Following [SPARK-26024](https://issues.apache.org/jira/browse/SPARK-26024), I noticed the number of elements in each partition after repartitioning using `df.repartitionByRange` can vary for the same setup:
```scala
// Shuffle numbers from 0 to 1000, and make a DataFrame
val df = Random.shuffle(0.to(1000)).toDF("val")
// Repartition it using 3 partitions
// Sum up number of elements in each partition, and collect it.
// And do it several times
for (i <- 0 to 9) {
var counts = df.repartitionByRange(3, col("val"))
.mapPartitions{part => Iterator(part.size)}
.collect()
println(counts.toList)
}
// -> the number of elements in each partition varies
```
This is expected as for performance reasons this method uses sampling to estimate the ranges (with default size of 100). Hence, the output may not be consistent, since sampling can return different values. But documentation was not mentioning it at all, leading to misunderstanding.
## What changes were proposed in this pull request?
Update the documentation (Spark & PySpark) to mention the impact of `spark.sql.execution.rangeExchange.sampleSizePerPartition` on the resulting partitioned DataFrame.
Closes#23025 from JulienPeloton/SPARK-26024.
Authored-by: Julien <peloton@lal.in2p3.fr>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
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