sadikovi commented on code in PR #36745:
URL: https://github.com/apache/spark/pull/36745#discussion_r887433430
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/catalog/SessionCatalog.scala:
##########
@@ -427,6 +428,7 @@ class SessionCatalog(
tableDefinition.copy(identifier = tableIdentifier)
}
+
ResolveDefaultColumns.checkDataSourceSupportsDefaultColumns(tableDefinition)
Review Comment:
Do we also need to handle DataFrame API? For example, I create a table using
this:
```
val schema = ...
val df = spark.createDataFrame(schema, ...)
df.write.saveAsTable("...")
```
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/ResolveDefaultColumnsUtil.scala:
##########
@@ -231,4 +232,18 @@ object ResolveDefaultColumns {
}
}
}
+
+ def checkDataSourceSupportsDefaultColumns(table: CatalogTable): Unit = {
+ if (table.schema.fields.map(_.metadata).exists { m =>
+ m.contains(CURRENT_DEFAULT_COLUMN_METADATA_KEY) ||
+ m.contains(EXISTS_DEFAULT_COLUMN_METADATA_KEY)
+ }) {
+ table.provider.getOrElse("").toLowerCase() match {
+ case "csv" | "json" | "parquet" | "orc" =>
Review Comment:
Would you like to make a configuration option? Or does it depend on the data
source implementation. For example, can we have DEFAULT in JDBC?
Do we also need to consider DataSource V2? Will this feature work there?
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