viirya commented on a change in pull request #31993:
URL: https://github.com/apache/spark/pull/31993#discussion_r614490313
##########
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SchemaPruning.scala
##########
@@ -32,11 +33,10 @@ object SchemaPruning {
// in the resulting schema may differ from their ordering in the logical
relation's
// original schema
val mergedSchema = requestedRootFields
- .map { case root: RootField => StructType(Array(root.field)) }
+ .map { root: RootField => StructType(Array(root.field)) }
.reduceLeft(_ merge _)
- val dataSchemaFieldNames = dataSchema.fieldNames.toSet
val mergedDataSchema =
- StructType(mergedSchema.filter(f =>
dataSchemaFieldNames.contains(f.name)))
+ StructType(dataSchema.map(s =>
mergedSchema.find(_.name.equals(s.name)).getOrElse(s)))
Review comment:
It is because `requestedColumnIds` will check if given data schema has
less fields than physical schema in ORC file.
Under nested column pruning, Spark will let data source use pruned schema as
data schema to read files. E.g., Spark prune `_col1`, for the above example.
But the ORC file has three top-level fields `_col0`, `_col1`, and `_col2`, so
the check in `requestedColumnIds` will fail on the case.
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