Which of the following code blocks reduces a DataFrame from 12 to 6 partitions and performs a full shuffle?

DataFrame.repartition(6)

Correct. repartition() always triggers a full shuffle (different from coalesce()).

DataFrame.repartition(12)

No, this would just leave the DataFrame with 12 partitions and not 6.

DataFrame.coalesce(6)

coalesce does not perform a full shuffle of the data. Whenever you see "full shuffle", you know that

you are not dealing with coalesce(). While coalesce() can perform a partial shuffle when required,

it will try to minimize shuffle operations, so the amount of data that is sent between executors.

Here, 12 partitions can easily be repartitioned to be 6 partitions simply by stitching every two

partitions into one.

DataFrame.coalesce(6, shuffle=True) and DataFrame.coalesce(6).shuffle()

These statements are not valid Spark API syntax.

More info: Spark Repartition & Coalesce - Explained and Repartition vs Coalesce in Apache Spark -

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