How to write better code with Python Descriptors

The python descriptor protocol offers a neat, yet powerful way to handle the access of class and instance attributes.

Here is what we will learn in this blog post:

What is the descriptor protocol?

How do I apply the descriptor protocol?

Close look at the descriptor API

Applications of the descriptor protocol.

Use descriptors and decorators to implements the @property annotation

Descriptor Protocol

The Python descriptor protocol is an addition to the Python data model. You can use descriptors to customize the access of class and object instance attributes.

Sounds pretty abstract, doesn't it? ;-)

class Person:

    name = "Mustermann"

    def __init__(self):
        self.first_name = "Max"


person = Person()

# read
person.name
person.first_name

# write
person.name = "Mareike"
person.first_name = "Maike"

# delete
del person.name
del person.first_name

Our Person class contains two attributes.

The class attribute name

The object instance attribute first_name

The code above demonstrates how we can access these two attributes through the read, write, and delete operations.

Most of you already know this syntax.
But it is interesting to have a deeper look into the Python data model to see how the access truly works.

Python stores methods and attributes in a special variable called __dict__.
The __dict__ variable is a dictionary.
The dictionary keys reference the name of an attribute or method, and the value is the attribute value or method object.

person = Person()

print(person.__dict__.keys())

print(Person.__dict__.keys())

dict_keys([])
dict_keys(['__module__', 'name', '__dict__', '__weakref__', '__doc__'])

As you can see, object instances and class objects have their own __dict__ variable.

This separation is necessary to have independent object instance attributes and methods (state).

You can spot the name class attribute in the output of Person.__dict__ and the first_name object instance attribute in the output of person.__dict__.

This is a very simple, yet powerful concept.

The read, write and delete operations that we demonstrated above are just syntactic sugar.
Your interpreter will operate on the respective __dict__ data structures to carry out the operations.

The accessor functions that the interpreter uses therefore are getattr, setattr, and delattr.

person = Person()

# read
getattr(Person, "name")
getattr(person, "first_name")

# write
setattr(Person, "name", "Mareike")
setattr(person, "name", "Maike")

# delete
delattr(Person, "name")
delattr(person, "first_name")

I guess some of you are already familiar with those methods.

They become very useful when refactoring code and make class and object instance attributes dynamically accessible.

But why do we not operate on the __dict__ variables directly?

# read
Person.__dict__["name"]

# write
Person.__dict__["name"] = "Mareike"

# delete
del Person.__dict__["name"]

---------------------------------------------------------------------------

TypeError                                 Traceback (most recent call last)

<ipython-input-68-7ab3e836bd7a> in <module>
      3 
      4 # write
----> 5 Person.__dict__["name"] = "Mareike"
      6 
      7 # delete


TypeError: 'mappingproxy' object does not support item assignment

You cannot write to the __dict__ data structure directly.

The reason for this becomes clear when we have a look at the descriptor protocol.

Descriptors allow us to customize the way getattr, setattr and delattr access instance or class attributes.

Here is a simplified version of the Person example with a descriptor class attribute.

import logging

logging.basicConfig(level=logging.INFO)

class MyStringField:

    def __get__(self, obj, obj_type=None):
        logging.info("__get__: self._name -> %r", obj._name)
        return obj._name

    def __set__(self, obj, value):
        logging.info("__set__: self._name <- %r", value)
        obj._name = value

    def __delete__(self, obj):
        logging.info("__delete__: self._name")
        del obj._name


class Person:

    name = MyStringField()

What do you think? Does our name descriptor attribute customize the access for an instance or for a class attribute?

person = Person()

person.name = "Max Mustermann"
person.name
del person.name

INFO:root:__set__: self._name <- 'Max Mustermann'
INFO:root:__get__: self._name -> 'Max Mustermann'
INFO:root:__delete__: self._name

The descriptor attribute name is a class attribute but it handles the access of the instance attribute _name.

What happens when we put our descriptor in an instance attribute?

class Person:

    def __init__(self):
      self.name = MyStringField()

person = Person()

person.name = "Max Mustermann"
person.name
del person.name

You can see on the missing log output that our descriptor methods __get__, __set__ and __delete__ do not get invoked.

This is one of the descriptor protocol oddities, descriptors only work on class attributes!

Descriptor API

I guess by now you understood how the descriptor methods __get__, __set__ and __delete__ is connected to the attribute access.

The accessor methods getattr, setattr and delattr invoke the descriptor methods if they exist.
That is also the reason why we cannot modify the __dict__ data structures directly.
Modifying the __dict__ data structures directly would not invoke the descriptor methods of an attribute.

Here is a listing of all descriptor protocol methods.

"""
Override read access behavior.

:param obj: reference to the object instance (nullable)
:param obj_type: reference to the object instance type (nullable)
"""
__get__(self, obj, obj_type=None) -> object

"""
Override write access behavior.

:param obj: @see __get__
:param value: the next value that the caller assigns
"""
__set__(self, obj, value) -> None

"""
Inform the Descriptor about its name.

E.g.
class MyDescriptor:

   __set_name__(self, obj_type, name):
       ...

class Person:
  first_name = MyDescriptor()


The call `name = MyDescriptor()` invokes 
__set_name__ with the name="first_name".

:param obj: @see __get__
:param name: descriptor name

"""
__set_name(self, obj_type, name) -> None


"""
Override delete behavior

:param obj: @see __get__
"""
__delete__(self, obj) -> None

The listing contains a new function __set_name__ which we will explore in the next section.

But for now, let us have a look at the obj and obj_type parameters.

obj yields a reference to the object instance on which the descriptor got invoked while
obj_type references the type.

Sounds confusing? Let's explain it with an example:

import logging

logging.basicConfig(level=logging.INFO)

class MyStringField:

    def __get__(self, obj, obj_type=None):
        logging.info("__get__: obj: %r, obj_type: %r", obj, obj_type)
        return obj._name

    def __set__(self, obj, value):
        logging.info("__set__: obj: %r", obj)
        obj._name = value

    def __delete__(self, obj):
        logging.info("__delete__: obj: %r", obj)
        del obj._name


class Person:

    name = MyStringField()


person = Person()

person.name = "Max Mustermann"
person.name
del person.name

INFO:root:__set__: obj: <__main__.Person object at 0x7f4bf024b7f0>
INFO:root:__get__: obj: <__main__.Person object at 0x7f4bf024b7f0>, obj_type: <class '__main__.Person'>
INFO:root:__delete__: obj: <__main__.Person object at 0x7f4bf024b7f0>

You can see in the log output that obj references a person object instance, while obj_type references the Person type.

Our descriptors can customize the access to class and instance attributes through the obj and obj_type parameters.

__set_name__

All descriptors that we wrote so far had to know the name of the property for which they handle the access.
So to say, the property name was hardcoded.

The __set_name__ descriptor method is there to make our descriptors more generic.

import logging


class MyProperty:

    def __init__(self, prop_type):
        self._type = prop_type

    def __set_name__(self, obj_type, name):
        logging.info("__set_name__ with name: %r", name)
        self.property_name = "_name"

    def __get__(self, obj, obj_type=None):
        value = getattr(obj, self.property_name)
        logging.info("__get__: self.%r -> %r", self.property_name, value)
        return value

    def __set__(self, obj, value):

        if type(value) is not self._type:
            raise ValueError("expected type {} but got {} instead".format(self._type, type(value)))

        setattr(obj, self.property_name, value)
        logging.info("__set__: self.%r <- %r", self.property_name, value)

class Person:

    name = MyProperty(prop_type=str)
    first_name = MyProperty(prop_type=str)


person = Person()

person._name = "Mustermann"
person._first_name = 5

logging.info("attributes (after): %r", person.__dict__.keys())

logging.info("person.name: %r", person._name)
logging.info("person.first_name: %r", person._first_name)

INFO:root:__set_name__ with name: 'name'
INFO:root:__set_name__ with name: 'first_name'
INFO:root:attributes (after): dict_keys(['_name', '_first_name'])
INFO:root:person.name: 'Mustermann'
INFO:root:person.first_name: 5

MyProperty saves the attribute name that we received through the __set_name__ call as an instance attribute.

Note, we prefixed the property name with a _ which is a common practice to name private instance attributes in Python.

And that's it.
You now know how to apply the descriptor protocol!

Let us dedicate the rest of the blog post to practical examples that incorporate the descriptor protocol.

Validator Descriptor

This example demonstrates how we can build our own property types.

We want to build an abstract Validator attribute class that executes the validate method before the attribute gets modified.

import logging
import re
from abc import ABC, abstractmethod

logging.basicConfig(level=logging.INFO)


class ValidationException(Exception):
    pass

class PropertyWithValidator(ABC):

    def __set_name__(self, owner, name):
        self.property_name = "_" + name

    def __get__(self, obj, obj_type=None):
        value = getattr(obj, self.property_name)
        logging.info("__get__: %r -> %r", self.property_name, value)
        return value

    def __set__(self, obj, value):
        self.validate(value)
        logging.info("__set__: %r <- %r", self.property_name, value)
        setattr(obj, self.property_name, value)

    @abstractmethod
    def validate(self, value):
        pass


class IPv4(PropertyWithValidator):

    def validate(self, value):

        if type(value) is not str:
            raise ValidationException("{} must be a string to be a valid ipv4 address".format(value))

        ipv4_pattern = re.compile(r'^(?:[0-9]{1,3}\.){3}[0-9]{1,3}$')
        if not ipv4_pattern.match(value):
            raise ValidationException("{} is not a valid ipv4 address".format(value))

class Flow:

    src_ipv4 = IPv4()
    dst_ipv4 = IPv4()

PropertyWithValidator is an abstract class (@see ABC) that invokes the abstract validate method before you can assign a new value to the descriptor attribute.

We used this abstract class in IPv4 to implement a concrete Validator class.
All we had to do for this was to implement a validate method.

In our case, the method validate method uses the Python regexp lib re to ensure that values meet the IPv4 standard.

flow = Flow()

logging.info("attributes: %r", flow.__dict__.keys())

flow.src_ipv4 = "2.2.2.2"
flow.dst_ipv4 = "5.5.5.5"
flow.src_ipv4
flow.dst_ipv4

INFO:root:attributes: dict_keys([])
INFO:root:__set__: '_src_ipv4' <- '2.2.2.2'
INFO:root:__set__: '_dst_ipv4' <- '5.5.5.5'
INFO:root:__get__: '_src_ipv4' -> '2.2.2.2'
INFO:root:__get__: '_dst_ipv4' -> '5.5.5.5'





'5.5.5.5'

As you can see, we can assign valid IPv4 addresses to flow.src_ipv4 and flow.dst_ipv4.

But see what happens when we assign an invalid IPv4 string:

flow.src_ipv4 = "5.5.5."

---------------------------------------------------------------------------

ValidationException                       Traceback (most recent call last)

<ipython-input-53-86168850f280> in <module>
----> 1 flow.src_ipv4 = "5.5.5."


<ipython-input-48-f8c1cbfc54dd> in __set__(self, obj, value)
     20 
     21     def __set__(self, obj, value):
---> 22         self.validate(value)
     23         logging.info("__set__: %r <- %r", self.property_name, value)
     24         setattr(obj, self.property_name, value)


<ipython-input-48-f8c1cbfc54dd> in validate(self, value)
     38         ipv4_pattern = re.compile(r'^(?:[0-9]{1,3}\.){3}[0-9]{1,3}$')
     39         if not ipv4_pattern.match(value):
---> 40             raise ValidationException("{} is not a valid ipv4 address".format(value))
     41 
     42 class Flow:


ValidationException: 5.5.5. is not a valid ipv4 address

Runtime Access Protection

Our next example uses the descriptor protocol to implement a runtime access protection for a password attribute.

from abc import ABC, abstractmethod

class UnauthorizedAccessException(Exception):
    REASON_UNAUTHORIZED_CALLER = "Caller is not authorized to perform {operation} operation on attribute {attribute}"

class ProtectedAttribute(ABC):

    def __set_name__(self, obj, name):
        self._property_name = "_" + name

    def __get__(self, obj, obj_type=None):

        if not self.is_access_authorized(obj):
            self._raise_unauthorized("read")

        return getattr(obj, self._property_name)

    def __set__(self, obj, value):

        if not self.is_access_authorized(obj):
            self._raise_unauthorized("write")

        setattr(obj, self._property_name, value)

    def _raise_unauthorized(self, operation):
        raise UnauthorizedAccessException(
                UnauthorizedAccessException.REASON_UNAUTHORIZED_CALLER.format(
                    operation=operation, 
                    attribute=self._property_name
                )
            )

    @abstractmethod
    def is_access_authorized(self, obj):
        pass

class SecurityContext:

    current_session = { "user_name": "Max Mustermann" }

class Password(ProtectedAttribute):

    def __init__(self, authorized_users):
        self._authorized_users = authorized_users

    def is_access_authorized(self, obj):

        return SecurityContext.current_session["user_name"] in self._authorized_users

class User:

    password = Password(authorized_users=["Maike Mareike"])

The __set__ and __get__ descriptor methods invoke the abstract is_access_authorized method before accessing the descriptor attribute value.

user = User()

user.password = "my_secret"

---------------------------------------------------------------------------

UnauthorizedAccessException               Traceback (most recent call last)

<ipython-input-80-b925f049f61b> in <module>
      1 user = User()
      2 
----> 3 user.password = "my_secret"


<ipython-input-79-2e1ac42e3056> in __set__(self, obj, value)
     19 
     20         if not self.is_access_authorized(obj):
---> 21             self._raise_unauthorized("write")
     22 
     23         setattr(obj, self._property_name, value)


<ipython-input-79-2e1ac42e3056> in _raise_unauthorized(self, operation)
     24 
     25     def _raise_unauthorized(self, operation):
---> 26         raise UnauthorizedAccessException(
     27                 UnauthorizedAccessException.REASON_UNAUTHORIZED_CALLER.format(
     28                     operation=operation,


UnauthorizedAccessException: Caller is not authorized to perform write operation on attribute _password

This is a very simple example of runtime protection through the descriptor protocol.
But the idea behind it is very powerful.
Especially if you consider that the Python language lacks encapsulation.

Programming languages such as Java offer modifiers like private to encapsulate your code access.

While we cannot achieve an encapsulation through the typing system in Python, we can ensure it through descriptors at runtime.

There is no clean way to circumvent the is_access_authorized method while accessing the user.password field.

(We have to relativize this expression.
Python makes it possible to change the method implementation at runtime, also called "monkey patching".

Thus, it is possible to override the setattr and getattr implementation for the Password class at runtime.)

Invoke Side Effects

Our next example is a Configuration attribute class. The Configuration gets represented as a dictionary data structure in memory, and as a JSON file on the file system.

The challenge with the Configruation attribute class is to keep it's value in memory in sync with the JSON file on disk.

import json
import logging

logging.basicConfig(level=logging.INFO)

class JSONConfiguration:

    def __init__(self, file_path):
        self._file_path = file_path

    def __set_name__(self, obj, name):
        self._property_name = "_" + name

    def _write_to_disk(self, config_dict):

        logging.info("write config back to disk on path %r", self._file_path)
        json_str = json.dumps(config_dict)

        with open(self._file_path) as config_file:
            confoig_file.write(json_str)

    def __get__(self, obj, obj_type=None):
        return getattr(obj, self._property_name)

    def __set__(self, obj, value):
        setattr(obj, self._property_name, value)
        self._write_to_disk(value)


class Application:

    config = JSONConfiguration(
        file_path="/tmp/config.json", 
    )

The Application.config object behaves like a dictionary from the perspective of the caller.

It completely hides the _write_to_disk method that gets invoked by the __set__ descriptor method.
This simplifies the contract between the Application.config attribute and the caller.

Without the use of descriptors, the contract would make the caller responsible for calling the _write_to_disk method after modifying the config dictionary data structure.

application = Application()
application.config = { "host": "localhost", "port": "8088"}

INFO:root:write config back to disk on path '/tmp/config.json'



---------------------------------------------------------------------------

FileNotFoundError                         Traceback (most recent call last)

<ipython-input-70-0ccf8f4311f0> in <module>
      1 application = Application()
----> 2 application.config = { "host": "localhost", "port": "8088"}


<ipython-input-64-06078f3aa6fa> in __set__(self, obj, value)
     25     def __set__(self, obj, value):
     26         setattr(obj, self._property_name, value)
---> 27         self._write_to_disk(value)
     28 
     29 


<ipython-input-64-06078f3aa6fa> in _write_to_disk(self, config_dict)
     17         json_str = json.dumps(config_dict)
     18 
---> 19         with open(self._file_path) as config_file:
     20             confoig_file.write(json_str)
     21 


FileNotFoundError: [Errno 2] No such file or directory: '/tmp/config.json'

(These code examples come from a Jupyter notebook, so please ignore the FileNotFoundError exception.)

@property

You may remember that I told you in the abstract how the descriptor protocol may help you understand the python standard library code?

This example demonstrates how the popular @popular annotation can be implemented through the descriptor and decorator protocols.

import logging

logging.basicConfig(level=logging.INFO)

class prop:

    def __init__(self, fget=None, fset=None, fdel=None):
        self._fget = fget  
        self._fset = fset
        self._fdel = fdel

    def __get__(self, obj, obj_type):
        value = self._fget(obj)
        logging.info("read: {}".format(value))
        return value

    def __set__(self, obj, value):
        logging.info("write: {}".format(value))
        self._fset(obj, value)

    def __del__(self, obj):
        logging.info("del")
        self._fdel(obj)

    def __call__(self, fget):
        return type(self)(fget, self._fset, self._fdel)

    def setter(self, fset):
        return type(self)(self._fget, fset, self._fdel)

    def deleter(self, fdel):
        return type(self)(self._fget, self._fset, fdel)

class Person:

    @prop
    def name(self):
        return self._name

    @name.setter
    def set_name(self, value):
        self._name = value

    @name.deleter
    def delete_name(self):
        del self._name

    @prop
    def first_name(self):
        return self._first_name

    @first_name.setter
    def set_first_name(self, value):
        self._first_name = value

    @first_name.deleter
    def delete_first_name(self):
        del self._first_name

This code may look scary for you at first.
Especially if you are not accustomed to the decorator protocol.

(Spoiler alert, we will have a blog post for the decorator protocol next ;-) )

I'll walk you through the code quickly.

First, let us understand how the decorator notion of the code works.

The __init__ method gets called when you annotate the @property decorator.
The arguments of the __init__ method are the parameters that you pass to the decorator call.

We use a little trick in our example.

Our @property descriptor requires getter, setter, and deleter methods for our property.
But we do not want to pass all of the methods right away @property(fget=..., fset=..., fdel=...).

We want to annotate the getter, setter and deleter separately to make our API even more elegant.
Thus, we use None as the default parameter for our fget, fset, and fdel methods.

Let us now see how we fill the decorator attributes fget, fset, and fdel with real methods.

The __call__ method gets invoked when the decorator gets called. The __call__ method takes a method as an argument (in our case the fget => getter method) and returns a method again.

Our __call__ methods returns a new decorator that uses fget as getter method. The line
type(self)(fget, self._fset, self._fdel) returns the decorator instance itself while adding the fget method.

Last but not least, our descriptor methods invoke the fget, fset, and fdel methods.

person = Person()
person.set_name = "Max"
person.set_first_name = "Mustermann"

logging.info("attributes: %r", person.__dict__.keys())

INFO:root:write: Max
INFO:root:write: Mustermann
INFO:root:attributes: dict_keys(['_name', '_first_name'])

Summary

So, that's it.
My first ever blog post and you made it right to the end, thank you! :-)

In this post, we learned a few things about the Python data model.
We demonstrated the accessor methods getattr, setattr, and delattr and explained how they relate to the Python __dict__ data structure and to the descriptor protocol.

Later on, we saw applications of the descriptor protocol.
Finally, we saw how the popular @property annotation can be implemented through a synergy of the decorator and descriptor protocol.

Learning new protocols and syntaxes in a programming language is very exciting.
But we should not get carried away by this excitement.

The descriptor protocol proves useful in very specific scenarios, some of which we showed in the blog post.
Personally, I see the strength of the descriptor protocol especially in the design of APIs in Python libraries.

The protocol does a very good job at hiding implementation details and thus simplifying the contract between descriptor attribute and caller.

But it is not a golden hammer!

Do not write every attribute as descriptor attribute.

In general, programming should be about working yourself from the problem to the solution, not the other way around.

Thus, do not use design patterns or language constructs wherever you see fit!

I hope you could learn one or two things about Python through this post.
I surely did!

Please consider sharing a link to the post if you enjoyed reading it.
It helps me to build an audience and is greatly appreciated.

cheers,
Dennis

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