When there are so many different languages out there to choose from, how do you determine which is going to be the best fit for your project? Take Python and Rust, for example—they have a lot in common when it comes to how APIs should function, but that's where many of the similarities end. One is an interpreted language, while the other is compiled, and the principles underlying them are completely different. You'll find loyal adopters on both sides that swear that one is better than the other.
But is one side right? Let's find out!
In this article, we'll provide answers to some of the most frequently asked questions for both languages, and by the end, you'll hopefully be able to determine which of the two will best suit the needs of your project.
You can find Python being used across most if not all programming environments, and it's often used to build applications for websites, operating systems, machine learning applications, data analysis, and science.
In general, the applications for Python are vast, but there are a few standout areas where it can be most beneficial. We've broken them down below.
System administrators face a multitude of difficulties and obstacles on a regular basis. Between controlling users and managing disk space, operations, tools, and backups—there's a lot that can go wrong. Shell scripts can be used to make these tasks a bit easier, but unfortunately, they come with some rather frustrating restrictions. If someone were to use Python instead, however, this issue would be resolved because Python is a full-featured scripting language.
When it comes to system administration, automation is key, so many administrators end up turning to Python. Not only is the language straightforward and robust, it also supports special packages that make it even more efficient in this capacity. Additionally, it's installed by default on Linux servers, which makes it an even more attractive option.
Scientific research often requires a massive amount of data and rigorous computing in order to carry out engineering tasks efficiently, and the outcome of this research is generally to acquire analytics and insights based on the data collected.
Many people make use of applications like Microsoft Excel and Google Sheets to carry out data operations. While these can be powerful tools in the right hands, they have limitations that can be overcome simply by using Python.
Python also has a number of libraries that are ideal for anyone in research and computing. These libraries make learning Python in this capacity easy, and many scientists, physicists, and mathematicians choose Python for this reason. If you'd like to take a look at some of the most popular ones, check out NumPy, SciPy, and Matplotlib.
Data science includes estimating and concluding valuable insights and information via a huge amount of data, and these massive data sets are not sorted and preprocessed. Data science is capable of making associations between distinct datasets but acquires powerful computational sophistry.
Currently, more than 70,000 libraries in the Python Package Index that offer support to help someone get data science tasks done. Python offers a powerful toolset to carry out numerous operations, from predictive causal analytics to prescriptive analytics. Even Google sees the benefit of using Python for its data science efforts—they use it to index websites.
Today, Python is consistently used over other programming languages in various industries as a result of its cutting-edge machine learning and artificial intelligence functionality. It is versatile, neat, quick to write, and easy to manage.
Python’s biggest advantage is the massive availability of libraries and frameworks that it has to offer. It integrates with enterprise-wide databases and software, and it is easily integrable with PHP and .NET. Python’s asynchronous coding style is flexible and easy to handle across complex coding.
When it comes to finding a job in development, having Python on your resume makes you more likely to land a role. A large number of startups and large organizations use Python as their primary backend programming language.
Despite its popularity, Python does have a few drawbacks. As it's an interpreted language, it is slower than compiled languages like C++ and Java. The dynamic nature of Python makes it easy to debug, but errors often don't show up until runtime because programmers do not need to declare variables as they code. As long as you’re constantly testing your code, this need not be too much of an issue.
Rust is an open-source programming language that was sponsored and released by Mozilla Research back in 2010. Since then, it's become the second-fastest programming language in terms of growth and adoption by developers, and it was voted the most loved and wanted language in a 2020 StackOverflow survey.
The growth and adoption that Rust has experienced has been driven by many of its strengths including security and high levels of performance. It is a statically-typed programming language invented for safe concurrency and memory management, and it's also C/C++ compliant!
Rust has a lot of different use cases, with more being added every day, but there are a few standout areas where it can be most beneficial. We've broken them down below.
Rust is used to create software applications like game engines, operating systems, file systems, browser components, and simulation engines for virtual reality. It makes systems programming accessible by combining power with ergonomics, and in this way, the software can be made that is less prone to bugs and security exploits. It also includes powerful features like zero-cost abstractions, safe memory management, and fearless concurrency.
Rust is an ideal choice for blockchain technology. Blockchains require a high speed of work within the network with a low level of severe load, and building infrastructure in Rust is more productive than using other languages. Many blockchains use C++ functionality to implement such conditions, so its compatibility makes it an ideal language to use for this.
Web applications are complex, distributed systems, and users interact with web or mobile front-end user interfaces. With that being said, users rarely see the complex environment consisting of backend services and the software infrastructure components that respond to user requests.
Web services can be built in Rust, and it can be used to build efficient, reliable applications more quickly while maintaining flexibility and low-level control. Developers can use Rust to build fast and secure web applications using frameworks like Rocket.
Machine learning and neural network algorithms can be written using Rust libraries. NeuroFlow is a neural network Rust crate that relies on three pillars: speed, reliability, and speed again.
Rust can be used to write optimized low-level code with minimal dependencies, and the language looks quite attractive for machine learning as a result of its low-level memory handling, use of high-level abstractions, and high performance of the language.
Rust is a great programming language for projects, and it offers developers with the ability to compile native, static binaries that can be easily deployed. Rust programming offers speed and safety for various tasks like developing web applications, building distributed services, and creating cross-platform applications that employ powerful command-line tools.
Rust enforces strict safe memory allocations, secure coding, and concurrency support. It can accomplish goals without having a garbage collector, and it provides several mechanisms for concurrency and parallelism.
Rust doesn’t have the level of monkey patching supported by languages like Python and Perl. It's not as easy to develop code in comparison to scripting languages like Python or Ruby, and the compiler is slow compared to other programming languages. Learning Rust is also not as easy of a task in comparison to other languages, so developers may take longer to understand and navigate its complexities.
Python is a general-purpose programming language but Rust is decidedly for systems programming. Python can be used to quickly develop scalable systems as a result of its simple features, and this makes it easier to write code. In Rust, there are many complex features which makes it more challenging and time-consuming to learn.
🏆 WINNER: Python
Pure Rust code offers the best performance between the two, and it's around 12x faster than Python. Its performance matches that of C/C++, and Python is slower in comparison. Rust also provides developers with a good combination of high performance and security when compared to Python, and it improves processing speed.
🏆 WINNER: Rust
Both languages are extensively learned, and the official documentation of both is pretty comprehensive. In Python, the introduction to the language is well defined, and there are many examples available that make it easier to understand. The Python community has done a fantastic job with documentation, and most functions are described in simple English. Documentation of Rust is well-organized and has rich content when it comes to an introduction and functions. When comparing these two, Rust's documentation is more complex than that of Python, so Python wins this round.
🏆 WINNER: Python
Error processing for Rust and Python is quite different from one another. Python handles errors by throwing an exception, and Rust handles errors by returning a value. In Rust, there are errors in the function signature and very visible, but bugs in Python are pretty hidden and not obvious until runtime. As a result, Rust has the best error processing compared to Python.
🏆 WINNER: Rust
Rust and Python both were ranked as two of the most loved languages by StackOverflow with Rust coming in at first place and Python in third. Rust has 37.3K GitHub stars and Python has 25.3K.
When it comes to which are more readily adopted by organizations, however, Python is a clear winner. Uber, Spotify, and Netflix all use Python. With that being said, as Rust becomes more mainstream, it's likely to have more mainstream adoption as well. Currently, Dropbox, Sentry, and Roundscope Ukraine Labs use Rust primarily. Until Rust picks up more steam, Python is the winner for this round.
🏆 WINNER: Python
As it's more established, Python has the largest community of the two languages, and as both are open source, community contributes greatly to the development of the languages.
🏆 WINNER: Python
As we've already touched on, Rust has a high barrier to entry. Most languages take one to two days to start production, but Rust will take one or two weeks. Rust is relatively new, so it may not have the libraries that you need, but this is not the case with Python. Python is significantly easier to learn in comparison to Rust.
🏆 WINNER: Python
Despite its popularity, only 5% of developers use Rust and most of that adoption is spurred by enthusiasm for the language. If you're looking to develop games, graphics, and operating systems you will likely find Rust useful. However, you might be discouraged by the limited talent pool available for this language. Python, on the other hand, has a broad talent pool, so it's easy to hire for. While Rust may catch up down the road, this one also goes to Python.
🏆 WINNER: Python
One of the biggest benefits of using Rust is its emphasis on safety. In Python, programmers must allocate and deallocate memory on the garbage collector. With Rust, there's a greater focus on fixing the memory leaks. Rust wins this round!
🏆 WINNER: Rust
Python is the standard language in the industry for ML. It has many libraries related to machine learning, and there are packages for data preprocessing and visualization. In this instance, Python is the most suitable for machine learning software.
🏆 WINNER: Python
Although Rust is certainly making big waves in the development community, we still feel that it has a ways to go if it wants to best Python. In our books, Python is still the top contender, but as always, it's important to choose a language that best meets the needs of the project that you're working on. Hopefully, with the help of this guide, you'll be on the right track to making an educated decision!
At Crowdbotics, we use RADStack, a unique combination of React Native and Django for our development operations. This way, we enable end users to create self-contained, standalone applications using the same open-source frameworks powering Instagram, Facebook, and SnapChat. React Native is a well-maintained, cross-platform framework, enabling end users to create universal applications (web, desktop, iOS, and Android) from a single codebase. Django is a well-supported framework for backend development that is secure and built for hyperscale. This unique combination makes us an ideal solution for large organizations in various sectors. If you'd like to learn more about our development process and get a quote for your build, reach out to us today!
August 27, 2021