scala - 重命名 Spark DataFrame 中的嵌套结构列

Question

我正在尝试更改 scala 中 DataFrame 列的名称。我可以轻松更改直接字段的列名称，但在转换数组结构列时遇到困难。

下面是我的 DataFrame 架构。

|-- _VkjLmnVop: string (nullable = true)
|-- _KaTasLop: string (nullable = true)
|-- AbcDef: struct (nullable = true)
 |    |-- UvwXyz: struct (nullable = true)
 |    |    |-- _MnoPqrstUv: string (nullable = true)
 |    |    |-- _ManDevyIxyz: string (nullable = true)

但我需要如下所示的架构

|-- vkj_lmn_vop: string (nullable = true)
|-- ka_tas_lop: string (nullable = true)
|-- abc_def: struct (nullable = true)
 |    |-- uvw_xyz: struct (nullable = true)
 |    |    |-- mno_pqrst_uv: string (nullable = true)
 |    |    |-- man_devy_ixyz: string (nullable = true)

对于非结构列，我将按以下方式更改列名称

def aliasAllColumns(df: DataFrame): DataFrame = {
  df.select(df.columns.map { c =>
    df.col(c)
      .as(
        c.replaceAll("_", "")
          .replaceAll("([A-Z])", "_$1")
          .toLowerCase
          .replaceFirst("_", ""))
  }: _*)
}
aliasAllColumns(file_data_df).show(1)

如何动态更改结构列名称？

Answer 1

您可以创建一个递归方法来遍历 DataFrame 架构以重命名列:

import org.apache.spark.sql.types._

def renameAllCols(schema: StructType, rename: String => String): StructType = {
  def recurRename(schema: StructType): Seq[StructField] = schema.fields.map{
      case StructField(name, dtype: StructType, nullable, meta) =>
        StructField(rename(name), StructType(recurRename(dtype)), nullable, meta)
      case StructField(name, dtype: ArrayType, nullable, meta) if dtype.elementType.isInstanceOf[StructType] =>
        StructField(rename(name), ArrayType(StructType(recurRename(dtype.elementType.asInstanceOf[StructType])), true), nullable, meta)
      case StructField(name, dtype, nullable, meta) =>
        StructField(rename(name), dtype, nullable, meta)
    }
  StructType(recurRename(schema))
}

使用以下示例进行测试:

import org.apache.spark.sql.functions._
import spark.implicits._

val renameFcn = (s: String) =>
  s.replace("_", "").replaceAll("([A-Z])", "_$1").toLowerCase.dropWhile(_ == '_')

case class C(A_Bc: Int, D_Ef: Int)

val df = Seq(
  (10, "a", C(1, 2), Seq(C(11, 12), C(13, 14)), Seq(101, 102)),
  (20, "b", C(3, 4), Seq(C(15, 16)), Seq(103))
).toDF("_VkjLmnVop", "_KaTasLop", "AbcDef", "ArrStruct", "ArrInt")

val newDF = spark.createDataFrame(df.rdd, renameAllCols(df.schema, renameFcn))

newDF.printSchema
// root
//  |-- vkj_lmn_vop: integer (nullable = false)
//  |-- ka_tas_lop: string (nullable = true)
//  |-- abc_def: struct (nullable = true)
//  |    |-- a_bc: integer (nullable = false)
//  |    |-- d_ef: integer (nullable = false)
//  |-- arr_struct: array (nullable = true)
//  |    |-- element: struct (containsNull = true)
//  |    |    |-- a_bc: integer (nullable = false)
//  |    |    |-- d_ef: integer (nullable = false)
//  |-- arr_int: array (nullable = true)
//  |    |-- element: integer (containsNull = false)