srielau commented on code in PR #46707:
URL: https://github.com/apache/spark/pull/46707#discussion_r1628771638
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sql/core/src/test/scala/org/apache/spark/sql/connector/DataSourceV2SQLSuite.scala:
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@@ -3488,6 +3488,35 @@ class DataSourceV2SQLSuiteV1Filter
}
}
+ test("SPARK-36680: Supports Dynamic Table Options for Spark SQL") {
+ val t1 = s"${catalogAndNamespace}table"
+ withTable(t1) {
+ sql(s"CREATE TABLE $t1 (id bigint, data string) USING $v2Format")
+ sql(s"INSERT INTO $t1 VALUES (1, 'a'), (2, 'b')")
+
+ var df = sql(s"SELECT * FROM $t1")
+ var collected = df.queryExecution.optimizedPlan.collect {
+ case scan: DataSourceV2ScanRelation =>
+ assert(scan.relation.options.isEmpty)
+ }
+ assert (collected.size == 1)
+ checkAnswer(df, Seq(Row(1, "a"), Row(2, "b")))
+
+ df = sql(s"SELECT * FROM $t1 WITH (`split-size` = 5)")
Review Comment:
Pure TVF tableName(named parameterlist), while elegant on one level may be
rather confusing to users.
There is no more visual cue what is a TVF and what is a table , and of
course we would need a tie breaker for whom to resolve for first. This adds
unwanted complexity.
Adding a builtin Polymorphic TVF uses existing (and standard) language.
I would like to know some examples. Teh PR mentions federation (?).
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