ahshahid commented on code in PR #48252:
URL: https://github.com/apache/spark/pull/48252#discussion_r1847229554
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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:
Here the encoder possible can only be of type Java or Kryo Serializable type.
As if it had been of type UDT, the code will not reach this point. In that
case it would have get handled in the case matching UDT type.
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