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One job, three branches, the same expensive steps rerun three separate times.
What you'll have at the end
One cached table reused by all three branches, with a matching drop in job time
You need
A PySpark job where one DataFrame, already built from a filter and a join, feeds three or more separate downstream actions, each one currently triggering its own full run of that filter and join.
Not covered
Truncating a long transformation lineage with a checkpoint, which fixes an iterative job whose chain of steps grows too deep, not three branches reusing one already-short result.
Where exactly is a slow Spark job losing its time?
start there first if you don't yet know which stage is actually slow; this recipe assumes you've already found the repeated filter and join and just need to stop it running three times.
One skewed key is stalling your Spark join
reach for that one when a single task inside a join is stuck, not when the same join is simply repeated for three separate branches.
Checked 25 Aug 2026
Part of the PySpark cookbook