How Rust Achieves Memory Safety Without Sacrificing Performance or Expressiveness

Memory safety is a crucial aspect of software development, as it can prevent many common and dangerous bugs that can compromise the security and performance of applications and systems. However, not all programming languages are designed to ensure memory safety, and some of the most widely used ones, such as C and C++, are notorious for allowing memory errors that can lead to crashes, vulnerabilities, and exploits.

Rust is a relatively new programming language that aims to provide a memory-safe alternative to C and C++, while also offering low-level control, high performance, and concurrency support. Rust achieves memory safety through a novel system of ownership, borrowing, and lifetimes, which are enforced by the compiler at compile time. This system prevents memory errors such as out-of-bounds access, use-after-free, and data races, without requiring a garbage collector or runtime checks. Rust also allows users to write unsafe code when necessary, but only in a restricted and explicit way, making it easier to audit and isolate.

In this opinion piece, I will argue that Rust is a superior choice for memory-safe programming, compared to other languages that either sacrifice performance, flexibility, or expressiveness, or rely on runtime mechanisms that can introduce overhead, unpredictability, or complexity. I will also discuss some of the challenges and limitations of Rust, and how they can be overcome or mitigated.

Why Rust is better than C and C++

C and C++ are the dominant languages for systems programming, as they offer direct access to hardware resources, manual memory management, and low-level abstractions. However, these languages also expose programmers to a wide range of memory errors, such as buffer overflows, dangling pointers, memory leaks, and undefined behavior. These errors can cause crashes, data corruption, or security breaches, and are often hard to detect and fix. According to various reports, memory errors account for a majority of vulnerabilities in software products from companies like Google, Microsoft, and Apple.

Rust is designed to eliminate these memory errors, by enforcing a set of rules and guarantees at compile time. The core concept of Rust is ownership, which means that every value in Rust has a unique owner, which is responsible for managing its memory. When the owner goes out of scope, the value is automatically deallocated. Ownership can be transferred between variables, or borrowed by references, which allow temporary access to a value without taking ownership. However, Rust imposes restrictions on how references can be used, to ensure that no two references can modify the same value at the same time, or that a reference cannot outlive its owner. These restrictions are checked by the compiler, using a mechanism called the borrow checker, which analyzes the lifetimes of variables and references, and reports any violations as compile-time errors.

By using ownership, borrowing, and lifetimes, Rust can prevent memory errors such as out-of-bounds access, use-after-free, and data races, without requiring a garbage collector or runtime checks. This means that Rust can achieve memory safety without sacrificing performance, predictability, or control. Rust also allows users to write unsafe code, which can bypass the compiler’s checks and use raw pointers, external libraries, or inline assembly. However, unsafe code is clearly marked with the unsafe keyword, and can only perform a limited set of actions. This makes it easier to audit and isolate unsafe code, and to ensure that it does not compromise the safety of the rest of the program.

Why Rust is better than other memory-safe languages

While Rust is not the only memory-safe programming language, it offers some advantages over other languages that also claim to ensure memory safety. Some of these languages are:

Garbage-collected languages, such as Java, C#, Python, and Ruby. These languages use a garbage collector (GC) to automatically manage memory, by reclaiming memory that is no longer used by the program. While this can simplify programming and avoid memory leaks, it also introduces some drawbacks, such as:

• Performance overhead: GCs consume CPU cycles and memory, and can cause pauses or slowdowns in the program execution, especially when collecting large amounts of memory or when running in concurrent or distributed environments.
• Unpredictability: GCs can run at arbitrary times, and their behavior can depend on various factors, such as the GC algorithm, the heap size, the allocation pattern, and the workload. This can make it hard to reason about the performance and memory usage of the program, and to optimize it for specific scenarios or requirements.
• Lack of control: GCs abstract away the details of memory management, and often do not expose any knobs or options to tune or customize their behavior. This can limit the ability of programmers to fine-tune the memory management for their needs, or to interact with low-level or external resources that require manual memory management.

Reference-counted languages, such as Swift, Objective-C, and Python (for some objects). These languages use reference counting to keep track of the number of references to each object, and to deallocate objects when their reference count drops to zero. While this can avoid some of the drawbacks of GCs, such as pauses and unpredictability, it also has some limitations, such as:

• Performance overhead: Reference counting requires updating the reference count of each object whenever a reference is created or destroyed, which can incur significant overhead, especially for frequently accessed or shared objects.
• Memory leaks: Reference counting cannot handle cycles, where two or more objects refer to each other, and prevent each other from being deallocated. This can cause memory leaks, unless the programmer manually breaks the cycles, or uses a cycle detector, which can add more complexity and overhead.
• Lack of control: Reference counting also abstracts away the details of memory management, and often does not allow programmers to control when and how objects are deallocated, or to interact with low-level or external resources that require manual memory management.

Linear and affine languages, such as ATS, Clean, and Cyclone. These languages use linear or affine types to ensure that each value is used exactly once or at most once, respectively. This allows the compiler to statically determine when a value is no longer needed, and to deallocate it automatically. While this can achieve memory safety without requiring a GC or reference counting, it also imposes some challenges, such as:

• Expressiveness: Linear and affine types can be restrictive and cumbersome to use, as they limit the ways that values can be manipulated, copied, or shared. For example, they often require explicit annotations or annotations or wrappers to indicate the linearity or affinity of types, or to enable common operations such as branching, looping, or recursion. They also often prohibit or complicate the use of data structures that involve aliasing, such as graphs, trees, or hash tables.
• Compatibility: Linear and affine types can be incompatible with existing languages, libraries, or paradigms that do not adhere to their rules or assumptions. For example, they can make it hard to interoperate with C or C++ code, or to use object-oriented or functional programming features, such as inheritance, polymorphism, or higher-order functions.

Rust avoids these drawbacks, by using a more flexible and pragmatic approach to memory management, which balances safety, performance, and expressiveness. Rust’s ownership system is based on the idea of affine types, but it relaxes some of the restrictions, by allowing values to be borrowed by references, which can be either mutable or immutable, and can have different lifetimes. This allows Rust to support common programming patterns and data structures, such as branching, looping, recursion, aliasing, and sharing, while still ensuring memory safety. Rust also supports compatibility and interoperability with other languages and libraries, by allowing users to write unsafe code, or to use abstractions such as smart pointers, which can encapsulate different memory management strategies, such as reference counting, GC, or foreign function interface (FFI).