richardc-db commented on code in PR #46312:
URL: https://github.com/apache/spark/pull/46312#discussion_r1586782740
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sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/ResolveDefaultColumnsUtil.scala:
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@@ -84,9 +84,16 @@ object ResolveDefaultColumns extends QueryErrorsBase
if (SQLConf.get.enableDefaultColumns) {
val newFields: Seq[StructField] = tableSchema.fields.map { field =>
if (field.metadata.contains(CURRENT_DEFAULT_COLUMN_METADATA_KEY)) {
- val analyzed: Expression = analyze(field, statementType)
+ val defaultSql: String = if
(field.dataType.isInstanceOf[VariantType]) {
+ // A variant's SQL/string representation is its JSON string which
cannot be directly
+ // casted to a variant type. Thus, we lazily evaluate the default
expression to avoid
Review Comment:
hmm, I'm not sure if we have a literal presentation for the variant type
(its a struct of two binary fields), which is why we have this problem. Similar
to the year month time interval/calendar interval, for example, we always rely
on `parse_json` (which can accept a json string) to create a literal variant.
The variant's `toString` method essentially calls `toJson` which converts
the encoded variant into a JSON string
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