Rename nested struct columns in a Spark DataFrame [duplicate] - scala

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Rename nested field in spark dataframe
(5 answers)
Closed 3 years ago.
I am trying to change the names of a DataFrame columns in scala. I am easily able to change the column names for direct fields but I'm facing difficulty while converting array struct columns.
Below is my DataFrame schema.
|-- _VkjLmnVop: string (nullable = true)
|-- _KaTasLop: string (nullable = true)
|-- AbcDef: struct (nullable = true)
| |-- UvwXyz: struct (nullable = true)
| | |-- _MnoPqrstUv: string (nullable = true)
| | |-- _ManDevyIxyz: string (nullable = true)
But I need the schema like below
|-- 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)
For Non Struct columns I'm changing column names by below
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)
How I can change Struct column names dynamically?

Answer 1

You can create a recursive method to traverse the DataFrame schema for renaming the columns:
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))
}
Testing it with the following example:
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)

Answer 2

as far as I know, it's not possible to rename nested fields directly.
From one side, you could try moving to a flat object.
However, if you need to keep the structure, you can play with spark.sql.functions.struct(*cols).
Creates a new struct column.
Parameters: cols – list of column names (string) or list of Column expressions
You will need to decompose all the schema, generate the aliases that you need and then compose it again using the struct function.
It's not the best solution. But it's something :)
Pd: I'm attaching the PySpark doc since it contains a better explanation than the Scala one.