Github user gatorsmile commented on a diff in the pull request: https://github.com/apache/spark/pull/18266#discussion_r134797984 --- Diff: sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JdbcUtils.scala --- @@ -768,6 +769,25 @@ object JdbcUtils extends Logging { } /** + * Parses the user specified customDataFrameColumnTypes option value string, and returns + */ + def parseUserSpecifiedColumnTypes( + schema: StructType, + columnTypes: String, + nameEquality: Resolver): StructType = { + val userSchema = CatalystSqlParser.parseTableSchema(columnTypes) + // This is resolved by names, only check the column names. + userSchema.fieldNames.foreach { col => + schema.find(f => nameEquality(f.name, col)).getOrElse { + throw new AnalysisException( + s"${JDBCOptions.JDBC_CUSTOM_DATAFRAME_COLUMN_TYPES} option column $col not found in " + + s"schema ${schema.catalogString}") + } + } + userSchema --- End diff -- What is your expected behaviors when users-specified schema does not include all the columns of the underlying table schema?
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