Github user sureshthalamati commented on a diff in the pull request: https://github.com/apache/spark/pull/16209#discussion_r107562605 --- Diff: sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JdbcUtils.scala --- @@ -680,19 +681,63 @@ object JdbcUtils extends Logging { /** * Compute the schema string for this RDD. */ - def schemaString(schema: StructType, url: String): String = { + def schemaString( + schema: StructType, + url: String, + createTableColumnTypes: Option[String] = None): String = { val sb = new StringBuilder() val dialect = JdbcDialects.get(url) + val userSpecifiedColTypesMap = createTableColumnTypes + .map(parseUserSpecifiedCreateTableColumnTypes(schema, _)) + .getOrElse(Map.empty[String, String]) schema.fields foreach { field => val name = dialect.quoteIdentifier(field.name) - val typ: String = getJdbcType(field.dataType, dialect).databaseTypeDefinition + val typ: String = userSpecifiedColTypesMap.get(field.name) + .getOrElse(getJdbcType(field.dataType, dialect).databaseTypeDefinition) val nullable = if (field.nullable) "" else "NOT NULL" sb.append(s", $name $typ $nullable") } if (sb.length < 2) "" else sb.substring(2) } /** + * Parses the user specified createTableColumnTypes option value string specified in the same + * format as create table ddl column types, and returns Map of field name and the data type to + * use in-place of the default data type. + */ + private def parseUserSpecifiedCreateTableColumnTypes(schema: StructType, + createTableColumnTypes: String): Map[String, String] = { + val userSchema = CatalystSqlParser.parseTableSchema(createTableColumnTypes) + val userColNames = userSchema.fieldNames + // check duplicate columns in the user specified column types. + if (userColNames.distinct.length != userColNames.length) { + val duplicates = userColNames.groupBy(identity).collect { + case (x, ys) if ys.length > 1 => x + }.mkString(", ") + throw new AnalysisException( + s"Found duplicate column(s) in createTableColumnTypes option value: $duplicates") + } + // check user specified column names exists in the data frame schema. + val commonNames = userColNames.intersect(schema.fieldNames) --- End diff -- Thank you for the review. Good question., updated the PR with case-sensitive handling. Now column names from user specified schema are matched with data frame schema based on the SQLConf.CASE_SENSITIVE flag.
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