A PySpark job reads a Delta table into a DataFrame that currently has 200 partitions spread across the cluster. Before writing the result, the team wants to end up with 8 partitions to avoid producing hundreds of tiny output files, but without paying for a full shuffle of the data. Which operation should they use?
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Correct answer: coalesce(8), which merges existing partitions into fewer ones without a full shuffle
Reference: https://docs.databricks.com/aws/en/pyspark/reference
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Official Answer (C): coalesce(8), which merges existing partitions into fewer ones without a full shuffle
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