Flatten a Spark DataFrame schema (include struct and array type)

flatten_all_spark_schema.py

import typing as T

import cytoolz.curried as tz
import pyspark
from pyspark.sql.functions import explode



def schema_to_columns(schema: pyspark.sql.types.StructType) -> T.List[T.List[str]]:

    columns = list()
    
    def helper(schm: pyspark.sql.types.StructType, prefix: list = None):
        
        if prefix is None:
            prefix = list()
            
        for item in schm.fields:
            if isinstance(item.dataType, pyspark.sql.types.StructType):
                helper(item.dataType, prefix + [item.name])
            else:
                columns.append(prefix + [item.name])
    
    helper(schema)
    
    return columns

   
  
  
def flatten_array(frame: pyspark.sql.DataFrame) -> (pyspark.sql.DataFrame, BooleanType):
    have_array = False
    aliased_columns = list()
    i=0
    for column, t_column in frame.dtypes:
        if t_column.startswith('array<') and i == 0:
            have_array = True 
            c = explode(frame[column]).alias(column)            
            i = i+ 1
        else:
            c = tz.get_in([column], frame)
        aliased_columns.append(c)
    
     
    return (frame.select(aliased_columns), have_array)
    
    
    
    
    
def flatten_frame(frame: pyspark.sql.DataFrame) -> pyspark.sql.DataFrame:

    aliased_columns = list()
    
    for col_spec in schema_to_columns(frame.schema):
        c = tz.get_in(col_spec, frame)
        if len(col_spec) == 1:
            aliased_columns.append(c)
        else:
            aliased_columns.append(c.alias(':'.join(col_spec)))
    
    return frame.select(aliased_columns)

def flatten_all(frame: pyspark.sql.DataFrame) -> pyspark.sql.DataFrame:
    frame = flatten_frame(frame)
    (frame, have_array) = flatten_array(frame)
    if have_array:
        return flatten_all(frame)
    else:
        return frame