org.apache.spark.sql.jdbc.OracleDialect.scala Maven / Gradle / Ivy
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* http://www.apache.org/licenses/LICENSE-2.0
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package org.apache.spark.sql.jdbc
import java.sql.Types
import org.apache.spark.sql.types._
private case object OracleDialect extends JdbcDialect {
override def canHandle(url: String): Boolean = url.startsWith("jdbc:oracle")
override def getCatalystType(
sqlType: Int, typeName: String, size: Int, md: MetadataBuilder): Option[DataType] = {
// Handle NUMBER fields that have no precision/scale in special way
// because JDBC ResultSetMetaData converts this to 0 precision and -127 scale
// For more details, please see
// https://github.com/apache/spark/pull/8780#issuecomment-145598968
// and
// https://github.com/apache/spark/pull/8780#issuecomment-144541760
if (sqlType == Types.NUMERIC && size == 0) {
// This is sub-optimal as we have to pick a precision/scale in advance whereas the data
// in Oracle is allowed to have different precision/scale for each value.
Option(DecimalType(DecimalType.MAX_PRECISION, 10))
} else if (sqlType == Types.NUMERIC && md.build().getLong("scale") == -127) {
// Handle FLOAT fields in a special way because JDBC ResultSetMetaData converts
// this to NUMERIC with -127 scale
// Not sure if there is a more robust way to identify the field as a float (or other
// numeric types that do not specify a scale.
Option(DecimalType(DecimalType.MAX_PRECISION, 10))
} else {
None
}
}
override def getJDBCType(dt: DataType): Option[JdbcType] = dt match {
case StringType => Some(JdbcType("VARCHAR2(255)", java.sql.Types.VARCHAR))
case _ => None
}
}