Pyspark S3A Access Denied Exception for cross account STS assume role - apache-spark

Question

I setup an AWS Glue job to process S3 files present in another AWS account B. The IAM role in Account A(glue job IAM role) is using STS to assume a role in Account B which provides access to my desired files. Account B's IAM role have Trust relationship to the Glue job role in Account A. I was able to print access key and secret key, so assuming the STS is working well.
I get below error:
An error occurred while calling o83.json. com.amazon.ws.emr.hadoop.fs.shaded.com.amazonaws.services.s3.model.AmazonS3Exception: Access Denied (Service: Amazon S3; Status Code: 403; Error Code: AccessDenied;
what is the right implementation of S3A connector as i get Access Denied Exception.
Here is my code:
from __future__ import print_function
from pyspark import SparkContext
from pyspark import SparkConf
from pyspark import SQLContext
from pyspark.sql import SparkSession
from pyspark.sql import Row
from pyspark.sql.functions import col
from pyspark.sql.types import *
from pyspark.sql import HiveContext
from pyspark.sql.functions import explode
from pyspark.sql.functions import explode_outer
from pyspark.sql.functions import substring_index
from pyspark.sql.functions import input_file_name
from pyspark.sql import functions as f
import sys
import os
import os
import boto3
import sys
import errno
import time
import datetime
from datetime import timedelta, date
from pyspark.sql.functions import split
from pyspark.sql.functions import substring
from boto3.session import Session
spark = SparkSession\
.builder\
.appName("JsonInputFormat")\
.enableHiveSupport()\
.getOrCreate()\
sc = spark.sparkContext
hive_context = HiveContext(sc)
hive_context.setConf("hive.exec.dynamic.partition", "true")
hive_context.setConf("hive.exec.dynamic.partition.mode", "nonstrict")
hive_context.setConf("hive.serialization.extend.nesting.levels","true")
sqlCtx = HiveContext(sc)
client = boto3.client('sts')
response = client.assume_role(RoleArn='ROLE_TO_ASSUME', RoleSessionName='AssumeRoleSession1')
credentials = response['Credentials']
ACCESS_KEY = credentials['AccessKeyId']
SECRET_KEY = credentials['SecretAccessKey']
print('access key is {}'.format(ACCESS_KEY))
print('secret key is {}'.format(SECRET_KEY))
print("Hadoop version: " + sc._gateway.jvm.org.apache.hadoop.util.VersionInfo.getVersion())
session = Session(aws_access_key_id=ACCESS_KEY, aws_secret_access_key=SECRET_KEY)
s3 = session.resource('s3')
spark._jsc.hadoopConfiguration().set("fs.s3a.access.key", ACCESS_KEY)
spark._jsc.hadoopConfiguration().set("fs.s3a.secret.key", SECRET_KEY)
spark._jsc.hadoopConfiguration().set("com.amazonaws.services.s3a.enableV4", "true")
spark._jsc.hadoopConfiguration().set("fs.s3a.endpoint", "s3-us-east-1.amazonaws.com")
spark._jsc.hadoopConfiguration().set("fs.s3a.impl","org.apache.hadoop.fs.s3a.S3AFileSystem")
def flatten_schema(schema):
"""Take schema as returned from schema().jsonValue()
and return list of field names with full path"""
def _flatten(schema, path="", accum=None):
# Extract name of the current element
name = schema.get("name")
# If there is a name extend path
if name is not None:
path = "{0}.{1}".format(path, name) if path else name
print('path is {}'.format(path))
# It is some kind of struct
if isinstance(schema.get("fields"), list):
for field in schema.get("fields"):
_flatten(field, path, accum)
elif isinstance(schema.get("type"), dict):
_flatten(schema.get("type"), path, accum)
# It is an atomic type
else:
accum.append(path)
accum = []
_flatten(schema, "", accum)
return accum
sqlCtx.sql("set spark.sql.caseSensitive=true")
yesterday = date.today() - timedelta(1)
daybefore=yesterday.strftime("%Y-%m-%d")
currentdate=time.strftime("%Y-%m-%d")
key = 'KEY={}'.format(str(daybefore))
bucket = 'BUKCET_NAME'
df_base=spark.read.json('s3a://{}/{}/*/'.format(bucket,key))
base_schema=df_base.schema
datePrefix=str(daybefore)
source='s3a://{}/{}'.format(bucket,key)
df1=spark.read.json(source,schema=base_schema)
schema=df1.schema.jsonValue()
columns_list=flatten_schema(schema)
print('columns list is {}'.format(columns_list))
df2 = df1.select(*(col(x).alias(x.replace('.','_')) for x in columns_list))
print('df2 is {}'.format(df2))
df3=df2.select("*",explode_outer(df2.contents).alias("contents_flat"))
df3=df3.drop("contents")
print('df3 is {}'.format(df3))
schema4=df3.schema.jsonValue()
columns_list4=flatten_schema(schema4)
print('columns list 4 is {}'.format(columns_list4))
df5 = df3.select(*(col(x).alias(x.replace('.','_')) for x in columns_list4))
print('df5 is {}'.format(df5))
schema5=df5.schema.jsonValue()
columns_list5=flatten_schema(schema5)
print('columns list 5 is {}'.format(columns_list5))
df6 = df5.select(*(col(x).alias(x.replace('contents_flat','contents')) for x in columns_list5))
print('df6 is {}'.format(df6))
schema6=df6.schema.jsonValue()
columns_list6=flatten_schema(schema6)
print('column list 6 is {}'.format(columns_list6))
df7 = df6.select(*(col(x) for x in columns_list6)) #above line edited down here
schema7=df7.schema.jsonValue()
print('schema7 is {}'.format(schema7))
columns_list7=flatten_schema(schema7)
print('columns list 7 is {}'.format(columns_list7))
df7 = df7.select(*(col(x).alias(x.replace('.','_')) for x in columns_list7))
df7=df7.select("*",explode_outer(df7.business_address_latLng_warnings).alias("business_address_latLng_warnings_flat"))
df7=df7.drop("business_address_latLng_warnings")
print('df7 is {}'.format(df7))
df8 = df7.withColumn("filename",input_file_name())
split_col = split(df8['filename'], 'short_date=')
df9 = df8.withColumn('shortfilename', split_col.getItem(1))
df_final = df9.withColumn('filedate', substring('shortfilename',1,10)).drop('shortfilename')
print('df_final is {}'.format(df_final))
df_final.write.mode('append').csv('s3://{bucket}/{folder1}/', header='true')
spark.stop()```