Choosing Between Go and Rust: Simplifying Language Selection for Your Next Project

Rust and Go are two relatively new programming languages. Go has been around since 2009, making it more widespread compared to Rust, which was released in 2015. So, in terms of popularity, Go comes out ahead. However, Rust is continuously improving with the support of its growing community.

In this article, we won’t directly answer the question of whether you should use Rust or Go. Both languages are excellent and have gained recognition in the industry, with big companies like Google using both. Instead, we’ll focus on comparing the performance of Rust and Go in different situations.

Go vs Rust: similarities and differences

Go and Rust are modern programming languages designed to address issues found in older languages like C and C++. Rob Pikes, one of the co-creators of Go, created the language to tackle challenges in concurrent computation faced by C++ at that time. Both Rust and Go focus on reliable memory management to reduce software bugs related to memory safety.

However, a significant difference between Rust and Go lies in their development approaches. After releasing Go version 1, the creators decided to keep the language stable and simple by limiting major changes. This means, on average, only about two features are added to Go each year.

On the other hand, Rust is a more complex language with nearly 15 new features added each year. While this indicates rapid evolution, it can also disrupt the development process for Rust developers, as they need to constantly update their knowledge of the language.

To better understand the key differences and similarities between Go and Rust, let’s compare certain characteristics and create a Go vs Rust benchmark.

Memory Management: 

The main difference between Go and Rust lies in how they handle memory management. Both languages prioritize memory efficiency and preventing memory leaks. However, Go uses a garbage collector, while Rust takes it a step further with its ownership and borrowing model, along with the Resource Acquisition Is Initialization (RAII) concept. The Rust compiler (rustc) is also adept at catching memory-related bugs during compilation.

Go vs Rust Performance: 

Both languages are fast and offer high software performance. Rust’s advantage is its ability to provide zero-cost abstractions, meaning you can use high-level programming concepts without sacrificing program performance, though it may lead to slightly longer compile times. Go, on the other hand, doesn’t offer this feature. While Go is not designed to be extremely high-performance, it still offers sufficient performance for quickly writing programs without excessive optimization.

Learning Curve of Go vs Rust: 

Learning and becoming productive with Go is generally easier and faster compared to Rust. Many developers find Rust more challenging, especially when dealing with its memory safety rules, type conversions, and type checks.

Rust vs Go Speed at Compile Time: 

Rust’s extensive compiler checks may result in longer development times. Go can be faster for creating initial prototypes, but debugging and testing might take longer. Rust, with its strict compiler checks, ensures fewer worries during deployment and testing since it won’t allow you to compile code with bugs.

Deployment: 

Both Rust and Go generate static binaries suitable for deployment. While the Go compiler is faster, the Rust compiler produces more optimized code.

Go vs Rust Concurrency: 

Concurrency is a major focus for Go, facilitated by goroutines—lightweight threads managed by the Go runtime. In contrast, Rust utilizes the Async/Await paradigm and threading for concurrency, which serves different use cases. Ensuring efficient concurrency with Go is generally simpler because all functions are asynchronous coroutines.

Use in Embedded Devices:

 When it comes to embedded systems programming, Rust surpasses Go. Rust is an excellent choice for embedded devices as it runs directly on hardware and delivers high performance.

Switching from Go to Rust: Sometimes, the promising benefits of Rust may persuade businesses to switch from Go to Rust. Let’s explore an example to understand the reasons behind such a switch.

Go vs Rust performance: example of switching from Go to Rust

Two years ago, Discord, the company behind a messaging app for gamers, made a significant change to their Read State service, which is crucial for tracking the messages and channels users read. Initially, they used Go for this service, but it didn’t meet their product requirements.

The issue with Go was related to performance. The Read State service had to handle tens of thousands of database writes per second, and with Go, they experienced frequent latency and CPU spikes every two minutes. The problem arose from how Go’s garbage collector managed memory. Instead of freeing memory right after it’s no longer needed, the memory stayed occupied until the garbage collector could verify if it was genuinely unused, causing slowdowns.

To address these performance challenges, Discord decided to switch to Rust. Rust’s more efficient memory management model allowed them to drastically improve the service’s performance.

However, it’s important to note that not every business should consider switching to Rust. Switching should only be done if there are valid reasons to do so. In the next section, we’ll explore situations where Rust and Go can be used most effectively.

When deciding between Rust and Go, consider their best use cases. Both languages are general-purpose and suitable for systems programming, but Rust is more versatile in this domain compared to Go.

Let’s take a brief look at some use cases where Go shines. Although Go has an automatic garbage collector, making some purists hesitate to call it a true systems programming language, it still fits well in certain systems. Go is a good choice for container technologies like Docker and Kubernetes, as well as database systems and various cloud infrastructure applications.

Typical Go use cases are as follows:

Cloud Computing: 

When migrating or deploying applications in the cloud, concurrency is crucial. As we discussed earlier in this article comparing Rust vs Go, Go excels in concurrency, making it the winner in this aspect. Go is well-suited for fulfilling concurrency needs in microservices architectures, scalable applications, and cloud solutions. If you’re considering SaaS migration strategy, explore our expertise in this area.

Web Application Development: 

Go seamlessly integrates with modern tools and databases, enabling rapid development of scalable web solutions. Its ability to compile as a static binary allows for fast deployment on various platforms. In the battle of Rust vs Go for web development, both languages come out as winners.

Command-line Interface (CLI): 

Go is an excellent choice for developing CLI applications, thanks to its fast compilation times. It offers convenient tools and libraries like Cobra and Viper for building CLI applications. Even GitHub used Go to create one of its CLI tools.

DevOps and SRE Solutions: 

The Go programming language enhances DevOps and SRE (Site Reliability Engineering) environments. Compared to Python, Go is faster and easier to debug. Go enables the development of server maintenance software, update scripts, and batch processing tools. An example is Drone, a continuous delivery system written in Go.

Rust is an ideal choice for various types of applications, including:

1. Operating Systems
2. Drivers and Kernels
3. File Systems
4. Gaming Engines
5. Large and Embedded Distributed Systems
6. Libraries for Heavy Mathematical Computations

With Rust, you can create software that is highly memory-efficient, has high bandwidth, and low latency. 

Among common Rust use cases are the following:

Rust’s Use Cases:

1. Microcontroller and RTOS-Based Embedded Systems: Rust is an excellent choice for programming microcontrollers and real-time operating system (RTOS) embedded systems. It offers high-level programming concepts and memory safety guarantees, making it perfect for embedded systems. With Rust’s robust package management solution (Cargo), embedded development becomes much easier compared to using the C language. An example of an RTOS-based embedded system written in Rust is Drone OS.
2. Edge Computing and IoT: Rust’s impressive computational power makes it highly beneficial for edge computing and IoT applications. It enables fast data capturing and processing. For instance, Dwelo, a smart home IoT startup, switched from Python to Rust for their IoT platform. Rust’s easy maintenance and thread safety made it an ideal secure and fast substitute for their original Python code.
3. Developer Tools: Similar to Go, Rust is great for creating CLI applications and diverse libraries for developers. It’s also suitable for CI/CD and automation tools. For instance, the open-source platform Sandstorm used Rust to develop the back end of their Collections application, ensuring fast startup times and a small memory footprint.
4. Web Services: Rust excels at increasing the performance and reliability of server-side applications. In the realm of Go vs Rust for web development, both languages shine. The Daily Edit, a news aggregator, uses Rust for its analysis engine and web back end.

It’s important to note that our list of Rust and Go use cases is not exhaustive; there are many other applications for both languages supported by real-world examples. Rust and Go have a wide range of uses and can significantly enhance various business operations.

Furthermore, we’ll discuss how Rust and Go contribute to enhancing data management operations. Inefficient data management remains a challenge for many enterprises, and breaking down data and application silos is a significant global digital trend in 2022.

Peculiarities of using Rust and Go in data science

In the fast-paced world of supercomputing, data scientists play a crucial role in processing and analyzing big data. Choosing the right programming language for data solutions is vital. Let’s take a look at the image below to understand the typical tasks performed by data scientists.

According to Tom Miller, the Faculty Director of Northwestern’s Data Science Program, Python and R are still widely used for most data science tasks. However, he points out that more modern options like Rust and Go are often overlooked, even though they can outperform Python and R in certain situations.

Now, let’s shift our focus to compare the popularity of Go and Rust for data science initiatives.

Go is frequently ranked among the top 10 best programming languages for data science. There are several reasons why Go is a suitable choice for data science tasks, as outlined in the graphic below. The data presented in the graphic is from the book “Machine Learning with Go” by Daniel Whitenack.

Go is consistently ranked among the top 10 best programming languages for data science. There are several key reasons why Go is well-suited for data science tasks, as explained below, based on insights from the book “Machine Learning with Go” by Daniel Whitenack.

1. Machine Efficiency: Go is highly efficient and excels in systems that require concurrent processing. Compared to Python, which is an interpreted language not ideal for compute-intensive tasks, Go performs better.
2. Statically Typed: Go’s static typing ensures data is parsed correctly, making data manipulation and parsing more efficient.
3. Database Interactions: Go seamlessly interacts with common databases and data stores like SQL, NoSQL, and key-value systems. Projects like Conduit at Yalantis use Go to create connectors for automated data interactions between various data stores.
4. Rich Set of Tools: Go offers a modern and robust set of tools for structuring and processing data. Packages like Gota (for data analysis) and Gonum (for numerical and scientific algorithms) make data science tasks easier.

The use of Go in data science is steadily gaining momentum. Its strong developer community continuously creates new packages and libraries, making it a compelling alternative to Python for developing data solutions empowered with machine learning and AI. From statistical analysis to predictive modeling, Go provides fast and simplified development for data science projects.

Next, let’s explore the benefits of using Rust in data science projects.

The value of Rust in data science is still relatively unexplored. According to Tim MacNamara, the author of “Rust in Action,” Rust can be highly useful in data science but may require pioneers willing to try it out for such problems today.

Rust has the potential to replace error-prone languages like C and C++ for creating the backend logic of machine learning and other data science projects. While maintaining the speed of C and C++ for data-intensive tasks, Rust offers greater safety and reliability, reducing the chances of failures when deploying compiled code to production.

However, compared to Python and Go, Rust’s libraries for data science are limited. The Rust toolkit for machine learning is still a work in progress, making it more experimental for confident engineers who are willing to develop some components from scratch.

One example of Rust’s potential in data science is Kamu, an open-source data fabric platform that switched from Scala and Apache Spark to Rust. The transition resulted in extremely responsive software, better portability, and smoother data management.

In conclusion, both Go and Rust are excellent building blocks for data science projects. As the data science ecosystems for these languages continue to evolve, teams can be multilingual and choose the best language for specific tasks, whether it’s Go, Rust, Python, R, or others.

Choosing the right programming language for your project can be tough, but we’re here to help. Before making a decision, we focus on your software architecture, as it’s a vital aspect of the technical solution.

Here’s how we assist you in finding the best-fit programming language(s):

1. Analyze Requirements: We carefully study both functional and non-functional requirements to determine which language (or a combination of languages) aligns with your business goals. This ensures that our custom technical solution meets your critical needs. For instance, if high performance and handling heavy loads are crucial non-functional requirements, Rust might be the ideal choice.
2. Consider Project Constraints: We take into account factors like deadlines, budget, and available resources. If speed is a priority and you need a quick solution, Go could be the better option. Its simplicity, extensive ecosystem, and fast development make it well-suited for such scenarios.
3. Design Software Architecture: Based on your requirements and constraints, we design a software architecture and then select languages and technologies to implement it efficiently.
4. Market Research: We study similar solutions in the market to identify potential language-related issues and avoid them in your project. For example, while Python and Java are common choices for certain solutions, if low latency and managing high loads are vital, Rust might be more suitable.

As a competent software development company, we have broad expertise and developers well-versed in diverse technologies, ready to start working on your project immediately. Rest assured, we’ll find the right programming language to make your project a success.

Partnering with a mature technology company offers numerous benefits when selecting a programming language. Here’s how Yalantis can help you:

1. Expertise in Multiple Languages: We have technical leads and software architects with vast experience in various programming languages, including Golang and Rust. They can design the right architecture, define necessary resources, and set up the entire development process for you.
2. Professional Consultancy: Our team provides consultancy services to solve your business pain points. For example, we can re-engineer your system using more suitable and sustainable technologies like Rust to reduce hosting expenses on platforms like Amazon.
3. Skilled Developers: We take charge of team augmentation, ensuring quick staffing, onboarding, management, and retention of skilled mid- and senior-level developers. Our developers can write high-quality code, enhancing the performance of your projects.
4. Technology Stack Changes: We can handle technology stack changes to stay flexible in the fast-evolving digital world. If technologies that worked well in the past aren’t meeting current goals, we can help you transition smoothly.
5. Avoiding Constant Re-engineering: By analyzing detailed technical and business requirements, we determine the right development path to avoid constant re-engineering in the future.

Choosing between Go and Rust in 2023 doesn’t have a clear-cut answer. The right decision depends on your specific project requirements and expectations. Consulting a software development agency with expertise in both Go and Rust can guide you in selecting the best-fit language for your use case. Our skilled Go and Rust programmers are prepared to tackle tasks of any complexity, and we embrace challenges to ensure successful outcomes for all our clients.

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