper “Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing.” by M Zaharia et al. While RDDs are the foundational API, many modern Spark applications, especially those dealing with structured data, now leverage the higher-level...