93 lines
2.7 KiB
Python
93 lines
2.7 KiB
Python
import csv
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import psycopg2
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import boto3
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import os
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from tqdm import tqdm
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from datetime import datetime, timedelta
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from pytz import timezone
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from dotenv import load_dotenv
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load_dotenv("../.env_prod")
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OUTPUT_NAME = "Jan"
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START_DATE = datetime(2024, 1, 1, tzinfo=timezone('Asia/Ho_Chi_Minh'))
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END_DATE = datetime(2024, 2, 1, tzinfo=timezone('Asia/Ho_Chi_Minh'))
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# Database connection details
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db_host = os.environ.get('DB_HOST', "")
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db_name = os.environ.get('DB_SCHEMA', "")
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db_user = os.environ.get('DB_USER', "")
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db_password = os.environ.get('DB_PASSWORD', "")
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# S3 bucket details
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s3_bucket_name = os.environ.get('S3_BUCKET_NAME', "")
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s3_folder_prefix = 'sbt_invoice'
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# S3 access credentials
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access_key = os.environ.get('S3_ACCESS_KEY', "")
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secret_key = os.environ.get('S3_SECRET_KEY', "")
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# Request IDs for filtering
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# Connect to the PostgreSQL database
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conn = psycopg2.connect(
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host=db_host,
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database=db_name,
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user=db_user,
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password=db_password
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)
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# Create a cursor
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cursor = conn.cursor()
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# Execute the SELECT query with the filter
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query = "SELECT * FROM fwd_api_subscriptionrequest WHERE created_at >= %s AND created_at <= %s"
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cursor.execute(query, (START_DATE, END_DATE))
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# Fetch the filtered data
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data = cursor.fetchall()
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# Define the CSV file path
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csv_file_path = f'{OUTPUT_NAME}.csv'
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# Write the data to the CSV file
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with open(csv_file_path, 'w', newline='') as csv_file:
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writer = csv.writer(csv_file)
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writer.writerow([desc[0] for desc in cursor.description]) # Write column headers
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writer.writerows(data) # Write the filtered data rows
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# Close the cursor and database connection
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cursor.close()
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conn.close()
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# # Download folders from S3
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# s3_client = boto3.client(
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# 's3',
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# aws_access_key_id=access_key,
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# aws_secret_access_key=secret_key
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# )
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# request_ids = []
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# for rq in data:
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# rq_id = rq[3]
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# request_ids.append(rq_id)
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# for request_id in tqdm(request_ids):
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# folder_key = f"{s3_folder_prefix}/{request_id}/" # Assuming folder structure like: s3_bucket_name/s3_folder_prefix/request_id/
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# local_folder_path = f"{OUTPUT_NAME}/{request_id}/" # Path to the local folder to save the downloaded files
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# os.makedirs(OUTPUT_NAME, exist_ok=True)
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# os.makedirs(local_folder_path, exist_ok=True)
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# # List objects in the S3 folder
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# response = s3_client.list_objects_v2(Bucket=s3_bucket_name, Prefix=folder_key)
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# objects = response.get('Contents', [])
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# for s3_object in objects:
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# object_key = s3_object['Key']
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# local_file_path = local_folder_path + object_key.split('/')[-1] # Extracting the file name from the object key
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# # Download the S3 object to the local file
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# s3_client.download_file(s3_bucket_name, object_key, local_file_path) |