Github user gatorsmile commented on a diff in the pull request:
https://github.com/apache/spark/pull/14482#discussion_r73471330
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlParser.scala ---
@@ -933,23 +933,6 @@ class SparkSqlAstBuilder(conf: SQLConf) extends
AstBuilder {
val properties =
Option(ctx.tablePropertyList).map(visitPropertyKeyValues).getOrElse(Map.empty)
val selectQuery = Option(ctx.query).map(plan)
- // Ensuring whether no duplicate name is used in table definition
- val colNames = dataCols.map(_.name)
- if (colNames.length != colNames.distinct.length) {
- val duplicateColumns = colNames.groupBy(identity).collect {
- case (x, ys) if ys.length > 1 => "\"" + x + "\""
- }
- operationNotAllowed(s"Duplicated column names found in table
definition of $name: " +
- duplicateColumns.mkString("[", ",", "]"), ctx)
- }
-
- // For Hive tables, partition columns must not be part of the schema
--- End diff --
wait. I found a conflict here.
For Hive Tables, the following query is not allowed.
```
CREATE TABLE tab1 (key INT, value STRING) PARTITIONED BY (key INT)
```
However, for data source tables, this query is allowed with a minor change.
```
CREATE TABLE tab1 (key INT, value STRING) USING json PARTITIONED BY (key)
```
Thus, they are different regarding whether partitioning columns should or
should not be included in data columns.
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