Applying Validator Descriptors in Practice
Now that you understand the concept of class descriptors and how to define _fields with validator descriptors, let's explore some practical applications and use cases.
One common use case for validator descriptors is to validate user input in web applications or command-line interfaces. By defining the validation rules in the descriptor, you can ensure that only valid data is accepted and stored in your class instances.
class User:
username = Validator(lambda x: isinstance(x, str) and len(x) >= 4, "Username must be a string of at least 4 characters")
email = Validator(lambda x: isinstance(x, str) and "@" in x, "Email must be a valid email address")
age = Validator(lambda x: isinstance(x, int) and x >= 18, "Age must be a non-negative integer and at least 18")
user = User()
user.username = "johndoe" ## Works
user.email = "johndoe@example.com" ## Works
user.age = 25 ## Works
user.username = "jd" ## Raises ValueError
user.email = "invalid_email" ## Raises ValueError
user.age = 17 ## Raises ValueError
Validating Database Models
Validator descriptors can also be used to define validation rules for database models, ensuring data integrity and consistency across your application.
from sqlalchemy import Column, Integer, String
from sqlalchemy.ext.declarative import declarative_base
Base = declarative_base()
class UserModel(Base):
__tablename__ = 'users'
id = Column(Integer, primary_key=True)
username = Validator(lambda x: isinstance(x, str) and len(x) >= 4, "Username must be a string of at least 4 characters")
email = Validator(lambda x: isinstance(x, str) and "@" in x, "Email must be a valid email address")
age = Validator(lambda x: isinstance(x, int) and x >= 18, "Age must be a non-negative integer and at least 18")
In this example, the UserModel class inherits from the SQLAlchemy Base class and defines the _fields using validator descriptors. This ensures that any data stored in the users table will adhere to the defined validation rules.
Caching and Memoization
Validator descriptors can also be used to implement caching and memoization, improving the performance of your application.
class CachedValue:
def __init__(self, func):
self.func = func
self.cache = {}
def __get__(self, instance, owner):
if instance not in self.cache:
self.cache[instance] = self.func(instance)
return self.cache[instance]
def __set__(self, instance, value):
self.cache[instance] = value
class Example:
@CachedValue
def expensive_computation(self):
## Perform some expensive computation
return random.randint(1, 100)
example = Example()
print(example.expensive_computation()) ## First call is slow, but subsequent calls are fast
print(example.expensive_computation()) ## Cached value is returned
In this example, the CachedValue descriptor is used to cache the results of the expensive_computation method, improving the overall performance of the Example class.
By exploring these practical applications, you can see how validator descriptors can be leveraged to create more robust, flexible, and efficient class designs in your Python projects.
