下面是一段代码。您应该考虑性能和列数是否很大。我已经收集了所有的Map字段，并将它们添加到dataframe中。

val jsonStr="""{"country":"US","ISD":"001","states":[{"state1":"NJ","state2":"NY","state3":"PA"}]}"""
import spark.implicits._

val countryDf = spark.read.json(Seq(jsonStr).toDS)

countryDf.show(false)
val statesDf = countryDf.select($"country", explode($"states").as("states"))

val index = statesDf.schema.fieldIndex("states")
val stateSchema = statesDf.schema(index).dataType.asInstanceOf[StructType]
var columns = mutable.LinkedHashSet[Column]()
stateSchema.fields.foreach(field =>{
  columns.add(lit(field.name))
  columns.add(col( "state." + field.name))
})

val s2 = statesDf
  .withColumn("statesMap", map(columns.toSeq: _*))

val allMapKeys = s2.select(explode($"statesMap")).select($"key").distinct.collect().map(_.get(0).toString)

val s3 = allMapKeys.foldLeft(s2)((a, b) => a.withColumn(b, a("statesMap")(b)))
  .drop("statesMap")
s3.show(false)

当读取嵌套的json并将其转换为数据集时，嵌套的部分将存储为结构类型。因此，您必须考虑在Dataframe中展平结构类型。

val jsonStr="""{"country":"US","ISD":"001","states":[{"state1":"NJ","state2":"NY","state3":"PA"}]}"""
val countryDf = spark.read.json(Seq(jsonStr).toDS)

countryDf.show(false)
+---+-------+--------------+
|ISD|country|states        |
+---+-------+--------------+
|001|US     |[[NJ, NY, PA]]|
+---+-------+--------------+

val countryDfExploded = countryDf.withColumn("states",explode($"states"))
countryDfExploded.show(false)
+---+-------+------------+
|ISD|country|states      |
+---+-------+------------+
|001|US     |[NJ, NY, PA]|
+---+-------+------------+

val countrySelectDf = countryDfExploded.select($"ISD", $"country")
countrySelectDf.show(false)
+---+-------+
|ISD|country|
+---+-------+
|001|US     |
+---+-------+

val statesDf = countryDfExploded.select( $"country",$"states.*")
statesDf.show(false)
+-------+------+------+------+
|country|state1|state2|state3|
+-------+------+------+------+
|US     |NJ    |NY    |PA    |
+-------+------+------+------+