S — Shuffle

Rules that flag patterns that cause expensive data movement across the cluster — wide transformations, unoptimized joins, and over-partitioning.

Rule	Title
S001	Missing .coalesce() after .union() / .unionByName()
S002	Join without a broadcast or merge hint
S003	.groupBy() followed by .distinct() or .dropDuplicates()
S004	Too many .distinct() operations in one file
S005	.repartition() with fewer partitions than the Spark default
S006	.repartition() with more partitions than the Spark default
S007	Avoid repartition(1) or coalesce(1)
S008	Overusing explode() / explode_outer()
S009	Prefer mapPartitions() over map() for row-level transforms
S010	Avoid crossJoin() — produces a Cartesian product
S011	Join without join conditions causes a nested-loop scan
S012	Avoid inner join followed by filter — prefer leftSemi join
S013	Avoid reduceByKey() — use DataFrame groupBy().agg() instead
S014	.distinct() or .dropDuplicates() called before .groupBy() — redundant shuffle