Question
A team uses Ray to launch millions of tiny Python tasks, each doing a few milliseconds of feature cleanup. Scheduler overhead dominates runtime, while Spark can express the same cleanup as column operations. What should the team evaluate?
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A team uses Ray to launch millions of tiny Python tasks, each doing a few milliseconds of feature cleanup. Scheduler overhead dominates runtime, while Spark can express the same cleanup as column operations. What should the team evaluate?
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