sunchao commented on code in PR #36427:
URL: https://github.com/apache/spark/pull/36427#discussion_r863072081
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sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetUtils.scala:
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@@ -208,6 +208,9 @@ object ParquetUtils {
case st: StructType =>
sqlConf.parquetVectorizedReaderNestedColumnEnabled &&
st.fields.forall(f => isBatchReadSupported(sqlConf, f.dataType))
+ case udt: UserDefinedType[_] =>
+ sqlConf.parquetVectorizedReaderNestedColumnEnabled &&
Review Comment:
why do we need to check
`sqlConf.parquetVectorizedReaderNestedColumnEnabled`? if `udt.sqlType` is
`AtomicType` then this should return true.
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetSchemaConverter.scala:
##########
@@ -174,9 +174,15 @@ class ParquetToSparkSchemaConverter(
*/
def convertField(
field: ColumnIO,
- sparkReadType: Option[DataType] = None): ParquetColumn = field match {
- case primitiveColumn: PrimitiveColumnIO =>
convertPrimitiveField(primitiveColumn, sparkReadType)
- case groupColumn: GroupColumnIO => convertGroupField(groupColumn,
sparkReadType)
+ sparkReadType: Option[DataType] = None): ParquetColumn = {
+ val targetType = sparkReadType.map {
Review Comment:
I think there is another edge case in `convertInternal` when `fieldReadType`
is `ArrayType` too - maybe we also need to check if the field type is UDT whose
`sqlType` is `ArrayType`.
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