返回首页返回书籍《The Rust Programming Language》
目录

目录 · CONTENTS

  1. 01The Rust Programming Language
  2. 02Foreword
  3. 03Introduction
  4. 04Getting Started
  5. 05Installation
  6. 06Hello, World!
  7. 07Hello, Cargo!
  8. 08Programming a Guessing Game
  9. 09Common Programming Concepts
  10. 10Variables and Mutability
  11. 11Data Types
  12. 12Functions
  13. 13Comments
  14. 14Control Flow
  15. 15Understanding Ownership
  16. 16What is Ownership?
  17. 17References and Borrowing
  18. 18The Slice Type
  19. 19Using Structs to Structure Related Data
  20. 20Defining and Instantiating Structs
  21. 21An Example Program Using Structs
  22. 22Methods
  23. 23Enums and Pattern Matching
  24. 24Defining an Enum
  25. 25The `match` Control Flow Construct
  26. 26Concise Control Flow with `if let` and `let...else`
  27. 27Packages, Crates, and Modules
  28. 28Packages and Crates
  29. 29Control Scope and Privacy with Modules
  30. 30Paths for Referring to an Item in the Module Tree
  31. 31Bringing Paths Into Scope with the `use` Keyword
  32. 32Separating Modules into Different Files
  33. 33Common Collections
  34. 34Storing Lists of Values with Vectors
  35. 35Storing UTF-8 Encoded Text with Strings
  36. 36Storing Keys with Associated Values in Hash Maps
  37. 37Error Handling
  38. 38Unrecoverable Errors with `panic!`
  39. 39Recoverable Errors with `Result`
  40. 40To `panic!` or Not to `panic!`
  41. 41Generic Types, Traits, and Lifetimes
  42. 42Generic Data Types
  43. 43Defining Shared Behavior with Traits
  44. 44Validating References with Lifetimes
  45. 45Writing Automated Tests
  46. 46How to Write Tests
  47. 47Controlling How Tests Are Run
  48. 48Test Organization
  49. 49An I/O Project: Building a Command Line Program
  50. 50Accepting Command Line Arguments
  51. 51Reading a File
  52. 52Refactoring to Improve Modularity and Error Handling
  53. 53Adding Functionality with Test Driven Development
  54. 54Working with Environment Variables
  55. 55Redirecting Errors to Standard Error
  56. 56Functional Language Features: Iterators and Closures
  57. 57Closures
  58. 58Processing a Series of Items with Iterators
  59. 59Improving Our I/O Project
  60. 60Performance in Loops vs. Iterators
  61. 61More About Cargo and Crates.io
  62. 62Customizing Builds with Release Profiles
  63. 63Publishing a Crate to Crates.io
  64. 64Cargo Workspaces
  65. 65Installing Binaries with `cargo install`
  66. 66Extending Cargo with Custom Commands
  67. 67Smart Pointers
  68. 68Using `Box<T>` to Point to Data on the Heap
  69. 69Treating Smart Pointers Like Regular References
  70. 70Running Code on Cleanup with the `Drop` Trait
  71. 71`Rc<T>`, the Reference Counted Smart Pointer
  72. 72`RefCell<T>` and the Interior Mutability Pattern
  73. 73Reference Cycles Can Leak Memory
  74. 74Fearless Concurrency
  75. 75Using Threads to Run Code Simultaneously
  76. 76Transfer Data Between Threads with Message Passing
  77. 77Shared-State Concurrency
  78. 78Extensible Concurrency with `Send` and `Sync`
  79. 79Fundamentals of Asynchronous Programming: Async, Await, Futures, and Streams
  80. 80extern crate trpl; // required for mdbook test
  81. 81Applying Concurrency with Async
  82. 82Working With Any Number of Futures
  83. 83Streams: Futures in Sequence
  84. 84A Closer Look at the Traits for Async
  85. 85Futures, Tasks, and Threads
  86. 86Object-Oriented Programming Features
  87. 87Characteristics of Object-Oriented Languages
  88. 88Using Trait Objects to Abstract over Shared Behavior
  89. 89Implementing an Object-Oriented Design Pattern
  90. 90Patterns and Matching
  91. 91All the Places Patterns Can Be Used
  92. 92Refutability: Whether a Pattern Might Fail to Match
  93. 93Pattern Syntax
  94. 94Advanced Features
  95. 95Unsafe Rust
  96. 96Advanced Traits
  97. 97Advanced Types
  98. 98Advanced Functions and Closures
  99. 99Macros
  100. 100Final Project: Building a Multithreaded Web Server
  101. 101Building a Single-Threaded Web Server
  102. 102From Single-Threaded to Multithreaded Server
  103. 103Graceful Shutdown and Cleanup
  104. 104Appendix
  105. 105A - Keywords
  106. 106B - Operators and Symbols
  107. 107C - Derivable Traits
  108. 108D - Useful Development Tools
  109. 109E - Editions
  110. 110F - Translations of the Book
  111. 111G - How Rust is Made and “Nightly Rust”
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第 41 节 · SECTION 41

Generic Types, Traits, and Lifetimes

Every programming language has tools for effectively handling the duplication of concepts. In Rust, one such tool is generics: abstract stand-ins for concrete types or other properties. We can express the behavior of generics or how they relate to other generics without knowing what will be in their place when compiling and running the code.

Functions can take parameters of some generic type, instead of a concrete type like i32 or String, in the same way they take parameters with unknown values to run the same code on multiple concrete values. In fact, we already used generics in Chapter 6 with Option<T>, in Chapter 8 with Vec<T> and HashMap<K, V>, and in Chapter 9 with Result<T, E>. In this chapter, you’ll explore how to define your own types, functions, and methods with generics!

First, we’ll review how to extract a function to reduce code duplication. We’ll then use the same technique to make a generic function from two functions that differ only in the types of their parameters. We’ll also explain how to use generic types in struct and enum definitions.

Then, you’ll learn how to use traits to define behavior in a generic way. You can combine traits with generic types to constrain a generic type to accept only those types that have a particular behavior, as opposed to just any type.

Finally, we’ll discuss lifetimes: a variety of generics that give the compiler information about how references relate to each other. Lifetimes allow us to give the compiler enough information about borrowed values so that it can ensure that references will be valid in more situations than it could without our help.

Removing Duplication by Extracting a Function

Generics allow us to replace specific types with a placeholder that represents multiple types to remove code duplication. Before diving into generics syntax, let’s first look at how to remove duplication in a way that doesn’t involve generic types by extracting a function that replaces specific values with a placeholder that represents multiple values. Then, we’ll apply the same technique to extract a generic function! By looking at how to recognize duplicated code you can extract into a function, you’ll start to recognize duplicated code that can use generics.

We’ll begin with the short program in Listing 10-1 that finds the largest number in a list.

Listing 10-1: Finding the largest number in a list of numbers Filename: src/main.rs

fn main() {
    let number_list = vec![34, 50, 25, 100, 65];

    let mut largest = &number_list[0];

    for number in &number_list {
        if number > largest {
            largest = number;
        }
    }

    println!("The largest number is {largest}");
}

We store a list of integers in the variable number_list and place a reference to the first number in the list in a variable named largest. We then iterate through all the numbers in the list, and if the current number is greater than the number stored in largest, we replace the reference in that variable. However, if the current number is less than or equal to the largest number seen so far, the variable doesn’t change, and the code moves on to the next number in the list. After considering all the numbers in the list, largest should refer to the largest number, which in this case is 100.

We’ve now been tasked with finding the largest number in two different lists of numbers. To do so, we can choose to duplicate the code in Listing 10-1 and use the same logic at two different places in the program, as shown in Listing 10-2.

Listing 10-2: Code to find the largest number in two lists of numbers Filename: src/main.rs

fn main() {
    let number_list = vec![34, 50, 25, 100, 65];

    let mut largest = &number_list[0];

    for number in &number_list {
        if number > largest {
            largest = number;
        }
    }

    println!("The largest number is {largest}");

    let number_list = vec![102, 34, 6000, 89, 54, 2, 43, 8];

    let mut largest = &number_list[0];

    for number in &number_list {
        if number > largest {
            largest = number;
        }
    }

    println!("The largest number is {largest}");
}

Although this code works, duplicating code is tedious and error-prone. We also have to remember to update the code in multiple places when we want to change it.

To eliminate this duplication, we’ll create an abstraction by defining a function that operates on any list of integers passed in as a parameter. This solution makes our code clearer and lets us express the concept of finding the largest number in a list abstractly.

In Listing 10-3, we extract the code that finds the largest number into a function named largest. Then, we call the function to find the largest number in the two lists from Listing 10-2. We could also use the function on any other list of i32 values we might have in the future.

Listing 10-3: Abstracted code to find the largest number in two lists Filename: src/main.rs

fn largest(list: &[i32]) -> &i32 {
    let mut largest = &list[0];

    for item in list {
        if item > largest {
            largest = item;
        }
    }

    largest
}

fn main() {
    let number_list = vec![34, 50, 25, 100, 65];

    let result = largest(&number_list);
    println!("The largest number is {result}");

    let number_list = vec![102, 34, 6000, 89, 54, 2, 43, 8];

    let result = largest(&number_list);
    println!("The largest number is {result}");
}

The largest function has a parameter called list, which represents any concrete slice of i32 values we might pass into the function. As a result, when we call the function, the code runs on the specific values that we pass in.

In summary, here are the steps we took to change the code from Listing 10-2 to Listing 10-3:

  1. Identify duplicate code.
  2. Extract the duplicate code into the body of the function, and specify the inputs and return values of that code in the function signature.
  3. Update the two instances of duplicated code to call the function instead.

Next, we’ll use these same steps with generics to reduce code duplication. In the same way that the function body can operate on an abstract list instead of specific values, generics allow code to operate on abstract types.

For example, say we had two functions: one that finds the largest item in a slice of i32 values and one that finds the largest item in a slice of char values. How would we eliminate that duplication? Let’s find out!