pang-wu commented on code in PR #41498:
URL: https://github.com/apache/spark/pull/41498#discussion_r1222252120
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connector/protobuf/src/main/scala/org/apache/spark/sql/protobuf/ProtobufDeserializer.scala:
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@@ -247,12 +247,86 @@ private[sql] class ProtobufDeserializer(
updater.setLong(ordinal, micros +
TimeUnit.NANOSECONDS.toMicros(nanoSeconds))
case (MESSAGE, StringType)
- if protoType.getMessageType.getFullName == "google.protobuf.Any" =>
+ if protoType.getMessageType.getFullName == "google.protobuf.Any" =>
(updater, ordinal, value) =>
// Convert 'Any' protobuf message to JSON string.
val jsonStr = jsonPrinter.print(value.asInstanceOf[DynamicMessage])
updater.set(ordinal, UTF8String.fromString(jsonStr))
+ // Handle well known wrapper types. We unpack the value field instead of
keeping
Review Comment:
> Better for Spark to preserve the same information, right?
I would say no, the issue here is after converting these data type to struct
spark erase the original type info, i.e. all user see is a struct, but this
struct could be a custom struct from the user rather than wrapper types. In
that case, we provide no additional information for a user to decide whether
special action needs to take, it adds the burden of understanding the original
schema to data consumer -- remember the data consumer may not have the
original schema at hand.
The idea behind wrapper type are these are structures defined to have
special meaning, so deserializers can leverage these type information to get
presence information and generate data that is more intuitive, thus the
implementaion in libraries like jsonpb.
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