What’s Holding Rust Back?
1. Limited Talent Pool
The scarcity of professionals experienced in Rust poses a significant hurdle for backend development, impeding its broader adoption for projects requiring this language’s prowess.
2. Ecosystem Complexity
Described as Lego-like, the Rust ecosystem can overwhelm beginners, especially with the introduction of asynchronous programming in 2019. The term “Lego-like” reflects the modular nature of the ecosystem, where numerous library implementations, known as crates, address similar or slightly different functionalities. However, just like Lego blocks that may not seamlessly interlock, not all Rust libraries can work together directly, necessitating a thoughtful and curated approach during project initiation. The introduction of asynchronous programming in 2019 further contributed to the complexity, leading to a surge in the number of crates. This abundance of crates requires developers to carefully select and integrate a curated set of them, ensuring coordinated versioning for improved interoperability within a project. Despite the initial challenges, the Lego-like nature of the Rust ecosystem empowers developers with flexibility and customization options, once they navigate and harness the diverse array of available crates effectively.
3. Learning Curve
Rust’s learning curve, especially when combined with asynchronous programming, can be suboptimal for beginners. The language’s advanced constructs, while powerful, might be intimidating, and the plethora of choices can be bewildering. The shortage of experienced mentors further exacerbates the learning curve.
How to address these challenges?
To overcome these challenges and foster Rust’s adoption in backend development, Luca Palmieri, the author of “Zero To Production In Rust,” proposes key focus areas:
1. High-Quality Error Messages
Rust frameworks should prioritize error messages that resonate with the language of backend developers, enhancing debugging and troubleshooting intuitiveness.
2. Compile-Time Error Catching
Rust frameworks should catch errors at compile time, ensuring early identification and resolution of issues for enhanced overall code robustness.
3. Boring Rust for Majority Tasks
Encouraging the use of “boring” Rust for routine backend tasks can simplify development without compromising performance. The concept of “boring” Rust revolves around adopting a pragmatic and straightforward approach to coding, where developers prioritize simplicity and ease of understanding over intricate details. This means writing code that is basic, reliable, and functional without delving into the complexities of the Rust codebase. By embracing this philosophy, developers can streamline the development process for routine tasks, allowing them to focus on functionality rather than getting bogged down by unnecessary intricacies. This approach not only enhances productivity but also facilitates collaboration within development teams, as codebases become more accessible and comprehensible to a broader audience. While “boring” Rust may lack the excitement of cutting-edge features, its emphasis on clarity and simplicity proves invaluable for efficiently handling everyday backend tasks, contributing to a more maintainable and scalable codebase in the long run.
4. Problem Domain-Focused APIs
Rust frameworks should emphasize building APIs with comprehensive features, addressing common concerns such as authentication, logging, and metrics to streamline development.
One notable early-stage effort is Pavex, a framework for building APIs in Rust, taking a unique route from other famous Rust web frameworks like Actix and Rocket. Pavex operates as a specialized compiler for building Rust APIs, generating a standalone API server SDK crate according to specifications.
Rustventures to another domain?
Despite challenges in backend engineering, Rust is gradually making strides into machine learning engineering (MLE) and data science (DS) fields. These domains demand optimized high-performance and efficient computing, areas where Rust excels. Notable examples include the Polars data frame interface, Qdrant vector databases, and Hugging Face’s Candle ML framework, showcasing Rust’s potential in these evolving fields. While choices for productivity are currently limited, Rust’s entry into these domains signals a promising trajectory.
In Conclusion
While Rust may not emerge as the default choice for mainstream backend development, its promise shines in niche areas requiring high-performance applications with a lower infrastructure footprint and stringent reliability requirements. As Rust continues to address ongoing challenges, it holds substantial potential in machine learning engineering and data science domains. The maturation of the ecosystem and the proliferation of expertise suggest that Rust’s role in backend development may expand, providing an appealing alternative for projects seeking both performance and reliability. The journey of Rust, marked by innovation and collaboration, stands as a testament to its evolving significance in the programming landscape. For those considering Rust, starting in machine learning engineering and data science fields may offer an acceptable entry point.
