j1wonpark commented on code in PR #56688:
URL: https://github.com/apache/spark/pull/56688#discussion_r3487104867
##########
sql/connect/client/jdbc/src/main/scala/org/apache/spark/sql/connect/client/jdbc/SparkConnectDatabaseMetaData.scala:
##########
@@ -816,4 +882,77 @@ object SparkConnectDatabaseMetaData {
)
private[jdbc] val TABLE_TYPES = Seq("TABLE", "VIEW")
+
+ // One row of the java.sql.DatabaseMetaData.getTypeInfo result.
+ private type TypeInfoRow =
+ (
+ String, // TYPE_NAME
+ Int, // DATA_TYPE
+ Int, // PRECISION
+ String, // LITERAL_PREFIX
+ String, // LITERAL_SUFFIX
+ String, // CREATE_PARAMS
+ Short, // NULLABLE
+ Boolean, // CASE_SENSITIVE
+ Short, // SEARCHABLE
+ Boolean, // UNSIGNED_ATTRIBUTE
+ Boolean, // FIXED_PREC_SCALE
+ Boolean, // AUTO_INCREMENT
+ String, // LOCAL_TYPE_NAME
+ Short, // MINIMUM_SCALE
+ Short, // MAXIMUM_SCALE
+ Int, // SQL_DATA_TYPE
+ Int, // SQL_DATETIME_SUB
+ Int // NUM_PREC_RADIX
+ )
+
+ // Fills the columns that are constant across all Spark atomic types: every
type is
+ // nullable and searchable, and none are unsigned, fixed-prec-scale, or
auto-increment.
+ // `literalQuote` is used as both the literal prefix and suffix.
+ private def typeRow(
+ typeName: String,
+ dataType: Int,
+ precision: Int,
+ literalQuote: String,
+ createParams: String,
+ caseSensitive: Boolean,
+ minScale: Short,
+ maxScale: Short,
+ numPrecRadix: Int): TypeInfoRow =
+ (
+ typeName,
+ dataType,
+ precision,
+ literalQuote,
+ literalQuote,
+ createParams,
+ typeNullable.toShort,
+ caseSensitive,
+ typeSearchable.toShort,
+ false,
+ false,
+ false,
+ null,
+ minScale,
+ maxScale,
+ 0,
+ 0,
+ numPrecRadix)
+
+ // Static JDBC type metadata for the Spark SQL atomic types, mirroring the
+ // JdbcTypeUtils type-code/precision mapping. Only STRING is case-sensitive.
+ // TIME and TIMESTAMP_NTZ are omitted (new and duplicate JDBC type codes).
+ private[jdbc] val TYPE_INFO: Seq[TypeInfoRow] = Seq(
+ typeRow("BOOLEAN", Types.BOOLEAN, 1, null, null, false, 0, 0, 0),
+ typeRow("TINYINT", Types.TINYINT, 3, null, null, false, 0, 0, 10),
+ typeRow("SMALLINT", Types.SMALLINT, 5, null, null, false, 0, 0, 10),
+ typeRow("INT", Types.INTEGER, 10, null, null, false, 0, 0, 10),
+ typeRow("BIGINT", Types.BIGINT, 19, null, null, false, 0, 0, 10),
+ typeRow("FLOAT", Types.FLOAT, 7, null, null, false, 0, 0, 10),
+ typeRow("DOUBLE", Types.DOUBLE, 15, null, null, false, 0, 0, 10),
+ typeRow("DECIMAL", Types.DECIMAL, 38, null, "precision,scale", false, 0,
38, 10),
+ typeRow("STRING", Types.VARCHAR, Int.MaxValue, "'", null, true, 0, 0, 0),
+ typeRow("BINARY", Types.VARBINARY, Int.MaxValue, "'", null, false, 0, 0,
0),
Review Comment:
Fixed in 1303ab8: `BINARY` now reports `LITERAL_PREFIX` `X'` and
`LITERAL_SUFFIX` `'` to match the hex literal syntax.
##########
sql/connect/client/jdbc/src/test/scala/org/apache/spark/sql/connect/client/jdbc/SparkConnectDatabaseMetaDataSuite.scala:
##########
@@ -808,4 +808,100 @@ class SparkConnectDatabaseMetaDataSuite extends
ConnectFunSuite with RemoteSpark
}
}
}
+
+ test("SparkConnectDatabaseMetaData getPrimaryKeys") {
+ withConnection { conn =>
+ val metadata = conn.getMetaData
+ // Spark has no primary keys, so an empty result set with the JDBC
schema is returned.
+ Using.resource(metadata.getPrimaryKeys(null, null, null)) { rs =>
+ val rsmd = rs.getMetaData
+ assert((1 to rsmd.getColumnCount).map(rsmd.getColumnName) === Seq(
+ "TABLE_CAT", "TABLE_SCHEM", "TABLE_NAME", "COLUMN_NAME", "KEY_SEQ",
"PK_NAME"))
+ assert(!rs.next())
+ }
+ }
+ }
+
+ test("SparkConnectDatabaseMetaData getImportedKeys and getExportedKeys") {
+ withConnection { conn =>
+ val metadata = conn.getMetaData
+ val foreignKeySchema = Seq(
+ "PKTABLE_CAT", "PKTABLE_SCHEM", "PKTABLE_NAME", "PKCOLUMN_NAME",
+ "FKTABLE_CAT", "FKTABLE_SCHEM", "FKTABLE_NAME", "FKCOLUMN_NAME",
+ "KEY_SEQ", "UPDATE_RULE", "DELETE_RULE", "FK_NAME", "PK_NAME",
"DEFERRABILITY")
+ // Spark has no foreign keys, so both return an empty result set with
the JDBC schema.
+ Seq(
+ () => metadata.getImportedKeys(null, null, null),
+ () => metadata.getExportedKeys(null, null, null)).foreach {
getForeignKeys =>
+ Using.resource(getForeignKeys()) { rs =>
+ val rsmd = rs.getMetaData
+ assert((1 to rsmd.getColumnCount).map(rsmd.getColumnName) ===
foreignKeySchema)
+ assert(!rs.next())
+ }
+ }
+ }
+ }
+
+ test("SparkConnectDatabaseMetaData getTypeInfo") {
+ withConnection { conn =>
+ val metadata = conn.getMetaData
+ Using.resource(metadata.getTypeInfo) { rs =>
+ val rsmd = rs.getMetaData
+ assert((1 to rsmd.getColumnCount).map(rsmd.getColumnName) === Seq(
Review Comment:
Done in eef0e8a: the getTypeInfo test now asserts PRECISION, CREATE_PARAMS,
(MINIMUM_SCALE, MAXIMUM_SCALE), the literal quotes, and NUM_PREC_RADIX for
every type, so the per-type fidelity is locked in.
##########
sql/connect/client/jdbc/src/main/scala/org/apache/spark/sql/connect/client/jdbc/SparkConnectDatabaseMetaData.scala:
##########
@@ -816,4 +882,77 @@ object SparkConnectDatabaseMetaData {
)
private[jdbc] val TABLE_TYPES = Seq("TABLE", "VIEW")
+
+ // One row of the java.sql.DatabaseMetaData.getTypeInfo result.
+ private type TypeInfoRow =
Review Comment:
Done in 684e0ec: `TypeInfoRow` and `typeRow` are now `private[jdbc]`.
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