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April 23, 2024 21:21
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| from datetime import datetime | |
| from airflow import DAG | |
| from airflow.decorators import dag, task | |
| from sqlalchemy import create_engine, select | |
| import pandas as pd | |
| import psycopg2 | |
| import os | |
| import json | |
| default_args = { | |
| 'owner': 'airflow', | |
| 'start_date': datetime(2024, 3, 29), | |
| 'retries': 3, | |
| 'schedule_interval': '@hourly', | |
| 'tags': ['test-tag'], | |
| 'catchup':"False" | |
| } | |
| sqlalchemy_db = os.getenv("SQLALCHEMY_DB") | |
| sqlalchemy_db_jdbc = os.getenv("SQLALCHEMY_DB_JDBC") | |
| sqlalchemy_username = os.getenv("SQLALCHEMY_USERNAME") | |
| sqlalchemy_password = os.getenv("SQLALCHEMY_PASSWORD") | |
| sqlalchemy_host_address = os.getenv("SQLALCHEMY_HOST_ADDRESS") | |
| sqlalchemy_host_port = os.getenv("SQLALCHEMY_HOST_PORT") | |
| # this is railway not data analytics | |
| sqlalchemy_host_database = os.getenv("SQLALCHEMY_HOST_DATABASE") | |
| @dag("test_scheduled_dag_20minutes", | |
| description="This dag should be scheduled for every 20 minutes", | |
| default_args=default_args, | |
| schedule_interval='*/20 * * * *' | |
| ) | |
| def basic_dag(): | |
| @task() | |
| def extract(): | |
| print("Extracting data") | |
| # this should be a secret, consider making all that a .env | |
| engine = create_engine(f"{sqlalchemy_db}+{sqlalchemy_db_jdbc}://{sqlalchemy_username}:{sqlalchemy_password}@{sqlalchemy_host_address}:{sqlalchemy_host_port}/{sqlalchemy_host_database}") | |
| try: | |
| stmt = """ | |
| SELECT * | |
| FROM prun_data."Company Orders" | |
| """ | |
| dataframe = pd.read_sql( | |
| sql=stmt, | |
| con=engine | |
| ) | |
| jsoned_dataframe = dataframe.to_json() | |
| return jsoned_dataframe | |
| except Exception as e: | |
| print("Task failed due to: ", e) | |
| @task() | |
| def transform(jsonified_data: str): | |
| print("Transforming data") | |
| order_cost = 0 | |
| if jsonified_data is not None: | |
| data_dict = json.loads(jsonified_data) | |
| for individual_data in data_dict.values(): | |
| # Assuming each individual_data is a dictionary with a 'cost' key | |
| if 'cost' in individual_data: | |
| order_cost += individual_data['cost'] | |
| print(f"Cost found: {individual_data['cost']}") | |
| else: | |
| print(f"No 'cost' key found in {individual_data}") | |
| else: | |
| print("No data to transform") | |
| return {"total_order_value": order_cost} | |
| @task() | |
| def load(json_value: dict): | |
| print("Loading data") | |
| try: | |
| # Assuming json_value is a dictionary with a single key-value pair | |
| # where the key is the column name and the value is the data | |
| df = pd.DataFrame.from_dict(json_value, orient='index', columns=['total_order_value']) | |
| display(df) | |
| except Exception as e: | |
| print("Task failed due to: ", e) | |
| print(json_value) | |
| return df | |
| @task() | |
| def upload_to_db(df: pd.DataFrame): | |
| print("Uploading to postgresql in schema prun_data and table order_summary") | |
| try: | |
| df = df | |
| engine = create_engine(f"{sqlalchemy_db}+{sqlalchemy_db_jdbc}://{sqlalchemy_username}:{sqlalchemy_password}@{sqlalchemy_host_address}:{sqlalchemy_host_port}/{sqlalchemy_host_database}") | |
| df.to_sql('order_summary', con=engine, if_exists='replace', schema='prun_data') | |
| except Exception as e: | |
| print("Task failed due to: ", e) | |
| print(df) | |
| user_total_order_value = extract() | |
| order_summary = transform(user_total_order_value) | |
| df = load(order_summary) | |
| upload_to_db(df) | |
| basic_dag() |
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