Category : Apache Spark | Sub Category : Apache Spark Programs | By Prasad Bonam Last updated: 2023-10-14 23:40:20 Viewed : 253
In Apache Spark, RDD stands for Resilient Distributed Dataset. RDD is a fundamental data structure in Spark that represents a collection of elements that can be divided across multiple nodes in a cluster and can be operated on in parallel. It is a distributed memory abstraction that allows users to perform in-memory computations on large clusters in a fault-tolerant manner.
Some key characteristics of RDDs in Apache Spark include:
Resilient: RDDs are fault-tolerant, meaning they can be reconstructed in case of node failures. Spark keeps track of the lineage of transformations used to build the RDD so that it can recompute the lost partitions.
Distributed: RDDs are distributed across multiple nodes in a cluster, enabling parallel processing and efficient use of resources.
Immutable: RDDs are immutable, meaning that they cannot be modified once created. However, you can perform various transformation operations on them to create new RDDs.
Lazy Evaluated: Transformations on RDDs are lazily evaluated, which means that the actual computations are not executed until an action is triggered. This optimization allows Spark to optimize the execution plan before running the actual job.
RDDs provide a programming interface for users to perform various data transformations and actions. Some common operations that can be performed on RDDs include map, filter, reduce, join, and aggregate. These operations allow users to manipulate the data in a distributed manner without needing to explicitly manage the underlying parallelism.
While RDDs have been the primary abstraction in earlier versions of Apache Spark, newer versions have introduced higher-level abstractions such as DataFrames and Datasets that provide a more structured and efficient way of working with data, especially for structured and semi-structured data processing. However, RDDs still serve as a foundational concept in Apache Spark and are widely used in various contexts for data processing and analysis.