Cost Tracking with CUR test

import boto3 import gzip import pandas as pd from io import StringIO from datetime import datetime, timedelta from botocore.exceptions import ParamValidationError #BUCKET_NAME = 'dagknowscostreport' #BASE_PATH = 'costreport/dagknowscostreport/' # Is the path from bucket root directory to the 'dated folders' which contain the CUR Reports #FILENAME = 'dagknowscostreport-00001.csv.gz' #last_n_days = 100 # Retrieve AWS credentials from the vault creds = _get_creds(cred_label)['creds'] access_key = creds['username'] secret_key = creds['password'] s3_client = boto3.client('s3',aws_access_key_id=access_key,aws_secret_access_key=secret_key) def list_s3_keys(bucket, prefix): s3 = boto3.client('s3',aws_access_key_id=access_key,aws_secret_access_key=secret_key) keys = [] kwargs = {'Bucket': bucket, 'Prefix': prefix} while True: response = s3.list_objects_v2(**kwargs) for obj in response.get('Contents', []): keys.append(obj['Key']) try: kwargs['ContinuationToken'] = response['NextContinuationToken'] except KeyError: break return keys def fetch_data_from_s3(file_key): try: s3 = boto3.client('s3',aws_access_key_id=access_key,aws_secret_access_key=secret_key) response = s3.get_object(Bucket=BUCKET_NAME, Key=file_key) gz_content = response['Body'].read() csv_content = gzip.decompress(gz_content).decode('utf-8') return pd.read_csv(StringIO(csv_content), low_memory=False) except Exception as e: print(f"Error fetching data from S3 for key {file_key}: {e}") return None # Function to get the end of the previous month def get_end_of_last_month(date): return (date.replace(day=1) - timedelta(days=1)).replace(hour=0, minute=0, second=0, microsecond=0) def check_column_existence(df, column_name): if column_name not in df.columns: print(f"Warning: Column '{column_name}' not found in the DataFrame!") return False print(f"Column '{column_name}' exists in the DataFrame.") return True def list_folders(prefix): paginator = s3_client.get_paginator('list_objects_v2') folders = [] for page in paginator.paginate(Bucket=BUCKET_NAME, Prefix=prefix, Delimiter='/'): folders.extend([content['Prefix'] for content in page.get('CommonPrefixes', [])]) return folders def list_csv_gz_files(folder): response = s3_client.list_objects_v2(Bucket=BUCKET_NAME, Prefix=folder) files = [{'key': obj['Key'], 'last_modified': obj['LastModified']} for obj in response.get('Contents', []) if obj['Key'].endswith('.csv.gz')] return files ''' def print_last_file_info(files): if not files: print("No CSV GZ files found in the folder.") return # Sort files by last modified time last_file = sorted(files, key=lambda x: x['last_modified'], reverse=True)[0] print(f"Last file: {last_file['key']}, Last modified: {last_file['last_modified']}") ''' def process_data(bucket_name, base_path, last_n_days): try: # Setup for fetching data end_date = datetime.utcnow() - timedelta(days=1) start_date = end_date - timedelta(days=last_n_days) # Modified to use the list_folders function for getting month range folders month_ranges = list_folders(base_path) all_keys = [] for month_range in month_ranges: # Logic to fetch the last .csv.gz file from the last folder of the month keys_for_month = list_csv_gz_files(month_range) if keys_for_month: # Sort files by last modified time and get the last file last_file = sorted(keys_for_month, key=lambda x: x['last_modified'], reverse=True)[0] all_keys.append(last_file['key']) print(f"Last file for {month_range}: {last_file['key']}, Last modified: {last_file['last_modified']}") else: print(f"No CSV GZ files found in the folder {month_range}.") # Fetch and process data from identified keys dfs = [fetch_data_from_s3(key) for key in all_keys] df = pd.concat(dfs, ignore_index=True) if dfs else pd.DataFrame() # Further processing on the DataFrame df['lineItem/UsageStartDate'] = pd.to_datetime(df['lineItem/UsageStartDate']) df['day'] = df['lineItem/UsageStartDate'].dt.date # Exclude the latest date from the DataFrame latest_date = df['day'].max() df = df[df['day'] < latest_date] # Check required columns required_columns = [ 'lineItem/ProductCode', 'lineItem/UnblendedCost', 'lineItem/BlendedCost', 'lineItem/UsageStartDate', 'lineItem/UsageAccountId', 'lineItem/NormalizedUsageAmount', 'product/productFamily', 'product/instanceType' ] # Check if all required columns exist if not all([check_column_existence(df, col) for col in required_columns]): print("One or more required columns are missing.") return pd.DataFrame(), False # Returning an empty DataFrame and False return df, len(dfs) > 0 except Exception as e: print(f"ERROR: {e}") return pd.DataFrame(), False folders = list_folders(BASE_PATH) for folder in folders: print(f"Processing folder: {folder}") files = list_csv_gz_files(folder) #print_last_file_info(files) df, data_fetched = process_data(BUCKET_NAME, BASE_PATH, last_n_days) if data_fetched: print("Proceeding with further operations") else: print("No data fetched. Exiting operation.") context.proceed = False print("Script Execution End")