aleksandar-trajkovic-db commented on code in PR #59634:
URL: https://github.com/apache/spark/pull/59634#discussion_r4324160833
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sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JdbcUtils.scala:
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@@ -304,18 +301,17 @@ object JdbcUtils extends Logging with SQLConfHelper {
/**
* Takes a [[ResultSet]] and returns its Catalyst schema.
*
+ * @param options The JDBC options of the read, which control how column
types are mapped.
* @param alwaysNullable If true, all the columns are nullable.
- * @param isTimestampNTZ If true, all timestamp columns are interpreted as
TIMESTAMP_NTZ.
* @return A [[StructType]] giving the Catalyst schema.
* @throws SQLException if the schema contains an unsupported type.
*/
def getSchema(
conn: Connection,
resultSet: ResultSet,
dialect: JdbcDialect,
- alwaysNullable: Boolean = false,
- isTimestampNTZ: Boolean = false,
- preferTimestampNanos: Boolean = false): StructType = {
+ options: JDBCOptions,
Review Comment:
The JdbcOption I mentioned should affect mapping as well, not just execution
(right now doesn't, I will refactor that, as the current way of reading the
config is proving a hassle outside of OSS). For the mapping of Oracle's Date to
Spark's Timestamp, there exists the option oracle.jdbc.mapDateToTimestamp,
which is default true and doesn't exist as a defined JdbcOptions, but does get
passed through JdbcOptions to the driver.
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