Github user cloud-fan commented on a diff in the pull request:
https://github.com/apache/spark/pull/22938#discussion_r231762733
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/jsonExpressions.scala
---
@@ -550,15 +550,33 @@ case class JsonToStructs(
s"Input schema ${nullableSchema.catalogString} must be a struct, an
array or a map.")
}
- // This converts parsed rows to the desired output by the given schema.
@transient
- lazy val converter = nullableSchema match {
- case _: StructType =>
- (rows: Iterator[InternalRow]) => if (rows.hasNext) rows.next() else
null
- case _: ArrayType =>
- (rows: Iterator[InternalRow]) => if (rows.hasNext)
rows.next().getArray(0) else null
- case _: MapType =>
- (rows: Iterator[InternalRow]) => if (rows.hasNext)
rows.next().getMap(0) else null
+ private lazy val castRow = nullableSchema match {
+ case _: StructType => (row: InternalRow) => row
+ case _: ArrayType => (row: InternalRow) =>
+ if (row.isNullAt(0)) {
+ new GenericArrayData(Array())
--- End diff --
I think this is the place `from_json` is different from json data source. A
data source must produce data as rows, while the `from_json` can return array
or map.
I think the previous behavior also makes sense. For array/map, we don't
have the corrupted column, and returning null is reasonable. Actually I prefer
null over empty array/map, but we need more discussion about this behavior.
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