SQLAlchemy ON DUPLICATE KEY UPDATE Explained: Python, MySQL, SQLAlchemy

Understanding ON DUPLICATE KEY UPDATE

• MySQL Feature: This functionality is specific to MySQL databases. It allows you to perform an INSERT operation and, if a row with the same unique key already exists, update specific columns in that existing row.
• Efficiency: This approach is efficient because it combines INSERT and UPDATE logic into a single statement, reducing database roundtrips.

Limitations of SQLAlchemy ORM

• No Built-in Support: SQLAlchemy's Object Relational Mapper (ORM) doesn't directly provide an interface for ON DUPLICATE KEY UPDATE. It lacks a built-in way to express this functionality within the ORM layer.

Alternative Approaches

Here are common workarounds to achieve "upsert" (insert or update) behavior in SQLAlchemy with MySQL:

1. @compiles Decorator (Manual SQL):

   • Define a custom function using the @compiles decorator for the model class.
   • Within this function, construct the raw SQL INSERT ... ON DUPLICATE KEY UPDATE statement, including the columns to be updated.
   • This approach gives you fine-grained control over the SQL, but requires manual SQL management.

2. session.merge() (Primary Key Only):

   • Leverage SQLAlchemy's session.merge() method.
   • This method can be used for upserts if the unique key you're checking is the primary key of your model.
   • session.merge() attempts to insert or update the object based on its primary key.
   • However, it doesn't offer control over which columns to update in case of a duplicate.

Example (Using @compiles Decorator):

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

# Custom function for @compiles decorator
def insert_or_update(stmt):
    if isinstance(stmt, insert):
        pk_cols = [col for col in stmt.table.columns if col.primary_key]
        stmt.compile(compile_kwargs={'use_bind': stmt.bind})
        update = update(stmt.table)
        for col in stmt.table.columns:
            if col not in pk_cols:
                update.values({col: getattr(stmt._inserted_values, col.name)})
        update.where(pk_cols[0] == stmt._inserted_values[pk_cols[0].name])
        stmt.suffixes.append('ON DUPLICATE KEY UPDATE ' + ', '.join(
            f"{col.name} = VALUES({col.name})" for col in stmt.table.columns if col not in pk_cols
        ))

Base = declarative_base()

class User(Base):
    __tablename__ = 'users'

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

    @compiles(insert)
    def insert_or_update_on_compile(self, insert, compiler, **kw):
        insert_or_update(insert)

engine = create_engine('mysql+pymysql://user:password@host/database')
Base.metadata.create_all(engine)

Session = sessionmaker(bind=engine)
session = Session()

user1 = User(name='Alice', email='[email protected]')
user2 = User(name='Bob', email='[email protected]')  # Duplicate email

session.add(user1)
session.add(user2)
session.commit()  # Updates email for existing '[email protected]' row

Choosing the Right Approach:

• If you need precise control over the SQL generated and the columns to update, the @compiles decorator method offers more flexibility.
• If you simply want to upsert based on the primary key and don't require column-level update control, session.merge() can be a simpler option.

Remember that these are workarounds since SQLAlchemy's ORM doesn't natively support ON DUPLICATE KEY UPDATE. Consider the trade-offs between control and simplicity when selecting the approach that best suits your requirements.

This example demonstrates how to create a custom function using the @compiles decorator for your model class to construct the INSERT ... ON DUPLICATE KEY UPDATE statement:

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

# Custom function for @compiles decorator
def insert_or_update(stmt):
    if isinstance(stmt, insert):
        pk_cols = [col for col in stmt.table.columns if col.primary_key]
        stmt.compile(compile_kwargs={'use_bind': stmt.bind})
        update = update(stmt.table)
        for col in stmt.table.columns:
            if col not in pk_cols:
                update.values({col: getattr(stmt._inserted_values, col.name)})
        update.where(pk_cols[0] == stmt._inserted_values[pk_cols[0].name])
        stmt.suffixes.append('ON DUPLICATE KEY UPDATE ' + ', '.join(
            f"{col.name} = VALUES({col.name})" for col in stmt.table.columns if col not in pk_cols
        ))

Base = declarative_base()

class User(Base):
    __tablename__ = 'users'

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

    @compiles(insert)
    def insert_or_update_on_compile(self, insert, compiler, **kw):
        insert_or_update(insert)

engine = create_engine('mysql+pymysql://user:password@host/database')
Base.metadata.create_all(engine)

Session = sessionmaker(bind=engine)
session = Session()

user1 = User(name='Alice', email='[email protected]')
user2 = User(name='Bob', email='[email protected]')  # Duplicate email

session.add(user1)
session.add(user2)
session.commit()  # Updates email for existing '[email protected]' row

Explanation:

1. We define a custom function insert_or_update that's decorated with @compiles.
2. This function checks if the statement is an INSERT operation.
3. It then extracts the primary key columns from the table.
4. It constructs an UPDATE statement for the same table, targeting columns that are not part of the primary key.
5. The WHERE clause ensures the update happens only for rows with the same primary key value as the inserted row.
6. Finally, it appends the ON DUPLICATE KEY UPDATE clause with the specific column assignments to the INSERT statement.

This example shows how to use the session.merge() method for upserts, but it's limited to updating the primary key:

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

Base = declarative_base()

class User(Base):
    __tablename__ = 'users'

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

engine = create_engine('mysql+pymysql://user:password@host/database')
Base.metadata.create_all(engine)

Session = sessionmaker(bind=engine)
session = Session()

user1 = User(name='Alice', email='[email protected]')
user2 = User(name='Bob', email='[email protected]')  # Duplicate email (updates primary key for '[email protected]')

session.add(user1)
session.add(user2)
session.commit()

1. We create a simple User model with an email as the unique key.
2. We use session.merge() to add both user1 and user2.
3. Since email is unique, session.merge() will attempt to update the existing row with [email protected] (created by user1) by replacing its primary key (id) with the values from user2.

**Choosing

1. Raw SQL Execution:

   • If you prefer maximum control over the SQL statement, you can construct and execute raw SQL queries using SQLAlchemy's engine.
   • This method allows you to write the complete INSERT ... ON DUPLICATE KEY UPDATE statement directly.
   • However, it involves managing raw SQL strings, which can be less maintainable and susceptible to SQL injection vulnerabilities if not handled carefully.

2. Custom Logic with execute():

   • You can build your own logic using SQLAlchemy's engine.execute() method.
   • First, attempt an INSERT using execute().
   • If there's a IntegrityError due to a duplicate key, construct and execute an UPDATE statement targeting the desired columns.
   • This approach provides some level of control but requires error handling and separate INSERT and UPDATE statements.

3. Third-Party Libraries:

   • Consider exploring external libraries like sqlalchemy-upsert or sqlalchemy-mate.
   • These libraries often provide convenient wrappers or extensions to handle ON DUPLICATE KEY UPDATE functionality within SQLAlchemy.
   • Evaluate the documentation and community support for these libraries before integrating them into your project.

• If you need the most control and are comfortable with raw SQL, consider using raw SQL execution.
• For a more controlled approach involving separate INSERT and UPDATE logic, explore the custom logic with execute().
• If you prefer a pre-built solution and value convenience, investigate third-party libraries like sqlalchemy-upsert or sqlalchemy-mate.

Remember to prioritize maintainability, readability, and security while selecting the best approach for your specific use case.