AWS Glue hanging up & consuming lot of time in ETL job - amazon-web-services

Question

I am using AWS Glue where I want to dump records from Oracle table (which has 80 million rows) to Redshift. However, almost 2 hrs go,it remains in hanging state & still nothing gets written to Amazon S3 & eventually I have to stop the job.
My code:
import sys
import boto3
import json
from awsglue.transforms import *
from awsglue.utils import getResolvedOptions
from pyspark.context import SparkContext
from awsglue.context import GlueContext
from awsglue.dynamicframe import DynamicFrame
from awsglue.job import Job
args = getResolvedOptions(sys.argv, ['JOB_NAME'])
sc = SparkContext()
glueContext = GlueContext(sc)
spark = glueContext.spark_session
job = Job(glueContext)
job.init(args['JOB_NAME'], args)
db_username = [removed]
db_password = [removed]
db_url = [removed]
table_name = [removed]
jdbc_driver_name = "oracle.jdbc.OracleDriver"
s3_output = [removed]
df = glueContext.read.format("jdbc").option("url", db_url).option("user", db_username).option("password", db_password).option("dbtable", table_name).option("driver", jdbc_driver_name).load()
df.printSchema()
datasource0 = DynamicFrame.fromDF(df, glueContext, "datasource0")
datasource0.schema()
datasource0.show()
applymapping1 = ApplyMapping.apply(frame = datasource0, mappings = [("correlation_id", "decimal", "correlation_id", "bigint"), ("machine_pin","varchar","machine_pin","varchar"),("messageguid","varchar","messageguid","varchar"), ("originating_domain_object_id", "decimal", "originating_domain_object_id", "bigint"), ("originating_message_type_id", "bigint", "originating_message_type_id", "bigint"), ("source_messageguid","varchar","source_messageguid","varchar"), ("timestamp_of_request","timestamp","timestamp_of_request","timestamp"),("token","varchar","token","varchar"),("id","decimal","id","bigint"),("file_attachment","decimal","file_attachment","bigint")], transformation_ctx = "applymapping1")
resolvechoice2 = ResolveChoice.apply(frame = applymapping1,choice = "make_cols", transformation_ctx = "resolvechoice2")
dropnullfields3 = DropNullFields.apply(frame = resolvechoice2, transformation_ctx = "dropnullfields3")
datasink4 = glueContext.write_dynamic_frame.from_jdbc_conf(frame = dropnullfields3, catalog_connection = "us01-isg-analytics", connection_options = {"dbtable": "analytics_team_data.message_details", "database": "jk_test"}, redshift_tmp_dir = "s3://aws-glue-scripts-823837687343-us-east-1/glue_op/", transformation_ctx = "datasink4")
When I use Apache Spark,it takes less than 1 hr to dump the data to Redshift.What modifications need to be required for performance optimization so that Glue dumps the data in a speedy manner?