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Your custom Python function works, but it sends every row across the JVM boundary and the stage crawls.
What you'll have at the end
The same logic in built-in Spark functions, with no Python stage left in the plan
You need
A PySpark job that already builds a DataFrame and adds one or more derived columns through a registered Python function, plus a place to run that job, or a representative slice of its data, where you can read its query plan and rerun it a few times.
Not covered
Picking pandas_udf or a Scala function for a piece of logic that genuinely has no built-in equivalent; this recipe only covers the case where every piece can be rebuilt from Spark's own built-in expressions.
Leans on
Where exactly is a slow Spark job losing its time?
start here first when you're not sure the UDF is even the slow part of a bigger job; this recipe assumes the slowness is already narrowed down to one specific function.
Write a pytest suite that catches a broken PySpark transformation
reach for this once the native version ships, to lock it in with a test so a future edit can't quietly reintroduce the row-by-row version.
Checked 25 Aug 2026
Part of the PySpark cookbook