Github user gatorsmile commented on a diff in the pull request: https://github.com/apache/spark/pull/15664#discussion_r94094625 --- Diff: sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JdbcUtils.scala --- @@ -211,6 +211,55 @@ object JdbcUtils extends Logging { } /** + * Returns the schema if the table already exists in the JDBC database. + */ + def getSchema(conn: Connection, url: String, table: String): Option[StructType] = { + val dialect = JdbcDialects.get(url) + + try { + val statement = conn.prepareStatement(dialect.getSchemaQuery(table)) + try { + Some(getSchema(statement.executeQuery(), dialect)) + } catch { + case _: SQLException => None + } finally { + statement.close() + } + } catch { + case _: SQLException => None + } + } + + /** + * Returns a schema using rddSchema's column sequence and tableSchema's column names. + * + * When appending data into some case-sensitive DBMSs like PostgreSQL/Oracle, we need to respect + * the existing case-sensitive column names instead of RDD column names for user convenience. + * See SPARK-18123 for more details. + */ + def normalizeSchema( + rddSchema: StructType, + tableSchema: StructType, + caseSensitive: Boolean): StructType = { --- End diff -- After rethinking about it, the result of this function `normalizeSchema` is a mixed of two schemas (source/rdd schema + target/table schema). We should not mix them together.
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