suresh What is RDD (Resilient Distributed Dataset) in Apache Spark and how does it help in fault tolerance?
RDD (Resilient Distributed Dataset) is a fundamental abstraction in Apache Spark that represents a distributed collection of elements that can be operated on in parallel. RDDs are fault-tolerant, immutable data structures that allow Spark to efficiently recover from node failures or data loss.
When an RDD is created in Spark, it is divided into smaller partitions that are distributed across multiple nodes in a cluster. Each partition of the RDD is replicated to ensure fault tolerance. If a node fails, Spark can recompute the lost partitions from the replicated copies on other nodes, allowing the computation to continue without any data loss.
By using RDDs, Apache Spark is able to achieve fault tolerance by replicating data across multiple nodes and providing the ability to recompute lost data partitions in case of failures. This ensures the reliability and resilience of Spark applications, making it a powerful framework for processing large-scale data in distributed environments.
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