Python

In Python, there’s a saying that “design patterns are anti-patterns”. Also, in the realm of dynamic languages, design patterns have the notoriety of injecting additional abstraction layers to the core logic and making the flow gratuitously obscure. Python’s dynamic nature and the treatment of functions as first-class objects often make Java-ish design patterns redundant.

Instead of littering your code with seemingly over-engineered patterns, you can almost always take the advantage of Python’s first-class objects, duck-typing, monkey-patching etc to accomplish the task at hand. However, recently there is one design pattern that I find myself using over and over again to write more maintainable code and that is the Proxy pattern. So I thought I’d document it here for future reference.

Updated on 2023-09-11: Fix broken URLs.

Recently, I was working with Mapbox’s Route optimization API. It tries to solve the traveling salesman problem where you provide the API with coordinates of multiple places and it returns a duration-optimized route between those locations. This is a perfect usecase where Redis caching can come handy. Redis is a fast and lightweight in-memory database with additional persistence options; making it a perfect candidate for the task at hand. Here, caching can save you from making redundant API requests and also, it can dramatically improve the response time as well.

Updated on 2022-02-13: Change functools import style.

When I first learned about Python decorators, using them felt like doing voodoo magic. Decorators can give you the ability to add new functionalities to any callable without actually touching or changing the code inside it. This can typically yield better encapsulation and help you write cleaner and more understandable code. However, decorator is considered as a fairly advanced topic in Python since understanding and writing it requires you to have command over multiple additional concepts like first class objects, higher order functions, closures etc. First, I’ll try to introduce these concepts as necessary and then unravel the core concept of decorator layer by layer. So let’s dive in.

Writing concurrent code in Python can be tricky. Before you even start, you have to worry about all this icky stuff like whether the task at hand is I/O or CPU bound or whether putting the extra effort to achieve concurrency is even going to give you the boost you need. Also, the presence of Global Interpreter Lock, GIL foists further limitations on writing truly concurrent code. But for the sake of sanity, you can oversimplify it like this without being blatantly incorrect:

When I first encountered Python’s pathlib module for path manipulation, I brushed it aside assuming it to be just an OOP way of doing what os.path already does quite well. The official doc also dubs it as the Object-oriented filesystem paths. However, back in 2019 when an issue confirmed that Django was replacing os.path with pathlib, I got curious.

The os.path module has always been the de facto standard for working with paths in Python. But the API can feel massive as it performs a plethora of other loosely coupled system related jobs. I’ve to look things up constantly even to perform some of the most basic tasks like joining multiple paths, listing all the files in a folder having a particular extension, opening multiple files in a directory etc. The pathlib module can do nearly everything that os.path offers and comes with some additional cherries on top.

Pre-commit hooks can be a neat way to run automated ad-hoc tasks before submitting a new git commit. These tasks may include linting, trimming trailing whitespaces, running code formatter before code reviews etc. Let’s see how multiple Python linters and formatters can be applied automatically before each commit to impose strict conformity on your codebase.

To keep my sanity, I only use three linters in all of my python projects:

Updated on 2022-02-13: Change import style of functools.singledispatch.

Recently, I was refactoring a portion of a Python function that somewhat looked like this:

def process(data):
    if cond0 and cond1:
        # apply func01 on data that satisfies the cond0 & cond1
        return func01(data)
    elif cond2 or cond3:
        # apply func23 on data that satisfies the cond2 & cond3
        return func23(data)
    elif cond4 and cond5:
        # apply func45 on data that satisfies cond4 & cond5
        return func45(data)


def func01(data):
    ...


def func23(data):
    ...


def func45(data):
    ...

This pattern gets tedious when the number of conditions and actionable functions start to grow. I was looking for a functional approach to avoid defining and calling three different functions that do very similar things. Situations like this is where parametric polymorphism comes into play. The idea is, you have to define a single function that’ll be dynamically overloaded with alternative implementations based on the type of the function arguments.

Python’s context managers are great for resource management and stopping the propagation of leaked abstractions. You’ve probably used it while opening a file or a database connection. Usually it starts with a with statement like this:

with open("file.txt", "wt") as f:
    f.write("contents go here")

In the above case, file.txt gets automatically closed when the execution flow goes out of the scope. This is equivalent to writing:

try:
    f = open("file.txt", "wt")
    text = f.write("contents go here")
finally:
    f.close()

Writing custom context managers

To write a custom context manager, you need to create a class that includes the __enter__ and __exit__ methods. Let’s recreate a custom context manager that will execute the same workflow as above.

Recently, my work needed me to create lots of custom data types and draw comparison among them. So, my code was littered with many classes that somewhat looked like this:

class CartesianPoint:
    def __init__(self, x, y, z):
        self.x = x
        self.y = y
        self.z = z

    def __repr__(self):
        return f"CartesianPoint(x = {self.x}, y = {self.y}, z = {self.z})"


print(CartesianPoint(1, 2, 3))

>>> CartesianPoint(x = 1, y = 2, z = 3)

This class only creates a CartesianPoint type and shows a pretty output of the instances created from it. However, it already has two methods inside, __init__ and __repr__ that don’t do much.