Rust for Systems Programming: Why a Python Developer Should Take a Look

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Why Rust? A View from the Python World

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You write in Python, and everything suits you. Scripts fly, libraries for every taste, and Django or FastAPI solve 90% of business tasks. But at some point, you hit a wall: you need to write a high-performance module for real-time data processing, a device driver, or simply reduce memory consumption. Python starts to "lag," and C/C++ scares you with segfaults and memory leaks.

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And then Rust for systems programming enters the stage — a language that promises the speed of C and safety you never even dreamed of. Without a garbage collector. Without null pointers. With a compiler that catches errors at build time, not in production.

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In this article, we'll break down why Rust is becoming the standard for systems programming, show real code examples, and explain how it differs from the familiar Python. Let's go!

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What is Systems Programming and What Does Rust Have to Do With It?

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Systems programming is the creation of software that works "close to the metal": operating systems, drivers, embedded systems, game engines, databases. Key requirements: maximum performance, memory control, and predictable behavior.

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For many years, C and C++ ruled here. But they have a fundamental problem: the programmer is responsible for memory management. One mistake — and you get a vulnerability or a crash. Rust for systems programming solves this at the language level: the ownership and borrowing system guarantees memory safety without a garbage collector.

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How is Rust Different from Python?

Speed: Rust compiles to machine code, Python is interpreted. Rust is 10–100 times faster on computationally intensive tasks.
Memory: In Python, memory is managed by a garbage collector (stop-the-world pauses). In Rust, a strict compiler checks the correctness of memory access.
Typing: Rust is a strongly typed language with a powerful type system (algebraic types, traits). Python has dynamic typing, which leads to runtime errors.
Concurrency: Rust allows you to write safe concurrent code without data races — the compiler won't let you shoot yourself in the foot.

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Code Examples: Rust vs Python

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Let's compare a simple task: reading a file and counting the number of lines. First in Python:

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# Python\nwith open("data.txt", "r") as f:\n    lines = f.readlines()\n    print(f"Number of lines: {len(lines)}")\n

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And now in Rust:

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// Rust\nuse std::fs::File;\nuse std::io::{BufRead, BufReader};\n\nfn main() {\n    let file = File::open("data.txt").expect("Failed to open file");\n    let reader = BufReader::new(file);\n    let count = reader.lines().count();\n    println!("Number of lines: {}", count);\n}\n

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Python is shorter, but Rust gives you control over every step: we explicitly handle the error (expect), use buffered reading. In large projects, this pays off with stability.

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Memory Safety: The Borrow Checker in Action

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The main "feature" of Rust is the compiler that ensures every reference is valid. Try to compile this code:

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fn main() {\n    let mut s = String::from("hello");\n    let r1 = &s; // immutable reference\n    let r2 = &mut s; // mutable reference — error!\n    println!("{}, {}", r1, r2);\n}\n

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The compiler will output an error: cannot borrow `s` as mutable because it is also borrowed as immutable. In Python, you would get unexpected behavior or a crash. Rust prevents this at compile time. This is Rust memory safety in action.

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