SQLAlchemy ORM: Filter Records Based on 'NOT LIKE' Criteria

In SQLAlchemy, the Object-Relational Mapper (ORM) allows you to work with database objects using Python classes. The "NOT LIKE" operator is used in SQL queries to filter results that don't contain a specific pattern within a text column.

Here's how to achieve this in your SQLAlchemy ORM queries:

Method 1: Using Negation (~)

1. Import Necessary Modules:

   from sqlalchemy import orm
   

2. Define Your Query:

   session = MyModel.query  # Replace `MyModel` with your actual model class
   
   # Filter for records where the `name` column doesn't contain 'pattern'
   query = session.filter(~MyModel.name.contains('pattern'))
   

   • session.query: Creates a SQLAlchemy query object.
   • ~MyModel.name.contains('pattern'): Applies the negation operator (~) to negate the contains method. This translates to "NOT LIKE 'pattern'" in the generated SQL query.

Method 2: Using sqlalchemy.ColumnOperators.is_not

1. session = MyModel.query
   
   # Filter for records where the `name` column doesn't contain 'pattern'
   query = session.filter(MyModel.name.is_not(like='%pattern%'))
   

   • ColumnOperators.is_not(like='%pattern%'): This approach uses the is_not method from ColumnOperators along with the like operator to achieve the same result. The '%pattern%' wildcard pattern matches any string containing 'pattern'.

Choosing the Method

Both methods (~ and is_not) are valid and achieve the same outcome. The choice often depends on personal preference and code style:

• is_not is more verbose but potentially clearer, especially for developers new to SQLAlchemy.
• ~ is more concise but might be less explicit for those unfamiliar with SQLAlchemy's operator negation.

Additional Considerations

• For more complex pattern matching, consider using regular expressions with the ilike operator (case-insensitive LIKE) or database-specific functions.
• Escape special characters in your pattern string if necessary to prevent unintended behavior in the SQL query.

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

# Define a sample model class
Base = declarative_base()

class User(Base):
    __tablename__ = 'users'

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

# Create a database engine (replace 'your_database_url' with your actual connection string)
engine = create_engine('your_database_url')

# Create a session
Session = orm.sessionmaker(bind=engine)
session = Session()

# Filter for users where name doesn't contain 'admin'
not_admin_users = session.query(User).filter(~User.name.contains('admin'))

# Process or print the results (e.g., names of non-admin users)
for user in not_admin_users:
    print(user.name)

# Close the session
session.close()

Explanation:

• Finally, we close the session.
• The results are iterated through, and you can modify this section to process or print the desired information (e.g., user names).
• The query filters for users where the name column doesn't contain 'admin' using the ~ operator on contains.
• We create a database engine and a SQLAlchemy session.
• We define a sample User model with name and email columns.

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

# Define a sample model class (same as Example 1)

# Create a database engine (replace 'your_database_url' with your actual connection string)
engine = create_engine('your_database_url')

# Create a session
Session = orm.sessionmaker(bind=engine)
session = Session()

# Filter for users where name doesn't contain 'admin'
not_admin_users = session.query(User).filter(User.name.is_not(like='%admin%'))

# Process or print the results (same as Example 1)

# Close the session
session.close()

• The rest of the code is identical to Example 1.
• The query filters for users where the name column doesn't contain 'admin' using ColumnOperators.is_not with the like operator and the wildcard pattern.
• This example uses the same model and setup as Example 1.

This method involves writing the raw SQL query with the "NOT LIKE" operator and executing it using Session.execute. Here's an example:

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

# ... (same model and engine setup as previous examples)

# Define the SQL query
sql = f"""
SELECT * FROM users
WHERE name NOT LIKE '%admin%'
"""

# Execute the query and fetch results
results = session.execute(sql)

# Process or print the results
for row in results:
    user_id, name, email = row  # Assuming column order
    print(f"User ID: {user_id}, Name: {name}, Email: {email}")

# Close the session
session.close()

• We iterate through the results and process them manually.
• We use Session.execute to execute the query and fetch the results as tuples.
• We construct the SQL query directly with the "NOT LIKE" operator.

Pros:

• This method offers more control over the exact SQL query being executed.

Cons:

• You need to handle result fetching and processing manually.
• It can be less readable and maintainable compared to the ORM approach.

Using Regular Expressions (Limited Support):

Some databases support regular expressions in LIKE operators. If your database supports it, you can potentially use regular expressions for more complex pattern matching. However, be aware that SQLAlchemy ORM might not provide direct support for regular expressions within contains or like methods. You might need to resort to raw SQL or database-specific functions.

Important Note:

Always consult your database documentation to verify support for regular expressions in LIKE operators before attempting this approach.

• If you need more control over the SQL query or require complex pattern matching (and your database supports it), consider the textual SQL approach or explore database-specific functions for regular expressions.
• For simple "NOT LIKE" queries, methods 1 and 2 (~ and is_not) are generally recommended.