Deconstructing the Rust Discourse


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Hodong Kim <hodong@nimfsoft.art>

Preface

This book aims to analyze specific technical and social discourses surrounding the Rust programming language. The analysis includes claims such as “memory safety is achievable only through Rust” or “C++ is no longer a modern systems programming language.” The book investigates how Rust’s core principles—’safety’, ‘performance’, and ‘concurrency’—are the result of engineering trade-offs (design exchanges where one benefit is gained at the cost of another) and examines the historical and technical contexts of these concepts.

To this end, the book includes discussions on the design and history of several programming languages, including C++, Java, C#, Go, and Ada. Therefore, this book is intended for readers with an understanding of various systems programming paradigms and fundamental principles of computer science.

This book references Ada and its subset SPARK as comparative subjects, in addition to C++, which is predominantly featured in Rust discourse. Ada/SPARK are used as an analytical means of comparison to demonstrate how the same goal of ‘safety’ can be achieved through different philosophies and engineering trade-offs. This allows for an analysis of Rust’s design as one of several possible approaches and presents historical precedents for the principle of ‘memory safety without a garbage collector (GC)1’. This approach extends the scope of technical evaluation beyond the dichotomous framework often established with C++ and contributes to assessing Rust’s engineering aspects against objective criteria.

It is clarified that the subject of this book’s analysis is not Rust technology itself, nor the official positions of the Rust Foundation or development teams. The Rust project’s official channels recognize the technical challenges discussed in this book as areas for improvement and are exploring solutions. This book analyzes the formation and proliferation of specific discourses observed in some online technical forums and social media, rather than these official activities. Therefore, this analysis is not an evaluation of any specific group but aims to provide an understanding of the discourse structure within the technology ecosystem. In this book, ‘Rust discourse’ refers to specific, selected trends for analysis, not the consensus of the entire community.

This book does not intend to devalue Rust’s technical achievements. Its premise is that because Rust is a widely adopted technology, it necessitates detailed and multifaceted discussion. The purpose of this book is not to advocate for or criticize any specific technology, but to objectively analyze engineering trade-offs and the formation process of technical discourse.

Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Table of Contents

Part 1: The Appearance of Rust and Its Technical Characteristics

Part 1 analyzes how the programming language Rust approached challenges in the system programming field and the features through which it is discussed.

The first chapter (Chapter 1) examines the trade-off between ‘performance’ and ‘safety’ that formed the background of Rust’s creation, and introduces key technical features adopted to address this, including the ownership model, the Zero-Cost Abstractions (ZCA) philosophy, and the ecosystem represented by Cargo.

The second chapter (Chapter 2) analyzes the complex factors through which this technical foundation interacted with Developer Experience (DX), narrative, and institutional sponsorship to influence adoption.

1. Introduction to the Rust Language and Its Key Features

This chapter analyzes the design philosophy of the Rust language and the technical features that implement it. The discussion begins by examining the system programming field’s problem that Rust sought to address, namely the trade-off between ‘performance’ and ‘safety’.

Subsequently, to achieve this goal, its key features are reviewed in order: the memory management model introduced by Rust (ownership, borrowing, lifetimes), the ‘Zero-Cost Abstractions’ principle that follows the C++ lineage, the method of ensuring safety through the type system, and the standardized development tool ecosystem that supports this.

1.1 Background: The Trade-off Between ‘Performance’ and ‘Safety’

While programming languages are developed for various reasons, Rust gained attention by approaching existing paradigms in a new way. To understand Rust’s origin, it is necessary to examine the long-discussed choices within the field of systems programming.

In the past, developers of low-level systems were faced with a choice between ‘performance’ and ‘safety.’ On one side were languages like C/C++, which provided high performance and control by directly managing hardware, but assigned the responsibility of handling memory errors such as segmentation faults, buffer overflows, and data races to the programmer. On the other side were languages like Ada, which pursued a high level of safety and predictability at the language level and were used in specific high-reliability systems. Another example was garbage collector (GC)-based languages like Java or C#, which provided memory safety through automatic memory management but were limited in some system domains like real-time systems or operating system kernels due to the GC’s runtime overhead and unpredictable behavior.

Started as a research project at Mozilla, Rust was developed with the goal of resolving the trade-off between ‘performance and safety.’ The aim was to create “a language that has C++-level performance while ensuring memory safety without a GC.” To achieve this, Rust established the core objectives of safety, performance, and concurrency from the early stages of its design.

Safety

One of Rust’s core design principles is memory safety. It aims to prevent issues such as abnormal program termination, data corruption, and system control hijacking that can arise from memory errors by checking memory usage rules at compile time. This is an approach where the compiler blocks the compilation of code that could cause errors, in order to reduce the possibility of programmer mistakes.

Performance

Rust is a systems programming language and considers performance one of its core objectives. It is designed to efficiently use hardware performance without relying on a runtime like a GC. The ‘Zero-Cost Abstractions’ principle represents Rust’s design philosophy of ensuring that no additional runtime cost is incurred even when developers use high-level abstraction features.

Concurrency

In a multi-core processor environment, writing code where multiple threads share data without conflict is a complex problem. Rust’s ownership system finds and prevents concurrency-related errors like ‘data races’ at compile time. Through this, developers can experience the concept of ‘fearless concurrency.’ Here, ‘fearless’ means that developers can write concurrent code based on the technical guarantee that certain types of bugs, such as data races, are prevented by the compiler.

In conclusion, the question ‘Why Rust?’ can be answered at the intersection of these three objectives. Rust is an attempt to implement the values of performance, safety, and concurrency—which were previously difficult to achieve simultaneously—within a single language. To achieve this goal, Rust introduced the concept of ‘ownership’ as a core feature of the language.

1.2 Memory Management via Ownership, Borrowing, and Lifetimes

Rust’s goal of ‘memory safety without a garbage collector (GC)’ is distinct from existing methods. Manual management in C/C++ has the potential for errors, while Java’s GC incurs runtime overhead. To solve this problem, Rust introduced a system that enforces memory management rules at compile-time rather than runtime. It consists of three concepts: ownership, borrowing, and lifetimes.

1. Ownership: Every value has an owner

Rust’s memory management is based on ‘ownership’ rules.

  • Every value has only one owner variable.
  • When the owner goes out of scope, the value is automatically dropped (deallocated) from memory.
  • Ownership can be ‘moved’ to another variable, after which the original owner is no longer valid.

These rules prevent ‘double free’ errors. Furthermore, because the previous variable cannot be used after ownership moves, ‘use-after-free’ errors are also prevented at compile-time.

2. Borrowing: Accessing without ownership

If moving ownership were the only way to pass data, inefficiencies could arise. To address this, Rust provides the concept of ‘borrowing’. This allows temporary access (a reference) to data within a specific scope without transferring ownership.

The following rules apply to ‘borrowing’:

  • Multiple ‘immutable borrows’ (&T) of a particular piece of data can exist simultaneously.
  • Only one ‘mutable borrow’ (&mut T) can exist, and no other borrows are allowed during its validity.

Through these rules, the compiler blocks attempts at concurrent modification or simultaneous reading and modification of the same data at compile-time. This is the principle by which Rust prevents ‘data races’.

3. Lifetimes: Guaranteeing the validity period of borrowed data

A ‘lifetime’ specifies to the compiler the scope for which a ‘borrow (reference)’ is valid—its ‘duration of survival’.

Through lifetime analysis, the compiler prevents ‘dangling pointer’ problems, which occur when borrowed data is deallocated by its owner prematurely. That is, it does not permit a situation where “the lifetime of a reference to data is longer than the actual lifetime of the data itself.” While the compiler often infers lifetimes automatically, developers can explicitly specify lifetimes in cases where inference is difficult.

This system of managing resource lifecycles via ownership, controlling data access via borrowing, and preventing dangling pointers via lifetimes is enforced by a part of the compiler called the ‘borrow checker’. This checker is the mechanism that implements ‘safety without performance overhead’.

1.3 The Genealogy of Zero-Cost Abstractions

High-Level Abstraction and Low-Level Control

In programming languages, ‘abstraction level’ and ‘performance’ have long been recognized as a trade-off. High-level languages like Python and Java provide developers with abstraction features, but there was a tendency for runtime costs (overhead) to occur as more high-level features were used. Conversely, low-level languages like C offered relatively high performance, but developers had to manually control memory, etc., which could affect code readability and maintainability.

C++ and Rust present a philosophy regarding this trade-off: “You don’t pay for what you don’t use.” This is the ‘Zero-Cost Abstractions (ZCA)’ principle. ZCA is the principle aiming for the compiled end-result to have performance identical or similar to manually optimized low-level code, even when the developer uses high-level abstraction features like iterators, generics, and traits.

The origins of this principle can be found in C. C provided mechanisms for programmers to manually write cost-free code, such as allowing developers to control memory layout via structs and reducing function call costs via inline functions or macros. For example, using the sizeof operator to calculate the precise memory size of a data structure at compile time, or utilizing #define macros to expand code before compilation, can be seen as early forms of ZCA in C that implemented specific features without runtime overhead.

C++ built language-level abstractions on this foundation. The core elements were templates and RAII (Resource Acquisition Is Initialization).

  • Templates perform type checking and generate code for multiple types at compile time.
  • RAII is a pattern that automates resource management via destructors.

Rust inherits the ZCA philosophy of C/C++ and adds the ‘ownership’ system. That is, costs associated with abstraction are processed at compile time rather than runtime to minimize performance degradation, while simultaneously enforcing that all abstractions adhere to memory safety rules via the borrow checker.

A representative example is the iterator.

// Code to find the sum of the squares of multiples of 3 from 1 to 99
let sum = (1..100).filter(|&x| x % 3 == 0).map(|x| x * x).sum::<u32>();

This code declaratively describes the task using a chain of high-level methods like filter, map, and sum. In C, an imperative style using for loops, if conditions, and a separate sum variable is common. The Rust compiler optimizes this high-level iterator code to generate machine code with performance similar to a manually written for loop. Intermediate call costs, such as filter or map, can be eliminated during the compilation process through inlining, etc., and memory access is also checked for safety at compile time.

This compile-time optimization is achieved through Rust’s type system, generics, and compiler techniques like inlining and monomorphization. This approach performs more processing at compile time in exchange for reducing runtime costs.

1.4 Ensuring Safety Through the Type System and Pattern Matching

Compile-Time Error Checking

Rust’s ‘safety’ extends beyond just memory management. Rust is designed to explicitly express the various states and error possibilities a program might encounter at the code level through its static type system, and to have the compiler enforce this. This is a method of increasing program stability by catching potential runtime errors at compile time. This approach is based on Rust’s type system and pattern matching.

One of the features of Rust’s type system is the enum. Unlike enums in some languages which are used merely to list constants, Rust’s enum is a data structure where each variant can hold different types and quantities of data. Rust utilizes this to handle uncertain states within a program.

A representative example is the Option<T> type, which is used to handle issues related to null pointers. null does not exist in Rust. Instead, situations where a value may or may not be present are expressed using the Option enum, which has two states: Some(value) or None. Through this, the compiler requires the developer to handle the None case, checking the possibility of runtime errors like ‘null pointer dereferencing’ at compile time. Similarly, operations that might succeed or fail are made to explicitly return an Ok(value) or Err(error) state via the Result<T, E> type, preventing cases where error handling is omitted.

The tool used to handle these types is pattern matching. Rust’s match expression forces the compiler to check every possible case of an enum like Option or Result. This is called exhaustiveness checking.

let maybe_number: Option<i32> = Some(10);

// The `match` expression forces all possible cases to be checked,
// which is called 'exhaustiveness checking'.
// Therefore, omitting the code to handle the `None` case will cause a compile error.
match maybe_number {
    Some(number) => println!("Number: {}", number),
    None => println!("No number."),
}

In this way, when a programmer omits the handling of a specific state or error case, the compiler prevents it by raising an error.

In summary, Rust’s type system enables the explicit modeling of program states, and pattern matching enforces that all those states are handled. This is an example of Rust’s design principle of aiming to catch errors that could occur at runtime during compile time.

1.5 Ecosystem: Cargo and Crates.io

Build System and Package Manager

The adoption of a programming language is influenced by its ecosystem and tools, in addition to the features of the language itself. Some systems programming languages, like C/C++, lacked officially designated package managers or build systems, sometimes causing developers to use different tools (Makefile, CMake, etc.) and manage dependencies for each project.

Rust set the goal of providing tools related to the development environment as one of its objectives from the early stages of language design. The results are Cargo, the official build system and package manager, and Crates.io, the official package repository.

Cargo is a command-line tool that manages the project lifecycle, including more than just code compilation. Developers can perform the following tasks via commands:

  • Project Creation (cargo new): Creates a new project with a standardized directory structure.
  • Dependency Management: By specifying the name and version of required libraries (referred to as ‘crates’ in Rust) in the cargo.toml configuration file, Cargo automatically downloads and manages those libraries and their sub-dependencies.
  • Build and Run (cargo build, cargo run): Compiles and runs the project via commands.
  • Test and Documentation (cargo test, cargo doc): Runs test code included in the project and generates HTML documentation based on source code comments.

Crates.io is a centralized package repository, similar to Node.js’s NPM or Python’s PyPI. It serves as a platform where Rust developers can share and use libraries.

Cargo aims to reduce the burden of development environment setup and dependency management by integrating processes like project setup, dependency management, building, and testing into a single, standardized tool.

2. Rust Adoption Factors: The Interaction of Technology, Ecosystem, and Narrative

In the programming language market, where numerous languages have appeared and disappeared, Rust has gained developer preference and adoption by major technology companies in a short period. To understand this phenomenon, it is necessary to analyze the complex factors that have contributed to Rust’s adoption.

Rust’s adoption is difficult to explain with a single factor and can be seen as the result of the interaction between its technical background, developer experience, narrative, and the demands of the era. This chapter analyzes these factors to examine the process by which Rust has come to occupy a specific position in the software development ecosystem.

2.1 Technical Background: Memory Safety and Performance Goals

One of the main factors in Rust’s adoption lies in its technical approach to the goal of ‘memory safety without performance degradation,’ which has been a challenge in the systems programming field.

C/C++ provided hardware control and performance, but handling memory errors fell within the developer’s area of responsibility. In contrast, Garbage Collector (GC) based languages like Java or C# provided memory safety, but their use was limited in certain systems areas (such as operating systems, browser engines, etc.) due to runtime overhead and potential pauses caused by GC operations.

Rust presented a different model from the approaches of C/C++ and GC-based languages. Through a compile-time static analysis model of ownership and the borrow checker, it aims to prevent memory errors without a GC, while targeting runtime performance similar to C++.

This approach presents a technical design different from the conventional view that ‘safety and performance are a trade-off.’ After security incidents like Heartbleed, the industry’s demand for memory safety increased, and Rust gained attention in this context.

2.2 Developer Experience (DX): ‘Cargo’ and the Toolchain

One of the factors considered when discussing Rust’s adoption process is the Developer Experience (DX) centered around Cargo, the official build system and package manager.

While the C/C++ ecosystem used various build systems like Makefile, CMake, and autotools and lacked a standardized dependency management method, Rust provided a unified toolchain from its early design. Developers can perform project creation, dependency management, building, testing, and documentation generation using commands like cargo new, cargo build, and cargo test.

Similar to npm (JavaScript) or pip (Python), Cargo has functioned as an infrastructure that contributed to the growth of the Rust ecosystem. Apart from Rust’s learning curve, some developers positively evaluate productivity based on the toolchain.

2.3 Narrative Building and ‘Agenda-Setting’ Analysis

The adoption of a technology interacts not only with technical factors but also with the narrative surrounding it and public perception. In Rust’s case, specific narrative strategies are observed.

  • Value Proposition: Slogans such as “fearless concurrency” and “safety without performance degradation” presented the problems Rust aimed to solve and its values.
  • ‘Agenda-Setting’ Analysis: The Rust discourse highlighted ‘memory safety’ as a criterion for evaluating systems programming languages through a comparative framework with C/C++. By bringing this value to the center of the discussion, ‘memory safety’ became a major evaluation criterion. This can be analyzed as a case of a technical community shaping public perception and setting an agenda around a specific value.

This narrative provided developers with motivation to learn and use Rust, and it influenced the formation of an internal community identity.

2.4 Institutional Sponsorship and Community Culture

Rust received sponsorship from Mozilla from its early stages. Subsequently, the Rust Foundation was established, with participation from Google, Microsoft, Amazon, and others. This sponsorship from institutions and corporations became a factor in spreading the perception that Rust is a project aimed at solving industry problems.

At the same time, the Rust project officially adopted a Code of Conduct and emphasized a culture regarding new participants. Official documentation, such as “The Rust Programming Language” (colloquially “The Book”), was utilized as learning material for developers, which influenced the barrier to entry.

2.5 Summary of Adoption Factors

Rust’s adoption can be seen as the result of the interaction of the various factors analyzed above.

  1. Regarding the problem of ‘memory safety without performance degradation,’
  2. it presented a technical approach,
  3. provided a developer experience including Cargo,
  4. conveyed its value through narrative, and
  5. built the foundation of its ecosystem through institutional sponsorship and community.

An understanding of these multifaceted adoption factors provides a background for evaluating Rust’s technical limitations and discourse issues discussed in other chapters of this book.

Part 2: Technical Analysis of Key Design Principles

Part 1 examined the technical features of Rust and its associated narrative. Part 2 aims to technically analyze Rust’s key design principles: ‘safety’ and ‘ownership’.

This part will conduct a multi-faceted analysis of the context in which these principles are evaluated as ‘innovation’, the engineering trade-offs that exist, and how these concepts relate to historical precedents in programming languages like C++ and Ada. Through this, this part aims to establish an analytical foundation for understanding Rust’s core design philosophy.

3. A Multifaceted Analysis of the ‘Safety’ Narrative

The identity of the Rust programming language is based on the primary attribute of ‘safety’. In Rust discourse, ‘safety’ is emphasized as a key feature that solves the memory error problems of C/C++. However, the term ‘safety’ carries multi-layered meanings in technical, historical, and discursive contexts.

This Chapter 3 aims to analyze this ‘safety’ narrative from multiple perspectives.

First, it examines the historical context of how Rust’s core concepts, evaluated as ‘innovative’, relate to preceding technologies like C++ and Ada (Section 3.1). Second, it clearly defines the technical definition of ‘safety’ that Rust guarantees, its boundaries (unsafe, panic), and its limitations (memory leaks, logical errors) (Section 3.2). Third, through a comparative analysis with C++, Ada/SPARK, and GC-based languages, it analyzes the assurance levels of ‘safety’ and the trade-offs chosen by different engineering approaches (Sections 3.3-3.5). Finally, based on this technical analysis, it examines how the concept of ‘safety’ functions within the discourse (Section 3.6) and presents the trade-offs in programming language design as a conclusion (Section 3.7).

3.1 The Meaning of ‘Innovation’ and Analysis of Historical Precedents

Rust is evaluated as ‘innovative’ in that it simultaneously pursues the goals of ‘performance’ and ‘safety’, presenting a new approach to existing design methods in systems programming. To analyze the meaning of this ‘innovation’ from engineering and historical perspectives, this section examines the technical precedents upon which Rust’s core concepts are based.

Advancements in software engineering are achieved through the succession of existing ideas and their new applications. This section analyzes how Rust’s core concepts connect with ideas developed in languages such as C++, Ada, and functional languages.

In particular, this section references Ada and its subset SPARK as a basis for comparison. This is because Ada/SPARK is a historical precedent that achieved Rust’s goal of ‘safety without GC’ decades ago, albeit through different methods. Therefore, comparing the two technologies is used as an analytical tool to understand where Rust’s approach possesses originality.

Ownership and Resource Management: Succession of the C++ RAII Pattern

Rust’s ‘ownership’ model is related to resource management techniques developed in C++. C++ established the RAII (Resource Acquisition Is Initialization) design pattern, which links the lifecycle of a resource to the lifecycle of an object to automatically release the resource upon destructor call, and substantiated this through smart pointers.

The concept itself of managing memory via resource ‘ownership’ was first established in C++. Rust’s distinguishing feature is that it transformed this idea from an optionally used pattern into a mandatory rule enforced by the compiler across all areas of the language. (A detailed analysis of C++ RAII and smart pointers follows in Section 4.1.)

Safety without GC: The Precedent of Ada/SPARK

One of Rust’s main features is ‘memory safety without a garbage collector (GC)’. This goal was first pursued in the 1980s by the Ada language, developed under the leadership of the U.S. Department of Defense. Ada was designed for high-reliability systems, preventing errors like null pointer access and buffer overflows without a GC through its type system and runtime checks.

SPARK, a subset of Ada, went further by introducing formal verification techniques.2 This is a technology that mathematically proves specific program properties (e.g., the absence of runtime errors), providing a different scope and level of reliability than the memory safety guarantees offered by Rust’s borrow checker. (A detailed comparison follows in Section 3.4.)

Rust’s borrow checker has a practical difference in that it approaches memory safety problems in a more automated way than formal verification. However, the goal itself of ‘achieving safety without a GC’ has a historical precedent, having first been implemented in the Ada/SPARK ecosystem.

Explicit Error Handling: The Influence of Functional Programming

Rust’s explicit error handling method using Result and Option also has its roots in existing programming paradigms. It borrows from the ‘Algebraic Data Type (ADT)’ and monadic error handling techniques developed in ML-family functional languages like Haskell and OCaml. These languages have long used their type systems to explicitly represent ‘a state without a value’ or ‘a state where an error occurred’, enforcing that the compiler handles all cases.

Integration and Enforcement of Concepts

Rust’s core concepts did not arise independently; they are the result of integrating ideas from existing languages. Examples include the RAII principle from C++, the pursuit of safety without GC from Ada/SPARK, and the type-based error handling methods from functional languages.

Therefore, Rust’s design feature can be analyzed as an attempt to provide safety guarantees across a wide range of code by integrating multiple concepts into one language and ‘enforcing’ them as the language’s default rules via the compiler.

3.2 The Definition, Boundaries, and Limitations of Rust’s ‘Safety’

Rust’s ‘safety’ does not imply comprehensive ‘bug-free’ status, but rather refers to a clearly defined technical scope of guarantee. Understanding the precise meaning and scope of this ‘safety’ is necessary for analyzing Rust’s engineering design.

This Section 3.2 first identifies the core definition of ‘safety’ that Rust guarantees (3.2.1), then sequentially analyzes the boundaries of this guarantee represented by the unsafe keyword (3.2.2), the panic failure model (3.2.3), and problems not included in the guarantee’s scope, such as memory leaks and logical errors (3.2.4, 3.2.5), to clarify its limitations.

3.2.1 The Definition of ‘Safety’: Preventing Undefined Behavior (UB)

In Rust discourse, ‘safety’ is presented as a core concept. The technical definition of this term requires clear stipulation. In the Rust language model, ‘safety’ is not used to mean the absence of all kinds of bugs, but in a more specific and limited sense: guaranteeing the absence of ‘Undefined Behavior (UB)’.

‘Undefined Behavior’ refers to unpredictable actions in languages like C/C++ where a program enters a state not prescribed by the language specification, potentially causing system crashes, data corruption, or security vulnerabilities.

One of Rust’s design goals is to-statically prevent such UB from occurring at compile-time within code regions classified as ‘Safe Rust’. The Rust compiler (especially the borrow checker) blocks the causes of UB such as use-after-free, null pointer dereferencing, buffer overflows, and data races between threads.

This definition is specified in Rust’s official document, ‘The Rustonomicon’. “When we say that code is ‘Safe’, we are making a promise: this code will not exhibit any Undefined Behavior (UB).”3

Therefore, Rust’s ‘safety guarantee’ is concentrated in the specific areas of ‘memory safety’ and ‘thread safety (prevention of data races)’. This technical definition has a scope difference from the general perception of the term ‘safety’ (e.g., logical correctness of the program, absence of runtime errors), and this serves as a baseline for understanding the limitations (memory leaks, panics, etc.) to be analyzed in later sections.

3.2.2 The unsafe Keyword and C ABI Dependency

Rust’s compile-time safety guarantee is valid in areas classified as ‘Safe Rust’. However, Rust provides an explicit path to bypass the compiler’s rules (e.g., ownership, borrow rules) via the unsafe keyword. Within an unsafe block, a developer can perform operations that could cause Undefined Behavior (UB), such as dereferencing raw pointers or accessing mutable static variables. The existence of unsafe defines the scope and boundaries of Rust’s safety guarantees.

One of the primary purposes of the unsafe keyword is for Foreign Function Interface (FFI). Many modern operating systems, hardware drivers, and core libraries use the C language’s Application Binary Interface (ABI) as a de facto standard interface. For a Rust program to use operating system features like the file system, networking, or low-level hardware control, it often needs to call system APIs implemented with a C ABI.

These FFI calls require an unsafe block. This is because the Rust compiler cannot verify the behavior of the C code beyond the FFI boundary (e.g., whether a passed pointer is valid, or if a buffer size is correct). In other words, Rust has a structural dependency at the point of interaction with the C ABI, and at this point, the responsibility for the safety guarantee shifts from the compiler to the developer writing the unsafe code.

Beyond FFI, unsafe is also used for other low-level tasks:

  • Implementing high-performance data structures that the compiler cannot verify (e.g., the internal memory allocation management of Vec<T>).
  • Directly controlling hardware registers in OS kernels or embedded environments.

The Rust ecosystem uses a pattern of abstracting and encapsulating this unsafe code within a ‘safe’ interface. However, in this structure, if a defect exists in the unsafe implementation, memory errors can occur even in code written as ‘Safe Rust’. The unsafe keyword is a necessary mechanism for Rust to interact with low-level systems, including the C ABI, while simultaneously serving to explicitly delineate the boundary where Rust’s static safety guarantees do not apply.

3.2.3 The Meaning of ‘Safe Failure’ and panic

Rust’s error handling model includes the concept of ‘safe failure’, which is related to the panic mechanism. To analyze the meaning of panic, ‘failure’ can be distinguished from two perspectives:

  • Memory Integrity Perspective (Safe Failure): This refers to a controlled program termination, distinct from failures that cause Undefined Behavior (UB) or data corruption (e.g., a segmentation fault in C/C++). By default, a Rust panic unwinds the stack, calls the destructor (drop) for each object, and terminates the thread while preserving memory integrity. From this perspective, a panic is a ‘safe failure’ because it does not cause UB.

  • Service Continuity Perspective (Unrecoverable Halt): This refers to a state where the thread terminates without recovering logic or continuing service through exception handling when an error occurs. From this perspective, a panic corresponds to an ‘unrecoverable halt’.

Technically, a panic has the engineering function of preserving memory integrity and aiding debugging. However, this is a concept distinct from the system’s continuous survival or the service’s ‘resilience’.

Rust provides the std::panic::catch_unwind function, which offers a path to prevent a panic from propagating across thread boundaries and to attempt recovery.4 This can be seen as an exceptional means to manage the ‘unrecoverable halt’ characteristic of a panic.

Comparison of Default Failure Modes: The Trade-off Between Availability and Integrity

This difference can be analyzed through a comparison of ‘default failure modes’ with other languages. In particular, how the system reacts when a developer does not perform error handling serves as the criterion for comparison.

In Java or C# environments, even if a developer omits exception handling, a ‘Fail-Safe’ structure operates where exceptions automatically propagate upward and are captured at the framework level. This is a ‘service survival’-centric design that ensures unhandled exceptions do not lead to a total service outage.

In contrast, in Rust, unwrap() is sometimes selected (commission) instead of the complex handling of Result types, which leads to ‘Safe Failure’. In other words, when a developer chooses the ‘path of least resistance’, structurally, Java is likely to lead to ‘service continuity’, whereas Rust is likely to lead to ‘service interruption’. This suggests that Rust has a structural tendency to prioritize ‘data integrity’ over ‘service availability’.

3.2.4 The ‘Safe’ Memory Leak Problem

Rust’s definition of ‘safety’ (Section 3.2.1) focuses on preventing Undefined Behavior (UB), and memory leaks are not included in this scope of guarantee. A memory leak is a phenomenon where a program fails to release allocated memory, causing the system’s available memory to gradually decrease.

From Rust’s perspective, a memory leak is not UB (Undefined Behavior) and is therefore classified as a ‘safe’ operation. This is because, while memory not being freed (leaking) can cause resource exhaustion problems for the program, it does not lead to memory corruption or system crashes, unlike accessing freed memory (use-after-free) or freeing the same memory twice (double-free).

Memory leaks can occur even within ‘Safe Rust’ code. One such case is a reference cycle that occurs when using the reference-counting smart pointer Rc<T> together with RefCell<T>, which provides interior mutability.

When two or more Rc instances form a circular structure by mutually referencing each other (e.g., via RefCell), the reference count of each instance can never reach zero. Even if other parts of the program can no longer access this cycle, the counts within the cycle remain non-zero, the destructors (drop) are not called, and the associated memory is not freed.

This is a logical problem that occurs within ‘safe’ code that does not violate Rust’s ownership and borrow rules, and it demonstrates that Rust’s safety model does not automatically solve all types of memory-related problems.

3.2.5 Problems Outside the Scope of Guarantee (Logical Errors, Deadlocks, etc.)

As defined in Section 3.2.1, the ‘safety’ guarantee provided by the Rust compiler is concentrated in the specific areas of ‘memory safety (preventing UB)’ and ‘preventing data races’. The compiler does not prevent all types of bugs that fall outside this scope.

The following are major types of problems that are outside Rust’s safety guarantee and fall within the developer’s area of responsibility:

  • Logical Errors These are cases where the program’s logic itself is written incorrectly according to intent. Examples include business logic errors like applying an interest rate incorrectly in financial calculations or processing a discount logic redundantly. Rust’s borrow checker validates the validity of memory access, but it does not validate whether the code’s business logic operates correctly as intended.

  • Deadlocks Rust’s concurrency guarantee prevents ‘data races’, which occur when multiple threads try to ‘write simultaneously’ to the same data. However, it does not prevent ‘deadlocks’, where two or more threads hold different resources (e.g., Mutex A, Mutex B) while indefinitely waiting for the other’s resource (B, A). This is not a memory safety issue, but a logical flaw in concurrency design.

  • Integer Overflow This occurs when an operation on an integer variable exceeds its representable range. In debug builds, Rust will panic; however, in release (deployment) builds, the value wraps around by default. This is not Undefined Behavior (UB), but it can be a source of calculation errors or logical bugs if not explicitly handled by the developer.

  • Resource Exhaustion Beyond the memory leaks in Section 3.2.4, this involves problems where limited system resources—such as file handles, network sockets, or database connections—are not released due to logical errors. While Rust’s RAII pattern (Drop trait) assists in resource deallocation, the language does not guarantee the automatic management of all types of resources.

The limitations of this guarantee scope can be confirmed by the CVE-2024-24576 vulnerability discovered in 2024. This vulnerability, rated CVSS 10.0 (Critical), occurred in Rust’s ‘safe’ standard library API (std::process::Command). The cause was not a memory error, but a Command Injection vulnerability (a logical error, CWE-78) that occurred from failing to properly escape arguments when processing commands in a Windows environment.

This case demonstrates that even though Rust prevents memory-related UB, logical security vulnerabilities can still occur outside its scope of guarantee.

3.3 Comparative Analysis 1: C++’s Multi-Layered Approach to Safety

Discourse addressing Rust’s safety often explains its value through comparison with C/C++. In this process, it is necessary to set the ‘modern C/C++ ecosystem’—which has accumulated changes over time—as the subject of comparison, rather than the C/C++ of the 1990s.

The C++ language and its ecosystem have long used a multi-layered approach spanning the language, tools, and methodologies to ensure safety. The difference in this approach, unlike Rust’s built-in compiler guarantee, is that it demands developer choice, additional costs, and discipline.

1. Language Evolution: Modern C++ and ‘Optional’ Safety

‘Modern C++’ (post-C++11 standard) introduced smart pointers (std::unique_ptr, std::shared_ptr) supporting the RAII (Resource Acquisition Is Initialization) pattern into its standard library. This is a method to prevent some of C++’s existing memory-related problems at the language level by specifying resource ownership and managing memory automatically.

However, the use of smart pointers in C++ is a ‘best practice’, not an enforced rule. Developers can still use raw pointers, and the compiler does not prevent this. The responsibility for safety relies on developer discipline.

2. The Tooling Ecosystem: An Approach Demanding ‘Cost and Expertise’

The C/C++ development environment can utilize the following automated tools to ensure safety:

  • Static Analysis: Tools like Coverity, PVS-Studio, and Clang Static Analyzer analyze the codebase before the compilation stage to find potential bugs.
  • Dynamic Analysis: Tools like Valgrind and AddressSanitizer monitor memory access during program execution to detect runtime errors.
  • Real-time Linting: Linters like Clang-Tidy enforce rules from the C++ Core Guidelines5 to guide a specific coding style.

While these tools improve safety, some commercial tools incur costs, and they require expertise to configure and interpret the analysis results. This differs in accessibility and cost from the static analysis capabilities provided by default in Rust’s official toolchain (cargo).

3. Methodology for Mission-Critical Systems: The ‘Specialized Field’ Approach

In ‘mission-critical’ systems fields that demand high reliability, such as automotive, aviation, and medical devices, specific methodologies are applied.

  • Enforcing Coding Standards: Coding standards like MISRA C/C++ are used to restrict the use of potentially risky language features, such as dynamic allocation.
  • Performing Static Verification: Static code verification tools like Polyspace and Frama-C are used to mathematically verify the possibility of runtime errors (e.g., overflows).

These approaches can increase the safety of C/C++ code, but they are limited to specific fields and involve costs and efforts that impact development productivity, making them difficult to apply in general software development.

Conclusion: The Difference Between ‘Optional Effort’ and ‘Enforced Default’

The C++ ecosystem has used a multi-layered methodology to ensure safety. The exchange of ideas between programming paradigms is also occurring, as seen with std::expected introduced in the C++23 standard, which takes an approach similar to Rust’s Result.

However, the use of such features and tools in C++ is a ‘best practice’ that relies on developer ‘choice’. These standards and methodologies may not be consistently applied across many projects, and memory-related security incidents continue to occur.6

In conclusion, Rust’s compiler-enforced safety features differ from C++’s multi-layered approach in that they provide ‘safety’ as an ‘enforced default’ rather than an ‘option’.

3.4 Comparative Analysis 2: Ada/SPARK’s Mathematical Proof and Assurance Level

This section utilizes Ada and its subset SPARK as an ‘analytical tool’ to describe where Rust’s safety model is positioned on the safety assurance spectrum of systems programming. Through a comparative analysis with SPARK’s ‘mathematically proven correctness’, it explores the engineering characteristics and guarantee scope of the Rust model. This comparison is intended to understand the trade-offs selected by different design philosophies.

Rust’s Safety Guarantee: Preventing ‘Undefined Behavior (UB)’

As analyzed in Section 3.2, Rust’s core safety guarantee is the prevention of memory access errors and data races that cause Undefined Behavior (UB) at compile-time, achieved through ‘ownership’ and ‘borrowing’ rules.

However, this guarantee does not assure the program’s logical correctness or the absence of all kinds of runtime errors. For example, errors like integer overflows or array index out-of-bounds lead to a panic (Section 3.2.3), which is distinct from guaranteeing stable system ‘execution’.

Ada/SPARK’s Safety Guarantee: Proving ‘Program Correctness’

In contrast, the Ada/SPARK ecosystem targets a broader scope of correctness.

  1. Ada’s Default Safety and Resilience: Ada, at the language level, attempts to prevent logical errors through its type system and ‘Design by Contract’. By default, it raises an exception upon a runtime error, including integer overflows. This is a design oriented toward ‘resilience’, allowing the system to continue its mission via error handling routines.

  2. SPARK’s Mathematical Proof: SPARK, a subset of Ada, uses formal verification tools to mathematically analyze the logical properties of the code. Through this, it can ‘prove’ at compile-time that runtime errors (including integer overflows, array index out-of-bounds, etc.) will not occur.

Difference in Error Handling Design: Recover vs. Panic

These technical differences stem from the design philosophy regarding error handling.

  • Ada: Treats runtime errors as ‘exceptions’ and supports system recovery. This reflects the requirements of ‘mission-critical (availability-focused)’ systems, where the entire system must not stop and must remain in an available state even if an error occurs.
  • Rust: Treats the same errors as ‘bugs’ in the program and causes the execution flow to panic (halt). This is a design that prioritizes ‘memory safety (integrity-focused)’ to prevent secondary issues (such as memory corruption) that could arise from continuing execution in an incorrect state.

This difference goes beyond the presence or absence of features and represents a difference in design goals based on the nature of the systems each language targets.

Comparison of Assurance Levels Between the Two Languages

Error Type Rust Ada (Default) SPARK
Memory Errors (UB) Blocked at Compile-Time (Guaranteed) Blocked at Compile/Run-Time (Guaranteed) Mathematically Proven Absent
Data Races Blocked at Compile-Time (Guaranteed) Blocked at Run-Time (Guaranteed) Mathematically Proven Absent
Integer Overflow panic (Debug) / Wraps (Release) Runtime Exception (Recoverable) Mathematically Proven Absent
Array Out-of-Bounds panic (Unrecoverable Halt) Runtime Exception (Recoverable) Mathematically Proven Absent
Logical Errors Programmer Responsibility Partially prevented by Design by Contract Provably absent based on contracts

Conclusion: Position on the Safety Spectrum

This comparative analysis shows that Rust’s safety model is positioned at a specific point on the ‘safety’ spectrum. Whereas SPARK requires explicit developer effort (annotations, contracts) and the use of specialized tools for ‘mathematical proof’, Rust provides automated safety by focusing on a limited guarantee scope (UB prevention) and paying the cost of the developer’s learning curve (the borrow checker).

The two technologies present solutions to different engineering problems, and evaluating Rust’s safety only in comparison to C/C++ may limit the understanding of its technical position within the full spectrum of systems programming.

3.5 Comparative Analysis 3: Re-evaluation of Alternative Memory Management (GC)

Rust’s memory management method is often compared to the manual management of C/C++. However, the systems programming spectrum also includes languages (e.g., Go, C#, Java) that achieve memory safety and productivity through a garbage collector (GC).

In some Rust-related discourse, it is argued that GC-based languages are unsuitable for certain systems programming domains, citing the ‘Stop-the-World’ pauses and runtime overhead of GC. While these claims may have applied to past GC technology, they may not reflect the characteristics of modern GC.

Modern GCs implemented in mainstream languages manage memory while minimizing application pauses by using techniques like generational GC, concurrent GC, and parallel GC. For example, the Go language’s GC is designed to target pause times in the microsecond (µs) range and is used in network servers and cloud infrastructure. Java’s ZGC and Shenandoah GC target millisecond (ms) pauses even with very large heaps.

Rust’s ownership model and GC can be seen as a difference in design philosophy regarding ‘how costs are paid’:

  • Rust’s Approach: Minimizes runtime costs, instead transferring those costs to compile time and the developer’s cognitive load (learning curve, borrow checker)—i.e., ‘development time’.
  • GC Language Approach: Reduces the developer’s cognitive load and development time, instead paying the cost in ‘machine time’—runtime CPU and memory resources.

Domains where GC use is restricted, such as hardware-constrained embedded systems or hard real-time operating systems, do exist. However, generalizing these specific requirements to evaluate the practicality of all GC-based languages may fail to consider the demands of various business environments. In some commercial settings, development speed and time-to-market can be more critical than runtime performance, in which case a GC language can be a viable option.

3.6 Discourse Analysis: Redefining ‘Practicality’ and ‘Responsibility’

The preceding sections (3.1-3.5) analyzed Rust’s ‘safety’ model from technical and historical perspectives and compared it with other approaches like C++, Ada/SPARK, and GC languages. In this process, Rust’s conceptual precedents (3.1) and technical limitations (3.2) were also addressed.

This Section 3.6 shifts the focus of the analysis from ‘technical facts’ to ‘technical discourse’. That is, it analyzes how these technical facts are communicated and interpreted within the Rust ecosystem, and how the core narrative of ‘safety’ is maintained and defended.

First, it examines the way the meaning of ‘innovation’ is redefined as ‘practicality’ (3.6.1) and the way ‘responsibility’ for technical limitations, such as memory leaks or unsafe bugs, is attributed (3.6.2).

3.6.1 The Discursive Function of ‘Practical Innovation’

Section 3.1 analyzed how Rust’s core concepts are based on preceding technologies like C++ and Ada. In response to such analysis, the argument is raised that Rust’s innovation lies not in the ‘invention of concepts’ but in the ‘democratization of value’ or ‘practical innovation’.

The logic of this argument is as follows: The ‘safety without GC’ of Ada/SPARK required high costs (learning curve, specialized tools, development speed) in specific fields like aviation and defense, and thus did not spread to the general developer population. In contrast, Rust, through its tooling ecosystem (like Cargo) and community, is said to have disseminated this concept into the general systems programming domain. In short, the argument is that a technology usable by the many has greater engineering significance than a technology used by the few.

The point of analysis for this book is how this claim of ‘practical innovation’ functions within technical discourse. When this claim is used as a response to the critical question of ‘a lack of conceptual originality’, it is observed to function as a rhetorical tool.

Responding to the question “Is A conceptually new?” with “A is used in the market and is practical” may not be a direct answer to the former question. This can be seen as a topic shift, diverting the category of discussion from ‘conceptual origin’ to ‘practical utility’.

This logical shift can lead to a discourse that implies ‘conceptual uniqueness’ based on Rust’s ‘practical achievements’. As a result, the historical and engineering outcomes of languages like Ada and C++ may be relatively undervalued or excluded from the discussion. The concept of ‘practical innovation’ can simultaneously explain Rust’s achievements while also performing a discursive function that evades a critical examination of the original meaning of the term ‘innovation’.

3.6.2 The Attribution of ‘Responsibility’: The Discussion of Memory Leaks and unsafe

When Rust’s technical limitations (Section 3.2) are discussed, the way ‘responsibility’ for those issues is attributed shows a specific discursive pattern. This can be analyzed as a logical boundary-setting exercise intended to preserve the language’s core concept of ‘safety’.

1. Memory Leaks: Separating Responsibility via the Definition of ‘Safety’

As analyzed in Section 3.2.4, memory leaks can occur in Rust even within ‘safe’ code, such as through reference cycles.

When this technical fact is raised as a criticism of Rust’s ‘memory safety’, the discourse often refers to the technical definition from Section 3.2.1 (Safety = UB Prevention). The logic is that since a memory leak does not cause Undefined Behavior (UB), it is not an ‘unsafe’ operation, and therefore does not fall within the scope of the compiler’s ‘safety guarantee’.

This approach separates ‘memory problems’ into ‘dangerous problems that cause UB’ and ‘logical problems that do not cause UB (safe) (memory leaks)’. Consequently, the responsibility for preventing memory leaks is transferred from the compiler’s guarantee domain to the developer’s logical responsibility domain. This shows a difference from how memory management is treated in the C/C++ community as a comprehensive developer ‘responsibility’.

2. unsafe Bugs: Isolating Responsibility via the unsafe Boundary

As explained in Section 3.2.2, code inside an unsafe block bypasses the compiler’s safety checks, and bugs in this part can undermine the stability of ‘Safe Rust’ code.

When a memory error occurs in a library’s unsafe code, the discourse tends to emphasize that the ‘Safe Rust’ guarantee itself has not failed. The cause of the error is attributed not to the ‘Safe Rust’ model, but clearly to the ‘responsibility of the developer who wrote the unsafe code’.

The unsafe keyword, while marking a specific area of code as ‘not trusted’, also serves to isolate the responsibility for problems arising in that area to the developer. This contrasts with C/C++, where a library bug might be accepted as a manifestation of the language’s inherent risks.

In conclusion, these two discussion methods function as mechanisms to maintain Rust’s core concept: the ‘memory safety guarantee of Safe Rust’. By (1) limiting the definition of ‘safety’ (to UB prevention) and (2) separating responsibility via the explicit unsafe boundary, the core narrative—that the ‘Safe Rust’ guarantee remains valid despite actual problems occurring in the ecosystem (memory leaks, unsafe bugs)—is preserved.

3.7 Conclusion: The Trade-off between Performance, Safety, and Productivity

In software engineering, it is difficult for a single tool to satisfy all requirements. This also applies to programming language design. Engineering design is generally a process of coordinating trade-offs between multiple goals.

The design direction of a programming language is typically determined based on three factors: performance and memory control, development productivity, and compiler-level safety. Each language and ecosystem selects a specific point among these three factors, each having different characteristics and costs.

  • C/C++: Prioritizes hardware control and execution performance. For this, developers must take direct responsibility, including memory management (Section 3.3), and safety relies on external tools or discipline.
  • Go, Java/C#: Focuses on development productivity through a garbage collector (GC) and runtime (Section 3.5). This design pays the cost of runtime overhead.
  • Ada/SPARK: Aims for the highest level of mathematically provable safety and correctness (Section 3.4). This requires a high level of development cost and expertise.
  • Rust: Aims to achieve both C++-level performance and ‘memory safety (UB prevention)’ without a GC (Section 3.2). Instead of runtime costs, this demands ‘development time’ and ‘cognitive cost’ from the developer, who must learn and apply the ownership and borrow checker models.

Due to these design differences, each language may show different suitability for specific development scenarios. For example, a web service backend might choose Go for its productivity, an aircraft control system might choose SPARK for its provable stability, and a system where GC is a constraint might choose Rust’s model.

In conclusion, ‘safety’ is not a singular concept but a multi-layered spectrum (see table in Section 3.4), and all languages possess specific characteristics and associated costs according to their unique design goals. Therefore, analyzing the constraints and requirements of a specific problem domain to select the appropriate tool corresponds to an engineering approach.

4. Re-evaluation of the ‘Ownership’ Model and Design Philosophy

First, Section 4.1 analyzes how this concept originated from C++’s RAII patterns and smart pointers. Next, Section 4.2 analyzes that Rust’s characteristic lies not in the ‘invention’ of the concept, but in the compiler’s role in transforming C++’s ‘optional pattern’ into a ‘mandatory rule’. Finally, Section 4.3 compares it with Ada/SPARK’s ‘Design by Contract’ to examine what trade-offs the ownership model has when implementing specific data structures.

4.1 Origin of the Ownership Concept: C++’s RAII Pattern and Smart Pointers

To understand the historical background of Rust’s (Rust) ownership model, one can examine how resource management has evolved in the C/C++ languages.

C’s Manual Memory Management and Its Limitations

The C language gives programmers control over dynamic memory through the malloc() and free() functions. This design provides flexibility and performance, but it places the responsibility on the programmer to free all allocated memory at the correct time, and exactly once.

This manual management model can lead to the following memory errors if a programmer makes a mistake:

  • Memory leak: A phenomenon where allocated memory is not freed, gradually reducing available memory.
  • Double free: A phenomenon where already-freed memory is freed again, corrupting the memory manager’s state.
  • Use-after-free: An issue where a freed memory region is accessed, leading to data corruption or security vulnerabilities.

Due to these problems, paradigms were sought in C++ to solve this systemically, beyond relying on the programmer’s individual responsibility.

C++’s Evolution: The RAII Pattern and Smart Pointers

C++ introduced the RAII (Resource Acquisition Is Initialization) pattern to transfer the responsibility of resource management from the individual programmer to the language’s object lifecycle management rules. RAII is a method where resources are acquired in an object’s constructor and automatically released in its destructor. The C++ compiler guarantees that the destructor is called when an object goes out of scope (including normal termination and exception cases), thus preventing resource leaks from being missed.

A representative case of applying this RAII pattern to dynamic memory management is smart pointers. Smart pointers introduced since the C++11 standard show similarity to Rust’s ownership model.

  • std::unique_ptr (Unique Ownership): Expresses exclusive ownership over a specific resource. The concept of copy being forbidden and only ‘moving’ ownership being allowed directly connects to Rust’s default ownership model and move semantics.
  • std::shared_ptr (Shared Ownership): Provides a way for multiple pointers to safely co-own a single resource through reference counting. This is the concept that forms the basis for Rust’s Rc<T> and Arc<T>.

C++ established the concept of ‘resource ownership’ through RAII and smart pointers, and presented solutions for handling it.

4.2 Rust’s Ownership Model: Not ‘Invention of the Concept’, but ‘Compiler’s Enforcement’

The preceding Section 4.1 analyzed that Rust’s ownership concept is connected to C++’s RAII patterns and smart pointers. Rust’s characteristic lies not in the ‘invention’ of the concept itself, but in the ‘method of enforcement’ of existing ownership principles at the language level.

Transition from Optional Pattern to Mandatory Rule

In C++, the use of smart pointers like std::unique_ptr is a design pattern and an ‘option’ for the developer. A developer can choose not to follow this pattern and use raw pointers (raw pointer), and the compiler does not prevent this. The responsibility for ensuring safety lies with the developer.

Rust, on the other hand, established ownership rules as a mandatory rule embedded in the language’s type system, not as an optional pattern. All values follow this rule, and a static analysis tool called the borrow checker verifies compliance at compile-time. Unless an unsafe block is used, a rule violation leads to a compile error, blocking program generation.

This design differs from C++ in that it shifts the entity responsible for safety assurance from the ‘developer’ to the ‘compiler’s static analysis’. However, at this point, it is necessary to consider the impact of dependency on tools on securing runtime safety.

In the C language environment, awareness of potential risks in code tends to induce the practice of defensive coding. On the other hand, trust in the compiler’s safety guarantees can be a factor that reduces defensive approaches to runtime logical errors or exceptional situations. For example, using unwrap() instead of explicitly performing error handling for the Result type can be interpreted as a result of prioritizing convenience based on the safety net provided by the language.

Trade-offs from the Perspective of an Experienced Developer

This characteristic of ‘compiler enforcement’ has a duality of usefulness and constraints from the perspective of a C/C++ developer.

Some C/C++ developers can recognize that Rust’s ownership rules align with existing best practices.

  • Rust’s move semantics are similar to the ownership transfer pattern using std::unique_ptr and std::move in C++.
  • Rust’s immutable (&T) and mutable (&mut T) references share a context with the design principles in C++ of using const T& to guarantee data immutability or to prevent simultaneous modification.

In these respects, Rust can be evaluated as a tool that explicitly enforces existing ‘implicit disciplines’ via the compiler.

However, this enforcement also acts as a limitation. When implementing certain data structures or performing performance optimizations, a developer might employ memory management patterns that are beyond the borrow checker’s analysis capabilities. Since the borrow checker cannot prove all valid programs, a situation arises where logically safe code is rejected simply because ‘the compiler cannot prove it’.

In conclusion, Rust’s ownership model functions to enhance the safety level of code through rule enforcement. At the same time, due to a design philosophy that prioritizes fixed rules, it contains a trade-off that constrains development flexibility in certain situations.

4.3 Design Philosophy Comparison: Ownership Model and Design by Contract

Programming languages adopt different design philosophies to ensure correctness. The ownership and borrowing model used by Rust focuses on automatically preventing specific types of errors at compile-time. In contrast, Design by Contract, as used in languages like Ada/SPARK, employs a method where tools verify logical ‘contracts’ specified by the developer.

To analyze the differences between these two philosophies and their respective engineering trade-offs, the implementation of a doubly-linked list, a data structure in computer science, will be used as a case study.

1. Approach 1: Rust’s Ownership Model

A doubly-linked list has a structure where each node mutually references its previous and next nodes. This structure, which can be implemented in other languages using pointers or references, directly conflicts with Rust’s basic rules. This is because Rust’s ownership system, by default, does not allow reference cycles or multiple mutable references to a single piece of data.

Therefore, a node definition that attempts to directly represent this structure with references is treated as a compile error by the borrow checker.

// Code that does not compile
struct Node<'a> {
    value: i32,
    prev: Option<&'a Node<'a>>,
    next: Option<&'a Node<'a>>,
}

To resolve these constraints within ‘safe’ Rust code, one must use specific features provided by the language. That is, Rc<T> for shared ownership, RefCell<T> for interior mutability, and Weak<T> to break reference cycles are used in combination.

// Implementation example using Rc, RefCell, and Weak
use std::rc::{Rc, Weak};
use std::cell::RefCell;

type Link<T> = Option<Rc<Node<T>>>;

struct Node<T> {
    value: T,
    next: RefCell<Link<T>>,
    prev: RefCell<Option<Weak<Node<T>>>>,
}
  • Analysis: This approach provides the benefit of the compiler automatically preventing certain types of concurrency problems, such as data races. The ownership rules enforce specific memory safety rules, and in cases requiring shared state, like a doubly-linked list, it guides the developer to explicitly handle that state using Rc, RefCell, etc. The cognitive cost and code verbosity that arise in this process are the costs of this design philosophy. The developer’s focus may shift more to satisfying the compiler’s rules than to the logical structure of the problem.

2. Approach 2: Ada/SPARK’s Pointers and Design by Contract

Ada supports C/C++-like pointer usage through access types, allowing the structure of a doubly-linked list to be expressed.

-- Expression using Ada
type Node;
type Node_Access is access all Node;
type Node is record
  value : Integer;
  prev  : Node_Access;
  next  : Node_Access;
end record;

By default, Ada ensures safety by checking for errors like null pointer (null access) dereferencing at runtime, raising a Constraint_Error exception.

Furthermore, SPARK, a subset of Ada, provides a method to mathematically prove the absence of runtime errors at compile-time through Design by Contract. The developer specifies preconditions (Pre) and postconditions (Post) for procedures or functions, and a static analysis tool verifies whether the code always satisfies these contracts.

-- Example of safety proof via SPARK contract
procedure Process_Node (Item : in Node_Access)
  with Pre => Item /= null; -- Specifies the contract: 'Item is not null'
  • Analysis: This approach allows the developer to express data structures using a pointer model similar to C/C++. Safety is secured through runtime checks, or through explicit contracts written by the developer and proven by static analysis tools. The cost of this design philosophy is the responsibility and effort required for the developer to consider all potential error paths and write them as formalized contracts. If contracts are omitted or written incorrectly, the safety guarantee may be incomplete, which entails a different kind of risk than a method relying on automated rules.

3. Design Philosophy Comparison and Conclusion

The two approaches allocate the responsibility and cost for ensuring software correctness to different entities and at different times.

Category Rust Ada/SPARK
Safety Assurance Entity Compiler (Automatic enforcement of implicit rules) Developer + Tool (Explicit contract writing and static proof)
Default Paradigm Restrictive by default, opt-in complexity Permissive by default, opt-in safety proof (via contracts)
Primary Cost Cognitive overhead and code complexity when implementing  
certain patterns Need to write formal specifications for all interactions  
Primary Benefit Automatic prevention of specific error classes (e.g., data races) Direct expression of developer’s design intent & ability to prove broad logical properties

In conclusion, rather than being evaluated from a binary perspective of ‘innovation’ or ‘flaw’, Rust’s ownership model is analyzed as a design philosophy with advantages and corresponding costs. This philosophy has the function of preventing specific types of bugs, and in the process, it contains a trade-off that requires developers to incur learning costs and specific solution methods for certain problems. The suitability of the language can be evaluated differently based on the type of problem to be solved, the team’s capabilities, and the values prioritized by the project (e.g., automated safety guarantees vs. design flexibility).

Part 3: Ecosystem Realities and Structural Costs

Part 3 will analyze the realistic challenges faced by the Rust ecosystem and the structural costs behind them. When evaluating Rust’s developer experience (DX), the ‘zero-cost abstraction’ principle, and the constraints of real-world industrial application, it is important to understand that the problems we encounter can be divided into two categories:

  1. Problems of ‘maturity’: These are issues that can be naturally resolved or mitigated as time passes and the community’s efforts accumulate, such as a lack of libraries, instability of some tools, or insufficient documentation. These are maturity issues common to all growing technology ecosystems.

  2. Inherent ‘trade-offs’ in design: These are the result of intentionally sacrificing other values (e.g., ease of learning, compile speed, flexibility in implementing certain patterns) to achieve the language’s core values (e.g., runtime performance, memory safety without a GC). This is a matter of ‘choice,’ not a ‘flaw,’ and therefore is unlikely to disappear completely over time.

Based on this analytical framework, this chapter aims to clearly distinguish and evaluate which category of problem Rust’s various technical challenges fall into.

5. ‘Developer Experience (DX)’ Outcomes and Costs

Chapter 5 analyzes the various aspects of the ‘Developer Experience (DX)’ encountered by developers using Rust and the costs it entails.

The discussion begins with the impact of Rust’s ‘borrow checker’ and ‘learning curve’ on productivity (5.1). It then examines the ‘generalization tendency’ in technology selection (5.2), followed by a review of the complexity and trade-offs in specific technical areas such as ‘asynchronous programming’ (5.3) and the ‘error handling model’ (5.4). Finally, the discussion on developer experience concludes by analyzing the challenges of the ‘library ecosystem’ (5.5) and the ‘development toolchain’ (5.6, 5.7).

5.1 The Borrow Checker, Learning Curve, and Productivity Trade-offs

The key technology implementing Rust’s (Rust) safety model is the borrow checker, which statically enforces ownership, borrowing, and lifetimes rules at compile-time. This mechanism, due to its strictness, forms a trade-off with development productivity. Developers accustomed to other programming paradigms must reconfigure their existing approaches to apply Rust’s model, which leads to a learning curve.

The Duality of the Trade-off: Learning Cost and Safety Assurance

The rules applied by the borrow checker incur a cognitive cost during development, but simultaneously provide the benefit of preventing specific types of runtime errors.

  1. Cost and Benefit of the Ownership and Borrowing Model: Developers must apply a single-owner rule for all values and adhere to immutable or mutable borrow rules when accessing data. In this process, developers may expend additional effort beyond logic implementation to satisfy the compiler’s rules. However, through this cost, the compiler prevents concurrency issues like data races at compile-time and eliminates the possibility of memory errors such as use-after-free.
  2. Cost and Benefit of Explicit Lifetimes: In cases where the compiler cannot automatically infer the validity of a reference, the developer must explicitly specify lifetime parameters ('a). This is a task that demands additional abstract thinking to pass the compiler’s static analysis. However, through this explicit notation, the compiler can statically verify and block the possibility of errors referencing invalid memory, such as dangling pointers.
  3. Constraints and Alternatives for Implementing Specific Design Patterns: The borrow checker’s analysis model makes it difficult to implement structures like doubly-linked lists or graphs requiring circular references using only the basic rules. This shows that there are limits to the range of programs the borrow checker model can express. In such cases, developers can use Rc<T>, RefCell<T>, or unsafe blocks to explicitly handle exceptions to the rules and implement the desired data structures.

Impact on Productivity and Related Discourse

These technical characteristics affect a project’s productivity. When new members join a development team, an adaptation period and training costs may arise (initial productivity decline), and feature implementation may be delayed due to resolving compile errors, potentially reducing the predictability of the project schedule. In a business environment that uses development time as a resource, this acts as a cost and risk.

This learning curve is part of the design trade-off selected for the goal of ‘safety without performance degradation’. In some online discussions, a discourse is observed where this learning difficulty is reinterpreted as a sign of developer enhancement or professionalism. This perspective can lead to criticisms that it reduces discussions about the learning process’s difficulty to a matter of individual competence, acting as an entry barrier for new developers or limiting discussions on improving the tool’s usability.

5.2 Generalization Tendency in Technology Selection and Engineering Trade-offs

When a new technology emerges, a tendency to expand its application scope beyond its original purpose is observed. This is a phenomenon known as the ‘law of the instrument’ and can be seen as a common socio-psychological dynamic in technology adoption.

The Rust language provides a case study for analyzing this phenomenon. The value of ‘memory safety’ provided by the language and the learning time required to master it cause developers to invest significant effort into the technology. This investment can lead to attempts to expand the technology’s utility beyond the specific areas where its strengths are demonstrated, into broader domains.

This section analyzes two aspects of this ‘generalization’ tendency as it appears in Rust-related discussions. First, it examines the tendency for Rust’s key features (e.g., absence of GC, runtime performance) to be used as exclusive evaluation criteria when assessing other programming languages. Second, it uses general web application development as a case study to explore how trade-off analysis, considering the problem’s characteristics and constraints, can be applied differently.

Bias in Comparison Methods with Other Technologies

The generalization tendency in technology selection can be accompanied by a specific bias in comparison methods with other programming languages.

In some cases, Rust’s characteristics of ‘memory safety without a garbage collector (GC)’ and ‘high runtime performance’ are applied as the primary criteria for evaluating technology. From this perspective, other languages may be evaluated as follows:

  • C/C++: The absence of memory safety becomes the main basis for evaluation, more so than other aspects (e.g., ecosystem, hardware control capabilities).
  • Go, Java, C#: The presence of a GC is analyzed as a potential cause of performance degradation, and the development productivity or ecosystem value of these languages may be relatively underrated.
  • Python, JavaScript: The absence of a static type system is presented as a basis for stability problems, and these languages’ features, such as rapid prototyping and development speed, may be considered secondary factors.

Engineering evaluation comprehensively considers various trade-offs. A method that selectively emphasizes only specific criteria may have limitations in assessing each technology’s suitability for different problem domains.

Case Study: Generalization in Web Backend Development

One example of this generalization is the argument for applying Rust to some web backend development.

Rust can be an option in specific web service fields requiring high performance and low latency, such as high-performance API gateways or real-time communication servers. Memory safety is also a factor that enhances server stability.

However, this argument can be seen as generalizing the requirements of a specific area where Rust’s features are prominent to other web backend areas. In the development of many general web applications (e.g., SaaS, internal management systems, e-commerce platforms), the following business and engineering factors are considered alongside performance:

  • Development speed and time-to-market
  • Ecosystem maturity (completeness of libraries for authentication, payments, ORMs, etc.)
  • Ease of learning for new personnel and the size of the developer talent pool

On these metrics, languages with existing ecosystems such as Go, C#/.NET, Java/Spring, and Python/Django may be suitable options. Arguing for the broad application of a specific technology without considering the problem’s characteristics and business constraints can be seen as an approach that does not sufficiently consider engineering trade-offs.

5.3 Asynchronous Programming Model Complexity and Engineering Trade-offs

Rust’s (Rust) asynchronous programming model (async/await) is designed based on the ‘Zero-Cost Abstractions’ (ZCA) principle, aiming to achieve runtime performance without a garbage collector or green threads (Green Thread). This is a design goal set in the system programming domain that utilizes operating system threads.

However, this design choice entails costs to be borne by the developer, namely conceptual complexity and debugging difficulty.

Cause of Technical Complexity

Rust’s async/await operates by having the compiler transform asynchronous code into a state machine. This process can create ‘self-referential structs’ that contain references to themselves in memory, and Rust introduced a pointer type called Pin<T> to ensure the memory address stability of these structs.

Pin<T> and its related concepts like Generators are abstract concepts not found in other mainstream languages, requiring study to understand their operation. This complexity can be seen as a form of ‘leaky abstraction’, and developers in Rust’s asynchronous ecosystem have also mentioned the learning curve for these concepts in blogs and talks, raising the need for usability improvements.

Practical Impact on Development Experience

The internal complexity of the async model causes the following difficulties in the actual development and maintenance process:

  1. Increased Debugging Difficulty: The stack traces output when an error occurs in async code are often composed of internal functions of the async runtime and state machine calls generated by the compiler, making it difficult to trace the root cause of the error. Furthermore, unlike synchronous code, local variables in async functions are captured inside the state machine object, making state tracking via a debugger challenging.
  2. Cost Shifting: Consequently, Rust’s async model minimizes runtime CPU and memory usage (machine time) but transfers that cost to the developer’s learning time and debugging difficulty (developer time), which is a design trade-off.

Comparative Analysis with Alternative Models

This trade-off becomes clearer when compared with alternative asynchronous models like Go’s Goroutines. Goroutines provide a simplified concurrency programming model to developers through lightweight threads (green threads) managed by the language runtime.

Category Rust async/await Go Goroutine
Design Goal Zero runtime overhead Development productivity & simplicity
Runtime Cost Minimized Cost exists due to scheduler, GC
Learning Curve High (Requires Pin, etc.) Low (go keyword)
Debugging Difficult (Complex stack trace) Easy (Clear stack trace)

For CPU-bound tasks, Rust’s model may have a performance advantage. However, in typical I/O-bound work environments where network latency or database response speed is the bottleneck, the development and debugging complexity cost required by Rust’s model may be greater than the runtime cost accepted by Go’s model.

A tendency is sometimes observed in parts of the Rust community to evaluate the Go model based on it “not being zero-cost”. However, this can be an approach that evaluates technology using only ‘runtime performance’ as a single metric, overlooking other engineering values such as ‘development productivity’ or ‘ease of maintenance’.

5.4 Reconsidering the Practicality of the Explicit Error Handling Model (Result<T, E>)

Rust adopts an explicit error handling model that enforces error handling at compile-time through the Result<T, E> enum, pattern matching, and the ? operator. This model functions to prevent omitted error handling. This section analyzes the practicality of this model by comparing it with alternative error handling methods, analyzing its conceptual origins, and examining the costs incurred in actual use.

1. Comparison with Alternative Models: try-catch Exception Handling

When discussing Rust’s Result model, try-catch-based exception handling models are often criticized for unpredictable control flow. However, exception handling mechanisms possess the following engineering characteristics:

  • Separation of Concerns: Normal logic can be described in the try block and exception handling in the catch block, separating them. Since control flow is immediately transferred from the point where an error occurs to the point where it is handled, the method of manually propagating errors (return Err(...)) through multiple function steps can be avoided.
  • Compile-Time Checking: The criticism “you don’t know what exception will be thrown” does not apply in all cases. For example, Java’s ‘Checked Exceptions’ require specifying the exceptions a function can throw in its signature, and the compiler enforces their handling. This is an example of achieving the goal of preventing error omission in a different way than the Result type.
  • System Resilience: Exception handling systems play a role in preventing abnormal program termination and maintaining stable service operation through error logging, resource cleanup (finally), and error recovery logic.

2. Conceptual Origins: Functional Programming

The explicit error and state handling method via Result and Option is not unique to Rust; it is an adoption of a previously existing concept. The roots of this idea lie in the functional programming camp.

Haskell’s Maybe a and Either a b types, or the sum types in ML-family languages like OCaml and F#, have for decades used a method of representing the absence of a value or an error state within the type system, compelling the compiler to handle all cases.

Therefore, Rust’s contribution can be analyzed not as ‘inventing’ this concept, but as ‘reinterpreting’ it in the context of a systems programming language and ‘popularizing’ it through syntactic conveniences like the ? operator.

3. Practical Cost: Verbosity of Error Type Conversion

The ? operator is used in scenarios propagating the same error type, but it shows its limitations in real-world applications that use various external libraries. Different libraries return their own unique error types (e.g., std::io::Error, sqlx::Error), and developers must repeatedly write boilerplate code to convert them into a single application error type.

// Example of converting various different error types into a single application error type
fn load_config_and_user(id: Uuid) -> Result<Config, MyAppError> {
    let file_content = fs::read_to_string("config.toml")
        .map_err(MyAppError::Io)?; // std::io::Error -> MyAppError

    let config: Config = toml::from_str(&file_content)
        .map_err(MyAppError::Toml)?; // toml::de::Error -> MyAppError

    // ...
    Ok(config)
}

External libraries like anyhow and thiserror are used to resolve this repetitive conversion. However, the fact that the use of external libraries for specific functions (in this case, flexible error handling) is considered a de facto standard in the ecosystem suggests that there are additional requirements for practical application development using only the language’s basic features.

4. Case Study: The Cloudflare Outage and the Use of unwrap()

How Rust’s error handling model operates in a real-world operational environment can be confirmed through the Cloudflare service outage that occurred in November 2025.7 This accident occurred because unwrap() was used, causing a panic, instead of handling the error case with match or the ? operator in a function returning a Result type.

Rust enforces developers to explicitly handle errors through the Result type. However, at the same time, it provides a means to bypass that enforcement through a method called unwrap(). Theoretically, unwrap() is mainly used for prototyping or test code, but in the actual development process, there are cases where it is used in production code to reduce the cost of writing complex error handling logic.

This case suggests that the language’s enforcement cannot completely rule out choices that prioritize developer convenience. Even if the compiler enforces rules, if a developer selects a path (unwrap()) that bypasses safety mechanisms for implementation convenience, the result can lead to system interruption. This is a case showing the limitations that can occur when Rust’s ‘enforced safety’ model is combined with human factors in actual engineering fields.

5.5 Rust Ecosystem’s Qualitative Maturity Challenges and Community Discourse Analysis

Rust’s official package manager, Cargo, and its central repository, Crates.io, played a role in the language’s rapid adoption and growth. This led to a quantitative expansion, with libraries, or crates, being shared. However, behind this quantitative growth lies the challenge of qualitative maturity in ensuring stability and reliability in production environments. This section analyzes the main qualitative challenges facing the Rust ecosystem and examines the community’s characteristic discourse structure in response to these issues.

1. Key Challenges Related to the Crate Ecosystem’s Qualitative Maturity

Developers using Rust in production environments may face the following practical problems related to the library ecosystem:

  • Lack of API Stability: A significant number of crates are often maintained at 0.x versions, below semantic versioning 1.0.0, for extended periods. This implies that the library’s public API is not stabilized and that breaking changes, which do not guarantee backward compatibility, could occur. In projects with production dependencies, this acts as a factor increasing potential maintenance costs and risks.
  • Variation in Documentation: Despite the ability to generate standardized documentation via cargo doc, the documentation level of actual crates varies greatly. Some crates lack specific usage examples or explanations of design philosophy beyond an API list, causing developers to have to analyze the source code directly to use the library. This can be a factor hindering the goal of library use, which is productivity improvement.
  • Maintenance Sustainability Issues: As a problem common to many open-source ecosystems, even core crates are sometimes maintained by a small number of volunteers. If a core maintainer stops the project for personal reasons, there is a risk that follow-up on security vulnerabilities or major bugs could be delayed for a long time. This could affect the stability of the entire ecosystem dependent on that crate.

2. Analysis of Criticism of Ecosystem Issues and Observed Response Patterns

When criticism about the ecosystem’s qualitative issues is raised, a specific discourse pattern is often observed in various public discussion spaces, such as certain online forums, that steers the conversation in a direction different from the technical essence of the problem. This is a tendency to lead the discussion in a direction different from the technical essence of the problem.

  • Shifting Responsibility via ‘Encouraging Participation’: Responses like “Pull Requests are welcome” or “If you need it, contribute it yourself” are expressions that encourage voluntary participation, a value of open source. However, when such expressions are used as answers to criticism about library flaws or lack of documentation, they also perform the rhetorical function of shifting the responsibility for solving the problem onto the original critic. Considering the reality that not all users have the expertise or time to modify libraries, such reactions can stifle the feedback loop.
  • Success Case Representativeness and Statistical Perspective: In response to criticism about the overall qualitative maturity of the ecosystem, cases of a few successfully managed core crates, like tokio and serde, are sometimes presented as counterarguments. It is meaningful that these success stories show the potential of the Rust ecosystem and the level of quality that can be achieved. It is meaningful that these success stories show the potential of the Rust ecosystem and the level of quality that can be achieved. However, this line of argument can be examined from the perspective of ‘representativeness of the sample’. This is because it is difficult to see a few successful cases as representative of the average maturity of the entire ecosystem, which is composed of numerous libraries, or the reality faced by the average developer. This is less about pointing out a logical fallacy and more an engineering and statistical question of whether a specific sample (success cases) is sufficient to describe the characteristics of the entire population (the ecosystem). This approach can lead to an overestimation of the ecosystem’s current state by limiting the focus to a few top-tier cases, instead of comprehensively surveying the practical problems faced by individual libraries.

5.6 Development Toolchain’s Technical Challenges and Productivity

The developer experience of the Rust language is accompanied by specific features as well as several technical challenges. These challenges can affect the development productivity of large-scale projects. This section analyzes current issues from the aspects of compiler resource usage, IDE integration and debugging environments, and build system flexibility.

5.6.1 Compiler Resource Usage and Its Impact

The Rust compiler (rustc) tends to require time and memory resources during the compilation process. This stems from the language design, such as the monomorphization strategy to implement the ‘Zero-Cost Abstractions’ (ZCA) principle and dependencies on the LLVM backend.

  • Compile Time: Monomorphization generates code for each generic type, which increases the amount of code the compiler must process and optimize. This slows the ‘code modification → compile → test’ development feedback loop, and this can be a factor hindering developer productivity, especially as project size increases. While tools like cargo check provide syntax checking, a full build and test may still require time.
  • Memory Usage: Memory usage during compilation can cause problems in resource-constrained development environments (e.g., personal laptops, low-spec CI/CD build servers). In large-scale projects, the compiler process may exceed the system’s available memory, sometimes resulting in it being forcibly terminated by the operating system’s OOM (Out of Memory) Killer. This is a factor that hinders the stability of the development experience.

However, these costs are not fixed. The Rust project and community recognize compile time as a subject for improvement and are exploring solutions. The development of the Cranelift backend to improve debug build speeds and attempts to strengthen the rustc compiler’s own parallel processing capabilities are examples showing that this engineering trade-off is being managed.

5.6.2 IDE Integration and Debugging Environment: The Cost Behind Abstractions

When discussing Rust’s developer experience, the IDE integration and debugging environment is an area that shows how the language’s design philosophy creates costs for the developer’s actual work. Rust supports language servers and standard debuggers, but its complexity and abstraction model introduce points that cause cognitive load and productivity decline.

The Reality and Limits of the Language Server (rust-analyzer)

The language server rust-analyzer provides features like code completion, type inference, and error checking by analyzing Rust’s complex type system and macro features in real-time. It is evaluated as a tool that has improved the productivity of the Rust ecosystem.

However, this very depth of analysis acts as a cost. rust-analyzer keeps code, including project dependencies, resident in memory and must recalculate complex trait resolution and macro expansion every time the developer modifies the code. This leads to the following practical problems:

  • Resource Usage: In large-scale projects, the rust-analyzer process itself can occupy several gigabytes (GB) of memory, which can be a burden in resource-constrained development environments.
  • Analysis Instability: In code where complex generic types or procedural macros are used, cases may arise where type inference fails or provides an inaccurate diagnosis, which can lead to situations where the developer relies on the compiler’s (rustc) final diagnosis rather than trusting the language server’s results.

This can be interpreted not as an issue with rust-analyzer itself, but as a limitation of a language server that must process the compiler’s work in real-time, and as evidence of the Rust language’s complexity.

The Trade-off Between Abstraction and Debugging

Rust’s ‘Zero-Cost Abstractions’ (ZCA) principle generates its cost for the developer during the debugging process. Although standard debuggers like LLDB or GDB are used, the experience of debugging Rust’s abstracted types differs from that of other languages.

For example, when inspecting a Vec<String> type variable in a debugger, a developer in a Java or C# integrated IDE environment might see the collection’s contents, like ["hello", "world"]. However, in the Rust debugger, the Vec struct’s fields are displayed, such as the memory layout: a pointer (ptr) to heap memory, the total allocated capacity capacity, and the current number of elements length.

`)]

This approach creates a cognitive load for the developer, who must interpret the low-level memory structure shown by the debugger to grasp the program’s logical state. This is a trade-off where the price for the abstraction that removed runtime cost manifests as reduced debugging convenience.

Asynchronous Code Debugging

This aspect appears when debugging async/await code. As analyzed in Section 5.3, Rust’s async functions are transformed into a state machine by the compiler. This makes stack-based debugging difficult to operate.

Even if you stop at an error point and check the call stack, the logical flow of function_a calling function_b written by the developer does not appear. Instead, what is visible are the internal functions of the async runtime (e.g., tokio) scheduler, and the state machine’s poll function calls generated by the compiler, which the developer must interpret. Consequently, it can be difficult to answer the question, “How did this code get here?”

This forms a contrast with other ecosystems, such as C#’s Visual Studio or Java’s IntelliJ IDEA, which reconstruct and display the logical call stack for asynchronous code. Rust’s asynchronous debugging environment can be considered a case that shows how a design philosophy of minimizing runtime overhead can lead to complexity costs in the development and maintenance phases.

5.6.3 Build System (Cargo) Flexibility

Rust’s official build system, Cargo, provides productivity based on the ‘convention over configuration’ philosophy, including standardized project management and dependency resolution. This is a feature of Cargo.

However, this feature can act as inflexibility when project requirements go beyond the standard scope. In cases requiring complex code generation or special integration with external libraries, it is often difficult to cope flexibly with just build.rs scripts. Furthermore, in large-scale monorepo environments, the combination of feature flags can become complex, making dependency management another maintenance cost. This can be a constraint in large-scale industrial environments that must respond to various build scenarios.

These factors show that the developer experience provided by Rust is accompanied by specific advantages as well as technical challenges. Therefore, rather than evaluating the development environment fragmentarily, it can be understood as the result of different design philosophies. The following section aims to move away from the perspective of defining each ecosystem by a single philosophy, to compare the two options of ‘separated toolchain’ and ‘integrated experience’, and to explore the essence of the debate by considering the variable of ‘maturity’.

5.7 Development Environment Comparison: The Intersection of Maturity and Design Philosophy

The previous section analyzed the technical challenges of Rust’s development environment. However, such analysis is often at risk of leading to a binary comparison that only contrasts the features of each ecosystem, such as “Java/C#’s integrated IDE” versus “Rust’s VS Code environment”. This approach can overlook the fact that both ecosystems offer two options: a ‘separated toolchain’ and an ‘integrated experience’.

Therefore, a comparison needs to place each philosophy side-by-side and consider the variable of ‘ecosystem maturity’ on top of them.

1. First Comparison: The ‘Separated Toolchain’ Environment (VS Code)

The advent of the Language Server Protocol (LSP) has laid the groundwork for multiple languages to receive similar support in editors like Visual Studio Code. In this environment, the situation for each ecosystem is as follows:

  • For Java/C#: Eclipse JDT LS, Red Hat’s Java extension, and C#’s Roslyn LSP have secured stability and maturity through years of development and corporate support. They provide code completion, diagnostics, and refactoring functions in enterprise projects.

  • For Rust: rust-analyzer has contributed to the Rust ecosystem’s growth. However, as analyzed in Section 5.6, it faces maturity challenges, such as showing instability or requiring system resources due to the language’s own complexity (macros, trait resolution, etc.).

  • Analysis: Under the same condition of a ‘separated toolchain’, Java/C#’s LSPs show maturity, having developed over a long history and on a relatively stable language specification. In contrast, Rust’s rust-analyzer is in a situation of having to solve linguistic challenges. This does not show the superiority of one over the other, but rather the differences in each ecosystem’s historical path and technical challenges.

2. Second Comparison: The ‘Integrated Experience’ Environment (Professional IDEs)

Both ecosystems also offer integrated environments beyond LSP’s capabilities.

  • For Java/C#: IntelliJ IDEA and Visual Studio, based on accumulated experience, provide ‘project intelligence’ beyond code analysis. The refactoring, debugging, and profiling experiences offered by analyzing the code’s semantic structure are why these IDEs are classified as ‘development platforms’. This is a case showing the maturity of the ‘integrated’ philosophy.

  • For Rust: JetBrains’ RustRover and CLion show that the ‘integrated experience’ option also exists in the Rust ecosystem. These IDEs attempt to provide debugger integration and refactoring functions through their own analysis engines, in addition to rust-analyzer. This is a step forward for the Rust developer experience.

  • Analysis: In this area, the ‘maturity gap’ is revealed. RustRover is in its early stages compared to IntelliJ’s Java support features. It is a challenge to implement the Java ecosystem’s refactoring patterns and debugging-related functions in a short period. This can be interpreted as a process that growing technologies go through, rather than a technical limitation of Rust.

3. Conclusion: Reframing the Comparison

Directly comparing “Java/C#’s integrated IDEs” with “Rust’s VS Code” is an asymmetrical frame that cross-compares the mature parts and the popular parts of each ecosystem.

The content derived from the comparison is as follows:

  1. Both ecosystems provide development environments for both philosophies.
  2. In both the ‘separated toolchain’ and ‘integrated experience’ areas, the Java/C# ecosystem shows maturity through a longer history and investment.
  3. Rust’s ecosystem development environment is advancing, but it faces maturity challenges due to the language’s own complexity and the ecosystem’s lack of historical time.

Therefore, it is difficult to conclude that the difference between the two development environments is due to one side’s ‘dependency’ or the superiority/inferiority of a specific philosophy. As analyzed in the text, the point is that the ‘stage of maturity’ each ecosystem has reached is different. The Java/C# ecosystem, through time and investment, has achieved completeness in both ‘integrated’ and ‘separated’ methods, whereas the Rust ecosystem is in the process of growing while solving the language’s complexity. Engineering evaluation should start from selecting the tools and philosophies suitable for a given project’s requirements, in a state of acknowledging this reality.

6. Analysis of the Actual Costs of ‘Zero-Cost Abstractions’

Chapter 6 analyzes the actual costs associated with Rust’s design principle of ‘Zero-Cost Abstractions (ZCA)’.

The first section (Section 6.1) examines how ZCA’s runtime cost is shifted, through a mechanism called ‘monomorphization’, into the costs of increased compile time and binary size. Following this, Section 6.2 analyzes the binary size problem, examining its relationship with ABI instability and static linking, and its impact on application fields through practical examples.

6.1 The Mechanism of Cost Shifting: The Role of Monomorphization

One of Rust’s (Rust) design principles is ‘Zero-Cost Abstractions (ZCA)’. This principle means that even if a developer uses abstraction features like Generics or Iterators, it should not cause runtime performance degradation.

This principle is connected to C++’s design philosophy. The principle “You don’t pay for what you don’t use,” presented by Bjarne Stroustrup, the creator of C++, is the same as the essence of ZCA. C++ has long implemented a method of eliminating runtime overhead by generating code at compile-time through features like Templates.

Rust inherits this ZCA philosophy and has been implemented to ensure memory safety by combining it with ownership and the borrow checker. However, the term ‘zero-cost’ only means ‘zero runtime cost’; it does not mean the cost required for abstraction does not exist. Rust’s ZCA can be understood as a cost-shifting mechanism that secures runtime performance but transfers that cost to other stages of the development cycle.

This cost shifting is related to a compilation strategy called monomorphization. This is a method where, when compiling generic code like Vec<T>, separate, specialized code is generated for each concrete type used in the code, such as Vec<i32> and Vec<String>. This strategy aims to increase execution speed by eliminating indirect costs like runtime type checking or virtual function calls, but it incurs the following two costs:

  1. Increased Compile Time: The compiler must duplicate the code for as many generic types as are used and optimize each one individually. This increases the amount of code the compiler (especially the LLVM backend) must process, becoming a cause of increased overall compile time.
  2. Increased Binary Size: All the generated specialized code is included in the final executable file. This results in multiple copies of the same logic existing, causing the final binary size to grow. This is particularly pronounced when combined with static linking.

As an alternative to monomorphization, Rust provides a dynamic dispatch method using trait objects (&dyn Trait). This method generates a single function instead of duplicating code and finds the necessary implementation at runtime to call. Thus, it presents a trade-off: incurring a runtime cost in exchange for reducing compile time and binary size.

In conclusion, Rust’s ‘Zero-Cost Abstractions’ are a product of a design philosophy that considers runtime performance. However, the costs of increased compile time and binary size that occur in this process affect development productivity and the deployment environment. These cost-shifting aspects are factors that should be considered when evaluating the ZCA principle. This is a design trade-off: paying the cost of compile time and binary size to achieve the goal of ‘zero runtime cost’.

6.2 Binary Size Analysis: The Impact of Design Principles on Application Fields

Rust programs tend to have larger executable file (binary) sizes compared to programs with similar functionality written in C/C++. This becomes a consideration in the resource-constrained systems programming domain where Rust is discussed as an alternative to C/C++. This section analyzes the technical causes of this phenomenon and examines its ripple effects through specific case comparisons.

1. Technical Cause: ABI Instability and Static Linking

One of the causes of Rust’s binary size increase lies in the design characteristic that the ABI (Application Binary Interface) of the standard library (libstd) is not stably maintained. The C language supports dynamic linking, where multiple programs share and use shared libraries installed on the system, based on a stable libc ABI that has existed for decades. This allows C program executables to maintain a small size by including only their own unique code.

On the other hand, Rust did not stabilize its ABI, allowing for changes to the internal implementation of libstd for the improvement and evolution of the language and libraries. This is a design choice that prioritizes ‘rapid evolution’ over ‘stable compatibility’. As a result of this choice, static linking, which includes the necessary library code within the program’s executable file, was adopted as the default method instead of dynamic linking, which is difficult to guarantee compatibility between versions. Therefore, even for a program, the relevant features of libstd are included in the binary, causing the size to increase.

2. Case Study: Comparison of CLI Tools and Core Utilities

The impact of this design can be confirmed through a size comparison of actual programs.

Case 1: grep and ripgrep ripgrep is a text search tool written in Rust, often compared to the C-based grep. However, on a typical Linux system, a dynamically linked grep is tens of kilobytes (KB) in size, whereas a statically linked ripgrep reaches several megabytes (MB). This facilitates dependency management when deploying a single application, but it can act as an increase in total capacity in a scenario of replacing all of the operating system’s basic tools.

Case 2: BusyBox and uutils In resource-constrained embedded Linux environments, BusyBox is used, which provides multiple commands like ls and cat in a single binary. BusyBox, written in C, has a total size of less than 1MB. In contrast, uutils, developed in Rust for a similar purpose, has a size reaching several MB. While the specific size may vary depending on each project’s version and compile environment, this tendency can be seen as a structural result stemming from the differences in the standard library design and default build methods of the two languages. The table below is a comparison based on Alpine Linux packages.

Table 6.2: Package Size Comparison of Core Utility Implementations (Alpine Linux v3.22 standard)8

Package Language Structure Installed Size (Approx.)
busybox 1.37.0-r18 C Single Binary 798.2KiB
coreutils 9.7-r1 C Individual Binaries 1.0 MiB
uutils 0.1.0-r0 Rust Single Binary 6.3 MiB

This data shows that Rust’s default build method has differences from the requirements of the embedded environments targeted by BusyBox.

Package sizes were referenced from the ‘Installed size’ provided in the official package database of the Alpine Linux v3.22 stable release. The purpose of this table is not to compare the latest performance at a specific point in time, but to show the structural tendency of how each language ecosystem’s design method impacts binary size. This fundamental tendency is not significantly swayed by minor patch updates or version changes that can occur within a stable release, so a specific stable release was adopted as the standard for data reproducibility and consistency of the argument. The referenced versions of each package are as specified in the table.

3. Size Reduction Techniques and Their Trade-offs

Several techniques exist to reduce Rust binary size, which are shared through guidelines like min-sized-rust. The techniques are as follows:

  • Changing Panic Handling Strategy (panic = 'abort'): Instead of unwinding the stack when a panic occurs, the program aborts immediately, removing related code and metadata. This reduces size, but skips the resource cleanup process and makes panic recovery via catch_unwind impossible. In other words, it entails an engineering trade-off between binary size optimization and the ability to secure system resilience.
  • Exclude Standard Library (no_std): Does not use libstd, which provides OS-dependent features like heap memory allocation, threading, and file I/O. This can reduce the size, but it comes with the constraint of having to implement data structures and features like Vec<T> and String oneself or rely on external crates.

As such, to implement C/C++ level binaries in Rust, one must disable the features and some safety mechanisms provided by the language by default. This suggests that Rust’s default design philosophy places more emphasis on features and runtime performance than on binary size.

The increased compile time and binary size caused by the ‘Zero-Cost Abstractions’ principle and its implementation method, monomorphization, are cases that show Rust’s design philosophy.

These costs are not a ‘maturity problem’, but an ‘inherent trade-off’ where ‘development time’ and ‘deployment size’ are exchanged to secure the value of ‘runtime performance’. This demonstrates the engineering principle that “costs do not disappear, they are just transferred elsewhere.” Therefore, developers need to understand this cost-shifting mechanism behind the term ‘zero-cost’ and evaluate whether their project’s constraints (e.g., compile speed, binary size) align with Rust’s design philosophy.

7. Industrial Application Constraints

Chapter 7 analyzes the constraints Rust faces when applied in practical industrial fields.

The discussion begins with the challenges in specialized fields such as ‘Embedded and Kernel Environments’ (7.1) and ‘Mission-Critical Systems’ (7.2). It then examines the barriers to adoption in ‘General Industry’ (7.3), and finally concludes the discussion with a multi-faceted analysis of the ‘Big Tech Adoption’ narrative (7.4).

7.1 Embedded and Kernel Environments: Application Reality and Engineering Challenges

One of the areas where Rust is evaluated as an alternative to C/C++ is in embedded systems and operating system kernel development. However, applying Rust in these two fields presents several engineering challenges. Just as the C language cannot use user-space standard libraries like glibc in a kernel environment, Rust also cannot use its standard library (libstd), which depends on operating system features.

Therefore, the challenge is not the existence of no_std itself, but the development model differences and the resulting costs experienced by Rust developers, who are accustomed to the std environment, when they switch to no_std. While the C language development model assumes a low-level environment, for Rust developers accustomed to the std ecosystem, the transition to no_std requires a cognitive cost. This is because the absence of features like heap memory allocation, threading, and standard data structures (e.g., Vec<T>, String), as well as the inability to use libraries dependent on std, limits the available ecosystem.

One of the attempts to solve these challenges is the ‘Rust for Linux’ project, and its approach can be summarized by the following characteristics:

  1. Building Safe Abstractions: One of the project’s aims is to ‘safely’ wrap the ‘unsafe’ low-level APIs written in C from the existing Linux kernel, using Rust’s ownership and lifetime rules. For example, elements like the kernel’s memory allocation functions (kmalloc, kfree), locking mechanisms, and reference counting are abstracted into ‘safe’ data structures similar to Rust’s Box<T>, Mutex<T>, and Arc<T>. This allows developers to focus on high-level logic by utilizing the compile-time safety checks provided by Rust, instead of directly handling the kernel’s internal operations.
  2. Utilization of unsafe: However, at the bottom of this abstraction layer, the use of unsafe code is necessary to call C functions or directly access hardware registers. This is a result of the FFI (Foreign Function Interface) design for interoperating with the C ecosystem. That is, the strategy is to isolate unsafe code at specific boundaries and to write ‘safe’ code on top of it.
  3. Practical Application Cases and Cultural Challenges: On this foundation, Rust is currently being adopted experimentally in parts of actual systems, such as Android’s Binder IPC driver and Apple M1/M2 GPU drivers. This process involves not only technical barriers but also the skeptical views of some C kernel developers and the cultural and philosophical debates on the Linux Kernel Mailing List (LKML) as part of the integration process.

To quantitatively analyze the actual integration status in the Linux kernel, the source code of Linux kernel v6.15.5 (as of July 9, 2025), distributed from kernel.org, was analyzed using the cloc v2.04 tool.9 The analysis showed that the total lines of code (SLOC), excluding comments and blank lines, were 28,790,641, of which Rust code accounted for 14,194 lines, or about 0.05% of the total.

This figure shows the status at a specific point in time. Rust’s integration into the kernel is an ongoing project, so this proportion may change in the future. This data, as of mid-2025, shows Rust’s relative scale and integration status within the kernel’s C language codebase. The quantitative proportion of code does not represent the importance or technical impact of that code. Looking at the content of the currently included code, it can be seen that its role is focused on building the basic infrastructure for writing drivers. Meanwhile, how criticism based on such data is accepted and defended within certain technical discourses will be analyzed again in the case study in Section 8.4.

The table below summarizes the distribution of languages with a high proportion of code lines within that kernel version.

Table 7.1: Language Proportion in Linux Kernel v6.15.5 (Unit: Lines, %)¹

Rank Language Lines of Code Ratio (%)
1 C & C/C++ Header 26,602,887 92.40
2 JSON 518,853 1.80
3 reStructuredText 506,910 1.76
4 YAML 421,053 1.46
5 Assembly 231,400 0.80
… … … …
14 Rust 14,194 0.05

¹Based on a total of 28,790,641 lines of code. Some languages are omitted.

7.2 Mission-Critical Systems and the Absence of International Standards

In the mission-critical systems field, such as aviation, defense, and medicine, which require high reliability, industry standards and ecosystem maturity are among the main criteria for selecting a language, in addition to technical performance.

These fields often require compliance with international standards (e.g., ISO/IEC) to ensure software stability and predictability. A standardized language has a fixed specification, which supports long-term maintenance, and serves as the foundation for a commercial ecosystem where various vendors provide compatible compilers, static analysis tools, and certification support services. Languages like C, C++, and Ada have these standardization procedures and vendor ecosystems.

However, Rust is not a language established as an international standard, and it adopts a model of changing its language specification for evolution. This ‘rapid evolution’ model can contribute to short-term feature improvements, but it may conflict with the requirements of the mission-critical field, which is conservative about change and values long-term stability. Consequently, related regulatory compliance and certification procedures become complicated, and it is difficult to get support from commercial vendors, which acts as a structural barrier to entry into the field.

7.3 Barriers to General Industry Adoption

The following barriers exist for Rust to spread beyond specific fields to the general industry at large.

  1. Workforce Supply and Training Costs: The talent pool of Rust developers is limited compared to other languages like Java, C#, and Python. This can lead to hiring difficulties and high labor cost burdens for companies. Furthermore, transitioning existing developers to Rust entails learning costs for concepts like the ownership model and an initial period of reduced productivity.
  2. Enterprise Ecosystem Maturity: In areas of the ecosystem used for large-scale enterprise application development, such as ORM (Object-Relational Mapping) frameworks, cloud service SDKs, and authentication/authorization libraries, there is a lack of maturity compared to Java or .NET. This can act as a barrier to adoption in corporate environments that prioritize development speed and stability.
  3. Legacy System Integration and Migration Costs: Most companies already operate legacy systems built with C++, Java, etc. A full rewrite of these systems in Rust involves high costs and unpredictable risks. Therefore, gradual integration or interfacing is an alternative, but interoperation between languages via FFI (Foreign Function Interface) inherently involves technical complexity and the potential for errors.

These factors are business and engineering constraints that companies must consider when choosing a technology stack, separate from the language’s technical features.

7.4 Multi-faceted Analysis of the ‘Big Tech Adoption’ Narrative: Context, Limits, and Strategic Implications

One of the arguments for Rust’s (Rust) practicality and future value is the adoption cases by technology companies like Google, Microsoft, and Amazon. The fact that these companies use Rust is used as an indicator of Rust’s technical features and its ability to solve specific problems.

However, for an engineering evaluation, it is necessary to analyze the specific ‘context’, ‘scale’, and ‘conditions’ of that adoption, beyond the mere fact of ‘which company uses it’. This multi-faceted analysis helps in understanding the technical reality and strategic implications behind the ‘big tech adoption’ narrative.

1. Review of Adoption Context, Scale, and Conditions

First is the context of application. These companies are not adopting Rust wholesale for all systems and products, but are applying it ‘selectively’ to specific areas where Rust’s features are prominent. For example, low-level components of operating system kernels, security-sensitive parts of web browser rendering engines, and high-performance infrastructure where even minor garbage collector delays are not permissible are among the targets. This means that in the reality where these companies still use C#, Java, Go, and C++ as their mainstays in much broader areas, Rust is being utilized as a ‘strategic tool’ rather than a ‘full replacement’.

Second is the scale of adoption. The word ‘adoption’ often implies organization-wide acceptance, but the reality can be different. Compared to the total number of software projects or the developer talent pool at these companies, the proportion occupied by Rust is in a growth phase. A ‘halo effect’ may occur, where the adoption by some teams is magnified through the company’s logo as if it were the standard technology for the entire organization.

Third is the condition of adoption. Technology companies possess the resources to bear the costs associated with adopting new technology. This includes developer training costs due to the learning curve, internal tooling and library development costs to address gaps in the ecosystem, and the temporal and financial leeway to accept an initial drop in productivity. Presenting cases from specific companies as ‘universal proof’ that can be equally applied to general companies with limited personnel and budgets, without considering the reality of these resources, may overlook the ‘representativeness of the sample’ problem. It is difficult to assume that results observed in a specific sample group (technology companies) will be reproduced in the same way in the entire industrial ecosystem population. This also connects to the ‘representativeness of the sample’ problem pointed out in Section 5.5.

2. Implications of Strategic Adoption

The fact that these companies ‘strategically chose’ Rust is linked to ‘what problems’ they introduced Rust to solve. Google’s Android, Microsoft’s Windows kernel, and the Chrome browser, for example, operate on top of an existing C++ codebase of hundreds of millions of lines. Achieving memory safety in these systems without performance degradation was a challenge.

In this situation, Rust was chosen as a ‘technical solution that can gradually introduce memory safety in a scalable way to a large-scale codebase, while maintaining existing C++ performance and control levels’. This shows that Rust can be used to solve problems faced by engineering organizations.

This choice can be interpreted as a leading indicator that the paradigm of systems programming is changing, beyond Rust just solving ‘niche market’ problems.

3. Conclusion: Multi-faceted Analysis

In conclusion, the adoption of Rust by large corporations can be analyzed dually. On one hand, its specific context and limitations can be analyzed, rather than it being used as proof for all problem situations. On the other hand, this selective adoption shows Rust’s characteristics in solving specific problems in the systems programming field and can be interpreted as a sign heralding a paradigm shift.

Engineering judgment can be made through multi-faceted analysis and can start from evaluating the limitations and potential of a specific technology.

The industrial application constraints of Rust analyzed in this chapter are the result of a complex interplay of two factors: ‘maturity problems’ and ‘inherent trade-offs’.

The barrier to entry into mission-critical systems, arising from the absence of international standards or ABI stability issues, can be seen as an ‘inherent trade-off’ stemming from Rust’s development model that prioritizes ‘rapid evolution’.

On the other hand, the lack of a developer talent pool or the immaturity of the library ecosystem in certain enterprise areas is a ‘maturity problem’ that can be alleviated as technology adoption spreads and the community grows.

In conclusion, for Rust to expand beyond its current application fields into broader industrial sectors, it faces the challenge of addressing both types of barriers. Along with the maturation of the ecosystem, a review is needed of how the language’s design philosophy can align with the requirements of various industries.

Part 4: Technical Community Discourse Analysis

Having analyzed the technical features of Rust and the engineering trade-offs behind them up to Part 3, Part 4 will now shift its focus to critically deconstruct the social phenomenon surrounding Rust, namely, the ‘discourse.’

The analysis in this part will be approached as a case study examining the formation process of defensive discourse in a particular technical community and its logical patterns. It is clarified that the object of analysis is not the official position of the Rust project, but is limited to a specific tendency observed in some online discussion spaces. It is clearly stated that this is not an attempt to over-interpret the voices of a few as the opinion of the entire community. Nevertheless, the reason this book focuses on such informal discourse is that, even if it is the voice of a few, it shapes a new developer’s first impression of the technology and has a real impact on their experience of entering the ecosystem. Furthermore, this public discourse has significant analytical value because it can become the training data for Large Language Models (LLMs), leading to the technical re-learning and amplification of existing biases. This part aims to achieve a deep understanding of the universal formation process of such technical discourse through the specific case of Rust. Chapter 8 will analyze how the ‘silver bullet narrative’10 is formed and how it functions as a collective defense mechanism when faced with criticism, and Chapter 9 will consider the realistic impact of this discourse on a developer’s technology choices and the sustainability of the ecosystem. Finally, Chapter 10 will synthesize all the preceding analyses to present the challenges and prospects for the Rust ecosystem and conclude.

Ultimately, Part 4 aims to help developers cultivate a more mature and balanced perspective by moving beyond blind advocacy or criticism of a particular technology and understanding the way a technology ecosystem operates.

8. Formation of the ‘Silver Bullet Narrative’ and Group Defense Mechanisms

Chapter 8 analyzes how the ‘silver bullet narrative’ is formed and how it functions as a ‘group defense mechanism’ when faced with criticism. The discussion begins by examining the formation process and effects of this narrative (8.1). Next, it examines the limits of the ‘complete replacement’ narrative (8.2) and the historical precedents of technical discourse (8.3). It then analyzes specific argumentation patterns in response to critical discourse (8.4), gatekeeping (8.5), the governance controversy (8.6), and cases of citing external agency reports (8.7). Finally, the chapter concludes by examining the official improvement efforts and governance on the other side of this discourse (8.8).

8.1 The Formation Process and Effect of the ‘Silver Bullet Narrative’

The analysis of the ‘silver bullet narrative’ in this chapter limits its scope. This analysis does not cover the official positions of the Rust Foundation or the core development team, and it clarifies that it is not an attempt to generalize the entire Rust community as a single group. The point this chapter focuses on is a specific discourse that shows a different tendency from the official self-critical culture of the Rust project.

In fact, Rust’s core developers and the Foundation recognize the complexity of async, compile times, and toolchain issues described in the previous chapters of this book as tasks for improvement. They specify technical limitations through the Request for Comments (RFC) process or official blogs and are seeking solutions with the community.

Therefore, the subject of this chapter’s analysis is limited to the defensive or generalized rhetoric of certain supporters observed in some online technology forums or social media, separate from these official improvement activities11. Since it is difficult to measure the quantitative share of this informal discourse, this analysis focuses on analyzing its ‘logical structure’ and ‘effect’ rather than its ‘frequency’.

As analyzed in Section 2.3, one of the factors that influenced Rust’s growth was the narrative formed around values ​​such as ‘safety without performance degradation’. This narrative is evaluated as having contributed to ecosystem growth by forming the community’s identity and inducing contributions from volunteers.

However, when this narrative is faced with external criticism or technical limitations, a tendency is sometimes observed for it to be simplified into a ‘silver bullet narrative’10 (“Rust solves all systems programming problems”) and lead to group defense mechanisms. To analyze the social drivers of this phenomenon, some concepts from social psychology can be utilized as an analytical framework. This is not an attempt to ‘diagnose’ the psychology of a specific group or individual, but rather an approach to explain the formation structure and effect of discourse appearing in a technical community with an identity.

For example, cognitive dissonance theory is a concept that describes the state that occurs when an individual is faced with information that conflicts with their efforts or beliefs. Applying this framework, one can assume a situation where a developer has invested time and effort to overcome Rust’s learning curve. Facing criticism about the language’s disadvantages or limitations after such an investment can cause a state of dissonance that conflicts with the motive to justify one’s efforts. As a result, the individual may show a tendency to describe the advantages of the technology they chose by emphasizing them and downplaying the disadvantages, in order to resolve this state.

Furthermore, from the perspective of social identity theory, when mastery of a specific technology is linked to a developer’s professional identity, the community has a tendency to form an ‘in-group’. In this case, external criticism may be perceived as a challenge to the ‘in-group’s’ values ​​or identity, rather than being accepted as a technical review. This dynamic can act as a factor in the formation of a defensive discourse that relatively devalues ​​the ‘out-group’ of other technical ecosystems.

This in-group/out-group dynamic can be reinforced through the ‘echo chamber effect’ in certain online spaces. An echo chamber refers to a phenomenon where similar opinions are amplified through repetition within a closed system. In this environment, information that conforms to the community’s dominant narrative is mainly shared, while critical opinions or alternative perspectives may tend to be marginalized from discussion. As a result, participants’ existing beliefs are strengthened, which can function as a mechanism to solidify the ‘silver bullet narrative’ and maintain a defensive posture against external criticism.

On this psychological basis, the ‘silver bullet narrative’ appears to be reinforced through specific information framing.

Structural Cause Analysis of Selective Framing

The phenomenon where Rust-related discourse selectively emphasizes the confrontation with C/C++ and does not significantly cover alternatives like Ada/SPARK cannot be explained solely by the intention of ‘securing discourse leadership’. The following structural causes, inherent in the way the developer ecosystem operates, act in combination here:

  1. Asymmetry in Information Accessibility and Learning Resources: The process by which software developers learn and compare specific technologies heavily depends on the quantity and quality of available information. C/C++ has books, university lectures, online tutorials, and community discussion materials accumulated over decades. Rust also built a learning ecosystem through official documentation (“The Book”) and its community. On the other hand, Ada/SPARK has developed mainly in specific high-reliability industrial fields such as aviation and defense, so up-to-date learning materials or public community discussions that general developers can access are relatively lacking. This difference in information accessibility acts as a background that leads developers to perceive C/C++ as the main comparison target.
  2. Industrial Relevance and Changes in Market Demand: Technical discourse tends to form around technologies that are currently being actively used and competing in the market. C/C++ is the foundational technology for various industries such as operating systems, game engines, and financial systems, while Rust is emerging as an alternative to C/C++ in high-performance system areas such as cloud-native, web infrastructure, and blockchain. That is, the two languages are in a relationship where they directly compete or are considered as substitutes in the actual industrial field. On the other hand, the mission-critical systems market where Ada/SPARK is mainly used has different requirements and ecosystems from the general software development market, so the need for direct comparison is relatively low.
  3. Educational Curriculum and Developers’ Shared Experience: In computer science education courses, C/C++ is adopted as the practical language for subjects such as operating systems, compilers, and computer architecture, playing a role like a ‘common language’ for programmers. Therefore, the memory management problems of C/C++ are a shared experience and a common problem awareness that many developers have experienced firsthand. The reason why Rust discourse gets empathy when pointing out the problems of C/C++ is because this shared background exists. Compared to this, Ada is not covered in most standard educational courses, so there is a limit to forming a consensus among developers to make it a comparison target.

Synthesizing these structural factors, the C/C++-centric confrontational framework is analyzed as a result of the complex interplay of the asymmetry of the information ecosystem, the realistic demands of the market, and the shared educational background of developers, rather than the intentional exclusion by a specific group.

‘Memory Safety’ Agenda Preemption and Discourse Leadership

One of the results that emerged from this narrative formation process is the preemption of the ‘memory safety’ agenda in the field of systems programming.

Originally, mainstream languages ​​such as Java, C#, and Go have provided memory safety by default through GC, etc. However, in these ecosystems, ‘memory safety’ was a premise and thus not a subject of discussion.

Some discourse supporting Rust emphasized ‘memory safety’ as a differentiating point of the language and a value in the confrontation with C/C++. As a result, an ‘agenda-setting’ effect occurred, where developers came to recognize the term ‘memory safety’ through Rust. This can be analyzed as a case of bringing a specific value to the center of discourse, forming public perception of the concept, and turning it into a brand asset.

In conclusion, the ‘silver bullet narrative’ was formed by some supporters through methods of selective framing of comparison targets and preemption of the agenda. This had an effect on promoting Rust and strengthening the community’s identity, and at the same time, leaves room for critical review that it could hinder a specific perspective on the technical ecosystem.

Ripple Effects on the Information Ecosystem and AI Learning Data

When a dominant discourse about a specific technology is formed, it can spread beyond the boundaries of that community and affect the broader technical information ecosystem.

First, it affects the information accessibility of new learners. When searching for information about a specific field (e.g., safe systems programming), quantitatively large discourses online are likely to occupy the top search results. In this case, learners will primarily encounter Rust as an alternative to C/C++, and may not be aware of the existence of other technical alternatives that are covered less, such as Ada/SPARK. This can act as a factor limiting the opportunity for balanced technology selection.

Second, it can cause bias in the learning data of large language models (LLMs). LLMs learn information based on text data from the internet, so the quantitative distribution of training data affects the model’s answer generation tendency. If a framing that emphasizes the advantages of a specific technology (Rust) dominates the discourse, the LLM, in response to questions like “What is the safest systems programming language?”, is likely to mention Rust first or cover it more heavily than other technical alternatives (Ada/SPARK), based on its frequency of appearance in the training data. This can lead to a result where existing discursive biases are relearned and amplified by artificial intelligence.

8.2 Limits of the ‘Complete Replacement’ Narrative

The ‘silver bullet narrative’ often extends to the prospect that “Rust will eventually replace existing systems programming languages.” However, this ‘complete replacement’ narrative may not consider the following constraints of the software ecosystem:

  • Technical Constraint: Dependency on C ABI (Application Binary Interface) Modern operating systems, hardware drivers, and libraries use the C language’s calling convention as the standard interface. Rust also must use the C ABI to interoperate with this existing ecosystem. This means that Rust is in a structural relationship where it must ‘coexist’ or ‘interoperate’ with the C ecosystem, rather than ‘replacing’ it.
  • Market Constraint: Existing Application Ecosystem The value of the software market is determined by the specific applications (games, professional software, etc.) created in that language, not the language itself. The commercial and open-source application assets accumulated in C/C++ over decades act as a market entry barrier that is difficult to replace with technical features alone.

8.3 Historical Precedent of Technical Discourse: 1990s-2000s Operating System Competition

The narrative and group identity formed around a specific technology are not unique to Rust. This is a pattern observed repeatedly throughout the history of technology. A case in point is the ‘Linux vs. Microsoft Windows’ competition in the 1990s and early 2000s.

At that time, voices coexisted in the Linux community, but among them, a narrative was formed centered on the value of ‘freedom and sharing’. They considered themselves a technical/moral alternative to the ‘giant monopoly corporation’, and this identity also led to referring to a specific company as ‘M$’.12 The following similar patterns appeared in this narrative formation process:

  • Confrontational Framing: A binary frame such as ‘openness’ vs. ‘closedness’, ‘hacker culture’ vs. ‘commercialism’ was used.
  • Sense of Technical Superiority: The text-based CLI (Command-Line Interface) and kernel compilation abilities were considered the capabilities of a ‘true developer’, serving as a standard to distinguish from the user base that relied on GUIs.
  • Response to Criticism: Criticisms about usability issues or hardware compatibility problems were dismissed as the user’s ‘lack of effort’ or ‘lack of understanding’. (e.g., “RTFM, Read The Fucking Manual”)13
  • Optimism about the Future: Regardless of objective market share, a belief in the victory of the ‘Year of the Linux Desktop’ was shared within the community.

Such historical examples show the phenomenon that occurs when the discourse of a specific technical community is formed around values ​​and identity, in addition to technical features. This suggests that when analyzing some phenomena in the Rust community, it may be possible to approach it from a techno-sociological perspective, in addition to individual psychological characteristics.

8.4 Analysis of Argumentation Patterns against Critical Discourse

In communities where a discourse about a specific technology has formed, specific response patterns may appear in response to critical discourse that opposes it. This section analyzes these response patterns through examples of specific argumentation structures. These patterns are a tendency observed on tech blog comments where various technologies are compared, or on online platforms such as X (formerly Twitter), Hacker News, and Reddit. The purpose of this section is not to verify the factual relationship of a specific incident, but to exemplify the argumentation structure appearing in these public discussions by linking them to the logical fallacies in the appendix.

Case Study 1: Response to Objective Data

Situation: On an online bulletin board, objective data was presented that the proportion of Rust code within the Linux kernel was less than 0.1% according to cloc tool analysis. Based on this, criticism was raised pointing out the realistic limitations of the claim that “Rust will replace all systems programming.”

Observed Response Pattern: In response to this data-based criticism, some users showed a tendency to respond in the following ways:

  1. Red Herring: Instead of directly refuting the core of the criticism, ‘Rust’s low proportion’, they shifted the subject of the discussion by saying, “Other languages ​​like Ada haven’t even entered the kernel,” or questioned the motive of the criticism by saying, “The critic is biased because they are a supporter of a specific language.”14
  2. Ad Hominem: Responses appeared that mentioned the intelligence or character of the person who raised the criticism, not the content of the criticism, such as, “You lack the intellectual ability to understand such logic,” or “Seeing that attitude, I know your level.”15
  3. Presenting Other Cases: Instead of responding to the specific data on the proportion within the Linux kernel, they tried to defend the original claim by selectively presenting other cases, such as “Big companies like Google/MS use Rust.” This can be related to ‘cherry picking’ or the ‘hasty generalization fallacy’.

Analysis: The response patterns above correspond to types of logical fallacies. This is a case showing that when data-based criticism conflicts with the existing narrative, other types of reactions may appear.

Case Study 2: Discussion on the Boundaries of the ‘Safety’ Definition

Situation: A developer pointed out that a memory leak caused by a circular reference in Rc<RefCell<T>> could cause problems in a long-running server application. (Linked to the discussion in Section 3.3)

Observed Response Pattern: In response to this point, some users showed a tendency to respond by focusing on the ‘definition’ of the term.

  1. Argument by Definition: “Rust’s ‘memory safety’ means the absence of Undefined Behavior (UB). A memory leak is not UB, so this is an issue unrelated to Rust’s safety guarantees. Therefore, your point is off-topic,” presenting the language’s official technical definition as the basis.
  2. Locus of Responsibility: “Creating a circular reference is the developer’s mistake, and Rust provides solutions like Weak<T>. It is unreasonable to attribute the responsibility for not using the features provided by the tool correctly to the language’s limitations,” mentioning the cause of the problem as the individual developer’s responsibility.

Case Study 3: Discussion on ‘Intellectual Honesty’ and Inter-Community Conflict

Situation: A non-profit security foundation released a Rust-ported version of a video decoder written in C, and a controversy arose when they offered prize money for performance improvements.

The technical issues and conflicts raised in this debate are summarized as follows:

  1. Performance and ‘Safety’ Claims: The Rust-ported version mentioned ‘memory safety’, but the actual performance came from the assembly code of the original C project. This code was called through an unsafe block that bypassed Rust’s safety checks.
  2. Criticism of ‘Intellectual Honesty’ Raised: Regarding this structure, criticism was raised, mainly from the original C decoder developer community. The content of the criticism was, “Even though the actual source of performance is the C/Assembly code, promoting it as if it were the achievement of ‘Safe Rust’ does not fairly acknowledge the contribution of the original project.”
  3. Maintenance Model: The Rust-ported version had a structure that required manually backporting updates from the original C project. This received criticism from the C developer community as an “asymmetrical contribution structure” that “relies on the original C project for core R&D while only utilizing its achievements.”

Case Study 4: Discussion on CVSS 10.0 Vulnerability and ‘Memory Safety’

Situation: In April 2024, a command injection vulnerability (CVE-2024-24576) with a CVSS 10.0 (Critical) rating was discovered in the Rust standard library (std::process::Command). This was a security flaw that occurred in ‘safe’ Rust code.

Observed Response Pattern: Regarding the occurrence of this vulnerability, a discussion was observed in some online discourse that this incident did not undermine Rust’s ‘safety’ guarantees.

  1. Limiting the Issue to ‘Memory Safety’: The logic used was, “This is a bug, but it is not a memory safety vulnerability.” The CVE was a logical error (CWE-78), not a memory error (e.g., buffer overflow).
  2. Mentioning External Factors: The passage from the official Rust blog explaining that “the cause of the vulnerability is the complexity of cmd.exe” was quoted, and the logic was mentioned that the cause of the problem lies in the design of the Windows operating system API.

8.5 Defining ‘Qualification’ and ‘Normality’: Gatekeeping and Discursive Exclusion

Some discourse responding to technical criticism may appear in a way that questions the ‘qualification’ of the critic or the subject, rather than directly addressing the raised issue. This is a rhetorical strategy that shifts the topic of the discussion from technical validity to a matter of identity and status, observed mainly in two forms: ‘gatekeeping’ and ‘defining normality’.

1. Gatekeeping: Setting the Qualifications for a ‘True Developer’

Gatekeeping is a social act of setting membership conditions for a specific group and excluding the opinions of outsiders who do not meet those standards from the discussion. In a technical community, this appears as an attempt to question the validity of criticism about a specific technology based on the critic’s ‘lack of expertise’, etc.

  • Case Analysis: When a game developer, based on three years of Rust usage experience, reflected on the difficulties due to the immaturity of the ecosystem, the following response might appear:

    “You are talking about systems programming, but you are only focusing on business logic. True systems programming is about directly handling core elements like event loops or schedulers. What you are doing is not real systems programming.”

  • Discursive Function: This response presents a specific standard of ‘true systems programming’ instead of responding to the raised problem (ecosystem immaturity). And by claiming that the other person does not meet that standard, it questions the validity of the experience that is the premise of the criticism. This method can be seen as an example of the ‘No True Scotsman Fallacy’16, which redefines the subject of the discussion when a counterargument is raised to maintain the original claim. This gatekeeping has the effect of shifting the focus from the content of the criticism to the qualification of the critic.

2. Defining Normality: Evaluation Based on a Specific Ecosystem

‘Defining normality’ is an argumentation method that sets the characteristics of a specific technical ecosystem as ‘normal’ or ‘standard’ and evaluates the approaches of other ecosystems according to that standard.

  • Case Analysis: When evaluating the toolchain of another language, there are cases of expressing it as follows:

    “In fact, any normal language has this as a basic feature.”

  • Discursive Function: This claim functions to set the characteristics of the Rust ecosystem used by the speaker (e.g., the ‘separated toolchain’ centered on Cargo and rust-analyzer) as the standard for ‘normal’. Applying this standard, the ‘integrated experience’ provided by the integrated IDEs (IntelliJ, Visual Studio) of the Java/C# ecosystem can be evaluated as deviating from the standard.

    Such a frame tends not to include the fact that the Java/C# ecosystem also supports a ‘separated toolchain’ environment through VS Code and language servers. As a result, it simplifies the reality where multiple approaches coexist into a binary structure of ‘normal’ and ‘abnormal’, laying the groundwork for emphasizing the validity of a specific model.

In conclusion, discourses that define ‘qualification’ and ‘normality’ can function to support a specific perspective rather than a comprehensive analysis of technical facts. This argumentation method can act as a barrier for the community to accept other external technical perspectives, resulting in limiting the scope of the discussion.

8.6 The 2023 Trademark Policy Controversy and Reflection on Governance

In the process of an open-source project growing and becoming institutionalized, conflicts between existing informal practices and new official policies may arise, and the governance model may be put to the test. The controversy surrounding the draft Rust trademark policy in 2023 is a case study that shows this process.

In April 2023, the Rust Foundation released a new draft trademark policy regarding the use of the Rust name and logo and requested community feedback. However, as the perception spread that the content of the released draft was restrictive compared to the existing informal practices of the community, it provoked criticism and backlash from the community. The main content of the criticism was the concern that the policy constrained the use of the Rust trademark for community events, project names, crate names, etc., and could stifle the ecosystem’s activities.17

This controversy led to several results.

First, the community backlash led to public discussion of the possibility of a language fork named ‘Crab-lang’. This was an event that showed that dissatisfaction with the policy could lead to the possibility of a project split.

Second, this incident revealed the differences in communication methods and perceptions between the Rust Foundation and the developer community that makes up the project. Criticism was raised that the Foundation, in the process of fulfilling its legal responsibility to protect the trademark, had not considered the culture and values ​​that the community had maintained.

As a result, the Rust Foundation accepted the community’s feedback, withdrew the policy draft, and announced its position to re-develop the policy from scratch with the community.18

This case is recorded as an incident that raised questions about the trust relationship and governance model between the Rust project’s leadership and the community. It shows the process by which an open-source project establishes a formal governance structure and the necessity of communication and consensus-building with the community in that process.

8.7 Discourse Analysis of Securing Technical Legitimacy by Citing US Government Agency Reports

In the process of arguing for a specific technology, announcements from external agencies are often used as a basis to strengthen the legitimacy of the claim. In the technical discourse related to the Rust language, a pattern is observed where two reports published by the US National Security Agency (NSA) and the White House are selectively linked and cited. This section analyzes what content each of these two reports contains, and how they are combined and interpreted within the technical community to be used to support a specific conclusion.

1. NSA’s Presentation of a List of Memory-Safe Languages (2022-2023)

In November 2022, the US National Security Agency (NSA) released an information report titled “Software Memory Safety.” This report emphasized the importance of ensuring memory safety in software development and recommended switching to memory-safe languages. In this report, the NSA explicitly listed C#, Go, Java, Ruby, Rust, and Swift as specific examples of memory-safe languages, and later, through an update in April 2023, also included Python, Delphi/Object Pascal, and Ada.19

The release of this report began to be used as a basis that Rust was mentioned in the same category as other memory-safe languages by an agency that discusses reliability at the national security level.

2. White House’s Urge to Switch to Memory-Safe Languages (2024)

In February 2024, the US White House Office of the National Cyber Director (ONCD) released a report emphasizing the need for the tech ecosystem to switch to memory-safe languages.20 The report pointed out the serious threat to national cyber security from vulnerabilities arising from memory-unsafe languages ​​like C/C++ and urged developers to adopt memory-safe languages ​​by default. The report did not present a specific list of languages, but it mentioned Rust as ‘an example’ of a memory-safe language.

3. Discourse Formation through Linking and Selective Interpretation of the Two Reports

These two reports, due to the difference in their content and time of release, have structural features that can be selectively linked and interpreted to construct a specific logic. The logical construction can take the form of the following step-by-step reasoning:

  1. Premise 1 (NSA Report): A technical agency (NSA) presented a specific list of memory-safe languages.
  2. Premise 2 (White House Report): The nation’s chief executive agency declared that the transition to memory-safe languages ​​is an urgent national task.
  3. Inference and Filtering: Based on these two premises, a process of selecting a language that meets the specific purpose of systems programming from the list presented by the NSA proceeds.
    • First, languages ​​that use a garbage collector (GC), such as Python, Java, C#, Go, and Swift, tend to be excluded from the discussion on the grounds that they are unsuitable for the systems programming domain due to ‘runtime overhead’.
    • Second, in this process, the mention of Ada, one of the non-GC languages ​​included in the NSA list, is omitted or not given weight.
  4. Conclusion Drawing: After this selective filtering, a conclusion is reached that “Among the safe languages ​​presented by the NSA, the only and realistic alternative that can perform the systems programming memory safety task urged by the White House without a GC is Rust.”

This reasoning process is an analysis case showing how materials with different purposes and contexts can be linked, and how specific criteria (e.g., ‘absence of GC’) can be selectively applied to be used to draw a conclusion that conforms to the initial premises.

8.8 The Other Side of the Discourse: Official Improvement Efforts and Community Governance

This chapter analyzed the defensive discourse patterns shown by some supporters in response to specific technical criticisms. This phenomenon does not represent the entire picture of the Rust ecosystem. Alongside this informal discourse, official efforts to acknowledge and improve Rust’s technical limitations coexist.

One of the features of the Rust project is the governance model represented by the Request for Comments (RFC) process. Language changes or new feature proposals are publicly discussed through RFC documents. In this process, developers discuss technical validity, potential problems, and compatibility with the existing ecosystem, and final decisions are made through this. This is a case showing a culture that advances technology by accepting criticism, rather than avoiding it.

Furthermore, Rust’s developers and various Working Groups are seeking solutions by setting the several technical challenges pointed out in this book as improvement goals. For example, regarding the complexity and learning curve issues of the async model, developers have acknowledged the difficulties and presented improvement visions on their blogs, and shortening compile time is one of the compiler team’s tasks, with research and development being carried out.

In conclusion, to understand a technology ecosystem, one can distinguish between the defensive voices of some appearing in informal online spaces and the improvement efforts made through the project’s official channels. The fact that this official feedback loop is operating within the Rust ecosystem can be interpreted as evidence showing the long-term potential and development possibility of this technology.

Part 5: Comprehensive Analysis and Conclusion

Part 5 analyzes the utility and constraints of Rust based on the technical analysis and the current state of the ecosystem.

Chapter 9 re-evaluates Rust’s technical strengths and limitations, the developer competence model, and the community culture. Chapter 10 presents tasks for the maturity and expansion of the ecosystem, proposes an analytical framework for technology selection, and concludes the discussion.

9. Re-evaluation of Rust: Utility, Constraints, and Technology Selection Strategy

Chapter 9 comprehensively analyzes the utility and constraints of Rust based on the technical features and the reality of the ecosystem discussed earlier.

First, Section 9.1 examines how the technical feature of compile-time memory safety assurance is utilized in actual industrial fields and what its position in the market is. Next, Section 9.2 analyzes the relationship between the discourse on technology preference and the actual job market, and considers the impact of Rust’s abstraction level on developer competence. Finally, Section 9.3 discusses the role of community culture and feedback loops that affect the sustainability of the technology ecosystem.

9.1 Analysis of Rust’s Technical Characteristics and Application Fields

1. Strength: Compile-Time Memory Safety Assurance

One of the technical characteristics of the Rust language is preventing specific types of memory errors at the language and compiler level. Problems such as buffer overflow, use-after-free, and null pointer dereference, which have been causes of security vulnerabilities in languages like C/C++, are statically analyzed and blocked at compile-time through Rust’s ownership and borrow checker model.

This is a characteristic that shifts the paradigm of software safety assurance from ‘error detection and defense at runtime’ to ‘prevention of error sources at compile-time’. If the code compiles successfully, it can be guaranteed that such types of memory-related vulnerabilities do not exist.

This memory safety contributes not only to preventing system control hijacking but also to preventing sensitive information leakage. The Heartbleed vulnerability of 2014 is a case showing that omitting memory bounds checks can lead to information leakage. Rust structurally lowers the possibility of these types of bugs by performing bounds checks by default when accessing arrays and vectors, and by prohibiting access to already freed memory through the ownership system.

In fact, tech companies like Microsoft and Google have analyzed that about 70% of security vulnerabilities occurring in their product lines stem from memory safety issues.21 22 This external environmental analysis shows the utility of the structural safety assurance provided by Rust.

2. Application Fields: The Intersection of Performance and Stability

Rust’s technical characteristics are utilized in cloud-native infrastructure and network service fields. These fields require maintaining low latency without garbage collector (GC) pauses and security and stability against external attacks.

  • Case Study 1: Solving Discord’s Performance Issues
    Discord, which provides voice and text chat services, experienced latency spike issues due to GC in services written in Go. In real-time communication, such latency affects user experience. The Discord team rewrote backend services (e.g., ‘Read States’ service) in Rust. As a result, they achieved low latency by eliminating GC, prevented risks associated with C++’s manual memory management, and secured memory safety. This is a case where Rust was used as an alternative to GC constraints.23

  • Case Study 2: Linkerd’s Proxy Implementation
    The service mesh project Linkerd implemented its data plane proxy (linkerd-proxy) in Rust. Since service meshes are deployed in infrastructure, proxies require low resource footprint, speed, stability, and security. Rust provides performance and low memory usage at the C/C++ level through the ‘Zero-Cost Abstractions’ principle, and lowers the possibility of security vulnerabilities occurring in infrastructure components through compile-time safety assurance. This shows that Rust is used for ‘system component’ development that requires both performance and safety.24

In addition, cloud companies such as Cloudflare and Amazon Web Services (AWS) are adopting Rust for network services and virtualization technologies (e.g., Firecracker), and Figma is utilizing Rust for graphics rendering in the WebAssembly environment. This shows that Rust is being utilized in specific markets.

3. Position and Limitations in the Market

Rust is being used as an alternative to existing languages in specific areas where ‘performance’ and ‘safety’ are required and GC usage is restricted.

However, this utilization does not extend to all software development areas.

  • Traditional Systems Programming (C/C++): Code assets and ecosystems accumulated in C/C++ for decades, such as operating systems, embedded systems, and game engines, act as entry barriers.
  • Enterprise Business Applications (Java/C#): In large-scale enterprise environments, development productivity, library ecosystems, and workforce supply often become evaluation criteria in addition to runtime performance. Especially in web backend environments where changes in business logic and service continuity are required, garbage collection (GC) and exception handling mechanisms may be more advantageous for securing productivity and availability than strict memory management.

Therefore, Rust’s current position can be analyzed as a ‘specialized tool’ solving problems in specific markets, and to become a mainstream general-purpose language, it needs to solve technical and ecosystem challenges in other areas.

9.2 Reality of the Technology Ecosystem and Developer Competence Model

Rust’s technical characteristics and ecosystem status are related to developers’ technology selection and competence development strategies.

1. Analysis of the Gap between Technology Preference Discourse and the Actual Job Market

In surveys like the Stack Overflow Developer Survey, Rust has been selected in the ‘Most Loved Language’ category, showing developer preference. Also, adoption cases by tech companies form a perception of the language’s potential.

However, a gap exists between this technology preference discourse and the demand in the actual job market. As of 2025, the hiring demand for Rust developers is on an increasing trend, but it occupies a small proportion compared to the market size of Java, Python, C++, etc.

This gap can be interpreted as a result of factors considered by the industry when adopting new technologies, such as learning costs, ecosystem maturity, and integration costs with existing systems. This suggests that developers should consider market size and ecosystem maturity in addition to the technology’s popularity or potential when planning their careers.

2. Relationship between Language Abstraction Level and Basic Computer Science Knowledge

Rust’s ownership and lifetimes model requires developers to understand memory management principles, which influences the cultivation of systems programming competence.

However, the abstractions provided by Rust may limit direct experience with some basic computer science principles. For example, since Rust enforces memory safety at the language level, developers have fewer opportunities to directly experience and solve errors like memory leaks or double frees that occur during manual memory management (malloc/free) in C/C++.

Similarly, using standard library data structures like Vec<T> or HashMap<K, V> is a different dimension of learning from directly implementing linked lists or hash tables in a low-level language and experiencing memory layout design or pointer arithmetic.

This shows that learning a specific language cannot cover all the basics of computer science. Direct memory and data structure implementation experience through low-level languages can be a foundation for understanding the value of abstractions provided by languages like Rust and their internal operating principles. Therefore, basic computer science knowledge such as data structures, algorithms, and operating systems can be said to be valid independently of mastering specific language technologies.

3. The Relationship Between Tool Dependency and Defensive Coding

Additionally, an element covered in the developer competence model is the awareness of the tool’s limitations. As analyzed in the preceding Section 4.2, the proposition that “the language is safe” does not mean that “the written code is safe.” The Rust compiler prevents memory corruption (UB), but it does not prevent service interruptions (panic) or degradation of availability caused by logic errors.

Reliance on the language’s safety guarantees can act as a factor that reduces the practice of defensive coding, such as exception situation verification. Therefore, an approach is required to identify the scope of safety guarantees provided by the language and to apply separate verification and discipline to areas outside that scope (logical errors, system resilience, etc.).

9.3 Culture of the Technical Community and Ecosystem Sustainability

The sustainability of a specific programming language is related not only to the characteristics of the technology itself but also to the culture of the community. The way the community accepts criticism and the attitude towards new participants affect the sustainability of the ecosystem.

1. The Role of Criticism and Feedback Loops

In a technology ecosystem, external criticism or internal problem-raising functions as a feedback mechanism. Discussions with language communities with different design philosophies, such as C++, Ada, and Go, provide opportunities to examine the characteristics and limitations of a specific technology.

Therefore, the way a community accepts and processes external feedback relates to ecosystem maturity. As observed in some online discussions, a tendency to take a defensive attitude toward technical criticism can reduce technical exchange. Conversely, a culture that integrates this into official procedures, like the Rust project’s RFC process, can contribute to ecosystem development.

2. Impact of New Participant Onboarding and Knowledge Sharing Culture

Ecosystem sustainability is related to the inflow of new participants. The Rust project officially has a Code of Conduct.

However, apart from this official orientation, patterns of responding to beginners’ questions in the following ways are observed in some online technical forums.

  • Pointing out Lack of Knowledge: A method of mentioning the questioner’s lack of knowledge or effort (“Read the official documentation first”) or denying the premise of the question itself (“That approach is not needed”) rather than the content of the question. Such interaction can affect the questioner, leading to delayed problem solving or hindering the willingness to participate in the community.
  • Providing Information and Suggesting Alternatives: A method of empathizing with the difficulties faced by the questioner, explaining that the cause of the problem lies in the complexity of the technology itself rather than individual competence, and suggesting information or alternatives for a solution. Such interaction helps new participants acquire knowledge and forms a perception of the community, laying the foundation for growing into contributors.

In conclusion, an attitude of accepting criticism beyond support for technology and a knowledge-sharing culture towards new participants are factors that influence the technology ecosystem’s move towards social maturity.

10. Conclusion: Challenges and Prospects for Ecosystem Sustainability

Chapter 10 presents the challenges of the Rust ecosystem and synthesizes the discussions of this book. First, Section 10.1 analyzes the technical and policy challenges for the qualitative maturity of the ecosystem and the expansion of industrial fields. Next, Section 10.2 redefines Rust’s values of ‘safety’ and ‘performance’ in an engineering context, proposes an analytical framework for technology selection, and concludes the book.

10.1 Challenges for Structural Improvement of the Ecosystem

For Rust to expand into a general-purpose systems programming language, the qualitative maturity of the entire ecosystem, along with the technical features of the language, is presented as a challenge. This section analyzes technical and policy challenges that could affect the future Rust ecosystem.

1. Technical Challenge: The Trade-off between ABI Stability and Design Philosophy

Currently, Rust does not provide a stable ABI (Application Binary Interface) for the standard library (libstd), and most programs use static linking. This is one of the causes of increased binary size, acting as a constraint on expansion into resource-constrained systems.

While this design enables improvement and optimization of the language and libraries, the absence of dynamic linking limits integration with other languages or usability as a system library. Therefore, whether to stabilize libstd’s ABI will be a technical point of contention that the Rust project must choose between the two values of ‘evolution’ and ‘compatibility’.

2. Ecosystem Challenge: Securing Library Stability and Reliability

The Rust library ecosystem centered on crates.io has grown quantitatively, but there is room for improvement in qualitative aspects. Many core libraries remain at versions below 1.0, implying API instability, and maintenance models relying on contributions from a few individuals act as potential risk factors for securing long-term reliability.

To solve these problems, the following methods are utilized in other open-source ecosystems:

  • Financial/Personnel Support for Core Libraries: Supporting the maintenance of core projects through foundation or corporate sponsorship.
  • Introduction of Maturity Models: Introducing a rating system that evaluates library stability, documentation level, maintenance status, etc., to help user selection.

These institutional mechanisms can play a role in the Rust ecosystem moving towards qualitative maturity.

3. Scalability Challenge: Flexibility for Application to Industrial Fields

For Rust’s application fields to expand, securing the flexibility of the language and ecosystem is presented as a challenge.

  • Usability of Language and Tools: Tasks related to cognitive cost and productivity, such as the ‘Polonius’ project which changes the borrow checker’s analysis method, are linked to the language’s accessibility.
  • Consideration of Execution Models: Currently, Rust’s async model is based on ‘Zero-Cost Abstractions’. A scheme to optionally provide a Green Thread model like Go’s Goroutines could be a variable in Rust adoption in the network service field.
  • Ecosystem Expansion: Library development for fields such as desktop GUI and data science, and FFI (Foreign Function Interface) technologies, can affect Rust’s scope of utilization.

These challenges are being discussed through the Rust community and Working Groups, and the results will affect Rust’s standing.

10.2 Synthesis

This book analyzed the features and discourse of the Rust language and described engineering trade-offs through comparison with other technical alternatives.

Meanings of ‘Safety’ and ‘Performance’

Rust’s ‘safety’ and ‘performance’ can be considered in an engineering context beyond their technical definitions.

  • Extension of Safety: Compile-time memory safety assurance is a function of Rust. The reliability of a software system can be treated as a concept that includes the logical correctness of the program, resilience to sustain service in the event of errors, and the community’s collaborative environment.
  • Extension of Performance: Rust was designed considering runtime performance optimization. The efficiency of a software development project includes development productivity, the speed of the feedback loop including compile time, and maintenance costs, in addition to runtime performance. The balance between runtime performance and other efficiency metrics is a consideration for the ecosystem.

Analytical Framework for Technology Selection

When evaluating technology, the following analytical framework can be applied to examine factors:

  1. Problem Domain: What are the requirements of the problem to be solved? Is it runtime performance and latency (e.g., Rust, C++)? Is it development productivity and time-to-market (e.g., Go, C#)? Or is it mathematical provability (e.g., Ada/SPARK)?
  2. Cost Analysis: What are the costs associated with adopting the technology, and what are the organization’s resources? What is the trade-off relationship between runtime cost (GC) and developer learning cost and compile time? Is investment in commercial analysis tools or specialized personnel required?
  3. Ecosystem Maturity: Does the current ecosystem meet the project’s requirements? How are the stability and reliability of essential libraries? How is the level of official documentation and community support? What is the current status of the supply of relevant technical personnel?
  4. Discourse Transparency: Does the relevant technical community discuss the advantages and limitations of the technology? How are discussions about external criticism conducted? Is there an environment established that supports questions and learning for new participants?
  5. Allowable Range of Failure Scenarios: Is it a mission-critical environment where the system must not halt due to a single panic? In such environments, runtime resilience (Java/C#) or proof of flawlessness (Ada/SPARK) may be prioritized over compile-time memory safety.

These questions can be utilized to make engineering decisions according to constraints and goals by considering various aspects of technology.

Epilogue

This book analyzed the technical characteristics and discourse of the Rust language in historical and engineering contexts. The analysis confirms that Rust provides compile-time memory safety assurance functions.

Rust’s design principles—the ownership model, zero-cost abstractions, and error handling via the type system—are the result of integrating and enforcing existing ideas such as C++’s RAII, Ada/SPARK’s safety model, and functional programming. In this process, it entails engineering trade-offs such as learning curves, compile times, binary sizes, and the complexity of implementing design patterns.

Furthermore, it was observed that when narratives emphasizing technical superiority are formed within a technical community, they can affect the evaluation of technology and interactions with other ecosystems. This is a characteristic appearing in some discourses that are the subject of this book’s analysis, not the entire community. This phenomenon is a pattern also found in past operating system competition cases and can be interpreted as social dynamics that appear when technical choices are linked to group identity.

In conclusion, the purpose of this book’s analysis is not to evaluate a specific technology. It is an attempt to analyze the process by which technology is treated as a social phenomenon and the structure of its discourse. This discussion suggests that developers and technical communities should consider an engineering approach to selecting suitable tools for problem-solving.

Appendix: Analysis of Logical Fallacy Cases Observed in Technical Discussions

This appendix analyzes the types of argumentation patterns observed in online technical discussions to explain the communication methods discussed in the main text. The cases presented are examples to explain logical fallacies. Each case has been anonymized, and the aim is to analyze the argumentation structure and its impact on the discussion.

Case 1: Ad Hominem Fallacy

  • Context: When a developer pointed out the impact of Rust’s learning curve and the complexity of async on productivity, a tendency was observed among some users to respond by mentioning the speaker rather than the technical point.
  • Observed Response: “Honestly, the fact that you don’t understand async is not a problem with Rust, but a problem with your ability. You probably aren’t ready to handle complex systems. Consider going back to an easier language.”
  • Analysis: Instead of discussing the raised technical criticism (learning curve, complexity of async), this response mentions the competence and qualities of the individual who made the claim. This corresponds to the ad hominem fallacy, which attacks the opponent by deviating from the essence of the point. This method of argumentation can act as a factor affecting technical discussion.
  • Social-Technical Cause Analysis: This type of reaction can be linked to the Rust community’s identity regarding ‘safety’. When memory safety is considered a value or philosophy of Rust beyond a simple technical function, criticism of safety implementation elements like async or the borrow checker can be perceived as a challenge to the technology itself. As a result, the discussion shifts from “What is the problem with this feature?” to “Why can’t you understand this feature?”, creating an environment conducive to ad hominem fallacies that attribute the subject of criticism to personal competence issues rather than the technology.

Case 2: Genetic Fallacy and Circumstantial Ad Hominem

  • Context: Rust’s borrow checker is a feature that prevents errors like data races by inspecting memory access rules at compile time. A C++ user pointed out that this borrow checker could constrain a developer’s flexibility in certain situations. In response to this claim, some users showed a tendency to mention the background or motive rather than the content of the claim.
  • Observed Response: “The fact that you feel Rust’s rules are a ‘constraint’ is just you showing ‘resistance’ to a new paradigm because you are used to the ‘unsafe’ ways of C++ for decades. It is a biased view stemming from attachment to existing methods.”
  • Analysis: Rather than refuting the content of the claim, this response takes issue with the motive or background (familiarity with C++) for making the claim. This can be seen as a form of the genetic fallacy, which evaluates a claim based on its source or motive, and has the effect of shifting the technical point to a psychological analysis.
  • Social-Technical Cause Analysis: These fallacies are based on the ‘C++ alternative narrative’ that shapes Rust discourse. Within this narrative, C++ is often defined as the ‘unsafe past’. Therefore, criticism from a user with a C++ background can be regarded as the perspective of a person used to ‘past ways’, regardless of its content. This creates an environment conducive to genetic fallacies that attempt to dismiss claims by questioning the source rather than exploring the technical essence of the criticism.

Case 3: Straw Man Fallacy

  • Context: When a claim comparing Rust’s Result type and Java’s ‘Checked Exceptions’ was presented in a blog post, some users showed a pattern of transforming and attacking it.
  • Observed Response: “So is your claim that ‘Rust’s error handling is useless’? You don’t understand at all how panic and Result solved the null pointer problem. You just want to do lazy coding that wraps everything in try...catch.”
  • Analysis: This response transforms the original comparative analysis (“…has shortcomings compared to…”) into the claim that it “is useless,” and then attacks that transformed claim. This corresponds to the straw man fallacy, which attacks a straw man made easy to attack rather than the opponent’s actual claim, and affects the discussion.

  1. A system that automatically finds and cleans up memory that is no longer in use by a program. ↩

  2. Ada and SPARK use formal verification techniques to allow specific properties (e.g., absence of runtime errors, logical correctness) to be mathematically proven for all possible program execution paths. This provides a comprehensive level of stability different from the memory safety guarantees of Rust’s borrow checker and has been used in fields requiring specific levels of safety and reliability, such as air traffic control and nuclear power plant control systems. (References: AdaCore documentation, SPARK User’s Guide, etc.) ↩

  3. The Rustonomicon, “Meet Safe and Unsafe”. “When we say that code is Safe, we are making a promise: this code will not exhibit any Undefined Behavior.” https://doc.rust-lang.org/nomicon/meet-safe-and-unsafe.html ↩

  4. This feature is primarily designed for purposes such as handling exceptions at the boundary with external C libraries (FFI) or managing thread pools where the failure of a specific thread should not lead to a full system halt. ↩

  5. C++ Core Guidelines: A set of coding guidelines initiated by Bjarne Stroustrup and Herb Sutter. It provides recommendations for C++ programming, covering areas such as ownership, resource management, and interface design. Various static analysis tools support the automated checking of these rules. (See: https://isocpp.github.io/CppCoreGuidelines/) ↩

  6. JetBrains, “The State of Developer Ecosystem 2023”, C++ section. According to the report, while C++17 and C++20 are widely used standards, a significant number of projects are still using pre-C++11 standards. ↩

  7. Matthew Prince, “Cloudflare outage on November 18, 2025”, Cloudflare Blog, 2025-11-18. https://blog.cloudflare.com/18-november-2025-outage/ ↩

  8. Package sizes were referenced from the ‘Installed size’ provided in the official package database of the Alpine Linux v3.22 stable release. The purpose of this table is not to compare the latest performance at a specific point in time, but to show the structural tendency of how each language ecosystem’s design method impacts binary size. This fundamental tendency is not significantly swayed by minor patch updates or version changes that can occur within a stable release, so a specific stable release was adopted as the standard for data reproducibility and consistency of the argument. The referenced versions of each package are as specified in the table. ↩

  9. The analysis was performed by decompressing the linux-6.15.5.tar.xz archive and then running the cloc . command without any options from the source code root directory. This information is provided so that the reader can verify the analysis results using the same method. ↩

  10. The term ‘silver bullet narrative’ used in this text is not intended to disparage any particular technology or community, but is an analytical term widely used in the sociology of technology. It refers to the tendency to believe that there is a single, overly simplified, perfect technical solution to a complex problem, and it shares a context with ‘technological triumphalism.’ This term is used to more objectively describe the structure of the discourse in question. ↩ ↩2

  11. The discourse analysis conducted in this Part 4 does not target specific individuals or private communities. The basis for the analysis is based on qualitative observation of repetitive argumentation patterns appearing in publicly accessible information, such as public discussions on online platforms like X (formerly Twitter), Hacker News, and Reddit (e.g., r/rust, r/programming), numerous tech blog posts on the theme of “Why Rust?”, and Q&A sessions from related tech conference presentations. The purpose of this analysis is not to measure the statistical frequency of these discourses, but to understand their structure and logic. ↩

  12. ‘M$’ is an expression used in the 1990s by some in the Linux and open-source communities to criticize Microsoft’s (Microsoft) commercial policies. It contains the intention of mocking the company’s commercialism by replacing the ‘S’ in ‘Microsoft’ with a dollar sign ($), which symbolizes money (M$, Micro$oft). ↩

  13. RTFM is an abbreviation for ‘Read The Fucking Manual’, an informal expression meaning ‘Read that damn manual’. It was used as a term showing an exclusive aspect of the 1990s hacker culture, demanding that users who asked basic questions find the answers themselves. ↩

  14. This method of evaluating the value of a claim based on its origin or motive, not its content, corresponds to the ‘genetic fallacy’. (See Appendix ‘Case 2: Genetic Fallacy’) ↩

  15. Questioning the capabilities or qualities of the individual who made the argument, rather than the validity of the raised criticism, corresponds to the ‘ad hominem fallacy’. (See Appendix ‘Case 1: Ad Hominem Fallacy’) ↩

  16. No True Scotsman Fallacy: An argumentative fallacy named by the British philosopher Antony Flew. For example, claiming “No Scotsman puts sugar on his porridge,” and when countered with “But my Scotsman acquaintance puts sugar on his,” changing the statement to “‘True’ Scotsmen don’t.” It refers to the attempt to avoid refutation by limiting the subject of the argument with an arbitrary standard of ‘true’. ↩

  17. Thomas Claburn, “Rust Foundation apologizes for bungled trademark policy”, The Register, April 17, 2023. https://www.theregister.com/2023/04/17/rust_foundation_apologizes_trademark_policy/ ↩

  18. Rust Foundation, “Rust Trademark Policy Draft Revision & Next Steps,” Rust Foundation Blog, April 11, 2023. https://rustfoundation.org/media/rust-trademark-policy-draft-revision-next-steps/ ↩

  19. National Security Agency, “Software Memory Safety,” CSI-001-22, November 2022. https://media.defense.gov/2022/Nov/10/2003112742/-1/-1/0/CSI_SOFTWARE_MEMORY_SAFETY.PDF ↩

  20. Office of the National Cyber Director, “Back to the Building Blocks: A Path Toward Secure and Measurable Software,” February 2024. https://bidenwhitehouse.archives.gov/wp-content/uploads/2024/02/Final-ONCD-Technical-Report.pdf ↩

  21. Microsoft Security Response Center, “A Proactive Approach to More Secure Code”, 2019-07-16. https://msrc.microsoft.com/blog/2019/07/16/a-proactive-approach-to-more-secure-code/ ↩

  22. Google has emphasized the importance of memory safety in several projects.
    Chrome: “The Chromium project finds that around 70% of our serious security bugs are memory safety problems.”, The Chromium Projects, “Memory-Safe Languages in Chrome”, https://www.chromium.org/Home/chromium-security/memory-safety/ (This page is continuously updated)
    Android: “Memory safety bugs are a top cause of stability issues, and consistently represent ~70% of Android’s high severity security vulnerabilities.”, Google Security Blog, “Memory Safe Languages in Android 13”, 2022-12-01. https://security.googleblog.com/2022/12/memory-safe-languages-in-android-13.html ↩

  23. Discord Engineering, “Why Discord is switching from Go to Rust”, 2020-02-04. https://discord.com/blog/why-discord-is-switching-from-go-to-rust ↩

  24. Linkerd, “Under the Hood of Linkerd’s Magic”, Linkerd Docs. https://linkerd.io/2/reference/architecture/#proxy ↩