java.lang.UnsupportedOperationException：bean类中不能有循环引用

Question

在执行以下Spark命令时：

Dataset<Row> xDF = sparkSession.createDataFrame(xs, X.class);

我遇到以下异常：

Exception in thread "main" java.lang.UnsupportedOperationException: Cannot have circular references in bean class, but got the circular reference of class class java.lang.ClassLoader
    at org.apache.spark.sql.catalyst.JavaTypeInference$.org$apache$spark$sql$catalyst$JavaTypeInference$$inferDataType(JavaTypeInference.scala:123)
    at org.apache.spark.sql.catalyst.JavaTypeInference$$anonfun$1.apply(JavaTypeInference.scala:133)
    at org.apache.spark.sql.catalyst.JavaTypeInference$$anonfun$1.apply(JavaTypeInference.scala:131)
    at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
    at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
    at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
    at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:186)
    at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
    at scala.collection.mutable.ArrayOps$ofRef.map(ArrayOps.scala:186)
    at org.apache.spark.sql.catalyst.JavaTypeInference$.org$apache$spark$sql$catalyst$JavaTypeInference$$inferDataType(JavaTypeInference.scala:131)
    at org.apache.spark.sql.catalyst.JavaTypeInference$$anonfun$1.apply(JavaTypeInference.scala:133)
    at org.apache.spark.sql.catalyst.JavaTypeInference$$anonfun$1.apply(JavaTypeInference.scala:131)
    at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
    at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
    at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
    at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:186)
    at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
    at scala.collection.mutable.ArrayOps$ofRef.map(ArrayOps.scala:186)
    at org.apache.spark.sql.catalyst.JavaTypeInference$.org$apache$spark$sql$catalyst$JavaTypeInference$$inferDataType(JavaTypeInference.scala:131)
    at org.apache.spark.sql.catalyst.JavaTypeInference$$anonfun$1.apply(JavaTypeInference.scala:133)
    at org.apache.spark.sql.catalyst.JavaTypeInference$$anonfun$1.apply(JavaTypeInference.scala:131)
    at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
    at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
    at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
    at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:186)
    at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
    at scala.collection.mutable.ArrayOps$ofRef.map(ArrayOps.scala:186)
    at org.apache.spark.sql.catalyst.JavaTypeInference$.org$apache$spark$sql$catalyst$JavaTypeInference$$inferDataType(JavaTypeInference.scala:131)
    at org.apache.spark.sql.catalyst.JavaTypeInference$$anonfun$1.apply(JavaTypeInference.scala:133)
    at org.apache.spark.sql.catalyst.JavaTypeInference$$anonfun$1.apply(JavaTypeInference.scala:131)
    at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
    at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
    at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
    at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:186)
    at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
    at scala.collection.mutable.ArrayOps$ofRef.map(ArrayOps.scala:186)
    at org.apache.spark.sql.catalyst.JavaTypeInference$.org$apache$spark$sql$catalyst$JavaTypeInference$$inferDataType(JavaTypeInference.scala:131)
    at org.apache.spark.sql.catalyst.JavaTypeInference$$anonfun$1.apply(JavaTypeInference.scala:133)
    at org.apache.spark.sql.catalyst.JavaTypeInference$$anonfun$1.apply(JavaTypeInference.scala:131)
    at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
    at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
    at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
    at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:186)
    at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
    at scala.collection.mutable.ArrayOps$ofRef.map(ArrayOps.scala:186)
    at org.apache.spark.sql.catalyst.JavaTypeInference$.org$apache$spark$sql$catalyst$JavaTypeInference$$inferDataType(JavaTypeInference.scala:131)
    at org.apache.spark.sql.catalyst.JavaTypeInference$$anonfun$1.apply(JavaTypeInference.scala:133)
    at org.apache.spark.sql.catalyst.JavaTypeInference$$anonfun$1.apply(JavaTypeInference.scala:131)
    at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
    at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
    at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
    at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:186)
    at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
    at scala.collection.mutable.ArrayOps$ofRef.map(ArrayOps.scala:186)
    at org.apache.spark.sql.catalyst.JavaTypeInference$.org$apache$spark$sql$catalyst$JavaTypeInference$$inferDataType(JavaTypeInference.scala:131)
    at org.apache.spark.sql.catalyst.JavaTypeInference$.inferDataType(JavaTypeInference.scala:55)
    at org.apache.spark.sql.SparkSession.getSchema(SparkSession.scala:745)
    at org.apache.spark.sql.SparkSession.createDataFrame(SparkSession.scala:384)
    at org.apache.spark.sql.SparkSession.createDataFrame(SparkSession.scala:402)

类X中有Set<>和ZonedDateTime字段。是否由于这些对象而导致出现此问题？ 我还有另一个类，里面只有Double和String字段。对于该对象的JavaRDD，DataFrame的创建不会失败。

因此，对于X类，上述问题来了：

@Builder
@Value
@Wither
The Class X {

   ZonedDateTime dateTime;
   Set<String> set;
   Double d;
   String str;
}

对于Y，它不会出现：

@Builder
@Value
Class Y {
    Double d;
    String str;
}