Rust vs Python – Is THIS the Language of the FUTURE?

With Rust's popularity rising among the developer community, it is essential to ask, will Rust replace Python? Which one is best for you? And, should you start learning Rust?

This article gives you a complete picture of the Rust vs. Python landscape. By the end of the article, you'll have a clear idea of what Rust and Python offer. In addition, you know to pick the right programming language for your project.

Let's get started.

What is Rust?

Rust is a multi-purpose, high-performance programming language. It is statically typed with a similar syntax to C++. With Rust, developers can create memory-efficient and performance-optimized applications.

Rust's core idea is to compile everything before the program runs. Therefore, it doesn't house a runtime solution, making it ideal for writing critical applications. Moreover, it offers excellent concurrency and memory management rules powered by its ownership model.

The best way to describe Rust is to have low-level access similar to C++ while having the safety of a high-level programming language such as Python.

Who uses Rust?

Rust is popular among startups and big companies. Rust is a multi-purpose programming language that uses servers, tooling, web applications, game development, data science, etc.

Some prominent industry names using Rust include Coursera, Dropbox, Figma, Microsoft, Facebook, and Discord.

Each company uses Rust differently depending on requirements. For example, Figma, a collaborative web design tool, uses Rust to boost its platform's front-end performance. They have entirely written Figma's multiplayer syncing engine from TypeScript to Rust.

Coursera, a popular online learning platform, uses Rust to power web-based assignment solutions for students. In addition, Dropbox uses Rust for its file synchronization engine due to better testing and debugging, making it easy to manage complex databases and concurrent code.

Rust key features

Rust excels as a multi-paradigm programming language. However, its key features separate it from Python. It offers a good combination of low-level access with high-level usability, matching C++ performance. Its key features include:

• Memory Safety: Rust is a memory-safe programming language. Thanks to its ownership model, it achieves memory safety without relying on a garbage collector. It also has a robust type system that ensures no runtime errors can occur as Rust's compiler analyze memory during compile time. 
• Zero-cost abstraction: Rust doesn't punish developers for deploying abstractions. This means you get no performance penalty when you use abstraction, and the code runs without overhead. The zero-cost conception helps do code maintenance or add new features.
• Thread safety: Rust's ownership model approach to concurrency includes a thread-safe approach. It protects the program from running into race conditions at run time. Programmers using Rust can use Rust's mutable option to give access to a variable and then purge it once not needed, leading to a thread-safe environment.

Apart from these, Rust also offers easy-to-understand error messages, type interface, pattern matching, and move semantics.

What is Python?

Python is a high-level programming language. It is known for its scripting capabilities and ease of use. Moreover, it is also an excellent pick for visualization, automation, testing, and much more.

Python is interpreted and supports object-oriented paradigm. It also supports dynamic typing and binding, which is crucial for Rapid Application Development(RAD).

Who uses Python?

Python is a popular programming language. It is used widely in different sectors, including Finance, Government, Education, and IT. Python's use cases include web development, data analysis & machine learning, scripting, automation, software prototyping, and more.

Google is at the forefront of using Python for its projects. Their founders are vocal about how Python can help them achieve better results, as Python offers better prototyping and ease of maintenance. 

Next, we have Facebook. They released a post in 2016 showcasing how Python is used to write and maintain multiple infrastructure management services. These include Dapper, FBOSS, and TORconfig; each offers unique functionality to Facebook's core infrastructure.

Spotify also uses Python to power its back-end services, including data analysis. According to Spotify, they like Python's ability to fit in a fast development pipeline.

Python key features

Python is a popular programming language. The key features that make it so popular among developers and learners include the following:

• English-like syntax: Python syntax is similar to English in many ways. It is identical to writing pseudo code, which appeals to most learners and engineers. Apart from learning advantages, Python's English-like syntax makes development faster. 
• Interpreted: Python is an interpreted language. It enables developers to have flexibility compared to a compiled language. Moreover, interpreted languages are genuinely platform-independent. Python, an interpreted language, also brings other benefits, including faster prototyping and easy error identification.
• Dynamically typed: Python is dynamically typed, which makes it easier to understand. The interpreter takes care of determining variable data types at runtime.
• High-level language: Python is a high-level language that makes it easy to work with.
• Object-oriented programming language: Python supports an object-oriented model that lets programmers implement concepts such as Inheritance, Polymorphism, and Encapsulation. It helps developers to map things similar to how the real world works, enabling them to write code faster and more efficiently.

Apart from these features, other Python's key features include a large community, an extensive standard library, open source, platform-independent, GUI support, etc.

Rust Pros and Cons

This section takes a look at Rust's pros and cons.

Pros of Rust

• Rust has low memory usage. This is possible because of Rust's ability to offer excellent memory control without using Garbage Collector.
• Rust is an excellent performance out of the box. It manages to match C++ performance. That's because it is designed to perform. Moreover, it also offers LLVM optimizations. The other Rust features that improve the system's performance include slices, HashMap API, and more.
• Rust also offers easy-to-understand error messages. That's a boon compared to other programming languages that fail to do so. The compiler provides a detailed error so you can work with them with as much information as possible. 
• Rust offers a solid toolchain for better development workflow. Here, you get a Cargo package manager capable of providing package management, documentation generation, and test runner.
• Rust is statically typed, ensuring that type errors don't creep in during runtime.
• Rust supports cross-platform development.

Cons of Rust

• Rust's borrower checker introduces complexity. This requires Rust programmers to think as a borrow checker and resolve any issues with it.
• Rust doesn't capture stack traces, which makes errors hard to debug. However, community-based tools already provide a workaround, such as using the error chain library.
• Rust is a new programming language and still growing. Its early phase means developers must deploy workarounds to overcome drawbacks, and libraries are still in progress.
• Rust's documentation is still a work in progress. It can lead many developers to look for other resources or longer debug times. 
• Rust compile time is slower due to several checks to ensure error-free code development.

Python Pros and Cons

This section covers Python's pros and cons.

Pros of Python

• Python is an easy-to-learn programming language thanks to its English-like syntax.
• Python offers an extensive library for easier and faster development across different spheres, including machine learning, web development, testing, automation, scraping, visualization, etc.
• Python is great for rapid application development. This is because of multiple factors, including dynamic typing, easy-to-learn, shorter program code, and extensive availability of libraries.
• Python is interpreted, which means benefits like faster error checking and the ability to execute independently of the platform.
• Python is truly portable and follows the write once, run anywhere philosophy. As Python is pre-compiled, its Python code can run anywhere using the platform-specific Python Virtual Machine(PVM)
• Python is dynamically typed, which doesn't require programmers to type variable types. Its interpreter then determines the data type during runtime.

Cons of Python

• As Python is not a low-level language, it is not the fastest programming language. However, it doesn't mean it is not ideal for real-world applications, but if you're building a critical application, there are better choices than Python.
• Due to the dynamic type approach and use of an interpreter, Python suffers from runtime errors. This makes it harder to debug.
• Python memory consumption is higher due to the flexible data type, making it unideal for memory-sensitive apps.
• Python is not optimized for database access. So, if you're working with a data-focused application that requires writing complex queries, there are better fits than Python.
• Python is not an ideal pick for developing mobile or desktop applications due to its high memory consumption and limited availability of Python mobile frameworks.

Rust vs. Python Comparison

In this section, we'll look closely at Rust vs Python. Then, we'll compare them via different criteria. If you're in a hurry, check out the Rust vs Python table.

Popularity

When it comes to popularity, Python is the winner. This is because of multiple reasons, including its ease of use, usage in the ML/AI sphere, and the most obvious factor, age. 

Python is almost 3 decades old, from its inception in 1991 when Dutch programmer Guido van Rossum released its first version. In contrast, Rust was released in 2006. And, only recently, it has started gaining popularity.

Even though Python's popularity is more than Rust, it doesn't make Rust a loser. 

On the contrary, in the last 7 consecutive years, Rust has been the most loved programming language in the StackOverflow developer survey. However, it may not gain as much popularity as Python, considering Rust has a steep learning curve.

Learning Curve

Python is easy to learn and get started with. Its ease of learning begins with its English-like syntax and dynamic type approach. On top of that, there are excellent resources for learning Python, making it more accessible to the learning audience.

Rust, on the other hand, has a steep learning curve. Its core philosophy is to offer a tool to create memory-safe programs. To do so, it introduces many new concepts, such as the ownership model. If you're a beginner, you may find it way more challenging. Experienced programmers must invest time and effort to become proficient at Rust.

Apart from that, Python also has a rich ecosystem where you get access to frameworks, libraries, and modules. Furthermore, it makes it easy to use Python, considering it offers good documentation. Lastly, you can write a program in fewer lines with Python than with Rust.

Performance

Performance-wise, Rust wins hands down. It can interact directly with the hardware as the code is compiled into machine code. Rust's memory management approach also means better performance as it enforces memory checks during code compilation.

Python lacks speed because it is interpreted. This means that the Python Virtual Machine(PVM) must first interpret the code before it can interact with the hardware. However, Python's goal is never to be fast but to offer more flexibility and ease to use.

Memory Management

Both Python and Rust aim to be memory-safe. However, Rust takes memory management too seriously and implements it through its unique ownership model, where variables need to be borrowed to use. Once the borrowing period ends, Rust's borrow checker removes variable references automatically, ensuring proper memory management.

Additionally, the Rust ownership model forces programmers to use stack and heap to store values in the memory. Heap offers a less organized way to store values, whereas stack provides a faster, more organized way to store and access values. However, programmers can use the heap to store unorganized values and then use the stack to store the heap pointer for faster access.

On the other hand, Python is also memory safe but uses a garbage collector, which can lead to runtime race conditions.

Security

Rust takes security very seriously, and that's obvious from its ownership model. It also offers proper Secure Rust guidelines for developers who want to build bullet-proof applications. Also, as Rust is compiled before the program runs, most errors and security issues are highlighted beforehand. Lastly, the Rust team actively patches any security vulnerabilities, ensuring a safe Rust ecosystem.

So, when you start developing with Rust, you use their default, “safe-by-default” mode. It also offers another mode known as unsafe Rust, which gives developers more flexibility to work with their code and makes them responsible for the app's security.

Python is also very secure. However, it does lack in both memory and thread safety. This means developers must do the grunt work(such as memory management) to secure their applications.

Error Handling

When it comes to error handling, Python uses exceptions to handle errors. So, a developer can explicitly mention exception which works in addition to already available built-in exceptions.

In Rust, error handling works differently. Rust offers two error categories, known as recoverable errors and unrecoverable errors. The developer must resolve the unrecoverable errors because Rust enters the panic macro once it happens. Here, you get every detail of how to resolve the issue. In case of a recoverable error, the user must enter the correct input or resolve from its side. However, Rust's inability to provide stack traces can leave developers who need help to be knowledgeable. Plenty of chain tools provide the much-necessary stack information for debugging.

Overall, Rust offers a better error-handling model, which guarantees memory safety, improves user-friendliness, and offers reliability & consistency.

Employability

Most learners learn a programming language because of the prospect of making money. So, where do Rust and Python stand when making money? 

Here, Python seems to win by a large margin. This is because most industry players use Python more often in their processes. That's why most IT companies are looking for skilled Python programmers. Additionally, Python's broad use cases mean plenty of roles across the industry, including data analyst, machine learning expert, full-stack developer, and so on.

• Practice Python interview questions

Rust, on the other hand, has a limited market demand. However, even if it is relatively new, its demand grows as more companies embrace Rust in their product or service. This means there is good demand for Rust programmers, but lower than that of Python programmers.

• Practice Rust interview questions

Both Rust and Python programmers are paid adequately according to industry standards.

Garbage Collection

Python relies on garbage collection to free up memory. Rust, however, doesn't use a garbage collector as it handles memory at compile-time rather than run time.

Scalability

Regarding core design, Rust offers a more scalable architecture thanks to proper memory management, error handling, and the ability to interact with the hardware directly. However, Python can also be used to build scalable solutions, primarily because of available vertical or horizontal scaling options.

Community and Documentation

Python has a rich ecosystem and a vast community. It also offers excellent documentation which covers almost every aspect of its programming language. So, if you're developing in Python, you'll find help instantly in official documentation, video, or community.

Rust does fairly in community and documentation. The fact that it is new means a small community with fewer resources to fiddle with. However, its documentation has grown strong each year and offers a comprehensive outlook on what Rust offers.

Which one should you learn?

Choosing between Python and Rust takes work. Both are excellent programming languages. However, you may find that each has its strengths and weaknesses. Rust's primary goal is to provide developers with the tool to create bug-free, high-performance apps, and services. Its memory management is unmatched, but it can take longer for teams to ship prototypes and final products due to its strict development rules.

In short, you should learn Rust if:

• You want solutions that use concurrency at its core.
• You want to scale your applications to meet users' demands.
• You want to do low-level programming with access to logical and functional syntax.
• You want your code to be easily maintainable, as Rust's old codes are compatible with newer versions.
• You want to benefit from the Rust high demand in the market.

Python, on the other hand, excels at prototyping. As a result, learning is easy, and teams can start with Python faster than Rust or any other programming language. Moreover, Python's modern features, such as functional programming, object-oriented support, and abstractions, are well known to aid development. On top of that, Python is easy to maintain.

You should learn Python if:

• You want to do a data science project and benefit from its extensible visualization options.
• You want to create web apps that are easy to develop and maintain.
• You want to become in demand as a Python developer.
• You want to do web scraping or data extraction.

If you're entirely new to programming, it is better to start with Python and shift to Rust. For experienced programmers, Rust can feel more powerful. However, you should consider future goals before learning any of these programming language.

In short, always remember that Python and Rust are programming language tools.

They are a way for you to create your desired output. So, pick a tool that lets you reach your goals.

When to use it?

In the previous section, we discussed which one should learn. However, it doesn't guide you as you still need to figure out when to use Rust or Python. So let's resolve it in this section.

When to use Rust?

Rust's low-level performance, zero-cost abstraction, and excellent memory management make it ideal for the following purposes:

• Programming an operating system or microcontroller applications as you can connect to the hardware with Rust's ability to do low-level programming.
• Rust is also an excellent pick for projects where you need to rewrite replacement software parts' programming. Rust offers an excellent foreign-function interface so that you can write better-performing code for these parts.
• If you're working with Embedded systems, you can make the most out of Rust as it can directly access the underlying hardware, including CPU, memory, and storage.

These're not all the use cases. To better understand Rust's ability to solve problems, you can see how companies like Figma or Coursera solve their proprietary platform-specific issues.

When to use Python?

Python is a general-purpose programming language that fits various use cases. These include:

• Python is an excellent pick for web development. It has a vibrant web ecosystem where you can use Python frameworks such as Django and Flask to create your web app.
• Python is very effective in data analysis and visualization. It has a rich visualization library and frameworks.
• Python is also a popular machine learning and artificial intelligence choice. It offers dedicated tools and libraries to do ML and AI projects.

Conclusion: Which One is Better?

There is no real winner when it comes to programming language. At the core of each programming language, a philosophy guides it. Python and Rust's core philosophies are different.

Python's philosophy is ease-of-use, prototype-friendly, and maintenance-friendly. On the other hand, Rust primarily focuses on giving programmers the tool to develop bug-free, high-performance solutions. This makes Rust hard to learn and use. It also means developers spend more time perfecting the solution before releasing it. However, an almost perfect Rust app means less technical debt and maintenance.

You can choose Python for scientific research, data science, and web development. However, if you're doing neural network development, WebAssembly development, or blockchain development, Rust seems to be a better choice.