amaliujia commented on code in PR #38166:
URL: https://github.com/apache/spark/pull/38166#discussion_r991855071
##########
connector/connect/src/test/scala/org/apache/spark/sql/connect/planner/SparkConnectProtoSuite.scala:
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@@ -81,6 +81,31 @@ class SparkConnectProtoSuite extends PlanTest with
SparkConnectPlanTest {
}
}
+ test("Test union, except, intersect") {
+ for (isAll <- Seq(true, false)) {
+ val connectPlan = {
+ import org.apache.spark.sql.connect.dsl.plans._
+ transform(connectTestRelation.except(connectTestRelation, isAll))
+ }
+ val sparkPlan = sparkTestRelation.except(sparkTestRelation, isAll)
+ comparePlans(connectPlan.analyze, sparkPlan.analyze, false)
+
+ val connectPlan2 = {
+ import org.apache.spark.sql.connect.dsl.plans._
+ transform(connectTestRelation.intersect(connectTestRelation, isAll))
+ }
+ val sparkPlan2 = sparkTestRelation.intersect(sparkTestRelation, isAll)
+ comparePlans(connectPlan2.analyze, sparkPlan2.analyze, false)
+ }
+
+ val connectPlan3 = {
+ import org.apache.spark.sql.connect.dsl.plans._
+ transform(connectTestRelation.union(connectTestRelation))
Review Comment:
Another reason actually to follow Catalyst: our clients, by best effort,
should follow existing data frame API to achieve "changing imports and rest of
code will work".
So it's expected that users will use `union().distinct()` to construct
`Union(isAll=false)`. If we ask clients to smartly identify these two API can
be coalesced together, that will be too much for clients. Instead clients just
convert each API to a plan node and connect proto follow Catalyst as
`Distinct(Union)` then it will work.
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