Spark Performance and Cost Factors
More executors help while tasks await slots. Skew, layout, memory, and serialization can set limits that extra machines do not remove.
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Massively Parallel Processing (MPP)
MPP splits one query across many nodes working on different parts of the data at once — fast until the data has to move between them or divides unevenly.
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Data Skew
Diagnose uneven Spark work using records, bytes, and runtime metrics, and distinguish a hot key from other causes of slow tasks.
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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.