aokolnychyi commented on code in PR #51002:
URL: https://github.com/apache/spark/pull/51002#discussion_r2114467809
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/connector/catalog/CatalogV2Util.scala:
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@@ -597,11 +597,31 @@ private[sql] object CatalogV2Util {
// Note: the back-fill here is a logical concept. The data source can
keep the existing
// data unchanged and let the data reader to return "exist
default" for missing
// columns.
- val existingDefault = Literal(default.getValue.value(),
default.getValue.dataType()).sql
-
f.withExistenceDefaultValue(existingDefault).withCurrentDefaultValue(default.getSql)
+ val existsDefault = extractExistsDefault(default)
+ val (sql, expr) = extractCurrentDefault(default)
+ val newMetadata = new MetadataBuilder()
+ .withMetadata(f.metadata)
+ .putString(EXISTS_DEFAULT_COLUMN_METADATA_KEY, existsDefault)
+ .putExpression(CURRENT_DEFAULT_COLUMN_METADATA_KEY, sql, expr)
+ .build()
+ f.copy(metadata = newMetadata)
}.getOrElse(f)
}
+ private def extractExistsDefault(default: ColumnDefaultValue): String = {
+ Literal(default.getValue.value(), default.getValue.dataType()).sql
+ }
+
+ private def extractCurrentDefault(default: ColumnDefaultValue): (String,
Option[Expression]) = {
+ val expr =
Option(default.getExpression).flatMap(V2ExpressionUtils.toCatalyst)
Review Comment:
Generally yes, but I am not sure we would want to allow them in default
values.
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