5 More Must-Know Python Concepts 🐍

Let's take a look at five more fundamental concepts that every Python developer should have in their toolkit. 🛠️

Read: https://www.kdnuggets.com/5-more-must-know-python-concepts 🔗

#Python #Programming #Coding #Developer #TechTips #LearnPython

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✍️ Pyneng — a large base for Python and network automation!

Detailed documentation and educational materials. The site contains lessons on Python syntax, working with files, functions, OOP, as well as separate sections on network technologies. The materials are presented with a large number of examples and practical tasks.

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When you're doing a parser or migrating a site, there's often a pile of unreadable HTML markup on the screen. Converting this into neat Markdown is usually a hassle.

In the open code, I found a convenient tool called python-markdownify, which precisely solves the problem of converting HTML to Markdown.

The logic is simple: you take bulky HTML and get a clear and well-structured Markdown as a result.

The tool is easily customizable. You can clean up the necessary tags, change the format of headings, and neatly process tables and images. All of this can be configured.

It's installed via pip. It can be used both from Python code and from the command line, converting files in batches.

pip install python-markdownify

If desired, you can inherit and redefine the conversion rules for your own cases. The extensibility is fine there.

If you have to process large amounts of text or migrate a blog, the library saves a lot of time that would otherwise be spent on tedious work with regular expressions.

➡️ Link to GitHub
http://github.com/matthewwithanm/python-markdownify

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Advice for Python, UV, and Docker 🐍🐳

Sometimes dependencies are better installed separately from the code — this noticeably speeds up the compilation of Docker images 🚀

The idea is simple: first, we install dependencies, then we add the project 🛠

Why is this necessary:
• Docker caches layers and does not rebuild them unnecessarily ⚡️
• if only the code changes — the dependencies are taken from the cache 💾
• if the dependencies change — only the corresponding layer is rebuilt 🔁
• without this, any minor change triggers a full reinstallation 🔄

Example:

RUN --mount=type=cache,target=/root/.cache/uv  --mount=type=bind,source=uv.lock,target=uv.lock  --mount=type=bind,source=pyproject.toml,target=pyproject.toml  uv sync --locked --no-install-project

COPY . /app
RUN --mount=type=cache,target=/root/.cache/uv  uv sync --locked

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Limiting program resources using the resource module 🛡️

import resource
import sys

# 1. Limiting the size of RAM (soft and hard limits in bytes)
# Limit the memory to ~50 MB
memory_limit = 50 * 1024 * 1024 
resource.setrlimit(resource.RLIMIT_AS, (memory_limit, memory_limit))

# 2. Checking the protection's working
try:
    print("Trying to allocate a huge array of memory...")
    huge_list = [i for i in range(10_000_000)]
except MemoryError:
    print("The limit worked! The program didn't crash, but caught the error.")

# 3. Finding out how many resources the script has already consumed
usage = resource.getrusage(resource.RUSAGE_SELF)
print(f"Peak memory consumption (in KB): {usage.ru_maxrss}")

Protecting the server from "greedy" code 🔧

When you run someone else's code, process user files, or write parsers, there's always a risk of a memory leak or an infinite loop. If such a script runs on the server, it can fill up all the RAM and bring down neighboring important processes (for example, the database). The built-in resource module (works on Unix/Linux/macOS) allows you to strictly limit the program's appetites.

— Safe environment: You can limit not only RAM (RLIMIT_AS), but also CPU time (RLIMIT_CPU). If the code goes into an infinite loop, the system will gracefully terminate it after a specified number of seconds.

— File system control: Using RLIMIT_FSIZE, you can prevent the script from creating files larger than a certain size. This will save the server's disks from being accidentally overwritten by gigantic logs.

— Precise audit: The getrusage function provides detailed statistics on the current process: how much time the CPU spent on calculations, how many I/O operations there were, and what the maximum amount of memory used was during the entire operation.

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⚡️ How Redis counts billions of unique values while barely using memory

There's an algorithm called HyperLogLog. It allows you to roughly estimate how many unique elements have passed through the system, using about 12 KB of memory.

The idea is simple: Redis doesn't store the elements themselves.

It does the following:

- Takes an element
- Calculates a hash from it
- Uses part of the hash as a cell number
- Checks the other part to see how many consecutive zeros it contains
- If the new number is larger than the old one, it updates the cell

Why does this work?

Because a long series of zeros in the hash is rare.

For example:

- 1 consecutive zero - quite common
- 5 consecutive zeros - less common
- 10 consecutive zeros - about a 1 in 1024 chance
- 20 consecutive zeros - a very rare event

If Redis sees a very rare pattern, it means that many different elements have likely passed through it.

Redis uses 16,384 small counters. Each stores the maximum "rarity" it has seen for its group of elements.

Then Redis combines these values mathematically to get an estimate of unique elements.

Not an exact number, but a very close approximation.

The main trick of HyperLogLog:

it can handle millions or even billions of values, but memory hardly increases at all.

That's why Redis can count unique users, IPs, requests, or events without huge tables and lists.

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