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How is fault tolerance achieved in Apache Spark?
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Answer / Isha Vishnoi
Apache Spark achieves fault tolerance through resilient distributed datasets (RDDs). RDDs are immutable distributed collections of data partitioned across nodes in a cluster. When an action is triggered on an RDD, the system checks the status of each node to ensure that all tasks have completed successfully. If any task fails, Spark will automatically re-execute the failed task on another available node, ensuring that the final result remains consistent.
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