Narrow and Wide Dependencies
Some operations keep each partition’s work local; others need rows from everywhere. That difference is where stage boundaries and expensive recovery come from.
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Spark Execution Plans, Stages, and Shuffles
Row computation waits for an action, while planning may happen earlier. See how plans become stages and tasks, and where shuffle boundaries cost time.
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Spark Partitioning, Shuffles, and Data Skew
Understand partition distributions, salted aggregates and joins, broadcast plans, coalesce, and Parquet file counts while preserving query semantics.
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Apache Spark Architecture and Execution
Understand Spark drivers, executors, partitions, memory limits, caching, and the conditions for recomputation and recovery.