Python

I’ve been a happy user of pydantic1 settings to manage all my app configurations since the 1.0 era. When pydantic 2.0 was released, the settings portion became a separate package called pydantic_settings2. It does two things that I love: it automatically reads the environment variables from the .env file and allows you to declaratively convert the string values to their desired types like integers, booleans, etc. Plus, it lets you override the variables defined in ....

I love dynamically typed languages as much as the next person. They let us make ergonomic API calls like this: import httpx # Sync call for simplicity r = httpx.get("https://dummyjson.com/products/1").json() print(r["id"], r["title"], r["description"]) or this: fetch("https://dummyjson.com/products/1") .then((res) => res.json()) .then((json) => console.log(json.id, json.type, json.description)); In both cases, running the snippets will return: 1 'iPhone 9' 'An apple mobile which is nothing like apple' Unless you’ve worked with a statically typed language that enforces more constraints, it’s hard to appreciate how incredibly convenient it is to be able to call and use an API endpoint without having to deal with types or knowing anything about its payload structure....

You can use @dataclass(frozen=True) to make instances of a data class immutable during runtime. However, there’s a small caveat—instantiating a frozen data class is slightly slower than a non-frozen one. This is because, when you enable frozen=True, Python has to generate __setattr__ and __delattr__ methods during class definition time and invoke them for each instantiation. Below is a quick benchmark comparing the instantiation times of a mutable dataclass and a frozen one (in Python 3....

Despite using VSCode as my primary editor, I never really bothered to set up the native debugger to step through application code running inside Docker containers. Configuring the debugger to work with individual files, libraries, or natively running servers is trivial1. So, I use it in those cases and just resort back to my terminal for debugging containerized apps running locally. However, after seeing a colleague’s workflow in a pair-programming session, I wanted to configure the debugger to cover this scenario too....

Data classes are containers for your data—not behavior. The delineation is right there in the name. Yet, I see state-mutating methods getting crammed into data classes and polluting their semantics all the time. While this text will primarily talk about data classes in Python, the message remains valid for any language that supports data classes and allows you to add state-mutating methods to them, e.g., Kotlin, Swift, etc. By state-mutating method, I mean methods that change attribute values during runtime....

The 100k context window of Claude 21 has been a huge boon for me since now I can paste a moderately complex problem to the chat window and ask questions about it. In that spirit, it recently refactored some pretty gnarly conditional logic for me in such an elegant manner that it absolutely blew me away. Now, I know how bitmasks2 work and am aware of the existence of enum.Flag3 in Python....

Unless I’m hand rolling my own ORM-like feature or validation logic, I rarely need to write custom descriptors in Python. The built-in descriptor magics like @classmethod, @property, @staticmethod, and vanilla instance methods usually get the job done. However, every time I need to dig my teeth into descriptors, I reach for this fantastic how to1 guide by Raymond Hettinger. You should definitely set aside the time to read it if you haven’t already....

Python offers a ton of ways like os.system or os.spawn* to create new processes and run arbitrary commands in your system. However, the documentation usually encourages you to use the subprocess1 module for creating and managing child processes. The subprocess module exposes a high-level run() function that provides a simple interface for running a subprocess and waiting for it to complete. It accepts the command to run as a list of strings, starts the subprocess, waits for it to finish, and then returns a CompletedProcess object with information about the result....

The current title of this post is probably incorrect and may even be misleading. I had a hard time coming up with a suitable name for it. But the idea goes like this: sometimes you might find yourself in a situation where you need to iterate through a generator more than once. Sure, you can use an iterable like a tuple or list to allow multiple iterations, but if the number of elements is large, that’ll cause an OOM error....

Over the years, I’ve used the template pattern1 across multiple OO languages with varying degrees of success. It was one of the first patterns I learned in the primordial hours of my software engineering career, and for some reason, it just feels like the natural way to tackle many real-world code-sharing problems. Yet, even before I jumped on board with the composition over inheritance2 camp, I couldn’t help but notice how using this particular inheritance technique spawns all sorts of design and maintenance headaches as the codebase starts to grow....

One major drawback of Python’s huge ecosystem is the significant variances in workflows among people trying to accomplish different things. This holds true for dependency management as well. Depending on what you’re doing with Python—whether it’s building reusable libraries, writing web apps, or diving into data science and machine learning—your workflow can look completely different from someone else’s. That being said, my usual approach to any development process is to pick a method and give it a shot to see if it works for my specific needs....

I was watching this amazing lightning talk1 by Karla Burnett and wanted to understand how traceroute works in Unix. Traceroute is a tool that shows the route of a network packet from your computer to another computer on the internet. It also tells you how long it takes for the packet to reach each stop along the way. It’s useful when you want to know more about how your computer connects to other computers on the internet....

I needed a way to sort a Django queryset based on a custom sequence of an attribute. Typically, Django allows sorting a queryset by any attribute on the model or related to it in either ascending or descending order. However, what if you need to sort the queryset following a custom sequence of attribute values? Suppose, you’re working with a model called Product where you want to sort the rows of the table based on a list of product ids that are already sorted in a particular order....

Whenever I need to deduplicate the items of an iterable in Python, my usual approach is to create a set from the iterable and then convert it back into a list or tuple. However, this approach doesn’t preserve the original order of the items, which can be a problem if you need to keep the order unscathed. Here’s a naive approach that works: from __future__ import annotations from collections.abc import Iterable # Python >3....

In multi-page web applications, a common workflow is where a user: Loads a specific page or clicks on some button that triggers a long-running task. On the server side, a background worker picks up the task and starts processing it asynchronously. The page shouldn’t reload while the task is running. The backend then communicates the status of the long-running task in real-time. Once the task is finished, the client needs to display a success or an error message depending on the final status of the finished task....

I’ve always had a vague idea about what Unix domain sockets are from my experience working with Docker for the past couple of years. However, lately, I’m spending more time in embedded edge environments and had to explore Unix domain sockets in a bit more detail. This is a rough documentation of what I’ve explored to gain some insights. The dry definition Unix domain sockets (UDS) are similar to TCP sockets in a way that they allow two processes to communicate with each other, but there are some core differences....

While working on a multithreaded socket server in an embedded environment, I realized that the default behavior of Python’s socketserver.ThreadingTCPServer requires some extra work if you want to shut down the server gracefully in the presence of an interruption signal. The intended behavior here is that whenever any of SIGHUP, SIGINT, SIGTERM, or SIGQUIT signals are sent to the server, it should: Acknowledge the signal and log a message to the output console of the server....

I was working on a project where I needed to poll multiple data sources and consume the incoming data points in a single thread. In this particular case, the two data streams were coming from two different Redis lists. The correct way to consume them would be to write two separate consumers and spin them up as different processes. However, in this scenario, I needed a simple way to poll and consume data from one data source, wait for a bit, then poll and consume from another data source, and keep doing this indefinitely....

Consider this iterable: it = (1, 2, 3, 0, 4, 5, 6, 7) Let’s say you want to build another iterable that includes only the numbers that appear starting from the element 0. Usually, I’d do this: # This returns (0, 4, 5, 6, 7). from_zero = tuple(elem for idx, elem in enumerate(it) if idx >= it.index(0)) While this is quite terse and does the job, it won’t work with a generator....

I needed to write a socket server in Python that would allow me to intermittently pause the server loop for a while, run something else, then get back to the previous request-handling phase; repeating this iteration until the heat death of the universe. Initially, I opted for the low-level socket module to write something quick and dirty. However, the implementation got hairy pretty quickly. While the socket module gives you plenty of control over how you can tune the server’s behavior, writing a server with robust signal and error handling can be quite a bit of boilerplate work....