Pyspark Flatten json

from pyspark.sql.types import *
from pyspark.sql.functions import *

#Flatten array of structs and structs
def flatten(df):
   # compute Complex Fields (Lists and Structs) in Schema   
   complex_fields = dict([(field.name, field.dataType)
                             for field in df.schema.fields
                             if type(field.dataType) == ArrayType or  type(field.dataType) == StructType])
   while len(complex_fields)!=0:
      col_name=list(complex_fields.keys())[0]
      print ("Processing :"+col_name+" Type : "+str(type(complex_fields[col_name])))
    
      # if StructType then convert all sub element to columns.
      # i.e. flatten structs
      if (type(complex_fields[col_name]) == StructType):
         expanded = [col(col_name+'.'+k).alias(col_name+'_'+k) for k in [ n.name for n in  complex_fields[col_name]]]
         df=df.select("*", *expanded).drop(col_name)
    
      # if ArrayType then add the Array Elements as Rows using the explode function
      # i.e. explode Arrays
      elif (type(complex_fields[col_name]) == ArrayType):    
         df=df.withColumn(col_name,explode_outer(col_name))
    
      # recompute remaining Complex Fields in Schema       
      complex_fields = dict([(field.name, field.dataType)
                             for field in df.schema.fields
                             if type(field.dataType) == ArrayType or  type(field.dataType) == StructType])
   return df

df=flatten(df)
df.printSchema()

help me
When running the code above, everything worked, but I need to treat the data that comes in timestamp format to date. Can someone help me?

Exit:
_v | 0
action | To create
data_updatedAt | 2023-01-26T15:10:...
date$date | 1674745838876 <<<<<<<

I need:
_v | 0
action | To create
data_updatedAt | 2023-01-26T15:10:...
date$date | 2023-01-26T15:10:... <<<<<<<

Thanks a lot for your work, it works great.

Hello, I tried to use mapType in Spark Streaming but it's not working due to an issue in the code.

Below is the one giving issue while doing in Spark Streaming :

        keys = list(map(lambda row: row[0], keys_df.collect()))

Please let me know the best option to resolve it in Spark Structure Steaming.

This function flatten(), fails when there is nested array inside array, It failed to flatten these, "user_mentions": [
{
"screen_name": "AshJone15461246",
"name": "Ash Jones",
"id": 1589564629369462800,
"id_str": "1589564629369462784",
"indices": [
0,
16
]
},
{
"screen_name": "BariAWilliams",
"name": "Bärí A. Williams, Esq.",
"id": 4639656854,
"id_str": "4639656854",
"indices": [
17,
31
]
},
{
"screen_name": "bjorn_hefnoll",
"name": "Björn",
"id": 1374096417954881500,
"id_str": "1374096417954881548",
"indices": [
32,
46
]
},
{
"screen_name": "SpencerAlthouse",
"name": "Spencer Althouse",
"id": 38307346,
"id_str": "38307346",
"indices": [
47,
63
]
}
].