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Key RDD Concepts
- Fault Tolerance: RDDs automatically recover from node failures.
- Lazy Evaluation: Transformations are computed only when an action is invoked.
- Caching: Use persist() or cache() to keep RDDs in memory.
- Transformations & Actions: Transformations create new RDDs; actions return results to the driver.
- Partitioning: Data is divided across nodes for parallel processing.
- Shared Variables: Broadcast variables and accumulators help manage data and counters.
- Shuffling: Redistribution of data during aggregations or joins.
- SparkContext: Entry point for Spark functionality.
- Data Sources: RDDs can be created from files, collections, and various storage systems.
- Efficient Aggregation: Operations like reduceByKey optimize data processing.