以下是此问题的通用解决方案，它可以根据递归遍历中应用的任意函数在任何级别更新任意数量的嵌套值：
def mutate(df: DataFrame, fn: Column => Column): DataFrame = {
// Get a projection with fields mutated by `fn` and select it
// out of the original frame with the schema reassigned to the original
// frame (explained later)
df.sqlContext.createDataFrame(df.select(traverse(df.schema, fn):_*).rdd, df.schema)
}
def traverse(schema: StructType, fn: Column => Column, path: String = ""): Array[Column] = {
schema.fields.map(f => {
f.dataType match {
case s: StructType => struct(traverse(s, fn, path + f.name + "."): _*)
case _ => fn(col(path + f.name))
}
})
}
这实际上等同于通常的“将整个结构重新定义为投影”解决方案，但它使用原始结构自动重新嵌套字段并保留可空性/元数据（当您手动重新定义结构时会丢失）。令人讨厌的是，在创建投影（afact）时无法保留这些属性，因此上面的代码手动重新定义了架构。
一个示例应用程序：
case class Organ(name: String, count: Int)
case class Disease(id: Int, name: String, organ: Organ)
case class Drug(id: Int, name: String, alt: Array[String])
val df = Seq(
(1, Drug(1, "drug1", Array("x", "y")), Disease(1, "disease1", Organ("heart", 2))),
(2, Drug(2, "drug2", Array("a")), Disease(2, "disease2", Organ("eye", 3)))
).toDF("id", "drug", "disease")
df.show(false)
+---+------------------+-------------------------+
|id |drug |disease |
+---+------------------+-------------------------+
|1 |[1, drug1, [x, y]]|[1, disease1, [heart, 2]]|
|2 |[2, drug2, [a]] |[2, disease2, [eye, 3]] |
+---+------------------+-------------------------+
// Update the integer field ("count") at the lowest level:
val df2 = mutate(df, c => if (c.toString == "disease.organ.count") c - 1 else c)
df2.show(false)
+---+------------------+-------------------------+
|id |drug |disease |
+---+------------------+-------------------------+
|1 |[1, drug1, [x, y]]|[1, disease1, [heart, 1]]|
|2 |[2, drug2, [a]] |[2, disease2, [eye, 2]] |
+---+------------------+-------------------------+
// This will NOT necessarily be equal unless the metadata and nullability
// of all fields is preserved (as the code above does)
assertResult(df.schema.toString)(df2.schema.toString)
这样做的一个限制是它不能添加新字段，只能更新现有字段（尽管地图可以更改为 flatMap 和返回 Array[Column] 的函数，如果您不关心保留可空性/元数据）。
此外，这里有一个更通用的 Dataset[T] 版本：
case class Record(id: Int, drug: Drug, disease: Disease)
def mutateDS[T](df: Dataset[T], fn: Column => Column)(implicit enc: Encoder[T]): Dataset[T] = {
df.sqlContext.createDataFrame(df.select(traverse(df.schema, fn):_*).rdd, enc.schema).as[T]
}
// To call as typed dataset:
val fn: Column => Column = c => if (c.toString == "disease.organ.count") c - 1 else c
mutateDS(df.as[Record], fn).show(false)
// To call as untyped dataset:
implicit val encoder: ExpressionEncoder[Row] = RowEncoder(df.schema) // This is necessary regardless of sparkSession.implicits._ imports
mutateDS(df, fn).show(false)