简单的解决方案是将其中一个Dataframe进行类型转换以匹配另一个Dataframe，如下所示-

val df1 = spark.sql("select 'foo' name, named_struct('int', 1, 'long', null) count")
    df1.show(false)
    df1.printSchema()
    /**
      * +----+-----+
      * |name|count|
      * +----+-----+
      * |foo |[1,] |
      * +----+-----+
      *
      * root
      * |-- name: string (nullable = false)
      * |-- count: struct (nullable = false)
      * |    |-- int: integer (nullable = false)
      * |    |-- long: null (nullable = true)
      */
    val df2 = spark.sql("select 'bar' name, map('2', 3) count")
    df2.show(false)
    df2.printSchema()
    /**
      * +----+--------+
      * |name|count   |
      * +----+--------+
      * |bar |[2 -> 3]|
      * +----+--------+
      *
      * root
      * |-- name: string (nullable = false)
      * |-- count: map (nullable = false)
      * |    |-- key: string
      * |    |-- value: integer (valueContainsNull = false)
      */
    df1.withColumn("count",
      map($"count.int".cast("string"), $"count.long".cast("integer")))
      .union(df2)
      .show(false)
    /**
      * +----+--------+
      * |name|count   |
      * +----+--------+
      * |foo |[1 ->]  |
      * |bar |[2 -> 3]|
      * +----+--------+
      */