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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