feat(aggregation): create python aggregation and DAG

dags/aggregation.py

@@ -0,0 +1,51 @@
+from airflow import DAG
+from airflow.operators.python import PythonOperator
+from datetime import datetime, timedelta
+
+from data_utils.metabase_aggregation.aggregation import perform_and_insert_aggregated_data, aggregate_by_date_task, aggregate_by_participation_type_task, aggregate_by_budgets_task
+
+# Default arguments for the DAG tasks
+default_args = {
+    'owner': 'airflow',
+    'depends_on_past': False,
+    'email_on_failure': False,
+    'retries': 0,
+}
+
+# Define the DAG
+with DAG(
+        'daily_aggregated_client_data_dag',
+        default_args=default_args,
+        description='A DAG to aggregate data from multiple clients and insert it into a database',
+        schedule='@daily',
+        start_date=datetime(2024, 10, 21),  # DAG start date
+        catchup=False,
+) as dag:
+
+    # Task to run the aggregation and insertion function
+    aggregate_and_insert_task = PythonOperator(
+        task_id='perform_and_insert_aggregated_data_task',
+        python_callable=perform_and_insert_aggregated_data,
+        op_kwargs={},
+    )
+    aggregate_by_date_task = PythonOperator(
+        task_id='aggregate_by_date_task',
+        python_callable=aggregate_by_date_task,
+        op_kwargs={},
+    )
+    aggregate_by_participation_type_task = PythonOperator(
+        task_id='aggregate_by_participation_type_task',
+        python_callable=aggregate_by_participation_type_task,
+        op_kwargs={},
+    )
+    aggregate_by_budgets_task = PythonOperator(
+        task_id='aggregate_by_budgets_task',
+        python_callable=aggregate_by_budgets_task,
+        op_kwargs={},
+    )
+
+    aggregate_and_insert_task
+    aggregate_by_date_task
+    aggregate_by_participation_type_task
+    aggregate_by_budgets_task
+

dags/client_list.py

@@ -1,19 +1,26 @@
 clients = [
-    "angers",
-    "casa",
-    "cdc",
-    "cea",
-    "cese",
-    "colombes",
-    "grand_nancy",
-    "lyon",
-    "marseille",
-    "montpellier",
-    "meyzieu",
-    "real_deal",
-    "sytral",
-    "thionville",
-    "toulouse",
-    "tours",
-    "valbonne"
+   "angers",
+   "arcueil",
+   "bagneux",
+   "casa",
+   "cdc",
+   "cea", 
+   "cese",
+   "chambery",
+   "colombes",
+   "cultuur_connect",
+   "grand_nancy",
+   "lyon",
+   "malakoff",
+   "marseille",
+   "montpellier",
+   "meyzieu",
+   "nanterre", 
+   "real_deal",
+   "sytral",
+   "thionville",
+   "toulouse",
+   "tours",
+   "valbonne",
+   "villeurbanne"
 ]

dags/data_utils/metabase_aggregation/aggregation.py

@@ -0,0 +1,164 @@
+from sqlalchemy import text
+from client_list import clients
+from ..postgres.get_postgres_connection import get_postgres_connection
+import pandas as pd
+
+db_cluster_preprod = 'db_cluster_name_data'
+db_cluster_prod = 'db_cluster_name_data_prod'
+
+
+def execute_queries(connection, queries):
+    """
+    Executes a list of queries on the specified connection.
+    """
+    results = {}
+    for query_name, query in queries.items():
+        try:
+            result = connection.execute(text(query))
+            count = result.fetchone()[0]
+            results[query_name] = count
+        except Exception as e:
+            print(f"Failed to execute query {query_name}: {e}")
+            results[query_name] = None  # Return None if the query fails
+    return results
+
+
+def aggregate_data_for_clients(db_cluster, client_databases, queries):
+    """
+    Performs data aggregation for each client and returns a DataFrame.
+    """
+    data = []
+
+    for client_db in client_databases:
+        connection = get_postgres_connection(db_cluster, client_db)
+        client_results = execute_queries(connection, queries)
+        client_results['client'] = client_db  # Add client name to results
+        data.append(client_results)
+        connection.close()  # Close the connection after query execution
+
+    # Convert results to DataFrame
+    df = pd.DataFrame(data)
+    return df
+
+def aggregate_data_for_clients_for_unique_query(db_cluster, client_databases, query):
+    """
+    Performs data aggregation for each client and returns a DataFrame.
+    """
+    data = []
+
+    for client_db in client_databases:
+        connection = get_postgres_connection(db_cluster, client_db)
+        result = connection.execute(text(query))
+        df = pd.DataFrame(result)
+        df['client'] = client_db  # Add client name to results
+        data.append(df)
+        connection.close()  # Close the connection after query execution
+
+    # Convert results to DataFrame
+    df = pd.concat(data)
+    return df
+
+def clean_data_in_postgres(connection, table):
+    """Deletes rows in the table."""
+    try:
+        delete_query = text(
+            f"DELETE FROM {table};"
+        )
+
+        connection.execute(delete_query)
+        print(f"Cleaned data in table {table}")
+    except Exception as e:
+        print(f"Failed to clean data in table {table}: {e}")
+
+def insert_data_to_aggregated_db(db_cluster, dataframe, target_database, target_table):
+    """
+    Inserts the aggregated data into a target PostgreSQL table.
+    """
+    try:
+        connection = get_postgres_connection(db_cluster, target_database)
+        clean_data_in_postgres(connection, target_table)
+        dataframe.to_sql(target_table, con=connection, if_exists='replace', index=False)
+        print(f"Data successfully inserted into {target_table} in {target_database}.")
+        connection.close()
+    except Exception as e:
+        print(f"Failed to insert data into {target_table}: {e}")
+        raise
+
+
+def perform_and_insert_aggregated_data():
+    client_databases = clients
+
+    # List of aggregation queries
+    queries = {
+        'user_count': "SELECT COUNT(*) AS user_count FROM prod.users;",
+        'participating_user_count': "SELECT COUNT(*) AS participating_user_count FROM prod.users WHERE has_answered_survey OR is_endorsing OR is_following OR has_authored_comment OR has_authored_proposal OR has_voted_on_project OR has_voted_on_proposal;",
+        'participatory_process_count': "SELECT COUNT(*) AS participatory_process_count FROM prod.stg_decidim_participatory_processes;",
+        'participations_count': "SELECT COUNT(*) AS participations_count FROM prod.participations WHERE participation_type IS NOT NULL;",
+    }
+
+    # Perform data aggregation for all clients
+    aggregated_data = aggregate_data_for_clients(db_cluster_preprod, client_databases, queries)
+
+    # Display the aggregated data (optional)
+    print(aggregated_data.head(5))
+
+    # Insert the aggregated data into a new database and table
+    target_database = "aggregated_client_data"
+    target_table = "aggregated_data"
+
+    insert_data_to_aggregated_db(db_cluster_preprod, aggregated_data, target_database, target_table)
+
+def aggregate_by_date_task():
+    client_databases = clients
+
+    query = """
+            SELECT 
+                COUNT(*) as users_count,
+                DATE(created_at) AS date_of_creation
+            FROM prod.users
+            GROUP BY date_of_creation
+            """
+
+    target_database = "aggregated_client_data"
+
+    target_table = "aggregate_by_date"
+    df = aggregate_data_for_clients_for_unique_query(db_cluster_preprod, client_databases, query)
+    insert_data_to_aggregated_db(db_cluster_preprod, df, target_database, target_table)
+
+def aggregate_by_participation_type_task():
+    client_databases = clients
+
+    query = """
+            SELECT 
+                COUNT (*),
+                participation_type
+            FROM prod.participations
+            GROUP BY participation_type
+            """
+
+    target_database = "aggregated_client_data"
+    target_table = "aggregate_by_participation_type"
+    df = aggregate_data_for_clients_for_unique_query(db_cluster_preprod, client_databases, query)
+    insert_data_to_aggregated_db(db_cluster_preprod, df, target_database, target_table)
+
+def aggregate_by_budgets_task():
+    client_databases = clients
+
+    query = """
+            SELECT
+                budget_id AS id, 
+                title, 
+                project_amount AS amount,
+                is_selected,
+                categories,
+				components.ps_title
+            FROM prod.budgets_projects
+			LEFT JOIN prod.components ON budgets_projects.decidim_component_id = components.id
+            GROUP BY budget_id, title, is_selected, resource_type, categories, project_amount, ps_title
+            ORDER BY project_amount ASC
+            """
+
+    target_database = "aggregated_client_data"
+    target_table = "aggregate_by_budgets"
+    df = aggregate_data_for_clients_for_unique_query(db_cluster_preprod, client_databases, query)
+    insert_data_to_aggregated_db(db_cluster_preprod, df, target_database, target_table)

dags/data_utils/postgres/get_postgres_connection.py

@@ -0,0 +1,26 @@
+from airflow.hooks.base import BaseHook
+from sqlalchemy import create_engine
+
+def get_postgres_connection(db_cluster, database):
+    """
+    Extracts PostgreSQL connection details from Airflow and establishes a connection using SQLAlchemy.
+    """
+    try:
+        # Retrieve the connection object from Airflow
+        connection = BaseHook.get_connection(db_cluster)
+
+        # Extract connection details
+        user = connection.login
+        password = connection.password
+        host = connection.host
+        port = connection.port
+
+        # Create the SQLAlchemy engine
+        engine = create_engine(f"postgresql://{user}:{password}@{host}:{port}/{database}")
+        conn = engine.connect()
+        print("Successfully connected to the PostgreSQL database via Airflow.")
+        return conn
+
+    except Exception as e:
+        print(f"Failed to connect to PostgreSQL via Airflow: {e}")
+        raise  # Raise exception to ensure the DAG fails if the connection cannot be established