Final answer:
When an RDD fails or suffers a system failure, it is rebuilt from its dependencies using lineage-based recovery.
Step-by-step explanation:
When an RDD fails or suffers a system failure, the RDD is rebuilt from its dependencies. This means that the RDD will be reconstructed using the data from its parent RDDs. The process is called lineage-based recovery, where the RDD's lineage graph is used to rebuild the lost or corrupted partitions. By using the lineage graph, the RDD can be reconstructed efficiently and accurately.
When an RDD (Resilient Distributed Dataset) fails or suffers a system failure, the RDD is rebuilt from its dependencies. This is one of the key features of Apache Spark, the engine that utilizes RDDs to process big data. It provides fault tolerance through the lineage of the RDD. Each RDD keeps track of how it was built from other datasets (its lineage), so if it fails, Spark can automatically use this information to rebuild the RDD from the last known good state.