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A join between your huge fact table and a tiny lookup table is shuffling every row of the big side across the network for nothing.
What you'll have at the end
The same join with no shuffle stage in the query plan, confirmed with explain()
You need
A PySpark job with an existing join between a large fact-style DataFrame and a much smaller lookup DataFrame, and the ability to see that join's own query plan for yourself.
Not covered
A join that's slow for a different reason, such as a skewed join key between two large tables rather than one small lookup table that should have been broadcast.
One skewed key is stalling your Spark join
reach for this instead when both sides of the join are genuinely large and one task is stuck, not when one side is small enough to skip the shuffle entirely.
Where exactly is a slow Spark job losing its time?
start here first if you don't yet know which stage or join is actually slow; this recipe assumes you've already pinned the slowdown on one specific fact-to-dimension join.
Checked 25 Aug 2026
Part of the PySpark cookbook