sunchao commented on a change in pull request #35657:
URL: https://github.com/apache/spark/pull/35657#discussion_r827157771
##########
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala
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@@ -305,6 +306,63 @@ case class HashPartitioning(expressions: Seq[Expression],
numPartitions: Int)
newChildren: IndexedSeq[Expression]): HashPartitioning = copy(expressions
= newChildren)
}
+/**
+ * Represents a partitioning where rows are split across partitions based on
transforms defined
+ * by `expressions`. `partitionValuesOpt`, if defined, should contain value of
partition key(s) in
+ * ascending order, after evaluated by the transforms in `expressions`, for
each input partition.
+ * In addition, its length must be the same as the number of input partitions
(and thus is a 1-1
+ * mapping), and each row in `partitionValuesOpt` must be unique.
+ *
+ * For example, if `expressions` is `[years(ts_col)]`, then a valid value of
`partitionValuesOpt` is
+ * `[0, 1, 2]`, which represents 3 input partitions with distinct partition
values. All rows
+ * in each partition have the same value for column `ts_col` (which is of
timestamp type), after
+ * being applied by the `years` transform.
Review comment:
Yes, I also think `HashPartitioning` is not accurate here. What do you
think of `DataSourcePartitioning` (different from the
`DataSourceHashPartitioning` in catalyst), or `DataStoragePartitioning`?
`KeyGroupedPartitioning` sounds fine too.
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