Both Rust and Python are widely-recognizable names in the developer’s world. Python stepped into the IT field more than three decades ago. Rust is a younger technology but it is gaining popularity, both when it comes to developers and businesses. The question is: is Rust better than Python? Or is it maybe the over way around?
This article will not provide simple answers because there are no winners or losers here. Both Python and Rust have a lot to offer, and they are great, robust technologies. This text will guide you through these programming languages’ features and show how they fit with specific projects - everything you need for the Rust/Python big picture to better understand when to choose one over the other.
Introducing Rust
Rust is a multi-paradigm, compiled language with safe concurrency capabilities and strong static typing. Rust can work as a standard high-level programming language but, at the same time, allows for performing low-level operations. Rust is a general-purpose programming language, so it is not focused on any specific project types.
A new survey, but old results – Rust is still the most beloved programming language, according to the Stack Overflow Developer Survey 2022. That’s the seventh year in a row Rust has taken that title.
Rust advantages and disadvantages
No solution is all roses. The shortlist below highlights the key Rust pros but also the cons, to give you better insight into this language. So, let’s take a look at them!
Rust advantages:
- Great memory safety.
- High-level performance.
- With Rust, it is possible to program high-level and also operate close to the hardware.
- Open-source solution.
Rust disadvantages:
- It can be considered complex compared to other programming languages.
- Many String (String, Str, OsString, Ostr, CString, Cstr) types can be confusing.
- Rust's code compiler is slightly slower compared to other languages.
Introducing Python
Let’s begin with a small clarification: the implementation described here is the classic CPython. Python is a multi-paradigm, interpreted language, with strong dynamic typing. What's more, Python is, like Rust, a general-purpose language. That means it is not predefined for any specific problem.
Due to Global Interpreter Lock, which allows only one thread to be active at the time, real concurrency is hard to achieve. This limitation was introduced to CPython mainly because Python Memory management is not thread-safe.
Python is a legendary programming language – this year, Python took fourth place in the most popular languages ranking. We do not know any single developer who is not familiar with this name. C++ is another legend. Do these languages have more in common other than being veterans on the market? Check it out by reading our Python vs. C++ comparison.
Python advantages and disadvantages
As in Rust's case, Python has both pros and cons, depending on project needs.
Python advantages:
- Beginner-friendly, easy to learn and understand.
- Simple, similar to English syntax.
- Many available libraries and tools.
- Open-source solution.
Python disadvantages:
- Not the best choice for memory-intensive tasks.
- Slow speed compared to other compiled languages.
- Requires more testing – some errors can occur only at runtime.
- Poor adaptability for mobile development.
What are Rust and Python used for?
Having introduced both technologies, now it is time to describe the project types in which these solutions can show the full range of their capabilities and spread their wings.
When to use Rust?
If your priorities are high performance, precision control over threads, and memory safety, then Rust might be your best choice. What exactly is Rust used for? Here's a shortlist of some usage examples:
Microcontroller applications or operating systems (OS) – Rust is an excellent technology for this kind of project as it can efficiently work close to the hardware.
Embedded systems – another example where you can benefit from the fact that Rust allows for direct access to hardware and memory.
Replacement software parts – with Rust onboard, it is possible to rewrite only those product parts that, for example, require better performance. Thanks to Rust's foreign-function interfaces, they can be replaced without problem.
However, these are only a few examples, not the complete list. Check out how other IT companies benefit from Rust in big projects.
For which projects is Python better?
As mentioned above, Python is a general-purpose programming language, making it fit for many areas. Below is a sample list of fields where Python is often considered the best option on the table.
Machine learning (ML) and artificial intelligence (AI) are probably the most popular Python usage examples. Projects connected with ML and AI have specific and often more significant requirements. Python is a stable and secure technology and offers dedicated tools, which means it is often chosen over other languages.
Web development – Python provides frameworks such as Flask, Django, and Pyramid for web development, to mention only the most popular choices. Python is often chosen for this type of project due to its database access, content management, data authorization functions, and simple syntax, which can help developers work faster.
Data visualization and analysis – Python allows for separating relevant data for better analysis of outputs and offers many libraries that facilitate writing code for this purpose.
Python can be used in so many project types that it is impossible to cram everything into only one article. You might want to read our previous publication if you are looking for more examples of what Python is used for.
Looking for some real-life cases? Below are some examples of how we have used Python for our clients:
- Connecting different VPC/VNets and extending on-premise resources to multiple clouds
- Using ONOS SDN controller with P4-programmable smartNICs to offload VNFs
- An automatic data center and low-level network infrastructure management solution
- Building CI/CD and testing for a full-stack monitoring and alerting service
Can Rust and Python successfully work together? We proved it with our client while building a web application for network monitoring in heterogeneous environments.
Rust vs. Python - a head to head comparison
After shedding some light on both technologies, now is the time to compare selected aspects of these programming languages to facilitate a decision on which one – Rust or Python – will work the best for you.
Performance – who is better?
High speed was never one of Python's strengths. Here, Rust will always be the winner – this language was created to achieve the best results in this field, so there is no surprise. Rust is faster because it is directly compiled into machine code without an interpreter between the code and the hardware.
What does Rust's performance level look like regarding low-level network programming? Is it still so great? How about the performance between C vs Rust?
Or maybe you are interested in numbers? In our previous publication, we compared Go vs. C++. Inside you will find a table with runtimes of these three programming languages on selected tasks.
What about security?
Both programming languages enable developers to take care of code security. Python prevents memory leaks and allows for custom configuration of memory management. On the other hand, Rust was designed with security in mind – it can be described as "safe-by-default." This language is entirely memory safe unless the developer decides to change that.
Rust offers two coding modes: Safe Rust and Unsafe Rust. Safe Rust forces restrictions on the programmer to ensure that the code works correctly. Unsafe Rust gives the developer more autonomy, but the responsibility for code safety is then on them.
Here, Rust leaves Python far behind. However, it is hard to overpraise Rust when it comes to safety. But, what is the result when comparing Rust vs Cpp?
Which code is easier to maintain?
When it comes to code security, ease of maintenance is an aspect worth a mention. Which code – Rust or Python's – is easier to maintain in the long run?
As an interpreted language with dynamic typing, Python performs type checking at runtime. On the other hand, Rust, being a compiled language with static typing, performs tasks connected with type checking at compile time. How does this difference influence code security and overall facility to maintain the code in good condition?
Dynamically-typed languages are generally more flexible, and developers can write code faster, because they do not have to worry about typing. In contrast to static typing languages (like Rust) it does not require that variable type has to be declared before receiving their values.
However, Python's flexibility can also lead to a situation when errors will not occur until runtime – the code can run even if it contains critical bugs. Rust does not let that happen. That's because code written in a statically-typed language will fail to compile until the errors have been fixed.
About garbage collection
A garbage collector (GC) is an example of automatic memory management. It manages the allocation of memory and its release for an application. A garbage collector eliminates the necessity to write dedicated code to perform memory management tasks and improve security by preventing memory leaks. GC often is not associated with great performance as it requires a halt in executing a program to clean up memory.
Rust does not use a garbage collector – instead of a GC, Rust gives a developer a decision on how data will be stored in memory. But to make their life easier and prevent memory leaks, Rust uses the concept of RAII (Resource Acquisition is Initialization). This mechanism puts the burden of creating memory cleanup code on the compiler.
On the other hand, Python does use a garbage collector. It is used to clean up unused memory while the program is running.
Error handling in Rust and Python
When it detects an error, Rust returns a value and gives developers a clue on how to fix the issue. Python only flags expectations. At first sight, error handling in Rust is more developer-friendly. It is easier to debug, and they can be found faster – waiting until runtime to find them is unnecessary. However, there are many IDEs for Python that reduce this limitation.
Does Golang handle errors similarly to Rust? Read the previous Rust or Golang comparison, to learn more.
Or if you are looking for more information about how the exception handling differed when we took a closer look at Golang and Python, check out our Go vs. Python comparison for further details.
Documentation – which has the better one?
When it comes to the variety of available documentation, Python, as the older programming language, comes out on top. Both Rust and Python provide developers with official documentation. However, Python will probably be easier to understand for more developers (especially those who have recently started their journey). It does not mean that Rust's documentation is worse – it is well-prepared but it is also complex. Rust, along with documentation, provides developers with an official open-source book, which is a great source for developers who want to learn this language.
Which language is easier to learn?
Rust is a more demanding language than Python. Its syntax is closer to that of low-level languages. That makes it probably not the best choice as the first technology to learn. The point has already been made that Python is not especially fast, but it could be said to be faster from a business perspective. With Python, as a rule the development process is shorter, and the overall product is easier to upgrade.
The simplicity and feels-like-English syntax are part of the answer to the question "Why is Python so popular?". In the text, we’ve already mentioned the most powerful features of this programming language.
Coding in Rust requires more effort and more time to master the language. What's more, sometimes, a knowledge of C++ or other object-oriented programming languages could be necessary.
Community – who has more fans?
Rust is chasing Python, but still has a way to go. Python has been on the market for longer, so it has had more time to build a large, solid community.
It is worth highlighting that both programming languages are open source, so they easily attract new developers who work on further improvements and try to develop the technologies even more, to adjust them better to their needs.
The Rust and Python communities are equally active and ready to help. However, Python provides more and easier-to-find resources and ready-to-grab solutions.
Conclusion
Developing a new project always requires many decisions that sometimes can affect how quickly the product will reach the market and even the product's overall success.
While deciding between Python and Rust is worth considering your current and future requirements. Python is a good option for projects that need easy extensibility – there are still more Python developers on the market ready to help you boost your productivity. However, Rust will be your solution when security is first on your priorities list.
