Both Rust and Go are relatively new programming languages, but because Go has been in use for a little longer (since 2009) than Rust (released in 2015), it is considered a more popular programming language. So when you compare Rust vs Go in terms of popularity, Go wins. Rust, however, gradually evolves and matures with the help of its growing community.
In this article we will not answer the question Should you use Rust or Go? They are both excellent and ahead of their time, and many leading companies (like Google) use both languages. Except that in some situations Rust fits better than Go and in other cases Go is a better choice. In particular, we will focus on the performance of both languages.
Go vs Rust: similarities and differences
Modern languages like Rust and Go focus on troubleshooting older programming languages like C and C++. As Rob Pikes, co-creator of Go, puts it, “One of the most important indeed reasons for creating Go was the difficulty of doing concurrent computations in C++ at the time.”
Also, most software bugs are related to memory safety, and Rust and Go places an emphasis on reliable memory management. However, one of the main differences between Rust and Golang is the approach of the teams behind them. After Go version 1 was released, Go developers promised that version 1 would be the biggest milestone and there would not be many changes in the language after that. This approach keeps Go stable and simple. On average, not more than two features are added to Go per year.
On the contrary, Rust is a complicated language, with an average of nearly 15 new features added every year. This can mean that the language is changing rapidly. At the same time, these frequent updates constantly interrupt the development process, forcing Rustaceans to brush up on their language skills.
Let’s compare Golang vs Rust in terms of specific features to better see the key differences and similarities between them and set a Go vs Rust benchmark.
Memory management. The main difference between these languages lies in memory management. While both languages emphasize memory efficiency and focus on preventing memory leaks, Go uses a garbage collector and Rust goes a step further by introducing the lending and ownership model along with the Resource Acquisition Is Initialization (RAII) concept. . Additionally, the Rust compiler, rustc, is efficient in identifying memory-related bugs at compile time.
Go vs Rust goals. Both languages are fast and guarantee high software performance. However, Rust allows free abstractions, which means you can use some high-level programming concepts (generic data, collections), but not at the expense of your program’s performance, just at the expense of a longer compile time. Rust’s cost-free abstraction feature enables the development of extremely powerful programs. Go does not offer such functionality. Golang shouldn’t be extremely powerful but should have enough horsepower to quickly write a program without thinking too much about speed. For a more detailed Rust vs Go performance comparison, you can check out this Rust vs Go performance benchmark.
Go language learning curve vs. Rust. Getting productive with Go is much easier and takes less time than with Rust. Many developers have a harder time understanding Rust, especially its memory safety rules, type conversions, and type checking. All of this makes Rust a more sophisticated programming language than Go.
Speed of Rust vs Go at compile time. Due to all the compiler checks that Rust performs, software development can take longer. With Go, it can be faster to develop an initial prototype, but debugging and testing can be tedious and take longer. When developing in Rust, you may need more development time but less hassle when deploying and testing because the compiler won’t let you compile code with errors.
Assignment. As far as deployment goes, the Rust vs Go benchmarks are quite similar. Both languages generate a static binary ready for deployment. The Golang compiler is faster, but the Rust compiler makes the code more optimized. Simultaneous Go vs Rest. For Go, competition is a crucial area of focus. This language is intended to provide simple and efficient concurrency using goroutines, a lightweight thread managed by the Go runtime.
Rust enables concurrency. using the Async/Await paradigm and threading, which are suitable for different use cases. So, when it comes to Rust vs Go concurrency, it’s much easier to ensure efficient concurrency with Go because all functions are asynchronous coroutines.
Use in embedded devices. In this battle between Rust and Golang, Rust beats Go when it comes to embedded devices. Rust is ideal for programming embedded systems. It works fast directly on the hardware.
This was a quick comparison of the main features of Rust and Go. If you want to compare Go to other languages, read our article on the differences between Go and Node.js.
Sometimes the promising benefits of Rust can convince companies to switch from Go to Rust. Let’s take a look at such an example and find out the reason for the switch.
Rust vs Go Performance: Go to Rust Example
Two years ago, Discord, the developer of an instant messaging app for gamers, changed their Read State service (needed to keep track of all messages and channels read by users) from Go to Rust.
The Discord team decided to switch to Rust because with the Go implementation, the Read State service did not meet the product requirements. Read state forces tens of thousands of database writes per second. Go experienced service latency and CPU spikes almost every two minutes. The reason is that the memory is not freed immediately after it is no longer in use, but is occupied for some time until the garbage collector can determine if it is truly unused, which slows down the program. By switching to Rust, Discord was able to significantly improve the performance of the service thanks to a more efficient memory management model.
However, that doesn’t necessarily mean that every business should consider switching to Rust. You should only switch to Rust if you have a valid reason to do so. In the next section, we’ll see when it’s best to use Rust and Go.
When to use Rust vs Go?
Rust and Go are generic languages suitable for system programming. However, Rust is a more versatile programming language for systems, while go is only suitable for certain types of systems.
Brief overview of Go usage scenarios
Since Go has an automatic garbage collector, calling it a true systems programming language is usually frowned upon. But there are systems that go well with Go: think of container technologies like Docker and Kubernetes, database systems and all kinds of cloud infrastructure.
Typical use cases for Go are as follows:
Cloud Computing. When it comes to cloud migration or deployment, competition is key. And as we established in the first part of this article when comparing the Rust language to Go, Go is the winner when it comes to competition. Go perfectly meets concurrency needs, especially those required by microservices architectures, scalable applications and cloud solutions.
Web application development. Go integrates well with all other modern tools and databases to quickly build scalable web solutions. This language allows for rapid deployment across platforms by compiling as a static binary file. When it comes to Rust vs Go for web development, both languages are winners.
Command Line Interface (CLI). Thanks to its fast compile times, Go is suitable for developing CLI applications. Go provides many handy tools and libraries like Cobra and Viper for creating CLI applications. GitHub used Go to build one of their CLI tools.
DevOps and SRE solutions. The Go programming language is capable of enhancing DevOps and SRE environments. In this regard, Go is faster and easier to debug than Python. With Go, it is possible to develop server maintenance software, write update scripts and create batch processing tools. For example, Drone is a continuous delivery system written in Go.
For a more in-depth analysis of the Go programming language, read this article.
Top companies using Go: BBC, Netflix, Twitter, Twitch, Dropbox, Intel, Facebook
Typical Rust Applications
Rust is perfect for developing operating systems, drivers, kernels, file systems, game engines, large and embedded distributed systems, and libraries that perform deep mathematical calculations. With Rust, it is possible to develop extremely memory-efficient software with high bandwidth and low latency.
Some of the most common use cases for Rust are:
Embedded systems based on microcontroller and RTOS. Rust is ideal for programming microcontrollers and embedded systems for real-time operating systems (RTOS). The ability to use high-level programming concepts and memory safety guarantees makes Rust a perfect solution for embedded systems. Additionally, Rust has a robust package management solution (Cargo) that makes embedded development much easier than using C. Drone OS is an example of an RTOS-based embedded system written in Rust.
Edge computing and IoT. The high computing power of Rust makes the implementation of this language particularly advantageous for edge computing and IoT applications. Rust can enable rapid data collection and processing. For example, the team from Dwelo (an IoT start-up for the smart home) rewrote the code for their IoT platform in Rust. Their original Python code had grown significantly over the years, and the company needed a safe and fast replacement for Python. Rust seemed like the right choice because it’s easy to maintain and safe for wires.
Development tools. Like Go, Rust is great for building CLI apps and various libraries for developers. Rust can also be used for CI/CD and automation tools. For example, Sandstorm (an open-source platform for self-hosted web apps) wrote their Collections app backend entirely in Rust for fast launch time and low memory footprint.
Web services. Rust can improve the performance and reliability of any server-side application, proving once again that in the Go vs Rust category for web development, both languages are great. For example, The Daily Edit news aggregator uses Rust for its analytics engine and web backend.
Top companies using Rust: Mozilla, Meta, Amazon, GitHub, Microsoft, Figma, Cloudflare, Atlassian
Our list of Rust and Go use cases is not exhaustive; we have listed only common ways to implement both languages, supported by real examples. Both languages have a wide range of uses and can help many businesses improve their operations.
Also, let’s discuss the Rust programming language versus Go in terms of improving data handling operations. Inefficient data management is still a pain point for many companies. One of the global digital trends in 2022 is the breaking down of data and application silos.
Peculiarities of using Rust and Go in data science
In the fast-paced world of supercomputing, where big data processing and analytics play a vital role, choosing the right programming language for data solutions is extremely important.
Tom Miller, faculty director of the North-western Data Science Program, says it’s still common to use Python and R for most data science tasks. More contemporary alternatives like Rust or Go are often overlooked, although they can do a lot more than Python and R.
However, let’s focus on the popularity of Go vs Rust for data science initiatives.
Go in terms of data science
- Go is often in the top 10 rankings of the best programming languages for data science. The main reasons why Go is a viable option for data science tasks are listed below.
- Go is a machine-efficient language that scales to systems that require concurrent processing. In this regard, Go is better than Python, as the latter is an interpreted language that is not suitable for computationally intensive tasks.
- Go is a statically typed language that ensures that data is parsed correctly. Go offers many possibilities for efficient data manipulation and analysis. Go seamlessly interacts with all major databases and data stores such as SQL, NoSQL, and Key-Value. In fact, at X-Strategy Services, we’re currently working on the Conduit project, which is building connectors in Go to enable automated data interactions between different data stores.
- Go provides a set of robust and modern tools for structuring and manipulating data, including Gota (a package for data analysis), Gonum (a set of packages for writing numerical and scientific algorithms), and other useful packages for data science purposes.
The use of Go in the context of data science is gradually gaining in importance.
Go has a strong developer community that continues to create many new and useful packages and libraries as alternatives to Python to ensure rapid and simplified development of machine learning and AI-powered data solutions, from statistical analysis to predictive modeling. Now let’s go to Rust to discover the benefits for data science projects.
How Rust adds value to data science
Rust is still largely unproven in data science. Rust in Action author Tim MacNamara writes on Quora in response to a question about Rust’s usefulness in data science: “Yes, very useful. But be prepared to be a pioneer if you want to apply Rust to today’s data science problems.”
First, Rust has great potential to replace error-prone C and C++ for building back-end logic for machine learning or other data science projects. While maintaining the speed of C and C++ for data-intensive projects, Rust is much more secure and reliable, allowing compiled code to go into production with significantly lower (if any) chance of failure.
However, Rust libraries for data science purposes are scarce compared to comparable libraries in Python and Go. So the Rust machine learning toolkit is still in the works, making this language more of an experimental language for confident engineers ready to develop some components from zero.
For example, the Kamu open source platform decided to choose Rust and realized the promising potential of the language for data science. The company previously relied on the Scala and Apache Spark programming language as its primary data engine, but the move to Rust resulted in:
- Extremely responsive software
- Better portability
- Smooth data management
After discussing Go and Rust in the context of data science, we can say that both are great building blocks for many data science projects. It is only a matter of time before these languages develop stable and productive data science ecosystems. However, you don’t need to choose just one programming language to develop compute-intensive data science software. Your programming team can be multilingual and know just when it’s best to use Go, Rust, Python, R, or another language.
5 practical tips to make the right choice
In this section, we’ll focus on simple steps you can take to make sure you’re choosing the right programming language and choosing between Rust and Go.
- Prioritize your project and define which project requirements are most critical. This way you know which language suits you best.
- Go vs Rest Speed: Define how important time to market and therefore development speed is. Go ensures your products get to market much faster than Rust. However, if you are willing to invest more time developing with Rust, this language will reveal its magic.
- During development, determine the level of risk you are comfortable with. If you want to experiment with Rust in the field of data science, do it. But be prepared for this path to be thorny.
- Consider hiring a multilingual team to get the best results and ensure you don’t end up with incomplete software that doesn’t meet all of your needs. If you’re not ready to choose, then don’t choose. Use both languages and see how this approach affects your business.
- Work with professionals. It’s an obvious tip, but finding real experts is harder than it looks, especially with a language as revolutionary as Rust. Look for relevant expertise and portfolio.
As you may have guessed, there is no right or wrong answer when it comes to Go vs Rust in 2022. There are pros and cons to both. Most importantly, approach each project individually and choose based on your core needs and expectations. The most reasonable course of action would be to consult a software development company that has experience in Go and Rust to help you determine which language is best for your particular use case. Our skilled Go and Rust programmers can help you with tasks of any complexity. Our team loves challenges and is happy to take them on to ensure the success of all our clients.