Rust vs Python: Technology And Business Comparison

Technology comparisons have become very popular nowadays. Since the wide variety of choices, people in business and companies have to pick the best ones from several technologies at once. That's why such quick guides are so useful.

Regular readers of our blog have already enjoyed a unique opportunity to get to know the differences between Python vs JavaScript, Python vs PHP and 

Python vs R.

Now it is time to take a look at the battle between Rust vs Python. 

In terms of the language, Rust is a completely different beast compared to Python. Not only because one is a compiled language, and the other is interpreted, but also because the principles underlying them are completely different. As different as the underlying languages are, they have a lot in common in ideas about how APIs should work.

Inside you'll find in-depth answers to the most frequently asked questions. Also, after reading the whole bit, you will be able to come up with an idea of what suits your project best!

Let's start!

What is Python?

Python is a powerful and clear object-oriented programming language comparable to Perl, Scheme, Ruby, or Java. Created by Guido Van Rossum, Python is a general-purpose programming language. Python is most appreciated for its elegant syntax and readable code. If you are just at the start of your programming career, Python is best for you.

Python has been ranked as the second most popular language among all programming languages. Python is used in almost all programming environments and applications such as websites, operating systems, machine learning applications, data analysis, and science, etc. Most universities and schools use Python both in their introductory programming courses and software development as it allows students to master the basics.

What is Rust?

Rust is a safe, simultaneous, and practical language. It is a systems programming language that combines robust compile-time correctness guarantees with high performance. 

It enhances the ideas of other system languages ​​such as C++ by providing guaranteed memory safety (no crashes, no data jumps) and complete control over the memory lifecycle.

"Technology from the past came to save the future from itself."

This is how Graydon Hoare, the creator of Rust, describes what he wants to achieve.

Rust has grown rapidly since Mozilla began sponsoring the project in 2009 and later announced it would be in development and release in 2010. As announced by Octoverse in 2019, Rust has become the second-fastest programming language in terms of growth and adoption by developers. Also, Rust was voted the most loved language in a 2020 StackOverflow survey. 

Rust's growth has been driven by its obvious strengths, namely high performance, and robust security. Its performance is C / C++ compliant, along with additional security checks and memory management. For example, Rust does not allow dangling pointers.

There is an opinion that software developers are some of the most progressive people on this planet. However, Rust is the exact opposite of the "act fast and break everything" mantra. However, any developer will almost certainly learn concepts that they have never heard of before.

From the innovation of systems programming for some developers on algebraic data types to Rust's approach to memory safety, everyone can find something new and extremely useful to learn. So many reasons to love Rust!

Why should we compare them?

Currently, there are many options for building various business projects, both in terms of programming languages ​​and in terms of frameworks, each of which has its strengths and weaknesses.

Many companies have been working with Python for quite some time and are very happy with it. They can quickly develop scalable systems thanks to Python's high performance and all the great features included in it.

However, there are times when performance is a major requirement, and in such situations, Python is simply not the best option. For this reason, famous brands decide to include a new language in their technology toolbox.

The client needs something that was able to offer the performance gains they were aiming for while still being (if possible) relatively easy to pick up for the people on their team, mostly used to Python and Javascript.

In this context, we ended up trying to choose between two options: Rust vs. Python.

Rust vs Python: Speed of Coding & Easiness of Coding

Coding experience is probably the most relevant but the most subjective aspect of this comparison. How easy was it to do things in each of these languages? At this point, it might be clear that when it comes to performance, Python is probably the language to go with.

The simplicity and fewer features of the language definitely make it easier to start writing code immediately, while, as mentioned earlier, the large number of new and relatively complex concepts in Rust makes it difficult to learn, and I feel it could take weeks before you will get comfortable with it. Even basic things like splitting code into different files in Rust required me to read several pages of documentation, while in Python, it was almost trivial.

The need for external dependencies was also a minor issue when working on the Rust implementation. The documentation for the frameworks and libraries I ended up using (which were popular, by the way) was often confusing, and I quickly got lost, deviating even a little from the examples given.

On the other hand, with Python, I felt much more comfortable trying different things, encountered fewer compilation errors, and navigating through the package documentation was also more intuitive.

Winner: Python

Python vs Rust: Performance

We've already had a glance at this criterion but let's discuss it in more detail.

Yes, Python is recognized for being "slow" in some cases, and the good news is that it doesn't matter, depending on the goals and priorities of your project. For most projects, this detail will not matter much.

However, you may run into the rare case where a single function or module takes too long and is found to be a performance bottleneck in your project, which often happens in string parsing and image processing.

The Computer Language Benchmarks Game lists speed comparisons between different languages when used with different algorithms. So you can see the exact figures. 

To sum up, there is no other leader between Rust and Python. Rust has a clear speed advantage. A smart way to combine the best of both worlds is if we code the most time-consuming parts of the application in Rust, we can increase the overall speed of Python code execution.

Winner: Rust

Rust vs Python: Documentation

When it comes to learning the basics, both languages ​​seem to be well learned. The official documentation for any of them is really great, and I had no problem finding unofficial resources.

When it comes to Python, it has a well-guided introduction to the language, really easy to read, and full of interactive examples that let you start trying something right away in the browser.

The Python community has done a great job of producing excellent documentation filled with simple English descriptions of functions. Compare this to other languages ​​like Java, where the documentation often contains dry API enumeration.

As a random example, consider the GUI toolkit documentation - tkinter documentation reads almost like a blog post, answering questions like "How do I ...", while Java Swing documentation contains dry descriptions that effectively repeat implementation code. In addition to this, most of the functions contain "Document Lines," which means that the documentation is often immediately available, even without having to search the Internet.

On the other hand, the Rust "Book," still great and well organized provides a much more detailed introduction to the language and many of the concepts found in Rust. From the get-go, it manages to seem more complex than Python, especially when it comes to some of the memory constraints, but while it may take a little time, I wouldn't recommend starting coding before reading at least one significant part of this book.

The clear advantages of Python are that it is easier for a beginner to learn due to its simpler translations and close similarities to English commands, as well as simpler syntax.

Winner: Python

Rust vs Python: Error Processing

Error processing in Python and Rust is entirely different. If in Python, errors are thrown as exceptions, then errors in Rust are passed back in the return value. This may sound weird at first, but it is actually a very good concept. Looking at the function, it's pretty clear what kind of error it is returning.

This works because a function in Rust can return a result. The result is a parameterized type that has two sides: success and failure. For example, Result <i32, MyError> means that the function either returns a 32-bit integer on success or MyError if an error occurs. What occurs if you need to return more than one error? Everything is different here from a philosophical point of view.

In Python, a function can fail with an error, and there is nothing you can do about it. If you've ever used the Python "requests" library and caught all request exceptions, and then annoyed you that SSL errors aren't being caught by this, you will understand the problem. It isn't much you can do if the library doesn't document what it returns.

In Rust, the situation is very different. The function signature includes an error. If you need to return two errors, the way to do it is to create your own error type and convert the internal errors to better ones. For example, if you have an HTTP library and internally it can fail with Unicode errors, I / O errors, SSL errors, all you need is to convert those errors to one type of error specific to your library and users, then you just have to deal with it. Rust provides error chaining where such an error can indicate the original error that caused it if you need to.

You can also use the Box <Error> type at any time, which will convert any error if you are too lazy to create your own error type.

While bugs spread invisibly in Python, in Rust, they spread visibly. This means that you can see when the function returns an error, even if you choose not to handle it there.

Winner: Rust

Rust vs Python: Popularity

Both technologies were ranked in the top 3 most loved languages by StackOverflow, with Rust being 1st and Python being 3rd.

As the data from stackshare.io suggests, "Great Libraries" is the main reason why over 1,022 developers love Python, and over 81 developers cite "Guaranteed Memory Safety" as their top reason for choosing Rust.

Python and Rust are open source tools. It seems that Rust with 37.3K GitHub stars and 5.85K forks on GitHub is in higher demand than Python with 25.3K GitHub stars and 10.5K GitHub forks.

Uber Technologies, Spotify, and Netflix are some of the popular companies using Python, while Rust is used by Dropbox, Sentry, and Roundscope Ukraine Labs. Python has gained wider acceptance, being mentioned in 2,826 company stacks and 3,632 developer stacks, compared to Rust, which has 39 company stacks and 105 developer stacks.

Looking at graphs from Google Trends data, Rust is no match for Python. Their stats are too different, with Rust going far behind its opponent.

Winner: Python

Rust vs Python: Community

Given its popularity, Python has the largest community contributing to the development of the language. Python has an active and helpful community like google groups comp.lang.python, StackOverflow, Reddit, etc.

As for the Rust, it can boast of a small but delightful community. There is crates.io where users can share libraries and docs.rs where they are documented. There is a compiler from Clippy and automatic formatting from rustfmt.

Also, there are official and unofficial chats, subreddits, user forums, questions on StackOverflow, and conferences around the world. What more could you want in a community that prioritizes friendliness?

Verdict: Draw

Rust vs Python: Easiness to Learn & Learning Curve

One of the demotivating things about Rust is the high entry threshold. While it takes 1–2 days to get started productively with most languages, Rust will likely take one or two weeks.

This is due to the need to master a lot of new concepts that are not used by other languages, as well as the fact that many errors occur during compilation. All exceptions will need to be handled immediately. You cannot just write the working code and add them later, as in the same Python.

Also, since Rust is still fairly new, it doesn't have all the libraries you might need. Apart from the official documentation and different questions on StackOverflow, there are not many tutorials out there.

The good news is that once you learn the concepts and compile the program, everything will go smoothly. Besides, thanks to backward compatibility, the programs will work in twenty years.

Given the robustness of the code and the fact that Rust is supported by several large corporations, it is worth spending a week or two studying it, regardless of the drawbacks.

But still, despite all the advantages, Python is easier to learn.

Winner: Python

Rust vs Python: Talent Pool

To start with, although only 5% of developers use Rust, they have a huge enthusiasm for the language.

And despite all that is written about learning easiness, there are still those who want to study this language (which is confirmed by the StackOverflow survey). 

Of course, the numbers tell us that it is much harder to find Rust devs but don't be so pessimistic. These guys are well worth it as well as the searching itself.

Today Rust is popular among game developers, graphics, and operating systems. However, for obvious reasons, the number of stationary places where narrow-profile Rust experts would be required in the world is extremely small. 

Nevertheless, while there are no prerequisites that the language will sink into oblivion, it looks more like a systematic capture of the world. 

This means that good skills in using Rust in the future will help devs find a high-paying, interesting job both in your country and abroad.

However, we can say firmly, Python's talent pool is much broader.

Winner: Python

Rust vs Python: Security

So, as you know, Rust is a systems programming language that is incredibly fast, prevents crashes, and (what is probably the most crucial for some) guarantees thread safety.

One of the key features of Rust is the use of technologies that are well known in the academic environment but rarely used in modern programming languages. Old, reliable, and sometimes forgotten technologies, but above all, extremely well-functioning.

These technologies are used primarily for one purpose: security.

Safely and efficiently managing computer memory is one of the toughest challenges in any programming language. Python, for instance, has a garbage collector that always looks for unused memory and cleans it up as the program runs.

In other languages, such as C and C ++, programmers must explicitly allocate and deallocate memory on the fly. Since memory issues are resolved before the program runs, this approach is better from a performance optimization standpoint.

Rust is more than secure: many of its core concepts focus on fixing memory leaks and other security issues. In an era where software is vital, security is imperative.

Winner: Rust

Machine Learning: Rust vs Python

Unlike Rust, Python is a great industry standard language that can handle both machine learning and web development tasks with ease. 

Our development team is very familiar with this language and have used it in various projects in the field of web learning and machine learning.

Python has many general machine learning-specific libraries, including TensorFlow, Pytorch, Pycaret, and Keras. It also has data processing and visualization packages such as Numpy, Pandas, and Matplotlib. 

If your software requires machine learning algorithms, big data processing, and an internal server, Python would be the way to go.

Winner: Python

Rust vs Python: Cost of Development

Talking about the costs future customers will have to spend, we have almost equal results.

As per the ranking of the most paying technologies, Rust developers have an average salary of $74k, while Python's employees can boast of $59k.

Therefore, Python development can cost you a bit less compared to Rust. Please, also add here the low probability to find as many developers to have a selection on a competitive basis.

However, you still have a chance to find cheap solutions for every language.

Winner: Python

Let’s sort all the gained knowledge out with this simple table. With its help, you can ease your pain of choice.

Main Pros and Cons

Pros of Python

• Great libraries
• Readable code
• Rapid development
• Large community
• Open-source
• Object-oriented
• Dynamic typing
• Easy to learn
• Great documentation
• Machine learning support
• Ease of development

Cons of Python

• Python programs are considered to be one of the slowest. 
• Strong language dependency on system libraries. 
• The Global Interpreter Lock (GIL) does not allow multiple Python threads to execute concurrently in a CPython implementation.

Pros of Rust

• Zero cost abstractions
• Move semantics
• Guaranteed memory safety
• No data streams
• Generics based on traits
• Pattern matching
• Type inference
• Minimum lead time
• Efficient C bindings

Cons of Rust

• Some code redundancy.
• High intensity of language development and, as a result, lack of good relevant literature for the study.
• The need to clearly and unambiguously prescribe the parameters for compilation.

What is Python good for?

Python is broadly used in many fields, from system administration to Data Science.

System administration

System administrators use Python to automate tasks. This language is simple, powerful, and supports special packages that make it more efficient. And best of all, it is installed by default on all Linux servers.

Python's brevity makes it easy to read code and find weaknesses. Language formatting is part of the syntax.

Scientific research

Python has certain libraries that are useful for research and computing:

• NumPy - an extension that adds support for matrices + multidimensional arrays. Also, it has mathematical functions for working with them;
• SciPy - a library with scientific tools;
• Matplotlib is a library for dealing with 2D and 3D graphics.

Because of the availability of libraries and the ease of learning the language, many scientists choose Python - it is especially popular among mathematicians and physicists.

Data Science

Python is one of the most used languages ​​in Data Science. It is used for writing machine learning algorithms and analytical applications. It caters to data storage and cloud services.

It also helps analyze data from the Internet. For example, Google uses Python to index sites.

What is Rust good for?

Rust code conforms to four main concepts in programming: procedural, parallel, functional, and OOP. Therefore, Rust is a versatile language that can be applied in many areas. For example:

• programming client applications and web servers;
• blockchain;
• creating your own operating systems;
• writing programs and applications for monitoring systems and servers;
• general-purpose software development;
• infrastructure creation;
• writing engines for browsers and games.

Software and operating systems

Rust was designed specifically for systems development. It has all the necessary memory and vulnerability mitigation functions to be used to create key programs and operating systems. The only thing that hinders this is the lack of support from the hardware, whose manufacturers are used to the fact that everyone uses C or C++. Here are some successful implementations:

• multi-threaded engine for Servo browser;
• Firecracker hypervisor, designed mainly for serverless architecture;
• microkernel OS Redox.

Blockchain projects

One of the most important requirements of blockchain networks is the high speed of work within the network with a low level of server load. Many blockchains (for example, Ethereum or Bitcoin) use C++ functionality to implement such conditions. With that said, building infrastructure in Rust will be more productive. Here are examples of blockchain usage:

• service for the development of blockchain projects Solana;
• framework for the same purposes Exonum;
• unofficial client for Ethereum and Bitcoin - Parity.io;
• decentralized platform for storing and working with information MaidSafe;
• heterogeneous blockchain networks polkadot.network.

Web applications

Rust has everything you need to develop web applications. Its functionality is suitable for both the frontend and backend. To create a frontend, you can use Yew, the React analog for Rust. Convenient and functional server development is available in actix-web, a highly efficient framework that includes working with HTTP / 2.0, WebSockets, and TLS. But, of course, Rust is not limited only to them. There are many technologies: pencil, conduit, rocket, gotham. Here are examples of projects:

• Coursera, an online learning service created by Stanford educators;
• file and cloud hosting Dropbox;
• an OpenDNS project that helps find available DNS servers.

Neural networks

Today, Rust neural networks are mostly experiments. The language looks quite attractive for machine learning: low-level memory handling and, at the same time, the use of high-level abstractions, as well as high performance of the language. All this works on the Rust API and promises to be very popular for it. 

But so far in the Rust ecosystem, there are practically no 100% tested and secure frameworks for developing neural networks that can compete with Python libraries.

Summary

We don't think there is a direct connection between Python and Rust. For example, Python shines at scientific computing, and we don't think this is something that Rust will be able to do soon, simply because of how much work it takes.

Likewise, it really doesn't make sense to write shell scripts in Rust if it can be done in Python. That being said, we are convinced, like many Python programmers who started learning Go, even more people will start looking at Rust in some of the areas in which they previously used Python.

It is a very powerful language with a solid foundation, a very liberal license, a very friendly community, and a democratic approach to language development.

However, we recommend that the choice of the most suitable technology is held under a thorough eye of a highly experienced consultant. 

At Ideamotive, you can find not only technical consultancy but also hire experienced Python developers and skilled Rust developers.

Don't hesitate and contact us right now!