Upsert in SQLAlchemy with PostgreSQL: Efficiency for Supported Databases

2024-07-27

  1. Query first, create if not found: This approach involves two steps:

    • Query: You write a query to check if the object exists in the database based on unique identifiers like an ID or a combination of fields.
    • Create: If the query returns nothing (meaning the object doesn't exist), you create a new object and add it to the database session.
  2. Upsert (insert or update): This functionality isn't built-in to SQLAlchemy, but some databases like PostgreSQL offer an "upsert" feature. Here's the idea:

    • You create an insert statement specifying the data for the new object.
    • You define a conflict resolution clause. This clause tells the database what to do if a record with the same unique identifier already exists (e.g., do nothing, update existing record).

Here's a comparison:

  • Query first, create if not found: This is more portable as it works with any database supported by SQLAlchemy. However, it requires two database calls (query and insert).
  • Upsert: This can be more efficient with databases that support it as it's a single database operation. However, it's not universally available.



from sqlalchemy import create_engine, Column, Integer, String, and_

# Define your database connection
engine = create_engine('your_database_uri')

# Define your model class
class User(Base):
  __tablename__ = 'users'
  id = Column(Integer, primary_key=True)
  name = Column(String)

# Function to check if user exists and create if not
def create_user_if_not_exists(session, username):
  # Query for existing user with specific username
  existing_user = session.query(User).filter(User.name == username).first()

  if not existing_user:
    # Create a new user object if not found
    new_user = User(name=username)
    session.add(new_user)
    session.commit()

# Example usage
session = Session(engine)
create_user_if_not_exists(session, "Alice")
session.close()

Upsert using PostgreSQL (example):

from sqlalchemy import create_engine, Column, Integer, String, and_, insert

# Define your database connection (PostgreSQL assumed here)
engine = create_engine('postgresql://user:password@host/database')

# Define your model class
class User(Base):
  __tablename__ = 'users'
  id = Column(Integer, primary_key=True)
  name = Column(String, unique=True)  # Unique constraint for upsert

# Function to upsert a user (insert or update)
def upsert_user(session, username):
  # Create insert statement with conflict resolution
  stmt = insert(User).values(name=username) \
      .on_conflict_do_nothing()  # Update existing if conflict (unique name)

  session.execute(stmt)
  session.commit()

# Example usage
session = Session(engine)
upsert_user(session, "Bob")
session.close()



SQLAlchemy's session.merge method allows merging objects into the current session. We can leverage this to check for existing objects and create new ones if needed. Here's how:

from sqlalchemy import create_engine, Column, Integer, String, and_

# Define your database connection
engine = create_engine('your_database_uri')

# Define your model class
class User(Base):
  __tablename__ = 'users'
  id = Column(Integer, primary_key=True)
  name = Column(String)

# Function to create or update user (using merge)
def create_or_update_user(session, username):
  # Create a new user object
  user = User(name=username)

  # Merge the object into the session, checking for conflicts
  merged_user = session.merge(user)

  # Detach the merged object if it's new (created)
  if merged_user is not user:
    session.add(merged_user)

  session.commit()

# Example usage
session = Session(engine)
create_or_update_user(session, "Charlie")
session.close()

This approach uses merge to handle both creating a new object and updating an existing one. We check if the merged_user is the same object we created (user). If they are different, it means a conflict occurred (existing object), and we detach the merged object (which has the correct database ID) and add it to the session.

Custom SQL with execute:

This method involves writing custom SQL to achieve the upsert functionality. Here's a basic example:

from sqlalchemy import create_engine, Column, Integer, String, and_, text

# Define your database connection
engine = create_engine('your_database_uri')

# Define your model class
class User(Base):
  __tablename__ = 'users'
  id = Column(Integer, primary_key=True)
  name = Column(String, unique=True)  # Unique constraint for insert

# Function to upsert a user (custom SQL)
def upsert_user_sql(session, username):
  # Define upsert SQL statement (assuming PostgreSQL)
  stmt = text("INSERT INTO users (name) VALUES (:name) ON CONFLICT (name) DO NOTHING")
  session.execute(stmt, {'name': username})
  session.commit()

# Example usage
session = Session(engine)
upsert_user_sql(session, "David")
session.close()

sqlalchemy




Creating One-to-One Relationships with Declarative in SQLAlchemy

Start by defining two Python classes that represent your database tables. These classes will typically inherit from sqlalchemy...



sqlalchemy

Leveraging External Libraries for Granular Result Set Caching in SQLAlchemy

This built-in feature caches the process of converting SQL statements into their string representation. When you execute the same query multiple times


Optimizing Memory Usage in SQLAlchemy Loops: When to Use `query` and `query.all()`

In SQLAlchemy, you use queries to interact with your database. These queries represent the selection criteria for fetching data from your tables


Unlocking New Databases with SQLAlchemy: Custom Dialect Development

SQLAlchemy provides a base class DefaultDialect you should subclass to create your dialect. This class has methods and attributes that need to be implemented or overridden to handle database-specific operations


Understanding BLOBs and SQLAlchemy: A Guide to Efficient Binary Data Storage

BLOBs are data types used in databases for storing large binary data such as images, audio files, documents, or any other kind of non-textual data


SQL, Database, SQLAlchemy: Working Together

Concepts:SQL (Structured Query Language): A language for interacting with relational databases, used for creating, reading