richardc-db commented on code in PR #46312:
URL: https://github.com/apache/spark/pull/46312#discussion_r1586798259
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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:
Yep, `parse_json('1')` works (even before this PR, but unintentionally).
This is because the current code inserts a cast to variant, which is ok in this
case because `CAST(1 as VARIANT)` succeeds
In the more complex case such as a default of `parse_json('{'k': 'v'}')`,
the analyzer actually fails because it tries to analyze the sql text `{'k':
'v'}` (note the lack of outer quotations). An alternative to the taken approach
in this PR is to create a string literal from `{'k': 'v'}` and wrap it with
`parse_json` (effectively coercing it to the correct variant type). This feels
inefficient, however, because we have variant->string->variant conversion
rather than lazily evaluating it once
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