Interacting with SQL Server Stored Procedures in Python Applications with SQLAlchemy

Stored Procedures

• In SQL Server (and other relational databases), stored procedures are pre-compiled blocks of SQL statements that perform specific tasks.
• They encapsulate complex logic, improve code reusability, and enhance security by centralizing data access.

SQLAlchemy

• SQLAlchemy is a popular Python object-relational mapper (ORM) that simplifies database interactions.
• It allows you to define Python classes that map to database tables and write Python code to interact with your database.

While SQLAlchemy doesn't directly provide a built-in method for stored procedures, you can effectively call them using the underlying database API's functionalities. Here's a common approach:

1. Establish a Connection:

   • Use SQLAlchemy's create_engine function to create a connection engine for your SQL Server database. This engine represents the communication channel between your Python code and the database.

2. Access the Raw Connection:

   • From the connection engine, obtain the underlying raw database connection object using the engine.raw_connection() method. This object provides access to low-level database features like calling stored procedures.

3. Execute the Stored Procedure:

   • Use the callproc method of the raw connection object. This method takes two arguments:
     • The stored procedure name (as a string).
     • A list of parameters (in the order they are defined in the stored procedure).

   Here's an example:

   import sqlalchemy
   
   # Database connection details
   engine = sqlalchemy.create_engine('mssql+pyodbc://user:password@server/database')
   
   # Get the raw connection
   connection = engine.raw_connection()
   
   # Stored procedure name and parameters
   procedure_name = 'MyStoredProcedure'
   param1 = 'value1'
   param2 = 42
   
   # Call the stored procedure
   cursor = connection.cursor()
   cursor.callproc(procedure_name, [param1, param2])
   
   # Process results (if the procedure returns data)
   results = cursor.fetchall()
   
   # Commit changes (if the procedure modifies data)
   connection.commit()
   
   # Close the cursor and connection
   cursor.close()
   connection.close()
   

Important Considerations

• Error Handling: Implement robust error handling to gracefully handle potential exceptions that might occur during execution.
• Parameter Handling: Carefully match the parameter types and order to those defined in the stored procedure.
• Result Processing: If the stored procedure returns data (output parameters or result sets), you'll need to fetch and process the results after calling cursor.callproc.

import sqlalchemy

def call_stored_procedure(engine, procedure_name, *parameters):
  """Calls a stored procedure in SQL Server using SQLAlchemy.

  Args:
      engine (sqlalchemy.engine.Engine): The SQLAlchemy engine object for the database.
      procedure_name (str): The name of the stored procedure.
      *parameters: The parameters to pass to the stored procedure (in order).

  Returns:
      list: A list of rows returned by the stored procedure (if applicable).
  """

  try:
    # Get raw connection
    connection = engine.raw_connection()

    # Create cursor
    cursor = connection.cursor()

    # Call the stored procedure
    cursor.callproc(procedure_name, parameters)

    # Process results (if the procedure returns data)
    results = cursor.fetchall()

    # Commit changes (if the procedure modifies data)
    connection.commit()

    return results

  except Exception as e:
    # Handle errors gracefully
    print("Error calling stored procedure:", e)
    raise  # Re-raise the exception for further handling

  finally:
    # Close cursor and connection
    if cursor:
      cursor.close()
    if connection:
      connection.close()

# Example usage
engine = sqlalchemy.create_engine('mssql+pyodbc://user:password@server/database')

# Stored procedure and parameters
procedure_name = 'GetCustomerDetails'
customer_id = 123

# Call the stored procedure
try:
  customer_data = call_stored_procedure(engine, procedure_name, customer_id)

  if customer_data:
    # Access data from the first row (assuming single customer)
    customer_name = customer_data[0][0]  # Assuming first column holds name
    print(f"Customer name: {customer_name}")
  else:
    print("No customer found with ID:", customer_id)

except Exception as e:
  print("An unexpected error occurred:", e)

This code defines a reusable function call_stored_procedure that encapsulates the logic for calling a stored procedure and handles errors and results. The example usage demonstrates how to call the GetCustomerDetails procedure and retrieve the name of the customer with the specified ID.

Using Textual SQL:

• This approach involves constructing the complete SQL statement, including the stored procedure call and parameter binding, using raw SQL. You can then execute this statement using SQLAlchemy's execute method.

Example:

import sqlalchemy

engine = sqlalchemy.create_engine('mssql+pyodbc://user:password@server/database')

procedure_name = 'UpdateCustomerEmail'
customer_id = 123
new_email = '[email protected]'

sql = f"""
EXEC {procedure_name} @customer_id = ?, @new_email = ?
"""

connection = engine.connect()
result = connection.execute(sql, customer_id, new_email)
connection.close()

Considerations:

• This method can be less secure as it exposes the full SQL statement within your code. Use parameterized queries to avoid potential SQL injection vulnerabilities.
• It might be less readable and maintainable for complex stored procedures with many parameters.

Using ORM-like Techniques (Limited Applicability):

• In specific scenarios, you might be able to leverage SQLAlchemy's ORM capabilities to achieve a similar outcome to calling a stored procedure. This involves defining custom logic within your Python models to handle the desired functionality.

Example (Illustrative - May not be suitable for all procedures):

from sqlalchemy import Column, Integer, String
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker

Base = declarative_base()

class Customer(Base):
  __tablename__ = 'customers'

  id = Column(Integer, primary_key=True)
  name = Column(String)
  email = Column(String)

  def update_email(self, new_email):
    self.email = new_email
    # ... Logic to update customer email in the database (potentially using SQLAlchemy's update method)

# Usage
session = sessionmaker(bind=engine)()
customer = session.query(Customer).get(123)
customer.update_email('[email protected]')
session.commit()
session.close()

• This approach is highly dependent on the specific logic implemented within your model and might not be suitable for all stored procedures.
• It potentially requires more code compared to directly calling the stored procedure.

Remember that the callproc method using the raw connection remains the most common and flexible approach for interacting with stored procedures in SQLAlchemy for SQL Server. Choose the method that best suits your specific requirements and security considerations.