ahshahid commented on code in PR #48252:
URL: https://github.com/apache/spark/pull/48252#discussion_r1844722744
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sql/api/src/main/scala/org/apache/spark/sql/catalyst/JavaTypeInference.scala:
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@@ -148,34 +163,180 @@ object JavaTypeInference {
// TODO: we should only collect properties that have getter and setter.
However, some tests
// pass in scala case class as java bean class which doesn't have
getter and setter.
val properties = getJavaBeanReadableProperties(c)
- // add type variables from inheritance hierarchy of the class
- val classTV = JavaTypeUtils.getTypeArguments(c,
classOf[Object]).asScala.toMap ++
- typeVariables
- // Note that the fields are ordered by name.
- val fields = properties.map { property =>
- val readMethod = property.getReadMethod
- val encoder = encoderFor(readMethod.getGenericReturnType, seenTypeSet
+ c, classTV)
- // The existence of `javax.annotation.Nonnull`, means this field is
not nullable.
- val hasNonNull = readMethod.isAnnotationPresent(classOf[Nonnull])
- EncoderField(
- property.getName,
- encoder,
- encoder.nullable && !hasNonNull,
- Metadata.empty,
- Option(readMethod.getName),
- Option(property.getWriteMethod).map(_.getName))
+
+ // if the properties is empty and this is not a top level enclosing
class, then we
+ // should not consider class as bean, as otherwise it will be treated as
empty schema
+ // and loose the data on deser.
+ if (properties.isEmpty && seenTypeSet.nonEmpty) {
+ findBestEncoder(Seq(c), seenTypeSet, typeVariables, None,
serializableEncodersOnly = true)
Review Comment:
will revert on this.. by going through the code.
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