amaliujia commented on code in PR #37256:
URL: https://github.com/apache/spark/pull/37256#discussion_r930307735
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sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala:
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@@ -2919,6 +2919,17 @@ object SQLConf {
.stringConf
.createWithDefault("csv,json,orc,parquet")
+ val ADD_DEFAULT_COLUMN_EXISTING_TABLE_BANNED_PROVIDERS =
+ buildConf("spark.sql.defaultColumn.addColumnExistingTableBannedProviders")
+ .internal()
+ .doc("List of table providers wherein SQL commands are NOT permitted to
assign DEFAULT " +
+ "values to new columns in existing tables, such as when using the
ALTER TABLE ... " +
+ "ADD COLUMNS command in SQL. Comma-separated list, whitespace ignored,
case-insensitive.")
Review Comment:
To me, it's ok to have a mixed known list of data formats that might cover
many usages (or re-use `DEFAULT_COLUMN_ALLOWED_PROVIDERS`) while leave room for
unknown or new but working data format checking.
I honestly don't have a good idea on this. Will also be happy to hear for
different opinions from Spark community side.
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