fastest way to insert 800 million data into postgres database

I have a script that read some files one by one, clean it and insert it ino postgres database.

i tried to use python multi-processing using Pools but actually i found that CPU usage still reach sometimes to 30% and return most of time to 6%. so it’s really so slow.

Any suggestion for speeding it up ?

thank you

import os
import multiprocessing


path="data/"
arr = os.listdir(path)

connection = psycopg2.connect(
    user="postgres", password="blabla", host="127.0.0.1", port="5432", database="test"
)
cursor = connection.cursor()

postgres_insert_query = """ INSERT INTO mobile (data1, data2) VALUES (%s,%s) 
                        ON CONFLICT (data1) 
                        DO 
                        UPDATE SET data2 = EXCLUDED.data1 ;"""


def insert_data(key,record_to_insert, item):
    print(key)
    try:
        cursor.executemany(postgres_insert_query, record_to_insert)

        connection.commit()
        count = cursor.rowcount
        print(count, "Record inserted successfully into mobile table", item)

    except (Exception, psycopg2.Error) as error:
        print("Failed to insert record into mobile table", error)


i = 1

def process_data(item):
    print(item)

    global i
    records = []

    i+=1
    with open(path+item,'r') as file:
        for line in file:
            line = dataCleansing(line)
            records.append((line+'-'+str(i),'data2-'+str(i)+line))
  
            if len(records)==50000:
                insert_data(i,records,item)
                records=[]

        insert_data(i,records,item)
        records=[]
            
if __name__ == '__main__':
    a_pool = multiprocessing.Pool(6)

    result = a_pool.map(process_data, arr)