RDD (Resilient Distributed Datasets) is a fundamental data structure in Apache Spark, which provides an abstraction for distributed data processing. RDDs are a collection of data distributed across multiple nodes in a cluster, with each partition containing a subset of the data. This structure is fault-tolerant, meaning that if a node fails, the data can be recomputed from other available data, hence “resilient.” RDDs are the basis for parallel data processing, enabling efficient execution of operations like map, filter, and reduce across large datasets.
https://en.wikipedia.org/wiki/Resilient_distributed_dataset
One of the primary features of RDDs is their ability to perform in-memory computations, which drastically improves the speed of iterative algorithms compared to traditional disk-based approaches. RDDs store data in memory (RAM) rather than on disk, which allows for quicker access and computation, especially in machine learning and data analytics workflows. This makes RDDs highly effective for iterative processes like those found in graph algorithms and machine learning model training, where the same data is repeatedly processed.
https://spark.apache.org/docs/latest/rdd-programming-guide.html
Additionally, RDDs provide fault tolerance through lineage information. Each RDD keeps track of its operations, forming a lineage graph that can be used to recompute any lost data if a failure occurs. This feature ensures that distributed computations can recover quickly without significant data loss. This ability to recover lost data without significant performance degradation is crucial for large-scale applications where failure is inevitable, especially when working with big data across distributed environments.