cloud-fan commented on code in PR #41585:
URL: https://github.com/apache/spark/pull/41585#discussion_r1233785056
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connector/connect/client/jvm/src/main/scala/org/apache/spark/sql/Dataset.scala:
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@@ -830,6 +830,66 @@ class Dataset[T] private[sql] (
builder.setJoinType(proto.Join.JoinType.JOIN_TYPE_CROSS)
}
+ /**
+ * Joins this Dataset returning a `Tuple2` for each pair where `condition`
evaluates to
+ * true.
+ *
+ * This is similar to the relation `join` function with one important
difference in the
+ * result schema. Since `joinWith` preserves objects present on either side
of the join, the
+ * result schema is similarly nested into a tuple under the column names
`_1` and `_2`.
+ *
+ * This type of join can be useful both for preserving type-safety with the
original object
+ * types as well as working with relational data where either side of the
join has column
+ * names in common.
+ *
+ * @param other Right side of the join.
+ * @param condition Join expression.
+ * @param joinType Type of join to perform. Default `inner`. Must be one of:
+ * `inner`, `cross`, `outer`, `full`,
`fullouter`,`full_outer`, `left`,
+ * `leftouter`, `left_outer`, `right`, `rightouter`,
`right_outer`.
+ *
+ * @group typedrel
+ * @since 3.5.0
+ */
+ def joinWith[U](other: Dataset[U], condition: Column, joinType: String):
Dataset[(T, U)] = {
Review Comment:
Then can we add that expression to the protocol to do positional column
lookup? I'm a bit worried about making the column resolution more complex with
`UnresolvedAttribute`. We already have special expressions `GetColumnByOrdinal`
and `GetViewColumnByNameAndOrdinal` to find-tune the column lookup logic and we
can add more.
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