Sampling 10 rows per groupb in PySpark

You need to use a window partition by and let the random number do the shuffle for you.

from pyspark.sql import Window

(
  df
  .withColumn(
    "random_number", F.rand()
  )
  .withColumn(
    "row_id",
    F.row_number().over(
      Window().partitionBy("level0", "level1", "level2").orderBy(F.col("random_number").desc())
    )
  ) 
  .where(F.col("row_id") < 10)
  .orderBy("level0", "level1", "level2")
  .show()
)