[SPARK-26297][SQL] improve the doc of Distribution/Partitioning

## What changes were proposed in this pull request?

Some documents of `Distribution/Partitioning` are stale and misleading, this PR fixes them:
1. `Distribution` never have intra-partition requirement
2. `OrderedDistribution` does not require tuples that share the same value being colocated in the same partition.
3. `RangePartitioning` can provide a weaker guarantee for a prefix of its `ordering` expressions.

## How was this patch tested?

comment-only PR.

Closes #23249 from cloud-fan/doc.

Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>

Author: cloud-fan

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala

@@ -22,13 +22,11 @@ import org.apache.spark.sql.types.{DataType, IntegerType}
 
 /**
  * Specifies how tuples that share common expressions will be distributed when a query is executed
- * in parallel on many machines.  Distribution can be used to refer to two distinct physical
- * properties:
- *  - Inter-node partitioning of data: In this case the distribution describes how tuples are
- *    partitioned across physical machines in a cluster.  Knowing this property allows some
- *    operators (e.g., Aggregate) to perform partition local operations instead of global ones.
- *  - Intra-partition ordering of data: In this case the distribution describes guarantees made
- *    about how tuples are distributed within a single partition.
+ * in parallel on many machines.
+ *
+ * Distribution here refers to inter-node partitioning of data. That is, it describes how tuples
+ * are partitioned across physical machines in a cluster. Knowing this property allows some
+ * operators (e.g., Aggregate) to perform partition local operations instead of global ones.
  */
 sealed trait Distribution {
   /**
@@ -70,9 +68,7 @@ case object AllTuples extends Distribution {
 
 /**
  * Represents data where tuples that share the same values for the `clustering`
- * [[Expression Expressions]] will be co-located. Based on the context, this
- * can mean such tuples are either co-located in the same partition or they will be contiguous
- * within a single partition.
+ * [[Expression Expressions]] will be co-located in the same partition.
  */
 case class ClusteredDistribution(
     clustering: Seq[Expression],
@@ -118,10 +114,12 @@ case class HashClusteredDistribution(
 
 /**
  * Represents data where tuples have been ordered according to the `ordering`
- * [[Expression Expressions]].  This is a strictly stronger guarantee than
- * [[ClusteredDistribution]] as an ordering will ensure that tuples that share the
- * same value for the ordering expressions are contiguous and will never be split across
- * partitions.
+ * [[Expression Expressions]]. Its requirement is defined as the following:
+ *   - Given any 2 adjacent partitions, all the rows of the second partition must be larger than or
+ *     equal to any row in the first partition, according to the `ordering` expressions.
+ *
+ * In other words, this distribution requires the rows to be ordered across partitions, but not
+ * necessarily within a partition.
  */
 case class OrderedDistribution(ordering: Seq[SortOrder]) extends Distribution {
   require(
@@ -241,12 +239,12 @@ case class HashPartitioning(expressions: Seq[Expression], numPartitions: Int)
 
 /**
  * Represents a partitioning where rows are split across partitions based on some total ordering of
- * the expressions specified in `ordering`.  When data is partitioned in this manner the following
- * two conditions are guaranteed to hold:
- *  - All row where the expressions in `ordering` evaluate to the same values will be in the same
- *    partition.
- *  - Each partition will have a `min` and `max` row, relative to the given ordering.  All rows
- *    that are in between `min` and `max` in this `ordering` will reside in this partition.
+ * the expressions specified in `ordering`.  When data is partitioned in this manner, it guarantees:
+ * Given any 2 adjacent partitions, all the rows of the second partition must be larger than any row
+ * in the first partition, according to the `ordering` expressions.
+ *
+ * This is a strictly stronger guarantee than what `OrderedDistribution(ordering)` requires, as
+ * there is no overlap between partitions.
  *
  * This class extends expression primarily so that transformations over expression will descend
  * into its child.
@@ -262,6 +260,22 @@ case class RangePartitioning(ordering: Seq[SortOrder], numPartitions: Int)
     super.satisfies0(required) || {
       required match {
         case OrderedDistribution(requiredOrdering) =>
+          // If `ordering` is a prefix of `requiredOrdering`:
+          //   Let's say `ordering` is [a, b] and `requiredOrdering` is [a, b, c]. According to the
+          //   RangePartitioning definition, any [a, b] in a previous partition must be smaller
+          //   than any [a, b] in the following partition. This also means any [a, b, c] in a
+          //   previous partition must be smaller than any [a, b, c] in the following partition.
+          //   Thus `RangePartitioning(a, b)` satisfies `OrderedDistribution(a, b, c)`.
+          //
+          // If `requiredOrdering` is a prefix of `ordering`:
+          //   Let's say `ordering` is [a, b, c] and `requiredOrdering` is [a, b]. According to the
+          //   RangePartitioning definition, any [a, b, c] in a previous partition must be smaller
+          //   than any [a, b, c] in the following partition. If there is a [a1, b1] from a previous
+          //   partition which is larger than a [a2, b2] from the following partition, then there
+          //   must be a [a1, b1 c1] larger than [a2, b2, c2], which violates RangePartitioning
+          //   definition. So it's guaranteed that, any [a, b] in a previous partition must not be
+          //   greater(i.e. smaller or equal to) than any [a, b] in the following partition. Thus
+          //   `RangePartitioning(a, b, c)` satisfies `OrderedDistribution(a, b)`.
           val minSize = Seq(requiredOrdering.size, ordering.size).min
           requiredOrdering.take(minSize) == ordering.take(minSize)
         case ClusteredDistribution(requiredClustering, _) =>