yyanyy commented on code in PR #58666:
URL: https://github.com/apache/spark/pull/58666#discussion_r4032100537
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/execution/datasources/v2/DataSourceV2Relation.scala:
##########
@@ -602,11 +604,23 @@ object DataSourceV2Relation {
val catalystColStat = ColumnStat(distinct, min, max, nullCount,
avgLen, maxLen, histogram)
- output.foreach(attribute => {
- if (attribute.name.equals(key.describe())) {
- colStats = colStats :+ (attribute -> catalystColStat)
+ // Catalyst statistics are keyed by top-level Attribute, so only
single-part references
+ // can be matched to an output column.
+ val fieldNames = key.fieldNames
+ if (fieldNames.length == 1) {
+ val fieldName = fieldNames.head
+ val matches = output.filter(attribute => resolver(attribute.name,
fieldName))
Review Comment:
I think `output.filter(resolver(...))` might not be using the same candidate
set as Spark’s top-level analyzer resolution. For example, the resolver equates
`s` and `ſ` (U+017F), while `AttributeSeq`’s `Locale.ROOT` buckets keep them
separate, so statistics can be attached to a column Spark would not resolve.
Could we reuse `AttributeSeq`’s candidate semantics, recover the original
attribute by `exprId`, and add an `s`/`ſ` regression test?
--
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
To unsubscribe, e-mail: [email protected]
For queries about this service, please contact Infrastructure at:
[email protected]
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
