Github user davies commented on a diff in the pull request:
https://github.com/apache/spark/pull/8509#discussion_r38775814
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystReadSupport.scala
---
@@ -160,4 +101,168 @@ private[parquet] object CatalystReadSupport {
val SPARK_ROW_REQUESTED_SCHEMA =
"org.apache.spark.sql.parquet.row.requested_schema"
val SPARK_METADATA_KEY = "org.apache.spark.sql.parquet.row.metadata"
+
+ /**
+ * Tailors `parquetSchema` according to `catalystSchema` by removing
column paths don't exist
+ * in `catalystSchema`, and adding those only exist in `catalystSchema`.
+ */
+ def clipParquetSchema(parquetSchema: MessageType, catalystSchema:
StructType): MessageType = {
+ val clippedParquetFields =
clipParquetGroupFields(parquetSchema.asGroupType(), catalystSchema)
+ Types.buildMessage().addFields(clippedParquetFields: _*).named("root")
+ }
+
+ private def clipParquetType(parquetType: Type, catalystType: DataType):
Type = {
+ catalystType match {
+ case t: ArrayType if !isPrimitiveCatalystType(t.elementType) =>
+ // Only clips array types with nested type as element type.
+ clipParquetListType(parquetType.asGroupType(), t.elementType)
+
+ case t: MapType if !isPrimitiveCatalystType(t.valueType) =>
+ // Only clips map types with nested type as value type.
+ clipParquetMapType(parquetType.asGroupType(), t.keyType,
t.valueType)
+
+ case t: StructType =>
+ clipParquetGroup(parquetType.asGroupType(), t)
+
+ case _ =>
+ parquetType
+ }
+ }
+
+ /**
+ * Whether a Catalyst [[DataType]] is primitive. Primitive [[DataType]]
is not equivalent to
+ * [[AtomicType]]. For example, [[CalendarIntervalType]] is primitive,
but it's not an
+ * [[AtomicType]].
+ */
+ private def isPrimitiveCatalystType(dataType: DataType): Boolean = {
+ dataType match {
+ case _: ArrayType | _: MapType | _: StructType => false
--- End diff --
After chatted with @liancheng offline, we should not handle UDT here
(leave it as it's).
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