beliefer commented on code in PR #49452:
URL: https://github.com/apache/spark/pull/49452#discussion_r1912016999
##########
connector/docker-integration-tests/src/test/scala/org/apache/spark/sql/jdbc/v2/V2JDBCTest.scala:
##########
@@ -986,4 +986,18 @@ private[v2] trait V2JDBCTest extends SharedSparkSession
with DockerIntegrationFu
test("scan with filter push-down with date time functions") {
testDatetime(s"$catalogAndNamespace.${caseConvert("datetime")}")
}
+
+ test("SPARK-50792 Format binary data as a binary literal in JDBC.") {
+ withTable(s"$catalogName.test_binary_literal") {
+ // Create a table with binary column
+ val binary = "X'123456'"
+ val tableName = "test_binary_literal"
+
+ sql(s"CREATE TABLE $catalogName.$tableName (binary_col BINARY)")
+ sql(s"INSERT INTO $catalogName.$tableName VALUES ($binary)")
+
+ val select = s"SELECT * FROM $catalogName.$tableName WHERE binary_col =
$binary"
+ assert(spark.sql(select).collect().length === 1, s"Binary literal test
failed: $select")
+ }
+ }
Review Comment:
Yes. If we update the basic class `JdbcDialect`, we should test all the
built-in integration tests.
`tablePreparation` used to customize the DDL, I'm afraid we can use Spark
SQL can covers all the built-in integration tests. But you could do your best
effort, let's see the result and make the decision.
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