beliefer commented on code in PR #44398:
URL: https://github.com/apache/spark/pull/44398#discussion_r1429868263
##########
sql/core/src/main/scala/org/apache/spark/sql/jdbc/JdbcDialects.scala:
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@@ -105,6 +105,25 @@ abstract class JdbcDialect extends Serializable with
Logging {
*/
def getJDBCType(dt: DataType): Option[JdbcType] = None
+ /**
+ * Converts an instance of `java.math.BigDecimal` to a `Decimal` value.
+ * @param d represents a specific `java.math.BigDecimal`.
+ * @param precision the precision for Decimal based on JDBC metadata.
+ * @param scale the scale for Decimal based on JDBC metadata.
+ * @return the `Decimal` value to convert to
+ */
+ @Since("4.0.0")
+ def convertBigDecimalToDecimal(d: BigDecimal, precision: Int, scale: Int):
Decimal =
+ // When connecting with Oracle DB through JDBC, the precision and scale of
BigDecimal
+ // object returned by ResultSet.getBigDecimal is not correctly matched to
the table
+ // schema reported by ResultSetMetaData.getPrecision and
ResultSetMetaData.getScale.
+ // If inserting values like 19999 into a column with NUMBER(12, 2) type,
you get through
+ // a BigDecimal object with scale as 0. But the dataframe schema has
correct type as
+ // DecimalType(12, 2). Thus, after saving the dataframe into parquet file
and then
+ // retrieve it, you will get wrong result 199.99.
+ // So it is needed to set precision and scale for Decimal based on JDBC
metadata.
+ Decimal(d, precision, scale)
Review Comment:
I don't know the background. So I let it as the default implementation.
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