The Rust Programming Language

by Steve Klabnik and Carol Nichols, with contributions from the Rust Community

This version of the text assumes you’re using Rust 1.61 (released 2022-05-18) or later. See the “Installation” section of Chapter 1 to install or update Rust.

The HTML format is available online at https://doc.rust-lang.org/stable/book/ and offline with installations of Rust made with rustup; run rustup docs --book to open.

Several community translations are also available.

This text is available in paperback and ebook format from No Starch Press.

Foreword

It wasn’t always so clear, but the Rust programming language is fundamentally about empowerment: no matter what kind of code you are writing now, Rust empowers you to reach farther, to program with confidence in a wider variety of domains than you did before.

Take, for example, “systems-level” work that deals with low-level details of memory management, data representation, and concurrency. Traditionally, this realm of programming is seen as arcane, accessible only to a select few who have devoted the necessary years learning to avoid its infamous pitfalls. And even those who practice it do so with caution, lest their code be open to exploits, crashes, or corruption.

Rust breaks down these barriers by eliminating the old pitfalls and providing a friendly, polished set of tools to help you along the way. Programmers who need to “dip down” into lower-level control can do so with Rust, without taking on the customary risk of crashes or security holes, and without having to learn the fine points of a fickle toolchain. Better yet, the language is designed to guide you naturally towards reliable code that is efficient in terms of speed and memory usage.

Programmers who are already working with low-level code can use Rust to raise their ambitions. For example, introducing parallelism in Rust is a relatively low-risk operation: the compiler will catch the classical mistakes for you. And you can tackle more aggressive optimizations in your code with the confidence that you won’t accidentally introduce crashes or vulnerabilities.

But Rust isn’t limited to low-level systems programming. It’s expressive and ergonomic enough to make CLI apps, web servers, and many other kinds of code quite pleasant to write — you’ll find simple examples of both later in the book. Working with Rust allows you to build skills that transfer from one domain to another; you can learn Rust by writing a web app, then apply those same skills to target your Raspberry Pi.

This book fully embraces the potential of Rust to empower its users. It’s a friendly and approachable text intended to help you level up not just your knowledge of Rust, but also your reach and confidence as a programmer in general. So dive in, get ready to learn—and welcome to the Rust community!

— Nicholas Matsakis and Aaron Turon

Introduction

Note: This edition of the book is the same as The Rust Programming Language available in print and ebook format from No Starch Press.

Welcome to The Rust Programming Language, an introductory book about Rust. The Rust programming language helps you write faster, more reliable software. High-level ergonomics and low-level control are often at odds in programming language design; Rust challenges that conflict. Through balancing powerful technical capacity and a great developer experience, Rust gives you the option to control low-level details (such as memory usage) without all the hassle traditionally associated with such control.

Who Rust Is For

Rust is ideal for many people for a variety of reasons. Let’s look at a few of the most important groups.

Teams of Developers

Rust is proving to be a productive tool for collaborating among large teams of developers with varying levels of systems programming knowledge. Low-level code is prone to a variety of subtle bugs, which in most other languages can be caught only through extensive testing and careful code review by experienced developers. In Rust, the compiler plays a gatekeeper role by refusing to compile code with these elusive bugs, including concurrency bugs. By working alongside the compiler, the team can spend their time focusing on the program’s logic rather than chasing down bugs.

Rust also brings contemporary developer tools to the systems programming world:

  • Cargo, the included dependency manager and build tool, makes adding, compiling, and managing dependencies painless and consistent across the Rust ecosystem.
  • Rustfmt ensures a consistent coding style across developers.
  • The Rust Language Server powers Integrated Development Environment (IDE) integration for code completion and inline error messages.

By using these and other tools in the Rust ecosystem, developers can be productive while writing systems-level code.

Students

Rust is for students and those who are interested in learning about systems concepts. Using Rust, many people have learned about topics like operating systems development. The community is very welcoming and happy to answer student questions. Through efforts such as this book, the Rust teams want to make systems concepts more accessible to more people, especially those new to programming.

Companies

Hundreds of companies, large and small, use Rust in production for a variety of tasks. Those tasks include command line tools, web services, DevOps tooling, embedded devices, audio and video analysis and transcoding, cryptocurrencies, bioinformatics, search engines, Internet of Things applications, machine learning, and even major parts of the Firefox web browser.

Open Source Developers

Rust is for people who want to build the Rust programming language, community, developer tools, and libraries. We’d love to have you contribute to the Rust language.

People Who Value Speed and Stability

Rust is for people who crave speed and stability in a language. By speed, we mean the speed of the programs that you can create with Rust and the speed at which Rust lets you write them. The Rust compiler’s checks ensure stability through feature additions and refactoring. This is in contrast to the brittle legacy code in languages without these checks, which developers are often afraid to modify. By striving for zero-cost abstractions, higher-level features that compile to lower-level code as fast as code written manually, Rust endeavors to make safe code be fast code as well.

The Rust language hopes to support many other users as well; those mentioned here are merely some of the biggest stakeholders. Overall, Rust’s greatest ambition is to eliminate the trade-offs that programmers have accepted for decades by providing safety and productivity, speed and ergonomics. Give Rust a try and see if its choices work for you.

Who This Book Is For

This book assumes that you’ve written code in another programming language but doesn’t make any assumptions about which one. We’ve tried to make the material broadly accessible to those from a wide variety of programming backgrounds. We don’t spend a lot of time talking about what programming is or how to think about it. If you’re entirely new to programming, you would be better served by reading a book that specifically provides an introduction to programming.

How to Use This Book

In general, this book assumes that you’re reading it in sequence from front to back. Later chapters build on concepts in earlier chapters, and earlier chapters might not delve into details on a topic; we typically revisit the topic in a later chapter.

You’ll find two kinds of chapters in this book: concept chapters and project chapters. In concept chapters, you’ll learn about an aspect of Rust. In project chapters, we’ll build small programs together, applying what you’ve learned so far. Chapters 2, 12, and 20 are project chapters; the rest are concept chapters.

Chapter 1 explains how to install Rust, how to write a “Hello, world!” program, and how to use Cargo, Rust’s package manager and build tool. Chapter 2 is a hands-on introduction to the Rust language. Here we cover concepts at a high level, and later chapters will provide additional detail. If you want to get your hands dirty right away, Chapter 2 is the place for that. At first, you might even want to skip Chapter 3, which covers Rust features similar to those of other programming languages, and head straight to Chapter 4 to learn about Rust’s ownership system. However, if you’re a particularly meticulous learner who prefers to learn every detail before moving on to the next, you might want to skip Chapter 2 and go straight to Chapter 3, returning to Chapter 2 when you’d like to work on a project applying the details you’ve learned.

Chapter 5 discusses structs and methods, and Chapter 6 covers enums, match expressions, and the if let control flow construct. You’ll use structs and enums to make custom types in Rust.

In Chapter 7, you’ll learn about Rust’s module system and about privacy rules for organizing your code and its public Application Programming Interface (API). Chapter 8 discusses some common collection data structures that the standard library provides, such as vectors, strings, and hash maps. Chapter 9 explores Rust’s error-handling philosophy and techniques.

Chapter 10 digs into generics, traits, and lifetimes, which give you the power to define code that applies to multiple types. Chapter 11 is all about testing, which even with Rust’s safety guarantees is necessary to ensure your program’s logic is correct. In Chapter 12, we’ll build our own implementation of a subset of functionality from the grep command line tool that searches for text within files. For this, we’ll use many of the concepts we discussed in the previous chapters.

Chapter 13 explores closures and iterators: features of Rust that come from functional programming languages. In Chapter 14, we’ll examine Cargo in more depth and talk about best practices for sharing your libraries with others. Chapter 15 discusses smart pointers that the standard library provides and the traits that enable their functionality.

In Chapter 16, we’ll walk through different models of concurrent programming and talk about how Rust helps you to program in multiple threads fearlessly. Chapter 17 looks at how Rust idioms compare to object-oriented programming principles you might be familiar with.

Chapter 18 is a reference on patterns and pattern matching, which are powerful ways of expressing ideas throughout Rust programs. Chapter 19 contains a smorgasbord of advanced topics of interest, including unsafe Rust, macros, and more about lifetimes, traits, types, functions, and closures.

In Chapter 20, we’ll complete a project in which we’ll implement a low-level multithreaded web server!

Finally, some appendices contain useful information about the language in a more reference-like format. Appendix A covers Rust’s keywords, Appendix B covers Rust’s operators and symbols, Appendix C covers derivable traits provided by the standard library, Appendix D covers some useful development tools, and Appendix E explains Rust editions.

There is no wrong way to read this book: if you want to skip ahead, go for it! You might have to jump back to earlier chapters if you experience any confusion. But do whatever works for you.

An important part of the process of learning Rust is learning how to read the error messages the compiler displays: these will guide you toward working code. As such, we’ll provide many examples that don’t compile along with the error message the compiler will show you in each situation. Know that if you enter and run a random example, it may not compile! Make sure you read the surrounding text to see whether the example you’re trying to run is meant to error. Ferris will also help you distinguish code that isn’t meant to work:

FerrisMeaning
Ferris with a question markThis code does not compile!
Ferris throwing up their handsThis code panics!
Ferris with one claw up, shruggingThis code does not produce the desired behavior.

In most situations, we’ll lead you to the correct version of any code that doesn’t compile.

Source Code

The source files from which this book is generated can be found on GitHub.

Getting Started

Let’s start your Rust journey! There’s a lot to learn, but every journey starts somewhere. In this chapter, we’ll discuss:

  • Installing Rust on Linux, macOS, and Windows
  • Writing a program that prints Hello, world!
  • Using cargo, Rust’s package manager and build system

Installation

The first step is to install Rust. We’ll download Rust through rustup, a command line tool for managing Rust versions and associated tools. You’ll need an internet connection for the download.

Note: If you prefer not to use rustup for some reason, please see the Other Rust Installation Methods page for more options.

The following steps install the latest stable version of the Rust compiler. Rust’s stability guarantees ensure that all the examples in the book that compile will continue to compile with newer Rust versions. The output might differ slightly between versions, because Rust often improves error messages and warnings. In other words, any newer, stable version of Rust you install using these steps should work as expected with the content of this book.

Command Line Notation

In this chapter and throughout the book, we’ll show some commands used in the terminal. Lines that you should enter in a terminal all start with $. You don’t need to type in the $ character; it’s the command line prompt shown to indicate the start of each command. Lines that don’t start with $ typically show the output of the previous command. Additionally, PowerShell-specific examples will use > rather than $.

Installing rustup on Linux or macOS

If you’re using Linux or macOS, open a terminal and enter the following command:

$ curl --proto '=https' --tlsv1.3 https://sh.rustup.rs -sSf | sh

The command downloads a script and starts the installation of the rustup tool, which installs the latest stable version of Rust. You might be prompted for your password. If the install is successful, the following line will appear:

Rust is installed now. Great!

You will also need a linker, which is a program that Rust uses to join its compiled outputs into one file. It is likely you already have one. If you get linker errors, you should install a C compiler, which will typically include a linker. A C compiler is also useful because some common Rust packages depend on C code and will need a C compiler.

On macOS, you can get a C compiler by running:

$ xcode-select --install

Linux users should generally install GCC or Clang, according to their distribution’s documentation. For example, if you use Ubuntu, you can install the build-essential package.

Installing rustup on Windows

On Windows, go to https://www.rust-lang.org/tools/install and follow the instructions for installing Rust. At some point in the installation, you’ll receive a message explaining that you’ll also need the MSVC build tools for Visual Studio 2013 or later. To acquire the build tools, you’ll need to install Visual Studio 2022. When asked which workloads to install, include:

  • “Desktop Development with C++”
  • The Windows 10 or 11 SDK
  • The English language pack component, along with any other language pack of your choosing

The rest of this book uses commands that work in both cmd.exe and PowerShell. If there are specific differences, we’ll explain which to use.

Troubleshooting

To check whether you have Rust installed correctly, open a shell and enter this line:

$ rustc --version

You should see the version number, commit hash, and commit date for the latest stable version that has been released in the following format:

rustc x.y.z (abcabcabc yyyy-mm-dd)

If you see this information, you have installed Rust successfully! If you don’t see this information and you’re on Windows, check that Rust is in your %PATH% system variable. If that’s all correct and Rust still isn’t working, there are a number of places you can get help. The easiest is the #beginners channel on the official Rust Discord. There, you can chat with other Rustaceans (a silly nickname we call ourselves) who can help you out. Other great resources include the Users forum and Stack Overflow.

Updating and Uninstalling

Once Rust is installed via rustup, when a new version of Rust is released, updating to the latest version is easy. From your shell, run the following update script:

$ rustup update

To uninstall Rust and rustup, run the following uninstall script from your shell:

$ rustup self uninstall

Local Documentation

The installation of Rust also includes a local copy of the documentation, so you can read it offline. Run rustup doc to open the local documentation in your browser.

Any time a type or function is provided by the standard library and you’re not sure what it does or how to use it, use the application programming interface (API) documentation to find out!

Hello, World!

Now that you’ve installed Rust, let’s write your first Rust program. It’s traditional when learning a new language to write a little program that prints the text Hello, world! to the screen, so we’ll do the same here!

Note: This book assumes basic familiarity with the command line. Rust makes no specific demands about your editing or tooling or where your code lives, so if you prefer to use an integrated development environment (IDE) instead of the command line, feel free to use your favorite IDE. Many IDEs now have some degree of Rust support; check the IDE’s documentation for details. Recently, the Rust team has been focusing on enabling great IDE support, and progress has been made rapidly on that front!

Creating a Project Directory

You’ll start by making a directory to store your Rust code. It doesn’t matter to Rust where your code lives, but for the exercises and projects in this book, we suggest making a projects directory in your home directory and keeping all your projects there.

Open a terminal and enter the following commands to make a projects directory and a directory for the “Hello, world!” project within the projects directory.

For Linux, macOS, and PowerShell on Windows, enter this:

$ mkdir ~/projects
$ cd ~/projects
$ mkdir hello_world
$ cd hello_world

For Windows CMD, enter this:

> mkdir "%USERPROFILE%\projects"
> cd /d "%USERPROFILE%\projects"
> mkdir hello_world
> cd hello_world

Writing and Running a Rust Program

Next, make a new source file and call it main.rs. Rust files always end with the .rs extension. If you’re using more than one word in your filename, the convention is to use an underscore to separate them. For example, use hello_world.rs rather than helloworld.rs.

Now open the main.rs file you just created and enter the code in Listing 1-1.

Filename: main.rs

fn main() {
    println!("Hello, world!");
}

Listing 1-1: A program that prints Hello, world!

Save the file and go back to your terminal window in the ~/projects/hello_world directory. On Linux or macOS, enter the following commands to compile and run the file:

$ rustc main.rs
$ ./main
Hello, world!

On Windows, enter the command .\main.exe instead of ./main:

> rustc main.rs
> .\main.exe
Hello, world!

Regardless of your operating system, the string Hello, world! should print to the terminal. If you don’t see this output, refer back to the “Troubleshooting” part of the Installation section for ways to get help.

If Hello, world! did print, congratulations! You’ve officially written a Rust program. That makes you a Rust programmer—welcome!

Anatomy of a Rust Program

Let’s review this “Hello, world!” program in detail. Here’s the first piece of the puzzle:

fn main() {

}

These lines define a function named main. The main function is special: it is always the first code that runs in every executable Rust program. Here, the first line declares a function named main that has no parameters and returns nothing. If there were parameters, they would go inside the parentheses ().

The function body is wrapped in {}. Rust requires curly brackets around all function bodies. It’s good style to place the opening curly bracket on the same line as the function declaration, adding one space in between.

Note: If you want to stick to a standard style across Rust projects, you can use an automatic formatter tool called rustfmt to format your code in a particular style (more on rustfmt in Appendix D). The Rust team has included this tool with the standard Rust distribution, like rustc, so it should already be installed on your computer!

The body of the the main function holds the following code:


#![allow(unused)]
fn main() {
    println!("Hello, world!");
}

This line does all the work in this little program: it prints text to the screen. There are four important details to notice here.

First, Rust style is to indent with four spaces, not a tab.

Second, println! calls a Rust macro. If it had called a function instead, it would be entered as println (without the !). We’ll discuss Rust macros in more detail in Chapter 19. For now, you just need to know that using a ! means that you’re calling a macro instead of a normal function, and that macros don’t always follow the same rules as functions.

Third, you see the "Hello, world!" string. We pass this string as an argument to println!, and the string is printed to the screen.

Fourth, we end the line with a semicolon (;), which indicates that this expression is over and the next one is ready to begin. Most lines of Rust code end with a semicolon.

Compiling and Running Are Separate Steps

You’ve just run a newly created program, so let’s examine each step in the process.

Before running a Rust program, you must compile it using the Rust compiler by entering the rustc command and passing it the name of your source file, like this:

$ rustc main.rs

If you have a C or C++ background, you’ll notice that this is similar to gcc or clang. After compiling successfully, Rust outputs a binary executable.

On Linux, macOS, and PowerShell on Windows, you can see the executable by entering the ls command in your shell. On Linux and macOS, you’ll see two files. With PowerShell on Windows, you’ll see the same three files that you would see using CMD.

$ ls
main  main.rs

With CMD on Windows, you would enter the following:

> dir /B %= the /B option says to only show the file names =%
main.exe
main.pdb
main.rs

This shows the source code file with the .rs extension, the executable file (main.exe on Windows, but main on all other platforms), and, when using Windows, a file containing debugging information with the .pdb extension. From here, you run the main or main.exe file, like this:

$ ./main # or .\main.exe on Windows

If your main.rs is your “Hello, world!” program, this line prints Hello, world! to your terminal.

If you’re more familiar with a dynamic language, such as Ruby, Python, or JavaScript, you might not be used to compiling and running a program as separate steps. Rust is an ahead-of-time compiled language, meaning you can compile a program and give the executable to someone else, and they can run it even without having Rust installed. If you give someone a .rb, .py, or .js file, they need to have a Ruby, Python, or JavaScript implementation installed (respectively). But in those languages, you only need one command to compile and run your program. Everything is a trade-off in language design.

Just compiling with rustc is fine for simple programs, but as your project grows, you’ll want to manage all the options and make it easy to share your code. Next, we’ll introduce you to the Cargo tool, which will help you write real-world Rust programs.

Hello, Cargo!

Cargo is Rust’s build system and package manager. Most Rustaceans use this tool to manage their Rust projects because Cargo handles a lot of tasks for you, such as building your code, downloading the libraries your code depends on, and building those libraries. (We call the libraries that your code needs dependencies.)

The simplest Rust programs, like the one we’ve written so far, don’t have any dependencies. If we had built the “Hello, world!” project with Cargo, it would only use the part of Cargo that handles building your code. As you write more complex Rust programs, you’ll add dependencies, and if you start a project using Cargo, adding dependencies will be much easier to do.

Because the vast majority of Rust projects use Cargo, the rest of this book assumes that you’re using Cargo too. Cargo comes installed with Rust if you used the official installers discussed in the “Installation” section. If you installed Rust through some other means, check whether Cargo is installed by entering the following into your terminal:

$ cargo --version

If you see a version number, you have it! If you see an error, such as command not found, look at the documentation for your method of installation to determine how to install Cargo separately.

Creating a Project with Cargo

Let’s create a new project using Cargo and look at how it differs from our original “Hello, world!” project. Navigate back to your projects directory (or wherever you decided to store your code). Then, on any operating system, run the following:

$ cargo new hello_cargo
$ cd hello_cargo

The first command creates a new directory and project called hello_cargo. We’ve named our project hello_cargo, and Cargo creates its files in a directory of the same name.

Go into the hello_cargo directory and list the files. You’ll see that Cargo has generated two files and one directory for us: a Cargo.toml file and a src directory with a main.rs file inside.

It has also initialized a new Git repository along with a .gitignore file. Git files won’t be generated if you run cargo new within an existing Git repository; you can override this behavior by using cargo new --vcs=git.

Note: Git is a common version control system. You can change cargo new to use a different version control system or no version control system by using the --vcs flag. Run cargo new --help to see the available options.

Open Cargo.toml in your text editor of choice. It should look similar to the code in Listing 1-2.

Filename: Cargo.toml

[package]
name = "hello_cargo"
version = "0.1.0"
edition = "2021"

[dependencies]

Listing 1-2: Contents of Cargo.toml generated by cargo new

This file is in the TOML (Tom’s Obvious, Minimal Language) format, which is Cargo’s configuration format.

The first line, [package], is a section heading that indicates that the following statements are configuring a package. As we add more information to this file, we’ll add other sections.

The next three lines set the configuration information Cargo needs to compile your program: the name, the version, and the edition of Rust to use. We’ll talk about the edition key in Appendix E.

The last line, [dependencies], is the start of a section for you to list any of your project’s dependencies. In Rust, packages of code are referred to as crates. We won’t need any other crates for this project, but we will in the first project in Chapter 2, so we’ll use this dependencies section then.

Now open src/main.rs and take a look:

Filename: src/main.rs

fn main() {
    println!("Hello, world!");
}

Cargo has generated a “Hello, world!” program for you, just like the one we wrote in Listing 1-1! So far, the differences between our project and the project Cargo generated are that Cargo placed the code in the src directory, and we have a Cargo.toml configuration file in the top directory.

Cargo expects your source files to live inside the src directory. The top-level project directory is just for README files, license information, configuration files, and anything else not related to your code. Using Cargo helps you organize your projects. There’s a place for everything, and everything is in its place.

If you started a project that doesn’t use Cargo, as we did with the “Hello, world!” project, you can convert it to a project that does use Cargo. Move the project code into the src directory and create an appropriate Cargo.toml file.

Building and Running a Cargo Project

Now let’s look at what’s different when we build and run the “Hello, world!” program with Cargo! From your hello_cargo directory, build your project by entering the following command:

$ cargo build
   Compiling hello_cargo v0.1.0 (file:///projects/hello_cargo)
    Finished dev [unoptimized + debuginfo] target(s) in 2.85 secs

This command creates an executable file in target/debug/hello_cargo (or target\debug\hello_cargo.exe on Windows) rather than in your current directory. You can run the executable with this command:

$ ./target/debug/hello_cargo # or .\target\debug\hello_cargo.exe on Windows
Hello, world!

If all goes well, Hello, world! should print to the terminal. Running cargo build for the first time also causes Cargo to create a new file at the top level: Cargo.lock. This file keeps track of the exact versions of dependencies in your project. This project doesn’t have dependencies, so the file is a bit sparse. You won’t ever need to change this file manually; Cargo manages its contents for you.

We just built a project with cargo build and ran it with ./target/debug/hello_cargo, but we can also use cargo run to compile the code and then run the resulting executable all in one command:

$ cargo run
    Finished dev [unoptimized + debuginfo] target(s) in 0.0 secs
     Running `target/debug/hello_cargo`
Hello, world!

Using cargo run is more convenient than having to remember to run cargo build and then use the whole path to the binary, so most developers use cargo run.

Notice that this time we didn’t see output indicating that Cargo was compiling hello_cargo. Cargo figured out that the files hadn’t changed, so it didn’t rebuild but just ran the binary. If you had modified your source code, Cargo would have rebuilt the project before running it, and you would have seen this output:

$ cargo run
   Compiling hello_cargo v0.1.0 (file:///projects/hello_cargo)
    Finished dev [unoptimized + debuginfo] target(s) in 0.33 secs
     Running `target/debug/hello_cargo`
Hello, world!

Cargo also provides a command called cargo check. This command quickly checks your code to make sure it compiles but doesn’t produce an executable:

$ cargo check
   Checking hello_cargo v0.1.0 (file:///projects/hello_cargo)
    Finished dev [unoptimized + debuginfo] target(s) in 0.32 secs

Why would you not want an executable? Often, cargo check is much faster than cargo build, because it skips the step of producing an executable. If you’re continually checking your work while writing the code, using cargo check will speed up the process of letting you know if your project is still compiling! As such, many Rustaceans run cargo check periodically as they write their program to make sure it compiles. Then they run cargo build when they’re ready to use the executable.

Let’s recap what we’ve learned so far about Cargo:

  • We can create a project using cargo new.
  • We can build a project using cargo build.
  • We can build and run a project in one step using cargo run.
  • We can build a project without producing a binary to check for errors using cargo check.
  • Instead of saving the result of the build in the same directory as our code, Cargo stores it in the target/debug directory.

An additional advantage of using Cargo is that the commands are the same no matter which operating system you’re working on. So, at this point, we’ll no longer provide specific instructions for Linux and macOS versus Windows.

Building for Release

When your project is finally ready for release, you can use cargo build --release to compile it with optimizations. This command will create an executable in target/release instead of target/debug. The optimizations make your Rust code run faster, but turning them on lengthens the time it takes for your program to compile. This is why there are two different profiles: one for development, when you want to rebuild quickly and often, and another for building the final program you’ll give to a user that won’t be rebuilt repeatedly and that will run as fast as possible. If you’re benchmarking your code’s running time, be sure to run cargo build --release and benchmark with the executable in target/release.

Cargo as Convention

With simple projects, Cargo doesn’t provide a lot of value over just using rustc, but it will prove its worth as your programs become more intricate. With complex projects composed of multiple crates, it’s much easier to let Cargo coordinate the build.

Even though the hello_cargo project is simple, it now uses much of the real tooling you’ll use in the rest of your Rust career. In fact, to work on any existing projects, you can use the following commands to check out the code using Git, change to that project’s directory, and build:

$ git clone example.org/someproject
$ cd someproject
$ cargo build

For more information about Cargo, check out its documentation.

Summary

You’re already off to a great start on your Rust journey! In this chapter, you’ve learned how to:

  • Install the latest stable version of Rust using rustup
  • Update to a newer Rust version
  • Open locally installed documentation
  • Write and run a “Hello, world!” program using rustc directly
  • Create and run a new project using the conventions of Cargo

This is a great time to build a more substantial program to get used to reading and writing Rust code. So, in Chapter 2, we’ll build a guessing game program. If you would rather start by learning how common programming concepts work in Rust, see Chapter 3 and then return to Chapter 2.

Programming a Guessing Game

Let’s jump into Rust by working through a hands-on project together! This chapter introduces you to a few common Rust concepts by showing you how to use them in a real program. You’ll learn about let, match, methods, associated functions, using external crates, and more! In the following chapters, we’ll explore these ideas in more detail. In this chapter, you’ll practice the fundamentals.

We’ll implement a classic beginner programming problem: a guessing game. Here’s how it works: the program will generate a random integer between 1 and 100. It will then prompt the player to enter a guess. After a guess is entered, the program will indicate whether the guess is too low or too high. If the guess is correct, the game will print a congratulatory message and exit.

Setting Up a New Project

To set up a new project, go to the projects directory that you created in Chapter 1 and make a new project using Cargo, like so:

$ cargo new guessing_game
$ cd guessing_game

The first command, cargo new, takes the name of the project (guessing_game) as the first argument. The second command changes to the new project’s directory.

Look at the generated Cargo.toml file:

Filename: Cargo.toml

[package]
name = "guessing_game"
version = "0.1.0"
edition = "2021"

# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html

[dependencies]

As you saw in Chapter 1, cargo new generates a “Hello, world!” program for you. Check out the src/main.rs file:

Filename: src/main.rs

fn main() {
    println!("Hello, world!");
}

Now let’s compile this “Hello, world!” program and run it in the same step using the cargo run command:

$ cargo run
   Compiling guessing_game v0.1.0 (file:///projects/guessing_game)
    Finished dev [unoptimized + debuginfo] target(s) in 1.50s
     Running `target/debug/guessing_game`
Hello, world!

The run command comes in handy when you need to rapidly iterate on a project, as we’ll do in this game, quickly testing each iteration before moving on to the next one.

Reopen the src/main.rs file. You’ll be writing all the code in this file.

Processing a Guess

The first part of the guessing game program will ask for user input, process that input, and check that the input is in the expected form. To start, we’ll allow the player to input a guess. Enter the code in Listing 2-1 into src/main.rs.

Filename: src/main.rs

use std::io;

fn main() {
    println!("Guess the number!");

    println!("Please input your guess.");

    let mut guess = String::new();

    io::stdin()
        .read_line(&mut guess)
        .expect("Failed to read line");

    println!("You guessed: {guess}");
}

Listing 2-1: Code that gets a guess from the user and prints it

This code contains a lot of information, so let’s go over it line by line. To obtain user input and then print the result as output, we need to bring the io input/output library into scope. The io library comes from the standard library, known as std:

use std::io;

fn main() {
    println!("Guess the number!");

    println!("Please input your guess.");

    let mut guess = String::new();

    io::stdin()
        .read_line(&mut guess)
        .expect("Failed to read line");

    println!("You guessed: {guess}");
}

By default, Rust has a set of items defined in the standard library that it brings into the scope of every program. This set is called the prelude, and you can see everything in it in the standard library documentation.

If a type you want to use isn’t in the prelude, you have to bring that type into scope explicitly with a use statement. Using the std::io library provides you with a number of useful features, including the ability to accept user input.

As you saw in Chapter 1, the main function is the entry point into the program:

use std::io;

fn main() {
    println!("Guess the number!");

    println!("Please input your guess.");

    let mut guess = String::new();

    io::stdin()
        .read_line(&mut guess)
        .expect("Failed to read line");

    println!("You guessed: {guess}");
}

The fn syntax declares a new function, the parentheses, (), indicate there are no parameters, and the curly bracket, {, starts the body of the function.

As you also learned in Chapter 1, println! is a macro that prints a string to the screen:

use std::io;

fn main() {
    println!("Guess the number!");

    println!("Please input your guess.");

    let mut guess = String::new();

    io::stdin()
        .read_line(&mut guess)
        .expect("Failed to read line");

    println!("You guessed: {guess}");
}

This code is printing a prompt stating what the game is and requesting input from the user.

Storing Values with Variables

Next, we’ll create a variable to store the user input, like this:

use std::io;

fn main() {
    println!("Guess the number!");

    println!("Please input your guess.");

    let mut guess = String::new();

    io::stdin()
        .read_line(&mut guess)
        .expect("Failed to read line");

    println!("You guessed: {guess}");
}

Now the program is getting interesting! There’s a lot going on in this little line. We use the let statement to create the variable. Here’s another example:

let apples = 5;

This line creates a new variable named apples and binds it to the value 5. In Rust, variables are immutable by default, meaning once we give the variable a value, the value won't change. We’ll be discussing this concept in detail in the “Variables and Mutability” section in Chapter 3. To make a variable mutable, we add mut before the variable name:

let apples = 5; // immutable
let mut bananas = 5; // mutable

Note: The // syntax starts a comment that continues until the end of the line. Rust ignores everything in comments. We’ll discuss comments in more detail in Chapter 3.

Returning to the guessing game program, you now know that let mut guess will introduce a mutable variable named guess. The equal sign (=) tells Rust we want to bind something to the variable now. On the right of the equals sign is the value that guess is bound to, which is the result of calling String::new, a function that returns a new instance of a String. String is a string type provided by the standard library that is a growable, UTF-8 encoded bit of text.

The :: syntax in the ::new line indicates that new is an associated function of the String type. An associated function is a function that’s implemented on a type, in this case String. This new function creates a new, empty string. You’ll find a new function on many types, because it’s a common name for a function that makes a new value of some kind.

In full, the let mut guess = String::new(); line has created a mutable variable that is currently bound to a new, empty instance of a String. Whew!

Receiving User Input

Recall that we included the input/output functionality from the standard library with use std::io; on the first line of the program. Now we’ll call the stdin function from the io module, which will allow us to handle user input:

use std::io;

fn main() {
    println!("Guess the number!");

    println!("Please input your guess.");

    let mut guess = String::new();

    io::stdin()
        .read_line(&mut guess)
        .expect("Failed to read line");

    println!("You guessed: {guess}");
}

If we hadn’t imported the io library with use std::io at the beginning of the program, we could still use the function by writing this function call as std::io::stdin. The stdin function returns an instance of std::io::Stdin, which is a type that represents a handle to the standard input for your terminal.

Next, the line .read_line(&mut guess) calls the read_line method on the standard input handle to get input from the user. We’re also passing &mut guess as the argument to read_line to tell it what string to store the user input in. The full job of read_line is to take whatever the user types into standard input and append that into a string (without overwriting its contents), so we therefore pass that string as an argument. The string argument needs to be mutable so the method can change the string’s content.

The & indicates that this argument is a reference, which gives you a way to let multiple parts of your code access one piece of data without needing to copy that data into memory multiple times. References are a complex feature, and one of Rust’s major advantages is how safe and easy it is to use references. You don’t need to know a lot of those details to finish this program. For now, all you need to know is that like variables, references are immutable by default. Hence, you need to write &mut guess rather than &guess to make it mutable. (Chapter 4 will explain references more thoroughly.)

Handling Potential Failure with the Result Type

We’re still working on this line of code. We’re now discussing a third line of text, but note that it’s still part of a single logical line of code. The next part is this method:

use std::io;

fn main() {
    println!("Guess the number!");

    println!("Please input your guess.");

    let mut guess = String::new();

    io::stdin()
        .read_line(&mut guess)
        .expect("Failed to read line");

    println!("You guessed: {guess}");
}

We could have written this code as:

io::stdin().read_line(&mut guess).expect("Failed to read line");

However, one long line is difficult to read, so it’s best to divide it. It’s often wise to introduce a newline and other whitespace to help break up long lines when you call a method with the .method_name() syntax. Now let’s discuss what this line does.

As mentioned earlier, read_line puts whatever the user enters into the string we pass to it, but it also returns a Result value. Result is an enumeration, often called an enum, which is a type that can be in one of multiple possible states. We call each possible state a variant.

Chapter 6 will cover enums in more detail. The purpose of these Result types is to encode error-handling information.

Result's variants are Ok and Err. The Ok variant indicates the operation was successful, and inside Ok is the successfully generated value. The Err variant means the operation failed, and Err contains information about how or why the operation failed.

Values of the Result type, like values of any type, have methods defined on them. An instance of Result has an expect method that you can call. If this instance of Result is an Err value, expect will cause the program to crash and display the message that you passed as an argument to expect. If the read_line method returns an Err, it would likely be the result of an error coming from the underlying operating system. If this instance of Result is an Ok value, expect will take the return value that Ok is holding and return just that value to you so you can use it. In this case, that value is the number of bytes in the user’s input.

If you don’t call expect, the program will compile, but you’ll get a warning:

$ cargo build
   Compiling guessing_game v0.1.0 (file:///projects/guessing_game)
warning: unused `Result` that must be used
  --> src/main.rs:10:5
   |
10 |     io::stdin().read_line(&mut guess);
   |     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
   |
   = note: `#[warn(unused_must_use)]` on by default
   = note: this `Result` may be an `Err` variant, which should be handled

warning: `guessing_game` (bin "guessing_game") generated 1 warning
    Finished dev [unoptimized + debuginfo] target(s) in 0.59s

Rust warns that you haven’t used the Result value returned from read_line, indicating that the program hasn’t handled a possible error.

The right way to suppress the warning is to actually write error handling, but in our case we just want to crash this program when a problem occurs, so we can use expect. You’ll learn about recovering from errors in Chapter 9.

Printing Values with println! Placeholders

Aside from the closing curly bracket, there’s only one more line to discuss in the code so far:

use std::io;

fn main() {
    println!("Guess the number!");

    println!("Please input your guess.");

    let mut guess = String::new();

    io::stdin()
        .read_line(&mut guess)
        .expect("Failed to read line");

    println!("You guessed: {guess}");
}

This line prints the string that now contains the user’s input. The {} set of curly brackets is a placeholder: think of {} as little crab pincers that hold a value in place. You can print more than one value using curly brackets: the first set of curly brackets holds the first value listed after the format string, the second set holds the second value, and so on. Printing multiple values in one call to println! would look like this:


#![allow(unused)]
fn main() {
let x = 5;
let y = 10;

println!("x = {} and y = {}", x, y);
}

This code would print x = 5 and y = 10.

Testing the First Part

Let’s test the first part of the guessing game. Run it using cargo run:

$ cargo run
   Compiling guessing_game v0.1.0 (file:///projects/guessing_game)
    Finished dev [unoptimized + debuginfo] target(s) in 6.44s
     Running `target/debug/guessing_game`
Guess the number!
Please input your guess.
6
You guessed: 6

At this point, the first part of the game is done: we’re getting input from the keyboard and then printing it.

Generating a Secret Number

Next, we need to generate a secret number that the user will try to guess. The secret number should be different every time so the game is fun to play more than once. We’ll use a random number between 1 and 100 so the game isn’t too difficult. Rust doesn’t yet include random number functionality in its standard library. However, the Rust team does provide a rand crate with said functionality.

Using a Crate to Get More Functionality

Remember that a crate is a collection of Rust source code files. The project we’ve been building is a binary crate, which is an executable. The rand crate is a library crate, which contains code intended to be used in other programs and can't be executed on its own.

Cargo’s coordination of external crates is where Cargo really shines. Before we can write code that uses rand, we need to modify the Cargo.toml file to include the rand crate as a dependency. Open that file now and add the following line to the bottom beneath the [dependencies] section header that Cargo created for you. Be sure to specify rand exactly as we have here, with this version number, or the code examples in this tutorial may not work.

Filename: Cargo.toml

rand = "0.8.3"

In the Cargo.toml file, everything that follows a header is part of that section that continues until another section starts. In [dependencies] you tell Cargo which external crates your project depends on and which versions of those crates you require. In this case, we specify the rand crate with the semantic version specifier 0.8.3. Cargo understands Semantic Versioning (sometimes called SemVer), which is a standard for writing version numbers. The number 0.8.3 is actually shorthand for ^0.8.3, which means any version that is at least 0.8.3 but below 0.9.0.

Cargo considers these versions to have public APIs compatible with version 0.8.3, and this specification ensures you’ll get the latest patch release that will still compile with the code in this chapter. Any version 0.9.0 or greater is not guaranteed to have the same API as what the following examples use.

Now, without changing any of the code, let’s build the project, as shown in Listing 2-2.

$ cargo build
    Updating crates.io index
  Downloaded rand v0.8.3
  Downloaded libc v0.2.86
  Downloaded getrandom v0.2.2
  Downloaded cfg-if v1.0.0
  Downloaded ppv-lite86 v0.2.10
  Downloaded rand_chacha v0.3.0
  Downloaded rand_core v0.6.2
   Compiling rand_core v0.6.2
   Compiling libc v0.2.86
   Compiling getrandom v0.2.2
   Compiling cfg-if v1.0.0
   Compiling ppv-lite86 v0.2.10
   Compiling rand_chacha v0.3.0
   Compiling rand v0.8.3
   Compiling guessing_game v0.1.0 (file:///projects/guessing_game)
    Finished dev [unoptimized + debuginfo] target(s) in 2.53s

Listing 2-2: The output from running cargo build after adding the rand crate as a dependency

You may see different version numbers (but they will all be compatible with the code, thanks to SemVer!), different lines (depending on the operating system), and the lines may be in a different order.

When we include an external dependency, Cargo fetches the latest versions of everything that dependency needs from the registry, which is a copy of data from Crates.io. Crates.io is where people in the Rust ecosystem post their open source Rust projects for others to use.

After updating the registry, Cargo checks the [dependencies] section and downloads any crates listed that aren’t already downloaded. In this case, although we only listed rand as a dependency, Cargo also grabbed other crates that rand depends on to work. After downloading the crates, Rust compiles them and then compiles the project with the dependencies available.

If you immediately run cargo build again without making any changes, you won’t get any output aside from the Finished line. Cargo knows it has already downloaded and compiled the dependencies, and you haven’t changed anything about them in your Cargo.toml file. Cargo also knows that you haven’t changed anything about your code, so it doesn’t recompile that either. With nothing to do, it simply exits.

If you open up the src/main.rs file, make a trivial change, and then save it and build again, you’ll only see two lines of output:

$ cargo build
   Compiling guessing_game v0.1.0 (file:///projects/guessing_game)
    Finished dev [unoptimized + debuginfo] target(s) in 2.53 secs

These lines show Cargo only updates the build with your tiny change to the src/main.rs file. Your dependencies haven’t changed, so Cargo knows it can reuse what it has already downloaded and compiled for those.

Ensuring Reproducible Builds with the Cargo.lock File

Cargo has a mechanism that ensures you can rebuild the same artifact every time you or anyone else builds your code: Cargo will use only the versions of the dependencies you specified until you indicate otherwise. For example, say that next week version 0.8.4 of the rand crate comes out, and that version contains an important bug fix, but it also contains a regression that will break your code. To handle this, Rust creates the Cargo.lock file the first time you run cargo build, so we now have this in the guessing_game directory.

When you build a project for the first time, Cargo figures out all the versions of the dependencies that fit the criteria and then writes them to the Cargo.lock file. When you build your project in the future, Cargo will see that the Cargo.lock file exists and use the versions specified there rather than doing all the work of figuring out versions again. This lets you have a reproducible build automatically. In other words, your project will remain at 0.8.3 until you explicitly upgrade, thanks to the Cargo.lock file. Because the Cargo.lock file is important for reproducible builds, it's often checked into source control with the rest of the code in your project.

Updating a Crate to Get a New Version

When you do want to update a crate, Cargo provides the command update, which will ignore the Cargo.lock file and figure out all the latest versions that fit your specifications in Cargo.toml. Cargo will then write those versions to the Cargo.lock file. Otherwise, by default, Cargo will only look for versions greater than 0.8.3 and less than 0.9.0. If the rand crate has released the two new versions 0.8.4 and 0.9.0 you would see the following if you ran cargo update:

$ cargo update
    Updating crates.io index
    Updating rand v0.8.3 -> v0.8.4

Cargo ignores the 0.9.0 release. At this point, you would also notice a change in your Cargo.lock file noting that the version of the rand crate you are now using is 0.8.4. To use rand version 0.9.0 or any version in the 0.9.x series, you’d have to update the Cargo.toml file to look like this instead:

[dependencies]
rand = "0.9.0"

The next time you run cargo build, Cargo will update the registry of crates available and reevaluate your rand requirements according to the new version you have specified.

There’s a lot more to say about Cargo and its ecosystem which we’ll discuss in Chapter 14, but for now, that’s all you need to know. Cargo makes it very easy to reuse libraries, so Rustaceans are able to write smaller projects that are assembled from a number of packages.

Generating a Random Number

Let’s start using rand to generate a number to guess. The next step is to update src/main.rs, as shown in Listing 2-3.

Filename: src/main.rs

use std::io;
use rand::Rng;

fn main() {
    println!("Guess the number!");

    let secret_number = rand::thread_rng().gen_range(1..=100);

    println!("The secret number is: {secret_number}");

    println!("Please input your guess.");

    let mut guess = String::new();

    io::stdin()
        .read_line(&mut guess)
        .expect("Failed to read line");

    println!("You guessed: {guess}");
}

Listing 2-3: Adding code to generate a random number

First, we add the line use rand::Rng. The Rng trait defines methods that random number generators implement, and this trait must be in scope for us to use those methods. Chapter 10 will cover traits in detail.

Next, we’re adding two lines in the middle. In the first line, we call the rand::thread_rng function that gives us the particular random number generator that we’re going to use: one that is local to the current thread of execution and seeded by the operating system. Then we call the gen_range method on the random number generator. This method is defined by the Rng trait that we brought into scope with the use rand::Rng statement. The gen_range method takes a range expression as an argument and generates a random number in the range. The kind of range expression we’re using here takes the form start..=end and is inclusive on the lower and upper bounds, so we need to specify 1..=100 to request a number between 1 and 100.

Note: You won’t just know which traits to use and which methods and functions to call from a crate, so each crate has documentation with instructions for using it. Another neat feature of Cargo is that running the cargo doc --open command will build documentation provided by all of your dependencies locally and open it in your browser. If you’re interested in other functionality in the rand crate, for example, run cargo doc --open and click rand in the sidebar on the left.

The second new line prints the secret number. This is useful while we’re developing the program to be able to test it, but we’ll delete it from the final version. It’s not much of a game if the program prints the answer as soon as it starts!

Try running the program a few times:

$ cargo run
   Compiling guessing_game v0.1.0 (file:///projects/guessing_game)
    Finished dev [unoptimized + debuginfo] target(s) in 2.53s
     Running `target/debug/guessing_game`
Guess the number!
The secret number is: 7
Please input your guess.
4
You guessed: 4

$ cargo run
    Finished dev [unoptimized + debuginfo] target(s) in 0.02s
     Running `target/debug/guessing_game`
Guess the number!
The secret number is: 83
Please input your guess.
5
You guessed: 5

You should get different random numbers, and they should all be numbers between 1 and 100. Great job!

Comparing the Guess to the Secret Number

Now that we have user input and a random number, we can compare them. That step is shown in Listing 2-4. Note that this code won’t compile quite yet, as we will explain.

Filename: src/main.rs

use rand::Rng;
use std::cmp::Ordering;
use std::io;

fn main() {
    // --snip--
    println!("Guess the number!");

    let secret_number = rand::thread_rng().gen_range(1..=100);

    println!("The secret number is: {secret_number}");

    println!("Please input your guess.");

    let mut guess = String::new();

    io::stdin()
        .read_line(&mut guess)
        .expect("Failed to read line");

    println!("You guessed: {guess}");

    match guess.cmp(&secret_number) {
        Ordering::Less => println!("Too small!"),
        Ordering::Greater => println!("Too big!"),
        Ordering::Equal => println!("You win!"),
    }
}

Listing 2-4: Handling the possible return values of comparing two numbers

First we add another use statement, bringing a type called std::cmp::Ordering into scope from the standard library. The Ordering type is another enum and has the variants Less, Greater, and Equal. These are the three outcomes that are possible when you compare two values.

Then we add five new lines at the bottom that use the Ordering type. The cmp method compares two values and can be called on anything that can be compared. It takes a reference to whatever you want to compare with: here it’s comparing the guess to the secret_number. Then it returns a variant of the Ordering enum we brought into scope with the use statement. We use a match expression to decide what to do next based on which variant of Ordering was returned from the call to cmp with the values in guess and secret_number.

A match expression is made up of arms. An arm consists of a pattern to match against, and the code that should be run if the value given to match fits that arm’s pattern. Rust takes the value given to match and looks through each arm’s pattern in turn. Patterns and the match construct are powerful Rust features that let you express a variety of situations your code might encounter and make sure that you handle them all. These features will be covered in detail in Chapter 6 and Chapter 18, respectively.

Let’s walk through an example with the match expression we use here. Say that the user has guessed 50 and the randomly generated secret number this time is 38. When the code compares 50 to 38, the cmp method will return Ordering::Greater, because 50 is greater than 38. The match expression gets the Ordering::Greater value and starts checking each arm’s pattern. It looks at the first arm’s pattern, Ordering::Less, and sees that the value Ordering::Greater does not match Ordering::Less, so it ignores the code in that arm and moves to the next arm. The next arm’s pattern is Ordering::Greater, which does match Ordering::Greater! The associated code in that arm will execute and print Too big! to the screen. The match expression ends after the first successful match, so it won’t look at the last arm in this scenario.

However, the code in Listing 2-4 won’t compile yet. Let’s try it:

$ cargo build
   Compiling libc v0.2.86
   Compiling getrandom v0.2.2
   Compiling cfg-if v1.0.0
   Compiling ppv-lite86 v0.2.10
   Compiling rand_core v0.6.2
   Compiling rand_chacha v0.3.0
   Compiling rand v0.8.3
   Compiling guessing_game v0.1.0 (file:///projects/guessing_game)
error[E0308]: mismatched types
  --> src/main.rs:22:21
   |
22 |     match guess.cmp(&secret_number) {
   |                     ^^^^^^^^^^^^^^ expected struct `String`, found integer
   |
   = note: expected reference `&String`
              found reference `&{integer}`

error[E0283]: type annotations needed for `{integer}`
   --> src/main.rs:8:44
    |
8   |     let secret_number = rand::thread_rng().gen_range(1..=100);
    |         -------------                      ^^^^^^^^^ cannot infer type for type `{integer}`
    |         |
    |         consider giving `secret_number` a type
    |
    = note: multiple `impl`s satisfying `{integer}: SampleUniform` found in the `rand` crate:
            - impl SampleUniform for i128;
            - impl SampleUniform for i16;
            - impl SampleUniform for i32;
            - impl SampleUniform for i64;
            and 8 more
note: required by a bound in `gen_range`
   --> /Users/carolnichols/.cargo/registry/src/github.com-1ecc6299db9ec823/rand-0.8.3/src/rng.rs:129:12
    |
129 |         T: SampleUniform,
    |            ^^^^^^^^^^^^^ required by this bound in `gen_range`
help: consider specifying the type arguments in the function call
    |
8   |     let secret_number = rand::thread_rng().gen_range::<T, R>(1..=100);
    |                                                     ++++++++

Some errors have detailed explanations: E0283, E0308.
For more information about an error, try `rustc --explain E0283`.
error: could not compile `guessing_game` due to 2 previous errors

The core of the error states that there are mismatched types. Rust has a strong, static type system. However, it also has type inference. When we wrote let mut guess = String::new(), Rust was able to infer that guess should be a String and didn’t make us write the type. The secret_number, on the other hand, is a number type. A few of Rust’s number types can have a value between 1 and 100: i32, a 32-bit number; u32, an unsigned 32-bit number; i64, a 64-bit number; as well as others. Unless otherwise specified, Rust defaults to an i32, which is the type of secret_number unless you add type information elsewhere that would cause Rust to infer a different numerical type. The reason for the error is that Rust cannot compare a string and a number type.

Ultimately, we want to convert the String the program reads as input into a real number type so we can compare it numerically to the secret number. We do so by adding this line to the main function body:

Filename: src/main.rs

use rand::Rng;
use std::cmp::Ordering;
use std::io;

fn main() {
    println!("Guess the number!");

    let secret_number = rand::thread_rng().gen_range(1..=100);

    println!("The secret number is: {secret_number}");

    println!("Please input your guess.");

    // --snip--

    let mut guess = String::new();

    io::stdin()
        .read_line(&mut guess)
        .expect("Failed to read line");

    let guess: u32 = guess.trim().parse().expect("Please type a number!");

    println!("You guessed: {guess}");

    match guess.cmp(&secret_number) {
        Ordering::Less => println!("Too small!"),
        Ordering::Greater => println!("Too big!"),
        Ordering::Equal => println!("You win!"),
    }
}

The line is:

let guess: u32 = guess.trim().parse().expect("Please type a number!");

We create a variable named guess. But wait, doesn’t the program already have a variable named guess? It does, but helpfully Rust allows us to shadow the previous value of guess with a new one. Shadowing lets us reuse the guess variable name rather than forcing us to create two unique variables, such as guess_str and guess for example. We’ll cover this in more detail in Chapter 3, but for now know that this feature is often used when you want to convert a value from one type to another type.

We bind this new variable to the expression guess.trim().parse(). The guess in the expression refers to the original guess variable that contained the input as a string. The trim method on a String instance will eliminate any whitespace at the beginning and end, which we must do to be able to compare the string to the u32, which can only contain numerical data. The user must press enter to satisfy read_line and input their guess, which adds a newline character to the string. For example, if the user types 5 and presses enter, guess looks like this: 5\n. The \n represents “newline”. (On Windows, pressing enter results in a carriage return and a newline, \r\n). The trim method eliminates \n or \r\n, resulting in just 5.

The parse method on strings converts a string to another type. Here, we use it to convert from a string to a number. We need to tell Rust the exact number type we want by using let guess: u32. The colon (:) after guess tells Rust we’ll annotate the variable’s type. Rust has a few built-in number types; the u32 seen here is an unsigned, 32-bit integer. It’s a good default choice for a small positive number. You’ll learn about other number types in Chapter 3. Additionally, the u32 annotation in this example program and the comparison with secret_number means that Rust will infer that secret_number should be a u32 as well. So now the comparison will be between two values of the same type!

The parse method will only work on characters that can logically be converted into numbers and so can easily cause errors. If, for example, the string contained A👍%, there would be no way to convert that to a number. Because it might fail, the parse method returns a Result type, much as the read_line method does (discussed earlier in “Handling Potential Failure with the Result Type”). We’ll treat this Result the same way by using the expect method again. If parse returns an Err Result variant because it couldn’t create a number from the string, the expect call will crash the game and print the message we give it. If parse can successfully convert the string to a number, it will return the Ok variant of Result, and expect will return the number that we want from the Ok value.

Let’s run the program now!

$ cargo run
   Compiling guessing_game v0.1.0 (file:///projects/guessing_game)
    Finished dev [unoptimized + debuginfo] target(s) in 0.43s
     Running `target/debug/guessing_game`
Guess the number!
The secret number is: 58
Please input your guess.
  76
You guessed: 76
Too big!

Nice! Even though spaces were added before the guess, the program still figured out that the user guessed 76. Run the program a few times to verify the different behavior with different kinds of input: guess the number correctly, guess a number that is too high, and guess a number that is too low.

We have most of the game working now, but the user can make only one guess. Let’s change that by adding a loop!

Allowing Multiple Guesses with Looping

The loop keyword creates an infinite loop. We’ll add a loop to give users more chances at guessing the number:

Filename: src/main.rs

use rand::Rng;
use std::cmp::Ordering;
use std::io;

fn main() {
    println!("Guess the number!");

    let secret_number = rand::thread_rng().gen_range(1..=100);

    // --snip--

    println!("The secret number is: {secret_number}");

    loop {
        println!("Please input your guess.");

        // --snip--


        let mut guess = String::new();

        io::stdin()
            .read_line(&mut guess)
            .expect("Failed to read line");

        let guess: u32 = guess.trim().parse().expect("Please type a number!");

        println!("You guessed: {guess}");

        match guess.cmp(&secret_number) {
            Ordering::Less => println!("Too small!"),
            Ordering::Greater => println!("Too big!"),
            Ordering::Equal => println!("You win!"),
        }
    }
}

As you can see, we’ve moved everything from the guess input prompt onward into a loop. Be sure to indent the lines inside the loop another four spaces each and run the program again. The program will now ask for another guess forever, which actually introduces a new problem. It doesn’t seem like the user can quit!

The user could always interrupt the program by using the keyboard shortcut ctrl-c. But there’s another way to escape this insatiable monster, as mentioned in the parse discussion in “Comparing the Guess to the Secret Number”: if the user enters a non-number answer, the program will crash. We can take advantage of that to allow the user to quit, as shown here:

$ cargo run
   Compiling guessing_game v0.1.0 (file:///projects/guessing_game)
    Finished dev [unoptimized + debuginfo] target(s) in 1.50s
     Running `target/debug/guessing_game`
Guess the number!
The secret number is: 59
Please input your guess.
45
You guessed: 45
Too small!
Please input your guess.
60
You guessed: 60
Too big!
Please input your guess.
59
You guessed: 59
You win!
Please input your guess.
quit
thread 'main' panicked at 'Please type a number!: ParseIntError { kind: InvalidDigit }', src/main.rs:28:47
note: run with `RUST_BACKTRACE=1` environment variable to display a backtrace

Typing quit will quit the game, but as you’ll notice so will entering any other non-number input. This is suboptimal to say the least; we want the game to also stop when the correct number is guessed.

Quitting After a Correct Guess

Let’s program the game to quit when the user wins by adding a break statement:

Filename: src/main.rs

use rand::Rng;
use std::cmp::Ordering;
use std::io;

fn main() {
    println!("Guess the number!");

    let secret_number = rand::thread_rng().gen_range(1..=100);

    println!("The secret number is: {secret_number}");

    loop {
        println!("Please input your guess.");

        let mut guess = String::new();

        io::stdin()
            .read_line(&mut guess)
            .expect("Failed to read line");

        let guess: u32 = guess.trim().parse().expect("Please type a number!");

        println!("You guessed: {guess}");

        // --snip--

        match guess.cmp(&secret_number) {
            Ordering::Less => println!("Too small!"),
            Ordering::Greater => println!("Too big!"),
            Ordering::Equal => {
                println!("You win!");
                break;
            }
        }
    }
}

Adding the break line after You win! makes the program exit the loop when the user guesses the secret number correctly. Exiting the loop also means exiting the program, because the loop is the last part of main.

Handling Invalid Input

To further refine the game’s behavior, rather than crashing the program when the user inputs a non-number, let’s make the game ignore a non-number so the user can continue guessing. We can do that by altering the line where guess is converted from a String to a u32, as shown in Listing 2-5.

Filename: src/main.rs

use rand::Rng;
use std::cmp::Ordering;
use std::io;

fn main() {
    println!("Guess the number!");

    let secret_number = rand::thread_rng().gen_range(1..=100);

    println!("The secret number is: {secret_number}");

    loop {
        println!("Please input your guess.");

        let mut guess = String::new();

        // --snip--

        io::stdin()
            .read_line(&mut guess)
            .expect("Failed to read line");

        let guess: u32 = match guess.trim().parse() {
            Ok(num) => num,
            Err(_) => continue,
        };

        println!("You guessed: {guess}");

        // --snip--

        match guess.cmp(&secret_number) {
            Ordering::Less => println!("Too small!"),
            Ordering::Greater => println!("Too big!"),
            Ordering::Equal => {
                println!("You win!");
                break;
            }
        }
    }
}

Listing 2-5: Ignoring a non-number guess and asking for another guess instead of crashing the program

We switch from an expect call to a match expression to move from crashing on an error to handling the error. Remember that parse returns a Result type and Result is an enum that has the variants Ok and Err. We’re using a match expression here, as we did with the Ordering result of the cmp method.

If parse is able to successfully turn the string into a number, it will return an Ok value that contains the resulting number. That Ok value will match the first arm’s pattern, and the match expression will just return the num value that parse produced and put inside the Ok value. That number will end up right where we want it in the new guess variable we’re creating.

If parse is not able to turn the string into a number, it will return an Err value that contains more information about the error. The Err value does not match the Ok(num) pattern in the first match arm, but it does match the Err(_) pattern in the second arm. The underscore, _, is a catchall value; in this example, we’re saying we want to match all Err values, no matter what information they have inside them. So the program will execute the second arm’s code, continue, which tells the program to go to the next iteration of the loop and ask for another guess. So, effectively, the program ignores all errors that parse might encounter!

Now everything in the program should work as expected. Let’s try it:

$ cargo run
   Compiling guessing_game v0.1.0 (file:///projects/guessing_game)
    Finished dev [unoptimized + debuginfo] target(s) in 4.45s
     Running `target/debug/guessing_game`
Guess the number!
The secret number is: 61
Please input your guess.
10
You guessed: 10
Too small!
Please input your guess.
99
You guessed: 99
Too big!
Please input your guess.
foo
Please input your guess.
61
You guessed: 61
You win!

Awesome! With one tiny final tweak, we will finish the guessing game. Recall that the program is still printing the secret number. That worked well for testing, but it ruins the game. Let’s delete the println! that outputs the secret number. Listing 2-6 shows the final code.

Filename: src/main.rs

use rand::Rng;
use std::cmp::Ordering;
use std::io;

fn main() {
    println!("Guess the number!");

    let secret_number = rand::thread_rng().gen_range(1..=100);

    loop {
        println!("Please input your guess.");

        let mut guess = String::new();

        io::stdin()
            .read_line(&mut guess)
            .expect("Failed to read line");

        let guess: u32 = match guess.trim().parse() {
            Ok(num) => num,
            Err(_) => continue,
        };

        println!("You guessed: {guess}");

        match guess.cmp(&secret_number) {
            Ordering::Less => println!("Too small!"),
            Ordering::Greater => println!("Too big!"),
            Ordering::Equal => {
                println!("You win!");
                break;
            }
        }
    }
}

Listing 2-6: Complete guessing game code

Summary

At this point, you’ve successfully built the guessing game. Congratulations!

This project was a hands-on way to introduce you to many new Rust concepts: let, match, functions, the use of external crates, and more. In the next few chapters, you’ll learn about these concepts in more detail. Chapter 3 covers concepts that most programming languages have, such as variables, data types, and functions, and shows how to use them in Rust. Chapter 4 explores ownership, a feature that makes Rust different from other languages. Chapter 5 discusses structs and method syntax, and Chapter 6 explains how enums work.

Common Programming Concepts

This chapter covers concepts that appear in almost every programming language and how they work in Rust. Many programming languages have much in common at their core. None of the concepts presented in this chapter are unique to Rust, but we’ll discuss them in the context of Rust and explain the conventions around using these concepts.

Specifically, you’ll learn about variables, basic types, functions, comments, and control flow. These foundations will be in every Rust program, and learning them early will give you a strong core to start from.

Keywords

The Rust language has a set of keywords that are reserved for use by the language only, much as in other languages. Keep in mind that you cannot use these words as names of variables or functions. Most of the keywords have special meanings, and you’ll be using them to do various tasks in your Rust programs; a few have no current functionality associated with them but have been reserved for functionality that might be added to Rust in the future. You can find a list of the keywords in Appendix A.

Variables and Mutability

As mentioned in the “Storing Values with Variables” section, by default variables are immutable. This is one of many nudges Rust gives you to write your code in a way that takes advantage of the safety and easy concurrency that Rust offers. However, you still have the option to make your variables mutable. Let’s explore how and why Rust encourages you to favor immutability and why sometimes you might want to opt out.

When a variable is immutable, once a value is bound to a name, you can’t change that value. To illustrate this, let’s generate a new project called variables in your projects directory by using cargo new variables.

Then, in your new variables directory, open src/main.rs and replace its code with the following code. This code won’t compile just yet, we’ll first examine the immutability error.

Filename: src/main.rs

fn main() {
    let x = 5;
    println!("The value of x is: {x}");
    x = 6;
    println!("The value of x is: {x}");
}

Save and run the program using cargo run. You should receive an error message, as shown in this output:

$ cargo run
   Compiling variables v0.1.0 (file:///projects/variables)
error[E0384]: cannot assign twice to immutable variable `x`
 --> src/main.rs:4:5
  |
2 |     let x = 5;
  |         -
  |         |
  |         first assignment to `x`
  |         help: consider making this binding mutable: `mut x`
3 |     println!("The value of x is: {x}");
4 |     x = 6;
  |     ^^^^^ cannot assign twice to immutable variable

For more information about this error, try `rustc --explain E0384`.
error: could not compile `variables` due to previous error

This example shows how the compiler helps you find errors in your programs. Compiler errors can be frustrating, but really they only mean your program isn’t safely doing what you want it to do yet; they do not mean that you’re not a good programmer! Experienced Rustaceans still get compiler errors.

The error message indicates that the cause of the error is that you cannot assign twice to immutable variable `x`, because you tried to assign a second value to the immutable x variable.

It’s important that we get compile-time errors when we attempt to change a value that’s designated as immutable because this very situation can lead to bugs. If one part of our code operates on the assumption that a value will never change and another part of our code changes that value, it’s possible that the first part of the code won’t do what it was designed to do. The cause of this kind of bug can be difficult to track down after the fact, especially when the second piece of code changes the value only sometimes. The Rust compiler guarantees that when you state a value won’t change, it really won’t change, so you don’t have to keep track of it yourself. Your code is thus easier to reason through.

But mutability can be very useful, and can make code more convenient to write. Although variables are immutable by default, you can make them mutable by adding mut in front of the variable name as you did in Chapter 2. Adding mut also conveys intent to future readers of the code by indicating that other parts of the code will be changing this variable’s value.

For example, let’s change src/main.rs to the following:

Filename: src/main.rs

fn main() {
    let mut x = 5;
    println!("The value of x is: {x}");
    x = 6;
    println!("The value of x is: {x}");
}

When we run the program now, we get this:

$ cargo run
   Compiling variables v0.1.0 (file:///projects/variables)
    Finished dev [unoptimized + debuginfo] target(s) in 0.30s
     Running `target/debug/variables`
The value of x is: 5
The value of x is: 6

We’re allowed to change the value bound to x from 5 to 6 when mut is used. Ultimately, deciding whether to use mutability or not is up to you and depends on what you think is clearest in that particular situation.

Constants

Like immutable variables, constants are values that are bound to a name and are not allowed to change, but there are a few differences between constants and variables.

First, you aren’t allowed to use mut with constants. Constants aren’t just immutable by default—they’re always immutable. You declare constants using the const keyword instead of the let keyword, and the type of the value must be annotated. We’re about to cover types and type annotations in the next section, “Data Types,” so don’t worry about the details right now. Just know that you must always annotate the type.

Constants can be declared in any scope, including the global scope, which makes them useful for values that many parts of code need to know about.

The last difference is that constants may be set only to a constant expression, not the result of a value that could only be computed at runtime.

Here’s an example of a constant declaration:


#![allow(unused)]
fn main() {
const THREE_HOURS_IN_SECONDS: u32 = 60 * 60 * 3;
}

The constant’s name is THREE_HOURS_IN_SECONDS and its value is set to the result of multiplying 60 (the number of seconds in a minute) by 60 (the number of minutes in an hour) by 3 (the number of hours we want to count in this program). Rust’s naming convention for constants is to use all uppercase with underscores between words. The compiler is able to evaluate a limited set of operations at compile time, which lets us choose to write out this value in a way that’s easier to understand and verify, rather than setting this constant to the value 10,800. See the Rust Reference’s section on constant evaluation for more information on what operations can be used when declaring constants.

Constants are valid for the entire time a program runs, within the scope they were declared in. This property makes constants useful for values in your application domain that multiple parts of the program might need to know about, such as the maximum number of points any player of a game is allowed to earn or the speed of light.

Naming hardcoded values used throughout your program as constants is useful in conveying the meaning of that value to future maintainers of the code. It also helps to have only one place in your code you would need to change if the hardcoded value needed to be updated in the future.

Shadowing

As you saw in the guessing game tutorial in Chapter 2, you can declare a new variable with the same name as a previous variable. Rustaceans say that the first variable is shadowed by the second, which means that the second variable is what the compiler will see when you use the name of the variable. In effect, the second variable overshadows the first, taking any uses of the variable name to itself until either it itself is shadowed or the scope ends. We can shadow a variable by using the same variable’s name and repeating the use of the let keyword as follows:

Filename: src/main.rs

fn main() {
    let x = 5;

    let x = x + 1;

    {
        let x = x * 2;
        println!("The value of x in the inner scope is: {x}");
    }

    println!("The value of x is: {x}");
}

This program first binds x to a value of 5. Then it creates a new variable x by repeating let x =, taking the original value and adding 1 so the value of x is then 6. Then, within an inner scope created with the curly brackets, the third let statement also shadows x and creates a new variable, multiplying the previous value by 2 to give x a value of 12. When that scope is over, the inner shadowing ends and x returns to being 6. When we run this program, it will output the following:

$ cargo run
   Compiling variables v0.1.0 (file:///projects/variables)
    Finished dev [unoptimized + debuginfo] target(s) in 0.31s
     Running `target/debug/variables`
The value of x in the inner scope is: 12
The value of x is: 6

Shadowing is different from marking a variable as mut, because we’ll get a compile-time error if we accidentally try to reassign to this variable without using the let keyword. By using let, we can perform a few transformations on a value but have the variable be immutable after those transformations have been completed.

The other difference between mut and shadowing is that because we’re effectively creating a new variable when we use the let keyword again, we can change the type of the value but reuse the same name. For example, say our program asks a user to show how many spaces they want between some text by inputting space characters, and then we want to store that input as a number:

fn main() {
    let spaces = "   ";
    let spaces = spaces.len();
}

The first spaces variable is a string type and the second spaces variable is a number type. Shadowing thus spares us from having to come up with different names, such as spaces_str and spaces_num; instead, we can reuse the simpler spaces name. However, if we try to use mut for this, as shown here, we’ll get a compile-time error:

fn main() {
    let mut spaces = "   ";
    spaces = spaces.len();
}

The error says we’re not allowed to mutate a variable’s type:

$ cargo run
   Compiling variables v0.1.0 (file:///projects/variables)
error[E0308]: mismatched types
 --> src/main.rs:3:14
  |
2 |     let mut spaces = "   ";
  |                      ----- expected due to this value
3 |     spaces = spaces.len();
  |              ^^^^^^^^^^^^ expected `&str`, found `usize`

For more information about this error, try `rustc --explain E0308`.
error: could not compile `variables` due to previous error

Now that we’ve explored how variables work, let’s look at more data types they can have.

Data Types

Every value in Rust is of a certain data type, which tells Rust what kind of data is being specified so it knows how to work with that data. We’ll look at two data type subsets: scalar and compound.

Keep in mind that Rust is a statically typed language, which means that it must know the types of all variables at compile time. The compiler can usually infer what type we want to use based on the value and how we use it. In cases when many types are possible, such as when we converted a String to a numeric type using parse in the “Comparing the Guess to the Secret Number” section in Chapter 2, we must add a type annotation, like this:


#![allow(unused)]
fn main() {
let guess: u32 = "42".parse().expect("Not a number!");
}

If we don’t add the : u32 type annotation above, Rust will display the following error, which means the compiler needs more information from us to know which type we want to use:

$ cargo build
   Compiling no_type_annotations v0.1.0 (file:///projects/no_type_annotations)
error[E0282]: type annotations needed
 --> src/main.rs:2:9
  |
2 |     let guess = "42".parse().expect("Not a number!");
  |         ^^^^^ consider giving `guess` a type

For more information about this error, try `rustc --explain E0282`.
error: could not compile `no_type_annotations` due to previous error

You’ll see different type annotations for other data types.

Scalar Types

A scalar type represents a single value. Rust has four primary scalar types: integers, floating-point numbers, Booleans, and characters. You may recognize these from other programming languages. Let’s jump into how they work in Rust.

Integer Types

An integer is a number without a fractional component. We used one integer type in Chapter 2, the u32 type. This type declaration indicates that the value it’s associated with should be an unsigned integer (signed integer types start with i, instead of u) that takes up 32 bits of space. Table 3-1 shows the built-in integer types in Rust. We can use any of these variants to declare the type of an integer value.

Table 3-1: Integer Types in Rust

LengthSignedUnsigned
8-biti8u8
16-biti16u16
32-biti32u32
64-biti64u64
128-biti128u128
archisizeusize

Each variant can be either signed or unsigned and has an explicit size. Signed and unsigned refer to whether it’s possible for the number to be negative—in other words, whether the number needs to have a sign with it (signed) or whether it will only ever be positive and can therefore be represented without a sign (unsigned). It’s like writing numbers on paper: when the sign matters, a number is shown with a plus sign or a minus sign; however, when it’s safe to assume the number is positive, it’s shown with no sign. Signed numbers are stored using two’s complement representation.

Each signed variant can store numbers from -(2n - 1) to 2n - 1 - 1 inclusive, where n is the number of bits that variant uses. So an i8 can store numbers from -(27) to 27 - 1, which equals -128 to 127. Unsigned variants can store numbers from 0 to 2n - 1, so a u8 can store numbers from 0 to 28 - 1, which equals 0 to 255.

Additionally, the isize and usize types depend on the architecture of the computer your program is running on, which is denoted in the table as “arch”: 64 bits if you’re on a 64-bit architecture and 32 bits if you’re on a 32-bit architecture.

You can write integer literals in any of the forms shown in Table 3-2. Note that number literals that can be multiple numeric types allow a type suffix, such as 57u8, to designate the type. Number literals can also use _ as a visual separator to make the number easier to read, such as 1_000, which will have the same value as if you had specified 1000.

Table 3-2: Integer Literals in Rust

Number literalsExample
Decimal98_222
Hex0xff
Octal0o77
Binary0b1111_0000
Byte (u8 only)b'A'

So how do you know which type of integer to use? If you’re unsure, Rust’s defaults are generally good places to start: integer types default to i32. The primary situation in which you’d use isize or usize is when indexing some sort of collection.

Integer Overflow

Let’s say you have a variable of type u8 that can hold values between 0 and 255. If you try to change the variable to a value outside of that range, such as 256, integer overflow will occur, which can result in one of two behaviors. When you’re compiling in debug mode, Rust includes checks for integer overflow that cause your program to panic at runtime if this behavior occurs. Rust uses the term panicking when a program exits with an error; we’ll discuss panics in more depth in the “Unrecoverable Errors with panic! section in Chapter 9.

When you’re compiling in release mode with the --release flag, Rust does not include checks for integer overflow that cause panics. Instead, if overflow occurs, Rust performs two’s complement wrapping. In short, values greater than the maximum value the type can hold “wrap around” to the minimum of the values the type can hold. In the case of a u8, the value 256 becomes 0, the value 257 becomes 1, and so on. The program won’t panic, but the variable will have a value that probably isn’t what you were expecting it to have. Relying on integer overflow’s wrapping behavior is considered an error.

To explicitly handle the possibility of overflow, you can use these families of methods provided by the standard library for primitive numeric types:

  • Wrap in all modes with the wrapping_* methods, such as wrapping_add
  • Return the None value if there is overflow with the checked_* methods
  • Return the value and a boolean indicating whether there was overflow with the overflowing_* methods
  • Saturate at the value’s minimum or maximum values with saturating_* methods

Floating-Point Types

Rust also has two primitive types for floating-point numbers, which are numbers with decimal points. Rust’s floating-point types are f32 and f64, which are 32 bits and 64 bits in size, respectively. The default type is f64 because on modern CPUs it’s roughly the same speed as f32 but is capable of more precision. All floating-point types are signed.

Here’s an example that shows floating-point numbers in action:

Filename: src/main.rs

fn main() {
    let x = 2.0; // f64

    let y: f32 = 3.0; // f32
}

Floating-point numbers are represented according to the IEEE-754 standard. The f32 type is a single-precision float, and f64 has double precision.

Numeric Operations

Rust supports the basic mathematical operations you’d expect for all of the number types: addition, subtraction, multiplication, division, and remainder. Integer division rounds down to the nearest integer. The following code shows how you’d use each numeric operation in a let statement:

Filename: src/main.rs

fn main() {
    // addition
    let sum = 5 + 10;

    // subtraction
    let difference = 95.5 - 4.3;

    // multiplication
    let product = 4 * 30;

    // division
    let quotient = 56.7 / 32.2;
    let floored = 2 / 3; // Results in 0

    // remainder
    let remainder = 43 % 5;
}

Each expression in these statements uses a mathematical operator and evaluates to a single value, which is then bound to a variable. Appendix B contains a list of all operators that Rust provides.

The Boolean Type

As in most other programming languages, a Boolean type in Rust has two possible values: true and false. Booleans are one byte in size. The Boolean type in Rust is specified using bool. For example:

Filename: src/main.rs

fn main() {
    let t = true;

    let f: bool = false; // with explicit type annotation
}

The main way to use Boolean values is through conditionals, such as an if expression. We’ll cover how if expressions work in Rust in the “Control Flow” section.

The Character Type

Rust’s char type is the language’s most primitive alphabetic type. Here’s some examples of declaring char values:

Filename: src/main.rs

fn main() {
    let c = 'z';
    let z: char = 'ℤ'; // with explicit type annotation
    let heart_eyed_cat = '😻';
}

Note that we specify char literals with single quotes, as opposed to string literals, which use double quotes. Rust’s char type is four bytes in size and represents a Unicode Scalar Value, which means it can represent a lot more than just ASCII. Accented letters; Chinese, Japanese, and Korean characters; emoji; and zero-width spaces are all valid char values in Rust. Unicode Scalar Values range from U+0000 to U+D7FF and U+E000 to U+10FFFF inclusive. However, a “character” isn’t really a concept in Unicode, so your human intuition for what a “character” is may not match up with what a char is in Rust. We’ll discuss this topic in detail in “Storing UTF-8 Encoded Text with Strings” in Chapter 8.

Compound Types

Compound types can group multiple values into one type. Rust has two primitive compound types: tuples and arrays.

The Tuple Type

A tuple is a general way of grouping together a number of values with a variety of types into one compound type. Tuples have a fixed length: once declared, they cannot grow or shrink in size.

We create a tuple by writing a comma-separated list of values inside parentheses. Each position in the tuple has a type, and the types of the different values in the tuple don’t have to be the same. We’ve added optional type annotations in this example:

Filename: src/main.rs

fn main() {
    let tup: (i32, f64, u8) = (500, 6.4, 1);
}

The variable tup binds to the entire tuple, because a tuple is considered a single compound element. To get the individual values out of a tuple, we can use pattern matching to destructure a tuple value, like this:

Filename: src/main.rs

fn main() {
    let tup = (500, 6.4, 1);

    let (x, y, z) = tup;

    println!("The value of y is: {y}");
}

This program first creates a tuple and binds it to the variable tup. It then uses a pattern with let to take tup and turn it into three separate variables, x, y, and z. This is called destructuring, because it breaks the single tuple into three parts. Finally, the program prints the value of y, which is 6.4.

We can also access a tuple element directly by using a period (.) followed by the index of the value we want to access. For example:

Filename: src/main.rs

fn main() {
    let x: (i32, f64, u8) = (500, 6.4, 1);

    let five_hundred = x.0;

    let six_point_four = x.1;

    let one = x.2;
}

This program creates the tuple x and then accesses each element of the tuple using their respective indices. As with most programming languages, the first index in a tuple is 0.

The tuple without any values has a special name, unit. This value and its corresponding type are both written () and represent an empty value or an empty return type. Expressions implicitly return the unit value if they don’t return any other value.

The Array Type

Another way to have a collection of multiple values is with an array. Unlike a tuple, every element of an array must have the same type. Unlike arrays in some other languages, arrays in Rust have a fixed length.

We write the values in an array as a comma-separated list inside square brackets:

Filename: src/main.rs

fn main() {
    let a = [1, 2, 3, 4, 5];
}

Arrays are useful when you want your data allocated on the stack rather than the heap (we will discuss the stack and the heap more in Chapter 4) or when you want to ensure you always have a fixed number of elements. An array isn’t as flexible as the vector type, though. A vector is a similar collection type provided by the standard library that is allowed to grow or shrink in size. If you’re unsure whether to use an array or a vector, chances are you should use a vector. Chapter 8 discusses vectors in more detail.

However, arrays are more useful when you know the number of elements will not need to change. For example, if you were using the names of the month in a program, you would probably use an array rather than a vector because you know it will always contain 12 elements:


#![allow(unused)]
fn main() {
let months = ["January", "February", "March", "April", "May", "June", "July",
              "August", "September", "October", "November", "December"];
}

You write an array’s type using square brackets with the type of each element, a semicolon, and then the number of elements in the array, like so:


#![allow(unused)]
fn main() {
let a: [i32; 5] = [1, 2, 3, 4, 5];
}

Here, i32 is the type of each element. After the semicolon, the number 5 indicates the array contains five elements.

You can also initialize an array to contain the same value for each element by specifying the initial value, followed by a semicolon, and then the length of the array in square brackets, as shown here:


#![allow(unused)]
fn main() {
let a = [3; 5];
}

The array named a will contain 5 elements that will all be set to the value 3 initially. This is the same as writing let a = [3, 3, 3, 3, 3]; but in a more concise way.

Accessing Array Elements

An array is a single chunk of memory of a known, fixed size that can be allocated on the stack. You can access elements of an array using indexing, like this:

Filename: src/main.rs

fn main() {
    let a = [1, 2, 3, 4, 5];

    let first = a[0];
    let second = a[1];
}

In this example, the variable named first will get the value 1, because that is the value at index [0] in the array. The variable named second will get the value 2 from index [1] in the array.

Invalid Array Element Access

Let’s see what happens if you try to access an element of an array that is past the end of the array. Say you run this code, similar to the guessing game in Chapter 2, to get an array index from the user:

Filename: src/main.rs

use std::io;

fn main() {
    let a = [1, 2, 3, 4, 5];

    println!("Please enter an array index.");

    let mut index = String::new();

    io::stdin()
        .read_line(&mut index)
        .expect("Failed to read line");

    let index: usize = index
        .trim()
        .parse()
        .expect("Index entered was not a number");

    let element = a[index];

    println!("The value of the element at index {index} is: {element}");
}

This code compiles successfully. If you run this code using cargo run and enter 0, 1, 2, 3, or 4, the program will print out the corresponding value at that index in the array. If you instead enter a number past the end of the array, such as 10, you’ll see output like this:

thread 'main' panicked at 'index out of bounds: the len is 5 but the index is 10', src/main.rs:19:19
note: run with `RUST_BACKTRACE=1` environment variable to display a backtrace

The program resulted in a runtime error at the point of using an invalid value in the indexing operation. The program exited with an error message and didn’t execute the final println! statement. When you attempt to access an element using indexing, Rust will check that the index you’ve specified is less than the array length. If the index is greater than or equal to the length, Rust will panic. This check has to happen at runtime, especially in this case, because the compiler can’t possibly know what value a user will enter when they run the code later.

This is an example of Rust’s memory safety principles in action. In many low-level languages, this kind of check is not done, and when you provide an incorrect index, invalid memory can be accessed. Rust protects you against this kind of error by immediately exiting instead of allowing the memory access and continuing. Chapter 9 discusses more of Rust’s error handling and how you can write readable, safe code that neither panics nor allows invalid memory access.

Functions

Functions are prevalent in Rust code. You’ve already seen one of the most important functions in the language: the main function, which is the entry point of many programs. You’ve also seen the fn keyword, which allows you to declare new functions.

Rust code uses snake case as the conventional style for function and variable names, in which all letters are lowercase and underscores separate words. Here’s a program that contains an example function definition:

Filename: src/main.rs

fn main() {
    println!("Hello, world!");

    another_function();
}

fn another_function() {
    println!("Another function.");
}

We define a function in Rust by entering fn followed by a function name and a set of parentheses. The curly brackets tell the compiler where the function body begins and ends.

We can call any function we’ve defined by entering its name followed by a set of parentheses. Because another_function is defined in the program, it can be called from inside the main function. Note that we defined another_function after the main function in the source code; we could have defined it before as well. Rust doesn’t care where you define your functions, only that they’re defined somewhere in a scope that can be seen by the caller.

Let’s start a new binary project named functions to explore functions further. Place the another_function example in src/main.rs and run it. You should see the following output:

$ cargo run
   Compiling functions v0.1.0 (file:///projects/functions)
    Finished dev [unoptimized + debuginfo] target(s) in 0.28s
     Running `target/debug/functions`
Hello, world!
Another function.

The lines execute in the order in which they appear in the main function. First, the “Hello, world!” message prints, and then another_function is called and its message is printed.

Parameters

We can define functions to have parameters, which are special variables that are part of a function’s signature. When a function has parameters, you can provide it with concrete values for those parameters. Technically, the concrete values are called arguments, but in casual conversation, people tend to use the words parameter and argument interchangeably for either the variables in a function’s definition or the concrete values passed in when you call a function.

In this version of another_function we add a parameter:

Filename: src/main.rs

fn main() {
    another_function(5);
}

fn another_function(x: i32) {
    println!("The value of x is: {x}");
}

Try running this program; you should get the following output:

$ cargo run
   Compiling functions v0.1.0 (file:///projects/functions)
    Finished dev [unoptimized + debuginfo] target(s) in 1.21s
     Running `target/debug/functions`
The value of x is: 5

The declaration of another_function has one parameter named x. The type of x is specified as i32. When we pass 5 in to another_function, the println! macro puts 5 where the pair of curly brackets containing x was in the format string.

In function signatures, you must declare the type of each parameter. This is a deliberate decision in Rust’s design: requiring type annotations in function definitions means the compiler almost never needs you to use them elsewhere in the code to figure out what type you mean. The compiler is also able to give more helpful error messages if it knows what types the function expects.

When defining multiple parameters, separate the parameter declarations with commas, like this:

Filename: src/main.rs

fn main() {
    print_labeled_measurement(5, 'h');
}

fn print_labeled_measurement(value: i32, unit_label: char) {
    println!("The measurement is: {value}{unit_label}");
}

This example creates a function named print_labeled_measurement with two parameters. The first parameter is named value and is an i32. The second is named unit_label and is type char. The function then prints text containing both the value and the unit_label.

Let’s try running this code. Replace the program currently in your functions project’s src/main.rs file with the preceding example and run it using cargo run:

$ cargo run
   Compiling functions v0.1.0 (file:///projects/functions)
    Finished dev [unoptimized + debuginfo] target(s) in 0.31s
     Running `target/debug/functions`
The measurement is: 5h

Because we called the function with 5 as the value for value and 'h' as the value for unit_label, the program output contains those values.

Statements and Expressions

Function bodies are made up of a series of statements optionally ending in an expression. So far, the functions we’ve covered haven’t included an ending expression, but you have seen an expression as part of a statement. Because Rust is an expression-based language, this is an important distinction to understand. Other languages don’t have the same distinctions, so let’s look at what statements and expressions are and how their differences affect the bodies of functions.

Statements are instructions that perform some action and do not return a value. Expressions evaluate to a resulting value. Let’s look at some examples.

We’ve actually already used statements and expressions. Creating a variable and assigning a value to it with the let keyword is a statement. In Listing 3-1, let y = 6; is a statement.

Filename: src/main.rs

fn main() {
    let y = 6;
}

Listing 3-1: A main function declaration containing one statement

Function definitions are also statements; the entire preceding example is a statement in itself.

Statements do not return values. Therefore, you can’t assign a let statement to another variable, as the following code tries to do; you’ll get an error:

Filename: src/main.rs

fn main() {
    let x = (let y = 6);
}

When you run this program, the error you’ll get looks like this:

$ cargo run
   Compiling functions v0.1.0 (file:///projects/functions)
error: expected expression, found statement (`let`)
 --> src/main.rs:2:14
  |
2 |     let x = (let y = 6);
  |              ^^^^^^^^^
  |
  = note: variable declaration using `let` is a statement

error[E0658]: `let` expressions in this position are unstable
 --> src/main.rs:2:14
  |
2 |     let x = (let y = 6);
  |              ^^^^^^^^^
  |
  = note: see issue #53667 <https://github.com/rust-lang/rust/issues/53667> for more information

warning: unnecessary parentheses around assigned value
 --> src/main.rs:2:13
  |
2 |     let x = (let y = 6);
  |             ^         ^
  |
  = note: `#[warn(unused_parens)]` on by default
help: remove these parentheses
  |
2 -     let x = (let y = 6);
2 +     let x = let y = 6;
  | 

For more information about this error, try `rustc --explain E0658`.
warning: `functions` (bin "functions") generated 1 warning
error: could not compile `functions` due to 2 previous errors; 1 warning emitted

The let y = 6 statement does not return a value, so there isn’t anything for x to bind to. This is different from what happens in other languages, such as C and Ruby, where the assignment returns the value of the assignment. In those languages, you can write x = y = 6 and have both x and y have the value 6; that is not the case in Rust.

Expressions evaluate to a value and make up most of the rest of the code that you’ll write in Rust. Consider a math operation, such as 5 + 6, which is an expression that evaluates to the value 11. Expressions can be part of statements: in Listing 3-1, the 6 in the statement let y = 6; is an expression that evaluates to the value 6. Calling a function is an expression. Calling a macro is an expression. A new scope block created with curly brackets is an expression, for example:

Filename: src/main.rs

fn main() {
    let y = {
        let x = 3;
        x + 1
    };

    println!("The value of y is: {y}");
}

This expression:

{
    let x = 3;
    x + 1
}

is a block that, in this case, evaluates to 4. That value gets bound to y as part of the let statement. Note that the x + 1 line doesn’t have a semicolon at the end, unlike most of the lines you’ve seen so far. Expressions do not include ending semicolons. If you add a semicolon to the end of an expression, you turn it into a statement, and it will then not return a value. Keep this in mind as you explore function return values and expressions next.

Functions with Return Values

Functions can return values to the code that calls them. We don’t name return values, but we must declare their type after an arrow (->). In Rust, the return value of the function is synonymous with the value of the final expression in the block of the body of a function. You can return early from a function by using the return keyword and specifying a value, but most functions return the last expression implicitly. Here’s an example of a function that returns a value:

Filename: src/main.rs

fn five() -> i32 {
    5
}

fn main() {
    let x = five();

    println!("The value of x is: {x}");
}

There are no function calls, macros, or even let statements in the five function—just the number 5 by itself. That’s a perfectly valid function in Rust. Note that the function’s return type is specified too, as -> i32. Try running this code; the output should look like this:

$ cargo run
   Compiling functions v0.1.0 (file:///projects/functions)
    Finished dev [unoptimized + debuginfo] target(s) in 0.30s
     Running `target/debug/functions`
The value of x is: 5

The 5 in five is the function’s return value, which is why the return type is i32. Let’s examine this in more detail. There are two important bits: first, the line let x = five(); shows that we’re using the return value of a function to initialize a variable. Because the function five returns a 5, that line is the same as the following:


#![allow(unused)]
fn main() {
let x = 5;
}

Second, the five function has no parameters and defines the type of the return value, but the body of the function is a lonely 5 with no semicolon because it’s an expression whose value we want to return.

Let’s look at another example:

Filename: src/main.rs

fn main() {
    let x = plus_one(5);

    println!("The value of x is: {x}");
}

fn plus_one(x: i32) -> i32 {
    x + 1
}

Running this code will print The value of x is: 6. But if we place a semicolon at the end of the line containing x + 1, changing it from an expression to a statement, we’ll get an error.

Filename: src/main.rs

fn main() {
    let x = plus_one(5);

    println!("The value of x is: {x}");
}

fn plus_one(x: i32) -> i32 {
    x + 1;
}

Compiling this code produces an error, as follows:

$ cargo run
   Compiling functions v0.1.0 (file:///projects/functions)
error[E0308]: mismatched types
 --> src/main.rs:7:24
  |
7 | fn plus_one(x: i32) -> i32 {
  |    --------            ^^^ expected `i32`, found `()`
  |    |
  |    implicitly returns `()` as its body has no tail or `return` expression
8 |     x + 1;
  |          - help: consider removing this semicolon

For more information about this error, try `rustc --explain E0308`.
error: could not compile `functions` due to previous error

The main error message, “mismatched types,” reveals the core issue with this code. The definition of the function plus_one says that it will return an i32, but statements don’t evaluate to a value, which is expressed by (), the unit type. Therefore, nothing is returned, which contradicts the function definition and results in an error. In this output, Rust provides a message to possibly help rectify this issue: it suggests removing the semicolon, which would fix the error.

Comments

All programmers strive to make their code easy to understand, but sometimes extra explanation is warranted. In these cases, programmers leave comments in their source code that the compiler will ignore but people reading the source code may find useful.

Here’s a simple comment:


#![allow(unused)]
fn main() {
// hello, world
}

In Rust, the idiomatic comment style starts a comment with two slashes, and the comment continues until the end of the line. For comments that extend beyond a single line, you’ll need to include // on each line, like this:


#![allow(unused)]
fn main() {
// So we’re doing something complicated here, long enough that we need
// multiple lines of comments to do it! Whew! Hopefully, this comment will
// explain what’s going on.
}

Comments can also be placed at the end of lines containing code:

Filename: src/main.rs

fn main() {
    let lucky_number = 7; // I’m feeling lucky today
}

But you’ll more often see them used in this format, with the comment on a separate line above the code it’s annotating:

Filename: src/main.rs

fn main() {
    // I’m feeling lucky today
    let lucky_number = 7;
}

Rust also has another kind of comment, documentation comments, which we’ll discuss in the “Publishing a Crate to Crates.io” section of Chapter 14.

Control Flow

The ability to run some code depending on if a condition is true, or run some code repeatedly while a condition is true, are basic building blocks in most programming languages. The most common constructs that let you control the flow of execution of Rust code are if expressions and loops.

if Expressions

An if expression allows you to branch your code depending on conditions. You provide a condition and then state, “If this condition is met, run this block of code. If the condition is not met, do not run this block of code.”

Create a new project called branches in your projects directory to explore the if expression. In the src/main.rs file, input the following:

Filename: src/main.rs

fn main() {
    let number = 3;

    if number < 5 {
        println!("condition was true");
    } else {
        println!("condition was false");
    }
}

All if expressions start with the keyword if, followed by a condition. In this case, the condition checks whether or not the variable number has a value less than 5. We place the block of code to execute if the condition is true immediately after the condition inside curly brackets. Blocks of code associated with the conditions in if expressions are sometimes called arms, just like the arms in match expressions that we discussed in the “Comparing the Guess to the Secret Number” section of Chapter 2.

Optionally, we can also include an else expression, which we chose to do here, to give the program an alternative block of code to execute should the condition evaluate to false. If you don’t provide an else expression and the condition is false, the program will just skip the if block and move on to the next bit of code.

Try running this code; you should see the following output:

$ cargo run
   Compiling branches v0.1.0 (file:///projects/branches)
    Finished dev [unoptimized + debuginfo] target(s) in 0.31s
     Running `target/debug/branches`
condition was true

Let’s try changing the value of number to a value that makes the condition false to see what happens:

fn main() {
    let number = 7;

    if number < 5 {
        println!("condition was true");
    } else {
        println!("condition was false");
    }
}

Run the program again, and look at the output:

$ cargo run
   Compiling branches v0.1.0 (file:///projects/branches)
    Finished dev [unoptimized + debuginfo] target(s) in 0.31s
     Running `target/debug/branches`
condition was false

It’s also worth noting that the condition in this code must be a bool. If the condition isn’t a bool, we’ll get an error. For example, try running the following code:

Filename: src/main.rs

fn main() {
    let number = 3;

    if number {
        println!("number was three");
    }
}

The if condition evaluates to a value of 3 this time, and Rust throws an error:

$ cargo run
   Compiling branches v0.1.0 (file:///projects/branches)
error[E0308]: mismatched types
 --> src/main.rs:4:8
  |
4 |     if number {
  |        ^^^^^^ expected `bool`, found integer

For more information about this error, try `rustc --explain E0308`.
error: could not compile `branches` due to previous error

The error indicates that Rust expected a bool but got an integer. Unlike languages such as Ruby and JavaScript, Rust will not automatically try to convert non-Boolean types to a Boolean. You must be explicit and always provide if with a Boolean as its condition. If we want the if code block to run only when a number is not equal to 0, for example, we can change the if expression to the following:

Filename: src/main.rs

fn main() {
    let number = 3;

    if number != 0 {
        println!("number was something other than zero");
    }
}

Running this code will print number was something other than zero.

Handling Multiple Conditions with else if

You can use multiple conditions by combining if and else in an else if expression. For example:

Filename: src/main.rs

fn main() {
    let number = 6;

    if number % 4 == 0 {
        println!("number is divisible by 4");
    } else if number % 3 == 0 {
        println!("number is divisible by 3");
    } else if number % 2 == 0 {
        println!("number is divisible by 2");
    } else {
        println!("number is not divisible by 4, 3, or 2");
    }
}

This program has four possible paths it can take. After running it, you should see the following output:

$ cargo run
   Compiling branches v0.1.0 (file:///projects/branches)
    Finished dev [unoptimized + debuginfo] target(s) in 0.31s
     Running `target/debug/branches`
number is divisible by 3

When this program executes, it checks each if expression in turn and executes the first body for which the condition holds true. Note that even though 6 is divisible by 2, we don’t see the output number is divisible by 2, nor do we see the number is not divisible by 4, 3, or 2 text from the else block. That’s because Rust only executes the block for the first true condition, and once it finds one, it doesn’t even check the rest.

Using too many else if expressions can clutter your code, so if you have more than one, you might want to refactor your code. Chapter 6 describes a powerful Rust branching construct called match for these cases.

Using if in a let Statement

Because if is an expression, we can use it on the right side of a let statement to assign the outcome to a variable, as in Listing 3-2.

Filename: src/main.rs

fn main() {
    let condition = true;
    let number = if condition { 5 } else { 6 };

    println!("The value of number is: {number}");
}

Listing 3-2: Assigning the result of an if expression to a variable

The number variable will be bound to a value based on the outcome of the if expression. Run this code to see what happens:

$ cargo run
   Compiling branches v0.1.0 (file:///projects/branches)
    Finished dev [unoptimized + debuginfo] target(s) in 0.30s
     Running `target/debug/branches`
The value of number is: 5

Remember that blocks of code evaluate to the last expression in them, and numbers by themselves are also expressions. In this case, the value of the whole if expression depends on which block of code executes. This means the values that have the potential to be results from each arm of the if must be the same type; in Listing 3-2, the results of both the if arm and the else arm were i32 integers. If the types are mismatched, as in the following example, we’ll get an error:

Filename: src/main.rs

fn main() {
    let condition = true;

    let number = if condition { 5 } else { "six" };

    println!("The value of number is: {number}");
}

When we try to compile this code, we’ll get an error. The if and else arms have value types that are incompatible, and Rust indicates exactly where to find the problem in the program:

$ cargo run
   Compiling branches v0.1.0 (file:///projects/branches)
error[E0308]: `if` and `else` have incompatible types
 --> src/main.rs:4:44
  |
4 |     let number = if condition { 5 } else { "six" };
  |                                 -          ^^^^^ expected integer, found `&str`
  |                                 |
  |                                 expected because of this

For more information about this error, try `rustc --explain E0308`.
error: could not compile `branches` due to previous error

The expression in the if block evaluates to an integer, and the expression in the else block evaluates to a string. This won’t work because variables must have a single type, and Rust needs to know at compile time what type the number variable is, definitively. Knowing the type of number lets the compiler verify the type is valid everywhere we use number. Rust wouldn’t be able to do that if the type of number was only determined at runtime; the compiler would be more complex and would make fewer guarantees about the code if it had to keep track of multiple hypothetical types for any variable.

Repetition with Loops

It’s often useful to execute a block of code more than once. For this task, Rust provides several loops, which will run through the code inside the loop body to the end and then start immediately back at the beginning. To experiment with loops, let’s make a new project called loops.

Rust has three kinds of loops: loop, while, and for. Let’s try each one.

Repeating Code with loop

The loop keyword tells Rust to execute a block of code over and over again forever or until you explicitly tell it to stop.

As an example, change the src/main.rs file in your loops directory to look like this:

Filename: src/main.rs

fn main() {
    loop {
        println!("again!");
    }
}

When we run this program, we’ll see again! printed over and over continuously until we stop the program manually. Most terminals support the keyboard shortcut ctrl-c to interrupt a program that is stuck in a continual loop. Give it a try:

$ cargo run
   Compiling loops v0.1.0 (file:///projects/loops)
    Finished dev [unoptimized + debuginfo] target(s) in 0.29s
     Running `target/debug/loops`
again!
again!
again!
again!
^Cagain!

The symbol ^C represents where you pressed ctrl-c . You may or may not see the word again! printed after the ^C, depending on where the code was in the loop when it received the interrupt signal.

Fortunately, Rust also provides a way to break out of a loop using code. You can place the break keyword within the loop to tell the program when to stop executing the loop. Recall that we did this in the guessing game in the “Quitting After a Correct Guess” section of Chapter 2 to exit the program when the user won the game by guessing the correct number.

We also used continue in the guessing game, which in a loop tells the program to skip over any remaining code in this iteration of the loop and go to the next iteration.

Returning Values from Loops

One of the uses of a loop is to retry an operation you know might fail, such as checking whether a thread has completed its job. You might also need to pass the result of that operation out of the loop to the rest of your code. To do this, you can add the value you want returned after the break expression you use to stop the loop; that value will be returned out of the loop so you can use it, as shown here:

fn main() {
    let mut counter = 0;

    let result = loop {
        counter += 1;

        if counter == 10 {
            break counter * 2;
        }
    };

    println!("The result is {result}");
}

Before the loop, we declare a variable named counter and initialize it to 0. Then we declare a variable named result to hold the value returned from the loop. On every iteration of the loop, we add 1 to the counter variable, and then check whether the counter is equal to 10. When it is, we use the break keyword with the value counter * 2. After the loop, we use a semicolon to end the statement that assigns the value to result. Finally, we print the value in result, which in this case is 20.

Loop Labels to Disambiguate Between Multiple Loops

If you have loops within loops, break and continue apply to the innermost loop at that point. You can optionally specify a loop label on a loop that we can then use with break or continue to specify that those keywords apply to the labeled loop instead of the innermost loop. Loop labels must begin with a single quote. Here’s an example with two nested loops:

fn main() {
    let mut count = 0;
    'counting_up: loop {
        println!("count = {count}");
        let mut remaining = 10;

        loop {
            println!("remaining = {remaining}");
            if remaining == 9 {
                break;
            }
            if count == 2 {
                break 'counting_up;
            }
            remaining -= 1;
        }

        count += 1;
    }
    println!("End count = {count}");
}

The outer loop has the label 'counting_up, and it will count up from 0 to 2. The inner loop without a label counts down from 10 to 9. The first break that doesn’t specify a label will exit the inner loop only. The break 'counting_up; statement will exit the outer loop. This code prints:

$ cargo run
   Compiling loops v0.1.0 (file:///projects/loops)
    Finished dev [unoptimized + debuginfo] target(s) in 0.58s
     Running `target/debug/loops`
count = 0
remaining = 10
remaining = 9
count = 1
remaining = 10
remaining = 9
count = 2
remaining = 10
End count = 2

Conditional Loops with while

A program will often need to evaluate a condition within a loop. While the condition is true, the loop runs. When the condition ceases to be true, the program calls break, stopping the loop. It’s possible to implement behavior like this using a combination of loop, if, else, and break; you could try that now in a program, if you’d like. However, this pattern is so common that Rust has a built-in language construct for it, called a while loop. In Listing 3-3, we use while to loop the program three times, counting down each time, and then, after the loop, print a message and exit.

Filename: src/main.rs

fn main() {
    let mut number = 3;

    while number != 0 {
        println!("{number}!");

        number -= 1;
    }

    println!("LIFTOFF!!!");
}

Listing 3-3: Using a while loop to run code while a condition holds true

This construct eliminates a lot of nesting that would be necessary if you used loop, if, else, and break, and it’s clearer. While a condition holds true, the code runs; otherwise, it exits the loop.

Looping Through a Collection with for

You can choose to use the while construct to loop over the elements of a collection, such as an array. For example, the loop in Listing 3-4 prints each element in the array a.

Filename: src/main.rs

fn main() {
    let a = [10, 20, 30, 40, 50];
    let mut index = 0;

    while index < 5 {
        println!("the value is: {}", a[index]);

        index += 1;
    }
}

Listing 3-4: Looping through each element of a collection using a while loop

Here, the code counts up through the elements in the array. It starts at index 0, and then loops until it reaches the final index in the array (that is, when index < 5 is no longer true). Running this code will print every element in the array:

$ cargo run
   Compiling loops v0.1.0 (file:///projects/loops)
    Finished dev [unoptimized + debuginfo] target(s) in 0.32s
     Running `target/debug/loops`
the value is: 10
the value is: 20
the value is: 30
the value is: 40
the value is: 50

All five array values appear in the terminal, as expected. Even though index will reach a value of 5 at some point, the loop stops executing before trying to fetch a sixth value from the array.

However, this approach is error prone; we could cause the program to panic if the index value or test condition are incorrect. For example, if you changed the definition of the a array to have four elements but forgot to update the condition to while index < 4, the code would panic. It’s also slow, because the compiler adds runtime code to perform the conditional check of whether the index is within the bounds of the array on every iteration through the loop.

As a more concise alternative, you can use a for loop and execute some code for each item in a collection. A for loop looks like the code in Listing 3-5.

Filename: src/main.rs

fn main() {
    let a = [10, 20, 30, 40, 50];

    for element in a {
        println!("the value is: {element}");
    }
}

Listing 3-5: Looping through each element of a collection using a for loop

When we run this code, we’ll see the same output as in Listing 3-4. More importantly, we’ve now increased the safety of the code and eliminated the chance of bugs that might result from going beyond the end of the array or not going far enough and missing some items.

Using the for loop, you wouldn’t need to remember to change any other code if you changed the number of values in the array, as you would with the method used in Listing 3-4.

The safety and conciseness of for loops make them the most commonly used loop construct in Rust. Even in situations in which you want to run some code a certain number of times, as in the countdown example that used a while loop in Listing 3-3, most Rustaceans would use a for loop. The way to do that would be to use a Range, provided by the standard library, which generates all numbers in sequence starting from one number and ending before another number.

Here’s what the countdown would look like using a for loop and another method we’ve not yet talked about, rev, to reverse the range:

Filename: src/main.rs

fn main() {
    for number in (1..4).rev() {
        println!("{number}!");
    }
    println!("LIFTOFF!!!");
}

This code is a bit nicer, isn’t it?

Summary

You made it! That was a sizable chapter: you learned about variables, scalar and compound data types, functions, comments, if expressions, and loops! To practice with the concepts discussed in this chapter, try building programs to do the following:

  • Convert temperatures between Fahrenheit and Celsius.
  • Generate the nth Fibonacci number.
  • Print the lyrics to the Christmas carol “The Twelve Days of Christmas,” taking advantage of the repetition in the song.

When you’re ready to move on, we’ll talk about a concept in Rust that doesn’t commonly exist in other programming languages: ownership.

Understanding Ownership

Ownership is Rust’s most unique feature and has deep implications for the rest of the language. It enables Rust to make memory safety guarantees without needing a garbage collector, so it’s important to understand how ownership works. In this chapter, we’ll talk about ownership as well as several related features: borrowing, slices, and how Rust lays data out in memory.

What Is Ownership?

Ownership is a set of rules that governs how a Rust program manages memory. All programs have to manage the way they use a computer’s memory while running. Some languages have garbage collection that regularly looks for no-longer used memory as the program runs; in other languages, the programmer must explicitly allocate and free the memory. Rust uses a third approach: memory is managed through a system of ownership with a set of rules that the compiler checks. If any of the rules are violated, the program won’t compile. None of the features of ownership will slow down your program while it’s running.

Because ownership is a new concept for many programmers, it does take some time to get used to. The good news is that the more experienced you become with Rust and the rules of the ownership system, the easier you’ll find it to naturally develop code that is safe and efficient. Keep at it!

When you understand ownership, you’ll have a solid foundation for understanding the features that make Rust unique. In this chapter, you’ll learn ownership by working through some examples that focus on a very common data structure: strings.

The Stack and the Heap

Many programming languages don’t require you to think about the stack and the heap very often. But in a systems programming language like Rust, whether a value is on the stack or the heap affects how the language behaves and why you have to make certain decisions. Parts of ownership will be described in relation to the stack and the heap later in this chapter, so here is a brief explanation in preparation.

Both the stack and the heap are parts of memory available to your code to use at runtime, but they are structured in different ways. The stack stores values in the order it gets them and removes the values in the opposite order. This is referred to as last in, first out. Think of a stack of plates: when you add more plates, you put them on top of the pile, and when you need a plate, you take one off the top. Adding or removing plates from the middle or bottom wouldn’t work as well! Adding data is called pushing onto the stack, and removing data is called popping off the stack. All data stored on the stack must have a known, fixed size. Data with an unknown size at compile time or a size that might change must be stored on the heap instead.

The heap is less organized: when you put data on the heap, you request a certain amount of space. The memory allocator finds an empty spot in the heap that is big enough, marks it as being in use, and returns a pointer, which is the address of that location. This process is called allocating on the heap and is sometimes abbreviated as just allocating (pushing values onto the stack is not considered allocating). Because the pointer to the heap is a known, fixed size, you can store the pointer on the stack, but when you want the actual data, you must follow the pointer. Think of being seated at a restaurant. When you enter, you state the number of people in your group, and the staff finds an empty table that fits everyone and leads you there. If someone in your group comes late, they can ask where you’ve been seated to find you.

Pushing to the stack is faster than allocating on the heap because the allocator never has to search for a place to store new data; that location is always at the top of the stack. Comparatively, allocating space on the heap requires more work, because the allocator must first find a big enough space to hold the data and then perform bookkeeping to prepare for the next allocation.

Accessing data in the heap is slower than accessing data on the stack because you have to follow a pointer to get there. Contemporary processors are faster if they jump around less in memory. Continuing the analogy, consider a server at a restaurant taking orders from many tables. It’s most efficient to get all the orders at one table before moving on to the next table. Taking an order from table A, then an order from table B, then one from A again, and then one from B again would be a much slower process. By the same token, a processor can do its job better if it works on data that’s close to other data (as it is on the stack) rather than farther away (as it can be on the heap).

When your code calls a function, the values passed into the function (including, potentially, pointers to data on the heap) and the function’s local variables get pushed onto the stack. When the function is over, those values get popped off the stack.

Keeping track of what parts of code are using what data on the heap, minimizing the amount of duplicate data on the heap, and cleaning up unused data on the heap so you don’t run out of space are all problems that ownership addresses. Once you understand ownership, you won’t need to think about the stack and the heap very often, but knowing that the main purpose of ownership is to manage heap data can help explain why it works the way it does.

Ownership Rules

First, let’s take a look at the ownership rules. Keep these rules in mind as we work through the examples that illustrate them:

  • Each value in Rust has an owner.
  • There can only be one owner at a time.
  • When the owner goes out of scope, the value will be dropped.

Variable Scope

Now that we’re past basic Rust syntax, we won’t include all the fn main() { code in examples, so if you’re following along, make sure to put the following examples inside a main function manually. As a result, our examples will be a bit more concise, letting us focus on the actual details rather than boilerplate code.

As a first example of ownership, we’ll look at the scope of some variables. A scope is the range within a program for which an item is valid. Take the following variable:


#![allow(unused)]
fn main() {
let s = "hello";
}

The variable s refers to a string literal, where the value of the string is hardcoded into the text of our program. The variable is valid from the point at which it’s declared until the end of the current scope. Listing 4-1 shows a program with comments annotating where the variable s would be valid.

fn main() {
    {                      // s is not valid here, it’s not yet declared
        let s = "hello";   // s is valid from this point forward

        // do stuff with s
    }                      // this scope is now over, and s is no longer valid
}

Listing 4-1: A variable and the scope in which it is valid

In other words, there are two important points in time here:

  • When s comes into scope, it is valid.
  • It remains valid until it goes out of scope.

At this point, the relationship between scopes and when variables are valid is similar to that in other programming languages. Now we’ll build on top of this understanding by introducing the String type.

The String Type

To illustrate the rules of ownership, we need a data type that is more complex than those we covered in the “Data Types” section of Chapter 3. The types covered previously are all a known size, can be stored on the stack and popped off the stack when their scope is over, and can be quickly and trivially copied to make a new, independent instance if another part of code needs to use the same value in a different scope. But we want to look at data that is stored on the heap and explore how Rust knows when to clean up that data, and the String type is a great example.

We’ll concentrate on the parts of String that relate to ownership. These aspects also apply to other complex data types, whether they are provided by the standard library or created by you. We’ll discuss String in more depth in Chapter 8.

We’ve already seen string literals, where a string value is hardcoded into our program. String literals are convenient, but they aren’t suitable for every situation in which we may want to use text. One reason is that they’re immutable. Another is that not every string value can be known when we write our code: for example, what if we want to take user input and store it? For these situations, Rust has a second string type, String. This type manages data allocated on the heap and as such is able to store an amount of text that is unknown to us at compile time. You can create a String from a string literal using the from function, like so:


#![allow(unused)]
fn main() {
let s = String::from("hello");
}

The double colon :: operator allows us to namespace this particular from function under the String type rather than using some sort of name like string_from. We’ll discuss this syntax more in the “Method Syntax” section of Chapter 5 and when we talk about namespacing with modules in “Paths for Referring to an Item in the Module Tree” in Chapter 7.

This kind of string can be mutated:

fn main() {
    let mut s = String::from("hello");

    s.push_str(", world!"); // push_str() appends a literal to a String

    println!("{}", s); // This will print `hello, world!`
}

So, what’s the difference here? Why can String be mutated but literals cannot? The difference is how these two types deal with memory.

Memory and Allocation

In the case of a string literal, we know the contents at compile time, so the text is hardcoded directly into the final executable. This is why string literals are fast and efficient. But these properties only come from the string literal’s immutability. Unfortunately, we can’t put a blob of memory into the binary for each piece of text whose size is unknown at compile time and whose size might change while running the program.

With the String type, in order to support a mutable, growable piece of text, we need to allocate an amount of memory on the heap, unknown at compile time, to hold the contents. This means:

  • The memory must be requested from the memory allocator at runtime.
  • We need a way of returning this memory to the allocator when we’re done with our String.

That first part is done by us: when we call String::from, its implementation requests the memory it needs. This is pretty much universal in programming languages.

However, the second part is different. In languages with a garbage collector (GC), the GC keeps track of and cleans up memory that isn’t being used anymore, and we don’t need to think about it. In most languages without a GC, it’s our responsibility to identify when memory is no longer being used and call code to explicitly free it, just as we did to request it. Doing this correctly has historically been a difficult programming problem. If we forget, we’ll waste memory. If we do it too early, we’ll have an invalid variable. If we do it twice, that’s a bug too. We need to pair exactly one allocate with exactly one free.

Rust takes a different path: the memory is automatically returned once the variable that owns it goes out of scope. Here’s a version of our scope example from Listing 4-1 using a String instead of a string literal:

fn main() {
    {
        let s = String::from("hello"); // s is valid from this point forward

        // do stuff with s
    }                                  // this scope is now over, and s is no
                                       // longer valid
}

There is a natural point at which we can return the memory our String needs to the allocator: when s goes out of scope. When a variable goes out of scope, Rust calls a special function for us. This function is called drop, and it’s where the author of String can put the code to return the memory. Rust calls drop automatically at the closing curly bracket.

Note: In C++, this pattern of deallocating resources at the end of an item’s lifetime is sometimes called Resource Acquisition Is Initialization (RAII). The drop function in Rust will be familiar to you if you’ve used RAII patterns.

This pattern has a profound impact on the way Rust code is written. It may seem simple right now, but the behavior of code can be unexpected in more complicated situations when we want to have multiple variables use the data we’ve allocated on the heap. Let’s explore some of those situations now.

Ways Variables and Data Interact: Move

Multiple variables can interact with the same data in different ways in Rust. Let’s look at an example using an integer in Listing 4-2.

fn main() {
    let x = 5;
    let y = x;
}

Listing 4-2: Assigning the integer value of variable x to y

We can probably guess what this is doing: “bind the value 5 to x; then make a copy of the value in x and bind it to y.” We now have two variables, x and y, and both equal 5. This is indeed what is happening, because integers are simple values with a known, fixed size, and these two 5 values are pushed onto the stack.

Now let’s look at the String version:

fn main() {
    let s1 = String::from("hello");
    let s2 = s1;
}

This looks very similar, so we might assume that the way it works would be the same: that is, the second line would make a copy of the value in s1 and bind it to s2. But this isn’t quite what happens.

Take a look at Figure 4-1 to see what is happening to String under the covers. A String is made up of three parts, shown on the left: a pointer to the memory that holds the contents of the string, a length, and a capacity. This group of data is stored on the stack. On the right is the memory on the heap that holds the contents.

String in memory

Figure 4-1: Representation in memory of a String holding the value "hello" bound to s1

The length is how much memory, in bytes, the contents of the String is currently using. The capacity is the total amount of memory, in bytes, that the String has received from the allocator. The difference between length and capacity matters, but not in this context, so for now, it’s fine to ignore the capacity.

When we assign s1 to s2, the String data is copied, meaning we copy the pointer, the length, and the capacity that are on the stack. We do not copy the data on the heap that the pointer refers to. In other words, the data representation in memory looks like Figure 4-2.

s1 and s2 pointing to the same value

Figure 4-2: Representation in memory of the variable s2 that has a copy of the pointer, length, and capacity of s1

The representation does not look like Figure 4-3, which is what memory would look like if Rust instead copied the heap data as well. If Rust did this, the operation s2 = s1 could be very expensive in terms of runtime performance if the data on the heap were large.

s1 and s2 to two places

Figure 4-3: Another possibility for what s2 = s1 might do if Rust copied the heap data as well

Earlier, we said that when a variable goes out of scope, Rust automatically calls the drop function and cleans up the heap memory for that variable. But Figure 4-2 shows both data pointers pointing to the same location. This is a problem: when s2 and s1 go out of scope, they will both try to free the same memory. This is known as a double free error and is one of the memory safety bugs we mentioned previously. Freeing memory twice can lead to memory corruption, which can potentially lead to security vulnerabilities.

To ensure memory safety, after the line let s2 = s1, Rust considers s1 as no longer valid. Therefore, Rust doesn’t need to free anything when s1 goes out of scope. Check out what happens when you try to use s1 after s2 is created; it won’t work:

fn main() {
    let s1 = String::from("hello");
    let s2 = s1;

    println!("{}, world!", s1);
}

You’ll get an error like this because Rust prevents you from using the invalidated reference:

$ cargo run
   Compiling ownership v0.1.0 (file:///projects/ownership)
error[E0382]: borrow of moved value: `s1`
 --> src/main.rs:5:28
  |
2 |     let s1 = String::from("hello");
  |         -- move occurs because `s1` has type `String`, which does not implement the `Copy` trait
3 |     let s2 = s1;
  |              -- value moved here
4 | 
5 |     println!("{}, world!", s1);
  |                            ^^ value borrowed here after move
  |
  = note: this error originates in the macro `$crate::format_args_nl` (in Nightly builds, run with -Z macro-backtrace for more info)

For more information about this error, try `rustc --explain E0382`.
error: could not compile `ownership` due to previous error

If you’ve heard the terms shallow copy and deep copy while working with other languages, the concept of copying the pointer, length, and capacity without copying the data probably sounds like making a shallow copy. But because Rust also invalidates the first variable, instead of calling it a shallow copy, it’s known as a move. In this example, we would say that s1 was moved into s2. So what actually happens is shown in Figure 4-4.

s1 moved to s2

Figure 4-4: Representation in memory after s1 has been invalidated

That solves our problem! With only s2 valid, when it goes out of scope, it alone will free the memory, and we’re done.

In addition, there’s a design choice that’s implied by this: Rust will never automatically create “deep” copies of your data. Therefore, any automatic copying can be assumed to be inexpensive in terms of runtime performance.

Ways Variables and Data Interact: Clone

If we do want to deeply copy the heap data of the String, not just the stack data, we can use a common method called clone. We’ll discuss method syntax in Chapter 5, but because methods are a common feature in many programming languages, you’ve probably seen them before.

Here’s an example of the clone method in action:

fn main() {
    let s1 = String::from("hello");
    let s2 = s1.clone();

    println!("s1 = {}, s2 = {}", s1, s2);
}

This works just fine and explicitly produces the behavior shown in Figure 4-3, where the heap data does get copied.

When you see a call to clone, you know that some arbitrary code is being executed and that code may be expensive. It’s a visual indicator that something different is going on.

Stack-Only Data: Copy

There’s another wrinkle we haven’t talked about yet. This code using integers – part of which was shown in Listing 4-2 – works and is valid:

fn main() {
    let x = 5;
    let y = x;

    println!("x = {}, y = {}", x, y);
}

But this code seems to contradict what we just learned: we don’t have a call to clone, but x is still valid and wasn’t moved into y.

The reason is that types such as integers that have a known size at compile time are stored entirely on the stack, so copies of the actual values are quick to make. That means there’s no reason we would want to prevent x from being valid after we create the variable y. In other words, there’s no difference between deep and shallow copying here, so calling clone wouldn’t do anything different from the usual shallow copying and we can leave it out.

Rust has a special annotation called the Copy trait that we can place on types that are stored on the stack, as integers are (we’ll talk more about traits in Chapter 10). If a type implements the Copy trait, variables that use it do not move, but rather are trivially copied, making them still valid after assignment to another variable.

Rust won’t let us annotate a type with Copy if the type, or any of its parts, has implemented the Drop trait. If the type needs something special to happen when the value goes out of scope and we add the Copy annotation to that type, we’ll get a compile-time error. To learn about how to add the Copy annotation to your type to implement the trait, see “Derivable Traits” in Appendix C.

So what types implement the Copy trait? You can check the documentation for the given type to be sure, but as a general rule, any group of simple scalar values can implement Copy, and nothing that requires allocation or is some form of resource can implement Copy. Here are some of the types that implement Copy:

  • All the integer types, such as u32.
  • The Boolean type, bool, with values true and false.
  • All the floating point types, such as f64.
  • The character type, char.
  • Tuples, if they only contain types that also implement Copy. For example, (i32, i32) implements Copy, but (i32, String) does not.

Ownership and Functions

The mechanics of passing a value to a function are similar to those when assigning a value to a variable. Passing a variable to a function will move or copy, just as assignment does. Listing 4-3 has an example with some annotations showing where variables go into and out of scope.

Filename: src/main.rs

fn main() {
    let s = String::from("hello");  // s comes into scope

    takes_ownership(s);             // s's value moves into the function...
                                    // ... and so is no longer valid here

    let x = 5;                      // x comes into scope

    makes_copy(x);                  // x would move into the function,
                                    // but i32 is Copy, so it's okay to still
                                    // use x afterward

} // Here, x goes out of scope, then s. But because s's value was moved, nothing
  // special happens.

fn takes_ownership(some_string: String) { // some_string comes into scope
    println!("{}", some_string);
} // Here, some_string goes out of scope and `drop` is called. The backing
  // memory is freed.

fn makes_copy(some_integer: i32) { // some_integer comes into scope
    println!("{}", some_integer);
} // Here, some_integer goes out of scope. Nothing special happens.

Listing 4-3: Functions with ownership and scope annotated

If we tried to use s after the call to takes_ownership, Rust would throw a compile-time error. These static checks protect us from mistakes. Try adding code to main that uses s and x to see where you can use them and where the ownership rules prevent you from doing so.

Return Values and Scope

Returning values can also transfer ownership. Listing 4-4 shows an example of a function that returns some value, with similar annotations as those in Listing 4-3.

Filename: src/main.rs

fn main() {
    let s1 = gives_ownership();         // gives_ownership moves its return
                                        // value into s1

    let s2 = String::from("hello");     // s2 comes into scope

    let s3 = takes_and_gives_back(s2);  // s2 is moved into
                                        // takes_and_gives_back, which also
                                        // moves its return value into s3
} // Here, s3 goes out of scope and is dropped. s2 was moved, so nothing
  // happens. s1 goes out of scope and is dropped.

fn gives_ownership() -> String {             // gives_ownership will move its
                                             // return value into the function
                                             // that calls it

    let some_string = String::from("yours"); // some_string comes into scope

    some_string                              // some_string is returned and
                                             // moves out to the calling
                                             // function
}

// This function takes a String and returns one
fn takes_and_gives_back(a_string: String) -> String { // a_string comes into
                                                      // scope

    a_string  // a_string is returned and moves out to the calling function
}

Listing 4-4: Transferring ownership of return values

The ownership of a variable follows the same pattern every time: assigning a value to another variable moves it. When a variable that includes data on the heap goes out of scope, the value will be cleaned up by drop unless ownership of the data has been moved to another variable.

While this works, taking ownership and then returning ownership with every function is a bit tedious. What if we want to let a function use a value but not take ownership? It’s quite annoying that anything we pass in also needs to be passed back if we want to use it again, in addition to any data resulting from the body of the function that we might want to return as well.

Rust does let us return multiple values using a tuple, as shown in Listing 4-5.

Filename: src/main.rs

fn main() {
    let s1 = String::from("hello");

    let (s2, len) = calculate_length(s1);

    println!("The length of '{}' is {}.", s2, len);
}

fn calculate_length(s: String) -> (String, usize) {
    let length = s.len(); // len() returns the length of a String

    (s, length)
}

Listing 4-5: Returning ownership of parameters

But this is too much ceremony and a lot of work for a concept that should be common. Luckily for us, Rust has a feature for using a value without transferring ownership, called references.

References and Borrowing

The issue with the tuple code in Listing 4-5 is that we have to return the String to the calling function so we can still use the String after the call to calculate_length, because the String was moved into calculate_length. Instead, we can provide a reference to the String value. A reference is like a pointer in that it’s an address we can follow to access the data stored at that address; that data is owned by some other variable. Unlike a pointer, a reference is guaranteed to point to a valid value of a particular type for the life of that reference.

Here is how you would define and use a calculate_length function that has a reference to an object as a parameter instead of taking ownership of the value:

Filename: src/main.rs

fn main() {
    let s1 = String::from("hello");

    let len = calculate_length(&s1);

    println!("The length of '{}' is {}.", s1, len);
}

fn calculate_length(s: &String) -> usize {
    s.len()
}

First, notice that all the tuple code in the variable declaration and the function return value is gone. Second, note that we pass &s1 into calculate_length and, in its definition, we take &String rather than String. These ampersands represent references, and they allow you to refer to some value without taking ownership of it. Figure 4-5 depicts this concept.

&String s pointing at String s1

Figure 4-5: A diagram of &String s pointing at String s1

Note: The opposite of referencing by using & is dereferencing, which is accomplished with the dereference operator, *. We’ll see some uses of the dereference operator in Chapter 8 and discuss details of dereferencing in Chapter 15.

Let’s take a closer look at the function call here:

fn main() {
    let s1 = String::from("hello");

    let len = calculate_length(&s1);

    println!("The length of '{}' is {}.", s1, len);
}

fn calculate_length(s: &String) -> usize {
    s.len()
}

The &s1 syntax lets us create a reference that refers to the value of s1 but does not own it. Because it does not own it, the value it points to will not be dropped when the reference stops being used.

Likewise, the signature of the function uses & to indicate that the type of the parameter s is a reference. Let’s add some explanatory annotations:

fn main() {
    let s1 = String::from("hello");

    let len = calculate_length(&s1);

    println!("The length of '{}' is {}.", s1, len);
}

fn calculate_length(s: &String) -> usize { // s is a reference to a String
    s.len()
} // Here, s goes out of scope. But because it does not have ownership of what
  // it refers to, it is not dropped.

The scope in which the variable s is valid is the same as any function parameter’s scope, but the value pointed to by the reference is not dropped when s stops being used because s doesn’t have ownership. When functions have references as parameters instead of the actual values, we won’t need to return the values in order to give back ownership, because we never had ownership.

We call the action of creating a reference borrowing. As in real life, if a person owns something, you can borrow it from them. When you’re done, you have to give it back. You don’t own it.

So what happens if we try to modify something we’re borrowing? Try the code in Listing 4-6. Spoiler alert: it doesn’t work!

Filename: src/main.rs

fn main() {
    let s = String::from("hello");

    change(&s);
}

fn change(some_string: &String) {
    some_string.push_str(", world");
}

Listing 4-6: Attempting to modify a borrowed value

Here’s the error:

$ cargo run
   Compiling ownership v0.1.0 (file:///projects/ownership)
error[E0596]: cannot borrow `*some_string` as mutable, as it is behind a `&` reference
 --> src/main.rs:8:5
  |
7 | fn change(some_string: &String) {
  |                        ------- help: consider changing this to be a mutable reference: `&mut String`
8 |     some_string.push_str(", world");
  |     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ `some_string` is a `&` reference, so the data it refers to cannot be borrowed as mutable

For more information about this error, try `rustc --explain E0596`.
error: could not compile `ownership` due to previous error

Just as variables are immutable by default, so are references. We’re not allowed to modify something we have a reference to.

Mutable References

We can fix the code from Listing 4-6 to allow us to modify a borrowed value with just a few small tweaks that use, instead, a mutable reference:

Filename: src/main.rs

fn main() {
    let mut s = String::from("hello");

    change(&mut s);
}

fn change(some_string: &mut String) {
    some_string.push_str(", world");
}

First, we change s to be mut. Then we create a mutable reference with &mut s where we call the change function, and update the function signature to accept a mutable reference with some_string: &mut String. This makes it very clear that the change function will mutate the value it borrows.

Mutable references have one big restriction: if you have a mutable reference to a value, you can have no other references to that value. This code that attempts to create two mutable references to s will fail:

Filename: src/main.rs

fn main() {
    let mut s = String::from("hello");

    let r1 = &mut s;
    let r2 = &mut s;

    println!("{}, {}", r1, r2);
}

Here’s the error:

$ cargo run
   Compiling ownership v0.1.0 (file:///projects/ownership)
error[E0499]: cannot borrow `s` as mutable more than once at a time
 --> src/main.rs:5:14
  |
4 |     let r1 = &mut s;
  |              ------ first mutable borrow occurs here
5 |     let r2 = &mut s;
  |              ^^^^^^ second mutable borrow occurs here
6 | 
7 |     println!("{}, {}", r1, r2);
  |                        -- first borrow later used here

For more information about this error, try `rustc --explain E0499`.
error: could not compile `ownership` due to previous error

This error says that this code is invalid because we cannot borrow s as mutable more than once at a time. The first mutable borrow is in r1 and must last until it’s used in the println!, but between the creation of that mutable reference and its usage, we tried to create another mutable reference in r2 that borrows the same data as r1.

The restriction preventing multiple mutable references to the same data at the same time allows for mutation but in a very controlled fashion. It’s something that new Rustaceans struggle with, because most languages let you mutate whenever you’d like. The benefit of having this restriction is that Rust can prevent data races at compile time. A data race is similar to a race condition and happens when these three behaviors occur:

  • Two or more pointers access the same data at the same time.
  • At least one of the pointers is being used to write to the data.
  • There’s no mechanism being used to synchronize access to the data.

Data races cause undefined behavior and can be difficult to diagnose and fix when you’re trying to track them down at runtime; Rust prevents this problem by refusing to compile code with data races!

As always, we can use curly brackets to create a new scope, allowing for multiple mutable references, just not simultaneous ones:

fn main() {
    let mut s = String::from("hello");

    {
        let r1 = &mut s;
    } // r1 goes out of scope here, so we can make a new reference with no problems.

    let r2 = &mut s;
}

Rust enforces a similar rule for combining mutable and immutable references. This code results in an error:

fn main() {
    let mut s = String::from("hello");

    let r1 = &s; // no problem
    let r2 = &s; // no problem
    let r3 = &mut s; // BIG PROBLEM

    println!("{}, {}, and {}", r1, r2, r3);
}

Here’s the error:

$ cargo run
   Compiling ownership v0.1.0 (file:///projects/ownership)
error[E0502]: cannot borrow `s` as mutable because it is also borrowed as immutable
 --> src/main.rs:6:14
  |
4 |     let r1 = &s; // no problem
  |              -- immutable borrow occurs here
5 |     let r2 = &s; // no problem
6 |     let r3 = &mut s; // BIG PROBLEM
  |              ^^^^^^ mutable borrow occurs here
7 | 
8 |     println!("{}, {}, and {}", r1, r2, r3);
  |                                -- immutable borrow later used here

For more information about this error, try `rustc --explain E0502`.
error: could not compile `ownership` due to previous error

Whew! We also cannot have a mutable reference while we have an immutable one to the same value.

Users of an immutable reference don’t expect the value to suddenly change out from under them! However, multiple immutable references are allowed because no one who is just reading the data has the ability to affect anyone else’s reading of the data.

Note that a reference’s scope starts from where it is introduced and continues through the last time that reference is used. For instance, this code will compile because the last usage of the immutable references, the println!, occurs before the mutable reference is introduced:

fn main() {
    let mut s = String::from("hello");

    let r1 = &s; // no problem
    let r2 = &s; // no problem
    println!("{} and {}", r1, r2);
    // variables r1 and r2 will not be used after this point

    let r3 = &mut s; // no problem
    println!("{}", r3);
}

The scopes of the immutable references r1 and r2 end after the println! where they are last used, which is before the mutable reference r3 is created. These scopes don’t overlap, so this code is allowed. The ability of the compiler to tell that a reference is no longer being used at a point before the end of the scope is called Non-Lexical Lifetimes (NLL for short), and you can read more about it in The Edition Guide.

Even though borrowing errors may be frustrating at times, remember that it’s the Rust compiler pointing out a potential bug early (at compile time rather than at runtime) and showing you exactly where the problem is. Then you don’t have to track down why your data isn’t what you thought it was.

Dangling References

In languages with pointers, it’s easy to erroneously create a dangling pointer--a pointer that references a location in memory that may have been given to someone else--by freeing some memory while preserving a pointer to that memory. In Rust, by contrast, the compiler guarantees that references will never be dangling references: if you have a reference to some data, the compiler will ensure that the data will not go out of scope before the reference to the data does.

Let’s try to create a dangling reference to see how Rust prevents them with a compile-time error:

Filename: src/main.rs

fn main() {
    let reference_to_nothing = dangle();
}

fn dangle() -> &String {
    let s = String::from("hello");

    &s
}

Here’s the error:

$ cargo run
   Compiling ownership v0.1.0 (file:///projects/ownership)
error[E0106]: missing lifetime specifier
 --> src/main.rs:5:16
  |
5 | fn dangle() -> &String {
  |                ^ expected named lifetime parameter
  |
  = help: this function's return type contains a borrowed value, but there is no value for it to be borrowed from
help: consider using the `'static` lifetime
  |
5 | fn dangle() -> &'static String {
  |                ~~~~~~~~

For more information about this error, try `rustc --explain E0106`.
error: could not compile `ownership` due to previous error

This error message refers to a feature we haven’t covered yet: lifetimes. We’ll discuss lifetimes in detail in Chapter 10. But, if you disregard the parts about lifetimes, the message does contain the key to why this code is a problem:

this function's return type contains a borrowed value, but there is no value
for it to be borrowed from

Let’s take a closer look at exactly what’s happening at each stage of our dangle code:

Filename: src/main.rs

fn main() {
    let reference_to_nothing = dangle();
}

fn dangle() -> &String { // dangle returns a reference to a String

    let s = String::from("hello"); // s is a new String

    &s // we return a reference to the String, s
} // Here, s goes out of scope, and is dropped. Its memory goes away.
  // Danger!

Because s is created inside dangle, when the code of dangle is finished, s will be deallocated. But we tried to return a reference to it. That means this reference would be pointing to an invalid String. That’s no good! Rust won’t let us do this.

The solution here is to return the String directly:

fn main() {
    let string = no_dangle();
}

fn no_dangle() -> String {
    let s = String::from("hello");

    s
}

This works without any problems. Ownership is moved out, and nothing is deallocated.

The Rules of References

Let’s recap what we’ve discussed about references:

  • At any given time, you can have either one mutable reference or any number of immutable references.
  • References must always be valid.

Next, we’ll look at a different kind of reference: slices.

The Slice Type

Slices let you reference a contiguous sequence of elements in a collection rather than the whole collection. A slice is a kind of reference, so it does not have ownership.

Here’s a small programming problem: write a function that takes a string of words separated by spaces and returns the first word it finds in that string. If the function doesn’t find a space in the string, the whole string must be one word, so the entire string should be returned.

Let’s work through how we’d write the signature of this function without using slices, to understand the problem that slices will solve:

fn first_word(s: &String) -> ?

The first_word function has a &String as a parameter. We don’t want ownership, so this is fine. But what should we return? We don’t really have a way to talk about part of a string. However, we could return the index of the end of the word, indicated by a space. Let’s try that, as shown in Listing 4-7.

Filename: src/main.rs

fn first_word(s: &String) -> usize {
    let bytes = s.as_bytes();

    for (i, &item) in bytes.iter().enumerate() {
        if item == b' ' {
            return i;
        }
    }

    s.len()
}

fn main() {}

Listing 4-7: The first_word function that returns a byte index value into the String parameter

Because we need to go through the String element by element and check whether a value is a space, we’ll convert our String to an array of bytes using the as_bytes method:

fn first_word(s: &String) -> usize {
    let bytes = s.as_bytes();

    for (i, &item) in bytes.iter().enumerate() {
        if item == b' ' {
            return i;
        }
    }

    s.len()
}

fn main() {}

Next, we create an iterator over the array of bytes using the iter method:

fn first_word(s: &String) -> usize {
    let bytes = s.as_bytes();

    for (i, &item) in bytes.iter().enumerate() {
        if item == b' ' {
            return i;
        }
    }

    s.len()
}

fn main() {}

We’ll discuss iterators in more detail in Chapter 13. For now, know that iter is a method that returns each element in a collection and that enumerate wraps the result of iter and returns each element as part of a tuple instead. The first element of the tuple returned from enumerate is the index, and the second element is a reference to the element. This is a bit more convenient than calculating the index ourselves.

Because the enumerate method returns a tuple, we can use patterns to destructure that tuple. We’ll be discussing patterns more in Chapter 6. In the for loop, we specify a pattern that has i for the index in the tuple and &item for the single byte in the tuple. Because we get a reference to the element from .iter().enumerate(), we use & in the pattern.

Inside the for loop, we search for the byte that represents the space by using the byte literal syntax. If we find a space, we return the position. Otherwise, we return the length of the string by using s.len():

fn first_word(s: &String) -> usize {
    let bytes = s.as_bytes();

    for (i, &item) in bytes.iter().enumerate() {
        if item == b' ' {
            return i;
        }
    }

    s.len()
}

fn main() {}

We now have a way to find out the index of the end of the first word in the string, but there’s a problem. We’re returning a usize on its own, but it’s only a meaningful number in the context of the &String. In other words, because it’s a separate value from the String, there’s no guarantee that it will still be valid in the future. Consider the program in Listing 4-8 that uses the first_word function from Listing 4-7.

Filename: src/main.rs

fn first_word(s: &String) -> usize {
    let bytes = s.as_bytes();

    for (i, &item) in bytes.iter().enumerate() {
        if item == b' ' {
            return i;
        }
    }

    s.len()
}

fn main() {
    let mut s = String::from("hello world");

    let word = first_word(&s); // word will get the value 5

    s.clear(); // this empties the String, making it equal to ""

    // word still has the value 5 here, but there's no more string that
    // we could meaningfully use the value 5 with. word is now totally invalid!
}

Listing 4-8: Storing the result from calling the first_word function and then changing the String contents

This program compiles without any errors and would also do so if we used word after calling s.clear(). Because word isn’t connected to the state of s at all, word still contains the value 5. We could use that value 5 with the variable s to try to extract the first word out, but this would be a bug because the contents of s have changed since we saved 5 in word.

Having to worry about the index in word getting out of sync with the data in s is tedious and error prone! Managing these indices is even more brittle if we write a second_word function. Its signature would have to look like this:

fn second_word(s: &String) -> (usize, usize) {

Now we’re tracking a starting and an ending index, and we have even more values that were calculated from data in a particular state but aren’t tied to that state at all. We have three unrelated variables floating around that need to be kept in sync.

Luckily, Rust has a solution to this problem: string slices.

String Slices

A string slice is a reference to part of a String, and it looks like this:

fn main() {
    let s = String::from("hello world");

    let hello = &s[0..5];
    let world = &s[6..11];
}

Rather than a reference to the entire String, hello is a reference to a portion of the String, specified in the extra [0..5] bit. We create slices using a range within brackets by specifying [starting_index..ending_index], where starting_index is the first position in the slice and ending_index is one more than the last position in the slice. Internally, the slice data structure stores the starting position and the length of the slice, which corresponds to ending_index minus starting_index. So in the case of let world = &s[6..11];, world would be a slice that contains a pointer to the byte at index 6 of s with a length value of 5.

Figure 4-6 shows this in a diagram.

world containing a pointer to the byte at index 6 of String s and a length 5

Figure 4-6: String slice referring to part of a String

With Rust’s .. range syntax, if you want to start at index zero, you can drop the value before the two periods. In other words, these are equal:


#![allow(unused)]
fn main() {
let s = String::from("hello");

let slice = &s[0..2];
let slice = &s[..2];
}

By the same token, if your slice includes the last byte of the String, you can drop the trailing number. That means these are equal:


#![allow(unused)]
fn main() {
let s = String::from("hello");

let len = s.len();

let slice = &s[3..len];
let slice = &s[3..];
}

You can also drop both values to take a slice of the entire string. So these are equal:


#![allow(unused)]
fn main() {
let s = String::from("hello");

let len = s.len();

let slice = &s[0..len];
let slice = &s[..];
}

Note: String slice range indices must occur at valid UTF-8 character boundaries. If you attempt to create a string slice in the middle of a multibyte character, your program will exit with an error. For the purposes of introducing string slices, we are assuming ASCII only in this section; a more thorough discussion of UTF-8 handling is in the “Storing UTF-8 Encoded Text with Strings” section of Chapter 8.

With all this information in mind, let’s rewrite first_word to return a slice. The type that signifies “string slice” is written as &str:

Filename: src/main.rs

fn first_word(s: &String) -> &str {
    let bytes = s.as_bytes();

    for (i, &item) in bytes.iter().enumerate() {
        if item == b' ' {
            return &s[0..i];
        }
    }

    &s[..]
}

fn main() {}

We get the index for the end of the word in the same way as we did in Listing 4-7, by looking for the first occurrence of a space. When we find a space, we return a string slice using the start of the string and the index of the space as the starting and ending indices.

Now when we call first_word, we get back a single value that is tied to the underlying data. The value is made up of a reference to the starting point of the slice and the number of elements in the slice.

Returning a slice would also work for a second_word function:

fn second_word(s: &String) -> &str {

We now have a straightforward API that’s much harder to mess up, because the compiler will ensure the references into the String remain valid. Remember the bug in the program in Listing 4-8, when we got the index to the end of the first word but then cleared the string so our index was invalid? That code was logically incorrect but didn’t show any immediate errors. The problems would show up later if we kept trying to use the first word index with an emptied string. Slices make this bug impossible and let us know we have a problem with our code much sooner. Using the slice version of first_word will throw a compile-time error:

Filename: src/main.rs

fn first_word(s: &String) -> &str {
    let bytes = s.as_bytes();

    for (i, &item) in bytes.iter().enumerate() {
        if item == b' ' {
            return &s[0..i];
        }
    }

    &s[..]
}

fn main() {
    let mut s = String::from("hello world");

    let word = first_word(&s);

    s.clear(); // error!

    println!("the first word is: {}", word);
}

Here’s the compiler error:

$ cargo run
   Compiling ownership v0.1.0 (file:///projects/ownership)
error[E0502]: cannot borrow `s` as mutable because it is also borrowed as immutable
  --> src/main.rs:18:5
   |
16 |     let word = first_word(&s);
   |                           -- immutable borrow occurs here
17 | 
18 |     s.clear(); // error!
   |     ^^^^^^^^^ mutable borrow occurs here
19 | 
20 |     println!("the first word is: {}", word);
   |                                       ---- immutable borrow later used here

For more information about this error, try `rustc --explain E0502`.
error: could not compile `ownership` due to previous error

Recall from the borrowing rules that if we have an immutable reference to something, we cannot also take a mutable reference. Because clear needs to truncate the String, it needs to get a mutable reference. The println! after the call to clear uses the reference in word, so the immutable reference must still be active at that point. Rust disallows the mutable reference in clear and the immutable reference in word from existing at the same time, and compilation fails. Not only has Rust made our API easier to use, but it has also eliminated an entire class of errors at compile time!

String Literals Are Slices

Recall that we talked about string literals being stored inside the binary. Now that we know about slices, we can properly understand string literals:


#![allow(unused)]
fn main() {
let s = "Hello, world!";
}

The type of s here is &str: it’s a slice pointing to that specific point of the binary. This is also why string literals are immutable; &str is an immutable reference.

String Slices as Parameters

Knowing that you can take slices of literals and String values leads us to one more improvement on first_word, and that’s its signature:

fn first_word(s: &String) -> &str {

A more experienced Rustacean would write the signature shown in Listing 4-9 instead because it allows us to use the same function on both &String values and &str values.

fn first_word(s: &str) -> &str {
    let bytes = s.as_bytes();

    for (i, &item) in bytes.iter().enumerate() {
        if item == b' ' {
            return &s[0..i];
        }
    }

    &s[..]
}

fn main() {
    let my_string = String::from("hello world");

    // `first_word` works on slices of `String`s, whether partial or whole
    let word = first_word(&my_string[0..6]);
    let word = first_word(&my_string[..]);
    // `first_word` also works on references to `String`s, which are equivalent
    // to whole slices of `String`s
    let word = first_word(&my_string);

    let my_string_literal = "hello world";

    // `first_word` works on slices of string literals, whether partial or whole
    let word = first_word(&my_string_literal[0..6]);
    let word = first_word(&my_string_literal[..]);

    // Because string literals *are* string slices already,
    // this works too, without the slice syntax!
    let word = first_word(my_string_literal);
}

Listing 4-9: Improving the first_word function by using a string slice for the type of the s parameter

If we have a string slice, we can pass that directly. If we have a String, we can pass a slice of the String or a reference to the String. This flexibility takes advantage of deref coercions, a feature we will cover in the “Implicit Deref Coercions with Functions and Methods” section of Chapter 15. Defining a function to take a string slice instead of a reference to a String makes our API more general and useful without losing any functionality:

Filename: src/main.rs

fn first_word(s: &str) -> &str {
    let bytes = s.as_bytes();

    for (i, &item) in bytes.iter().enumerate() {
        if item == b' ' {
            return &s[0..i];
        }
    }

    &s[..]
}

fn main() {
    let my_string = String::from("hello world");

    // `first_word` works on slices of `String`s, whether partial or whole
    let word = first_word(&my_string[0..6]);
    let word = first_word(&my_string[..]);
    // `first_word` also works on references to `String`s, which are equivalent
    // to whole slices of `String`s
    let word = first_word(&my_string);

    let my_string_literal = "hello world";

    // `first_word` works on slices of string literals, whether partial or whole
    let word = first_word(&my_string_literal[0..6]);
    let word = first_word(&my_string_literal[..]);

    // Because string literals *are* string slices already,
    // this works too, without the slice syntax!
    let word = first_word(my_string_literal);
}

Other Slices

String slices, as you might imagine, are specific to strings. But there’s a more general slice type, too. Consider this array:


#![allow(unused)]
fn main() {
let a = [1, 2, 3, 4, 5];
}

Just as we might want to refer to a part of a string, we might want to refer to part of an array. We’d do so like this:


#![allow(unused)]
fn main() {
let a = [1, 2, 3, 4, 5];

let slice = &a[1..3];

assert_eq!(slice, &[2, 3]);
}

This slice has the type &[i32]. It works the same way as string slices do, by storing a reference to the first element and a length. You’ll use this kind of slice for all sorts of other collections. We’ll discuss these collections in detail when we talk about vectors in Chapter 8.

Summary

The concepts of ownership, borrowing, and slices ensure memory safety in Rust programs at compile time. The Rust language gives you control over your memory usage in the same way as other systems programming languages, but having the owner of data automatically clean up that data when the owner goes out of scope means you don’t have to write and debug extra code to get this control.

Ownership affects how lots of other parts of Rust work, so we’ll talk about these concepts further throughout the rest of the book. Let’s move on to Chapter 5 and look at grouping pieces of data together in a struct.

Using Structs to Structure Related Data

A struct, or structure, is a custom data type that lets you package together and name multiple related values that make up a meaningful group. If you’re familiar with an object-oriented language, a struct is like an object’s data attributes. In this chapter, we’ll compare and contrast tuples with structs to build on what you already know and demonstrate when structs are a better way to group data.

We’ll demonstrate how to define and instantiate structs. We’ll discuss how to define associated functions, especially the kind of associated functions called methods, to specify behavior associated with a struct type. Structs and enums (discussed in Chapter 6) are the building blocks for creating new types in your program’s domain to take full advantage of Rust’s compile time type checking.

Defining and Instantiating Structs

Structs are similar to tuples, discussed in “The Tuple Type” section, in that both hold multiple related values. Like tuples, the pieces of a struct can be different types. Unlike with tuples, in a struct you’ll name each piece of data so it’s clear what the values mean. Adding these names means that structs are more flexible than tuples: you don’t have to rely on the order of the data to specify or access the values of an instance.

To define a struct, we enter the keyword struct and name the entire struct. A struct’s name should describe the significance of the pieces of data being grouped together. Then, inside curly brackets, we define the names and types of the pieces of data, which we call fields. For example, Listing 5-1 shows a struct that stores information about a user account.

struct User {
    active: bool,
    username: String,
    email: String,
    sign_in_count: u64,
}

fn main() {}

Listing 5-1: A User struct definition

To use a struct after we’ve defined it, we create an instance of that struct by specifying concrete values for each of the fields. We create an instance by stating the name of the struct and then add curly brackets containing key: value pairs, where the keys are the names of the fields and the values are the data we want to store in those fields. We don’t have to specify the fields in the same order in which we declared them in the struct. In other words, the struct definition is like a general template for the type, and instances fill in that template with particular data to create values of the type. For example, we can declare a particular user as shown in Listing 5-2.

struct User {
    active: bool,
    username: String,
    email: String,
    sign_in_count: u64,
}

fn main() {
    let user1 = User {
        email: String::from("someone@example.com"),
        username: String::from("someusername123"),
        active: true,
        sign_in_count: 1,
    };
}

Listing 5-2: Creating an instance of the User struct

To get a specific value from a struct, we use dot notation. For example, to access this user’s email address, we use user1.email. If the instance is mutable, we can change a value by using the dot notation and assigning into a particular field. Listing 5-3 shows how to change the value in the email field of a mutable User instance.

struct User {
    active: bool,
    username: String,
    email: String,
    sign_in_count: u64,
}

fn main() {
    let mut user1 = User {
        email: String::from("someone@example.com"),
        username: String::from("someusername123"),
        active: true,
        sign_in_count: 1,
    };

    user1.email = String::from("anotheremail@example.com");
}

Listing 5-3: Changing the value in the email field of a User instance

Note that the entire instance must be mutable; Rust doesn’t allow us to mark only certain fields as mutable. As with any expression, we can construct a new instance of the struct as the last expression in the function body to implicitly return that new instance.

Listing 5-4 shows a build_user function that returns a User instance with the given email and username. The active field gets the value of true, and the sign_in_count gets a value of 1.

struct User {
    active: bool,
    username: String,
    email: String,
    sign_in_count: u64,
}

fn build_user(email: String, username: String) -> User {
    User {
        email: email,
        username: username,
        active: true,
        sign_in_count: 1,
    }
}

fn main() {
    let user1 = build_user(
        String::from("someone@example.com"),
        String::from("someusername123"),
    );
}

Listing 5-4: A build_user function that takes an email and username and returns a User instance

It makes sense to name the function parameters with the same name as the struct fields, but having to repeat the email and username field names and variables is a bit tedious. If the struct had more fields, repeating each name would get even more annoying. Luckily, there’s a convenient shorthand!

Using the Field Init Shorthand

Because the parameter names and the struct field names are exactly the same in Listing 5-4, we can use the field init shorthand syntax to rewrite build_user so that it behaves exactly the same but doesn’t have the repetition of email and username, as shown in Listing 5-5.

struct User {
    active: bool,
    username: String,
    email: String,
    sign_in_count: u64,
}

fn build_user(email: String, username: String) -> User {
    User {
        email,
        username,
        active: true,
        sign_in_count: 1,
    }
}

fn main() {
    let user1 = build_user(
        String::from("someone@example.com"),
        String::from("someusername123"),
    );
}

Listing 5-5: A build_user function that uses field init shorthand because the email and username parameters have the same name as struct fields

Here, we’re creating a new instance of the User struct, which has a field named email. We want to set the email field’s value to the value in the email parameter of the build_user function. Because the email field and the email parameter have the same name, we only need to write email rather than email: email.

Creating Instances From Other Instances With Struct Update Syntax

It’s often useful to create a new instance of a struct that includes most of the values from another instance, but changes some. You can do this using struct update syntax.

First, in Listing 5-6 we show how to create a new User instance in user2 regularly, without the update syntax. We set a new value for email but otherwise use the same values from user1 that we created in Listing 5-2.

struct User {
    active: bool,
    username: String,
    email: String,
    sign_in_count: u64,
}

fn main() {
    // --snip--

    let user1 = User {
        email: String::from("someone@example.com"),
        username: String::from("someusername123"),
        active: true,
        sign_in_count: 1,
    };

    let user2 = User {
        active: user1.active,
        username: user1.username,
        email: String::from("another@example.com"),
        sign_in_count: user1.sign_in_count,
    };
}

Listing 5-6: Creating a new User instance using one of the values from user1

Using struct update syntax, we can achieve the same effect with less code, as shown in Listing 5-7. The syntax .. specifies that the remaining fields not explicitly set should have the same value as the fields in the given instance.

struct User {
    active: bool,
    username: String,
    email: String,
    sign_in_count: u64,
}

fn main() {
    // --snip--

    let user1 = User {
        email: String::from("someone@example.com"),
        username: String::from("someusername123"),
        active: true,
        sign_in_count: 1,
    };

    let user2 = User {
        email: String::from("another@example.com"),
        ..user1
    };
}

Listing 5-7: Using struct update syntax to set a new email value for a User instance but use the rest of the values from user1

The code in Listing 5-7 also creates an instance in user2 that has a different value for email but has the same values for the username, active, and sign_in_count fields from user1. The ..user1 must come last to specify that any remaining fields should get their values from the corresponding fields in user1, but we can choose to specify values for as many fields as we want in any order, regardless of the order of the fields in the struct’s definition.

Note that the struct update syntax uses = like an assignment; this is because it moves the data, just as we saw in the “Ways Variables and Data Interact: Move” section. In this example, we can no longer use user1 after creating user2 because the String in the username field of user1 was moved into user2. If we had given user2 new String values for both email and username, and thus only used the active and sign_in_count values from user1, then user1 would still be valid after creating user2. The types of active and sign_in_count are types that implement the Copy trait, so the behavior we discussed in the “Stack-Only Data: Copy” section would apply.

Using Tuple Structs without Named Fields to Create Different Types

Rust also supports structs that look similar to tuples, called tuple structs. Tuple structs have the added meaning the struct name provides but don’t have names associated with their fields; rather, they just have the types of the fields. Tuple structs are useful when you want to give the whole tuple a name and make the tuple a different type from other tuples, and when naming each field as in a regular struct would be verbose or redundant.

To define a tuple struct, start with the struct keyword and the struct name followed by the types in the tuple. For example, here we define and use two tuple structs named Color and Point:

struct Color(i32, i32, i32);
struct Point(i32, i32, i32);

fn main() {
    let black = Color(0, 0, 0);
    let origin = Point(0, 0, 0);
}

Note that the black and origin values are different types, because they’re instances of different tuple structs. Each struct you define is its own type, even though the fields within the struct might have the same types. For example, a function that takes a parameter of type Color cannot take a Point as an argument, even though both types are made up of three i32 values. Otherwise, tuple struct instances are similar to tuples in that you can destructure them into their individual pieces, and you can use a . followed by the index to access an individual value.

Unit-Like Structs Without Any Fields

You can also define structs that don’t have any fields! These are called unit-like structs because they behave similarly to (), the unit type that we mentioned in “The Tuple Type” section. Unit-like structs can be useful when you need to implement a trait on some type but don’t have any data that you want to store in the type itself. We’ll discuss traits in Chapter 10. Here’s an example of declaring and instantiating a unit struct named AlwaysEqual:

struct AlwaysEqual;

fn main() {
    let subject = AlwaysEqual;
}

To define AlwaysEqual, we use the struct keyword, the name we want, then a semicolon. No need for curly brackets or parentheses! Then we can get an instance of AlwaysEqual in the subject variable in a similar way: using the name we defined, without any curly brackets or parentheses. Imagine that later we’ll implement behavior for this type such that every instance of AlwaysEqual is always equal to every instance of any other type, perhaps to have a known result for testing purposes. We wouldn’t need any data to implement that behavior! You’ll see in Chapter 10 how to define traits and implement them on any type, including unit-like structs.

Ownership of Struct Data

In the User struct definition in Listing 5-1, we used the owned String type rather than the &str string slice type. This is a deliberate choice because we want each instance of this struct to own all of its data and for that data to be valid for as long as the entire struct is valid.

It’s also possible for structs to store references to data owned by something else, but to do so requires the use of lifetimes, a Rust feature that we’ll discuss in Chapter 10. Lifetimes ensure that the data referenced by a struct is valid for as long as the struct is. Let’s say you try to store a reference in a struct without specifying lifetimes, like the following; this won’t work:

Filename: src/main.rs

struct User {
    active: bool,
    username: &str,
    email: &str,
    sign_in_count: u64,
}

fn main() {
    let user1 = User {
        email: "someone@example.com",
        username: "someusername123",
        active: true,
        sign_in_count: 1,
    };
}

The compiler will complain that it needs lifetime specifiers:

$ cargo run
   Compiling structs v0.1.0 (file:///projects/structs)
error[E0106]: missing lifetime specifier
 --> src/main.rs:3:15
  |
3 |     username: &str,
  |               ^ expected named lifetime parameter
  |
help: consider introducing a named lifetime parameter
  |
1 ~ struct User<'a> {
2 |     active: bool,
3 ~     username: &'a str,
  |

error[E0106]: missing lifetime specifier
 --> src/main.rs:4:12
  |
4 |     email: &str,
  |            ^ expected named lifetime parameter
  |
help: consider introducing a named lifetime parameter
  |
1 ~ struct User<'a> {
2 |     active: bool,
3 |     username: &str,
4 ~     email: &'a str,
  |

For more information about this error, try `rustc --explain E0106`.
error: could not compile `structs` due to 2 previous errors

In Chapter 10, we’ll discuss how to fix these errors so you can store references in structs, but for now, we’ll fix errors like these using owned types like String instead of references like &str.

An Example Program Using Structs

To understand when we might want to use structs, let’s write a program that calculates the area of a rectangle. We’ll start by using single variables, and then refactor the program until we’re using structs instead.

Let’s make a new binary project with Cargo called rectangles that will take the width and height of a rectangle specified in pixels and calculate the area of the rectangle. Listing 5-8 shows a short program with one way of doing exactly that in our project’s src/main.rs.

Filename: src/main.rs

fn main() {
    let width1 = 30;
    let height1 = 50;

    println!(
        "The area of the rectangle is {} square pixels.",
        area(width1, height1)
    );
}

fn area(width: u32, height: u32) -> u32 {
    width * height
}

Listing 5-8: Calculating the area of a rectangle specified by separate width and height variables

Now, run this program using cargo run:

$ cargo run
   Compiling rectangles v0.1.0 (file:///projects/rectangles)
    Finished dev [unoptimized + debuginfo] target(s) in 0.42s
     Running `target/debug/rectangles`
The area of the rectangle is 1500 square pixels.

This code succeeds in figuring out the area of the rectangle by calling the area function with each dimension, but we can do more to make this code clear and readable.

The issue with this code is evident in the signature of area:

fn main() {
    let width1 = 30;
    let height1 = 50;

    println!(
        "The area of the rectangle is {} square pixels.",
        area(width1, height1)
    );
}

fn area(width: u32, height: u32) -> u32 {
    width * height
}

The area function is supposed to calculate the area of one rectangle, but the function we wrote has two parameters, and it’s not clear anywhere in our program that the parameters are related. It would be more readable and more manageable to group width and height together. We’ve already discussed one way we might do that in “The Tuple Type” section of Chapter 3: by using tuples.

Refactoring with Tuples

Listing 5-9 shows another version of our program that uses tuples.

Filename: src/main.rs

fn main() {
    let rect1 = (30, 50);

    println!(
        "The area of the rectangle is {} square pixels.",
        area(rect1)
    );
}

fn area(dimensions: (u32, u32)) -> u32 {
    dimensions.0 * dimensions.1
}

Listing 5-9: Specifying the width and height of the rectangle with a tuple

In one way, this program is better. Tuples let us add a bit of structure, and we’re now passing just one argument. But in another way, this version is less clear: tuples don’t name their elements, so we have to index into the parts of the tuple, making our calculation less obvious.

Mixing up the width and height wouldn’t matter for the area calculation, but if we want to draw the rectangle on the screen, it would matter! We would have to keep in mind that width is the tuple index 0 and height is the tuple index 1. This would be even harder for someone else to figure out and keep in mind if they were to use our code. Because we haven’t conveyed the meaning of our data in our code, it’s now easier to introduce errors.

Refactoring with Structs: Adding More Meaning

We use structs to add meaning by labeling the data. We can transform the tuple we’re using into a struct with a name for the whole as well as names for the parts, as shown in Listing 5-10.

Filename: src/main.rs

struct Rectangle {
    width: u32,
    height: u32,
}

fn main() {
    let rect1 = Rectangle {
        width: 30,
        height: 50,
    };

    println!(
        "The area of the rectangle is {} square pixels.",
        area(&rect1)
    );
}

fn area(rectangle: &Rectangle) -> u32 {
    rectangle.width * rectangle.height
}

Listing 5-10: Defining a Rectangle struct

Here we’ve defined a struct and named it Rectangle. Inside the curly brackets, we defined the fields as width and height, both of which have type u32. Then in main, we created a particular instance of Rectangle that has a width of 30 and a height of 50.

Our area function is now defined with one parameter, which we’ve named rectangle, whose type is an immutable borrow of a struct Rectangle instance. As mentioned in Chapter 4, we want to borrow the struct rather than take ownership of it. This way, main retains its ownership and can continue using rect1, which is the reason we use the & in the function signature and where we call the function.

The area function accesses the width and height fields of the Rectangle instance (note that accessing fields of a borrowed struct instance does not move the field values, which is why you often see borrows of structs). Our function signature for area now says exactly what we mean: calculate the area of Rectangle, using its width and height fields. This conveys that the width and height are related to each other, and it gives descriptive names to the values rather than using the tuple index values of 0 and 1. This is a win for clarity.

Adding Useful Functionality with Derived Traits

It’d be useful to be able to print an instance of Rectangle while we’re debugging our program and see the values for all its fields. Listing 5-11 tries using the println! macro as we have used in previous chapters. This won’t work, however.

Filename: src/main.rs

struct Rectangle {
    width: u32,
    height: u32,
}

fn main() {
    let rect1 = Rectangle {
        width: 30,
        height: 50,
    };

    println!("rect1 is {}", rect1);
}

Listing 5-11: Attempting to print a Rectangle instance

When we compile this code, we get an error with this core message:

error[E0277]: `Rectangle` doesn't implement `std::fmt::Display`

The println! macro can do many kinds of formatting, and by default, the curly brackets tell println! to use formatting known as Display: output intended for direct end user consumption. The primitive types we’ve seen so far implement Display by default, because there’s only one way you’d want to show a 1 or any other primitive type to a user. But with structs, the way println! should format the output is less clear because there are more display possibilities: Do you want commas or not? Do you want to print the curly brackets? Should all the fields be shown? Due to this ambiguity, Rust doesn’t try to guess what we want, and structs don’t have a provided implementation of Display to use with println! and the {} placeholder.

If we continue reading the errors, we’ll find this helpful note:

   = help: the trait `std::fmt::Display` is not implemented for `Rectangle`
   = note: in format strings you may be able to use `{:?}` (or {:#?} for pretty-print) instead

Let’s try it! The println! macro call will now look like println!("rect1 is {:?}", rect1);. Putting the specifier :? inside the curly brackets tells println! we want to use an output format called Debug. The Debug trait enables us to print our struct in a way that is useful for developers so we can see its value while we’re debugging our code.

Compile the code with this change. Drat! We still get an error:

error[E0277]: `Rectangle` doesn't implement `Debug`

But again, the compiler gives us a helpful note:

   = help: the trait `Debug` is not implemented for `Rectangle`
   = note: add `#[derive(Debug)]` to `Rectangle` or manually `impl Debug for Rectangle`

Rust does include functionality to print out debugging information, but we have to explicitly opt in to make that functionality available for our struct. To do that, we add the outer attribute #[derive(Debug)] just before the struct definition, as shown in Listing 5-12.

Filename: src/main.rs

#[derive(Debug)]
struct Rectangle {
    width: u32,
    height: u32,
}

fn main() {
    let rect1 = Rectangle {
        width: 30,
        height: 50,
    };

    println!("rect1 is {:?}", rect1);
}

Listing 5-12: Adding the attribute to derive the Debug trait and printing the Rectangle instance using debug formatting

Now when we run the program, we won’t get any errors, and we’ll see the following output:

$ cargo run
   Compiling rectangles v0.1.0 (file:///projects/rectangles)
    Finished dev [unoptimized + debuginfo] target(s) in 0.48s
     Running `target/debug/rectangles`
rect1 is Rectangle { width: 30, height: 50 }

Nice! It’s not the prettiest output, but it shows the values of all the fields for this instance, which would definitely help during debugging. When we have larger structs, it’s useful to have output that’s a bit easier to read; in those cases, we can use {:#?} instead of {:?} in the println! string. In this example, using the {:#?} style will output:

$ cargo run
   Compiling rectangles v0.1.0 (file:///projects/rectangles)
    Finished dev [unoptimized + debuginfo] target(s) in 0.48s
     Running `target/debug/rectangles`
rect1 is Rectangle {
    width: 30,
    height: 50,
}

Another way to print out a value using the Debug format is to use the dbg! macro, which takes ownership of an expression (as opposed to println! that takes a reference), prints the file and line number of where that dbg! macro call occurs in your code along with the resulting value of that expression, and returns ownership of the value.

Note: Calling the dbg! macro prints to the standard error console stream (stderr), as opposed to println! which prints to the standard output console stream (stdout). We’ll talk more about stderr and stdout in the ““Writing Error Messages to Standard Error Instead of Standard Output” section in Chapter 12.

Here’s an example where we’re interested in the value that gets assigned to the width field, as well as the value of the whole struct in rect1:

#[derive(Debug)]
struct Rectangle {
    width: u32,
    height: u32,
}

fn main() {
    let scale = 2;
    let rect1 = Rectangle {
        width: dbg!(30 * scale),
        height: 50,
    };

    dbg!(&rect1);
}

We can put dbg! around the expression 30 * scale and, because dbg! returns ownership of the expression’s value, the width field will get the same value as if we didn’t have the dbg! call there. We don’t want dbg! to take ownership of rect1, so we use a reference to rect1 in the next call. Here’s what the output of this example looks like:

$ cargo run
   Compiling rectangles v0.1.0 (file:///projects/rectangles)
    Finished dev [unoptimized + debuginfo] target(s) in 0.61s
     Running `target/debug/rectangles`
[src/main.rs:10] 30 * scale = 60
[src/main.rs:14] &rect1 = Rectangle {
    width: 60,
    height: 50,
}

We can see the first bit of output came from src/main.rs line 10, where we’re debugging the expression 30 * scale, and its resulting value is 60 (the Debug formatting implemented for integers is to print only their value). The dbg! call on line 14 of src/main.rs outputs the value of &rect1, which is the Rectangle struct. This output uses the pretty Debug formatting of the Rectangle type. The dbg! macro can be really helpful when you’re trying to figure out what your code is doing!

In addition to the Debug trait, Rust has provided a number of traits for us to use with the derive attribute that can add useful behavior to our custom types. Those traits and their behaviors are listed in Appendix C. We’ll cover how to implement these traits with custom behavior as well as how to create your own traits in Chapter 10. There are also many attributes other than derive; for more information, see the “Attributes” section of the Rust Reference.

Our area function is very specific: it only computes the area of rectangles. It would be helpful to tie this behavior more closely to our Rectangle struct, because it won’t work with any other type. Let’s look at how we can continue to refactor this code by turning the area function into an area method defined on our Rectangle type.

Method Syntax

Methods are similar to functions: we declare them with the fn keyword and a name, they can have parameters and a return value, and they contain some code that’s run when the method is called from somewhere else. Unlike functions, methods are defined within the context of a struct (or an enum or a trait object, which we cover in Chapters 6 and 17, respectively), and their first parameter is always self, which represents the instance of the struct the method is being called on.

Defining Methods

Let’s change the area function that has a Rectangle instance as a parameter and instead make an area method defined on the Rectangle struct, as shown in Listing 5-13.

Filename: src/main.rs

#[derive(Debug)]
struct Rectangle {
    width: u32,
    height: u32,
}

impl Rectangle {
    fn area(&self) -> u32 {
        self.width * self.height
    }
}

fn main() {
    let rect1 = Rectangle {
        width: 30,
        height: 50,
    };

    println!(
        "The area of the rectangle is {} square pixels.",
        rect1.area()
    );
}

Listing 5-13: Defining an area method on the Rectangle struct

To define the function within the context of Rectangle, we start an impl (implementation) block for Rectangle. Everything within this impl block will be associated with the Rectangle type. Then we move the area function within the impl curly brackets and change the first (and in this case, only) parameter to be self in the signature and everywhere within the body. In main, where we called the area function and passed rect1 as an argument, we can instead use method syntax to call the area method on our Rectangle instance. The method syntax goes after an instance: we add a dot followed by the method name, parentheses, and any arguments.

In the signature for area, we use &self instead of rectangle: &Rectangle. The &self is actually short for self: &Self. Within an impl block, the type Self is an alias for the type that the impl block is for. Methods must have a parameter named self of type Self for their first parameter, so Rust lets you abbreviate this with only the name self in the first parameter spot. Note that we still need to use the & in front of the self shorthand to indicate this method borrows the Self instance, just as we did in rectangle: &Rectangle. Methods can take ownership of self, borrow self immutably as we’ve done here, or borrow self mutably, just as they can any other parameter.

We’ve chosen &self here for the same reason we used &Rectangle in the function version: we don’t want to take ownership, and we just want to read the data in the struct, not write to it. If we wanted to change the instance that we’ve called the method on as part of what the method does, we’d use &mut self as the first parameter. Having a method that takes ownership of the instance by using just self as the first parameter is rare; this technique is usually used when the method transforms self into something else and you want to prevent the caller from using the original instance after the transformation.

The main reason for using methods instead of functions, in addition to providing method syntax and not having to repeat the type of self in every method’s signature, is for organization. We’ve put all the things we can do with an instance of a type in one impl block rather than making future users of our code search for capabilities of Rectangle in various places in the library we provide.

Note that we can choose to give a method the same name as one of the struct’s fields. For example, we can define a method on Rectangle also named width:

Filename: src/main.rs

#[derive(Debug)]
struct Rectangle {
    width: u32,
    height: u32,
}

impl Rectangle {
    fn width(&self) -> bool {
        self.width > 0
    }
}

fn main() {
    let rect1 = Rectangle {
        width: 30,
        height: 50,
    };

    if rect1.width() {
        println!("The rectangle has a nonzero width; it is {}", rect1.width);
    }
}

Here, we’re choosing to make the width method return true if the value in the instance’s width field is greater than 0, and false if the value is 0: we can use a field within a method of the same name for any purpose. In main, when we follow rect1.width with parentheses, Rust knows we mean the method width. When we don’t use parentheses, Rust knows we mean the field width.

Often, but not always, when we give methods with the same name as a field we want it to only return the value in the field and do nothing else. Methods like this are called getters, and Rust does not implement them automatically for struct fields as some other languages do. Getters are useful because you can make the field private but the method public and thus enable read-only access to that field as part of the type’s public API. We will be discussing what public and private are and how to designate a field or method as public or private in Chapter 7.

Where’s the -> Operator?

In C and C++, two different operators are used for calling methods: you use . if you’re calling a method on the object directly and -> if you’re calling the method on a pointer to the object and need to dereference the pointer first. In other words, if object is a pointer, object->something() is similar to (*object).something().

Rust doesn’t have an equivalent to the -> operator; instead, Rust has a feature called automatic referencing and dereferencing. Calling methods is one of the few places in Rust that has this behavior.

Here’s how it works: when you call a method with object.something(), Rust automatically adds in &, &mut, or * so object matches the signature of the method. In other words, the following are the same:


#![allow(unused)]
fn main() {
#[derive(Debug,Copy,Clone)]
struct Point {
    x: f64,
    y: f64,
}

impl Point {
   fn distance(&self, other: &Point) -> f64 {
       let x_squared = f64::powi(other.x - self.x, 2);
       let y_squared = f64::powi(other.y - self.y, 2);

       f64::sqrt(x_squared + y_squared)
   }
}
let p1 = Point { x: 0.0, y: 0.0 };
let p2 = Point { x: 5.0, y: 6.5 };
p1.distance(&p2);
(&p1).distance(&p2);
}

The first one looks much cleaner. This automatic referencing behavior works because methods have a clear receiver—the type of self. Given the receiver and name of a method, Rust can figure out definitively whether the method is reading (&self), mutating (&mut self), or consuming (self). The fact that Rust makes borrowing implicit for method receivers is a big part of making ownership ergonomic in practice.

Methods with More Parameters

Let’s practice using methods by implementing a second method on the Rectangle struct. This time, we want an instance of Rectangle to take another instance of Rectangle and return true if the second Rectangle can fit completely within self (the first Rectangle); otherwise it should return false. That is, once we’ve defined the can_hold method, we want to be able to write the program shown in Listing 5-14.

Filename: src/main.rs

fn main() {
    let rect1 = Rectangle {
        width: 30,
        height: 50,
    };
    let rect2 = Rectangle {
        width: 10,
        height: 40,
    };
    let rect3 = Rectangle {
        width: 60,
        height: 45,
    };

    println!("Can rect1 hold rect2? {}", rect1.can_hold(&rect2));
    println!("Can rect1 hold rect3? {}", rect1.can_hold(&rect3));
}

Listing 5-14: Using the as-yet-unwritten can_hold method

And the expected output would look like the following, because both dimensions of rect2 are smaller than the dimensions of rect1 but rect3 is wider than rect1:

Can rect1 hold rect2? true
Can rect1 hold rect3? false

We know we want to define a method, so it will be within the impl Rectangle block. The method name will be can_hold, and it will take an immutable borrow of another Rectangle as a parameter. We can tell what the type of the parameter will be by looking at the code that calls the method: rect1.can_hold(&rect2) passes in &rect2, which is an immutable borrow to rect2, an instance of Rectangle. This makes sense because we only need to read rect2 (rather than write, which would mean we’d need a mutable borrow), and we want main to retain ownership of rect2 so we can use it again after calling the can_hold method. The return value of can_hold will be a Boolean, and the implementation will check whether the width and height of self are both greater than the width and height of the other Rectangle, respectively. Let’s add the new can_hold method to the impl block from Listing 5-13, shown in Listing 5-15.

Filename: src/main.rs

#[derive(Debug)]
struct Rectangle {
    width: u32,
    height: u32,
}

impl Rectangle {
    fn area(&self) -> u32 {
        self.width * self.height
    }

    fn can_hold(&self, other: &Rectangle) -> bool {
        self.width > other.width && self.height > other.height
    }
}

fn main() {
    let rect1 = Rectangle {
        width: 30,
        height: 50,
    };
    let rect2 = Rectangle {
        width: 10,
        height: 40,
    };
    let rect3 = Rectangle {
        width: 60,
        height: 45,
    };

    println!("Can rect1 hold rect2? {}", rect1.can_hold(&rect2));
    println!("Can rect1 hold rect3? {}", rect1.can_hold(&rect3));
}

Listing 5-15: Implementing the can_hold method on Rectangle that takes another Rectangle instance as a parameter

When we run this code with the main function in Listing 5-14, we’ll get our desired output. Methods can take multiple parameters that we add to the signature after the self parameter, and those parameters work just like parameters in functions.

Associated Functions

All functions defined within an impl block are called associated functions because they’re associated with the type named after the impl. We can define associated functions that don’t have self as their first parameter (and thus are not methods) because they don’t need an instance of the type to work with. We’ve already used one function like this: the String::from function that’s defined on the String type.

Associated functions that aren’t methods are often used for constructors that will return a new instance of the struct. These are often called new, but new isn’t a special name and isn’t built into the language. For example, we could choose to provide an associated function named square that would have one dimension parameter and use that as both width and height, thus making it easier to create a square Rectangle rather than having to specify the same value twice:

Filename: src/main.rs

#[derive(Debug)]
struct Rectangle {
    width: u32,
    height: u32,
}

impl Rectangle {
    fn square(size: u32) -> Self {
        Self {
            width: size,
            height: size,
        }
    }
}

fn main() {
    let sq = Rectangle::square(3);
}

The Self keywords in the return type and in the body of the function are aliases for the type that appears after the impl keyword, which in this case is Rectangle.

To call this associated function, we use the :: syntax with the struct name; let sq = Rectangle::square(3); is an example. This function is namespaced by the struct: the :: syntax is used for both associated functions and namespaces created by modules. We’ll discuss modules in Chapter 7.

Multiple impl Blocks

Each struct is allowed to have multiple impl blocks. For example, Listing 5-15 is equivalent to the code shown in Listing 5-16, which has each method in its own impl block.

#[derive(Debug)]
struct Rectangle {
    width: u32,
    height: u32,
}

impl Rectangle {
    fn area(&self) -> u32 {
        self.width * self.height
    }
}

impl Rectangle {
    fn can_hold(&self, other: &Rectangle) -> bool {
        self.width > other.width && self.height > other.height
    }
}

fn main() {
    let rect1 = Rectangle {
        width: 30,
        height: 50,
    };
    let rect2 = Rectangle {
        width: 10,
        height: 40,
    };
    let rect3 = Rectangle {
        width: 60,
        height: 45,
    };

    println!("Can rect1 hold rect2? {}", rect1.can_hold(&rect2));
    println!("Can rect1 hold rect3? {}", rect1.can_hold(&rect3));
}

Listing 5-16: Rewriting Listing 5-15 using multiple impl blocks

There’s no reason to separate these methods into multiple impl blocks here, but this is valid syntax. We’ll see a case in which multiple impl blocks are useful in Chapter 10, where we discuss generic types and traits.

Summary

Structs let you create custom types that are meaningful for your domain. By using structs, you can keep associated pieces of data connected to each other and name each piece to make your code clear. In impl blocks, you can define functions that are associated with your type, and methods are a kind of associated function that let you specify the behavior that instances of your structs have.

But structs aren’t the only way you can create custom types: let’s turn to Rust’s enum feature to add another tool to your toolbox.

Enums and Pattern Matching

In this chapter we’ll look at enumerations, also referred to as enums. Enums allow you to define a type by enumerating its possible variants. First, we’ll define and use an enum to show how an enum can encode meaning along with data. Next, we’ll explore a particularly useful enum, called Option, which expresses that a value can be either something or nothing. Then we’ll look at how pattern matching in the match expression makes it easy to run different code for different values of an enum. Finally, we’ll cover how the if let construct is another convenient and concise idiom available to handle enums in your code.

Defining an Enum

Where structs give you a way of grouping together related fields and data, like a Rectangle with its width and height, enums give you a way of saying a value is one of a possible set of values. For example, we may want to say that Rectangle is one of a set of possible shapes that also includes Circle and Triangle. To do this, Rust allows us to encode these possibilities as an enum.

Let’s look at a situation we might want to express in code and see why enums are useful and more appropriate than structs in this case. Say we need to work with IP addresses. Currently, two major standards are used for IP addresses: version four and version six. Because these are the only possibilities for an IP address that our program will come across, we can enumerate all possible variants, which is where enumeration gets its name.

Any IP address can be either a version four or a version six address, but not both at the same time. That property of IP addresses makes the enum data structure appropriate, because an enum value can only be one of its variants. Both version four and version six addresses are still fundamentally IP addresses, so they should be treated as the same type when the code is handling situations that apply to any kind of IP address.

We can express this concept in code by defining an IpAddrKind enumeration and listing the possible kinds an IP address can be, V4 and V6. These are the variants of the enum:

enum IpAddrKind {
    V4,
    V6,
}

fn main() {
    let four = IpAddrKind::V4;
    let six = IpAddrKind::V6;

    route(IpAddrKind::V4);
    route(IpAddrKind::V6);
}

fn route(ip_kind: IpAddrKind) {}

IpAddrKind is now a custom data type that we can use elsewhere in our code.

Enum Values

We can create instances of each of the two variants of IpAddrKind like this:

enum IpAddrKind {
    V4,
    V6,
}

fn main() {
    let four = IpAddrKind::V4;
    let six = IpAddrKind::V6;

    route(IpAddrKind::V4);
    route(IpAddrKind::V6);
}

fn route(ip_kind: IpAddrKind) {}

Note that the variants of the enum are namespaced under its identifier, and we use a double colon to separate the two. This is useful because now both values IpAddrKind::V4 and IpAddrKind::V6 are of the same type: IpAddrKind. We can then, for instance, define a function that takes any IpAddrKind:

enum IpAddrKind {
    V4,
    V6,
}

fn main() {
    let four = IpAddrKind::V4;
    let six = IpAddrKind::V6;

    route(IpAddrKind::V4);
    route(IpAddrKind::V6);
}

fn route(ip_kind: IpAddrKind) {}

And we can call this function with either variant:

enum IpAddrKind {
    V4,
    V6,
}

fn main() {
    let four = IpAddrKind::V4;
    let six = IpAddrKind::V6;

    route(IpAddrKind::V4);
    route(IpAddrKind::V6);
}

fn route(ip_kind: IpAddrKind) {}

Using enums has even more advantages. Thinking more about our IP address type, at the moment we don’t have a way to store the actual IP address data; we only know what kind it is. Given that you just learned about structs in Chapter 5, you might be tempted to tackle this problem with structs as shown in Listing 6-1.

fn main() {
    enum IpAddrKind {
        V4,
        V6,
    }

    struct IpAddr {
        kind: IpAddrKind,
        address: String,
    }

    let home = IpAddr {
        kind: IpAddrKind::V4,
        address: String::from("127.0.0.1"),
    };

    let loopback = IpAddr {
        kind: IpAddrKind::V6,
        address: String::from("::1"),
    };
}

Listing 6-1: Storing the data and IpAddrKind variant of an IP address using a struct

Here, we’ve defined a struct IpAddr that has two fields: a kind field that is of type IpAddrKind (the enum we defined previously) and an address field of type String. We have two instances of this struct. The first is home, and it has the value IpAddrKind::V4 as its kind with associated address data of 127.0.0.1. The second instance is loopback. It has the other variant of IpAddrKind as its kind value, V6, and has address ::1 associated with it. We’ve used a struct to bundle the kind and address values together, so now the variant is associated with the value.

However, representing the same concept using just an enum is more concise: rather than an enum inside a struct, we can put data directly into each enum variant. This new definition of the IpAddr enum says that both V4 and V6 variants will have associated String values:

fn main() {
    enum IpAddr {
        V4(String),
        V6(String),
    }

    let home = IpAddr::V4(String::from("127.0.0.1"));

    let loopback = IpAddr::V6(String::from("::1"));
}

We attach data to each variant of the enum directly, so there is no need for an extra struct. Here it’s also easier to see another detail of how enums work: the name of each enum variant that we define also becomes a function that constructs an instance of the enum. That is, IpAddr::V4() is a function call that takes a String argument and returns an instance of the IpAddr type. We automatically get this constructor function defined as a result of defining the enum.

There’s another advantage to using an enum rather than a struct: each variant can have different types and amounts of associated data. Version four type IP addresses will always have four numeric components that will have values between 0 and 255. If we wanted to store V4 addresses as four u8 values but still express V6 addresses as one String value, we wouldn’t be able to with a struct. Enums handle this case with ease:

fn main() {
    enum IpAddr {
        V4(u8, u8, u8, u8),
        V6(String),
    }

    let home = IpAddr::V4(127, 0, 0, 1);

    let loopback = IpAddr::V6(String::from("::1"));
}

We’ve shown several different ways to define data structures to store version four and version six IP addresses. However, as it turns out, wanting to store IP addresses and encode which kind they are is so common that the standard library has a definition we can use! Let’s look at how the standard library defines IpAddr: it has the exact enum and variants that we’ve defined and used, but it embeds the address data inside the variants in the form of two different structs, which are defined differently for each variant:


#![allow(unused)]
fn main() {
struct Ipv4Addr {
    // --snip--
}

struct Ipv6Addr {
    // --snip--
}

enum IpAddr {
    V4(Ipv4Addr),
    V6(Ipv6Addr),
}
}

This code illustrates that you can put any kind of data inside an enum variant: strings, numeric types, or structs, for example. You can even include another enum! Also, standard library types are often not much more complicated than what you might come up with.

Note that even though the standard library contains a definition for IpAddr, we can still create and use our own definition without conflict because we haven’t brought the standard library’s definition into our scope. We’ll talk more about bringing types into scope in Chapter 7.

Let’s look at another example of an enum in Listing 6-2: this one has a wide variety of types embedded in its variants.

enum Message {
    Quit,
    Move { x: i32, y: i32 },
    Write(String),
    ChangeColor(i32, i32, i32),
}

fn main() {}

Listing 6-2: A Message enum whose variants each store different amounts and types of values

This enum has four variants with different types:

  • Quit has no data associated with it at all.
  • Move has named fields like a struct does.
  • Write includes a single String.
  • ChangeColor includes three i32 values.

Defining an enum with variants such as the ones in Listing 6-2 is similar to defining different kinds of struct definitions, except the enum doesn’t use the struct keyword and all the variants are grouped together under the Message type. The following structs could hold the same data that the preceding enum variants hold:

struct QuitMessage; // unit struct
struct MoveMessage {
    x: i32,
    y: i32,
}
struct WriteMessage(String); // tuple struct
struct ChangeColorMessage(i32, i32, i32); // tuple struct

fn main() {}

But if we used the different structs, which each have their own type, we couldn’t as easily define a function to take any of these kinds of messages as we could with the Message enum defined in Listing 6-2, which is a single type.

There is one more similarity between enums and structs: just as we’re able to define methods on structs using impl, we’re also able to define methods on enums. Here’s a method named call that we could define on our Message enum:

fn main() {
    enum Message {
        Quit,
        Move { x: i32, y: i32 },
        Write(String),
        ChangeColor(i32, i32, i32),
    }

    impl Message {
        fn call(&self) {
            // method body would be defined here
        }
    }

    let m = Message::Write(String::from("hello"));
    m.call();
}

The body of the method would use self to get the value that we called the method on. In this example, we’ve created a variable m that has the value Message::Write(String::from("hello")), and that is what self will be in the body of the call method when m.call() runs.

Let’s look at another enum in the standard library that is very common and useful: Option.

The Option Enum and Its Advantages Over Null Values

This section explores a case study of Option, which is another enum defined by the standard library. The Option type encodes the very common scenario in which a value could be something or it could be nothing.

For example, if you request the first of a list containing items, you would get a value. If you request the first item of an empty list, you would get nothing. Expressing this concept in terms of the type system means the compiler can check whether you’ve handled all the cases you should be handling; this functionality can prevent bugs that are extremely common in other programming languages.

Programming language design is often thought of in terms of which features you include, but the features you exclude are important too. Rust doesn’t have the null feature that many other languages have. Null is a value that means there is no value there. In languages with null, variables can always be in one of two states: null or not-null.

In his 2009 presentation “Null References: The Billion Dollar Mistake,” Tony Hoare, the inventor of null, has this to say:

I call it my billion-dollar mistake. At that time, I was designing the first comprehensive type system for references in an object-oriented language. My goal was to ensure that all use of references should be absolutely safe, with checking performed automatically by the compiler. But I couldn’t resist the temptation to put in a null reference, simply because it was so easy to implement. This has led to innumerable errors, vulnerabilities, and system crashes, which have probably caused a billion dollars of pain and damage in the last forty years.

The problem with null values is that if you try to use a null value as a not-null value, you’ll get an error of some kind. Because this null or not-null property is pervasive, it’s extremely easy to make this kind of error.

However, the concept that null is trying to express is still a useful one: a null is a value that is currently invalid or absent for some reason.

The problem isn’t really with the concept but with the particular implementation. As such, Rust does not have nulls, but it does have an enum that can encode the concept of a value being present or absent. This enum is Option<T>, and it is defined by the standard library as follows:


#![allow(unused)]
fn main() {
enum Option<T> {
    None,
    Some(T),
}
}

The Option<T> enum is so useful that it’s even included in the prelude; you don’t need to bring it into scope explicitly. Its variants are also included in the prelude: you can use Some and None directly without the Option:: prefix. The Option<T> enum is still just a regular enum, and Some(T) and None are still variants of type Option<T>.

The <T> syntax is a feature of Rust we haven’t talked about yet. It’s a generic type parameter, and we’ll cover generics in more detail in Chapter 10. For now, all you need to know is that <T> means the Some variant of the Option enum can hold one piece of data of any type, and that each concrete type that gets used in place of T makes the overall Option<T> type a different type. Here are some examples of using Option values to hold number types and string types:

fn main() {
    let some_number = Some(5);
    let some_char = Some('e');

    let absent_number: Option<i32> = None;
}

The type of some_number is Option<i32>. The type of some_char is Option<char>, which is a different type. Rust can infer these types because we’ve specified a value inside the Some variant. For absent_number, Rust requires us to annotate the overall Option type: the compiler can’t infer the type that the corresponding Some variant will hold by looking only at a None value. Here, we tell Rust that we mean for absent_number to be of type Option<i32>.

When we have a Some value, we know that a value is present and the value is held within the Some. When we have a None value, in some sense, it means the same thing as null: we don’t have a valid value. So why is having Option<T> any better than having null?

In short, because Option<T> and T (where T can be any type) are different types, the compiler won’t let us use an Option<T> value as if it were definitely a valid value. For example, this code won’t compile because it’s trying to add an i8 to an Option<i8>:

fn main() {
    let x: i8 = 5;
    let y: Option<i8> = Some(5);

    let sum = x + y;
}

If we run this code, we get an error message like this:

$ cargo run
   Compiling enums v0.1.0 (file:///projects/enums)
error[E0277]: cannot add `Option<i8>` to `i8`
 --> src/main.rs:5:17
  |
5 |     let sum = x + y;
  |                 ^ no implementation for `i8 + Option<i8>`
  |
  = help: the trait `Add<Option<i8>>` is not implemented for `i8`

For more information about this error, try `rustc --explain E0277`.
error: could not compile `enums` due to previous error

Intense! In effect, this error message means that Rust doesn’t understand how to add an i8 and an Option<i8>, because they’re different types. When we have a value of a type like i8 in Rust, the compiler will ensure that we always have a valid value. We can proceed confidently without having to check for null before using that value. Only when we have an Option<i8> (or whatever type of value we’re working with) do we have to worry about possibly not having a value, and the compiler will make sure we handle that case before using the value.

In other words, you have to convert an Option<T> to a T before you can perform T operations with it. Generally, this helps catch one of the most common issues with null: assuming that something isn’t null when it actually is.

Eliminating the risk of incorrectly assuming a not-null value helps you to be more confident in your code. In order to have a value that can possibly be null, you must explicitly opt in by making the type of that value Option<T>. Then, when you use that value, you are required to explicitly handle the case when the value is null. Everywhere that a value has a type that isn’t an Option<T>, you can safely assume that the value isn’t null. This was a deliberate design decision for Rust to limit null’s pervasiveness and increase the safety of Rust code.

So, how do you get the T value out of a Some variant when you have a value of type Option<T> so you can use that value? The Option<T> enum has a large number of methods that are useful in a variety of situations; you can check them out in its documentation. Becoming familiar with the methods on Option<T> will be extremely useful in your journey with Rust.

In general, in order to use an Option<T> value, you want to have code that will handle each variant. You want some code that will run only when you have a Some(T) value, and this code is allowed to use the inner T. You want some other code to run if you have a None value, and that code doesn’t have a T value available. The match expression is a control flow construct that does just this when used with enums: it will run different code depending on which variant of the enum it has, and that code can use the data inside the matching value.

The match Control Flow Construct

Rust has an extremely powerful control flow construct called match that allows you to compare a value against a series of patterns and then execute code based on which pattern matches. Patterns can be made up of literal values, variable names, wildcards, and many other things; Chapter 18 covers all the different kinds of patterns and what they do. The power of match comes from the expressiveness of the patterns and the fact that the compiler confirms that all possible cases are handled.

Think of a match expression as being like a coin-sorting machine: coins slide down a track with variously sized holes along it, and each coin falls through the first hole it encounters that it fits into. In the same way, values go through each pattern in a match, and at the first pattern the value “fits,” the value falls into the associated code block to be used during execution.

Speaking of coins, let’s use them as an example using match! We can write a function that takes an unknown United States coin and, in a similar way as the counting machine, determines which coin it is and return its value in cents, as shown here in Listing 6-3.

enum Coin {
    Penny,
    Nickel,
    Dime,
    Quarter,
}

fn value_in_cents(coin: Coin) -> u8 {
    match coin {
        Coin::Penny => 1,
        Coin::Nickel => 5,
        Coin::Dime => 10,
        Coin::Quarter => 25,
    }
}

fn main() {}

Listing 6-3: An enum and a match expression that has the variants of the enum as its patterns

Let’s break down the match in the value_in_cents function. First, we list the match keyword followed by an expression, which in this case is the value coin. This seems very similar to an expression used with if, but there’s a big difference: with if, the expression needs to return a Boolean value, but here, it can return any type. The type of coin in this example is the Coin enum that we defined on the first line.

Next are the match arms. An arm has two parts: a pattern and some code. The first arm here has a pattern that is the value Coin::Penny and then the => operator that separates the pattern and the code to run. The code in this case is just the value 1. Each arm is separated from the next with a comma.

When the match expression executes, it compares the resulting value against the pattern of each arm, in order. If a pattern matches the value, the code associated with that pattern is executed. If that pattern doesn’t match the value, execution continues to the next arm, much as in a coin-sorting machine. We can have as many arms as we need: in Listing 6-3, our match has four arms.

The code associated with each arm is an expression, and the resulting value of the expression in the matching arm is the value that gets returned for the entire match expression.

We don’t typically use curly brackets if the match arm code is short, as it is in Listing 6-3 where each arm just returns a value. If you want to run multiple lines of code in a match arm, you must use curly brackets, and the comma following the arm is then optional. For example, the following code prints “Lucky penny!” every time the method is called with a Coin::Penny, but still returns the last value of the block, 1:

enum Coin {
    Penny,
    Nickel,
    Dime,
    Quarter,
}

fn value_in_cents(coin: Coin) -> u8 {
    match coin {
        Coin::Penny => {
            println!("Lucky penny!");
            1
        }
        Coin::Nickel => 5,
        Coin::Dime => 10,
        Coin::Quarter => 25,
    }
}

fn main() {}

Patterns that Bind to Values

Another useful feature of match arms is that they can bind to the parts of the values that match the pattern. This is how we can extract values out of enum variants.

As an example, let’s change one of our enum variants to hold data inside it. From 1999 through 2008, the United States minted quarters with different designs for each of the 50 states on one side. No other coins got state designs, so only quarters have this extra value. We can add this information to our enum by changing the Quarter variant to include a UsState value stored inside it, which we’ve done here in Listing 6-4.

#[derive(Debug)] // so we can inspect the state in a minute
enum UsState {
    Alabama,
    Alaska,
    // --snip--
}

enum Coin {
    Penny,
    Nickel,
    Dime,
    Quarter(UsState),
}

fn main() {}

Listing 6-4: A Coin enum in which the Quarter variant also holds a UsState value

Let’s imagine that a friend is trying to collect all 50 state quarters. While we sort our loose change by coin type, we’ll also call out the name of the state associated with each quarter so if it’s one our friend doesn’t have, they can add it to their collection.

In the match expression for this code, we add a variable called state to the pattern that matches values of the variant Coin::Quarter. When a Coin::Quarter matches, the state variable will bind to the value of that quarter’s state. Then we can use state in the code for that arm, like so:

#[derive(Debug)]
enum UsState {
    Alabama,
    Alaska,
    // --snip--
}

enum Coin {
    Penny,
    Nickel,
    Dime,
    Quarter(UsState),
}

fn value_in_cents(coin: Coin) -> u8 {
    match coin {
        Coin::Penny => 1,
        Coin::Nickel => 5,
        Coin::Dime => 10,
        Coin::Quarter(state) => {
            println!("State quarter from {:?}!", state);
            25
        }
    }
}

fn main() {
    value_in_cents(Coin::Quarter(UsState::Alaska));
}

If we were to call value_in_cents(Coin::Quarter(UsState::Alaska)), coin would be Coin::Quarter(UsState::Alaska). When we compare that value with each of the match arms, none of them match until we reach Coin::Quarter(state). At that point, the binding for state will be the value UsState::Alaska. We can then use that binding in the println! expression, thus getting the inner state value out of the Coin enum variant for Quarter.

Matching with Option<T>

In the previous section, we wanted to get the inner T value out of the Some case when using Option<T>; we can also handle Option<T> using match as we did with the Coin enum! Instead of comparing coins, we’ll compare the variants of Option<T>, but the way that the match expression works remains the same.

Let’s say we want to write a function that takes an Option<i32> and, if there’s a value inside, adds 1 to that value. If there isn’t a value inside, the function should return the None value and not attempt to perform any operations.

This function is very easy to write, thanks to match, and will look like Listing 6-5.

fn main() {
    fn plus_one(x: Option<i32>) -> Option<i32> {
        match x {
            None => None,
            Some(i) => Some(i + 1),
        }
    }

    let five = Some(5);
    let six = plus_one(five);
    let none = plus_one(None);
}

Listing 6-5: A function that uses a match expression on an Option<i32>

Let’s examine the first execution of plus_one in more detail. When we call plus_one(five), the variable x in the body of plus_one will have the value Some(5). We then compare that against each match arm.

fn main() {
    fn plus_one(x: Option<i32>) -> Option<i32> {
        match x {
            None => None,
            Some(i) => Some(i + 1),
        }
    }

    let five = Some(5);
    let six = plus_one(five);
    let none = plus_one(None);
}

The Some(5) value doesn’t match the pattern None, so we continue to the next arm.

fn main() {
    fn plus_one(x: Option<i32>) -> Option<i32> {
        match x {
            None => None,
            Some(i) => Some(i + 1),
        }
    }

    let five = Some(5);
    let six = plus_one(five);
    let none = plus_one(None);
}

Does Some(5) match Some(i)? Why yes it does! We have the same variant. The i binds to the value contained in Some, so i takes the value 5. The code in the match arm is then executed, so we add 1 to the value of i and create a new Some value with our total 6 inside.

Now let’s consider the second call of plus_one in Listing 6-5, where x is None. We enter the match and compare to the first arm.

fn main() {
    fn plus_one(x: Option<i32>) -> Option<i32> {
        match x {
            None => None,
            Some(i) => Some(i + 1),
        }
    }

    let five = Some(5);
    let six = plus_one(five);
    let none = plus_one(None);
}

It matches! There’s no value to add to, so the program stops and returns the None value on the right side of =>. Because the first arm matched, no other arms are compared.

Combining match and enums is useful in many situations. You’ll see this pattern a lot in Rust code: match against an enum, bind a variable to the data inside, and then execute code based on it. It’s a bit tricky at first, but once you get used to it, you’ll wish you had it in all languages. It’s consistently a user favorite.

Matches Are Exhaustive

There’s one other aspect of match we need to discuss: the arms’ patterns must cover all possibilities. Consider this version of our plus_one function, which has a bug and won’t compile:

fn main() {
    fn plus_one(x: Option<i32>) -> Option<i32> {
        match x {
            Some(i) => Some(i + 1),
        }
    }

    let five = Some(5);
    let six = plus_one(five);
    let none = plus_one(None);
}

We didn’t handle the None case, so this code will cause a bug. Luckily, it’s a bug Rust knows how to catch. If we try to compile this code, we’ll get this error:

$ cargo run
   Compiling enums v0.1.0 (file:///projects/enums)
error[E0004]: non-exhaustive patterns: `None` not covered
   --> src/main.rs:3:15
    |
3   |         match x {
    |               ^ pattern `None` not covered
    |
note: `Option<i32>` defined here
    = note: the matched value is of type `Option<i32>`
help: ensure that all possible cases are being handled by adding a match arm with a wildcard pattern or an explicit pattern as shown
    |
4   ~             Some(i) => Some(i + 1),
5   ~             None => todo!(),
    |

For more information about this error, try `rustc --explain E0004`.
error: could not compile `enums` due to previous error

Rust knows that we didn’t cover every possible case and even knows which pattern we forgot! Matches in Rust are exhaustive: we must exhaust every last possibility in order for the code to be valid. Especially in the case of Option<T>, when Rust prevents us from forgetting to explicitly handle the None case, it protects us from assuming that we have a value when we might have null, thus making the billion-dollar mistake discussed earlier impossible.

Catch-all Patterns and the _ Placeholder

Using enums, we can also take special actions for a few particular values, but for all other values take one default action. Imagine we’re implementing a game where, if you roll a 3 on a dice roll, your player doesn’t move, but instead gets a new fancy hat. If you roll a 7, your player loses a fancy hat. For all other values, your player moves that number of spaces on the game board. Here’s a match that implements that logic, with the result of the dice roll hardcoded rather than a random value, and all other logic represented by functions without bodies because actually implementing them is out of scope for this example:

fn main() {
    let dice_roll = 9;
    match dice_roll {
        3 => add_fancy_hat(),
        7 => remove_fancy_hat(),
        other => move_player(other),
    }

    fn add_fancy_hat() {}
    fn remove_fancy_hat() {}
    fn move_player(num_spaces: u8) {}
}

For the first two arms, the patterns are the literal values 3 and 7. For the last arm that covers every other possible value, the pattern is the variable we’ve chosen to name other. The code that runs for the other arm uses the variable by passing it to the move_player function.

This code compiles, even though we haven’t listed all the possible values a u8 can have, because the last pattern will match all values not specifically listed. This catch-all pattern meets the requirement that match must be exhaustive. Note that we have to put the catch-all arm last because the patterns are evaluated in order. If we put the catch-all arm earlier, the other arms would never run, so Rust will warn us if we add arms after a catch-all!

Rust also has a pattern we can use when we want a catch-all but don’t want to use the value in the catch-all pattern: _ is a special pattern that matches any value and does not bind to that value. This tells Rust we aren’t going to use the value, so Rust won’t warn us about an unused variable.

Let’s change the rules of the game: now, if you roll anything other than a 3 or a 7, you must roll again. We no longer need to use the catch-all value, so we can change our code to use _ instead of the variable named other:

fn main() {
    let dice_roll = 9;
    match dice_roll {
        3 => add_fancy_hat(),
        7 => remove_fancy_hat(),
        _ => reroll(),
    }

    fn add_fancy_hat() {}
    fn remove_fancy_hat() {}
    fn reroll() {}
}

This example also meets the exhaustiveness requirement because we’re explicitly ignoring all other values in the last arm; we haven’t forgotten anything.

Finally, we’ll change the rules of the game one more time, so that nothing else happens on your turn if you roll anything other than a 3 or a 7. We can express that by using the unit value (the empty tuple type we mentioned in “The Tuple Type” section) as the code that goes with the _ arm:

fn main() {
    let dice_roll = 9;
    match dice_roll {
        3 => add_fancy_hat(),
        7 => remove_fancy_hat(),
        _ => (),
    }

    fn add_fancy_hat() {}
    fn remove_fancy_hat() {}
}

Here, we’re telling Rust explicitly that we aren’t going to use any other value that doesn’t match a pattern in an earlier arm, and we don’t want to run any code in this case.

There’s more about patterns and matching that we’ll cover in Chapter 18. For now, we’re going to move on to the if let syntax, which can be useful in situations where the match expression is a bit wordy.

Concise Control Flow with if let

The if let syntax lets you combine if and let into a less verbose way to handle values that match one pattern while ignoring the rest. Consider the program in Listing 6-6 that matches on an Option<u8> value in the config_max variable but only wants to execute code if the value is the Some variant.

fn main() {
    let config_max = Some(3u8);
    match config_max {
        Some(max) => println!("The maximum is configured to be {}", max),
        _ => (),
    }
}

Listing 6-6: A match that only cares about executing code when the value is Some

If the value is Some, we print out the value in the Some variant by binding the value to the variable max in the pattern. We don’t want to do anything with the None value. To satisfy the match expression, we have to add _ => () after processing just one variant, which is annoying boilerplate code to add.

Instead, we could write this in a shorter way using if let. The following code behaves the same as the match in Listing 6-6:

fn main() {
    let config_max = Some(3u8);
    if let Some(max) = config_max {
        println!("The maximum is configured to be {}", max);
    }
}

The syntax if let takes a pattern and an expression separated by an equal sign. It works the same way as a match, where the expression is given to the match and the pattern is its first arm. In this case, the pattern is Some(max), and the max binds to the value inside the Some. We can then use max in the body of the if let block in the same way as we used max in the corresponding match arm. The code in the if let block isn’t run if the value doesn’t match the pattern.

Using if let means less typing, less indentation, and less boilerplate code. However, you lose the exhaustive checking that match enforces. Choosing between match and if let depends on what you’re doing in your particular situation and whether gaining conciseness is an appropriate trade-off for losing exhaustive checking.

In other words, you can think of if let as syntax sugar for a match that runs code when the value matches one pattern and then ignores all other values.

We can include an else with an if let. The block of code that goes with the else is the same as the block of code that would go with the _ case in the match expression that is equivalent to the if let and else. Recall the Coin enum definition in Listing 6-4, where the Quarter variant also held a UsState value. If we wanted to count all non-quarter coins we see while also announcing the state of the quarters, we could do that with a match expression like this:

#[derive(Debug)]
enum UsState {
    Alabama,
    Alaska,
    // --snip--
}

enum Coin {
    Penny,
    Nickel,
    Dime,
    Quarter(UsState),
}

fn main() {
    let coin = Coin::Penny;
    let mut count = 0;
    match coin {
        Coin::Quarter(state) => println!("State quarter from {:?}!", state),
        _ => count += 1,
    }
}

Or we could use an if let and else expression like this:

#[derive(Debug)]
enum UsState {
    Alabama,
    Alaska,
    // --snip--
}

enum Coin {
    Penny,
    Nickel,
    Dime,
    Quarter(UsState),
}

fn main() {
    let coin = Coin::Penny;
    let mut count = 0;
    if let Coin::Quarter(state) = coin {
        println!("State quarter from {:?}!", state);
    } else {
        count += 1;
    }
}

If you have a situation in which your program has logic that is too verbose to express using a match, remember that if let is in your Rust toolbox as well.

Summary

We’ve now covered how to use enums to create custom types that can be one of a set of enumerated values. We’ve shown how the standard library’s Option<T> type helps you use the type system to prevent errors. When enum values have data inside them, you can use match or if let to extract and use those values, depending on how many cases you need to handle.

Your Rust programs can now express concepts in your domain using structs and enums. Creating custom types to use in your API ensures type safety: the compiler will make certain your functions get only values of the type each function expects.

In order to provide a well-organized API to your users that is straightforward to use and only exposes exactly what your users will need, let’s now turn to Rust’s modules.

Managing Growing Projects with Packages, Crates, and Modules

As you write large programs, organizing your code will become increasingly important. By grouping related functionality and separating code with distinct features, you’ll clarify where to find code that implements a particular feature and where to go to change how a feature works.

The programs we’ve written so far have been in one module in one file. As a project grows, you should organize code by splitting it into multiple modules and then multiple files. A package can contain multiple binary crates and optionally one library crate. As a package grows, you can extract parts into separate crates that become external dependencies. This chapter covers all these techniques. For very large projects comprising a set of interrelated packages that evolve together, Cargo provides workspaces, which we’ll cover in the “Cargo Workspaces” section in Chapter 14.

We’ll also discuss encapsulating implementation details, which lets you reuse code at a higher level: once you’ve implemented an operation, other code can call your code via its public interface without having to know how the implementation works. The way you write code defines which parts are public for other code to use and which parts are private implementation details that you reserve the right to change. This is another way to limit the amount of detail you have to keep in your head.

A related concept is scope: the nested context in which code is written has a set of names that are defined as “in scope.” When reading, writing, and compiling code, programmers and compilers need to know whether a particular name at a particular spot refers to a variable, function, struct, enum, module, constant, or other item and what that item means. You can create scopes and change which names are in or out of scope. You can’t have two items with the same name in the same scope; tools are available to resolve name conflicts.

Rust has a number of features that allow you to manage your code’s organization, including which details are exposed, which details are private, and what names are in each scope in your programs. These features, sometimes collectively referred to as the module system, include:

  • Packages: A Cargo feature that lets you build, test, and share crates
  • Crates: A tree of modules that produces a library or executable
  • Modules and use: Let you control the organization, scope, and privacy of paths
  • Paths: A way of naming an item, such as a struct, function, or module

In this chapter, we’ll cover all these features, discuss how they interact, and explain how to use them to manage scope. By the end, you should have a solid understanding of the module system and be able to work with scopes like a pro!

Packages and Crates

The first parts of the module system we’ll cover are packages and crates.

A crate is the smallest amount of code that the Rust compiler considers at a time. Even if you run rustc rather than cargo and pass a single source code file (as we did all the way back in the “Writing and Running a Rust Program” section of Chapter 1), the compiler considers that file to be a crate. Crates can contain modules, and the modules may be defined in other files that get compiled with the crate, as we’ll see in the coming sections.

A crate can come in one of two forms: a binary crate or a library crate. Binary crates are programs you can compile to an executable that you can run, such as a command-line program or a server. Each must have a function called main that defines what happens when the executable runs. All the crates we’ve created so far have been binary crates.

Library crates don’t have a main function, and they don’t compile to an executable. Instead, they define functionality intended to be shared with multiple projects. For example, the rand crate we used in Chapter 2 provides functionality that generates random numbers. Most of the time when Rustaceans say “crate”, they mean library crate, and they use “crate” interchangeably with the general programming concept of a “library".

The crate root is a source file that the Rust compiler starts from and makes up the root module of your crate (we’ll explain modules in depth in the “Defining Modules to Control Scope and Privacy” section).

A package is a bundle of one or more crates that provides a set of functionality. A package contains a Cargo.toml file that describes how to build those crates. Cargo is actually a package that contains the binary crate for the command-line tool you’ve been using to build your code. The Cargo package also contains a library crate that the binary crate depends on. Other projects can depend on the Cargo library crate to use the same logic the Cargo command-line tool uses.

A package can contain as many binary crates as you like, but at most only one library crate. A package must contain at least one crate, whether that’s a library or binary crate.

Let’s walk through what happens when we create a package. First, we enter the command cargo new:

$ cargo new my-project
     Created binary (application) `my-project` package
$ ls my-project
Cargo.toml
src
$ ls my-project/src
main.rs

After we run cargo new, we use ls to see what Cargo creates. In the project directory, there’s a Cargo.toml file, giving us a package. There’s also a src directory that contains main.rs. Open Cargo.toml in your text editor, and note there’s no mention of src/main.rs. Cargo follows a convention that src/main.rs is the crate root of a binary crate with the same name as the package. Likewise, Cargo knows that if the package directory contains src/lib.rs, the package contains a library crate with the same name as the package, and src/lib.rs is its crate root. Cargo passes the crate root files to rustc to build the library or binary.

Here, we have a package that only contains src/main.rs, meaning it only contains a binary crate named my-project. If a package contains src/main.rs and src/lib.rs, it has two crates: a binary and a library, both with the same name as the package. A package can have multiple binary crates by placing files in the src/bin directory: each file will be a separate binary crate.

Defining Modules to Control Scope and Privacy

In this section, we’ll talk about modules and other parts of the module system, namely paths that allow you to name items; the use keyword that brings a path into scope; and the pub keyword to make items public. We’ll also discuss the as keyword, external packages, and the glob operator.

First, we’re going to start with a list of rules for easy reference when you’re organizing your code in the future. Then we’ll explain each of the rules in detail.

Modules Cheat Sheet

Here we provide a quick reference on how modules, paths, the use keyword, and the pub keyword work in the compiler, and how most developers organize their code. We’ll be going through examples of each of these rules throughout this chapter, but this is a great place to refer to as a reminder of how modules work.

  • Start from the crate root: When compiling a crate, the compiler first looks in the crate root file (usually src/lib.rs for a library crate or src/main.rs for a binary crate) for code to compile.
  • Declaring modules: In the crate root file, you can declare new modules; say, you declare a “garden” module with mod garden;. The compiler will look for the module’s code in these places:
    • Inline, within curly brackets that replace the semicolon following mod garden
    • In the file src/garden.rs
    • In the file src/garden/mod.rs
  • Declaring submodules: In any file other than the crate root, you can declare submodules. For example, you might declare mod vegetables; in src/garden.rs. The compiler will look for the submodule’s code within the directory named for the parent module in these places:
    • Inline, directly following mod vegetables, within curly brackets instead of the semicolon
    • In the file src/garden/vegetables.rs
    • In the file src/garden/vegetables/mod.rs
  • Paths to code in modules: Once a module is part of your crate, you can refer to code in that module from anywhere else in that same crate, as long as the privacy rules allow, using the path to the code. For example, an Asparagus type in the garden vegetables module would be found at crate::garden::vegetables::Asparagus.
  • Private vs public: Code within a module is private from its parent modules by default. To make a module public, declare it with pub mod instead of mod. To make items within a public module public as well, use pub before their declarations.
  • The use keyword: Within a scope, the use keyword creates shortcuts to items to reduce repetition of long paths. In any scope that can refer to crate::garden::vegetables::Asparagus, you can create a shortcut with use crate::garden::vegetables::Asparagus; and from then on you only need to write Asparagus to make use of that type in the scope.

Here we create a binary crate named backyard that illustrates these rules. The crate’s directory, also named backyard, contains these files and directories:

backyard
├── Cargo.lock
├── Cargo.toml
└── src
    ├── garden
    │   └── vegetables.rs
    ├── garden.rs
    └── main.rs

The crate root file in this case is src/main.rs, and it contains:

Filename: src/main.rs

use crate::garden::vegetables::Asparagus;

pub mod garden;

fn main() {
    let plant = Asparagus {};
    println!("I'm growing {:?}!", plant);
}

The pub mod garden; line tells the compiler to include the code it finds in src/garden.rs, which is:

Filename: src/garden.rs

pub mod vegetables;

Here, pub mod vegetables; means the code in src/garden/vegetables.rs is included too. That code is:

#[derive(Debug)]
pub struct Asparagus {}

Now let’s get into the details of these rules and demonstrate them in action!

Modules let us organize code within a crate for readability and easy reuse. Modules also allow us to control the privacy of items, because code within a module is private by default. Private items are internal implementation details not available for outside use. We can choose to make modules and the items within them public, which exposes them to allow external code to use and depend on them.

As an example, let’s write a library crate that provides the functionality of a restaurant. We’ll define the signatures of functions but leave their bodies empty to concentrate on the organization of the code, rather than the implementation of a restaurant.

In the restaurant industry, some parts of a restaurant are referred to as front of house and others as back of house. Front of house is where customers are; this encompasses where the hosts seat customers, servers take orders and payment, and bartenders make drinks. Back of house is where the chefs and cooks work in the kitchen, dishwashers clean up, and managers do administrative work.

To structure our crate in this way, we can organize its functions into nested modules. Create a new library named restaurant by running cargo new --lib restaurant; then enter the code in Listing 7-1 into src/lib.rs to define some modules and function signatures. Here’s the front of house section:

Filename: src/lib.rs

mod front_of_house {
    mod hosting {
        fn add_to_waitlist() {}

        fn seat_at_table() {}
    }

    mod serving {
        fn take_order() {}

        fn serve_order() {}

        fn take_payment() {}
    }
}

Listing 7-1: A front_of_house module containing other modules that then contain functions

We define a module with the mod keyword followed by the name of the module (in this case, front_of_house). The body of the module then goes inside curly brackets. Inside modules, we can place other modules, as in this case with the modules hosting and serving. Modules can also hold definitions for other items, such as structs, enums, constants, traits, and—as in Listing 7-1—functions.

By using modules, we can group related definitions together and name why they’re related. Programmers using this code can navigate the code based on the groups rather than having to read through all the definitions, making it easier to find the definitions relevant to them. Programmers adding new functionality to this code would know where to place the code to keep the program organized.

Earlier, we mentioned that src/main.rs and src/lib.rs are called crate roots. The reason for their name is that the contents of either of these two files form a module named crate at the root of the crate’s module structure, known as the module tree.

Listing 7-2 shows the module tree for the structure in Listing 7-1.

crate
 └── front_of_house
     ├── hosting
     │   ├── add_to_waitlist
     │   └── seat_at_table
     └── serving
         ├── take_order
         ├── serve_order
         └── take_payment

Listing 7-2: The module tree for the code in Listing 7-1

This tree shows how some of the modules nest inside one another; for example, hosting nests inside front_of_house. The tree also shows that some modules are siblings to each other, meaning they’re defined in the same module; hosting and serving are siblings defined within front_of_house. If module A is contained inside module B, we say that module A is the child of module B and that module B is the parent of module A. Notice that the entire module tree is rooted under the implicit module named crate.

The module tree might remind you of the filesystem’s directory tree on your computer; this is a very apt comparison! Just like directories in a filesystem, you use modules to organize your code. And just like files in a directory, we need a way to find our modules.

Paths for Referring to an Item in the Module Tree

To show Rust where to find an item in a module tree, we use a path in the same way we use a path when navigating a filesystem. To call a function, we need to know its path.

A path can take two forms:

  • An absolute path is the full path starting from a crate root; for code from an external crate, the absolute path begins with the crate name, and for code from the current crate, it starts with the literal crate.
  • A relative path starts from the current module and uses self, super, or an identifier in the current module.

Both absolute and relative paths are followed by one or more identifiers separated by double colons (::).

Returning to Listing 7-1, say we want to call the add_to_waitlist function. This is the same as asking: what’s the path of the add_to_waitlist function? Listing 7-3 contains Listing 7-1 with some of the modules and functions removed.

We’ll show two ways to call the add_to_waitlist function from a new function eat_at_restaurant defined in the crate root. These paths are correct, but there’s another problem remaining that will prevent this example from compiling as-is. We’ll explain why in a bit.

The eat_at_restaurant function is part of our library crate’s public API, so we mark it with the pub keyword. In the “Exposing Paths with the pub Keyword” section, we’ll go into more detail about pub.

Filename: src/lib.rs

mod front_of_house {
    mod hosting {
        fn add_to_waitlist() {}
    }
}

pub fn eat_at_restaurant() {
    // Absolute path
    crate::front_of_house::hosting::add_to_waitlist();

    // Relative path
    front_of_house::hosting::add_to_waitlist();
}

Listing 7-3: Calling the add_to_waitlist function using absolute and relative paths

The first time we call the add_to_waitlist function in eat_at_restaurant, we use an absolute path. The add_to_waitlist function is defined in the same crate as eat_at_restaurant, which means we can use the crate keyword to start an absolute path. We then include each of the successive modules until we make our way to add_to_waitlist. You can imagine a filesystem with the same structure: we’d specify the path /front_of_house/hosting/add_to_waitlist to run the add_to_waitlist program; using the crate name to start from the crate root is like using / to start from the filesystem root in your shell.

The second time we call add_to_waitlist in eat_at_restaurant, we use a relative path. The path starts with front_of_house, the name of the module defined at the same level of the module tree as eat_at_restaurant. Here the filesystem equivalent would be using the path front_of_house/hosting/add_to_waitlist. Starting with a module name means that the path is relative.

Choosing whether to use a relative or absolute path is a decision you’ll make based on your project, and depends on whether you’re more likely to move item definition code separately from or together with the code that uses the item. For example, if we move the front_of_house module and the eat_at_restaurant function into a module named customer_experience, we’d need to update the absolute path to add_to_waitlist, but the relative path would still be valid. However, if we moved the eat_at_restaurant function separately into a module named dining, the absolute path to the add_to_waitlist call would stay the same, but the relative path would need to be updated. Our preference in general is to specify absolute paths because it’s more likely we’ll want to move code definitions and item calls independently of each other.

Let’s try to compile Listing 7-3 and find out why it won’t compile yet! The error we get is shown in Listing 7-4.

$ cargo build
   Compiling restaurant v0.1.0 (file:///projects/restaurant)
error[E0603]: module `hosting` is private
 --> src/lib.rs:9:28
  |
9 |     crate::front_of_house::hosting::add_to_waitlist();
  |                            ^^^^^^^ private module
  |
note: the module `hosting` is defined here
 --> src/lib.rs:2:5
  |
2 |     mod hosting {
  |     ^^^^^^^^^^^

error[E0603]: module `hosting` is private
  --> src/lib.rs:12:21
   |
12 |     front_of_house::hosting::add_to_waitlist();
   |                     ^^^^^^^ private module
   |
note: the module `hosting` is defined here
  --> src/lib.rs:2:5
   |
2  |     mod hosting {
   |     ^^^^^^^^^^^

For more information about this error, try `rustc --explain E0603`.
error: could not compile `restaurant` due to 2 previous errors

Listing 7-4: Compiler errors from building the code in Listing 7-3

The error messages say that module hosting is private. In other words, we have the correct paths for the hosting module and the add_to_waitlist function, but Rust won’t let us use them because it doesn’t have access to the private sections. In Rust, all items (functions, methods, structs, enums, modules, and constants) are private to parent modules by default. If you want to make an item like a function or struct private, you put it in a module.

Items in a parent module can’t use the private items inside child modules, but items in child modules can use the items in their ancestor modules. This is because child modules wrap and hide their implementation details, but the child modules can see the context in which they’re defined. To continue with our metaphor, think of the privacy rules as being like the back office of a restaurant: what goes on in there is private to restaurant customers, but office managers can see and do everything in the restaurant they operate.

Rust chose to have the module system function this way so that hiding inner implementation details is the default. That way, you know which parts of the inner code you can change without breaking outer code. However, Rust does give you the option to expose inner parts of child modules’ code to outer ancestor modules by using the pub keyword to make an item public.

Exposing Paths with the pub Keyword

Let’s return to the error in Listing 7-4 that told us the hosting module is private. We want the eat_at_restaurant function in the parent module to have access to the add_to_waitlist function in the child module, so we mark the hosting module with the pub keyword, as shown in Listing 7-5.

Filename: src/lib.rs

mod front_of_house {
    pub mod hosting {
        fn add_to_waitlist() {}
    }
}

pub fn eat_at_restaurant() {
    // Absolute path
    crate::front_of_house::hosting::add_to_waitlist();

    // Relative path
    front_of_house::hosting::add_to_waitlist();
}

Listing 7-5: Declaring the hosting module as pub to use it from eat_at_restaurant

Unfortunately, the code in Listing 7-5 still results in an error, as shown in Listing 7-6.

$ cargo build
   Compiling restaurant v0.1.0 (file:///projects/restaurant)
error[E0603]: function `add_to_waitlist` is private
 --> src/lib.rs:9:37
  |
9 |     crate::front_of_house::hosting::add_to_waitlist();
  |                                     ^^^^^^^^^^^^^^^ private function
  |
note: the function `add_to_waitlist` is defined here
 --> src/lib.rs:3:9
  |
3 |         fn add_to_waitlist() {}
  |         ^^^^^^^^^^^^^^^^^^^^

error[E0603]: function `add_to_waitlist` is private
  --> src/lib.rs:12:30
   |
12 |     front_of_house::hosting::add_to_waitlist();
   |                              ^^^^^^^^^^^^^^^ private function
   |
note: the function `add_to_waitlist` is defined here
  --> src/lib.rs:3:9
   |
3  |         fn add_to_waitlist() {}
   |         ^^^^^^^^^^^^^^^^^^^^

For more information about this error, try `rustc --explain E0603`.
error: could not compile `restaurant` due to 2 previous errors

Listing 7-6: Compiler errors from building the code in Listing 7-5

What happened? Adding the pub keyword in front of mod hosting makes the module public. With this change, if we can access front_of_house, we can access hosting. But the contents of hosting are still private; making the module public doesn’t make its contents public. The pub keyword on a module only lets code in its ancestor modules refer to it, not access its inner code. Because modules are containers, there’s not much we can do by only making the module public; we need to go further and choose to make one or more of the items within the module public as well.

The errors in Listing 7-6 say that the add_to_waitlist function is private. The privacy rules apply to structs, enums, functions, and methods as well as modules.

Let’s also make the add_to_waitlist function public by adding the pub keyword before its definition, as in Listing 7-7.

Filename: src/lib.rs

mod front_of_house {
    pub mod hosting {
        pub fn add_to_waitlist() {}
    }
}

pub fn eat_at_restaurant() {
    // Absolute path
    crate::front_of_house::hosting::add_to_waitlist();

    // Relative path
    front_of_house::hosting::add_to_waitlist();
}

Listing 7-7: Adding the pub keyword to mod hosting and fn add_to_waitlist lets us call the function from eat_at_restaurant

Now the code will compile! To see why adding the pub keyword lets us use these paths in add_to_waitlist with respect to the privacy rules, let’s look at the absolute and the relative paths.

In the absolute path, we start with crate, the root of our crate’s module tree. The front_of_house module is defined in the crate root. While front_of_house isn’t public, because the eat_at_restaurant function is defined in the same module as front_of_house (that is, eat_at_restaurant and front_of_house are siblings), we can refer to front_of_house from eat_at_restaurant. Next is the hosting module marked with pub. We can access the parent module of hosting, so we can access hosting. Finally, the add_to_waitlist function is marked with pub and we can access its parent module, so this function call works!

In the relative path, the logic is the same as the absolute path except for the first step: rather than starting from the crate root, the path starts from front_of_house. The front_of_house module is defined within the same module as eat_at_restaurant, so the relative path starting from the module in which eat_at_restaurant is defined works. Then, because hosting and add_to_waitlist are marked with pub, the rest of the path works, and this function call is valid!

If you plan on sharing your library crate so other projects can use your code, your public API is your contract with users of your crate that determines how they can interact with your code. There are many considerations around managing changes to your public API to make it easier for people to depend on your crate. These considerations are out of the scope of this book; if you’re interested in this topic, see The Rust API Guidelines.

Best Practices for Packages with a Binary and a Library

We mentioned a package can contain both a src/main.rs binary crate root as well as a src/lib.rs library crate root, and both crates will have the package name by default. Typically, packages with this pattern of containing both a library and a binary crate will have just enough code in the binary crate to start an executable that calls code with the library crate. This lets other projects benefit from the most functionality that the package provides, because the library crate’s code can be shared.

The module tree should be defined in src/lib.rs. Then, any public items can be used in the binary crate by starting paths with the name of the package. The binary crate becomes a user of the library crate just like a completely external crate would use the library crate: it can only use the public API. This helps you design a good API; not only are you the author, you’re also a client!

In Chapter 12, we’ll demonstrate this organizational practice with a command-line program that will contain both a binary crate and a library crate.

Starting Relative Paths with super

We can construct relative paths that begin in the parent module, rather than the current module or the crate root, by using super at the start of the path. This is like starting a filesystem path with the .. syntax. Using super allows us to reference an item that we know is in the parent module, which can make rearranging the module tree easier when the module is closely related to the parent, but the parent might be moved elsewhere in the module tree someday.

Consider the code in Listing 7-8 that models the situation in which a chef fixes an incorrect order and personally brings it out to the customer. The function fix_incorrect_order defined in the back_of_house module calls the function deliver_order defined in the parent module by specifying the path to deliver_order starting with super:

Filename: src/lib.rs

fn deliver_order() {}

mod back_of_house {
    fn fix_incorrect_order() {
        cook_order();
        super::deliver_order();
    }

    fn cook_order() {}
}

Listing 7-8: Calling a function using a relative path starting with super

The fix_incorrect_order function is in the back_of_house module, so we can use super to go to the parent module of back_of_house, which in this case is crate, the root. From there, we look for deliver_order and find it. Success! We think the back_of_house module and the deliver_order function are likely to stay in the same relationship to each other and get moved together should we decide to reorganize the crate’s module tree. Therefore, we used super so we’ll have fewer places to update code in the future if this code gets moved to a different module.

Making Structs and Enums Public

We can also use pub to designate structs and enums as public, but there are a few details extra to the usage of pub with structs and enums. If we use pub before a struct definition, we make the struct public, but the struct’s fields will still be private. We can make each field public or not on a case-by-case basis. In Listing 7-9, we’ve defined a public back_of_house::Breakfast struct with a public toast field but a private seasonal_fruit field. This models the case in a restaurant where the customer can pick the type of bread that comes with a meal, but the chef decides which fruit accompanies the meal based on what’s in season and in stock. The available fruit changes quickly, so customers can’t choose the fruit or even see which fruit they’ll get.

Filename: src/lib.rs

mod back_of_house {
    pub struct Breakfast {
        pub toast: String,
        seasonal_fruit: String,
    }

    impl Breakfast {
        pub fn summer(toast: &str) -> Breakfast {
            Breakfast {
                toast: String::from(toast),
                seasonal_fruit: String::from("peaches"),
            }
        }
    }
}

pub fn eat_at_restaurant() {
    // Order a breakfast in the summer with Rye toast
    let mut meal = back_of_house::Breakfast::summer("Rye");
    // Change our mind about what bread we'd like
    meal.toast = String::from("Wheat");
    println!("I'd like {} toast please", meal.toast);

    // The next line won't compile if we uncomment it; we're not allowed
    // to see or modify the seasonal fruit that comes with the meal
    // meal.seasonal_fruit = String::from("blueberries");
}

Listing 7-9: A struct with some public fields and some private fields

Because the toast field in the back_of_house::Breakfast struct is public, in eat_at_restaurant we can write and read to the toast field using dot notation. Notice that we can’t use the seasonal_fruit field in eat_at_restaurant because seasonal_fruit is private. Try uncommenting the line modifying the seasonal_fruit field value to see what error you get!

Also, note that because back_of_house::Breakfast has a private field, the struct needs to provide a public associated function that constructs an instance of Breakfast (we’ve named it summer here). If Breakfast didn’t have such a function, we couldn’t create an instance of Breakfast in eat_at_restaurant because we couldn’t set the value of the private seasonal_fruit field in eat_at_restaurant.

In contrast, if we make an enum public, all of its variants are then public. We only need the pub before the enum keyword, as shown in Listing 7-10.

Filename: src/lib.rs

mod back_of_house {
    pub enum Appetizer {
        Soup,
        Salad,
    }
}

pub fn eat_at_restaurant() {
    let order1 = back_of_house::Appetizer::Soup;
    let order2 = back_of_house::Appetizer::Salad;
}

Listing 7-10: Designating an enum as public makes all its variants public

Because we made the Appetizer enum public, we can use the Soup and Salad variants in eat_at_restaurant.

Enums aren’t very useful unless their variants are public; it would be annoying to have to annotate all enum variants with pub in every case, so the default for enum variants is to be public. Structs are often useful without their fields being public, so struct fields follow the general rule of everything being private by default unless annotated with pub.

There’s one more situation involving pub that we haven’t covered, and that is our last module system feature: the use keyword. We’ll cover use by itself first, and then we’ll show how to combine pub and use.

Bringing Paths into Scope with the use Keyword

Having to write out the paths to call functions can feel inconvenient and repetitive. In Listing 7-7, whether we chose the absolute or relative path to the add_to_waitlist function, every time we wanted to call add_to_waitlist we had to specify front_of_house and hosting too. Fortunately, there’s a way to simplify this process: we can create a shortcut to a path with the use keyword once, and then use the shorter name everywhere else in the scope.

In Listing 7-11, we bring the crate::front_of_house::hosting module into the scope of the eat_at_restaurant function so we only have to specify hosting::add_to_waitlist to call the add_to_waitlist function in eat_at_restaurant.

Filename: src/lib.rs

mod front_of_house {
    pub mod hosting {
        pub fn add_to_waitlist() {}
    }
}

use crate::front_of_house::hosting;

pub fn eat_at_restaurant() {
    hosting::add_to_waitlist();
}

Listing 7-11: Bringing a module into scope with use

Adding use and a path in a scope is similar to creating a symbolic link in the filesystem. By adding use crate::front_of_house::hosting in the crate root, hosting is now a valid name in that scope, just as though the hosting module had been defined in the crate root. Paths brought into scope with use also check privacy, like any other paths.

Note that use only creates the shortcut for the particular scope in which the use occurs. Listing 7-12 moves the eat_at_restaurant function into a new child module named customer, which is then a different scope than the use statement, so the function body won’t compile:

Filename: src/lib.rs

mod front_of_house {
    pub mod hosting {
        pub fn add_to_waitlist() {}
    }
}

use crate::front_of_house::hosting;

mod customer {
    pub fn eat_at_restaurant() {
        hosting::add_to_waitlist();
    }
}

Listing 7-12: A use statement only applies in the scope it’s in

The compiler error shows that the shortcut no longer applies within the customer module:

$ cargo build
   Compiling restaurant v0.1.0 (file:///projects/restaurant)
error[E0433]: failed to resolve: use of undeclared crate or module `hosting`
  --> src/lib.rs:11:9
   |
11 |         hosting::add_to_waitlist();
   |         ^^^^^^^ use of undeclared crate or module `hosting`

warning: unused import: `crate::front_of_house::hosting`
 --> src/lib.rs:7:5
  |
7 | use crate::front_of_house::hosting;
  |     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  |
  = note: `#[warn(unused_imports)]` on by default

For more information about this error, try `rustc --explain E0433`.
warning: `restaurant` (lib) generated 1 warning
error: could not compile `restaurant` due to previous error; 1 warning emitted

Notice there’s also a warning that the use is no longer used in its scope! To fix this problem, move the use within the customer module too, or reference the shortcut in the parent module with super::hosting within the child customer module.

Creating Idiomatic use Paths

In Listing 7-11, you might have wondered why we specified use crate::front_of_house::hosting and then called hosting::add_to_waitlist in eat_at_restaurant rather than specifying the use path all the way out to the add_to_waitlist function to achieve the same result, as in Listing 7-13.

Filename: src/lib.rs

mod front_of_house {
    pub mod hosting {
        pub fn add_to_waitlist() {}
    }
}

use crate::front_of_house::hosting::add_to_waitlist;

pub fn eat_at_restaurant() {
    add_to_waitlist();
}

Listing 7-13: Bringing the add_to_waitlist function into scope with use, which is unidiomatic

Although both Listing 7-11 and 7-13 accomplish the same task, Listing 7-11 is the idiomatic way to bring a function into scope with use. Bringing the function’s parent module into scope with use means we have to specify the parent module when calling the function. Specifying the parent module when calling the function makes it clear that the function isn’t locally defined while still minimizing repetition of the full path. The code in Listing 7-13 is unclear as to where add_to_waitlist is defined.

On the other hand, when bringing in structs, enums, and other items with use, it’s idiomatic to specify the full path. Listing 7-14 shows the idiomatic way to bring the standard library’s HashMap struct into the scope of a binary crate.

Filename: src/main.rs

use std::collections::HashMap;

fn main() {
    let mut map = HashMap::new();
    map.insert(1, 2);
}

Listing 7-14: Bringing HashMap into scope in an idiomatic way

There’s no strong reason behind this idiom: it’s just the convention that has emerged, and folks have gotten used to reading and writing Rust code this way.

The exception to this idiom is if we’re bringing two items with the same name into scope with use statements, because Rust doesn’t allow that. Listing 7-15 shows how to bring two Result types into scope that have the same name but different parent modules and how to refer to them.

Filename: src/lib.rs

use std::fmt;
use std::io;

fn function1() -> fmt::Result {
    // --snip--
    Ok(())
}

fn function2() -> io::Result<()> {
    // --snip--
    Ok(())
}

Listing 7-15: Bringing two types with the same name into the same scope requires using their parent modules.

As you can see, using the parent modules distinguishes the two Result types. If instead we specified use std::fmt::Result and use std::io::Result, we’d have two Result types in the same scope and Rust wouldn’t know which one we meant when we used Result.

Providing New Names with the as Keyword

There’s another solution to the problem of bringing two types of the same name into the same scope with use: after the path, we can specify as and a new local name, or alias, for the type. Listing 7-16 shows another way to write the code in Listing 7-15 by renaming one of the two Result types using as.

Filename: src/lib.rs

use std::fmt::Result;
use std::io::Result as IoResult;

fn function1() -> Result {
    // --snip--
    Ok(())
}

fn function2() -> IoResult<()> {
    // --snip--
    Ok(())
}

Listing 7-16: Renaming a type when it’s brought into scope with the as keyword

In the second use statement, we chose the new name IoResult for the std::io::Result type, which won’t conflict with the Result from std::fmt that we’ve also brought into scope. Listing 7-15 and Listing 7-16 are considered idiomatic, so the choice is up to you!

Re-exporting Names with pub use

When we bring a name into scope with the use keyword, the name available in the new scope is private. To enable the code that calls our code to refer to that name as if it had been defined in that code’s scope, we can combine pub and use. This technique is called re-exporting because we’re bringing an item into scope but also making that item available for others to bring into their scope.

Listing 7-17 shows the code in Listing 7-11 with use in the root module changed to pub use.

Filename: src/lib.rs

mod front_of_house {
    pub mod hosting {
        pub fn add_to_waitlist() {}
    }
}

pub use crate::front_of_house::hosting;

pub fn eat_at_restaurant() {
    hosting::add_to_waitlist();
}

Listing 7-17: Making a name available for any code to use from a new scope with pub use

Before this change, external code would have to call the add_to_waitlist function by using the path restaurant::front_of_house::hosting::add_to_waitlist(). Now that this pub use has re-exported the hosting module from the root module, external code can now use the path restaurant::hosting::add_to_waitlist() instead.

Re-exporting is useful when the internal structure of your code is different from how programmers calling your code would think about the domain. For example, in this restaurant metaphor, the people running the restaurant think about “front of house” and “back of house.” But customers visiting a restaurant probably won’t think about the parts of the restaurant in those terms. With pub use, we can write our code with one structure but expose a different structure. Doing so makes our library well organized for programmers working on the library and programmers calling the library. We’ll look at another example of pub use and how it affects your crate’s documentation in the “Exporting a Convenient Public API with pub use section of Chapter 14.

Using External Packages

In Chapter 2, we programmed a guessing game project that used an external package called rand to get random numbers. To use rand in our project, we added this line to Cargo.toml:

Filename: Cargo.toml

rand = "0.8.3"

Adding rand as a dependency in Cargo.toml tells Cargo to download the rand package and any dependencies from crates.io and make rand available to our project.

Then, to bring rand definitions into the scope of our package, we added a use line starting with the name of the crate, rand, and listed the items we wanted to bring into scope. Recall that in the “Generating a Random Number” section in Chapter 2, we brought the Rng trait into scope and called the rand::thread_rng function:

use std::io;
use rand::Rng;

fn main() {
    println!("Guess the number!");

    let secret_number = rand::thread_rng().gen_range(1..=100);

    println!("The secret number is: {secret_number}");

    println!("Please input your guess.");

    let mut guess = String::new();

    io::stdin()
        .read_line(&mut guess)
        .expect("Failed to read line");

    println!("You guessed: {guess}");
}

Members of the Rust community have made many packages available at crates.io, and pulling any of them into your package involves these same steps: listing them in your package’s Cargo.toml file and using use to bring items from their crates into scope.

Note that the standard std library is also a crate that’s external to our package. Because the standard library is shipped with the Rust language, we don’t need to change Cargo.toml to include std. But we do need to refer to it with use to bring items from there into our package’s scope. For example, with HashMap we would use this line:


#![allow(unused)]
fn main() {
use std::collections::HashMap;
}

This is an absolute path starting with std, the name of the standard library crate.

Using Nested Paths to Clean Up Large use Lists

If we’re using multiple items defined in the same crate or same module, listing each item on its own line can take up a lot of vertical space in our files. For example, these two use statements we had in the Guessing Game in Listing 2-4 bring items from std into scope:

Filename: src/main.rs

use rand::Rng;
// --snip--
use std::cmp::Ordering;
use std::io;
// --snip--

fn main() {
    println!("Guess the number!");

    let secret_number = rand::thread_rng().gen_range(1..=100);

    println!("The secret number is: {secret_number}");

    println!("Please input your guess.");

    let mut guess = String::new();

    io::stdin()
        .read_line(&mut guess)
        .expect("Failed to read line");

    println!("You guessed: {guess}");

    match guess.cmp(&secret_number) {
        Ordering::Less => println!("Too small!"),
        Ordering::Greater => println!("Too big!"),
        Ordering::Equal => println!("You win!"),
    }
}

Instead, we can use nested paths to bring the same items into scope in one line. We do this by specifying the common part of the path, followed by two colons, and then curly brackets around a list of the parts of the paths that differ, as shown in Listing 7-18.

Filename: src/main.rs

use rand::Rng;
// --snip--
use std::{cmp::Ordering, io};
// --snip--

fn main() {
    println!("Guess the number!");

    let secret_number = rand::thread_rng().gen_range(1..=100);

    println!("The secret number is: {secret_number}");

    println!("Please input your guess.");

    let mut guess = String::new();

    io::stdin()
        .read_line(&mut guess)
        .expect("Failed to read line");

    let guess: u32 = guess.trim().parse().expect("Please type a number!");

    println!("You guessed: {guess}");

    match guess.cmp(&secret_number) {
        Ordering::Less => println!("Too small!"),
        Ordering::Greater => println!("Too big!"),
        Ordering::Equal => println!("You win!"),
    }
}

Listing 7-18: Specifying a nested path to bring multiple items with the same prefix into scope

In bigger programs, bringing many items into scope from the same crate or module using nested paths can reduce the number of separate use statements needed by a lot!

We can use a nested path at any level in a path, which is useful when combining two use statements that share a subpath. For example, Listing 7-19 shows two use statements: one that brings std::io into scope and one that brings std::io::Write into scope.

Filename: src/lib.rs

use std::io;
use std::io::Write;

Listing 7-19: Two use statements where one is a subpath of the other

The common part of these two paths is std::io, and that’s the complete first path. To merge these two paths into one use statement, we can use self in the nested path, as shown in Listing 7-20.

Filename: src/lib.rs

use std::io::{self, Write};

Listing 7-20: Combining the paths in Listing 7-19 into one use statement

This line brings std::io and std::io::Write into scope.

The Glob Operator

If we want to bring all public items defined in a path into scope, we can specify that path followed by the * glob operator:


#![allow(unused)]
fn main() {
use std::collections::*;
}

This use statement brings all public items defined in std::collections into the current scope. Be careful when using the glob operator! Glob can make it harder to tell what names are in scope and where a name used in your program was defined.

The glob operator is often used when testing to bring everything under test into the tests module; we’ll talk about that in the “How to Write Tests” section in Chapter 11. The glob operator is also sometimes used as part of the prelude pattern: see the standard library documentation for more information on that pattern.

Separating Modules into Different Files

So far, all the examples in this chapter defined multiple modules in one file. When modules get large, you might want to move their definitions to a separate file to make the code easier to navigate.

For example, let’s start from the code in Listing 7-17 that had multiple restaurant modules. We’ll extract modules into files instead of having all the modules defined in the crate root file. In this case, the crate root file is src/lib.rs, but this procedure also works with binary crates whose crate root file is src/main.rs.

First, we’ll extract the front_of_house module to its own file. Remove the code inside the curly brackets for the front_of_house module, leaving only the mod front_of_house; declaration, so that src/lib.rs contains the code shown in Listing 7-21. Note that this won’t compile until we create the src/front_of_house.rs file in Listing 7-22.

Filename: src/lib.rs

mod front_of_house;

pub use crate::front_of_house::hosting;

pub fn eat_at_restaurant() {
    hosting::add_to_waitlist();
}

Listing 7-21: Declaring the front_of_house module whose body will be in src/front_of_house.rs

Next, place the code that was in the curly brackets into a new file named src/front_of_house.rs, as shown in Listing 7-22. The compiler knows to look in this file because it came across the module declaration in the crate root with the name front_of_house.

Filename: src/front_of_house.rs

pub mod hosting {
    pub fn add_to_waitlist() {}
}

Listing 7-22: Definitions inside the front_of_house module in src/front_of_house.rs

Note that you only need to load a file using a mod declaration once in your module tree. Once the compiler knows the file is part of the project (and knows where in the module tree the code resides because of where you’ve put the mod statement), other files in your project should refer to the loaded file’s code using a path to where it was declared, as covered in the “Paths for Referring to an Item in the Module Tree” section. In other words, mod is not an “include” operation that you may have seen in other programming languages.

Next, we’ll extract the hosting module to its own file. The process is a bit different because hosting is a child module of front_of_house, not of the root module. We’ll place the file for hosting in a new directory that will be named for its ancestors in the module tree, in this case src/front_of_house/.

To start moving hosting, we change src/front_of_house.rs to contain only the declaration of the hosting module:

Filename: src/front_of_house.rs

pub mod hosting;

Then we create a src/front_of_house directory and a file hosting.rs to contain the definitions made in the hosting module:

Filename: src/front_of_house/hosting.rs

pub fn add_to_waitlist() {}

If we instead put hosting.rs in the src directory, the compiler would expect the hosting.rs code to be in a hosting module declared in the crate root, and not declared as a child of the front_of_house module. The compiler’s rules for which files to check for which modules’ code means the directories and files more closely match the module tree.

Alternate File Paths

So far we’ve covered the most idiomatic file paths the Rust compiler uses, but Rust also supports an older style of file path. For a module named front_of_house declared in the crate root, the compiler will look for the module’s code in:

  • src/front_of_house.rs (what we covered)
  • src/front_of_house/mod.rs (older style, still supported path)

For a module named hosting that is a submodule of front_of_house, the compiler will look for the module’s code in:

  • src/front_of_house/hosting.rs (what we covered)
  • src/front_of_house/hosting/mod.rs (older style, still supported path)

If you use both styles for the same module, you’ll get a compiler error. Using a mix of both styles for different modules in the same project is allowed, but might be confusing for people navigating your project.

The main downside to the style that uses files named mod.rs is that your project can end up with many files named mod.rs, which can get confusing when you have them open in your editor at the same time.

We’ve moved each module’s code to a separate file, and the module tree remains the same. The function calls in eat_at_restaurant will work without any modification, even though the definitions live in different files. This technique lets you move modules to new files as they grow in size.

Note that the pub use crate::front_of_house::hosting statement in src/lib.rs also hasn’t changed, nor does use have any impact on what files are compiled as part of the crate. The mod keyword declares modules, and Rust looks in a file with the same name as the module for the code that goes into that module.

Summary

Rust lets you split a package into multiple crates and a crate into modules so you can refer to items defined in one module from another module. You can do this by specifying absolute or relative paths. These paths can be brought into scope with a use statement so you can use a shorter path for multiple uses of the item in that scope. Module code is private by default, but you can make definitions public by adding the pub keyword.

In the next chapter, we’ll look at some collection data structures in the standard library that you can use in your neatly organized code.

Common Collections

Rust’s standard library includes a number of very useful data structures called collections. Most other data types represent one specific value, but collections can contain multiple values. Unlike the built-in array and tuple types, the data these collections point to is stored on the heap, which means the amount of data does not need to be known at compile time and can grow or shrink as the program runs. Each kind of collection has different capabilities and costs, and choosing an appropriate one for your current situation is a skill you’ll develop over time. In this chapter, we’ll discuss three collections that are used very often in Rust programs:

  • A vector allows you to store a variable number of values next to each other.
  • A string is a collection of characters. We’ve mentioned the String type previously, but in this chapter we’ll talk about it in depth.
  • A hash map allows you to associate a value with a particular key. It’s a particular implementation of the more general data structure called a map.

To learn about the other kinds of collections provided by the standard library, see the documentation.

We’ll discuss how to create and update vectors, strings, and hash maps, as well as what makes each special.

Storing Lists of Values with Vectors

The first collection type we’ll look at is Vec<T>, also known as a vector. Vectors allow you to store more than one value in a single data structure that puts all the values next to each other in memory. Vectors can only store values of the same type. They are useful when you have a list of items, such as the lines of text in a file or the prices of items in a shopping cart.

Creating a New Vector

To create a new empty vector, we call the Vec::new function, as shown in Listing 8-1.

fn main() {
    let v: Vec<i32> = Vec::new();
}

Listing 8-1: Creating a new, empty vector to hold values of type i32

Note that we added a type annotation here. Because we aren’t inserting any values into this vector, Rust doesn’t know what kind of elements we intend to store. This is an important point. Vectors are implemented using generics; we’ll cover how to use generics with your own types in Chapter 10. For now, know that the Vec<T> type provided by the standard library can hold any type. When we create a vector to hold a specific type, we can specify the type within angle brackets. In Listing 8-1, we’ve told Rust that the Vec<T> in v will hold elements of the i32 type.

More often, you’ll create a Vec<T> with initial values and Rust will infer the type of value you want to store, so you rarely need to do this type annotation. Rust conveniently provides the vec! macro, which will create a new vector that holds the values you give it. Listing 8-2 creates a new Vec<i32> that holds the values 1, 2, and 3. The integer type is i32 because that’s the default integer type, as we discussed in the “Data Types” section of Chapter 3.

fn main() {
    let v = vec![1, 2, 3];
}

Listing 8-2: Creating a new vector containing values

Because we’ve given initial i32 values, Rust can infer that the type of v is Vec<i32>, and the type annotation isn’t necessary. Next, we’ll look at how to modify a vector.

Updating a Vector

To create a vector and then add elements to it, we can use the push method, as shown in Listing 8-3.

fn main() {
    let mut v = Vec::new();

    v.push(5);
    v.push(6);
    v.push(7);
    v.push(8);
}

Listing 8-3: Using the push method to add values to a vector

As with any variable, if we want to be able to change its value, we need to make it mutable using the mut keyword, as discussed in Chapter 3. The numbers we place inside are all of type i32, and Rust infers this from the data, so we don’t need the Vec<i32> annotation.

Reading Elements of Vectors

There are two ways to reference a value stored in a vector: via indexing or using the get method. In the following examples, we’ve annotated the types of the values that are returned from these functions for extra clarity.

Listing 8-4 shows both methods of accessing a value in a vector, with indexing syntax and the get method.

fn main() {
    let v = vec![1, 2, 3, 4, 5];

    let third: &i32 = &v[2];
    println!("The third element is {}", third);

    let third: Option<&i32> = v.get(2);
    match third {
        Some(third) => println!("The third element is {}", third),
        None => println!("There is no third element."),
    }
}

Listing 8-4: Using indexing syntax or the get method to access an item in a vector

Note a few details here. We use the index value of 2 to get the third element because vectors are indexed by number, starting at zero. Using & and [] gives us a reference to the element at the index value. When we use the get method with the index passed as an argument, we get an Option<&T> that we can use with match.

The reason Rust provides these two ways to reference an element is so you can choose how the program behaves when you try to use an index value outside the range of existing elements. As an example, let’s see what happens when we have a vector of five elements and then we try to access an element at index 100 with each technique, as shown in Listing 8-5.

fn main() {
    let v = vec![1, 2, 3, 4, 5];

    let does_not_exist = &v[100];
    let does_not_exist = v.get(100);
}

Listing 8-5: Attempting to access the element at index 100 in a vector containing five elements

When we run this code, the first [] method will cause the program to panic because it references a nonexistent element. This method is best used when you want your program to crash if there’s an attempt to access an element past the end of the vector.

When the get method is passed an index that is outside the vector, it returns None without panicking. You would use this method if accessing an element beyond the range of the vector may happen occasionally under normal circumstances. Your code will then have logic to handle having either Some(&element) or None, as discussed in Chapter 6. For example, the index could be coming from a person entering a number. If they accidentally enter a number that’s too large and the program gets a None value, you could tell the user how many items are in the current vector and give them another chance to enter a valid value. That would be more user-friendly than crashing the program due to a typo!

When the program has a valid reference, the borrow checker enforces the ownership and borrowing rules (covered in Chapter 4) to ensure this reference and any other references to the contents of the vector remain valid. Recall the rule that states you can’t have mutable and immutable references in the same scope. That rule applies in Listing 8-6, where we hold an immutable reference to the first element in a vector and try to add an element to the end. This program won’t work if we also try to refer to that element later in the function:

fn main() {
    let mut v = vec![1, 2, 3, 4, 5];

    let first = &v[0];

    v.push(6);

    println!("The first element is: {}", first);
}

Listing 8-6: Attempting to add an element to a vector while holding a reference to an item

Compiling this code will result in this error:

$ cargo run
   Compiling collections v0.1.0 (file:///projects/collections)
error[E0502]: cannot borrow `v` as mutable because it is also borrowed as immutable
 --> src/main.rs:6:5
  |
4 |     let first = &v[0];
  |                  - immutable borrow occurs here
5 | 
6 |     v.push(6);
  |     ^^^^^^^^^ mutable borrow occurs here
7 | 
8 |     println!("The first element is: {}", first);
  |                                          ----- immutable borrow later used here

For more information about this error, try `rustc --explain E0502`.
error: could not compile `collections` due to previous error

The code in Listing 8-6 might look like it should work: why should a reference to the first element care about changes at the end of the vector? This error is due to the way vectors work: because vectors put the values next to each other in memory, adding a new element onto the end of the vector might require allocating new memory and copying the old elements to the new space, if there isn’t enough room to put all the elements next to each other where the vector is currently stored. In that case, the reference to the first element would be pointing to deallocated memory. The borrowing rules prevent programs from ending up in that situation.

Note: For more on the implementation details of the Vec<T> type, see “The Rustonomicon”.

Iterating over the Values in a Vector

To access each element in a vector in turn, we would iterate through all of the elements rather than use indices to access one at a time. Listing 8-7 shows how to use a for loop to get immutable references to each element in a vector of i32 values and print them.

fn main() {
    let v = vec![100, 32, 57];
    for i in &v {
        println!("{}", i);
    }
}

Listing 8-7: Printing each element in a vector by iterating over the elements using a for loop

We can also iterate over mutable references to each element in a mutable vector in order to make changes to all the elements. The for loop in Listing 8-8 will add 50 to each element.

fn main() {
    let mut v = vec![100, 32, 57];
    for i in &mut v {
        *i += 50;
    }
}

Listing 8-8: Iterating over mutable references to elements in a vector

To change the value that the mutable reference refers to, we have to use the * dereference operator to get to the value in i before we can use the += operator. We’ll talk more about the dereference operator in the “Following the Pointer to the Value with the Dereference Operator” section of Chapter 15.

Iterating over a vector, whether immutably or mutably, is safe because of the borrow checker's rules. If we attempted to insert or remove items in the for loop bodies in Listing 8-7 and Listing 8-8, we would get a compiler error similar to the one we got with the code in Listing 8-6. The reference to the vector that the for loop holds prevents simultaneous modification of the whole vector.

Using an Enum to Store Multiple Types

Vectors can only store values that are the same type. This can be inconvenient; there are definitely use cases for needing to store a list of items of different types. Fortunately, the variants of an enum are defined under the same enum type, so when we need one type to represent elements of different types, we can define and use an enum!

For example, say we want to get values from a row in a spreadsheet in which some of the columns in the row contain integers, some floating-point numbers, and some strings. We can define an enum whose variants will hold the different value types, and all the enum variants will be considered the same type: that of the enum. Then we can create a vector to hold that enum and so, ultimately, holds different types. We’ve demonstrated this in Listing 8-9.

fn main() {
    enum SpreadsheetCell {
        Int(i32),
        Float(f64),
        Text(String),
    }

    let row = vec![
        SpreadsheetCell::Int(3),
        SpreadsheetCell::Text(String::from("blue")),
        SpreadsheetCell::Float(10.12),
    ];
}

Listing 8-9: Defining an enum to store values of different types in one vector

Rust needs to know what types will be in the vector at compile time so it knows exactly how much memory on the heap will be needed to store each element. We must also be explicit about what types are allowed in this vector. If Rust allowed a vector to hold any type, there would be a chance that one or more of the types would cause errors with the operations performed on the elements of the vector. Using an enum plus a match expression means that Rust will ensure at compile time that every possible case is handled, as discussed in Chapter 6.

If you don’t know the exhaustive set of types a program will get at runtime to store in a vector, the enum technique won’t work. Instead, you can use a trait object, which we’ll cover in Chapter 17.

Now that we’ve discussed some of the most common ways to use vectors, be sure to review the API documentation for all the many useful methods defined on Vec<T> by the standard library. For example, in addition to push, a pop method removes and returns the last element.

Dropping a Vector Drops Its Elements

Like any other struct, a vector is freed when it goes out of scope, as annotated in Listing 8-10.

fn main() {
    {
        let v = vec![1, 2, 3, 4];

        // do stuff with v
    } // <- v goes out of scope and is freed here
}

Listing 8-10: Showing where the vector and its elements are dropped

When the vector gets dropped, all of its contents are also dropped, meaning the integers it holds will be cleaned up. The borrow checker ensures that any references to contents of a vector are only used while the vector itself is valid.

Let’s move on to the next collection type: String!

Storing UTF-8 Encoded Text with Strings

We talked about strings in Chapter 4, but we’ll look at them in more depth now. New Rustaceans commonly get stuck on strings for a combination of three reasons: Rust’s propensity for exposing possible errors, strings being a more complicated data structure than many programmers give them credit for, and UTF-8. These factors combine in a way that can seem difficult when you’re coming from other programming languages.

We discuss strings in the context of collections because strings are implemented as a collection of bytes, plus some methods to provide useful functionality when those bytes are interpreted as text. In this section, we’ll talk about the operations on String that every collection type has, such as creating, updating, and reading. We’ll also discuss the ways in which String is different from the other collections, namely how indexing into a String is complicated by the differences between how people and computers interpret String data.

What Is a String?

We’ll first define what we mean by the term string. Rust has only one string type in the core language, which is the string slice str that is usually seen in its borrowed form &str. In Chapter 4, we talked about string slices, which are references to some UTF-8 encoded string data stored elsewhere. String literals, for example, are stored in the program’s binary and are therefore string slices.

The String type, which is provided by Rust’s standard library rather than coded into the core language, is a growable, mutable, owned, UTF-8 encoded string type. When Rustaceans refer to “strings” in Rust, they might be referring to either the String or the string slice &str types, not just one of those types. Although this section is largely about String, both types are used heavily in Rust’s standard library, and both String and string slices are UTF-8 encoded.

Creating a New String

Many of the same operations available with Vec<T> are available with String as well, because String is actually implemented as a wrapper around a vector of bytes with some extra guarantees, restrictions, and capabilities. An example of a function that works the same way with Vec<T> and String is the new function to create an instance, shown in Listing 8-11.

fn main() {
    let mut s = String::new();
}

Listing 8-11: Creating a new, empty String

This line creates a new empty string called s, which we can then load data into. Often, we’ll have some initial data that we want to start the string with. For that, we use the to_string method, which is available on any type that implements the Display trait, as string literals do. Listing 8-12 shows two examples.

fn main() {
    let data = "initial contents";

    let s = data.to_string();

    // the method also works on a literal directly:
    let s = "initial contents".to_string();
}

Listing 8-12: Using the to_string method to create a String from a string literal

This code creates a string containing initial contents.

We can also use the function String::from to create a String from a string literal. The code in Listing 8-13 is equivalent to the code from Listing 8-12 that uses to_string.

fn main() {
    let s = String::from("initial contents");
}

Listing 8-13: Using the String::from function to create a String from a string literal

Because strings are used for so many things, we can use many different generic APIs for strings, providing us with a lot of options. Some of them can seem redundant, but they all have their place! In this case, String::from and to_string do the same thing, so which you choose is a matter of style and readability.

Remember that strings are UTF-8 encoded, so we can include any properly encoded data in them, as shown in Listing 8-14.

fn main() {
    let hello = String::from("السلام عليكم");
    let hello = String::from("Dobrý den");
    let hello = String::from("Hello");
    let hello = String::from("שָׁלוֹם");
    let hello = String::from("नमस्ते");
    let hello = String::from("こんにちは");
    let hello = String::from("안녕하세요");
    let hello = String::from("你好");
    let hello = String::from("Olá");
    let hello = String::from("Здравствуйте");
    let hello = String::from("Hola");
}

Listing 8-14: Storing greetings in different languages in strings

All of these are valid String values.

Updating a String

A String can grow in size and its contents can change, just like the contents of a Vec<T>, if you push more data into it. In addition, you can conveniently use the + operator or the format! macro to concatenate String values.

Appending to a String with push_str and push

We can grow a String by using the push_str method to append a string slice, as shown in Listing 8-15.

fn main() {
    let mut s = String::from("foo");
    s.push_str("bar");
}

Listing 8-15: Appending a string slice to a String using the push_str method

After these two lines, s will contain foobar. The push_str method takes a string slice because we don’t necessarily want to take ownership of the parameter. For example, in the code in Listing 8-16, we want to be able to use s2 after appending its contents to s1.

fn main() {
    let mut s1 = String::from("foo");
    let s2 = "bar";
    s1.push_str(s2);
    println!("s2 is {}", s2);
}

Listing 8-16: Using a string slice after appending its contents to a String

If the push_str method took ownership of s2, we wouldn’t be able to print its value on the last line. However, this code works as we’d expect!

The push method takes a single character as a parameter and adds it to the String. Listing 8-17 adds the letter “l” to a String using the push method.

fn main() {
    let mut s = String::from("lo");
    s.push('l');
}

Listing 8-17: Adding one character to a String value using push

As a result, s will contain lol.

Concatenation with the + Operator or the format! Macro

Often, you’ll want to combine two existing strings. One way to do so is to use the + operator, as shown in Listing 8-18.

fn main() {
    let s1 = String::from("Hello, ");
    let s2 = String::from("world!");
    let s3 = s1 + &s2; // note s1 has been moved here and can no longer be used
}

Listing 8-18: Using the + operator to combine two String values into a new String value

The string s3 will contain Hello, world!. The reason s1 is no longer valid after the addition, and the reason we used a reference to s2, has to do with the signature of the method that’s called when we use the + operator. The + operator uses the add method, whose signature looks something like this:

fn add(self, s: &str) -> String {

In the standard library, you'll see add defined using generics and associated types. Here, we’ve substituted in concrete types, which is what happens when we call this method with String values. We’ll discuss generics in Chapter 10. This signature gives us the clues we need to understand the tricky bits of the + operator.

First, s2 has an &, meaning that we’re adding a reference of the second string to the first string. This is because of the s parameter in the add function: we can only add a &str to a String; we can’t add two String values together. But wait—the type of &s2 is &String, not &str, as specified in the second parameter to add. So why does Listing 8-18 compile?

The reason we’re able to use &s2 in the call to add is that the compiler can coerce the &String argument into a &str. When we call the add method, Rust uses a deref coercion, which here turns &s2 into &s2[..]. We’ll discuss deref coercion in more depth in Chapter 15. Because add does not take ownership of the s parameter, s2 will still be a valid String after this operation.

Second, we can see in the signature that add takes ownership of self, because self does not have an &. This means s1 in Listing 8-18 will be moved into the add call and will no longer be valid after that. So although let s3 = s1 + &s2; looks like it will copy both strings and create a new one, this statement actually takes ownership of s1, appends a copy of the contents of s2, and then returns ownership of the result. In other words, it looks like it’s making a lot of copies but isn’t; the implementation is more efficient than copying.

If we need to concatenate multiple strings, the behavior of the + operator gets unwieldy:

fn main() {
    let s1 = String::from("tic");
    let s2 = String::from("tac");
    let s3 = String::from("toe");

    let s = s1 + "-" + &s2 + "-" + &s3;
}

At this point, s will be tic-tac-toe. With all of the + and " characters, it’s difficult to see what’s going on. For more complicated string combining, we can instead use the format! macro:

fn main() {
    let s1 = String::from("tic");
    let s2 = String::from("tac");
    let s3 = String::from("toe");

    let s = format!("{}-{}-{}", s1, s2, s3);
}

This code also sets s to tic-tac-toe. The format! macro works like println!, but instead of printing the output to the screen, it returns a String with the contents. The version of the code using format! is much easier to read, and the code generated by the format! macro uses references so that this call doesn’t take ownership of any of its parameters.

Indexing into Strings

In many other programming languages, accessing individual characters in a string by referencing them by index is a valid and common operation. However, if you try to access parts of a String using indexing syntax in Rust, you’ll get an error. Consider the invalid code in Listing 8-19.

fn main() {
    let s1 = String::from("hello");
    let h = s1[0];
}

Listing 8-19: Attempting to use indexing syntax with a String

This code will result in the following error:

$ cargo run
   Compiling collections v0.1.0 (file:///projects/collections)
error[E0277]: the type `String` cannot be indexed by `{integer}`
 --> src/main.rs:3:13
  |
3 |     let h = s1[0];
  |             ^^^^^ `String` cannot be indexed by `{integer}`
  |
  = help: the trait `Index<{integer}>` is not implemented for `String`

For more information about this error, try `rustc --explain E0277`.
error: could not compile `collections` due to previous error

The error and the note tell the story: Rust strings don’t support indexing. But why not? To answer that question, we need to discuss how Rust stores strings in memory.

Internal Representation

A String is a wrapper over a Vec<u8>. Let’s look at some of our properly encoded UTF-8 example strings from Listing 8-14. First, this one:

fn main() {
    let hello = String::from("السلام عليكم");
    let hello = String::from("Dobrý den");
    let hello = String::from("Hello");
    let hello = String::from("שָׁלוֹם");
    let hello = String::from("नमस्ते");
    let hello = String::from("こんにちは");
    let hello = String::from("안녕하세요");
    let hello = String::from("你好");
    let hello = String::from("Olá");
    let hello = String::from("Здравствуйте");
    let hello = String::from("Hola");
}

In this case, len will be 4, which means the vector storing the string “Hola” is 4 bytes long. Each of these letters takes 1 byte when encoded in UTF-8. The following line, however, may surprise you. (Note that this string begins with the capital Cyrillic letter Ze, not the Arabic number 3.)

fn main() {
    let hello = String::from("السلام عليكم");
    let hello = String::from("Dobrý den");
    let hello = String::from("Hello");
    let hello = String::from("שָׁלוֹם");
    let hello = String::from("नमस्ते");
    let hello = String::from("こんにちは");
    let hello = String::from("안녕하세요");
    let hello = String::from("你好");
    let hello = String::from("Olá");
    let hello = String::from("Здравствуйте");
    let hello = String::from("Hola");
}

Asked how long the string is, you might say 12. In fact, Rust’s answer is 24: that’s the number of bytes it takes to encode “Здравствуйте” in UTF-8, because each Unicode scalar value in that string takes 2 bytes of storage. Therefore, an index into the string’s bytes will not always correlate to a valid Unicode scalar value. To demonstrate, consider this invalid Rust code:

let hello = "Здравствуйте";
let answer = &hello[0];

You already know that answer will not be З, the first letter. When encoded in UTF-8, the first byte of З is 208 and the second is 151, so it would seem that answer should in fact be 208, but 208 is not a valid character on its own. Returning 208 is likely not what a user would want if they asked for the first letter of this string; however, that’s the only data that Rust has at byte index 0. Users generally don’t want the byte value returned, even if the string contains only Latin letters: if &"hello"[0] were valid code that returned the byte value, it would return 104, not h.

The answer, then, is that to avoid returning an unexpected value and causing bugs that might not be discovered immediately, Rust doesn’t compile this code at all and prevents misunderstandings early in the development process.

Bytes and Scalar Values and Grapheme Clusters! Oh My!

Another point about UTF-8 is that there are actually three relevant ways to look at strings from Rust’s perspective: as bytes, scalar values, and grapheme clusters (the closest thing to what we would call letters).

If we look at the Hindi word “नमस्ते” written in the Devanagari script, it is stored as a vector of u8 values that looks like this:

[224, 164, 168, 224, 164, 174, 224, 164, 184, 224, 165, 141, 224, 164, 164,
224, 165, 135]

That’s 18 bytes and is how computers ultimately store this data. If we look at them as Unicode scalar values, which are what Rust’s char type is, those bytes look like this:

['न', 'म', 'स', '्', 'त', 'े']

There are six char values here, but the fourth and sixth are not letters: they’re diacritics that don’t make sense on their own. Finally, if we look at them as grapheme clusters, we’d get what a person would call the four letters that make up the Hindi word:

["न", "म", "स्", "ते"]

Rust provides different ways of interpreting the raw string data that computers store so that each program can choose the interpretation it needs, no matter what human language the data is in.

A final reason Rust doesn’t allow us to index into a String to get a character is that indexing operations are expected to always take constant time (O(1)). But it isn’t possible to guarantee that performance with a String, because Rust would have to walk through the contents from the beginning to the index to determine how many valid characters there were.

Slicing Strings

Indexing into a string is often a bad idea because it’s not clear what the return type of the string-indexing operation should be: a byte value, a character, a grapheme cluster, or a string slice. If you really need to use indices to create string slices, therefore, Rust asks you to be more specific.

Rather than indexing using [] with a single number, you can use [] with a range to create a string slice containing particular bytes:


#![allow(unused)]
fn main() {
let hello = "Здравствуйте";

let s = &hello[0..4];
}

Here, s will be a &str that contains the first 4 bytes of the string. Earlier, we mentioned that each of these characters was 2 bytes, which means s will be Зд.

If we were to try to slice only part of a character’s bytes with something like &hello[0..1], Rust would panic at runtime in the same way as if an invalid index were accessed in a vector:

$ cargo run
   Compiling collections v0.1.0 (file:///projects/collections)
    Finished dev [unoptimized + debuginfo] target(s) in 0.43s
     Running `target/debug/collections`
thread 'main' panicked at 'byte index 1 is not a char boundary; it is inside 'З' (bytes 0..2) of `Здравствуйте`', library/core/src/str/mod.rs:127:5
note: run with `RUST_BACKTRACE=1` environment variable to display a backtrace

You should use ranges to create string slices with caution, because doing so can crash your program.

Methods for Iterating Over Strings

The best way to operate on pieces of strings is to be explicit about whether you want characters or bytes. For individual Unicode scalar values, use the chars method. Calling chars on “Зд” separates out and returns two values of type char, and you can iterate over the result to access each element:


#![allow(unused)]
fn main() {
for c in "Зд".chars() {
    println!("{}", c);
}
}

This code will print the following:

З
д

Alternatively, the bytes method returns each raw byte, which might be appropriate for your domain:


#![allow(unused)]
fn main() {
for b in "Зд".bytes() {
    println!("{}", b);
}
}

This code will print the four bytes that make up this string:

208
151
208
180

But be sure to remember that valid Unicode scalar values may be made up of more than 1 byte.

Getting grapheme clusters from strings as with the Devanagari script is complex, so this functionality is not provided by the standard library. Crates are available on crates.io if this is the functionality you need.

Strings Are Not So Simple

To summarize, strings are complicated. Different programming languages make different choices about how to present this complexity to the programmer. Rust has chosen to make the correct handling of String data the default behavior for all Rust programs, which means programmers have to put more thought into handling UTF-8 data upfront. This trade-off exposes more of the complexity of strings than is apparent in other programming languages, but it prevents you from having to handle errors involving non-ASCII characters later in your development life cycle.

The good news is that the standard library offers a lot of functionality built off the String and &str types to help handle these complex situations correctly. Be sure to check out the documentation for useful methods like contains for searching in a string and replace for substituting parts of a string with another string.

Let’s switch to something a bit less complex: hash maps!

Storing Keys with Associated Values in Hash Maps

The last of our common collections is the hash map. The type HashMap<K, V> stores a mapping of keys of type K to values of type V using a hashing function, which determines how it places these keys and values into memory. Many programming languages support this kind of data structure, but they often use a different name, such as hash, map, object, hash table, dictionary, or associative array, just to name a few.

Hash maps are useful when you want to look up data not by using an index, as you can with vectors, but by using a key that can be of any type. For example, in a game, you could keep track of each team’s score in a hash map in which each key is a team’s name and the values are each team’s score. Given a team name, you can retrieve its score.

We’ll go over the basic API of hash maps in this section, but many more goodies are hiding in the functions defined on HashMap<K, V> by the standard library. As always, check the standard library documentation for more information.

Creating a New Hash Map

One way to create an empty hash map is using new and adding elements with insert. In Listing 8-20, we’re keeping track of the scores of two teams whose names are Blue and Yellow. The Blue team starts with 10 points, and the Yellow team starts with 50.

fn main() {
    use std::collections::HashMap;

    let mut scores = HashMap::new();

    scores.insert(String::from("Blue"), 10);
    scores.insert(String::from("Yellow"), 50);
}

Listing 8-20: Creating a new hash map and inserting some keys and values

Note that we need to first use the HashMap from the collections portion of the standard library. Of our three common collections, this one is the least often used, so it’s not included in the features brought into scope automatically in the prelude. Hash maps also have less support from the standard library; there’s no built-in macro to construct them, for example.

Just like vectors, hash maps store their data on the heap. This HashMap has keys of type String and values of type i32. Like vectors, hash maps are homogeneous: all of the keys must have the same type as each other, and all of the values must have the same type.

Accessing Values in a Hash Map

We can get a value out of the hash map by providing its key to the get method, as shown in Listing 8-21.

fn main() {
    use std::collections::HashMap;

    let mut scores = HashMap::new();

    scores.insert(String::from("Blue"), 10);
    scores.insert(String::from("Yellow"), 50);

    let team_name = String::from("Blue");
    let score = scores.get(&team_name);
}

Listing 8-21: Accessing the score for the Blue team stored in the hash map

Here, score will have the value that’s associated with the Blue team, and the result will be 10. The get method returns an Option<&V>; if there’s no value for that key in the hash map, get will return None. This program handles the Option by calling unwrap_or to set score to zero if scores doesn't have an entry for the key.

We can iterate over each key/value pair in a hash map in a similar manner as we do with vectors, using a for loop:

fn main() {
    use std::collections::HashMap;

    let mut scores = HashMap::new();

    scores.insert(String::from("Blue"), 10);
    scores.insert(String::from("Yellow"), 50);

    for (key, value) in &scores {
        println!("{}: {}", key, value);
    }
}

This code will print each pair in an arbitrary order:

Yellow: 50
Blue: 10

Hash Maps and Ownership

For types that implement the Copy trait, like i32, the values are copied into the hash map. For owned values like String, the values will be moved and the hash map will be the owner of those values, as demonstrated in Listing 8-22.

fn main() {
    use std::collections::HashMap;

    let field_name = String::from("Favorite color");
    let field_value = String::from("Blue");

    let mut map = HashMap::new();
    map.insert(field_name, field_value);
    // field_name and field_value are invalid at this point, try using them and
    // see what compiler error you get!
}

Listing 8-22: Showing that keys and values are owned by the hash map once they’re inserted

We aren’t able to use the variables field_name and field_value after they’ve been moved into the hash map with the call to insert.

If we insert references to values into the hash map, the values won’t be moved into the hash map. The values that the references point to must be valid for at least as long as the hash map is valid. We’ll talk more about these issues in the “Validating References with Lifetimes” section in Chapter 10.

Updating a Hash Map

Although the number of key and value pairs is growable, each unique key can only have one value associated with it at a time (but not vice versa: for example, both the Blue team and the Yellow team could have value 10 stored in the scores hash map).

When you want to change the data in a hash map, you have to decide how to handle the case when a key already has a value assigned. You could replace the old value with the new value, completely disregarding the old value. You could keep the old value and ignore the new value, only adding the new value if the key doesn’t already have a value. Or you could combine the old value and the new value. Let’s look at how to do each of these!

Overwriting a Value

If we insert a key and a value into a hash map and then insert that same key with a different value, the value associated with that key will be replaced. Even though the code in Listing 8-23 calls insert twice, the hash map will only contain one key/value pair because we’re inserting the value for the Blue team’s key both times.

fn main() {
    use std::collections::HashMap;

    let mut scores = HashMap::new();

    scores.insert(String::from("Blue"), 10);
    scores.insert(String::from("Blue"), 25);

    println!("{:?}", scores);
}

Listing 8-23: Replacing a value stored with a particular key

This code will print {"Blue": 25}. The original value of 10 has been overwritten.

Adding a Key and Value Only If a Key Isn’t Present

It’s common to check whether a particular key already exists in the hash map with a value then take the following actions: if the key does exist in the hash map, the existing value should remain the way it is. If the key doesn’t exist, insert it and a value for it.

Hash maps have a special API for this called entry that takes the key you want to check as a parameter. The return value of the entry method is an enum called Entry that represents a value that might or might not exist. Let’s say we want to check whether the key for the Yellow team has a value associated with it. If it doesn’t, we want to insert the value 50, and the same for the Blue team. Using the entry API, the code looks like Listing 8-24.

fn main() {
    use std::collections::HashMap;

    let mut scores = HashMap::new();
    scores.insert(String::from("Blue"), 10);

    scores.entry(String::from("Yellow")).or_insert(50);
    scores.entry(String::from("Blue")).or_insert(50);

    println!("{:?}", scores);
}

Listing 8-24: Using the entry method to only insert if the key does not already have a value

The or_insert method on Entry is defined to return a mutable reference to the value for the corresponding Entry key if that key exists, and if not, inserts the parameter as the new value for this key and returns a mutable reference to the new value. This technique is much cleaner than writing the logic ourselves and, in addition, plays more nicely with the borrow checker.

Running the code in Listing 8-24 will print {"Yellow": 50, "Blue": 10}. The first call to entry will insert the key for the Yellow team with the value 50 because the Yellow team doesn’t have a value already. The second call to entry will not change the hash map because the Blue team already has the value 10.

Updating a Value Based on the Old Value

Another common use case for hash maps is to look up a key’s value and then update it based on the old value. For instance, Listing 8-25 shows code that counts how many times each word appears in some text. We use a hash map with the words as keys and increment the value to keep track of how many times we’ve seen that word. If it’s the first time we’ve seen a word, we’ll first insert the value 0.

fn main() {
    use std::collections::HashMap;

    let text = "hello world wonderful world";

    let mut map = HashMap::new();

    for word in text.split_whitespace() {
        let count = map.entry(word).or_insert(0);
        *count += 1;
    }

    println!("{:?}", map);
}

Listing 8-25: Counting occurrences of words using a hash map that stores words and counts

This code will print {"world": 2, "hello": 1, "wonderful": 1}. You might see the same key/value pairs printed in a different order: recall from the “Accessing Values in a Hash Map” section that iterating over a hash map happens in an arbitrary order.

The split_whitespace method returns an iterator over sub-slices, separated by whitespace, of the value in text. The or_insert method returns a mutable reference (&mut V) to the value for the specified key. Here we store that mutable reference in the count variable, so in order to assign to that value, we must first dereference count using the asterisk (*). The mutable reference goes out of scope at the end of the for loop, so all of these changes are safe and allowed by the borrowing rules.

Hashing Functions

By default, HashMap uses a hashing function called SipHash that can provide resistance to Denial of Service (DoS) attacks involving hash tables1. This is not the fastest hashing algorithm available, but the trade-off for better security that comes with the drop in performance is worth it. If you profile your code and find that the default hash function is too slow for your purposes, you can switch to another function by specifying a different hasher. A hasher is a type that implements the BuildHasher trait. We’ll talk about traits and how to implement them in Chapter 10. You don’t necessarily have to implement your own hasher from scratch; crates.io has libraries shared by other Rust users that provide hashers implementing many common hashing algorithms.

Summary

Vectors, strings, and hash maps will provide a large amount of functionality necessary in programs when you need to store, access, and modify data. Here are some exercises you should now be equipped to solve:

  • Given a list of integers, use a vector and return the median (when sorted, the value in the middle position) and mode (the value that occurs most often; a hash map will be helpful here) of the list.
  • Convert strings to pig latin. The first consonant of each word is moved to the end of the word and “ay” is added, so “first” becomes “irst-fay.” Words that start with a vowel have “hay” added to the end instead (“apple” becomes “apple-hay”). Keep in mind the details about UTF-8 encoding!
  • Using a hash map and vectors, create a text interface to allow a user to add employee names to a department in a company. For example, “Add Sally to Engineering” or “Add Amir to Sales.” Then let the user retrieve a list of all people in a department or all people in the company by department, sorted alphabetically.

The standard library API documentation describes methods that vectors, strings, and hash maps have that will be helpful for these exercises!

We’re getting into more complex programs in which operations can fail, so, it’s a perfect time to discuss error handling. We’ll do that next!

Error Handling

Errors are a fact of life in software, so Rust has a number of features for handling situations in which something goes wrong. In many cases, Rust requires you to acknowledge the possibility of an error and take some action before your code will compile. This requirement makes your program more robust by ensuring that you’ll discover errors and handle them appropriately before you’ve deployed your code to production!

Rust groups errors into two major categories: recoverable and unrecoverable errors. For a recoverable error, such as a file not found error, we most likely just want to report the problem to the user and retry the operation. Unrecoverable errors are always symptoms of bugs, like trying to access a location beyond the end of an array, and so we want to immediately stop the program.

Most languages don’t distinguish between these two kinds of errors and handle both in the same way, using mechanisms such as exceptions. Rust doesn’t have exceptions. Instead, it has the type Result<T, E> for recoverable errors and the panic! macro that stops execution when the program encounters an unrecoverable error. This chapter covers calling panic! first and then talks about returning Result<T, E> values. Additionally, we’ll explore considerations when deciding whether to try to recover from an error or to stop execution.

Unrecoverable Errors with panic!

Sometimes, bad things happen in your code, and there’s nothing you can do about it. In these cases, Rust has the panic! macro. There are two ways to cause a panic in practice: by taking an action that causes our code to panic (such as accessing an array past the end) or by explicitly calling the panic! macro. In both cases, we cause a panic in our program. By default, these panics will print a failure message, unwind, clean up the stack, and quit. Via an environment variable, you can also have Rust display the call stack when a panic occurs to make it easier to track down the source of the panic.

Unwinding the Stack or Aborting in Response to a Panic

By default, when a panic occurs, the program starts unwinding, which means Rust walks back up the stack and cleans up the data from each function it encounters. However, this walking back and cleanup is a lot of work. Rust, therefore, allows you to choose the alternative of immediately aborting, which ends the program without cleaning up.

Memory that the program was using will then need to be cleaned up by the operating system. If in your project you need to make the resulting binary as small as possible, you can switch from unwinding to aborting upon a panic by adding panic = 'abort' to the appropriate [profile] sections in your Cargo.toml file. For example, if you want to abort on panic in release mode, add this:

[profile.release]
panic = 'abort'

Let’s try calling panic! in a simple program:

Filename: src/main.rs

fn main() {
    panic!("crash and burn");
}

When you run the program, you’ll see something like this:

$ cargo run
   Compiling panic v0.1.0 (file:///projects/panic)
    Finished dev [unoptimized + debuginfo] target(s) in 0.25s
     Running `target/debug/panic`
thread 'main' panicked at 'crash and burn', src/main.rs:2:5
note: run with `RUST_BACKTRACE=1` environment variable to display a backtrace

The call to panic! causes the error message contained in the last two lines. The first line shows our panic message and the place in our source code where the panic occurred: src/main.rs:2:5 indicates that it’s the second line, fifth character of our src/main.rs file.

In this case, the line indicated is part of our code, and if we go to that line, we see the panic! macro call. In other cases, the panic! call might be in code that our code calls, and the filename and line number reported by the error message will be someone else’s code where the panic! macro is called, not the line of our code that eventually led to the panic! call. We can use the backtrace of the functions the panic! call came from to figure out the part of our code that is causing the problem. We’ll discuss backtraces in more detail next.

Using a panic! Backtrace

Let’s look at another example to see what it’s like when a panic! call comes from a library because of a bug in our code instead of from our code calling the macro directly. Listing 9-1 has some code that attempts to access an index in a vector beyond the range of valid indexes.

Filename: src/main.rs

fn main() {
    let v = vec![1, 2, 3];

    v[99];
}

Listing 9-1: Attempting to access an element beyond the end of a vector, which will cause a call to panic!

Here, we’re attempting to access the 100th element of our vector (which is at index 99 because indexing starts at zero), but the vector has only 3 elements. In this situation, Rust will panic. Using [] is supposed to return an element, but if you pass an invalid index, there’s no element that Rust could return here that would be correct.

In C, attempting to read beyond the end of a data structure is undefined behavior. You might get whatever is at the location in memory that would correspond to that element in the data structure, even though the memory doesn’t belong to that structure. This is called a buffer overread and can lead to security vulnerabilities if an attacker is able to manipulate the index in such a way as to read data they shouldn’t be allowed to that is stored after the data structure.

To protect your program from this sort of vulnerability, if you try to read an element at an index that doesn’t exist, Rust will stop execution and refuse to continue. Let’s try it and see:

$ cargo run
   Compiling panic v0.1.0 (file:///projects/panic)
    Finished dev [unoptimized + debuginfo] target(s) in 0.27s
     Running `target/debug/panic`
thread 'main' panicked at 'index out of bounds: the len is 3 but the index is 99', src/main.rs:4:5
note: run with `RUST_BACKTRACE=1` environment variable to display a backtrace

This error points at line 4 of our main.rs where we attempt to access index 99. The next note line tells us that we can set the RUST_BACKTRACE environment variable to get a backtrace of exactly what happened to cause the error. A backtrace is a list of all the functions that have been called to get to this point. Backtraces in Rust work as they do in other languages: the key to reading the backtrace is to start from the top and read until you see files you wrote. That’s the spot where the problem originated. The lines above that spot are code that your code has called; the lines below are code that called your code. These before-and-after lines might include core Rust code, standard library code, or crates that you’re using. Let’s try getting a backtrace by setting the RUST_BACKTRACE environment variable to any value except 0. Listing 9-2 shows output similar to what you’ll see.

$ RUST_BACKTRACE=1 cargo run
thread 'main' panicked at 'index out of bounds: the len is 3 but the index is 99', src/main.rs:4:5
stack backtrace:
   0: rust_begin_unwind
             at /rustc/7eac88abb2e57e752f3302f02be5f3ce3d7adfb4/library/std/src/panicking.rs:483
   1: core::panicking::panic_fmt
             at /rustc/7eac88abb2e57e752f3302f02be5f3ce3d7adfb4/library/core/src/panicking.rs:85
   2: core::panicking::panic_bounds_check
             at /rustc/7eac88abb2e57e752f3302f02be5f3ce3d7adfb4/library/core/src/panicking.rs:62
   3: <usize as core::slice::index::SliceIndex<[T]>>::index
             at /rustc/7eac88abb2e57e752f3302f02be5f3ce3d7adfb4/library/core/src/slice/index.rs:255
   4: core::slice::index::<impl core::ops::index::Index<I> for [T]>::index
             at /rustc/7eac88abb2e57e752f3302f02be5f3ce3d7adfb4/library/core/src/slice/index.rs:15
   5: <alloc::vec::Vec<T> as core::ops::index::Index<I>>::index
             at /rustc/7eac88abb2e57e752f3302f02be5f3ce3d7adfb4/library/alloc/src/vec.rs:1982
   6: panic::main
             at ./src/main.rs:4
   7: core::ops::function::FnOnce::call_once
             at /rustc/7eac88abb2e57e752f3302f02be5f3ce3d7adfb4/library/core/src/ops/function.rs:227
note: Some details are omitted, run with `RUST_BACKTRACE=full` for a verbose backtrace.

Listing 9-2: The backtrace generated by a call to panic! displayed when the environment variable RUST_BACKTRACE is set

That’s a lot of output! The exact output you see might be different depending on your operating system and Rust version. In order to get backtraces with this information, debug symbols must be enabled. Debug symbols are enabled by default when using cargo build or cargo run without the --release flag, as we have here.

In the output in Listing 9-2, line 6 of the backtrace points to the line in our project that’s causing the problem: line 4 of src/main.rs. If we don’t want our program to panic, we should start our investigation at the location pointed to by the first line mentioning a file we wrote. In Listing 9-1, where we deliberately wrote code that would panic, the way to fix the panic is to not request an element beyond the range of the vector indexes. When your code panics in the future, you’ll need to figure out what action the code is taking with what values to cause the panic and what the code should do instead.

We’ll come back to panic! and when we should and should not use panic! to handle error conditions in the “To panic! or Not to panic! section later in this chapter. Next, we’ll look at how to recover from an error using Result.

Recoverable Errors with Result

Most errors aren’t serious enough to require the program to stop entirely. Sometimes, when a function fails, it’s for a reason that you can easily interpret and respond to. For example, if you try to open a file and that operation fails because the file doesn’t exist, you might want to create the file instead of terminating the process.

Recall from “Handling Potential Failure with the Result Type” in Chapter 2 that the Result enum is defined as having two variants, Ok and Err, as follows:


#![allow(unused)]
fn main() {
enum Result<T, E> {
    Ok(T),
    Err(E),
}
}

The T and E are generic type parameters: we’ll discuss generics in more detail in Chapter 10. What you need to know right now is that T represents the type of the value that will be returned in a success case within the Ok variant, and E represents the type of the error that will be returned in a failure case within the Err variant. Because Result has these generic type parameters, we can use the Result type and the functions defined on it in many different situations where the successful value and error value we want to return may differ.

Let’s call a function that returns a Result value because the function could fail. In Listing 9-3 we try to open a file.

Filename: src/main.rs

use std::fs::File;

fn main() {
    let greeting_file_result = File::open("hello.txt");
}

Listing 9-3: Opening a file

The return type of File::open is a Result<T, E>. The generic parameter T has been filled in by the implementation of File::open with the type of the success value, std::fs::File, which is a file handle. The type of E used in the error value is std::io::Error. This return type means the call to File::open might succeed and return a file handle that we can read from or write to. The function call also might fail: for example, the file might not exist, or we might not have permission to access the file. The File::open function needs to have a way to tell us whether it succeeded or failed and at the same time give us either the file handle or error information. This information is exactly what the Result enum conveys.

In the case where File::open succeeds, the value in the variable greeting_file_result will be an instance of Ok that contains a file handle. In the case where it fails, the value in greeting_file_result will be an instance of Err that contains more information about the kind of error that happened.

We need to add to the code in Listing 9-3 to take different actions depending on the value File::open returns. Listing 9-4 shows one way to handle the Result using a basic tool, the match expression that we discussed in Chapter 6.

Filename: src/main.rs

use std::fs::File;

fn main() {
    let greeting_file_result = File::open("hello.txt");

    let greeting_file = match greeting_file_result {
        Ok(file) => file,
        Err(error) => panic!("Problem opening the file: {:?}", error),
    };
}

Listing 9-4: Using a match expression to handle the Result variants that might be returned

Note that, like the Option enum, the Result enum and its variants have been brought into scope by the prelude, so we don’t need to specify Result:: before the Ok and Err variants in the match arms.

When the result is Ok, this code will return the inner file value out of the Ok variant, and we then assign that file handle value to the variable greeting_file. After the match, we can use the file handle for reading or writing.

The other arm of the match handles the case where we get an Err value from File::open. In this example, we’ve chosen to call the panic! macro. If there’s no file named hello.txt in our current directory and we run this code, we’ll see the following output from the panic! macro:

$ cargo run
   Compiling error-handling v0.1.0 (file:///projects/error-handling)
    Finished dev [unoptimized + debuginfo] target(s) in 0.73s
     Running `target/debug/error-handling`
thread 'main' panicked at 'Problem opening the file: Os { code: 2, kind: NotFound, message: "No such file or directory" }', src/main.rs:8:23
note: run with `RUST_BACKTRACE=1` environment variable to display a backtrace

As usual, this output tells us exactly what has gone wrong.

Matching on Different Errors

The code in Listing 9-4 will panic! no matter why File::open failed. However, we want to take different actions for different failure reasons: if File::open failed because the file doesn’t exist, we want to create the file and return the handle to the new file. If File::open failed for any other reason—for example, because we didn’t have permission to open the file—we still want the code to panic! in the same way as it did in Listing 9-4. For this we add an inner match expression, shown in Listing 9-5.

Filename: src/main.rs

use std::fs::File;
use std::io::ErrorKind;

fn main() {
    let greeting_file_result = File::open("hello.txt");

    let greeting_file = match greeting_file_result {
        Ok(file) => file,
        Err(error) => match error.kind() {
            ErrorKind::NotFound => match File::create("hello.txt") {
                Ok(fc) => fc,
                Err(e) => panic!("Problem creating the file: {:?}", e),
            },
            other_error => {
                panic!("Problem opening the file: {:?}", other_error);
            }
        },
    };
}

Listing 9-5: Handling different kinds of errors in different ways

The type of the value that File::open returns inside the Err variant is io::Error, which is a struct provided by the standard library. This struct has a method kind that we can call to get an io::ErrorKind value. The enum io::ErrorKind is provided by the standard library and has variants representing the different kinds of errors that might result from an io operation. The variant we want to use is ErrorKind::NotFound, which indicates the file we’re trying to open doesn’t exist yet. So we match on greeting_file_result, but we also have an inner match on error.kind().

The condition we want to check in the inner match is whether the value returned by error.kind() is the NotFound variant of the ErrorKind enum. If it is, we try to create the file with File::create. However, because File::create could also fail, we need a second arm in the inner match expression. When the file can’t be created, a different error message is printed. The second arm of the outer match stays the same, so the program panics on any error besides the missing file error.

Alternatives to Using match with Result<T, E>

That’s a lot of match! The match expression is very useful but also very much a primitive. In Chapter 13, you’ll learn about closures, which are used with many of the methods defined on Result<T, E>. These methods can be more concise than using match when handling Result<T, E> values in your code.

For example, here’s another way to write the same logic as shown in Listing 9-5, this time using closures and the unwrap_or_else method:

use std::fs::File;
use std::io::ErrorKind;

fn main() {
    let greeting_file = File::open("hello.txt").unwrap_or_else(|error| {
        if error.kind() == ErrorKind::NotFound {
            File::create("hello.txt").unwrap_or_else(|error| {
                panic!("Problem creating the file: {:?}", error);
            })
        } else {
            panic!("Problem opening the file: {:?}", error);
        }
    });
}

Although this code has the same behavior as Listing 9-5, it doesn’t contain any match expressions and is cleaner to read. Come back to this example after you’ve read Chapter 13, and look up the unwrap_or_else method in the standard library documentation. Many more of these methods can clean up huge nested match expressions when you’re dealing with errors.

Shortcuts for Panic on Error: unwrap and expect

Using match works well enough, but it can be a bit verbose and doesn’t always communicate intent well. The Result<T, E> type has many helper methods defined on it to do various, more specific tasks. The unwrap method is a shortcut method implemented just like the match expression we wrote in Listing 9-4. If the Result value is the Ok variant, unwrap will return the value inside the Ok. If the Result is the Err variant, unwrap will call the panic! macro for us. Here is an example of unwrap in action:

Filename: src/main.rs

use std::fs::File;

fn main() {
    let greeting_file = File::open("hello.txt").unwrap();
}

If we run this code without a hello.txt file, we’ll see an error message from the panic! call that the unwrap method makes:

thread 'main' panicked at 'called `Result::unwrap()` on an `Err` value: Os {
code: 2, kind: NotFound, message: "No such file or directory" }',
src/main.rs:4:49

Similarly, the expect method lets us also choose the panic! error message. Using expect instead of unwrap and providing good error messages can convey your intent and make tracking down the source of a panic easier. The syntax of expect looks like this:

Filename: src/main.rs

use std::fs::File;

fn main() {
    let greeting_file = File::open("hello.txt")
        .expect("hello.txt should be included in this project");
}

We use expect in the same way as unwrap: to return the file handle or call the panic! macro. The error message used by expect in its call to panic! will be the parameter that we pass to expect, rather than the default panic! message that unwrap uses. Here’s what it looks like:

thread 'main' panicked at 'hello.txt should be included in this project: Os {
code: 2, kind: NotFound, message: "No such file or directory" }',
src/main.rs:5:10

In production-quality code, most Rustaceans choose expect rather than unwrap and give more context about why the operation is expected to always succeed. That way, if your assumptions are ever proven wrong, you have more information to use in debugging.

Propagating Errors

When a function’s implementation calls something that might fail, instead of handling the error within the function itself, you can return the error to the calling code so that it can decide what to do. This is known as propagating the error and gives more control to the calling code, where there might be more information or logic that dictates how the error should be handled than what you have available in the context of your code.

For example, Listing 9-6 shows a function that reads a username from a file. If the file doesn’t exist or can’t be read, this function will return those errors to the code that called the function.

Filename: src/main.rs


#![allow(unused)]
fn main() {
use std::fs::File;
use std::io::{self, Read};

fn read_username_from_file() -> Result<String, io::Error> {
    let username_file_result = File::open("hello.txt");

    let mut username_file = match username_file_result {
        Ok(file) => file,
        Err(e) => return Err(e),
    };

    let mut username = String::new();

    match username_file.read_to_string(&mut username) {
        Ok(_) => Ok(username),
        Err(e) => Err(e),
    }
}
}

Listing 9-6: A function that returns errors to the calling code using match

This function can be written in a much shorter way, but we’re going to start by doing a lot of it manually in order to explore error handling; at the end, we’ll show the shorter way. Let’s look at the return type of the function first: Result<String, io::Error>. This means the function is returning a value of the type Result<T, E> where the generic parameter T has been filled in with the concrete type String, and the generic type E has been filled in with the concrete type io::Error.

If this function succeeds without any problems, the code that calls this function will receive an Ok value that holds a String—the username that this function read from the file. If this function encounters any problems, the calling code will receive an Err value that holds an instance of io::Error that contains more information about what the problems were. We chose io::Error as the return type of this function because that happens to be the type of the error value returned from both of the operations we’re calling in this function’s body that might fail: the File::open function and the read_to_string method.

The body of the function starts by calling the File::open function. Then we handle the Result value with a match similar to the match in Listing 9-4. If File::open succeeds, the file handle in the pattern variable file becomes the value in the mutable variable username_file and the function continues. In the Err case, instead of calling panic!, we use the return keyword to return early out of the function entirely and pass the error value from File::open, now in the pattern variable e, back to the calling code as this function’s error value.

So if we have a file handle in username_file, the function then creates a new String in variable username and calls the read_to_string method on the file handle in username_file to read the contents of the file into username. The read_to_string method also returns a Result because it might fail, even though File::open succeeded. So we need another match to handle that Result: if read_to_string succeeds, then our function has succeeded, and we return the username from the file that’s now in username wrapped in an Ok. If read_to_string fails, we return the error value in the same way that we returned the error value in the match that handled the return value of File::open. However, we don’t need to explicitly say return, because this is the last expression in the function.

The code that calls this code will then handle getting either an Ok value that contains a username or an Err value that contains an io::Error. It’s up to the calling code to decide what to do with those values. If the calling code gets an Err value, it could call panic! and crash the program, use a default username, or look up the username from somewhere other than a file, for example. We don’t have enough information on what the calling code is actually trying to do, so we propagate all the success or error information upward for it to handle appropriately.

This pattern of propagating errors is so common in Rust that Rust provides the question mark operator ? to make this easier.

A Shortcut for Propagating Errors: the ? Operator

Listing 9-7 shows an implementation of read_username_from_file that has the same functionality as in Listing 9-6, but this implementation uses the ? operator.

Filename: src/main.rs


#![allow(unused)]
fn main() {
use std::fs::File;
use std::io;
use std::io::Read;

fn read_username_from_file() -> Result<String, io::Error> {
    let mut username_file = File::open("hello.txt")?;
    let mut username = String::new();
    username_file.read_to_string(&mut username)?;
    Ok(username)
}
}

Listing 9-7: A function that returns errors to the calling code using the ? operator

The ? placed after a Result value is defined to work in almost the same way as the match expressions we defined to handle the Result values in Listing 9-6. If the value of the Result is an Ok, the value inside the Ok will get returned from this expression, and the program will continue. If the value is an Err, the Err will be returned from the whole function as if we had used the return keyword so the error value gets propagated to the calling code.

There is a difference between what the match expression from Listing 9-6 does and what the ? operator does: error values that have the ? operator called on them go through the from function, defined in the From trait in the standard library, which is used to convert values from one type into another. When the ? operator calls the from function, the error type received is converted into the error type defined in the return type of the current function. This is useful when a function returns one error type to represent all the ways a function might fail, even if parts might fail for many different reasons.

For example, we could change the read_username_from_file function in Listing 9-7 to return a custom error type named OurError that we define. If we also define impl From<io::Error> for OurError to construct an instance of OurError from an io::Error, then the ? operator calls in the body of read_username_from_file will call from and convert the error types without needing to add any more code to the function.

In the context of Listing 9-7, the ? at the end of the File::open call will return the value inside an Ok to the variable username_file. If an error occurs, the ? operator will return early out of the whole function and give any Err value to the calling code. The same thing applies to the ? at the end of the read_to_string call.

The ? operator eliminates a lot of boilerplate and makes this function’s implementation simpler. We could even shorten this code further by chaining method calls immediately after the ?, as shown in Listing 9-8.

Filename: src/main.rs


#![allow(unused)]
fn main() {
use std::fs::File;
use std::io;
use std::io::Read;

fn read_username_from_file() -> Result<String, io::Error> {
    let mut username = String::new();

    File::open("hello.txt")?.read_to_string(&mut username)?;

    Ok(username)
}
}

Listing 9-8: Chaining method calls after the ? operator

We’ve moved the creation of the new String in username to the beginning of the function; that part hasn’t changed. Instead of creating a variable username_file, we’ve chained the call to read_to_string directly onto the result of File::open("hello.txt")?. We still have a ? at the end of the read_to_string call, and we still return an Ok value containing username when both File::open and read_to_string succeed rather than returning errors. The functionality is again the same as in Listing 9-6 and Listing 9-7; this is just a different, more ergonomic way to write it.

Listing 9-9 shows a way to make this even shorter using fs::read_to_string.

Filename: src/main.rs


#![allow(unused)]
fn main() {
use std::fs;
use std::io;

fn read_username_from_file() -> Result<String, io::Error> {
    fs::read_to_string("hello.txt")
}
}

Listing 9-9: Using fs::read_to_string instead of opening and then reading the file

Reading a file into a string is a fairly common operation, so the standard library provides the convenient fs::read_to_string function that opens the file, creates a new String, reads the contents of the file, puts the contents into that String, and returns it. Of course, using fs::read_to_string doesn’t give us the opportunity to explain all the error handling, so we did it the longer way first.

Where The ? Operator Can Be Used

The ? operator can only be used in functions whose return type is compatible with the value the ? is used on. This is because the ? operator is defined to perform an early return of a value out of the function, in the same manner as the match expression we defined in Listing 9-6. In Listing 9-6, the match was using a Result value, and the early return arm returned an Err(e) value. The return type of the function has to be a Result so that it’s compatible with this return.

In Listing 9-10, let’s look at the error we’ll get if we use the ? operator in a main function with a return type incompatible with the type of the value we use ? on:

Filename: src/main.rs

use std::fs::File;

fn main() {
    let greeting_file = File::open("hello.txt")?;
}

Listing 9-10: Attempting to use the ? in the main function that returns () won’t compile

This code opens a file, which might fail. The ? operator follows the Result value returned by File::open, but this main function has the return type of (), not Result. When we compile this code, we get the following error message:

$ cargo run
   Compiling error-handling v0.1.0 (file:///projects/error-handling)
error[E0277]: the `?` operator can only be used in a function that returns `Result` or `Option` (or another type that implements `FromResidual`)
 --> src/main.rs:4:48
  |
3 | / fn main() {
4 | |     let greeting_file = File::open("hello.txt")?;
  | |                                                ^ cannot use the `?` operator in a function that returns `()`
5 | | }
  | |_- this function should return `Result` or `Option` to accept `?`
  |
  = help: the trait `FromResidual<Result<Infallible, std::io::Error>>` is not implemented for `()`

For more information about this error, try `rustc --explain E0277`.
error: could not compile `error-handling` due to previous error

This error points out that we’re only allowed to use the ? operator in a function that returns Result, Option, or another type that implements FromResidual.

To fix the error, you have two choices. One choice is to change the return type of your function to be compatible with the value you’re using the ? operator on as long as you have no restrictions preventing that. The other technique is to use a match or one of the Result<T, E> methods to handle the Result<T, E> in whatever way is appropriate.

The error message also mentioned that ? can be used with Option<T> values as well. As with using ? on Result, you can only use ? on Option in a function that returns an Option. The behavior of the ? operator when called on an Option<T> is similar to its behavior when called on a Result<T, E>: if the value is None, the None will be returned early from the function at that point. If the value is Some, the value inside the Some is the resulting value of the expression and the function continues. Listing 9-11 has an example of a function that finds the last character of the first line in the given text:

fn last_char_of_first_line(text: &str) -> Option<char> {
    text.lines().next()?.chars().last()
}

fn main() {
    assert_eq!(
        last_char_of_first_line("Hello, world\nHow are you today?"),
        Some('d')
    );

    assert_eq!(last_char_of_first_line(""), None);
    assert_eq!(last_char_of_first_line("\nhi"), None);
}

Listing 9-11: Using the ? operator on an Option<T> value

This function returns Option<char> because it’s possible that there is a character there, but it’s also possible that there isn’t. This code takes the text string slice argument and calls the lines method on it, which returns an iterator over the lines in the string. Because this function wants to examine the first line, it calls next on the iterator to get the first value from the iterator. If text is the empty string, this call to next will return None, in which case we use ? to stop and return None from last_char_of_first_line. If text is not the empty string, next will return a Some value containing a string slice of the first line in text.

The ? extracts the string slice, and we can call chars on that string slice to get an iterator of its characters. We’re interested in the last character in this first line, so we call last to return the last item in the iterator. This is an Option because it’s possible that the first line is the empty string, for example if text starts with a blank line but has characters on other lines, as in "\nhi". However, if there is a last character on the first line, it will be returned in the Some variant. The ? operator in the middle gives us a concise way to express this logic, allowing us to implement the function in one line. If we couldn’t use the ? operator on Option, we’d have to implement this logic using more method calls or a match expression.

Note that you can use the ? operator on a Result in a function that returns Result, and you can use the ? operator on an Option in a function that returns Option, but you can’t mix and match. The ? operator won’t automatically convert a Result to an Option or vice versa; in those cases, you can use methods like the ok method on Result or the ok_or method on Option to do the conversion explicitly.

So far, all the main functions we’ve used return (). The main function is special because it’s the entry and exit point of executable programs, and there are restrictions on what its return type can be for the programs to behave as expected.

Luckily, main can also return a Result<(), E>. Listing 9-12 has the code from Listing 9-10 but we’ve changed the return type of main to be Result<(), Box<dyn Error>> and added a return value Ok(()) to the end. This code will now compile:

use std::error::Error;
use std::fs::File;

fn main() -> Result<(), Box<dyn Error>> {
    let greeting_file = File::open("hello.txt")?;

    Ok(())
}

Listing 9-12: Changing main to return Result<(), E> allows the use of the ? operator on Result values

The Box<dyn Error> type is a trait object, which we’ll talk about in the “Using Trait Objects that Allow for Values of Different Types” section in Chapter 17. For now, you can read Box<dyn Error> to mean “any kind of error.” Using ? on a Result value in a main function with the error type Box<dyn Error> is allowed, because it allows any Err value to be returned early. Even though the body of this main function will only ever return errors of type std::io::Error, by specifying Box<dyn Error>, this signature will continue to be correct even if more code that returns other errors is added to the body of main.

When a main function returns a Result<(), E>, the executable will exit with a value of 0 if main returns Ok(()) and will exit with a nonzero value if main returns an Err value. Executables written in C return integers when they exit: programs that exit successfully return the integer 0, and programs that error return some integer other than 0. Rust also returns integers from executables to be compatible with this convention.

The main function may return any types that implement the std::process::Termination trait, which contains a function report that returns an ExitCode Consult the standard library documentation for more information on implementing the Termination trait for your own types.

Now that we’ve discussed the details of calling panic! or returning Result, let’s return to the topic of how to decide which is appropriate to use in which cases.

To panic! or Not to panic!

So how do you decide when you should call panic! and when you should return Result? When code panics, there’s no way to recover. You could call panic! for any error situation, whether there’s a possible way to recover or not, but then you’re making the decision that a situation is unrecoverable on behalf of the calling code. When you choose to return a Result value, you give the calling code options. The calling code could choose to attempt to recover in a way that’s appropriate for its situation, or it could decide that an Err value in this case is unrecoverable, so it can call panic! and turn your recoverable error into an unrecoverable one. Therefore, returning Result is a good default choice when you’re defining a function that might fail.

In situations such as examples, prototype code, and tests, it’s more appropriate to write code that panics instead of returning a Result. Let’s explore why, then discuss situations in which the compiler can’t tell that failure is impossible, but you as a human can. The chapter will conclude with some general guidelines on how to decide whether to panic in library code.

Examples, Prototype Code, and Tests

When you’re writing an example to illustrate some concept, also including robust error-handling code can make the example less clear. In examples, it’s understood that a call to a method like unwrap that could panic is meant as a placeholder for the way you’d want your application to handle errors, which can differ based on what the rest of your code is doing.

Similarly, the unwrap and expect methods are very handy when prototyping, before you’re ready to decide how to handle errors. They leave clear markers in your code for when you’re ready to make your program more robust.

If a method call fails in a test, you’d want the whole test to fail, even if that method isn’t the functionality under test. Because panic! is how a test is marked as a failure, calling unwrap or expect is exactly what should happen.

Cases in Which You Have More Information Than the Compiler

It would also be appropriate to call unwrap or expect when you have some other logic that ensures the Result will have an Ok value, but the logic isn’t something the compiler understands. You’ll still have a Result value that you need to handle: whatever operation you’re calling still has the possibility of failing in general, even though it’s logically impossible in your particular situation. If you can ensure by manually inspecting the code that you’ll never have an Err variant, it’s perfectly acceptable to call unwrap, and even better to document the reason you think you’ll never have an Err variant in the expect text. Here’s an example:

fn main() {
    use std::net::IpAddr;

    let home: IpAddr = "127.0.0.1"
        .parse()
        .expect("Hardcoded IP address should be valid");
}

We’re creating an IpAddr instance by parsing a hardcoded string. We can see that 127.0.0.1 is a valid IP address, so it’s acceptable to use expect here. However, having a hardcoded, valid string doesn’t change the return type of the parse method: we still get a Result value, and the compiler will still make us handle the Result as if the Err variant is a possibility because the compiler isn’t smart enough to see that this string is always a valid IP address. If the IP address string came from a user rather than being hardcoded into the program and therefore did have a possibility of failure, we’d definitely want to handle the Result in a more robust way instead. Mentioning the assumption that this IP address is hardcoded will prompt us to change expect to better error handling code if in the future, we need to get the IP address from some other source instead.

Guidelines for Error Handling

It’s advisable to have your code panic when it’s possible that your code could end up in a bad state. In this context, a bad state is when some assumption, guarantee, contract, or invariant has been broken, such as when invalid values, contradictory values, or missing values are passed to your code—plus one or more of the following:

  • The bad state is something that is unexpected, as opposed to something that will likely happen occasionally, like a user entering data in the wrong format.
  • Your code after this point needs to rely on not being in this bad state, rather than checking for the problem at every step.
  • There’s not a good way to encode this information in the types you use. We’ll work through an example of what we mean in the “Encoding States and Behavior as Types” section of Chapter 17.

If someone calls your code and passes in values that don’t make sense, it’s best to return an error if you can so the user of the library can decide what they want to do in that case. However, in cases where continuing could be insecure or harmful, the best choice might be to call panic! and alert the person using your library to the bug in their code so they can fix it during development. Similarly, panic! is often appropriate if you’re calling external code that is out of your control and it returns an invalid state that you have no way of fixing.

However, when failure is expected, it’s more appropriate to return a Result than to make a panic! call. Examples include a parser being given malformed data or an HTTP request returning a status that indicates you have hit a rate limit. In these cases, returning a Result indicates that failure is an expected possibility that the calling code must decide how to handle.

When your code performs an operation that could put a user at risk if it’s called using invalid values, your code should verify the values are valid first and panic if the values aren’t valid. This is mostly for safety reasons: attempting to operate on invalid data can expose your code to vulnerabilities. This is the main reason the standard library will call panic! if you attempt an out-of-bounds memory access: trying to access memory that doesn’t belong to the current data structure is a common security problem. Functions often have contracts: their behavior is only guaranteed if the inputs meet particular requirements. Panicking when the contract is violated makes sense because a contract violation always indicates a caller-side bug and it’s not a kind of error you want the calling code to have to explicitly handle. In fact, there’s no reasonable way for calling code to recover; the calling programmers need to fix the code. Contracts for a function, especially when a violation will cause a panic, should be explained in the API documentation for the function.

However, having lots of error checks in all of your functions would be verbose and annoying. Fortunately, you can use Rust’s type system (and thus the type checking done by the compiler) to do many of the checks for you. If your function has a particular type as a parameter, you can proceed with your code’s logic knowing that the compiler has already ensured you have a valid value. For example, if you have a type rather than an Option, your program expects to have something rather than nothing. Your code then doesn’t have to handle two cases for the Some and None variants: it will only have one case for definitely having a value. Code trying to pass nothing to your function won’t even compile, so your function doesn’t have to check for that case at runtime. Another example is using an unsigned integer type such as u32, which ensures the parameter is never negative.

Creating Custom Types for Validation

Let’s take the idea of using Rust’s type system to ensure we have a valid value one step further and look at creating a custom type for validation. Recall the guessing game in Chapter 2 in which our code asked the user to guess a number between 1 and 100. We never validated that the user’s guess was between those numbers before checking it against our secret number; we only validated that the guess was positive. In this case, the consequences were not very dire: our output of “Too high” or “Too low” would still be correct. But it would be a useful enhancement to guide the user toward valid guesses and have different behavior when a user guesses a number that’s out of range versus when a user types, for example, letters instead.

One way to do this would be to parse the guess as an i32 instead of only a u32 to allow potentially negative numbers, and then add a check for the number being in range, like so:

use rand::Rng;
use std::cmp::Ordering;
use std::io;

fn main() {
    println!("Guess the number!");

    let secret_number = rand::thread_rng().gen_range(1..=100);

    loop {
        // --snip--

        println!("Please input your guess.");

        let mut guess = String::new();

        io::stdin()
            .read_line(&mut guess)
            .expect("Failed to read line");

        let guess: i32 = match guess.trim().parse() {
            Ok(num) => num,
            Err(_) => continue,
        };

        if guess < 1 || guess > 100 {
            println!("The secret number will be between 1 and 100.");
            continue;
        }

        match guess.cmp(&secret_number) {
            // --snip--
            Ordering::Less => println!("Too small!"),
            Ordering::Greater => println!("Too big!"),
            Ordering::Equal => {
                println!("You win!");
                break;
            }
        }
    }
}

The if expression checks whether our value is out of range, tells the user about the problem, and calls continue to start the next iteration of the loop and ask for another guess. After the if expression, we can proceed with the comparisons between guess and the secret number knowing that guess is between 1 and 100.

However, this is not an ideal solution: if it was absolutely critical that the program only operated on values between 1 and 100, and it had many functions with this requirement, having a check like this in every function would be tedious (and might impact performance).

Instead, we can make a new type and put the validations in a function to create an instance of the type rather than repeating the validations everywhere. That way, it’s safe for functions to use the new type in their signatures and confidently use the values they receive. Listing 9-13 shows one way to define a Guess type that will only create an instance of Guess if the new function receives a value between 1 and 100.


#![allow(unused)]
fn main() {
pub struct Guess {
    value: i32,
}

impl Guess {
    pub fn new(value: i32) -> Guess {
        if value < 1 || value > 100 {
            panic!("Guess value must be between 1 and 100, got {}.", value);
        }

        Guess { value }
    }

    pub fn value(&self) -> i32 {
        self.value
    }
}
}

Listing 9-13: A Guess type that will only continue with values between 1 and 100

First, we define a struct named Guess that has a field named value that holds an i32. This is where the number will be stored.

Then we implement an associated function named new on Guess that creates instances of Guess values. The new function is defined to have one parameter named value of type i32 and to return a Guess. The code in the body of the new function tests value to make sure it’s between 1 and 100. If value doesn’t pass this test, we make a panic! call, which will alert the programmer who is writing the calling code that they have a bug they need to fix, because creating a Guess with a value outside this range would violate the contract that Guess::new is relying on. The conditions in which Guess::new might panic should be discussed in its public-facing API documentation; we’ll cover documentation conventions indicating the possibility of a panic! in the API documentation that you create in Chapter 14. If value does pass the test, we create a new Guess with its value field set to the value parameter and return the Guess.

Next, we implement a method named value that borrows self, doesn’t have any other parameters, and returns an i32. This kind of method is sometimes called a getter, because its purpose is to get some data from its fields and return it. This public method is necessary because the value field of the Guess struct is private. It’s important that the value field be private so code using the Guess struct is not allowed to set value directly: code outside the module must use the Guess::new function to create an instance of Guess, thereby ensuring there’s no way for a Guess to have a value that hasn’t been checked by the conditions in the Guess::new function.

A function that has a parameter or returns only numbers between 1 and 100 could then declare in its signature that it takes or returns a Guess rather than an i32 and wouldn’t need to do any additional checks in its body.

Summary

Rust’s error handling features are designed to help you write more robust code. The panic! macro signals that your program is in a state it can’t handle and lets you tell the process to stop instead of trying to proceed with invalid or incorrect values. The Result enum uses Rust’s type system to indicate that operations might fail in a way that your code could recover from. You can use Result to tell code that calls your code that it needs to handle potential success or failure as well. Using panic! and Result in the appropriate situations will make your code more reliable in the face of inevitable problems.

Now that you’ve seen useful ways that the standard library uses generics with the Option and Result enums, we’ll talk about how generics work and how you can use them in your code.

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 a function takes parameters with unknown values to run the same code on multiple concrete values. In fact, we’ve already used generics in Chapter 6 with Option<T>, Chapter 8 with Vec<T> and HashMap<K, V>, and 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 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, then, 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 begin with the short program in Listing 10-1 that finds the largest number in a list.

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);
    assert_eq!(*largest, 100);
}

Listing 10-1: Finding the largest number in a list of numbers

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, 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.

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);
}

Listing 10-2: Code to find the largest number in two lists of numbers

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 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.

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);
    assert_eq!(*result, 100);

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

    let result = largest(&number_list);
    println!("The largest number is {}", result);
    assert_eq!(*result, 6000);
}

Listing 10-3: Abstracted code to find the largest number in two lists

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!

Generic Data Types

We use generics to create definitions for items like function signatures or structs, which we can then use with many different concrete data types. Let’s first look at how to define functions, structs, enums, and methods using generics. Then we’ll discuss how generics affect code performance.

In Function Definitions

When defining a function that uses generics, we place the generics in the signature of the function where we would usually specify the data types of the parameters and return value. Doing so makes our code more flexible and provides more functionality to callers of our function while preventing code duplication.

Continuing with our largest function, Listing 10-4 shows two functions that both find the largest value in a slice. We'll then combine these into a single function that uses generics.

Filename: src/main.rs

fn largest_i32(list: &[i32]) -> &i32 {
    let mut largest = &list[0];

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

    largest
}

fn largest_char(list: &[char]) -> &char {
    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_i32(&number_list);
    println!("The largest number is {}", result);
    assert_eq!(*result, 100);

    let char_list = vec!['y', 'm', 'a', 'q'];

    let result = largest_char(&char_list);
    println!("The largest char is {}", result);
    assert_eq!(*result, 'y');
}

Listing 10-4: Two functions that differ only in their names and the types in their signatures

The largest_i32 function is the one we extracted in Listing 10-3 that finds the largest i32 in a slice. The largest_char function finds the largest char in a slice. The function bodies have the same code, so let’s eliminate the duplication by introducing a generic type parameter in a single function.

To parameterize the types in a new single function, we need to name the type parameter, just as we do for the value parameters to a function. You can use any identifier as a type parameter name. But we’ll use T because, by convention, parameter names in Rust are short, often just a letter, and Rust’s type-naming convention is CamelCase. Short for “type,” T is the default choice of most Rust programmers.

When we use a parameter in the body of the function, we have to declare the parameter name in the signature so the compiler knows what that name means. Similarly, when we use a type parameter name in a function signature, we have to declare the type parameter name before we use it. To define the generic largest function, place type name declarations inside angle brackets, <>, between the name of the function and the parameter list, like this:

fn largest<T>(list: &[T]) -> &T {

We read this definition as: the function largest is generic over some type T. This function has one parameter named list, which is a slice of values of type T. The largest function will return a reference to a value of the same type T.

Listing 10-5 shows the combined largest function definition using the generic data type in its signature. The listing also shows how we can call the function with either a slice of i32 values or char values. Note that this code won’t compile yet, but we’ll fix it later in this chapter.

Filename: src/main.rs

fn largest<T>(list: &[T]) -> &T {
    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 char_list = vec!['y', 'm', 'a', 'q'];

    let result = largest(&char_list);
    println!("The largest char is {}", result);
}

Listing 10-5: The largest function using generic type parameters; this doesn’t yet compile

If we compile this code right now, we’ll get this error:

$ cargo run
   Compiling chapter10 v0.1.0 (file:///projects/chapter10)
error[E0369]: binary operation `>` cannot be applied to type `&T`
 --> src/main.rs:5:17
  |
5 |         if item > largest {
  |            ---- ^ ------- &T
  |            |
  |            &T
  |
help: consider restricting type parameter `T`
  |
1 | fn largest<T: std::cmp::PartialOrd>(list: &[T]) -> &T {
  |             ++++++++++++++++++++++

For more information about this error, try `rustc --explain E0369`.
error: could not compile `chapter10` due to previous error

The help text mentions std::cmp::PartialOrd, which is a trait, and we’re going to talk about traits in the next section. For now, know that this error states that the body of largest won’t work for all possible types that T could be. Because we want to compare values of type T in the body, we can only use types whose values can be ordered. To enable comparisons, the standard library has the std::cmp::PartialOrd trait that you can implement on types (see Appendix C for more on this trait). By following the help text's suggestion, we restrict the types valid for T to only those that implement PartialOrd and this example will compile, because the standard library implements PartialOrd on both i32 and char.

In Struct Definitions

We can also define structs to use a generic type parameter in one or more fields using the <> syntax. Listing 10-6 defines a Point<T> struct to hold x and y coordinate values of any type.

Filename: src/main.rs

struct Point<T> {
    x: T,
    y: T,
}

fn main() {
    let integer = Point { x: 5, y: 10 };
    let float = Point { x: 1.0, y: 4.0 };
}

Listing 10-6: A Point<T> struct that holds x and y values of type T

The syntax for using generics in struct definitions is similar to that used in function definitions. First, we declare the name of the type parameter inside angle brackets just after the name of the struct. Then we use the generic type in the struct definition where we would otherwise specify concrete data types.

Note that because we’ve used only one generic type to define Point<T>, this definition says that the Point<T> struct is generic over some type T, and the fields x and y are both that same type, whatever that type may be. If we create an instance of a Point<T> that has values of different types, as in Listing 10-7, our code won’t compile.

Filename: src/main.rs

struct Point<T> {
    x: T,
    y: T,
}

fn main() {
    let wont_work = Point { x: 5, y: 4.0 };
}

Listing 10-7: The fields x and y must be the same type because both have the same generic data type T.

In this example, when we assign the integer value 5 to x, we let the compiler know that the generic type T will be an integer for this instance of Point<T>. Then when we specify 4.0 for y, which we’ve defined to have the same type as x, we’ll get a type mismatch error like this:

$ cargo run
   Compiling chapter10 v0.1.0 (file:///projects/chapter10)
error[E0308]: mismatched types
 --> src/main.rs:7:38
  |
7 |     let wont_work = Point { x: 5, y: 4.0 };
  |                                      ^^^ expected integer, found floating-point number

For more information about this error, try `rustc --explain E0308`.
error: could not compile `chapter10` due to previous error

To define a Point struct where x and y are both generics but could have different types, we can use multiple generic type parameters. For example, in Listing 10-8, we change the definition of Point to be generic over types T and U where x is of type T and y is of type U.

Filename: src/main.rs

struct Point<T, U> {
    x: T,
    y: U,
}

fn main() {
    let both_integer = Point { x: 5, y: 10 };
    let both_float = Point { x: 1.0, y: 4.0 };
    let integer_and_float = Point { x: 5, y: 4.0 };
}

Listing 10-8: A Point<T, U> generic over two types so that x and y can be values of different types

Now all the instances of Point shown are allowed! You can use as many generic type parameters in a definition as you want, but using more than a few makes your code hard to read. If you're finding you need lots of generic types in your code, it could indicate that your code needs restructuring into smaller pieces.

In Enum Definitions

As we did with structs, we can define enums to hold generic data types in their variants. Let’s take another look at the Option<T> enum that the standard library provides, which we used in Chapter 6:


#![allow(unused)]
fn main() {
enum Option<T> {
    Some(T),
    None,
}
}

This definition should now make more sense to you. As you can see, the Option<T> enum is generic over type T and has two variants: Some, which holds one value of type T, and a None variant that doesn’t hold any value. By using the Option<T> enum, we can express the abstract concept of an optional value, and because Option<T> is generic, we can use this abstraction no matter what the type of the optional value is.

Enums can use multiple generic types as well. The definition of the Result enum that we used in Chapter 9 is one example:


#![allow(unused)]
fn main() {
enum Result<T, E> {
    Ok(T),
    Err(E),
}
}

The Result enum is generic over two types, T and E, and has two variants: Ok, which holds a value of type T, and Err, which holds a value of type E. This definition makes it convenient to use the Result enum anywhere we have an operation that might succeed (return a value of some type T) or fail (return an error of some type E). In fact, this is what we used to open a file in Listing 9-3, where T was filled in with the type std::fs::File when the file was opened successfully and E was filled in with the type std::io::Error when there were problems opening the file.

When you recognize situations in your code with multiple struct or enum definitions that differ only in the types of the values they hold, you can avoid duplication by using generic types instead.

In Method Definitions

We can implement methods on structs and enums (as we did in Chapter 5) and use generic types in their definitions, too. Listing 10-9 shows the Point<T> struct we defined in Listing 10-6 with a method named x implemented on it.

Filename: src/main.rs

struct Point<T> {
    x: T,
    y: T,
}

impl<T> Point<T> {
    fn x(&self) -> &T {
        &self.x
    }
}

fn main() {
    let p = Point { x: 5, y: 10 };

    println!("p.x = {}", p.x());
}

Listing 10-9: Implementing a method named x on the Point<T> struct that will return a reference to the x field of type T

Here, we’ve defined a method named x on Point<T> that returns a reference to the data in the field x.

Note that we have to declare T just after impl so we can use T to specify that we’re implementing methods on the type Point<T>. By declaring T as a generic type after impl, Rust can identify that the type in the angle brackets in Point is a generic type rather than a concrete type. We could have chosen a different name for this generic parameter than the generic parameter declared in the struct definition, but using the same name is conventional. Methods written within an impl that declares the generic type will be defined on any instance of the type, no matter what concrete type ends up substituting for the generic type.

We can also specify constraints on generic types when defining methods on the type. We could, for example, implement methods only on Point<f32> instances rather than on Point<T> instances with any generic type. In Listing 10-10 we use the concrete type f32, meaning we don’t declare any types after impl.

Filename: src/main.rs

struct Point<T> {
    x: T,
    y: T,
}

impl<T> Point<T> {
    fn x(&self) -> &T {
        &self.x
    }
}

impl Point<f32> {
    fn distance_from_origin(&self) -> f32 {
        (self.x.powi(2) + self.y.powi(2)).sqrt()
    }
}

fn main() {
    let p = Point { x: 5, y: 10 };

    println!("p.x = {}", p.x());
}

Listing 10-10: An impl block that only applies to a struct with a particular concrete type for the generic type parameter T

This code means the type Point<f32> will have a distance_from_origin method; other instances of Point<T> where T is not of type f32 will not have this method defined. The method measures how far our point is from the point at coordinates (0.0, 0.0) and uses mathematical operations that are available only for floating point types.

Generic type parameters in a struct definition aren’t always the same as those you use in that same struct’s method signatures. Listing 10-11 uses the generic types X1 and Y1 for the Point struct and X2 Y2 for the mixup method signature to make the example clearer. The method creates a new Point instance with the x value from the self Point (of type X1) and the y value from the passed-in Point (of type Y2).

Filename: src/main.rs

struct Point<X1, Y1> {
    x: X1,
    y: Y1,
}

impl<X1, Y1> Point<X1, Y1> {
    fn mixup<X2, Y2>(self, other: Point<X2, Y2>) -> Point<X1, Y2> {
        Point {
            x: self.x,
            y: other.y,
        }
    }
}

fn main() {
    let p1 = Point { x: 5, y: 10.4 };
    let p2 = Point { x: "Hello", y: 'c' };

    let p3 = p1.mixup(p2);

    println!("p3.x = {}, p3.y = {}", p3.x, p3.y);
}

Listing 10-11: A method that uses generic types different from its struct’s definition

In main, we’ve defined a Point that has an i32 for x (with value 5) and an f64 for y (with value 10.4). The p2 variable is a Point struct that has a string slice for x (with value "Hello") and a char for y (with value c). Calling mixup on p1 with the argument p2 gives us p3, which will have an i32 for x, because x came from p1. The p3 variable will have a char for y, because y came from p2. The println! macro call will print p3.x = 5, p3.y = c.

The purpose of this example is to demonstrate a situation in which some generic parameters are declared with impl and some are declared with the method definition. Here, the generic parameters X1 and Y1 are declared after impl because they go with the struct definition. The generic parameters X2 and Y2 are declared after fn mixup, because they’re only relevant to the method.

Performance of Code Using Generics

You might be wondering whether there is a runtime cost when using generic type parameters. The good news is that using generic types won't make your program run any slower than it would with concrete types.

Rust accomplishes this by performing monomorphization of the code using generics at compile time. Monomorphization is the process of turning generic code into specific code by filling in the concrete types that are used when compiled. In this process, the compiler does the opposite of the steps we used to create the generic function in Listing 10-5: the compiler looks at all the places where generic code is called and generates code for the concrete types the generic code is called with.

Let’s look at how this works by using the standard library’s generic Option<T> enum:


#![allow(unused)]
fn main() {
let integer = Some(5);
let float = Some(5.0);
}

When Rust compiles this code, it performs monomorphization. During that process, the compiler reads the values that have been used in Option<T> instances and identifies two kinds of Option<T>: one is i32 and the other is f64. As such, it expands the generic definition of Option<T> into two definitions specialized to i32 and f64, thereby replacing the generic definition with the specific ones.

The monomorphized version of the code looks similar to the following (the compiler uses different names than what we’re using here for illustration):

Filename: src/main.rs

enum Option_i32 {
    Some(i32),
    None,
}

enum Option_f64 {
    Some(f64),
    None,
}

fn main() {
    let integer = Option_i32::Some(5);
    let float = Option_f64::Some(5.0);
}

The generic Option<T> is replaced with the specific definitions created by the compiler. Because Rust compiles generic code into code that specifies the type in each instance, we pay no runtime cost for using generics. When the code runs, it performs just as it would if we had duplicated each definition by hand. The process of monomorphization makes Rust’s generics extremely efficient at runtime.

Traits: Defining Shared Behavior

A trait defines functionality a particular type has and can share with other types. We can use traits to define shared behavior in an abstract way. We can use trait bounds to specify that a generic type can be any type that has certain behavior.

Note: Traits are similar to a feature often called interfaces in other languages, although with some differences.

Defining a Trait

A type’s behavior consists of the methods we can call on that type. Different types share the same behavior if we can call the same methods on all of those types. Trait definitions are a way to group method signatures together to define a set of behaviors necessary to accomplish some purpose.

For example, let’s say we have multiple structs that hold various kinds and amounts of text: a NewsArticle struct that holds a news story filed in a particular location and a Tweet that can have at most 280 characters along with metadata that indicates whether it was a new tweet, a retweet, or a reply to another tweet.

We want to make a media aggregator library crate named aggregator that can display summaries of data that might be stored in a NewsArticle or Tweet instance. To do this, we need a summary from each type, and we’ll request that summary by calling a summarize method on an instance. Listing 10-12 shows the definition of a public Summary trait that expresses this behavior.

Filename: src/lib.rs

pub trait Summary {
    fn summarize(&self) -> String;
}

Listing 10-12: A Summary trait that consists of the behavior provided by a summarize method

Here, we declare a trait using the trait keyword and then the trait’s name, which is Summary in this case. We’ve also declared the trait as pub so that crates depending on this crate can make use of this trait too, as we’ll see in a few examples. Inside the curly brackets, we declare the method signatures that describe the behaviors of the types that implement this trait, which in this case is fn summarize(&self) -> String.

After the method signature, instead of providing an implementation within curly brackets, we use a semicolon. Each type implementing this trait must provide its own custom behavior for the body of the method. The compiler will enforce that any type that has the Summary trait will have the method summarize defined with this signature exactly.

A trait can have multiple methods in its body: the method signatures are listed one per line and each line ends in a semicolon.

Implementing a Trait on a Type

Now that we’ve defined the desired signatures of the Summary trait’s methods, we can implement it on the types in our media aggregator. Listing 10-13 shows an implementation of the Summary trait on the NewsArticle struct that uses the headline, the author, and the location to create the return value of summarize. For the Tweet struct, we define summarize as the username followed by the entire text of the tweet, assuming that tweet content is already limited to 280 characters.

Filename: src/lib.rs

pub trait Summary {
    fn summarize(&self) -> String;
}

pub struct NewsArticle {
    pub headline: String,
    pub location: String,
    pub author: String,
    pub content: String,
}

impl Summary for NewsArticle {
    fn summarize(&self) -> String {
        format!("{}, by {} ({})", self.headline, self.author, self.location)
    }
}

pub struct Tweet {
    pub username: String,
    pub content: String,
    pub reply: bool,
    pub retweet: bool,
}

impl Summary for Tweet {
    fn summarize(&self) -> String {
        format!("{}: {}", self.username, self.content)
    }
}

Listing 10-13: Implementing the Summary trait on the NewsArticle and Tweet types

Implementing a trait on a type is similar to implementing regular methods. The difference is that after impl, we put the trait name we want to implement, then use the for keyword, and then specify the name of the type we want to implement the trait for. Within the impl block, we put the method signatures that the trait definition has defined. Instead of adding a semicolon after each signature, we use curly brackets and fill in the method body with the specific behavior that we want the methods of the trait to have for the particular type.

Now that the library has implemented the Summary trait on NewsArticle and Tweet, users of the crate can call the trait methods on instances of NewsArticle and Tweet in the same way we call regular methods. The only difference is that the user must bring the trait into scope as well as the types. Here’s an example of how a binary crate could use our aggregator library crate:

use aggregator::{Summary, Tweet};

fn main() {
    let tweet = Tweet {
        username: String::from("horse_ebooks"),
        content: String::from(
            "of course, as you probably already know, people",
        ),
        reply: false,
        retweet: false,
    };

    println!("1 new tweet: {}", tweet.summarize());
}

This code prints 1 new tweet: horse_ebooks: of course, as you probably already know, people.

Other crates that depend on the aggregator crate can also bring the Summary trait into scope to implement Summary on their own types. One restriction to note is that we can implement a trait on a type only if at least one of the trait or the type is local to our crate. For example, we can implement standard library traits like Display on a custom type like Tweet as part of our aggregator crate functionality, because the type Tweet is local to our aggregator crate. We can also implement Summary on Vec<T> in our aggregator crate, because the trait Summary is local to our aggregator crate.

But we can’t implement external traits on external types. For example, we can’t implement the Display trait on Vec<T> within our aggregator crate, because Display and Vec<T> are both defined in the standard library and aren’t local to our aggregator crate. This restriction is part of a property called coherence, and more specifically the orphan rule, so named because the parent type is not present. This rule ensures that other people’s code can’t break your code and vice versa. Without the rule, two crates could implement the same trait for the same type, and Rust wouldn’t know which implementation to use.

Default Implementations

Sometimes it’s useful to have default behavior for some or all of the methods in a trait instead of requiring implementations for all methods on every type. Then, as we implement the trait on a particular type, we can keep or override each method’s default behavior.

In Listing 10-14 we specify a default string for the summarize method of the Summary trait instead of only defining the method signature, as we did in Listing 10-12.

Filename: src/lib.rs

pub trait Summary {
    fn summarize(&self) -> String {
        String::from("(Read more...)")
    }
}

pub struct NewsArticle {
    pub headline: String,
    pub location: String,
    pub author: String,
    pub content: String,
}

impl Summary for NewsArticle {}

pub struct Tweet {
    pub username: String,
    pub content: String,
    pub reply: bool,
    pub retweet: bool,
}

impl Summary for Tweet {
    fn summarize(&self) -> String {
        format!("{}: {}", self.username, self.content)
    }
}

Listing 10-14: Defining a Summary trait with a default implementation of the summarize method

To use a default implementation to summarize instances of NewsArticle, we specify an empty impl block with impl Summary for NewsArticle {}.

Even though we’re no longer defining the summarize method on NewsArticle directly, we’ve provided a default implementation and specified that NewsArticle implements the Summary trait. As a result, we can still call the summarize method on an instance of NewsArticle, like this:

use aggregator::{self, NewsArticle, Summary};

fn main() {
    let article = NewsArticle {
        headline: String::from("Penguins win the Stanley Cup Championship!"),
        location: String::from("Pittsburgh, PA, USA"),
        author: String::from("Iceburgh"),
        content: String::from(
            "The Pittsburgh Penguins once again are the best \
             hockey team in the NHL.",
        ),
    };

    println!("New article available! {}", article.summarize());
}

This code prints New article available! (Read more...).

Creating a default implementation doesn’t require us to change anything about the implementation of Summary on Tweet in Listing 10-13. The reason is that the syntax for overriding a default implementation is the same as the syntax for implementing a trait method that doesn’t have a default implementation.

Default implementations can call other methods in the same trait, even if those other methods don’t have a default implementation. In this way, a trait can provide a lot of useful functionality and only require implementors to specify a small part of it. For example, we could define the Summary trait to have a summarize_author method whose implementation is required, and then define a summarize method that has a default implementation that calls the summarize_author method:

pub trait Summary {
    fn summarize_author(&self) -> String;

    fn summarize(&self) -> String {
        format!("(Read more from {}...)", self.summarize_author())
    }
}

pub struct Tweet {
    pub username: String,
    pub content: String,
    pub reply: bool,
    pub retweet: bool,
}

impl Summary for Tweet {
    fn summarize_author(&self) -> String {
        format!("@{}", self.username)
    }
}

To use this version of Summary, we only need to define summarize_author when we implement the trait on a type:

pub trait Summary {
    fn summarize_author(&self) -> String;

    fn summarize(&self) -> String {
        format!("(Read more from {}...)", self.summarize_author())
    }
}

pub struct Tweet {
    pub username: String,
    pub content: String,
    pub reply: bool,
    pub retweet: bool,
}

impl Summary for Tweet {
    fn summarize_author(&self) -> String {
        format!("@{}", self.username)
    }
}

After we define summarize_author, we can call summarize on instances of the Tweet struct, and the default implementation of summarize will call the definition of summarize_author that we’ve provided. Because we’ve implemented summarize_author, the Summary trait has given us the behavior of the summarize method without requiring us to write any more code.

use aggregator::{self, Summary, Tweet};

fn main() {
    let tweet = Tweet {
        username: String::from("horse_ebooks"),
        content: String::from(
            "of course, as you probably already know, people",
        ),
        reply: false,
        retweet: false,
    };

    println!("1 new tweet: {}", tweet.summarize());
}

This code prints 1 new tweet: (Read more from @horse_ebooks...).

Note that it isn’t possible to call the default implementation from an overriding implementation of that same method.

Traits as Parameters

Now that you know how to define and implement traits, we can explore how to use traits to define functions that accept many different types. We'll use the Summary trait we implemented on the NewsArticle and Tweet types in Listing 10-13 to define a notify function that calls the summarize method on its item parameter, which is of some type that implements the Summary trait. To do this, we use the impl Trait syntax, like this:

pub trait Summary {
    fn summarize(&self) -> String;
}

pub struct NewsArticle {
    pub headline: String,
    pub location: String,
    pub author: String,
    pub content: String,
}

impl Summary for NewsArticle {
    fn summarize(&self) -> String {
        format!("{}, by {} ({})", self.headline, self.author, self.location)
    }
}

pub struct Tweet {
    pub username: String,
    pub content: String,
    pub reply: bool,
    pub retweet: bool,
}

impl Summary for Tweet {
    fn summarize(&self) -> String {
        format!("{}: {}", self.username, self.content)
    }
}

pub fn notify(item: &impl Summary) {
    println!("Breaking news! {}", item.summarize());
}

Instead of a concrete type for the item parameter, we specify the impl keyword and the trait name. This parameter accepts any type that implements the specified trait. In the body of notify, we can call any methods on item that come from the Summary trait, such as summarize. We can call notify and pass in any instance of NewsArticle or Tweet. Code that calls the function with any other type, such as a String or an i32, won’t compile because those types don’t implement Summary.

Trait Bound Syntax

The impl Trait syntax works for straightforward cases but is actually syntax sugar for a longer form known as a trait bound; it looks like this:

pub fn notify<T: Summary>(item: &T) {
    println!("Breaking news! {}", item.summarize());
}

This longer form is equivalent to the example in the previous section but is more verbose. We place trait bounds with the declaration of the generic type parameter after a colon and inside angle brackets.

The impl Trait syntax is convenient and makes for more concise code in simple cases, while the fuller trait bound syntax can express more complexity in other cases. For example, we can have two parameters that implement Summary. Doing so with the impl Trait syntax looks like this:

pub fn notify(item1: &impl Summary, item2: &impl Summary) {

Using impl Trait is appropriate if we want this function to allow item1 and item2 to have different types (as long as both types implement Summary). If we want to force both parameters to have the same type, however, we must use a trait bound, like this:

pub fn notify<T: Summary>(item1: &T, item2: &T) {

The generic type T specified as the type of the item1 and item2 parameters constrains the function such that the concrete type of the value passed as an argument for item1 and item2 must be the same.

Specifying Multiple Trait Bounds with the + Syntax

We can also specify more than one trait bound. Say we wanted notify to use display formatting as well as summarize on item: we specify in the notify definition that item must implement both Display and Summary. We can do so using the + syntax:

pub fn notify(item: &(impl Summary + Display)) {

The + syntax is also valid with trait bounds on generic types:

pub fn notify<T: Summary + Display>(item: &T) {

With the two trait bounds specified, the body of notify can call summarize and use {} to format item.

Clearer Trait Bounds with where Clauses

Using too many trait bounds has its downsides. Each generic has its own trait bounds, so functions with multiple generic type parameters can contain lots of trait bound information between the function’s name and its parameter list, making the function signature hard to read. For this reason, Rust has alternate syntax for specifying trait bounds inside a where clause after the function signature. So instead of writing this:

fn some_function<T: Display + Clone, U: Clone + Debug>(t: &T, u: &U) -> i32 {

we can use a where clause, like this:

fn some_function<T, U>(t: &T, u: &U) -> i32
    where T: Display + Clone,
          U: Clone + Debug
{

This function’s signature is less cluttered: the function name, parameter list, and return type are close together, similar to a function without lots of trait bounds.

Returning Types that Implement Traits

We can also use the impl Trait syntax in the return position to return a value of some type that implements a trait, as shown here:

pub trait Summary {
    fn summarize(&self) -> String;
}

pub struct NewsArticle {
    pub headline: String,
    pub location: String,
    pub author: String,
    pub content: String,
}

impl Summary for NewsArticle {
    fn summarize(&self) -> String {
        format!("{}, by {} ({})", self.headline, self.author, self.location)
    }
}

pub struct Tweet {
    pub username: String,
    pub content: String,
    pub reply: bool,
    pub retweet: bool,
}

impl Summary for Tweet {
    fn summarize(&self) -> String {
        format!("{}: {}", self.username, self.content)
    }
}

fn returns_summarizable() -> impl Summary {
    Tweet {
        username: String::from("horse_ebooks"),
        content: String::from(
            "of course, as you probably already know, people",
        ),
        reply: false,
        retweet: false,
    }
}

By using impl Summary for the return type, we specify that the returns_summarizable function returns some type that implements the Summary trait without naming the concrete type. In this case, returns_summarizable returns a Tweet, but the code calling this function doesn’t need to know that.

The ability to specify a return type only by the trait it implements is especially useful in the context of closures and iterators, which we cover in Chapter 13. Closures and iterators create types that only the compiler knows or types that are very long to specify. The impl Trait syntax lets you concisely specify that a function returns some type that implements the Iterator trait without needing to write out a very long type.

However, you can only use impl Trait if you’re returning a single type. For example, this code that returns either a NewsArticle or a Tweet with the return type specified as impl Summary wouldn’t work:

pub trait Summary {
    fn summarize(&self) -> String;
}

pub struct NewsArticle {
    pub headline: String,
    pub location: String,
    pub author: String,
    pub content: String,
}

impl Summary for NewsArticle {
    fn summarize(&self) -> String {
        format!("{}, by {} ({})", self.headline, self.author, self.location)
    }
}

pub struct Tweet {
    pub username: String,
    pub content: String,
    pub reply: bool,
    pub retweet: bool,
}

impl Summary for Tweet {
    fn summarize(&self) -> String {
        format!("{}: {}", self.username, self.content)
    }
}

fn returns_summarizable(switch: bool) -> impl Summary {
    if switch {
        NewsArticle {
            headline: String::from(
                "Penguins win the Stanley Cup Championship!",
            ),
            location: String::from("Pittsburgh, PA, USA"),
            author: String::from("Iceburgh"),
            content: String::from(
                "The Pittsburgh Penguins once again are the best \
                 hockey team in the NHL.",
            ),
        }
    } else {
        Tweet {
            username: String::from("horse_ebooks"),
            content: String::from(
                "of course, as you probably already know, people",
            ),
            reply: false,
            retweet: false,
        }
    }
}

Returning either a NewsArticle or a Tweet isn’t allowed due to restrictions around how the impl Trait syntax is implemented in the compiler. We’ll cover how to write a function with this behavior in the “Using Trait Objects That Allow for Values of Different Types” section of Chapter 17.

Using Trait Bounds to Conditionally Implement Methods

By using a trait bound with an impl block that uses generic type parameters, we can implement methods conditionally for types that implement the specified traits. For example, the type Pair<T> in Listing 10-15 always implements the new function to return a new instance of Pair<T> (recall from the “Defining Methods” section of Chapter 5 that Self is a type alias for the type of the impl block, which in this case is Pair<T>). But in the next impl block, Pair<T> only implements the cmp_display method if its inner type T implements the PartialOrd trait that enables comparison and the Display trait that enables printing.

Filename: src/lib.rs

use std::fmt::Display;

struct Pair<T> {
    x: T,
    y: T,
}

impl<T> Pair<T> {
    fn new(x: T, y: T) -> Self {
        Self { x, y }
    }
}

impl<T: Display + PartialOrd> Pair<T> {
    fn cmp_display(&self) {
        if self.x >= self.y {
            println!("The largest member is x = {}", self.x);
        } else {
            println!("The largest member is y = {}", self.y);
        }
    }
}

Listing 10-15: Conditionally implementing methods on a generic type depending on trait bounds

We can also conditionally implement a trait for any type that implements another trait. Implementations of a trait on any type that satisfies the trait bounds are called blanket implementations and are extensively used in the Rust standard library. For example, the standard library implements the ToString trait on any type that implements the Display trait. The impl block in the standard library looks similar to this code:

impl<T: Display> ToString for T {
    // --snip--
}

Because the standard library has this blanket implementation, we can call the to_string method defined by the ToString trait on any type that implements the Display trait. For example, we can turn integers into their corresponding String values like this because integers implement Display:


#![allow(unused)]
fn main() {
let s = 3.to_string();
}

Blanket implementations appear in the documentation for the trait in the “Implementors” section.

Traits and trait bounds let us write code that uses generic type parameters to reduce duplication but also specify to the compiler that we want the generic type to have particular behavior. The compiler can then use the trait bound information to check that all the concrete types used with our code provide the correct behavior. In dynamically typed languages, we would get an error at runtime if we called a method on a type which didn’t define the method. But Rust moves these errors to compile time so we’re forced to fix the problems before our code is even able to run. Additionally, we don’t have to write code that checks for behavior at runtime because we’ve already checked at compile time. Doing so improves performance without having to give up the flexibility of generics.

Validating References with Lifetimes

Lifetimes are another kind of generic that we’ve already been using. Rather than ensuring that a type has the behavior we want, lifetimes ensure that references are valid as long as we need them to be.

One detail we didn’t discuss in the “References and Borrowing” section in Chapter 4 is that every reference in Rust has a lifetime, which is the scope for which that reference is valid. Most of the time, lifetimes are implicit and inferred, just like most of the time, types are inferred. We only must annotate types when multiple types are possible. In a similar way, we must annotate lifetimes when the lifetimes of references could be related in a few different ways. Rust requires us to annotate the relationships using generic lifetime parameters to ensure the actual references used at runtime will definitely be valid.

Annotating lifetimes is not even a concept most other programming languages have, so this is going to feel unfamiliar. Although we won’t cover lifetimes in their entirety in this chapter, we’ll discuss common ways you might encounter lifetime syntax so you can get comfortable with the concept.

Preventing Dangling References with Lifetimes

The main aim of lifetimes is to prevent dangling references, which cause a program to reference data other than the data it’s intended to reference. Consider the program in Listing 10-16, which has an outer scope and an inner scope.

fn main() {
    let r;

    {
        let x = 5;
        r = &x;
    }

    println!("r: {}", r);
}

Listing 10-16: An attempt to use a reference whose value has gone out of scope

Note: The examples in Listings 10-16, 10-17, and 10-23 declare variables without giving them an initial value, so the variable name exists in the outer scope. At first glance, this might appear to be in conflict with Rust’s having no null values. However, if we try to use a variable before giving it a value, we’ll get a compile-time error, which shows that Rust indeed does not allow null values.

The outer scope declares a variable named r with no initial value, and the inner scope declares a variable named x with the initial value of 5. Inside the inner scope, we attempt to set the value of r as a reference to x. Then the inner scope ends, and we attempt to print the value in r. This code won’t compile because the value r is referring to has gone out of scope before we try to use it. Here is the error message:

$ cargo run
   Compiling chapter10 v0.1.0 (file:///projects/chapter10)
error[E0597]: `x` does not live long enough
 --> src/main.rs:6:13
  |
6 |         r = &x;
  |             ^^ borrowed value does not live long enough
7 |     }
  |     - `x` dropped here while still borrowed
8 | 
9 |     println!("r: {}", r);
  |                       - borrow later used here

For more information about this error, try `rustc --explain E0597`.
error: could not compile `chapter10` due to previous error

The variable x doesn’t “live long enough.” The reason is that x will be out of scope when the inner scope ends on line 7. But r is still valid for the outer scope; because its scope is larger, we say that it “lives longer.” If Rust allowed this code to work, r would be referencing memory that was deallocated when x went out of scope, and anything we tried to do with r wouldn’t work correctly. So how does Rust determine that this code is invalid? It uses a borrow checker.

The Borrow Checker

The Rust compiler has a borrow checker that compares scopes to determine whether all borrows are valid. Listing 10-17 shows the same code as Listing 10-16 but with annotations showing the lifetimes of the variables.

fn main() {
    let r;                // ---------+-- 'a
                          //          |
    {                     //          |
        let x = 5;        // -+-- 'b  |
        r = &x;           //  |       |
    }                     // -+       |
                          //          |
    println!("r: {}", r); //          |
}                         // ---------+

Listing 10-17: Annotations of the lifetimes of r and x, named 'a and 'b, respectively

Here, we’ve annotated the lifetime of r with 'a and the lifetime of x with 'b. As you can see, the inner 'b block is much smaller than the outer 'a lifetime block. At compile time, Rust compares the size of the two lifetimes and sees that r has a lifetime of 'a but that it refers to memory with a lifetime of 'b. The program is rejected because 'b is shorter than 'a: the subject of the reference doesn’t live as long as the reference.

Listing 10-18 fixes the code so it doesn’t have a dangling reference and compiles without any errors.

fn main() {
    let x = 5;            // ----------+-- 'b
                          //           |
    let r = &x;           // --+-- 'a  |
                          //   |       |
    println!("r: {}", r); //   |       |
                          // --+       |
}                         // ----------+

Listing 10-18: A valid reference because the data has a longer lifetime than the reference

Here, x has the lifetime 'b, which in this case is larger than 'a. This means r can reference x because Rust knows that the reference in r will always be valid while x is valid.

Now that you know where the lifetimes of references are and how Rust analyzes lifetimes to ensure references will always be valid, let’s explore generic lifetimes of parameters and return values in the context of functions.

Generic Lifetimes in Functions

We’ll write a function that returns the longer of two string slices. This function will take two string slices and return a single string slice. After we’ve implemented the longest function, the code in Listing 10-19 should print The longest string is abcd.

Filename: src/main.rs

fn main() {
    let string1 = String::from("abcd");
    let string2 = "xyz";

    let result = longest(string1.as_str(), string2);
    println!("The longest string is {}", result);
}

Listing 10-19: A main function that calls the longest function to find the longer of two string slices

Note that we want the function to take string slices, which are references, rather than strings, because we don’t want the longest function to take ownership of its parameters. Refer to the “String Slices as Parameters” section in Chapter 4 for more discussion about why the parameters we use in Listing 10-19 are the ones we want.

If we try to implement the longest function as shown in Listing 10-20, it won’t compile.

Filename: src/main.rs

fn main() {
    let string1 = String::from("abcd");
    let string2 = "xyz";

    let result = longest(string1.as_str(), string2);
    println!("The longest string is {}", result);
}

fn longest(x: &str, y: &str) -> &str {
    if x.len() > y.len() {
        x
    } else {
        y
    }
}

Listing 10-20: An implementation of the longest function that returns the longer of two string slices but does not yet compile

Instead, we get the following error that talks about lifetimes:

$ cargo run
   Compiling chapter10 v0.1.0 (file:///projects/chapter10)
error[E0106]: missing lifetime specifier
 --> src/main.rs:9:33
  |
9 | fn longest(x: &str, y: &str) -> &str {
  |               ----     ----     ^ expected named lifetime parameter
  |
  = help: this function's return type contains a borrowed value, but the signature does not say whether it is borrowed from `x` or `y`
help: consider introducing a named lifetime parameter
  |
9 | fn longest<'a>(x: &'a str, y: &'a str) -> &'a str {
  |           ++++     ++          ++          ++

For more information about this error, try `rustc --explain E0106`.
error: could not compile `chapter10` due to previous error

The help text reveals that the return type needs a generic lifetime parameter on it because Rust can’t tell whether the reference being returned refers to x or y. Actually, we don’t know either, because the if block in the body of this function returns a reference to x and the else block returns a reference to y!

When we’re defining this function, we don’t know the concrete values that will be passed into this function, so we don’t know whether the if case or the else case will execute. We also don’t know the concrete lifetimes of the references that will be passed in, so we can’t look at the scopes as we did in Listings 10-17 and 10-18 to determine whether the reference we return will always be valid. The borrow checker can’t determine this either, because it doesn’t know how the lifetimes of x and y relate to the lifetime of the return value. To fix this error, we’ll add generic lifetime parameters that define the relationship between the references so the borrow checker can perform its analysis.

Lifetime Annotation Syntax

Lifetime annotations don’t change how long any of the references live. Rather, they describe the relationships of the lifetimes of multiple references to each other without affecting the lifetimes. Just as functions can accept any type when the signature specifies a generic type parameter, functions can accept references with any lifetime by specifying a generic lifetime parameter.

Lifetime annotations have a slightly unusual syntax: the names of lifetime parameters must start with an apostrophe (') and are usually all lowercase and very short, like generic types. Most people use the name 'a for the first lifetime annotation. We place lifetime parameter annotations after the & of a reference, using a space to separate the annotation from the reference’s type.

Here are some examples: a reference to an i32 without a lifetime parameter, a reference to an i32 that has a lifetime parameter named 'a, and a mutable reference to an i32 that also has the lifetime 'a.

&i32        // a reference
&'a i32     // a reference with an explicit lifetime
&'a mut i32 // a mutable reference with an explicit lifetime

One lifetime annotation by itself doesn’t have much meaning, because the annotations are meant to tell Rust how generic lifetime parameters of multiple references relate to each other. Let’s examine how the lifetime annotations relate to each other in the context of the longest function.

Lifetime Annotations in Function Signatures

To use lifetime annotations in function signatures, we need to declare the generic lifetime parameters inside angle brackets between the function name and the parameter list, just as we did with generic type parameters.

We want the signature to express the following constraint: the returned reference will be valid as long as both the parameters are valid. This is the relationship between lifetimes of the parameters and the return value. We’ll name the lifetime 'a and then add it to each reference, as shown in Listing 10-21.

Filename: src/main.rs

fn main() {
    let string1 = String::from("abcd");
    let string2 = "xyz";

    let result = longest(string1.as_str(), string2);
    println!("The longest string is {}", result);
}

fn longest<'a>(x: &'a str, y: &'a str) -> &'a str {
    if x.len() > y.len() {
        x
    } else {
        y
    }
}

Listing 10-21: The longest function definition specifying that all the references in the signature must have the same lifetime 'a

This code should compile and produce the result we want when we use it with the main function in Listing 10-19.

The function signature now tells Rust that for some lifetime 'a, the function takes two parameters, both of which are string slices that live at least as long as lifetime 'a. The function signature also tells Rust that the string slice returned from the function will live at least as long as lifetime 'a. In practice, it means that the lifetime of the reference returned by the longest function is the same as the smaller of the lifetimes of the values referred to by the function arguments. These relationships are what we want Rust to use when analyzing this code.

Remember, when we specify the lifetime parameters in this function signature, we’re not changing the lifetimes of any values passed in or returned. Rather, we’re specifying that the borrow checker should reject any values that don’t adhere to these constraints. Note that the longest function doesn’t need to know exactly how long x and y will live, only that some scope can be substituted for 'a that will satisfy this signature.

When annotating lifetimes in functions, the annotations go in the function signature, not in the function body. The lifetime annotations become part of the contract of the function, much like the types in the signature. Having function signatures contain the lifetime contract means the analysis the Rust compiler does can be simpler. If there’s a problem with the way a function is annotated or the way it is called, the compiler errors can point to the part of our code and the constraints more precisely. If, instead, the Rust compiler made more inferences about what we intended the relationships of the lifetimes to be, the compiler might only be able to point to a use of our code many steps away from the cause of the problem.

When we pass concrete references to longest, the concrete lifetime that is substituted for 'a is the part of the scope of x that overlaps with the scope of y. In other words, the generic lifetime 'a will get the concrete lifetime that is equal to the smaller of the lifetimes of x and y. Because we’ve annotated the returned reference with the same lifetime parameter 'a, the returned reference will also be valid for the length of the smaller of the lifetimes of x and y.

Let’s look at how the lifetime annotations restrict the longest function by passing in references that have different concrete lifetimes. Listing 10-22 is a straightforward example.

Filename: src/main.rs

fn main() {
    let string1 = String::from("long string is long");

    {
        let string2 = String::from("xyz");
        let result = longest(string1.as_str(), string2.as_str());
        println!("The longest string is {}", result);
    }
}

fn longest<'a>(x: &'a str, y: &'a str) -> &'a str {
    if x.len() > y.len() {
        x
    } else {
        y
    }
}

Listing 10-22: Using the longest function with references to String values that have different concrete lifetimes

In this example, string1 is valid until the end of the outer scope, string2 is valid until the end of the inner scope, and result references something that is valid until the end of the inner scope. Run this code, and you’ll see that the borrow checker approves; it will compile and print The longest string is long string is long.

Next, let’s try an example that shows that the lifetime of the reference in result must be the smaller lifetime of the two arguments. We’ll move the declaration of the result variable outside the inner scope but leave the assignment of the value to the result variable inside the scope with string2. Then we’ll move the println! that uses result to outside the inner scope, after the inner scope has ended. The code in Listing 10-23 will not compile.

Filename: src/main.rs

fn main() {
    let string1 = String::from("long string is long");
    let result;
    {
        let string2 = String::from("xyz");
        result = longest(string1.as_str(), string2.as_str());
    }
    println!("The longest string is {}", result);
}

fn longest<'a>(x: &'a str, y: &'a str) -> &'a str {
    if x.len() > y.len() {
        x
    } else {
        y
    }
}

Listing 10-23: Attempting to use result after string2 has gone out of scope

When we try to compile this code, we get this error:

$ cargo run
   Compiling chapter10 v0.1.0 (file:///projects/chapter10)
error[E0597]: `string2` does not live long enough
 --> src/main.rs:6:44
  |
6 |         result = longest(string1.as_str(), string2.as_str());
  |                                            ^^^^^^^^^^^^^^^^ borrowed value does not live long enough
7 |     }
  |     - `string2` dropped here while still borrowed
8 |     println!("The longest string is {}", result);
  |                                          ------ borrow later used here

For more information about this error, try `rustc --explain E0597`.
error: could not compile `chapter10` due to previous error

The error shows that for result to be valid for the println! statement, string2 would need to be valid until the end of the outer scope. Rust knows this because we annotated the lifetimes of the function parameters and return values using the same lifetime parameter 'a.

As humans, we can look at this code and see that string1 is longer than string2 and therefore result will contain a reference to string1. Because string1 has not gone out of scope yet, a reference to string1 will still be valid for the println! statement. However, the compiler can’t see that the reference is valid in this case. We’ve told Rust that the lifetime of the reference returned by the longest function is the same as the smaller of the lifetimes of the references passed in. Therefore, the borrow checker disallows the code in Listing 10-23 as possibly having an invalid reference.

Try designing more experiments that vary the values and lifetimes of the references passed in to the longest function and how the returned reference is used. Make hypotheses about whether or not your experiments will pass the borrow checker before you compile; then check to see if you’re right!

Thinking in Terms of Lifetimes

The way in which you need to specify lifetime parameters depends on what your function is doing. For example, if we changed the implementation of the longest function to always return the first parameter rather than the longest string slice, we wouldn’t need to specify a lifetime on the y parameter. The following code will compile:

Filename: src/main.rs

fn main() {
    let string1 = String::from("abcd");
    let string2 = "efghijklmnopqrstuvwxyz";

    let result = longest(string1.as_str(), string2);
    println!("The longest string is {}", result);
}

fn longest<'a>(x: &'a str, y: &str) -> &'a str {
    x
}

We’ve specified a lifetime parameter 'a for the parameter x and the return type, but not for the parameter y, because the lifetime of y does not have any relationship with the lifetime of x or the return value.

When returning a reference from a function, the lifetime parameter for the return type needs to match the lifetime parameter for one of the parameters. If the reference returned does not refer to one of the parameters, it must refer to a value created within this function. However, this would be a dangling reference because the value will go out of scope at the end of the function. Consider this attempted implementation of the longest function that won’t compile:

Filename: src/main.rs

fn main() {
    let string1 = String::from("abcd");
    let string2 = "xyz";

    let result = longest(string1.as_str(), string2);
    println!("The longest string is {}", result);
}

fn longest<'a>(x: &str, y: &str) -> &'a str {
    let result = String::from("really long string");
    result.as_str()
}

Here, even though we’ve specified a lifetime parameter 'a for the return type, this implementation will fail to compile because the return value lifetime is not related to the lifetime of the parameters at all. Here is the error message we get:

$ cargo run
   Compiling chapter10 v0.1.0 (file:///projects/chapter10)
error[E0515]: cannot return reference to local variable `result`
  --> src/main.rs:11:5
   |
11 |     result.as_str()
   |     ^^^^^^^^^^^^^^^ returns a reference to data owned by the current function

For more information about this error, try `rustc --explain E0515`.
error: could not compile `chapter10` due to previous error

The problem is that result goes out of scope and gets cleaned up at the end of the longest function. We’re also trying to return a reference to result from the function. There is no way we can specify lifetime parameters that would change the dangling reference, and Rust won’t let us create a dangling reference. In this case, the best fix would be to return an owned data type rather than a reference so the calling function is then responsible for cleaning up the value.

Ultimately, lifetime syntax is about connecting the lifetimes of various parameters and return values of functions. Once they’re connected, Rust has enough information to allow memory-safe operations and disallow operations that would create dangling pointers or otherwise violate memory safety.

Lifetime Annotations in Struct Definitions

So far, the structs we’ve defined all hold owned types. We can define structs to hold references, but in that case we would need to add a lifetime annotation on every reference in the struct’s definition. Listing 10-24 has a struct named ImportantExcerpt that holds a string slice.

Filename: src/main.rs

struct ImportantExcerpt<'a> {
    part: &'a str,
}

fn main() {
    let novel = String::from("Call me Ishmael. Some years ago...");
    let first_sentence = novel.split('.').next().expect("Could not find a '.'");
    let i = ImportantExcerpt {
        part: first_sentence,
    };
}

Listing 10-24: A struct that holds a reference, requiring a lifetime annotation

This struct has the single field part that holds a string slice, which is a reference. As with generic data types, we declare the name of the generic lifetime parameter inside angle brackets after the name of the struct so we can use the lifetime parameter in the body of the struct definition. This annotation means an instance of ImportantExcerpt can’t outlive the reference it holds in its part field.

The main function here creates an instance of the ImportantExcerpt struct that holds a reference to the first sentence of the String owned by the variable novel. The data in novel exists before the ImportantExcerpt instance is created. In addition, novel doesn’t go out of scope until after the ImportantExcerpt goes out of scope, so the reference in the ImportantExcerpt instance is valid.

Lifetime Elision

You’ve learned that every reference has a lifetime and that you need to specify lifetime parameters for functions or structs that use references. However, in Chapter 4 we had a function in Listing 4-9, shown again in Listing 10-25, that compiled without lifetime annotations.

Filename: src/lib.rs

fn first_word(s: &str) -> &str {
    let bytes = s.as_bytes();

    for (i, &item) in bytes.iter().enumerate() {
        if item == b' ' {
            return &s[0..i];
        }
    }

    &s[..]
}

fn main() {
    let my_string = String::from("hello world");

    // first_word works on slices of `String`s
    let word = first_word(&my_string[..]);

    let my_string_literal = "hello world";

    // first_word works on slices of string literals
    let word = first_word(&my_string_literal[..]);

    // Because string literals *are* string slices already,
    // this works too, without the slice syntax!
    let word = first_word(my_string_literal);
}

Listing 10-25: A function we defined in Listing 4-9 that compiled without lifetime annotations, even though the parameter and return type are references

The reason this function compiles without lifetime annotations is historical: in early versions (pre-1.0) of Rust, this code wouldn’t have compiled because every reference needed an explicit lifetime. At that time, the function signature would have been written like this:

fn first_word<'a>(s: &'a str) -> &'a str {

After writing a lot of Rust code, the Rust team found that Rust programmers were entering the same lifetime annotations over and over in particular situations. These situations were predictable and followed a few deterministic patterns. The developers programmed these patterns into the compiler’s code so the borrow checker could infer the lifetimes in these situations and wouldn’t need explicit annotations.

This piece of Rust history is relevant because it’s possible that more deterministic patterns will emerge and be added to the compiler. In the future, even fewer lifetime annotations might be required.

The patterns programmed into Rust’s analysis of references are called the lifetime elision rules. These aren’t rules for programmers to follow; they’re a set of particular cases that the compiler will consider, and if your code fits these cases, you don’t need to write the lifetimes explicitly.

The elision rules don’t provide full inference. If Rust deterministically applies the rules but there is still ambiguity as to what lifetimes the references have, the compiler won’t guess what the lifetime of the remaining references should be. Instead of guessing, the compiler will give you an error that you can resolve by adding the lifetime annotations.

Lifetimes on function or method parameters are called input lifetimes, and lifetimes on return values are called output lifetimes.

The compiler uses three rules to figure out the lifetimes of the references when there aren’t explicit annotations. The first rule applies to input lifetimes, and the second and third rules apply to output lifetimes. If the compiler gets to the end of the three rules and there are still references for which it can’t figure out lifetimes, the compiler will stop with an error. These rules apply to fn definitions as well as impl blocks.

The first rule is that the compiler assigns a lifetime parameter to each parameter that’s a reference. In other words, a function with one parameter gets one lifetime parameter: fn foo<'a>(x: &'a i32); a function with two parameters gets two separate lifetime parameters: fn foo<'a, 'b>(x: &'a i32, y: &'b i32); and so on.

The second rule is that, if there is exactly one input lifetime parameter, that lifetime is assigned to all output lifetime parameters: fn foo<'a>(x: &'a i32) -> &'a i32.

The third rule is that, if there are multiple input lifetime parameters, but one of them is &self or &mut self because this is a method, the lifetime of self is assigned to all output lifetime parameters. This third rule makes methods much nicer to read and write because fewer symbols are necessary.

Let’s pretend we’re the compiler. We’ll apply these rules to figure out the lifetimes of the references in the signature of the first_word function in Listing 10-25. The signature starts without any lifetimes associated with the references:

fn first_word(s: &str) -> &str {

Then the compiler applies the first rule, which specifies that each parameter gets its own lifetime. We’ll call it 'a as usual, so now the signature is this:

fn first_word<'a>(s: &'a str) -> &str {

The second rule applies because there is exactly one input lifetime. The second rule specifies that the lifetime of the one input parameter gets assigned to the output lifetime, so the signature is now this:

fn first_word<'a>(s: &'a str) -> &'a str {

Now all the references in this function signature have lifetimes, and the compiler can continue its analysis without needing the programmer to annotate the lifetimes in this function signature.

Let’s look at another example, this time using the longest function that had no lifetime parameters when we started working with it in Listing 10-20:

fn longest(x: &str, y: &str) -> &str {

Let’s apply the first rule: each parameter gets its own lifetime. This time we have two parameters instead of one, so we have two lifetimes:

fn longest<'a, 'b>(x: &'a str, y: &'b str) -> &str {

You can see that the second rule doesn’t apply because there is more than one input lifetime. The third rule doesn’t apply either, because longest is a function rather than a method, so none of the parameters are self. After working through all three rules, we still haven’t figured out what the return type’s lifetime is. This is why we got an error trying to compile the code in Listing 10-20: the compiler worked through the lifetime elision rules but still couldn’t figure out all the lifetimes of the references in the signature.

Because the third rule really only applies in method signatures, we’ll look at lifetimes in that context next to see why the third rule means we don’t have to annotate lifetimes in method signatures very often.

Lifetime Annotations in Method Definitions

When we implement methods on a struct with lifetimes, we use the same syntax as that of generic type parameters shown in Listing 10-11. Where we declare and use the lifetime parameters depends on whether they’re related to the struct fields or the method parameters and return values.

Lifetime names for struct fields always need to be declared after the impl keyword and then used after the struct’s name, because those lifetimes are part of the struct’s type.

In method signatures inside the impl block, references might be tied to the lifetime of references in the struct’s fields, or they might be independent. In addition, the lifetime elision rules often make it so that lifetime annotations aren’t necessary in method signatures. Let’s look at some examples using the struct named ImportantExcerpt that we defined in Listing 10-24.

First, we’ll use a method named level whose only parameter is a reference to self and whose return value is an i32, which is not a reference to anything:

struct ImportantExcerpt<'a> {
    part: &'a str,
}

impl<'a> ImportantExcerpt<'a> {
    fn level(&self) -> i32 {
        3
    }
}

impl<'a> ImportantExcerpt<'a> {
    fn announce_and_return_part(&self, announcement: &str) -> &str {
        println!("Attention please: {}", announcement);
        self.part
    }
}

fn main() {
    let novel = String::from("Call me Ishmael. Some years ago...");
    let first_sentence = novel.split('.').next().expect("Could not find a '.'");
    let i = ImportantExcerpt {
        part: first_sentence,
    };
}

The lifetime parameter declaration after impl and its use after the type name are required, but we’re not required to annotate the lifetime of the reference to self because of the first elision rule.

Here is an example where the third lifetime elision rule applies:

struct ImportantExcerpt<'a> {
    part: &'a str,
}

impl<'a> ImportantExcerpt<'a> {
    fn level(&self) -> i32 {
        3
    }
}

impl<'a> ImportantExcerpt<'a> {
    fn announce_and_return_part(&self, announcement: &str) -> &str {
        println!("Attention please: {}", announcement);
        self.part
    }
}

fn main() {
    let novel = String::from("Call me Ishmael. Some years ago...");
    let first_sentence = novel.split('.').next().expect("Could not find a '.'");
    let i = ImportantExcerpt {
        part: first_sentence,
    };
}

There are two input lifetimes, so Rust applies the first lifetime elision rule and gives both &self and announcement their own lifetimes. Then, because one of the parameters is &self, the return type gets the lifetime of &self, and all lifetimes have been accounted for.

The Static Lifetime

One special lifetime we need to discuss is 'static, which denotes that the affected reference can live for the entire duration of the program. All string literals have the 'static lifetime, which we can annotate as follows:


#![allow(unused)]
fn main() {
let s: &'static str = "I have a static lifetime.";
}

The text of this string is stored directly in the program’s binary, which is always available. Therefore, the lifetime of all string literals is 'static.

You might see suggestions to use the 'static lifetime in error messages. But before specifying 'static as the lifetime for a reference, think about whether the reference you have actually lives the entire lifetime of your program or not, and whether you want it to. Most of the time, an error message suggesting the 'static lifetime results from attempting to create a dangling reference or a mismatch of the available lifetimes. In such cases, the solution is fixing those problems, not specifying the 'static lifetime.

Generic Type Parameters, Trait Bounds, and Lifetimes Together

Let’s briefly look at the syntax of specifying generic type parameters, trait bounds, and lifetimes all in one function!

fn main() {
    let string1 = String::from("abcd");
    let string2 = "xyz";

    let result = longest_with_an_announcement(
        string1.as_str(),
        string2,
        "Today is someone's birthday!",
    );
    println!("The longest string is {}", result);
}

use std::fmt::Display;

fn longest_with_an_announcement<'a, T>(
    x: &'a str,
    y: &'a str,
    ann: T,
) -> &'a str
where
    T: Display,
{
    println!("Announcement! {}", ann);
    if x.len() > y.len() {
        x
    } else {
        y
    }
}

This is the longest function from Listing 10-21 that returns the longer of two string slices. But now it has an extra parameter named ann of the generic type T, which can be filled in by any type that implements the Display trait as specified by the where clause. This extra parameter will be printed using {}, which is why the Display trait bound is necessary. Because lifetimes are a type of generic, the declarations of the lifetime parameter 'a and the generic type parameter T go in the same list inside the angle brackets after the function name.

Summary

We covered a lot in this chapter! Now that you know about generic type parameters, traits and trait bounds, and generic lifetime parameters, you’re ready to write code without repetition that works in many different situations. Generic type parameters let you apply the code to different types. Traits and trait bounds ensure that even though the types are generic, they’ll have the behavior the code needs. You learned how to use lifetime annotations to ensure that this flexible code won’t have any dangling references. And all of this analysis happens at compile time, which doesn’t affect runtime performance!

Believe it or not, there is much more to learn on the topics we discussed in this chapter: Chapter 17 discusses trait objects, which are another way to use traits. There are also more complex scenarios involving lifetime annotations that you will only need in very advanced scenarios; for those, you should read the Rust Reference. But next, you’ll learn how to write tests in Rust so you can make sure your code is working the way it should.

Writing Automated Tests

In his 1972 essay “The Humble Programmer,” Edsger W. Dijkstra said that “Program testing can be a very effective way to show the presence of bugs, but it is hopelessly inadequate for showing their absence.” That doesn’t mean we shouldn’t try to test as much as we can!

Correctness in our programs is the extent to which our code does what we intend it to do. Rust is designed with a high degree of concern about the correctness of programs, but correctness is complex and not easy to prove. Rust’s type system shoulders a huge part of this burden, but the type system cannot catch everything. As such, Rust includes support for writing automated software tests.

Say we write a function add_two that adds 2 to whatever number is passed to it. This function’s signature accepts an integer as a parameter and returns an integer as a result. When we implement and compile that function, Rust does all the type checking and borrow checking that you’ve learned so far to ensure that, for instance, we aren’t passing a String value or an invalid reference to this function. But Rust can’t check that this function will do precisely what we intend, which is return the parameter plus 2 rather than, say, the parameter plus 10 or the parameter minus 50! That’s where tests come in.

We can write tests that assert, for example, that when we pass 3 to the add_two function, the returned value is 5. We can run these tests whenever we make changes to our code to make sure any existing correct behavior has not changed.

Testing is a complex skill: although we can’t cover every detail about how to write good tests in one chapter, we’ll discuss the mechanics of Rust’s testing facilities. We’ll talk about the annotations and macros available to you when writing your tests, the default behavior and options provided for running your tests, and how to organize tests into unit tests and integration tests.

How to Write Tests

Tests are Rust functions that verify that the non-test code is functioning in the expected manner. The bodies of test functions typically perform these three actions:

  1. Set up any needed data or state.
  2. Run the code you want to test.
  3. Assert the results are what you expect.

Let’s look at the features Rust provides specifically for writing tests that take these actions, which include the test attribute, a few macros, and the should_panic attribute.

The Anatomy of a Test Function

At its simplest, a test in Rust is a function that’s annotated with the test attribute. Attributes are metadata about pieces of Rust code; one example is the derive attribute we used with structs in Chapter 5. To change a function into a test function, add #[test] on the line before fn. When you run your tests with the cargo test command, Rust builds a test runner binary that runs the annotated functions and reports on whether each test function passes or fails.

Whenever we make a new library project with Cargo, a test module with a test function in it is automatically generated for us. This module gives you a template for writing your tests so you don’t have to look up the exact structure and syntax every time you start a new project. You can add as many additional test functions and as many test modules as you want!

We’ll explore some aspects of how tests work by experimenting with the template test before we actually test any code. Then we’ll write some real-world tests that call some code that we’ve written and assert that its behavior is correct.

Let’s create a new library project called adder that will add two numbers:

$ cargo new adder --lib
     Created library `adder` project
$ cd adder

The contents of the src/lib.rs file in your adder library should look like Listing 11-1.

Filename: src/lib.rs

#[cfg(test)]
mod tests {
    #[test]
    fn it_works() {
        let result = 2 + 2;
        assert_eq!(result, 4);
    }
}

Listing 11-1: The test module and function generated automatically by cargo new

For now, let’s ignore the top two lines and focus on the function. Note the #[test] annotation: this attribute indicates this is a test function, so the test runner knows to treat this function as a test. We might also have non-test functions in the tests module to help set up common scenarios or perform common operations, so we always need to indicate which functions are tests.

The example function body uses the assert_eq! macro to assert that result, which contains the result of adding 2 and 2, equals 4. This assertion serves as an example of the format for a typical test. Let’s run it to see that this test passes.

The cargo test command runs all tests in our project, as shown in Listing 11-2.

$ cargo test
   Compiling adder v0.1.0 (file:///projects/adder)
    Finished test [unoptimized + debuginfo] target(s) in 0.57s
     Running unittests src/lib.rs (target/debug/deps/adder-92948b65e88960b4)

running 1 test
test tests::it_works ... ok

test result: ok. 1 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.00s

   Doc-tests adder

running 0 tests

test result: ok. 0 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.00s

Listing 11-2: The output from running the automatically generated test

Cargo compiled and ran the test. We see the line running 1 test. The next line shows the name of the generated test function, called it_works, and that the result of running that test is ok. The overall summary test result: ok. means that all the tests passed, and the portion that reads 1 passed; 0 failed totals the number of tests that passed or failed.

It’s possible to mark a test as ignored so it doesn’t run in a particular instance; we’ll cover that in the “Ignoring Some Tests Unless Specifically Requested” section later in this chapter. Because we haven’t done that here, the summary shows 0 ignored. We can also pass an argument to the cargo test command to run only tests whose name matches a string; this is called filtering and we’ll cover that in the “Running a Subset of Tests by Name” section. We also haven’t filtered the tests being run, so the end of the summary shows 0 filtered out.

The 0 measured statistic is for benchmark tests that measure performance. Benchmark tests are, as of this writing, only available in nightly Rust. See the documentation about benchmark tests to learn more.

The next part of the test output starting at Doc-tests adder is for the results of any documentation tests. We don’t have any documentation tests yet, but Rust can compile any code examples that appear in our API documentation. This feature helps keep your docs and your code in sync! We’ll discuss how to write documentation tests in the “Documentation Comments as Tests” section of Chapter 14. For now, we’ll ignore the Doc-tests output.

Let’s start to customize the test to our own needs. First change the name of the it_works function to a different name, such as exploration, like so:

Filename: src/lib.rs

#[cfg(test)]
mod tests {
    #[test]
    fn exploration() {
        assert_eq!(2 + 2, 4);
    }
}

Then run cargo test again. The output now shows exploration instead of it_works:

$ cargo test
   Compiling adder v0.1.0 (file:///projects/adder)
    Finished test [unoptimized + debuginfo] target(s) in 0.59s
     Running unittests src/lib.rs (target/debug/deps/adder-92948b65e88960b4)

running 1 test
test tests::exploration ... ok

test result: ok. 1 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.00s

   Doc-tests adder

running 0 tests

test result: ok. 0 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.00s

Now we’ll add another test, but this time we’ll make a test that fails! Tests fail when something in the test function panics. Each test is run in a new thread, and when the main thread sees that a test thread has died, the test is marked as failed. In Chapter 9, we talked about how the simplest way to panic is to call the panic! macro. Enter the new test as a function named another, so your src/lib.rs file looks like Listing 11-3.

Filename: src/lib.rs

#[cfg(test)]
mod tests {
    #[test]
    fn exploration() {
        assert_eq!(2 + 2, 4);
    }

    #[test]
    fn another() {
        panic!("Make this test fail");
    }
}

Listing 11-3: Adding a second test that will fail because we call the panic! macro

Run the tests again using cargo test. The output should look like Listing 11-4, which shows that our exploration test passed and another failed.

$ cargo test
   Compiling adder v0.1.0 (file:///projects/adder)
    Finished test [unoptimized + debuginfo] target(s) in 0.72s
     Running unittests src/lib.rs (target/debug/deps/adder-92948b65e88960b4)

running 2 tests
test tests::another ... FAILED
test tests::exploration ... ok

failures:

---- tests::another stdout ----
thread 'main' panicked at 'Make this test fail', src/lib.rs:10:9
note: run with `RUST_BACKTRACE=1` environment variable to display a backtrace


failures:
    tests::another

test result: FAILED. 1 passed; 1 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.00s

error: test failed, to rerun pass '--lib'

Listing 11-4: Test results when one test passes and one test fails

Instead of ok, the line test tests::another shows FAILED. Two new sections appear between the individual results and the summary: the first displays the detailed reason for each test failure. In this case, we get the details that another failed because it panicked at 'Make this test fail' on line 10 in the src/lib.rs file. The next section lists just the names of all the failing tests, which is useful when there are lots of tests and lots of detailed failing test output. We can use the name of a failing test to run just that test to more easily debug it; we’ll talk more about ways to run tests in the “Controlling How Tests Are Run” section.

The summary line displays at the end: overall, our test result is FAILED. We had one test pass and one test fail.

Now that you’ve seen what the test results look like in different scenarios, let’s look at some macros other than panic! that are useful in tests.

Checking Results with the assert! Macro

The assert! macro, provided by the standard library, is useful when you want to ensure that some condition in a test evaluates to true. We give the assert! macro an argument that evaluates to a Boolean. If the value is true, nothing happens and the test passes. If the value is false, the assert! macro calls panic! to cause the test to fail. Using the assert! macro helps us check that our code is functioning in the way we intend.

In Chapter 5, Listing 5-15, we used a Rectangle struct and a can_hold method, which are repeated here in Listing 11-5. Let’s put this code in the src/lib.rs file, then write some tests for it using the assert! macro.

Filename: src/lib.rs

#[derive(Debug)]
struct Rectangle {
    width: u32,
    height: u32,
}

impl Rectangle {
    fn can_hold(&self, other: &Rectangle) -> bool {
        self.width > other.width && self.height > other.height
    }
}

Listing 11-5: Using the Rectangle struct and its can_hold method from Chapter 5

The can_hold method returns a Boolean, which means it’s a perfect use case for the assert! macro. In Listing 11-6, we write a test that exercises the can_hold method by creating a Rectangle instance that has a width of 8 and a height of 7 and asserting that it can hold another Rectangle instance that has a width of 5 and a height of 1.

Filename: src/lib.rs

#[derive(Debug)]
struct Rectangle {
    width: u32,
    height: u32,
}

impl Rectangle {
    fn can_hold(&self, other: &Rectangle) -> bool {
        self.width > other.width && self.height > other.height
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn larger_can_hold_smaller() {
        let larger = Rectangle {
            width: 8,
            height: 7,
        };
        let smaller = Rectangle {
            width: 5,
            height: 1,
        };

        assert!(larger.can_hold(&smaller));
    }
}

Listing 11-6: A test for can_hold that checks whether a larger rectangle can indeed hold a smaller rectangle

Note that we’ve added a new line inside the tests module: use super::*;. The tests module is a regular module that follows the usual visibility rules we covered in Chapter 7 in the “Paths for Referring to an Item in the Module Tree” section. Because the tests module is an inner module, we need to bring the code under test in the outer module into the scope of the inner module. We use a glob here so anything we define in the outer module is available to this tests module.

We’ve named our test larger_can_hold_smaller, and we’ve created the two Rectangle instances that we need. Then we called the assert! macro and passed it the result of calling larger.can_hold(&smaller). This expression is supposed to return true, so our test should pass. Let’s find out!

$ cargo test
   Compiling rectangle v0.1.0 (file:///projects/rectangle)
    Finished test [unoptimized + debuginfo] target(s) in 0.66s
     Running unittests src/lib.rs (target/debug/deps/rectangle-6584c4561e48942e)

running 1 test
test tests::larger_can_hold_smaller ... ok

test result: ok. 1 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.00s

   Doc-tests rectangle

running 0 tests

test result: ok. 0 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.00s

It does pass! Let’s add another test, this time asserting that a smaller rectangle cannot hold a larger rectangle:

Filename: src/lib.rs

#[derive(Debug)]
struct Rectangle {
    width: u32,
    height: u32,
}

impl Rectangle {
    fn can_hold(&self, other: &Rectangle) -> bool {
        self.width > other.width && self.height > other.height
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn larger_can_hold_smaller() {
        // --snip--
        let larger = Rectangle {
            width: 8,
            height: 7,
        };
        let smaller = Rectangle {
            width: 5,
            height: 1,
        };

        assert!(larger.can_hold(&smaller));
    }

    #[test]
    fn smaller_cannot_hold_larger() {
        let larger = Rectangle {
            width: 8,
            height: 7,
        };
        let smaller = Rectangle {
            width: 5,
            height: 1,
        };

        assert!(!smaller.can_hold(&larger));
    }
}

Because the correct result of the can_hold function in this case is false, we need to negate that result before we pass it to the assert! macro. As a result, our test will pass if can_hold returns false:

$ cargo test
   Compiling rectangle v0.1.0 (file:///projects/rectangle)
    Finished test [unoptimized + debuginfo] target(s) in 0.66s
     Running unittests src/lib.rs (target/debug/deps/rectangle-6584c4561e48942e)

running 2 tests
test tests::larger_can_hold_smaller ... ok
test tests::smaller_cannot_hold_larger ... ok

test result: ok. 2 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.00s

   Doc-tests rectangle

running 0 tests

test result: ok. 0 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.00s

Two tests that pass! Now let’s see what happens to our test results when we introduce a bug in our code. We’ll change the implementation of the can_hold method by replacing the greater-than sign with a less-than sign when it compares the widths:

#[derive(Debug)]
struct Rectangle {
    width: u32,
    height: u32,
}

// --snip--
impl Rectangle {
    fn can_hold(&self, other: &Rectangle) -> bool {
        self.width < other.width && self.height > other.height
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn larger_can_hold_smaller() {
        let larger = Rectangle {
            width: 8,
            height: 7,
        };
        let smaller = Rectangle {
            width: 5,
            height: 1,
        };

        assert!(larger.can_hold(&smaller));
    }

    #[test]
    fn smaller_cannot_hold_larger() {
        let larger = Rectangle {
            width: 8,
            height: 7,
        };
        let smaller = Rectangle {
            width: 5,
            height: 1,
        };

        assert!(!smaller.can_hold(&larger));
    }
}

Running the tests now produces the following:

$ cargo test
   Compiling rectangle v0.1.0 (file:///projects/rectangle)
    Finished test [unoptimized + debuginfo] target(s) in 0.66s
     Running unittests src/lib.rs (target/debug/deps/rectangle-6584c4561e48942e)

running 2 tests
test tests::larger_can_hold_smaller ... FAILED
test tests::smaller_cannot_hold_larger ... ok

failures:

---- tests::larger_can_hold_smaller stdout ----
thread 'main' panicked at 'assertion failed: larger.can_hold(&smaller)', src/lib.rs:28:9
note: run with `RUST_BACKTRACE=1` environment variable to display a backtrace


failures:
    tests::larger_can_hold_smaller

test result: FAILED. 1 passed; 1 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.00s

error: test failed, to rerun pass '--lib'

Our tests caught the bug! Because larger.width is 8 and smaller.width is 5, the comparison of the widths in can_hold now returns false: 8 is not less than 5.

Testing Equality with the assert_eq! and assert_ne! Macros

A common way to verify functionality is to test for equality between the result of the code under test and the value you expect the code to return. You could do this using the assert! macro and passing it an expression using the == operator. However, this is such a common test that the standard library provides a pair of macros—assert_eq! and assert_ne!—to perform this test more conveniently. These macros compare two arguments for equality or inequality, respectively. They’ll also print the two values if the assertion fails, which makes it easier to see why the test failed; conversely, the assert! macro only indicates that it got a false value for the == expression, without printing the values that led to the false value.

In Listing 11-7, we write a function named add_two that adds 2 to its parameter, then we test this function using the assert_eq! macro.

Filename: src/lib.rs

pub fn add_two(a: i32) -> i32 {
    a + 2
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn it_adds_two() {
        assert_eq!(4, add_two(2));
    }
}

Listing 11-7: Testing the function add_two using the assert_eq! macro

Let’s check that it passes!

$ cargo test
   Compiling adder v0.1.0 (file:///projects/adder)
    Finished test [unoptimized + debuginfo] target(s) in 0.58s
     Running unittests src/lib.rs (target/debug/deps/adder-92948b65e88960b4)

running 1 test
test tests::it_adds_two ... ok

test result: ok. 1 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.00s

   Doc-tests adder

running 0 tests

test result: ok. 0 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.00s

We pass 4 as the argument to assert_eq!, which is equal to the result of calling add_two(2). The line for this test is test tests::it_adds_two ... ok, and the ok text indicates that our test passed!

Let’s introduce a bug into our code to see what assert_eq! looks like when it fails. Change the implementation of the add_two function to instead add 3:

pub fn add_two(a: i32) -> i32 {
    a + 3
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn it_adds_two() {
        assert_eq!(4, add_two(2));
    }
}

Run the tests again:

$ cargo test
   Compiling adder v0.1.0 (file:///projects/adder)
    Finished test [unoptimized + debuginfo] target(s) in 0.61s
     Running unittests src/lib.rs (target/debug/deps/adder-92948b65e88960b4)

running 1 test
test tests::it_adds_two ... FAILED

failures:

---- tests::it_adds_two stdout ----
thread 'main' panicked at 'assertion failed: `(left == right)`
  left: `4`,
 right: `5`', src/lib.rs:11:9
note: run with `RUST_BACKTRACE=1` environment variable to display a backtrace


failures:
    tests::it_adds_two

test result: FAILED. 0 passed; 1 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.00s

error: test failed, to rerun pass '--lib'

Our test caught the bug! The it_adds_two test failed, and the message tells us that the assertion that fails was assertion failed: `(left == right)` and what the left and right values are. This message helps us start debugging: the left argument was 4 but the right argument, where we had add_two(2), was 5. You can imagine that this would be especially helpful when we have a lot of tests going on.

Note that in some languages and test frameworks, the parameters to equality assertion functions are called expected and actual, and the order in which we specify the arguments matters. However, in Rust, they’re called left and right, and the order in which we specify the value we expect and the value the code produces doesn’t matter. We could write the assertion in this test as assert_eq!(add_two(2), 4), which would result in the same failure message that displays assertion failed: `(left == right)`.

The assert_ne! macro will pass if the two values we give it are not equal and fail if they’re equal. This macro is most useful for cases when we’re not sure what a value will be, but we know what the value definitely shouldn’t be. For example, if we’re testing a function that is guaranteed to change its input in some way, but the way in which the input is changed depends on the day of the week that we run our tests, the best thing to assert might be that the output of the function is not equal to the input.

Under the surface, the assert_eq! and assert_ne! macros use the operators == and !=, respectively. When the assertions fail, these macros print their arguments using debug formatting, which means the values being compared must implement the PartialEq and Debug traits. All primitive types and most of the standard library types implement these traits. For structs and enums that you define yourself, you’ll need to implement PartialEq to assert equality of those types. You’ll also need to implement Debug to print the values when the assertion fails. Because both traits are derivable traits, as mentioned in Listing 5-12 in Chapter 5, this is usually as straightforward as adding the #[derive(PartialEq, Debug)] annotation to your struct or enum definition. See Appendix C, “Derivable Traits,” for more details about these and other derivable traits.

Adding Custom Failure Messages

You can also add a custom message to be printed with the failure message as optional arguments to the assert!, assert_eq!, and assert_ne! macros. Any arguments specified after the required arguments are passed along to the format! macro (discussed in Chapter 8 in the “Concatenation with the + Operator or the format! Macro” section), so you can pass a format string that contains {} placeholders and values to go in those placeholders. Custom messages are useful for documenting what an assertion means; when a test fails, you’ll have a better idea of what the problem is with the code.

For example, let’s say we have a function that greets people by name and we want to test that the name we pass into the function appears in the output:

Filename: src/lib.rs

pub fn greeting(name: &str) -> String {
    format!("Hello {}!", name)
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn greeting_contains_name() {
        let result = greeting("Carol");
        assert!(result.contains("Carol"));
    }
}

The requirements for this program haven’t been agreed upon yet, and we’re pretty sure the Hello text at the beginning of the greeting will change. We decided we don’t want to have to update the test when the requirements change, so instead of checking for exact equality to the value returned from the greeting function, we’ll just assert that the output contains the text of the input parameter.

Now let’s introduce a bug into this code by changing greeting to exclude name to see what the default test failure looks like:

pub fn greeting(name: &str) -> String {
    String::from("Hello!")
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn greeting_contains_name() {
        let result = greeting("Carol");
        assert!(result.contains("Carol"));
    }
}

Running this test produces the following:

$ cargo test
   Compiling greeter v0.1.0 (file:///projects/greeter)
    Finished test [unoptimized + debuginfo] target(s) in 0.91s
     Running unittests src/lib.rs (target/debug/deps/greeter-170b942eb5bf5e3a)

running 1 test
test tests::greeting_contains_name ... FAILED

failures:

---- tests::greeting_contains_name stdout ----
thread 'main' panicked at 'assertion failed: result.contains(\"Carol\")', src/lib.rs:12:9
note: run with `RUST_BACKTRACE=1` environment variable to display a backtrace


failures:
    tests::greeting_contains_name

test result: FAILED. 0 passed; 1 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.00s

error: test failed, to rerun pass '--lib'

This result just indicates that the assertion failed and which line the assertion is on. A more useful failure message would print the value from the greeting function. Let’s add a custom failure message composed of a format string with a placeholder filled in with the actual value we got from the greeting function:

pub fn greeting(name: &str) -> String {
    String::from("Hello!")
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn greeting_contains_name() {
        let result = greeting("Carol");
        assert!(
            result.contains("Carol"),
            "Greeting did not contain name, value was `{}`",
            result
        );
    }
}

Now when we run the test, we’ll get a more informative error message:

$ cargo test
   Compiling greeter v0.1.0 (file:///projects/greeter)
    Finished test [unoptimized + debuginfo] target(s) in 0.93s
     Running unittests src/lib.rs (target/debug/deps/greeter-170b942eb5bf5e3a)

running 1 test
test tests::greeting_contains_name ... FAILED

failures:

---- tests::greeting_contains_name stdout ----
thread 'main' panicked at 'Greeting did not contain name, value was `Hello!`', src/lib.rs:12:9
note: run with `RUST_BACKTRACE=1` environment variable to display a backtrace


failures:
    tests::greeting_contains_name

test result: FAILED. 0 passed; 1 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.00s

error: test failed, to rerun pass '--lib'

We can see the value we actually got in the test output, which would help us debug what happened instead of what we were expecting to happen.

Checking for Panics with should_panic

In addition to checking return values, it’s important to check that our code handles error conditions as we expect. For example, consider the Guess type that we created in Chapter 9, Listing 9-13. Other code that uses Guess depends on the guarantee that Guess instances will contain only values between 1 and 100. We can write a test that ensures that attempting to create a Guess instance with a value outside that range panics.

We do this by adding the attribute should_panic to our test function. The test passes if the code inside the function panics; the test fails if the code inside the function doesn’t panic.

Listing 11-8 shows a test that checks that the error conditions of Guess::new happen when we expect them to.

Filename: src/lib.rs

pub struct Guess {
    value: i32,
}

impl Guess {
    pub fn new(value: i32) -> Guess {
        if value < 1 || value > 100 {
            panic!("Guess value must be between 1 and 100, got {}.", value);
        }

        Guess { value }
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    #[should_panic]
    fn greater_than_100() {
        Guess::new(200);
    }
}

Listing 11-8: Testing that a condition will cause a panic!

We place the #[should_panic] attribute after the #[test] attribute and before the test function it applies to. Let’s look at the result when this test passes:

$ cargo test
   Compiling guessing_game v0.1.0 (file:///projects/guessing_game)
    Finished test [unoptimized + debuginfo] target(s) in 0.58s
     Running unittests src/lib.rs (target/debug/deps/guessing_game-57d70c3acb738f4d)

running 1 test
test tests::greater_than_100 - should panic ... ok

test result: ok. 1 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.00s

   Doc-tests guessing_game

running 0 tests

test result: ok. 0 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.00s

Looks good! Now let’s introduce a bug in our code by removing the condition that the new function will panic if the value is greater than 100:

pub struct Guess {
    value: i32,
}

// --snip--
impl Guess {
    pub fn new(value: i32) -> Guess {
        if value < 1 {
            panic!("Guess value must be between 1 and 100, got {}.", value);
        }

        Guess { value }
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    #[should_panic]
    fn greater_than_100() {
        Guess::new(200);
    }
}

When we run the test in Listing 11-8, it will fail:

$ cargo test
   Compiling guessing_game v0.1.0 (file:///projects/guessing_game)
    Finished test [unoptimized + debuginfo] target(s) in 0.62s
     Running unittests src/lib.rs (target/debug/deps/guessing_game-57d70c3acb738f4d)

running 1 test
test tests::greater_than_100 - should panic ... FAILED

failures:

---- tests::greater_than_100 stdout ----
note: test did not panic as expected

failures:
    tests::greater_than_100

test result: FAILED. 0 passed; 1 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.00s

error: test failed, to rerun pass '--lib'

We don’t get a very helpful message in this case, but when we look at the test function, we see that it’s annotated with #[should_panic]. The failure we got means that the code in the test function did not cause a panic.

Tests that use should_panic can be imprecise. A should_panic test would pass even if the test panics for a different reason from the one we were expecting. To make should_panic tests more precise, we can add an optional expected parameter to the should_panic attribute. The test harness will make sure that the failure message contains the provided text. For example, consider the modified code for Guess in Listing 11-9 where the new function panics with different messages depending on whether the value is too small or too large.

Filename: src/lib.rs

pub struct Guess {
    value: i32,
}

// --snip--

impl Guess {
    pub fn new(value: i32) -> Guess {
        if value < 1 {
            panic!(
                "Guess value must be greater than or equal to 1, got {}.",
                value
            );
        } else if value > 100 {
            panic!(
                "Guess value must be less than or equal to 100, got {}.",
                value
            );
        }

        Guess { value }
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    #[should_panic(expected = "less than or equal to 100")]
    fn greater_than_100() {
        Guess::new(200);
    }
}

Listing 11-9: Testing for a panic! with a panic message containing a specified substring

This test will pass because the value we put in the should_panic attribute’s expected parameter is a substring of the message that the Guess::new function panics with. We could have specified the entire panic message that we expect, which in this case would be Guess value must be less than or equal to 100, got 200. What you choose to specify depends on how much of the panic message is unique or dynamic and how precise you want your test to be. In this case, a substring of the panic message is enough to ensure that the code in the test function executes the else if value > 100 case.

To see what happens when a should_panic test with an expected message fails, let’s again introduce a bug into our code by swapping the bodies of the if value < 1 and the else if value > 100 blocks:

pub struct Guess {
    value: i32,
}

impl Guess {
    pub fn new(value: i32) -> Guess {
        if value < 1 {
            panic!(
                "Guess value must be less than or equal to 100, got {}.",
                value
            );
        } else if value > 100 {
            panic!(
                "Guess value must be greater than or equal to 1, got {}.",
                value
            );
        }

        Guess { value }
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    #[should_panic(expected = "less than or equal to 100")]
    fn greater_than_100() {
        Guess::new(200);
    }
}

This time when we run the should_panic test, it will fail:

$ cargo test
   Compiling guessing_game v0.1.0 (file:///projects/guessing_game)
    Finished test [unoptimized + debuginfo] target(s) in 0.66s
     Running unittests src/lib.rs (target/debug/deps/guessing_game-57d70c3acb738f4d)

running 1 test
test tests::greater_than_100 - should panic ... FAILED

failures:

---- tests::greater_than_100 stdout ----
thread 'main' panicked at 'Guess value must be greater than or equal to 1, got 200.', src/lib.rs:13:13
note: run with `RUST_BACKTRACE=1` environment variable to display a backtrace
note: panic did not contain expected string
      panic message: `"Guess value must be greater than or equal to 1, got 200."`,
 expected substring: `"less than or equal to 100"`

failures:
    tests::greater_than_100

test result: FAILED. 0 passed; 1 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.00s

error: test failed, to rerun pass '--lib'

The failure message indicates that this test did indeed panic as we expected, but the panic message did not include the expected string 'Guess value must be less than or equal to 100'. The panic message that we did get in this case was Guess value must be greater than or equal to 1, got 200. Now we can start figuring out where our bug is!

Using Result<T, E> in Tests

Our tests so far all panic when they fail. We can also write tests that use Result<T, E>! Here’s the test from Listing 11-1, rewritten to use Result<T, E> and return an Err instead of panicking:

#[cfg(test)]
mod tests {
    #[test]
    fn it_works() -> Result<(), String> {
        if 2 + 2 == 4 {
            Ok(())
        } else {
            Err(String::from("two plus two does not equal four"))
        }
    }
}

The it_works function now has the Result<(), String> return type. In the body of the function, rather than calling the assert_eq! macro, we return Ok(()) when the test passes and an Err with a String inside when the test fails.

Writing tests so they return a Result<T, E> enables you to use the question mark operator in the body of tests, which can be a convenient way to write tests that should fail if any operation within them returns an Err variant.

You can’t use the #[should_panic] annotation on tests that use Result<T, E>. To assert that an operation returns an Err variant, don’t use the question mark operator on the Result<T, E> value. Instead, use assert!(value.is_err()).

Now that you know several ways to write tests, let’s look at what is happening when we run our tests and explore the different options we can use with cargo test.

Controlling How Tests Are Run

Just as cargo run compiles your code and then runs the resulting binary, cargo test compiles your code in test mode and runs the resulting test binary. The default behavior of the binary produced by cargo test is to run all the tests in parallel and capture output generated during test runs, preventing the output from being displayed and making it easier to read the output related to the test results. You can, however, specify command line options to change this default behavior.

Some command line options go to cargo test, and some go to the resulting test binary. To separate these two types of arguments, you list the arguments that go to cargo test followed by the separator -- and then the ones that go to the test binary. Running cargo test --help displays the options you can use with cargo test, and running cargo test -- --help displays the options you can use after the separator.

Running Tests in Parallel or Consecutively

When you run multiple tests, by default they run in parallel using threads, meaning they finish running faster and you get feedback quicker. Because the tests are running at the same time, you must make sure your tests don’t depend on each other or on any shared state, including a shared environment, such as the current working directory or environment variables.

For example, say each of your tests runs some code that creates a file on disk named test-output.txt and writes some data to that file. Then each test reads the data in that file and asserts that the file contains a particular value, which is different in each test. Because the tests run at the same time, one test might overwrite the file in the time between another test writing and reading the file. The second test will then fail, not because the code is incorrect but because the tests have interfered with each other while running in parallel. One solution is to make sure each test writes to a different file; another solution is to run the tests one at a time.

If you don’t want to run the tests in parallel or if you want more fine-grained control over the number of threads used, you can send the --test-threads flag and the number of threads you want to use to the test binary. Take a look at the following example:

$ cargo test -- --test-threads=1

We set the number of test threads to 1, telling the program not to use any parallelism. Running the tests using one thread will take longer than running them in parallel, but the tests won’t interfere with each other if they share state.

Showing Function Output

By default, if a test passes, Rust’s test library captures anything printed to standard output. For example, if we call println! in a test and the test passes, we won’t see the println! output in the terminal; we’ll see only the line that indicates the test passed. If a test fails, we’ll see whatever was printed to standard output with the rest of the failure message.

As an example, Listing 11-10 has a silly function that prints the value of its parameter and returns 10, as well as a test that passes and a test that fails.

Filename: src/lib.rs

fn prints_and_returns_10(a: i32) -> i32 {
    println!("I got the value {}", a);
    10
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn this_test_will_pass() {
        let value = prints_and_returns_10(4);
        assert_eq!(10, value);
    }

    #[test]
    fn this_test_will_fail() {
        let value = prints_and_returns_10(8);
        assert_eq!(5, value);
    }
}

Listing 11-10: Tests for a function that calls println!

When we run these tests with cargo test, we’ll see the following output:

$ cargo test
   Compiling silly-function v0.1.0 (file:///projects/silly-function)
    Finished test [unoptimized + debuginfo] target(s) in 0.58s
     Running unittests src/lib.rs (target/debug/deps/silly_function-160869f38cff9166)

running 2 tests
test tests::this_test_will_fail ... FAILED
test tests::this_test_will_pass ... ok

failures:

---- tests::this_test_will_fail stdout ----
I got the value 8
thread 'main' panicked at 'assertion failed: `(left == right)`
  left: `5`,
 right: `10`', src/lib.rs:19:9
note: run with `RUST_BACKTRACE=1` environment variable to display a backtrace


failures:
    tests::this_test_will_fail

test result: FAILED. 1 passed; 1 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.00s

error: test failed, to rerun pass '--lib'

Note that nowhere in this output do we see I got the value 4, which is what is printed when the test that passes runs. That output has been captured. The output from the test that failed, I got the value 8, appears in the section of the test summary output, which also shows the cause of the test failure.

If we want to see printed values for passing tests as well, we can tell Rust to also show the output of successful tests with --show-output.

$ cargo test -- --show-output

When we run the tests in Listing 11-10 again with the --show-output flag, we see the following output:

$ cargo test -- --show-output
   Compiling silly-function v0.1.0 (file:///projects/silly-function)
    Finished test [unoptimized + debuginfo] target(s) in 0.60s
     Running unittests src/lib.rs (target/debug/deps/silly_function-160869f38cff9166)

running 2 tests
test tests::this_test_will_fail ... FAILED
test tests::this_test_will_pass ... ok

successes:

---- tests::this_test_will_pass stdout ----
I got the value 4


successes:
    tests::this_test_will_pass

failures:

---- tests::this_test_will_fail stdout ----
I got the value 8
thread 'main' panicked at 'assertion failed: `(left == right)`
  left: `5`,
 right: `10`', src/lib.rs:19:9
note: run with `RUST_BACKTRACE=1` environment variable to display a backtrace


failures:
    tests::this_test_will_fail

test result: FAILED. 1 passed; 1 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.00s

error: test failed, to rerun pass '--lib'

Running a Subset of Tests by Name

Sometimes, running a full test suite can take a long time. If you’re working on code in a particular area, you might want to run only the tests pertaining to that code. You can choose which tests to run by passing cargo test the name or names of the test(s) you want to run as an argument.

To demonstrate how to run a subset of tests, we’ll first create three tests for our add_two function, as shown in Listing 11-11, and choose which ones to run.

Filename: src/lib.rs

pub fn add_two(a: i32) -> i32 {
    a + 2
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn add_two_and_two() {
        assert_eq!(4, add_two(2));
    }

    #[test]
    fn add_three_and_two() {
        assert_eq!(5, add_two(3));
    }

    #[test]
    fn one_hundred() {
        assert_eq!(102, add_two(100));
    }
}

Listing 11-11: Three tests with three different names

If we run the tests without passing any arguments, as we saw earlier, all the tests will run in parallel:

$ cargo test
   Compiling adder v0.1.0 (file:///projects/adder)
    Finished test [unoptimized + debuginfo] target(s) in 0.62s
     Running unittests src/lib.rs (target/debug/deps/adder-92948b65e88960b4)

running 3 tests
test tests::add_three_and_two ... ok
test tests::add_two_and_two ... ok
test tests::one_hundred ... ok

test result: ok. 3 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.00s

   Doc-tests adder

running 0 tests

test result: ok. 0 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.00s

Running Single Tests

We can pass the name of any test function to cargo test to run only that test:

$ cargo test one_hundred
   Compiling adder v0.1.0 (file:///projects/adder)
    Finished test [unoptimized + debuginfo] target(s) in 0.69s
     Running unittests src/lib.rs (target/debug/deps/adder-92948b65e88960b4)

running 1 test
test tests::one_hundred ... ok

test result: ok. 1 passed; 0 failed; 0 ignored; 0 measured; 2 filtered out; finished in 0.00s

Only the test with the name one_hundred ran; the other two tests didn’t match that name. The test output lets us know we had more tests that didn’t run by displaying 2 filtered out at the end.

We can’t specify the names of multiple tests in this way; only the first value given to cargo test will be used. But there is a way to run multiple tests.

Filtering to Run Multiple Tests

We can specify part of a test name, and any test whose name matches that value will be run. For example, because two of our tests’ names contain add, we can run those two by running cargo test add:

$ cargo test add
   Compiling adder v0.1.0 (file:///projects/adder)
    Finished test [unoptimized + debuginfo] target(s) in 0.61s
     Running unittests src/lib.rs (target/debug/deps/adder-92948b65e88960b4)

running 2 tests
test tests::add_three_and_two ... ok
test tests::add_two_and_two ... ok

test result: ok. 2 passed; 0 failed; 0 ignored; 0 measured; 1 filtered out; finished in 0.00s

This command ran all tests with add in the name and filtered out the test named one_hundred. Also note that the module in which a test appears becomes part of the test’s name, so we can run all the tests in a module by filtering on the module’s name.

Ignoring Some Tests Unless Specifically Requested

Sometimes a few specific tests can be very time-consuming to execute, so you might want to exclude them during most runs of cargo test. Rather than listing as arguments all tests you do want to run, you can instead annotate the time-consuming tests using the ignore attribute to exclude them, as shown here:

Filename: src/lib.rs

#[test]
fn it_works() {
    assert_eq!(2 + 2, 4);
}

#[test]
#[ignore]
fn expensive_test() {
    // code that takes an hour to run
}

After #[test] we add the #[ignore] line to the test we want to exclude. Now when we run our tests, it_works runs, but expensive_test doesn’t:

$ cargo test
   Compiling adder v0.1.0 (file:///projects/adder)
    Finished test [unoptimized + debuginfo] target(s) in 0.60s
     Running unittests src/lib.rs (target/debug/deps/adder-92948b65e88960b4)

running 2 tests
test expensive_test ... ignored
test it_works ... ok

test result: ok. 1 passed; 0 failed; 1 ignored; 0 measured; 0 filtered out; finished in 0.00s

   Doc-tests adder

running 0 tests

test result: ok. 0 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.00s

The expensive_test function is listed as ignored. If we want to run only the ignored tests, we can use cargo test -- --ignored:

$ cargo test -- --ignored
   Compiling adder v0.1.0 (file:///projects/adder)
    Finished test [unoptimized + debuginfo] target(s) in 0.61s
     Running unittests src/lib.rs (target/debug/deps/adder-92948b65e88960b4)

running 1 test
test expensive_test ... ok

test result: ok. 1 passed; 0 failed; 0 ignored; 0 measured; 1 filtered out; finished in 0.00s

   Doc-tests adder

running 0 tests

test result: ok. 0 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.00s

By controlling which tests run, you can make sure your cargo test results will be fast. When you’re at a point where it makes sense to check the results of the ignored tests and you have time to wait for the results, you can run cargo test -- --ignored instead. If you want to run all tests whether they’re ignored or not, you can run cargo test -- --include-ignored.

Test Organization

As mentioned at the start of the chapter, testing is a complex discipline, and different people use different terminology and organization. The Rust community thinks about tests in terms of two main categories: unit tests and integration tests. Unit tests are small and more focused, testing one module in isolation at a time, and can test private interfaces. Integration tests are entirely external to your library and use your code in the same way any other external code would, using only the public interface and potentially exercising multiple modules per test.

Writing both kinds of tests is important to ensure that the pieces of your library are doing what you expect them to, separately and together.

Unit Tests

The purpose of unit tests is to test each unit of code in isolation from the rest of the code to quickly pinpoint where code is and isn’t working as expected. You’ll put unit tests in the src directory in each file with the code that they’re testing. The convention is to create a module named tests in each file to contain the test functions and to annotate the module with cfg(test).

The Tests Module and #[cfg(test)]

The #[cfg(test)] annotation on the tests module tells Rust to compile and run the test code only when you run cargo test, not when you run cargo build. This saves compile time when you only want to build the library and saves space in the resulting compiled artifact because the tests are not included. You’ll see that because integration tests go in a different directory, they don’t need the #[cfg(test)] annotation. However, because unit tests go in the same files as the code, you’ll use #[cfg(test)] to specify that they shouldn’t be included in the compiled result.

Recall that when we generated the new adder project in the first section of this chapter, Cargo generated this code for us:

Filename: src/lib.rs

#[cfg(test)]
mod tests {
    #[test]
    fn it_works() {
        let result = 2 + 2;
        assert_eq!(result, 4);
    }
}

This code is the automatically generated test module. The attribute cfg stands for configuration and tells Rust that the following item should only be included given a certain configuration option. In this case, the configuration option is test, which is provided by Rust for compiling and running tests. By using the cfg attribute, Cargo compiles our test code only if we actively run the tests with cargo test. This includes any helper functions that might be within this module, in addition to the functions annotated with #[test].

Testing Private Functions

There’s debate within the testing community about whether or not private functions should be tested directly, and other languages make it difficult or impossible to test private functions. Regardless of which testing ideology you adhere to, Rust’s privacy rules do allow you to test private functions. Consider the code in Listing 11-12 with the private function internal_adder.

Filename: src/lib.rs

pub fn add_two(a: i32) -> i32 {
    internal_adder(a, 2)
}

fn internal_adder(a: i32, b: i32) -> i32 {
    a + b
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn internal() {
        assert_eq!(4, internal_adder(2, 2));
    }
}

Listing 11-12: Testing a private function

Note that the internal_adder function is not marked as pub. Tests are just Rust code, and the tests module is just another module. As we discussed in the “Paths for Referring to an Item in the Module Tree” section, items in child modules can use the items in their ancestor modules. In this test, we bring all of the test module’s parent’s items into scope with use super::*, and then the test can call internal_adder. If you don’t think private functions should be tested, there’s nothing in Rust that will compel you to do so.

Integration Tests

In Rust, integration tests are entirely external to your library. They use your library in the same way any other code would, which means they can only call functions that are part of your library’s public API. Their purpose is to test whether many parts of your library work together correctly. Units of code that work correctly on their own could have problems when integrated, so test coverage of the integrated code is important as well. To create integration tests, you first need a tests directory.

The tests Directory

We create a tests directory at the top level of our project directory, next to src. Cargo knows to look for integration test files in this directory. We can then make as many test files as we want, and Cargo will compile each of the files as an individual crate.

Let’s create an integration test. With the code in Listing 11-12 still in the src/lib.rs file, make a tests directory, and create a new file named tests/integration_test.rs. Your directory structure should look like this:

adder
├── Cargo.lock
├── Cargo.toml
├── src
│   └── lib.rs
└── tests
    └── integration_test.rs

Enter the code in Listing 11-13 into the tests/integration_test.rs file:

Filename: tests/integration_test.rs

use adder;

#[test]
fn it_adds_two() {
    assert_eq!(4, adder::add_two(2));
}

Listing 11-13: An integration test of a function in the adder crate

Each file in the tests directory is a separate crate, so we need to bring our library into each test crate’s scope. For that reason we add use adder at the top of the code, which we didn’t need in the unit tests.

We don’t need to annotate any code in tests/integration_test.rs with #[cfg(test)]. Cargo treats the tests directory specially and compiles files in this directory only when we run cargo test. Run cargo test now:

$ cargo test
   Compiling adder v0.1.0 (file:///projects/adder)
    Finished test [unoptimized + debuginfo] target(s) in 1.31s
     Running unittests src/lib.rs (target/debug/deps/adder-1082c4b063a8fbe6)

running 1 test
test tests::internal ... ok

test result: ok. 1 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.00s

     Running tests/integration_test.rs (target/debug/deps/integration_test-1082c4b063a8fbe6)

running 1 test
test it_adds_two ... ok

test result: ok. 1 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.00s

   Doc-tests adder

running 0 tests

test result: ok. 0 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.00s

The three sections of output include the unit tests, the integration test, and the doc tests. Note that if any test in a section fails, the following sections will not be run. For example, if a unit test fails, there won’t be any output for integration and doc tests because those tests will only be run if all unit tests are passing.

The first section for the unit tests is the same as we’ve been seeing: one line for each unit test (one named internal that we added in Listing 11-12) and then a summary line for the unit tests.

The integration tests section starts with the line Running tests/integration_test.rs. Next, there is a line for each test function in that integration test and a summary line for the results of the integration test just before the Doc-tests adder section starts.

Each integration test file has its own section, so if we add more files in the tests directory, there will be more integration test sections.

We can still run a particular integration test function by specifying the test function’s name as an argument to cargo test. To run all the tests in a particular integration test file, use the --test argument of cargo test followed by the name of the file:

$ cargo test --test integration_test
   Compiling adder v0.1.0 (file:///projects/adder)
    Finished test [unoptimized + debuginfo] target(s) in 0.64s
     Running tests/integration_test.rs (target/debug/deps/integration_test-82e7799c1bc62298)

running 1 test
test it_adds_two ... ok

test result: ok. 1 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.00s

This command runs only the tests in the tests/integration_test.rs file.

Submodules in Integration Tests

As you add more integration tests, you might want to make more files in the tests directory to help organize them; for example, you can group the test functions by the functionality they’re testing. As mentioned earlier, each file in the tests directory is compiled as its own separate crate, which is useful for creating separate scopes to more closely imitate the way end users will be using your crate. However, this means files in the tests directory don’t share the same behavior as files in src do, as you learned in Chapter 7 regarding how to separate code into modules and files.

The different behavior of tests directory files is most noticeable when you have a set of helper functions to use in multiple integration test files and you try to follow the steps in the “Separating Modules into Different Files” section of Chapter 7 to extract them into a common module. For example, if we create tests/common.rs and place a function named setup in it, we can add some code to setup that we want to call from multiple test functions in multiple test files:

Filename: tests/common.rs

pub fn setup() {
    // setup code specific to your library's tests would go here
}

When we run the tests again, we’ll see a new section in the test output for the common.rs file, even though this file doesn’t contain any test functions nor did we call the setup function from anywhere:

$ cargo test
   Compiling adder v0.1.0 (file:///projects/adder)
    Finished test [unoptimized + debuginfo] target(s) in 0.89s
     Running unittests src/lib.rs (target/debug/deps/adder-92948b65e88960b4)

running 1 test
test tests::internal ... ok

test result: ok. 1 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.00s

     Running tests/common.rs (target/debug/deps/common-92948b65e88960b4)

running 0 tests

test result: ok. 0 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.00s

     Running tests/integration_test.rs (target/debug/deps/integration_test-92948b65e88960b4)

running 1 test
test it_adds_two ... ok

test result: ok. 1 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.00s

   Doc-tests adder

running 0 tests

test result: ok. 0 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.00s

Having common appear in the test results with running 0 tests displayed for it is not what we wanted. We just wanted to share some code with the other integration test files.

To avoid having common appear in the test output, instead of creating tests/common.rs, we’ll create tests/common/mod.rs. The project directory now looks like this:

├── Cargo.lock
├── Cargo.toml
├── src
│   └── lib.rs
└── tests
    ├── common
    │   └── mod.rs
    └── integration_test.rs

This is the older naming convention that Rust also understands that we mentioned in the “Alternate File Paths” section of Chapter 7. Naming the file this way tells Rust not to treat the common module as an integration test file. When we move the setup function code into tests/common/mod.rs and delete the tests/common.rs file, the section in the test output will no longer appear. Files in subdirectories of the tests directory don’t get compiled as separate crates or have sections in the test output.

After we’ve created tests/common/mod.rs, we can use it from any of the integration test files as a module. Here’s an example of calling the setup function from the it_adds_two test in tests/integration_test.rs:

Filename: tests/integration_test.rs

use adder;

mod common;

#[test]
fn it_adds_two() {
    common::setup();
    assert_eq!(4, adder::add_two(2));
}

Note that the mod common; declaration is the same as the module declaration we demonstrated in Listing 7-21. Then in the test function, we can call the common::setup() function.

Integration Tests for Binary Crates

If our project is a binary crate that only contains a src/main.rs file and doesn’t have a src/lib.rs file, we can’t create integration tests in the tests directory and bring functions defined in the src/main.rs file into scope with a use statement. Only library crates expose functions that other crates can use; binary crates are meant to be run on their own.

This is one of the reasons Rust projects that provide a binary have a straightforward src/main.rs file that calls logic that lives in the src/lib.rs file. Using that structure, integration tests can test the library crate with use to make the important functionality available. If the important functionality works, the small amount of code in the src/main.rs file will work as well, and that small amount of code doesn’t need to be tested.

Summary

Rust’s testing features provide a way to specify how code should function to ensure it continues to work as you expect, even as you make changes. Unit tests exercise different parts of a library separately and can test private implementation details. Integration tests check that many parts of the library work together correctly, and they use the library’s public API to test the code in the same way external code will use it. Even though Rust’s type system and ownership rules help prevent some kinds of bugs, tests are still important to reduce logic bugs having to do with how your code is expected to behave.

Let’s combine the knowledge you learned in this chapter and in previous chapters to work on a project!

An I/O Project: Building a Command Line Program

This chapter is a recap of the many skills you’ve learned so far and an exploration of a few more standard library features. We’ll build a command line tool that interacts with file and command line input/output to practice some of the Rust concepts you now have under your belt.

Rust’s speed, safety, single binary output, and cross-platform support make it an ideal language for creating command line tools, so for our project, we’ll make our own version of the classic command line search tool grep (globally search a regular expression and print). In the simplest use case, grep searches a specified file for a specified string. To do so, grep takes as its arguments a file path and a string. Then it reads the file, finds lines in that file that contain the string argument, and prints those lines.

Along the way, we’ll show how to make our command line tool use the terminal features that many other command line tools use. We’ll read the value of an environment variable to allow the user to configure the behavior of our tool. We’ll also print error messages to the standard error console stream (stderr) instead of standard output (stdout), so, for example, the user can redirect successful output to a file while still seeing error messages onscreen.

One Rust community member, Andrew Gallant, has already created a fully featured, very fast version of grep, called ripgrep. By comparison, our version will be fairly simple, but this chapter will give you some of the background knowledge you need to understand a real-world project such as ripgrep.

Our grep project will combine a number of concepts you’ve learned so far:

We’ll also briefly introduce closures, iterators, and trait objects, which Chapters 13 and 17 will cover in detail.

Accepting Command Line Arguments

Let’s create a new project with, as always, cargo new. We’ll call our project minigrep to distinguish it from the grep tool that you might already have on your system.

$ cargo new minigrep
     Created binary (application) `minigrep` project
$ cd minigrep

The first task is to make minigrep accept its two command line arguments: the file path and a string to search for. That is, we want to be able to run our program with cargo run, two hyphens to indicate the following arguments are for our program rather than for cargo, a string to search for, and a path to a file to search in, like so:

$ cargo run -- searchstring example-filename.txt

Right now, the program generated by cargo new cannot process arguments we give it. Some existing libraries on crates.io can help with writing a program that accepts command line arguments, but because you’re just learning this concept, let’s implement this capability ourselves.

Reading the Argument Values

To enable minigrep to read the values of command line arguments we pass to it, we’ll need the std::env::args function provided in Rust’s standard library. This function returns an iterator of the command line arguments passed to minigrep. We’ll cover iterators fully in Chapter 13. For now, you only need to know two details about iterators: iterators produce a series of values, and we can call the collect method on an iterator to turn it into a collection, such as a vector, that contains all the elements the iterator produces.

The code in Listing 12-1 allows your minigrep program to read any command line arguments passed to it and then collect the values into a vector.

Filename: src/main.rs

use std::env;

fn main() {
    let args: Vec<String> = env::args().collect();
    dbg!(args);
}

Listing 12-1: Collecting the command line arguments into a vector and printing them

First, we bring the std::env module into scope with a use statement so we can use its args function. Notice that the std::env::args function is nested in two levels of modules. As we discussed in Chapter 7, in cases where the desired function is nested in more than one module, we’ve chosen to bring the parent module into scope rather than the function. By doing so, we can easily use other functions from std::env. It’s also less ambiguous than adding use std::env::args and then calling the function with just args, because args might easily be mistaken for a function that’s defined in the current module.

The args Function and Invalid Unicode

Note that std::env::args will panic if any argument contains invalid Unicode. If your program needs to accept arguments containing invalid Unicode, use std::env::args_os instead. That function returns an iterator that produces OsString values instead of String values. We’ve chosen to use std::env::args here for simplicity, because OsString values differ per platform and are more complex to work with than String values.

On the first line of main, we call env::args, and we immediately use collect to turn the iterator into a vector containing all the values produced by the iterator. We can use the collect function to create many kinds of collections, so we explicitly annotate the type of args to specify that we want a vector of strings. Although we very rarely need to annotate types in Rust, collect is one function you do often need to annotate because Rust isn’t able to infer the kind of collection you want.

Finally, we print the vector using the debug macro. Let’s try running the code first with no arguments and then with two arguments:

$ cargo run
   Compiling minigrep v0.1.0 (file:///projects/minigrep)
    Finished dev [unoptimized + debuginfo] target(s) in 0.61s
     Running `target/debug/minigrep`
[src/main.rs:5] args = [
    "target/debug/minigrep",
]
$ cargo run -- needle haystack
   Compiling minigrep v0.1.0 (file:///projects/minigrep)
    Finished dev [unoptimized + debuginfo] target(s) in 1.57s
     Running `target/debug/minigrep needle haystack`
[src/main.rs:5] args = [
    "target/debug/minigrep",
    "needle",
    "haystack",
]

Notice that the first value in the vector is "target/debug/minigrep", which is the name of our binary. This matches the behavior of the arguments list in C, letting programs use the name by which they were invoked in their execution. It’s often convenient to have access to the program name in case you want to print it in messages or change behavior of the program based on what command line alias was used to invoke the program. But for the purposes of this chapter, we’ll ignore it and save only the two arguments we need.

Saving the Argument Values in Variables

The program is currently able to access the values specified as command line arguments. Now we need to save the values of the two arguments in variables so we can use the values throughout the rest of the program. We do that in Listing 12-2.

Filename: src/main.rs

use std::env;

fn main() {
    let args: Vec<String> = env::args().collect();

    let query = &args[1];
    let file_path = &args[2];

    println!("Searching for {}", query);
    println!("In file {}", file_path);
}

Listing 12-2: Creating variables to hold the query argument and file path argument

As we saw when we printed the vector, the program’s name takes up the first value in the vector at args[0], so we’re starting arguments at index 1. The first argument minigrep takes is the string we’re searching for, so we put a reference to the first argument in the variable query. The second argument will be the file path, so we put a reference to the second argument in the variable file_path.

We temporarily print the values of these variables to prove that the code is working as we intend. Let’s run this program again with the arguments test and sample.txt:

$ cargo run -- test sample.txt
   Compiling minigrep v0.1.0 (file:///projects/minigrep)
    Finished dev [unoptimized + debuginfo] target(s) in 0.0s
     Running `target/debug/minigrep test sample.txt`
Searching for test
In file sample.txt

Great, the program is working! The values of the arguments we need are being saved into the right variables. Later we’ll add some error handling to deal with certain potential erroneous situations, such as when the user provides no arguments; for now, we’ll ignore that situation and work on adding file-reading capabilities instead.

Reading a File

Now we’ll add functionality to read the file specified in the file_path argument. First, we need a sample file to test it with: we’ll use a file with a small amount of text over multiple lines with some repeated words. Listing 12-3 has an Emily Dickinson poem that will work well! Create a file called poem.txt at the root level of your project, and enter the poem “I’m Nobody! Who are you?”

Filename: poem.txt

I'm nobody! Who are you?
Are you nobody, too?
Then there's a pair of us - don't tell!
They'd banish us, you know.

How dreary to be somebody!
How public, like a frog
To tell your name the livelong day
To an admiring bog!

Listing 12-3: A poem by Emily Dickinson makes a good test case

With the text in place, edit src/main.rs and add code to read the file, as shown in Listing 12-4.

Filename: src/main.rs

use std::env;
use std::fs;

fn main() {
    // --snip--
    let args: Vec<String> = env::args().collect();

    let query = &args[1];
    let file_path = &args[2];

    println!("Searching for {}", query);
    println!("In file {}", file_path);

    let contents = fs::read_to_string(file_path)
        .expect("Should have been able to read the file");

    println!("With text:\n{contents}");
}

Listing 12-4: Reading the contents of the file specified by the second argument

First, we bring in a relevant part of the standard library with a use statement: we need std::fs to handle files.

In main, the new statement fs::read_to_string takes the file_path, opens that file, and returns a std::io::Result<String> of the file’s contents.

After that, we again add a temporary println! statement that prints the value of contents after the file is read, so we can check that the program is working so far.

Let’s run this code with any string as the first command line argument (because we haven’t implemented the searching part yet) and the poem.txt file as the second argument:

$ cargo run -- the poem.txt
   Compiling minigrep v0.1.0 (file:///projects/minigrep)
    Finished dev [unoptimized + debuginfo] target(s) in 0.0s
     Running `target/debug/minigrep the poem.txt`
Searching for the
In file poem.txt
With text:
I'm nobody! Who are you?
Are you nobody, too?
Then there's a pair of us - don't tell!
They'd banish us, you know.

How dreary to be somebody!
How public, like a frog
To tell your name the livelong day
To an admiring bog!

Great! The code read and then printed the contents of the file. But the code has a few flaws. At the moment, the main function has multiple responsibilities: generally, functions are clearer and easier to maintain if each function is responsible for only one idea. The other problem is that we’re not handling errors as well as we could. The program is still small, so these flaws aren’t a big problem, but as the program grows, it will be harder to fix them cleanly. It’s good practice to begin refactoring early on when developing a program, because it’s much easier to refactor smaller amounts of code. We’ll do that next.

Refactoring to Improve Modularity and Error Handling

To improve our program, we’ll fix four problems that have to do with the program’s structure and how it’s handling potential errors. First, our main function now performs two tasks: it parses arguments and reads files. As our program grows, the number of separate tasks the main function handles will increase. As a function gains responsibilities, it becomes more difficult to reason about, harder to test, and harder to change without breaking one of its parts. It’s best to separate functionality so each function is responsible for one task.

This issue also ties into the second problem: although query and file_path are configuration variables to our program, variables like contents are used to perform the program’s logic. The longer main becomes, the more variables we’ll need to bring into scope; the more variables we have in scope, the harder it will be to keep track of the purpose of each. It’s best to group the configuration variables into one structure to make their purpose clear.

The third problem is that we’ve used expect to print an error message when reading the file fails, but the error message just prints Should have been able to read the file. Reading a file can fail in a number of ways: for example, the file could be missing, or we might not have permission to open it. Right now, regardless of the situation, we’d print the same error message for everything, which wouldn’t give the user any information!

Fourth, we use expect repeatedly to handle different errors, and if the user runs our program without specifying enough arguments, they’ll get an index out of bounds error from Rust that doesn’t clearly explain the problem. It would be best if all the error-handling code were in one place so future maintainers had only one place to consult the code if the error-handling logic needed to change. Having all the error-handling code in one place will also ensure that we’re printing messages that will be meaningful to our end users.

Let’s address these four problems by refactoring our project.

Separation of Concerns for Binary Projects

The organizational problem of allocating responsibility for multiple tasks to the main function is common to many binary projects. As a result, the Rust community has developed guidelines for splitting the separate concerns of a binary program when main starts getting large. This process has the following steps:

  • Split your program into a main.rs and a lib.rs and move your program’s logic to lib.rs.
  • As long as your command line parsing logic is small, it can remain in main.rs.
  • When the command line parsing logic starts getting complicated, extract it from main.rs and move it to lib.rs.

The responsibilities that remain in the main function after this process should be limited to the following:

  • Calling the command line parsing logic with the argument values
  • Setting up any other configuration
  • Calling a run function in lib.rs
  • Handling the error if run returns an error

This pattern is about separating concerns: main.rs handles running the program, and lib.rs handles all the logic of the task at hand. Because you can’t test the main function directly, this structure lets you test all of your program’s logic by moving it into functions in lib.rs. The code that remains in main.rs will be small enough to verify its correctness by reading it. Let’s rework our program by following this process.

Extracting the Argument Parser

We’ll extract the functionality for parsing arguments into a function that main will call to prepare for moving the command line parsing logic to src/lib.rs. Listing 12-5 shows the new start of main that calls a new function parse_config, which we’ll define in src/main.rs for the moment.

Filename: src/main.rs

use std::env;
use std::fs;

fn main() {
    let args: Vec<String> = env::args().collect();

    let (query, file_path) = parse_config(&args);

    // --snip--

    println!("Searching for {}", query);
    println!("In file {}", file_path);

    let contents = fs::read_to_string(file_path)
        .expect("Should have been able to read the file");

    println!("With text:\n{contents}");
}

fn parse_config(args: &[String]) -> (&str, &str) {
    let query = &args[1];
    let file_path = &args[2];

    (query, file_path)
}

Listing 12-5: Extracting a parse_config function from main

We’re still collecting the command line arguments into a vector, but instead of assigning the argument value at index 1 to the variable query and the argument value at index 2 to the variable file_path within the main function, we pass the whole vector to the parse_config function. The parse_config function then holds the logic that determines which argument goes in which variable and passes the values back to main. We still create the query and file_path variables in main, but main no longer has the responsibility of determining how the command line arguments and variables correspond.

This rework may seem like overkill for our small program, but we’re refactoring in small, incremental steps. After making this change, run the program again to verify that the argument parsing still works. It’s good to check your progress often, to help identify the cause of problems when they occur.

Grouping Configuration Values

We can take another small step to improve the parse_config function further. At the moment, we’re returning a tuple, but then we immediately break that tuple into individual parts again. This is a sign that perhaps we don’t have the right abstraction yet.

Another indicator that shows there’s room for improvement is the config part of parse_config, which implies that the two values we return are related and are both part of one configuration value. We’re not currently conveying this meaning in the structure of the data other than by grouping the two values into a tuple; we’ll instead put the two values into one struct and give each of the struct fields a meaningful name. Doing so will make it easier for future maintainers of this code to understand how the different values relate to each other and what their purpose is.

Listing 12-6 shows the improvements to the parse_config function.

Filename: src/main.rs

use std::env;
use std::fs;

fn main() {
    let args: Vec<String> = env::args().collect();

    let config = parse_config(&args);

    println!("Searching for {}", config.query);
    println!("In file {}", config.file_path);

    let contents = fs::read_to_string(config.file_path)
        .expect("Should have been able to read the file");

    // --snip--

    println!("With text:\n{contents}");
}

struct Config {
    query: String,
    file_path: String,
}

fn parse_config(args: &[String]) -> Config {
    let query = args[1].clone();
    let file_path = args[2].clone();

    Config { query, file_path }
}

Listing 12-6: Refactoring parse_config to return an instance of a Config struct

We’ve added a struct named Config defined to have fields named query and file_path. The signature of parse_config now indicates that it returns a Config value. In the body of parse_config, where we used to return string slices that reference String values in args, we now define Config to contain owned String values. The args variable in main is the owner of the argument values and is only letting the parse_config function borrow them, which means we’d violate Rust’s borrowing rules if Config tried to take ownership of the values in args.

There are a number of ways we could manage the String data; the easiest, though somewhat inefficient, route is to call the clone method on the values. This will make a full copy of the data for the Config instance to own, which takes more time and memory than storing a reference to the string data. However, cloning the data also makes our code very straightforward because we don’t have to manage the lifetimes of the references; in this circumstance, giving up a little performance to gain simplicity is a worthwhile trade-off.

The Trade-Offs of Using clone

There’s a tendency among many Rustaceans to avoid using clone to fix ownership problems because of its runtime cost. In Chapter 13, you’ll learn how to use more efficient methods in this type of situation. But for now, it’s okay to copy a few strings to continue making progress because you’ll make these copies only once and your file path and query string are very small. It’s better to have a working program that’s a bit inefficient than to try to hyperoptimize code on your first pass. As you become more experienced with Rust, it’ll be easier to start with the most efficient solution, but for now, it’s perfectly acceptable to call clone.

We’ve updated main so it places the instance of Config returned by parse_config into a variable named config, and we updated the code that previously used the separate query and file_path variables so it now uses the fields on the Config struct instead.

Now our code more clearly conveys that query and file_path are related and that their purpose is to configure how the program will work. Any code that uses these values knows to find them in the config instance in the fields named for their purpose.

Creating a Constructor for Config

So far, we’ve extracted the logic responsible for parsing the command line arguments from main and placed it in the parse_config function. Doing so helped us to see that the query and file_path values were related and that relationship should be conveyed in our code. We then added a Config struct to name the related purpose of query and file_path and to be able to return the values’ names as struct field names from the parse_config function.

So now that the purpose of the parse_config function is to create a Config instance, we can change parse_config from a plain function to a function named new that is associated with the Config struct. Making this change will make the code more idiomatic. We can create instances of types in the standard library, such as String, by calling String::new. Similarly, by changing parse_config into a new function associated with Config, we’ll be able to create instances of Config by calling Config::new. Listing 12-7 shows the changes we need to make.

Filename: src/main.rs

use std::env;
use std::fs;

fn main() {
    let args: Vec<String> = env::args().collect();

    let config = Config::new(&args);

    println!("Searching for {}", config.query);
    println!("In file {}", config.file_path);

    let contents = fs::read_to_string(config.file_path)
        .expect("Should have been able to read the file");

    println!("With text:\n{contents}");

    // --snip--
}

// --snip--

struct Config {
    query: String,
    file_path: String,
}

impl Config {
    fn new(args: &[String]) -> Config {
        let query = args[1].clone();
        let file_path = args[2].clone();

        Config { query, file_path }
    }
}

Listing 12-7: Changing parse_config into Config::new

We’ve updated main where we were calling parse_config to instead call Config::new. We’ve changed the name of parse_config to new and moved it within an impl block, which associates the new function with Config. Try compiling this code again to make sure it works.

Fixing the Error Handling

Now we’ll work on fixing our error handling. Recall that attempting to access the values in the args vector at index 1 or index 2 will cause the program to panic if the vector contains fewer than three items. Try running the program without any arguments; it will look like this:

$ cargo run
   Compiling minigrep v0.1.0 (file:///projects/minigrep)
    Finished dev [unoptimized + debuginfo] target(s) in 0.0s
     Running `target/debug/minigrep`
thread 'main' panicked at 'index out of bounds: the len is 1 but the index is 1', src/main.rs:27:21
note: run with `RUST_BACKTRACE=1` environment variable to display a backtrace

The line index out of bounds: the len is 1 but the index is 1 is an error message intended for programmers. It won’t help our end users understand what they should do instead. Let’s fix that now.

Improving the Error Message

In Listing 12-8, we add a check in the new function that will verify that the slice is long enough before accessing index 1 and 2. If the slice isn’t long enough, the program panics and displays a better error message.

Filename: src/main.rs

use std::env;
use std::fs;

fn main() {
    let args: Vec<String> = env::args().collect();

    let config = Config::new(&args);

    println!("Searching for {}", config.query);
    println!("In file {}", config.file_path);

    let contents = fs::read_to_string(config.file_path)
        .expect("Should have been able to read the file");

    println!("With text:\n{contents}");
}

struct Config {
    query: String,
    file_path: String,
}

impl Config {
    // --snip--
    fn new(args: &[String]) -> Config {
        if args.len() < 3 {
            panic!("not enough arguments");
        }
        // --snip--

        let query = args[1].clone();
        let file_path = args[2].clone();

        Config { query, file_path }
    }
}

Listing 12-8: Adding a check for the number of arguments

This code is similar to the Guess::new function we wrote in Listing 9-13, where we called panic! when the value argument was out of the range of valid values. Instead of checking for a range of values here, we’re checking that the length of args is at least 3 and the rest of the function can operate under the assumption that this condition has been met. If args has fewer than three items, this condition will be true, and we call the panic! macro to end the program immediately.

With these extra few lines of code in new, let’s run the program without any arguments again to see what the error looks like now:

$ cargo run
   Compiling minigrep v0.1.0 (file:///projects/minigrep)
    Finished dev [unoptimized + debuginfo] target(s) in 0.0s
     Running `target/debug/minigrep`
thread 'main' panicked at 'not enough arguments', src/main.rs:26:13
note: run with `RUST_BACKTRACE=1` environment variable to display a backtrace

This output is better: we now have a reasonable error message. However, we also have extraneous information we don’t want to give to our users. Perhaps using the technique we used in Listing 9-13 isn’t the best to use here: a call to panic! is more appropriate for a programming problem than a usage problem, as discussed in Chapter 9. Instead, we’ll use the other technique you learned about in Chapter 9—returning a Result that indicates either success or an error.

Returning a Result Instead of Calling panic!

We can instead return a Result value that will contain a Config instance in the successful case and will describe the problem in the error case. We’re also going to change the function name from new to build because many programmers expect new functions to never fail. When Config::build is communicating to main, we can use the Result type to signal there was a problem. Then we can change main to convert an Err variant into a more practical error for our users without the surrounding text about thread 'main' and RUST_BACKTRACE that a call to panic! causes.

Listing 12-9 shows the changes we need to make to the return value of the function we’re now calling Config::build and the body of the function needed to return a Result. Note that this won’t compile until we update main as well, which we’ll do in the next listing.

Filename: src/main.rs

use std::env;
use std::fs;

fn main() {
    let args: Vec<String> = env::args().collect();

    let config = Config::new(&args);

    println!("Searching for {}", config.query);
    println!("In file {}", config.file_path);

    let contents = fs::read_to_string(config.file_path)
        .expect("Should have been able to read the file");

    println!("With text:\n{contents}");
}

struct Config {
    query: String,
    file_path: String,
}

impl Config {
    fn build(args: &[String]) -> Result<Config, &'static str> {
        if args.len() < 3 {
            return Err("not enough arguments");
        }

        let query = args[1].clone();
        let file_path = args[2].clone();

        Ok(Config { query, file_path })
    }
}

Listing 12-9: Returning a Result from Config::build

Our build function now returns a Result with a Config instance in the success case and a &'static str in the error case. Our error values will always be string literals that have the 'static lifetime.

We’ve made two changes in the body of the function: instead of calling panic! when the user doesn’t pass enough arguments, we now return an Err value, and we’ve wrapped the Config return value in an Ok. These changes make the function conform to its new type signature.

Returning an Err value from Config::build allows the main function to handle the Result value returned from the build function and exit the process more cleanly in the error case.

Calling Config::build and Handling Errors

To handle the error case and print a user-friendly message, we need to update main to handle the Result being returned by Config::build, as shown in Listing 12-10. We’ll also take the responsibility of exiting the command line tool with a nonzero error code away from panic! and instead implement it by hand. A nonzero exit status is a convention to signal to the process that called our program that the program exited with an error state.

Filename: src/main.rs

use std::env;
use std::fs;
use std::process;

fn main() {
    let args: Vec<String> = env::args().collect();

    let config = Config::build(&args).unwrap_or_else(|err| {
        println!("Problem parsing arguments: {err}");
        process::exit(1);
    });

    // --snip--

    println!("Searching for {}", config.query);
    println!("In file {}", config.file_path);

    let contents = fs::read_to_string(config.file_path)
        .expect("Should have been able to read the file");

    println!("With text:\n{contents}");
}

struct Config {
    query: String,
    file_path: String,
}

impl Config {
    fn build(args: &[String]) -> Result<Config, &'static str> {
        if args.len() < 3 {
            return Err("not enough arguments");
        }

        let query = args[1].clone();
        let file_path = args[2].clone();

        Ok(Config { query, file_path })
    }
}

Listing 12-10: Exiting with an error code if building a Config fails

In this listing, we’ve used a method we haven’t covered in detail yet: unwrap_or_else, which is defined on Result<T, E> by the standard library. Using unwrap_or_else allows us to define some custom, non-panic! error handling. If the Result is an Ok value, this method’s behavior is similar to unwrap: it returns the inner value Ok is wrapping. However, if the value is an Err value, this method calls the code in the closure, which is an anonymous function we define and pass as an argument to unwrap_or_else. We’ll cover closures in more detail in Chapter 13. For now, you just need to know that unwrap_or_else will pass the inner value of the Err, which in this case is the static string "not enough arguments" that we added in Listing 12-9, to our closure in the argument err that appears between the vertical pipes. The code in the closure can then use the err value when it runs.

We’ve added a new use line to bring process from the standard library into scope. The code in the closure that will be run in the error case is only two lines: we print the err value and then call process::exit. The process::exit function will stop the program immediately and return the number that was passed as the exit status code. This is similar to the panic!-based handling we used in Listing 12-8, but we no longer get all the extra output. Let’s try it:

$ cargo run
   Compiling minigrep v0.1.0 (file:///projects/minigrep)
    Finished dev [unoptimized + debuginfo] target(s) in 0.48s
     Running `target/debug/minigrep`
Problem parsing arguments: not enough arguments

Great! This output is much friendlier for our users.

Extracting Logic from main

Now that we’ve finished refactoring the configuration parsing, let’s turn to the program’s logic. As we stated in “Separation of Concerns for Binary Projects”, we’ll extract a function named run that will hold all the logic currently in the main function that isn’t involved with setting up configuration or handling errors. When we’re done, main will be concise and easy to verify by inspection, and we’ll be able to write tests for all the other logic.

Listing 12-11 shows the extracted run function. For now, we’re just making the small, incremental improvement of extracting the function. We’re still defining the function in src/main.rs.

Filename: src/main.rs

use std::env;
use std::fs;
use std::process;

fn main() {
    // --snip--

    let args: Vec<String> = env::args().collect();

    let config = Config::build(&args).unwrap_or_else(|err| {
        println!("Problem parsing arguments: {err}");
        process::exit(1);
    });

    println!("Searching for {}", config.query);
    println!("In file {}", config.file_path);

    run(config);
}

fn run(config: Config) {
    let contents = fs::read_to_string(config.file_path)
        .expect("Should have been able to read the file");

    println!("With text:\n{contents}");
}

// --snip--

struct Config {
    query: String,
    file_path: String,
}

impl Config {
    fn build(args: &[String]) -> Result<Config, &'static str> {
        if args.len() < 3 {
            return Err("not enough arguments");
        }

        let query = args[1].clone();
        let file_path = args[2].clone();

        Ok(Config { query, file_path })
    }
}

Listing 12-11: Extracting a run function containing the rest of the program logic

The run function now contains all the remaining logic from main, starting from reading the file. The run function takes the Config instance as an argument.

Returning Errors from the run Function

With the remaining program logic separated into the run function, we can improve the error handling, as we did with Config::build in Listing 12-9. Instead of allowing the program to panic by calling expect, the run function will return a Result<T, E> when something goes wrong. This will let us further consolidate the logic around handling errors into main in a user-friendly way. Listing 12-12 shows the changes we need to make to the signature and body of run.

Filename: src/main.rs

use std::env;
use std::fs;
use std::process;
use std::error::Error;

// --snip--


fn main() {
    let args: Vec<String> = env::args().collect();

    let config = Config::build(&args).unwrap_or_else(|err| {
        println!("Problem parsing arguments: {err}");
        process::exit(1);
    });

    println!("Searching for {}", config.query);
    println!("In file {}", config.file_path);

    run(config);
}

fn run(config: Config) -> Result<(), Box<dyn Error>> {
    let contents = fs::read_to_string(config.file_path)?;

    println!("With text:\n{contents}");

    Ok(())
}

struct Config {
    query: String,
    file_path: String,
}

impl Config {
    fn build(args: &[String]) -> Result<Config, &'static str> {
        if args.len() < 3 {
            return Err("not enough arguments");
        }

        let query = args[1].clone();
        let file_path = args[2].clone();

        Ok(Config { query, file_path })
    }
}

Listing 12-12: Changing the run function to return Result

We’ve made three significant changes here. First, we changed the return type of the run function to Result<(), Box<dyn Error>>. This function previously returned the unit type, (), and we keep that as the value returned in the Ok case.

For the error type, we used the trait object Box<dyn Error> (and we’ve brought std::error::Error into scope with a use statement at the top). We’ll cover trait objects in Chapter 17. For now, just know that Box<dyn Error> means the function will return a type that implements the Error trait, but we don’t have to specify what particular type the return value will be. This gives us flexibility to return error values that may be of different types in different error cases. The dyn keyword is short for “dynamic.”

Second, we’ve removed the call to expect in favor of the ? operator, as we talked about in Chapter 9. Rather than panic! on an error, ? will return the error value from the current function for the caller to handle.

Third, the run function now returns an Ok value in the success case. We’ve declared the run function’s success type as () in the signature, which means we need to wrap the unit type value in the Ok value. This Ok(()) syntax might look a bit strange at first, but using () like this is the idiomatic way to indicate that we’re calling run for its side effects only; it doesn’t return a value we need.

When you run this code, it will compile but will display a warning:

$ cargo run the poem.txt
   Compiling minigrep v0.1.0 (file:///projects/minigrep)
warning: unused `Result` that must be used
  --> src/main.rs:19:5
   |
19 |     run(config);
   |     ^^^^^^^^^^^^
   |
   = note: `#[warn(unused_must_use)]` on by default
   = note: this `Result` may be an `Err` variant, which should be handled

warning: `minigrep` (bin "minigrep") generated 1 warning
    Finished dev [unoptimized + debuginfo] target(s) in 0.71s
     Running `target/debug/minigrep the poem.txt`
Searching for the
In file poem.txt
With text:
I'm nobody! Who are you?
Are you nobody, too?
Then there's a pair of us - don't tell!
They'd banish us, you know.

How dreary to be somebody!
How public, like a frog
To tell your name the livelong day
To an admiring bog!

Rust tells us that our code ignored the Result value and the Result value might indicate that an error occurred. But we’re not checking to see whether or not there was an error, and the compiler reminds us that we probably meant to have some error-handling code here! Let’s rectify that problem now.

Handling Errors Returned from run in main

We’ll check for errors and handle them using a technique similar to one we used with Config::build in Listing 12-10, but with a slight difference:

Filename: src/main.rs

use std::env;
use std::error::Error;
use std::fs;
use std::process;

fn main() {
    // --snip--

    let args: Vec<String> = env::args().collect();

    let config = Config::build(&args).unwrap_or_else(|err| {
        println!("Problem parsing arguments: {err}");
        process::exit(1);
    });

    println!("Searching for {}", config.query);
    println!("In file {}", config.file_path);

    if let Err(e) = run(config) {
        println!("Application error: {e}");

        process::exit(1);
    }
}

fn run(config: Config) -> Result<(), Box<dyn Error>> {
    let contents = fs::read_to_string(config.file_path)?;

    println!("With text:\n{contents}");

    Ok(())
}

struct Config {
    query: String,
    file_path: String,
}

impl Config {
    fn build(args: &[String]) -> Result<Config, &'static str> {
        if args.len() < 3 {
            return Err("not enough arguments");
        }

        let query = args[1].clone();
        let file_path = args[2].clone();

        Ok(Config { query, file_path })
    }
}

We use if let rather than unwrap_or_else to check whether run returns an Err value and call process::exit(1) if it does. The run function doesn’t return a value that we want to unwrap in the same way that Config::build returns the Config instance. Because run returns () in the success case, we only care about detecting an error, so we don’t need unwrap_or_else to return the unwrapped value, which would only be ().

The bodies of the if let and the unwrap_or_else functions are the same in both cases: we print the error and exit.

Splitting Code into a Library Crate

Our minigrep project is looking good so far! Now we’ll split the src/main.rs file and put some code into the src/lib.rs file. That way we can test the code and have a src/main.rs file with fewer responsibilities.

Let’s move all the code that isn’t the main function from src/main.rs to src/lib.rs:

  • The run function definition
  • The relevant use statements
  • The definition of Config
  • The Config::build function definition

The contents of src/lib.rs should have the signatures shown in Listing 12-13 (we’ve omitted the bodies of the functions for brevity). Note that this won’t compile until we modify src/main.rs in Listing 12-14.

Filename: src/lib.rs

use std::error::Error;
use std::fs;

pub struct Config {
    pub query: String,
    pub file_path: String,
}

impl Config {
    pub fn build(args: &[String]) -> Result<Config, &'static str> {
        // --snip--
        if args.len() < 3 {
            return Err("not enough arguments");
        }

        let query = args[1].clone();
        let file_path = args[2].clone();

        Ok(Config { query, file_path })
    }
}

pub fn run(config: Config) -> Result<(), Box<dyn Error>> {
    // --snip--
    let contents = fs::read_to_string(config.file_path)?;

    println!("With text:\n{contents}");

    Ok(())
}

Listing 12-13: Moving Config and run into src/lib.rs

We’ve made liberal use of the pub keyword: on Config, on its fields and its new method, and on the run function. We now have a library crate that has a public API we can test!

Now we need to bring the code we moved to src/lib.rs into the scope of the binary crate in src/main.rs, as shown in Listing 12-14.

Filename: src/main.rs

use std::env;
use std::process;

use minigrep::Config;

fn main() {
    // --snip--
    let args: Vec<String> = env::args().collect();

    let config = Config::build(&args).unwrap_or_else(|err| {
        println!("Problem parsing arguments: {err}");
        process::exit(1);
    });

    println!("Searching for {}", config.query);
    println!("In file {}", config.file_path);

    if let Err(e) = minigrep::run(config) {
        // --snip--
        println!("Application error: {e}");

        process::exit(1);
    }
}

Listing 12-14: Using the minigrep library crate in src/main.rs

We add a use minigrep::Config line to bring the Config type from the library crate into the binary crate’s scope, and we prefix the run function with our crate name. Now all the functionality should be connected and should work. Run the program with cargo run and make sure everything works correctly.

Whew! That was a lot of work, but we’ve set ourselves up for success in the future. Now it’s much easier to handle errors, and we’ve made the code more modular. Almost all of our work will be done in src/lib.rs from here on out.

Let’s take advantage of this newfound modularity by doing something that would have been difficult with the old code but is easy with the new code: we’ll write some tests!

Developing the Library’s Functionality with Test-Driven Development

Now that we’ve extracted the logic into src/lib.rs and left the argument collecting and error handling in src/main.rs, it’s much easier to write tests for the core functionality of our code. We can call functions directly with various arguments and check return values without having to call our binary from the command line.

In this section, we’ll add the searching logic to the minigrep program using the test-driven development (TDD) process with the following steps:

  1. Write a test that fails and run it to make sure it fails for the reason you expect.
  2. Write or modify just enough code to make the new test pass.
  3. Refactor the code you just added or changed and make sure the tests continue to pass.
  4. Repeat from step 1!

Though it’s just one of many ways to write software, TDD can help drive code design. Writing the test before you write the code that makes the test pass helps to maintain high test coverage throughout the process.

We’ll test drive the implementation of the functionality that will actually do the searching for the query string in the file contents and produce a list of lines that match the query. We’ll add this functionality in a function called search.

Writing a Failing Test

Because we don’t need them anymore, let’s remove the println! statements from src/lib.rs and src/main.rs that we used to check the program’s behavior. Then, in src/lib.rs, add a tests module with a test function, as we did in Chapter 11. The test function specifies the behavior we want the search function to have: it will take a query and the text to search, and it will return only the lines from the text that contain the query. Listing 12-15 shows this test, which won’t compile yet.

Filename: src/lib.rs

use std::error::Error;
use std::fs;

pub struct Config {
    pub query: String,
    pub file_path: String,
}

impl Config {
    pub fn build(args: &[String]) -> Result<Config, &'static str> {
        if args.len() < 3 {
            return Err("not enough arguments");
        }

        let query = args[1].clone();
        let file_path = args[2].clone();

        Ok(Config { query, file_path })
    }
}

pub fn run(config: Config) -> Result<(), Box<dyn Error>> {
    let contents = fs::read_to_string(config.file_path)?;

    Ok(())
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn one_result() {
        let query = "duct";
        let contents = "\
Rust:
safe, fast, productive.
Pick three.";

        assert_eq!(vec!["safe, fast, productive."], search(query, contents));
    }
}

Listing 12-15: Creating a failing test for the search function we wish we had

This test searches for the string "duct". The text we’re searching is three lines, only one of which contains "duct" (Note that the backslash after the opening double quote tells Rust not to put a newline character at the beginning of the contents of this string literal). We assert that the value returned from the search function contains only the line we expect.

We aren’t yet able to run this test and watch it fail because the test doesn’t even compile: the search function doesn’t exist yet! In accordance with TDD principles, we’ll add just enough code to get the test to compile and run by adding a definition of the search function that always returns an empty vector, as shown in Listing 12-16. Then the test should compile and fail because an empty vector doesn’t match a vector containing the line "safe, fast, productive."

Filename: src/lib.rs

use std::error::Error;
use std::fs;

pub struct Config {
    pub query: String,
    pub file_path: String,
}

impl Config {
    pub fn build(args: &[String]) -> Result<Config, &'static str> {
        if args.len() < 3 {
            return Err("not enough arguments");
        }

        let query = args[1].clone();
        let file_path = args[2].clone();

        Ok(Config { query, file_path })
    }
}

pub fn run(config: Config) -> Result<(), Box<dyn Error>> {
    let contents = fs::read_to_string(config.file_path)?;

    Ok(())
}

pub fn search<'a>(query: &str, contents: &'a str) -> Vec<&'a str> {
    vec![]
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn one_result() {
        let query = "duct";
        let contents = "\
Rust:
safe, fast, productive.
Pick three.";

        assert_eq!(vec!["safe, fast, productive."], search(query, contents));
    }
}

Listing 12-16: Defining just enough of the search function so our test will compile

Notice that we need to define an explicit lifetime 'a in the signature of search and use that lifetime with the contents argument and the return value. Recall in Chapter 10 that the lifetime parameters specify which argument lifetime is connected to the lifetime of the return value. In this case, we indicate that the returned vector should contain string slices that reference slices of the argument contents (rather than the argument query).

In other words, we tell Rust that the data returned by the search function will live as long as the data passed into the search function in the contents argument. This is important! The data referenced by a slice needs to be valid for the reference to be valid; if the compiler assumes we’re making string slices of query rather than contents, it will do its safety checking incorrectly.

If we forget the lifetime annotations and try to compile this function, we’ll get this error:

$ cargo build
   Compiling minigrep v0.1.0 (file:///projects/minigrep)
error[E0106]: missing lifetime specifier
  --> src/lib.rs:28:51
   |
28 | pub fn search(query: &str, contents: &str) -> Vec<&str> {
   |                      ----            ----         ^ expected named lifetime parameter
   |
   = help: this function's return type contains a borrowed value, but the signature does not say whether it is borrowed from `query` or `contents`
help: consider introducing a named lifetime parameter
   |
28 | pub fn search<'a>(query: &'a str, contents: &'a str) -> Vec<&'a str> {
   |              ++++         ++                 ++              ++

For more information about this error, try `rustc --explain E0106`.
error: could not compile `minigrep` due to previous error

Rust can’t possibly know which of the two arguments we need, so we need to tell it explicitly. Because contents is the argument that contains all of our text and we want to return the parts of that text that match, we know contents is the argument that should be connected to the return value using the lifetime syntax.

Other programming languages don’t require you to connect arguments to return values in the signature, but this practice will get easier over time. You might want to compare this example with the “Validating References with Lifetimes” section in Chapter 10.

Now let’s run the test:

$ cargo test
   Compiling minigrep v0.1.0 (file:///projects/minigrep)
    Finished test [unoptimized + debuginfo] target(s) in 0.97s
     Running unittests src/lib.rs (target/debug/deps/minigrep-9cd200e5fac0fc94)

running 1 test
test tests::one_result ... FAILED

failures:

---- tests::one_result stdout ----
thread 'main' panicked at 'assertion failed: `(left == right)`
  left: `["safe, fast, productive."]`,
 right: `[]`', src/lib.rs:44:9
note: run with `RUST_BACKTRACE=1` environment variable to display a backtrace


failures:
    tests::one_result

test result: FAILED. 0 passed; 1 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.00s

error: test failed, to rerun pass '--lib'

Great, the test fails, exactly as we expected. Let’s get the test to pass!

Writing Code to Pass the Test

Currently, our test is failing because we always return an empty vector. To fix that and implement search, our program needs to follow these steps:

  • Iterate through each line of the contents.
  • Check whether the line contains our query string.
  • If it does, add it to the list of values we’re returning.
  • If it doesn’t, do nothing.
  • Return the list of results that match.

Let’s work through each step, starting with iterating through lines.

Iterating Through Lines with the lines Method

Rust has a helpful method to handle line-by-line iteration of strings, conveniently named lines, that works as shown in Listing 12-17. Note this won’t compile yet.

Filename: src/lib.rs

use std::error::Error;
use std::fs;

pub struct Config {
    pub query: String,
    pub file_path: String,
}

impl Config {
    pub fn build(args: &[String]) -> Result<Config, &'static str> {
        if args.len() < 3 {
            return Err("not enough arguments");
        }

        let query = args[1].clone();
        let file_path = args[2].clone();

        Ok(Config { query, file_path })
    }
}

pub fn run(config: Config) -> Result<(), Box<dyn Error>> {
    let contents = fs::read_to_string(config.file_path)?;

    Ok(())
}

pub fn search<'a>(query: &str, contents: &'a str) -> Vec<&'a str> {
    for line in contents.lines() {
        // do something with line
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn one_result() {
        let query = "duct";
        let contents = "\
Rust:
safe, fast, productive.
Pick three.";

        assert_eq!(vec!["safe, fast, productive."], search(query, contents));
    }
}

Listing 12-17: Iterating through each line in contents

The lines method returns an iterator. We’ll talk about iterators in depth in Chapter 13, but recall that you saw this way of using an iterator in Listing 3-5, where we used a for loop with an iterator to run some code on each item in a collection.

Searching Each Line for the Query

Next, we’ll check whether the current line contains our query string. Fortunately, strings have a helpful method named contains that does this for us! Add a call to the contains method in the search function, as shown in Listing 12-18. Note this still won’t compile yet.

Filename: src/lib.rs

use std::error::Error;
use std::fs;

pub struct Config {
    pub query: String,
    pub file_path: String,
}

impl Config {
    pub fn build(args: &[String]) -> Result<Config, &'static str> {
        if args.len() < 3 {
            return Err("not enough arguments");
        }

        let query = args[1].clone();
        let file_path = args[2].clone();

        Ok(Config { query, file_path })
    }
}

pub fn run(config: Config) -> Result<(), Box<dyn Error>> {
    let contents = fs::read_to_string(config.file_path)?;

    Ok(())
}

pub fn search<'a>(query: &str, contents: &'a str) -> Vec<&'a str> {
    for line in contents.lines() {
        if line.contains(query) {
            // do something with line
        }
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn one_result() {
        let query = "duct";
        let contents = "\
Rust:
safe, fast, productive.
Pick three.";

        assert_eq!(vec!["safe, fast, productive."], search(query, contents));
    }
}

Listing 12-18: Adding functionality to see whether the line contains the string in query

At the moment, we’re building up functionality. To get it to compile, we need to return a value from the body as we indicated we would in the function signature.

Storing Matching Lines

To finish this function, we need a way to store the matching lines that we want to return. For that, we can make a mutable vector before the for loop and call the push method to store a line in the vector. After the for loop, we return the vector, as shown in Listing 12-19.

Filename: src/lib.rs

use std::error::Error;
use std::fs;

pub struct Config {
    pub query: String,
    pub file_path: String,
}

impl Config {
    pub fn build(args: &[String]) -> Result<Config, &'static str> {
        if args.len() < 3 {
            return Err("not enough arguments");
        }

        let query = args[1].clone();
        let file_path = args[2].clone();

        Ok(Config { query, file_path })
    }
}

pub fn run(config: Config) -> Result<(), Box<dyn Error>> {
    let contents = fs::read_to_string(config.file_path)?;

    Ok(())
}

pub fn search<'a>(query: &str, contents: &'a str) -> Vec<&'a str> {
    let mut results = Vec::new();

    for line in contents.lines() {
        if line.contains(query) {
            results.push(line);
        }
    }

    results
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn one_result() {
        let query = "duct";
        let contents = "\
Rust:
safe, fast, productive.
Pick three.";

        assert_eq!(vec!["safe, fast, productive."], search(query, contents));
    }
}

Listing 12-19: Storing the lines that match so we can return them

Now the search function should return only the lines that contain query, and our test should pass. Let’s run the test:

$ cargo test
   Compiling minigrep v0.1.0 (file:///projects/minigrep)
    Finished test [unoptimized + debuginfo] target(s) in 1.22s
     Running unittests src/lib.rs (target/debug/deps/minigrep-9cd200e5fac0fc94)

running 1 test
test tests::one_result ... ok

test result: ok. 1 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.00s

     Running unittests src/main.rs (target/debug/deps/minigrep-9cd200e5fac0fc94)

running 0 tests

test result: ok. 0 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.00s

   Doc-tests minigrep

running 0 tests

test result: ok. 0 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.00s

Our test passed, so we know it works!

At this point, we could consider opportunities for refactoring the implementation of the search function while keeping the tests passing to maintain the same functionality. The code in the search function isn’t too bad, but it doesn’t take advantage of some useful features of iterators. We’ll return to this example in Chapter 13, where we’ll explore iterators in detail, and look at how to improve it.

Using the search Function in the run Function

Now that the search function is working and tested, we need to call search from our run function. We need to pass the config.query value and the contents that run reads from the file to the search function. Then run will print each line returned from search:

Filename: src/lib.rs

use std::error::Error;
use std::fs;

pub struct Config {
    pub query: String,
    pub file_path: String,
}

impl Config {
    pub fn build(args: &[String]) -> Result<Config, &'static str> {
        if args.len() < 3 {
            return Err("not enough arguments");
        }

        let query = args[1].clone();
        let file_path = args[2].clone();

        Ok(Config { query, file_path })
    }
}

pub fn run(config: Config) -> Result<(), Box<dyn Error>> {
    let contents = fs::read_to_string(config.file_path)?;

    for line in search(&config.query, &contents) {
        println!("{line}");
    }

    Ok(())
}

pub fn search<'a>(query: &str, contents: &'a str) -> Vec<&'a str> {
    let mut results = Vec::new();

    for line in contents.lines() {
        if line.contains(query) {
            results.push(line);
        }
    }

    results
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn one_result() {
        let query = "duct";
        let contents = "\
Rust:
safe, fast, productive.
Pick three.";

        assert_eq!(vec!["safe, fast, productive."], search(query, contents));
    }
}

We’re still using a for loop to return each line from search and print it.

Now the entire program should work! Let’s try it out, first with a word that should return exactly one line from the Emily Dickinson poem, “frog”:

$ cargo run -- frog poem.txt
   Compiling minigrep v0.1.0 (file:///projects/minigrep)
    Finished dev [unoptimized + debuginfo] target(s) in 0.38s
     Running `target/debug/minigrep frog poem.txt`
How public, like a frog

Cool! Now let’s try a word that will match multiple lines, like “body”:

$ cargo run -- body poem.txt
   Compiling minigrep v0.1.0 (file:///projects/minigrep)
    Finished dev [unoptimized + debuginfo] target(s) in 0.0s
     Running `target/debug/minigrep body poem.txt`
I'm nobody! Who are you?
Are you nobody, too?
How dreary to be somebody!

And finally, let’s make sure that we don’t get any lines when we search for a word that isn’t anywhere in the poem, such as “monomorphization”:

$ cargo run -- monomorphization poem.txt
   Compiling minigrep v0.1.0 (file:///projects/minigrep)
    Finished dev [unoptimized + debuginfo] target(s) in 0.0s
     Running `target/debug/minigrep monomorphization poem.txt`

Excellent! We’ve built our own mini version of a classic tool and learned a lot about how to structure applications. We’ve also learned a bit about file input and output, lifetimes, testing, and command line parsing.

To round out this project, we’ll briefly demonstrate how to work with environment variables and how to print to standard error, both of which are useful when you’re writing command line programs.

Working with Environment Variables

We’ll improve minigrep by adding an extra feature: an option for case-insensitive searching that the user can turn on via an environment variable. We could make this feature a command line option and require that users enter it each time they want it to apply, but by instead making it an environment variable, we allow our users to set the environment variable once and have all their searches be case insensitive in that terminal session.

Writing a Failing Test for the Case-Insensitive search Function

We first add a new search_case_insensitive function that will be called when the environment variable has a value. We’ll continue to follow the TDD process, so the first step is again to write a failing test. We’ll add a new test for the new search_case_insensitive function and rename our old test from one_result to case_sensitive to clarify the differences between the two tests, as shown in Listing 12-20.

Filename: src/lib.rs

use std::error::Error;
use std::fs;

pub struct Config {
    pub query: String,
    pub file_path: String,
}

impl Config {
    pub fn build(args: &[String]) -> Result<Config, &'static str> {
        if args.len() < 3 {
            return Err("not enough arguments");
        }

        let query = args[1].clone();
        let file_path = args[2].clone();

        Ok(Config { query, file_path })
    }
}

pub fn run(config: Config) -> Result<(), Box<dyn Error>> {
    let contents = fs::read_to_string(config.file_path)?;

    for line in search(&config.query, &contents) {
        println!("{line}");
    }

    Ok(())
}

pub fn search<'a>(query: &str, contents: &'a str) -> Vec<&'a str> {
    let mut results = Vec::new();

    for line in contents.lines() {
        if line.contains(query) {
            results.push(line);
        }
    }

    results
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn case_sensitive() {
        let query = "duct";
        let contents = "\
Rust:
safe, fast, productive.
Pick three.
Duct tape.";

        assert_eq!(vec!["safe, fast, productive."], search(query, contents));
    }

    #[test]
    fn case_insensitive() {
        let query = "rUsT";
        let contents = "\
Rust:
safe, fast, productive.
Pick three.
Trust me.";

        assert_eq!(
            vec!["Rust:", "Trust me."],
            search_case_insensitive(query, contents)
        );
    }
}

Listing 12-20: Adding a new failing test for the case-insensitive function we’re about to add

Note that we’ve edited the old test’s contents too. We’ve added a new line with the text "Duct tape." using a capital D that shouldn’t match the query "duct" when we’re searching in a case-sensitive manner. Changing the old test in this way helps ensure that we don’t accidentally break the case-sensitive search functionality that we’ve already implemented. This test should pass now and should continue to pass as we work on the case-insensitive search.

The new test for the case-insensitive search uses "rUsT" as its query. In the search_case_insensitive function we’re about to add, the query "rUsT" should match the line containing "Rust:" with a capital R and match the line "Trust me." even though both have different casing from the query. This is our failing test, and it will fail to compile because we haven’t yet defined the search_case_insensitive function. Feel free to add a skeleton implementation that always returns an empty vector, similar to the way we did for the search function in Listing 12-16 to see the test compile and fail.

Implementing the search_case_insensitive Function

The search_case_insensitive function, shown in Listing 12-21, will be almost the same as the search function. The only difference is that we’ll lowercase the query and each line so whatever the case of the input arguments, they’ll be the same case when we check whether the line contains the query.

Filename: src/lib.rs

use std::error::Error;
use std::fs;

pub struct Config {
    pub query: String,
    pub file_path: String,
}

impl Config {
    pub fn build(args: &[String]) -> Result<Config, &'static str> {
        if args.len() < 3 {
            return Err("not enough arguments");
        }

        let query = args[1].clone();
        let file_path = args[2].clone();

        Ok(Config { query, file_path })
    }
}

pub fn run(config: Config) -> Result<(), Box<dyn Error>> {
    let contents = fs::read_to_string(config.file_path)?;

    for line in search(&config.query, &contents) {
        println!("{line}");
    }

    Ok(())
}

pub fn search<'a>(query: &str, contents: &'a str) -> Vec<&'a str> {
    let mut results = Vec::new();

    for line in contents.lines() {
        if line.contains(query) {
            results.push(line);
        }
    }

    results
}

pub fn search_case_insensitive<'a>(
    query: &str,
    contents: &'a str,
) -> Vec<&'a str> {
    let query = query.to_lowercase();
    let mut results = Vec::new();

    for line in contents.lines() {
        if line.to_lowercase().contains(&query) {
            results.push(line);
        }
    }

    results
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn case_sensitive() {
        let query = "duct";
        let contents = "\
Rust:
safe, fast, productive.
Pick three.
Duct tape.";

        assert_eq!(vec!["safe, fast, productive."], search(query, contents));
    }

    #[test]
    fn case_insensitive() {
        let query = "rUsT";
        let contents = "\
Rust:
safe, fast, productive.
Pick three.
Trust me.";

        assert_eq!(
            vec!["Rust:", "Trust me."],
            search_case_insensitive(query, contents)
        );
    }
}

Listing 12-21: Defining the search_case_insensitive function to lowercase the query and the line before comparing them

First, we lowercase the query string and store it in a shadowed variable with the same name. Calling to_lowercase on the query is necessary so no matter whether the user’s query is "rust", "RUST", "Rust", or "rUsT", we’ll treat the query as if it were "rust" and be insensitive to the case. While to_lowercase will handle basic Unicode, it won’t be 100% accurate. If we were writing a real application, we’d want to do a bit more work here, but this section is about environment variables, not Unicode, so we’ll leave it at that here.

Note that query is now a String rather than a string slice, because calling to_lowercase creates new data rather than referencing existing data. Say the query is "rUsT", as an example: that string slice doesn’t contain a lowercase u or t for us to use, so we have to allocate a new String containing "rust". When we pass query as an argument to the contains method now, we need to add an ampersand because the signature of contains is defined to take a string slice.

Next, we add a call to to_lowercase on each line to lowercase all characters. Now that we’ve converted line and query to lowercase, we’ll find matches no matter what the case of the query is.

Let’s see if this implementation passes the tests:

$ cargo test
   Compiling minigrep v0.1.0 (file:///projects/minigrep)
    Finished test [unoptimized + debuginfo] target(s) in 1.33s
     Running unittests src/lib.rs (target/debug/deps/minigrep-9cd200e5fac0fc94)

running 2 tests
test tests::case_insensitive ... ok
test tests::case_sensitive ... ok

test result: ok. 2 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.00s

     Running unittests src/main.rs (target/debug/deps/minigrep-9cd200e5fac0fc94)

running 0 tests

test result: ok. 0 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.00s

   Doc-tests minigrep

running 0 tests

test result: ok. 0 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.00s

Great! They passed. Now, let’s call the new search_case_insensitive function from the run function. First, we’ll add a configuration option to the Config struct to switch between case-sensitive and case-insensitive search. Adding this field will cause compiler errors because we aren’t initializing this field anywhere yet:

Filename: src/lib.rs

use std::error::Error;
use std::fs;

pub struct Config {
    pub query: String,
    pub file_path: String,
    pub ignore_case: bool,
}

impl Config {
    pub fn build(args: &[String]) -> Result<Config, &'static str> {
        if args.len() < 3 {
            return Err("not enough arguments");
        }

        let query = args[1].clone();
        let file_path = args[2].clone();

        Ok(Config { query, file_path })
    }
}

pub fn run(config: Config) -> Result<(), Box<dyn Error>> {
    let contents = fs::read_to_string(config.file_path)?;

    let results = if config.ignore_case {
        search_case_insensitive(&config.query, &contents)
    } else {
        search(&config.query, &contents)
    };

    for line in results {
        println!("{line}");
    }

    Ok(())
}

pub fn search<'a>(query: &str, contents: &'a str) -> Vec<&'a str> {
    let mut results = Vec::new();

    for line in contents.lines() {
        if line.contains(query) {
            results.push(line);
        }
    }

    results
}

pub fn search_case_insensitive<'a>(
    query: &str,
    contents: &'a str,
) -> Vec<&'a str> {
    let query = query.to_lowercase();
    let mut results = Vec::new();

    for line in contents.lines() {
        if line.to_lowercase().contains(&query) {
            results.push(line);
        }
    }

    results
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn case_sensitive() {
        let query = "duct";
        let contents = "\
Rust:
safe, fast, productive.
Pick three.
Duct tape.";

        assert_eq!(vec!["safe, fast, productive."], search(query, contents));
    }

    #[test]
    fn case_insensitive() {
        let query = "rUsT";
        let contents = "\
Rust:
safe, fast, productive.
Pick three.
Trust me.";

        assert_eq!(
            vec!["Rust:", "Trust me."],
            search_case_insensitive(query, contents)
        );
    }
}

We added the ignore_case field that holds a Boolean. Next, we need the run function to check the ignore_case field’s value and use that to decide whether to call the search function or the search_case_insensitive function, as shown in Listing 12-22. This still won’t compile yet.

Filename: src/lib.rs

use std::error::Error;
use std::fs;

pub struct Config {
    pub query: String,
    pub file_path: String,
    pub ignore_case: bool,
}

impl Config {
    pub fn build(args: &[String]) -> Result<Config, &'static str> {
        if args.len() < 3 {
            return Err("not enough arguments");
        }

        let query = args[1].clone();
        let file_path = args[2].clone();

        Ok(Config { query, file_path })
    }
}

pub fn run(config: Config) -> Result<(), Box<dyn Error>> {
    let contents = fs::read_to_string(config.file_path)?;

    let results = if config.ignore_case {
        search_case_insensitive(&config.query, &contents)
    } else {
        search(&config.query, &contents)
    };

    for line in results {
        println!("{line}");
    }

    Ok(())
}

pub fn search<'a>(query: &str, contents: &'a str) -> Vec<&'a str> {
    let mut results = Vec::new();

    for line in contents.lines() {
        if line.contains(query) {
            results.push(line);
        }
    }

    results
}

pub fn search_case_insensitive<'a>(
    query: &str,
    contents: &'a str,
) -> Vec<&'a str> {
    let query = query.to_lowercase();
    let mut results = Vec::new();

    for line in contents.lines() {
        if line.to_lowercase().contains(&query) {
            results.push(line);
        }
    }

    results
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn case_sensitive() {
        let query = "duct";
        let contents = "\
Rust:
safe, fast, productive.
Pick three.
Duct tape.";

        assert_eq!(vec!["safe, fast, productive."], search(query, contents));
    }

    #[test]
    fn case_insensitive() {
        let query = "rUsT";
        let contents = "\
Rust:
safe, fast, productive.
Pick three.
Trust me.";

        assert_eq!(
            vec!["Rust:", "Trust me."],
            search_case_insensitive(query, contents)
        );
    }
}

Listing 12-22: Calling either search or search_case_insensitive based on the value in config.ignore_case

Finally, we need to check for the environment variable. The functions for working with environment variables are in the env module in the standard library, so we bring that module into scope at the top of src/lib.rs. Then we’ll use the var function from the env module to check to see if any value has been set for an environment variable named IGNORE_CASE, as shown in Listing 12-23.

Filename: src/lib.rs

use std::env;
// --snip--

use std::error::Error;
use std::fs;

pub struct Config {
    pub query: String,
    pub file_path: String,
    pub ignore_case: bool,
}

impl Config {
    pub fn build(args: &[String]) -> Result<Config, &'static str> {
        if args.len() < 3 {
            return Err("not enough arguments");
        }

        let query = args[1].clone();
        let file_path = args[2].clone();

        let ignore_case = env::var("IGNORE_CASE").is_ok();

        Ok(Config {
            query,
            file_path,
            ignore_case,
        })
    }
}

pub fn run(config: Config) -> Result<(), Box<dyn Error>> {
    let contents = fs::read_to_string(config.file_path)?;

    let results = if config.ignore_case {
        search_case_insensitive(&config.query, &contents)
    } else {
        search(&config.query, &contents)
    };

    for line in results {
        println!("{line}");
    }

    Ok(())
}

pub fn search<'a>(query: &str, contents: &'a str) -> Vec<&'a str> {
    let mut results = Vec::new();

    for line in contents.lines() {
        if line.contains(query) {
            results.push(line);
        }
    }

    results
}

pub fn search_case_insensitive<'a>(
    query: &str,
    contents: &'a str,
) -> Vec<&'a str> {
    let query = query.to_lowercase();
    let mut results = Vec::new();

    for line in contents.lines() {
        if line.to_lowercase().contains(&query) {
            results.push(line);
        }
    }

    results
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn case_sensitive() {
        let query = "duct";
        let contents = "\
Rust:
safe, fast, productive.
Pick three.
Duct tape.";

        assert_eq!(vec!["safe, fast, productive."], search(query, contents));
    }

    #[test]
    fn case_insensitive() {
        let query = "rUsT";
        let contents = "\
Rust:
safe, fast, productive.
Pick three.
Trust me.";

        assert_eq!(
            vec!["Rust:", "Trust me."],
            search_case_insensitive(query, contents)
        );
    }
}

Listing 12-23: Checking for any value in an environment variable named IGNORE_CASE

Here, we create a new variable ignore_case. To set its value, we call the env::var function and pass it the name of the IGNORE_CASE environment variable. The env::var function returns a Result that will be the successful Ok variant that contains the value of the environment variable if the environment variable is set to any value. It will return the Err variant if the environment variable is not set.

We’re using the is_ok method on the Result to check whether the environment variable is set, which means the program should do a case-insensitive search. If the IGNORE_CASE environment variable isn’t set to anything, is_ok will return false and the program will perform a case-sensitive search. We don’t care about the value of the environment variable, just whether it’s set or unset, so we’re checking is_ok rather than using unwrap, expect, or any of the other methods we’ve seen on Result.

We pass the value in the ignore_case variable to the Config instance so the run function can read that value and decide whether to call search_case_insensitive or search, as we implemented in Listing 12-22.

Let’s give it a try! First, we’ll run our program without the environment variable set and with the query to, which should match any line that contains the word “to” in all lowercase:

$ cargo run -- to poem.txt
   Compiling minigrep v0.1.0 (file:///projects/minigrep)
    Finished dev [unoptimized + debuginfo] target(s) in 0.0s
     Running `target/debug/minigrep to poem.txt`
Are you nobody, too?
How dreary to be somebody!

Looks like that still works! Now, let’s run the program with IGNORE_CASE set to 1 but with the same query to.

$ IGNORE_CASE=1 cargo run -- to poem.txt

If you’re using PowerShell, you will need to set the environment variable and run the program as separate commands:

PS> $Env:IGNORE_CASE=1; cargo run -- to poem.txt

This will make IGNORE_CASE persist for the remainder of your shell session. It can be unset with the Remove-Item cmdlet:

PS> Remove-Item Env:IGNORE_CASE

We should get lines that contain “to” that might have uppercase letters:

Are you nobody, too?
How dreary to be somebody!
To tell your name the livelong day
To an admiring bog!

Excellent, we also got lines containing “To”! Our minigrep program can now do case-insensitive searching controlled by an environment variable. Now you know how to manage options set using either command line arguments or environment variables.

Some programs allow arguments and environment variables for the same configuration. In those cases, the programs decide that one or the other takes precedence. For another exercise on your own, try controlling case sensitivity through either a command line argument or an environment variable. Decide whether the command line argument or the environment variable should take precedence if the program is run with one set to case sensitive and one set to ignore case.

The std::env module contains many more useful features for dealing with environment variables: check out its documentation to see what is available.

Writing Error Messages to Standard Error Instead of Standard Output

At the moment, we’re writing all of our output to the terminal using the println! macro. In most terminals, there are two kinds of output: standard output (stdout) for general information and standard error (stderr) for error messages. This distinction enables users to choose to direct the successful output of a program to a file but still print error messages to the screen.

The println! macro is only capable of printing to standard output, so we have to use something else to print to standard error.

Checking Where Errors Are Written

First, let’s observe how the content printed by minigrep is currently being written to standard output, including any error messages we want to write to standard error instead. We’ll do that by redirecting the standard output stream to a file while intentionally causing an error. We won’t redirect the standard error stream, so any content sent to standard error will continue to display on the screen.

Command line programs are expected to send error messages to the standard error stream so we can still see error messages on the screen even if we redirect the standard output stream to a file. Our program is not currently well-behaved: we’re about to see that it saves the error message output to a file instead!

To demonstrate this behavior, we’ll run the program with > and the file path, output.txt, that we want to redirect the standard output stream to. We won’t pass any arguments, which should cause an error:

$ cargo run > output.txt

The > syntax tells the shell to write the contents of standard output to output.txt instead of the screen. We didn’t see the error message we were expecting printed to the screen, so that means it must have ended up in the file. This is what output.txt contains:

Problem parsing arguments: not enough arguments

Yup, our error message is being printed to standard output. It’s much more useful for error messages like this to be printed to standard error so only data from a successful run ends up in the file. We’ll change that.

Printing Errors to Standard Error

We’ll use the code in Listing 12-24 to change how error messages are printed. Because of the refactoring we did earlier in this chapter, all the code that prints error messages is in one function, main. The standard library provides the eprintln! macro that prints to the standard error stream, so let’s change the two places we were calling println! to print errors to use eprintln! instead.

Filename: src/main.rs

use std::env;
use std::process;

use minigrep::Config;

fn main() {
    let args: Vec<String> = env::args().collect();

    let config = Config::build(&args).unwrap_or_else(|err| {
        eprintln!("Problem parsing arguments: {err}");
        process::exit(1);
    });

    if let Err(e) = minigrep::run(config) {
        eprintln!("Application error: {e}");

        process::exit(1);
    }
}

Listing 12-24: Writing error messages to standard error instead of standard output using eprintln!

Let’s now run the program again in the same way, without any arguments and redirecting standard output with >:

$ cargo run > output.txt
Problem parsing arguments: not enough arguments

Now we see the error onscreen and output.txt contains nothing, which is the behavior we expect of command line programs.

Let’s run the program again with arguments that don’t cause an error but still redirect standard output to a file, like so:

$ cargo run -- to poem.txt > output.txt

We won’t see any output to the terminal, and output.txt will contain our results:

Filename: output.txt

Are you nobody, too?
How dreary to be somebody!

This demonstrates that we’re now using standard output for successful output and standard error for error output as appropriate.

Summary

This chapter recapped some of the major concepts you’ve learned so far and covered how to perform common I/O operations in Rust. By using command line arguments, files, environment variables, and the eprintln! macro for printing errors, you’re now prepared to write command line applications. Combined with the concepts in previous chapters, your code will be well organized, store data effectively in the appropriate data structures, handle errors nicely, and be well tested.

Next, we’ll explore some Rust features that were influenced by functional languages: closures and iterators.

Functional Language Features: Iterators and Closures

Rust’s design has taken inspiration from many existing languages and techniques, and one significant influence is functional programming. Programming in a functional style often includes using functions as values by passing them in arguments, returning them from other functions, assigning them to variables for later execution, and so forth.

In this chapter, we won’t debate the issue of what functional programming is or isn’t but will instead discuss some features of Rust that are similar to features in many languages often referred to as functional.

More specifically, we’ll cover:

  • Closures, a function-like construct you can store in a variable
  • Iterators, a way of processing a series of elements
  • How to use closures and iterators to improve the I/O project in Chapter 12
  • The performance of closures and iterators (Spoiler alert: they’re faster than you might think!)

We’ve already covered some other Rust features, such as pattern matching and enums, that are also influenced by the functional style. Because mastering closures and iterators is an important part of writing idiomatic, fast Rust code, we’ll devote this entire chapter to them.

Closures: Anonymous Functions that Capture Their Environment

Rust’s closures are anonymous functions you can save in a variable or pass as arguments to other functions. You can create the closure in one place and then call the closure elsewhere to evaluate it in a different context. Unlike functions, closures can capture values from the scope in which they’re defined. We’ll demonstrate how these closure features allow for code reuse and behavior customization.

Capturing the Environment with Closures

We’ll first examine how we can use closures to capture values from the environment they’re defined in for later use. Here’s the scenario: Every so often, our t-shirt company gives away an exclusive, limited-edition shirt to someone on our mailing list as a promotion. People on the mailing list can optionally add their favorite color to their profile. If the person chosen for a free shirt has their favorite color set, they get that color shirt. If the person hasn’t specified a favorite color, they get whatever color the company currently has the most of.

There are many ways to implement this. For this example, we’re going to use an enum called ShirtColor that has the variants Red and Blue (limiting the number of colors available for simplicity). We represent the company’s inventory with an Inventory struct that has a field named shirts that contains a Vec<ShirtColor> representing the shirt colors currently in stock. The method shirt_giveaway defined on Inventory gets the optional shirt color preference of the free shirt winner, and returns the shirt color the person will get. This setup is shown in Listing 13-1:

Filename: src/main.rs

#[derive(Debug, PartialEq, Copy, Clone)]
enum ShirtColor {
    Red,
    Blue,
}

struct Inventory {
    shirts: Vec<ShirtColor>,
}

impl Inventory {
    fn giveaway(&self, user_preference: Option<ShirtColor>) -> ShirtColor {
        user_preference.unwrap_or_else(|| self.most_stocked())
    }

    fn most_stocked(&self) -> ShirtColor {
        let mut num_red = 0;
        let mut num_blue = 0;

        for color in &self.shirts {
            match color {
                ShirtColor::Red => num_red += 1,
                ShirtColor::Blue => num_blue += 1,
            }
        }
        if num_red > num_blue {
            ShirtColor::Red
        } else {
            ShirtColor::Blue
        }
    }
}

fn main() {
    let store = Inventory {
        shirts: vec![ShirtColor::Blue, ShirtColor::Red, ShirtColor::Blue],
    };

    let user_pref1 = Some(ShirtColor::Red);
    let giveaway1 = store.giveaway(user_pref1);
    println!(
        "The user with preference {:?} gets {:?}",
        user_pref1, giveaway1
    );

    let user_pref2 = None;
    let giveaway2 = store.giveaway(user_pref2);
    println!(
        "The user with preference {:?} gets {:?}",
        user_pref2, giveaway2
    );
}

Listing 13-1: Shirt company giveaway situation

The store defined in main has two blue shirts and one red shirt remaining to distribute for this limited-edition promotion. We call the giveaway method for a user with a preference for a red shirt and a user without any preference.

Again, this code could be implemented in many ways, and here, to focus on closures, we’ve stuck to concepts you’ve already learned except for the body of the giveaway method that uses a closure. In the giveaway method, we get the user preference as a parameter of type Option<ShirtColor> and call the unwrap_or_else method on user_preference. The unwrap_or_else method on Option<T> is defined by the standard library. It takes one argument: a closure without any arguments that returns a value T (the same type stored in the Some variant of the Option<T>, in this case ShirtColor). If the Option<T> is the Some variant, unwrap_or_else returns the value from within the Some. If the Option<T> is the None variant, unwrap_or_else calls the closure and returns the value returned by the closure.

We specify the closure expression || self.most_stocked() as the argument to unwrap_or_else. This is a closure that takes no parameters itself (if the closure had parameters, they would appear between the two vertical bars). The body of the closure calls self.most_stocked(). We’re defining the closure here, and the implementation of unwrap_or_else will evaluate the closure later if the result is needed.

Running this code prints:

$ cargo run
   Compiling shirt-company v0.1.0 (file:///projects/shirt-company)
    Finished dev [unoptimized + debuginfo] target(s) in 0.27s
     Running `target/debug/shirt-company`
The user with preference Some(Red) gets Red
The user with preference None gets Blue

One interesting aspect here is that we’ve passed a closure that calls self.most_stocked() on the current Inventory instance. The standard library didn’t need to know anything about the Inventory or ShirtColor types we defined, or the logic we want to use in this scenario. The closure captures an immutable reference to the self Inventory instance and passes it with the code we specify to the unwrap_or_else method. Functions, on the other hand, are not able to capture their environment in this way.

Closure Type Inference and Annotation

There are more differences between functions and closures. Closures don’t usually require you to annotate the types of the parameters or the return value like fn functions do. Type annotations are required on functions because the types are part of an explicit interface exposed to your users. Defining this interface rigidly is important for ensuring that everyone agrees on what types of values a function uses and returns. Closures, on the other hand, aren’t used in an exposed interface like this: they’re stored in variables and used without naming them and exposing them to users of our library.

Closures are typically short and relevant only within a narrow context rather than in any arbitrary scenario. Within these limited contexts, the compiler can infer the types of the parameters and the return type, similar to how it’s able to infer the types of most variables (there are rare cases where the compiler needs closure type annotations too).

As with variables, we can add type annotations if we want to increase explicitness and clarity at the cost of being more verbose than is strictly necessary. Annotating the types for a closure would look like the definition shown in Listing 13-2. In this example, we’re defining a closure and storing it in a variable rather than defining the closure in the spot we pass it as an argument as we did in Listing 13-1.

Filename: src/main.rs

use std::thread;
use std::time::Duration;

fn generate_workout(intensity: u32, random_number: u32) {
    let expensive_closure = |num: u32| -> u32 {
        println!("calculating slowly...");
        thread::sleep(Duration::from_secs(2));
        num
    };

    if intensity < 25 {
        println!("Today, do {} pushups!", expensive_closure(intensity));
        println!("Next, do {} situps!", expensive_closure(intensity));
    } else {
        if random_number == 3 {
            println!("Take a break today! Remember to stay hydrated!");
        } else {
            println!(
                "Today, run for {} minutes!",
                expensive_closure(intensity)
            );
        }
    }
}

fn main() {
    let simulated_user_specified_value = 10;
    let simulated_random_number = 7;

    generate_workout(simulated_user_specified_value, simulated_random_number);
}

Listing 13-2: Adding optional type annotations of the parameter and return value types in the closure

With type annotations added, the syntax of closures looks more similar to the syntax of functions. Here we define a function that adds 1 to its parameter and a closure that has the same behavior, for comparison. We’ve added some spaces to line up the relevant parts. This illustrates how closure syntax is similar to function syntax except for the use of pipes and the amount of syntax that is optional:

fn  add_one_v1   (x: u32) -> u32 { x + 1 }
let add_one_v2 = |x: u32| -> u32 { x + 1 };
let add_one_v3 = |x|             { x + 1 };
let add_one_v4 = |x|               x + 1  ;

The first line shows a function definition, and the second line shows a fully annotated closure definition. In the third line, we remove the type annotations from the closure definition. In the fourth line, we remove the brackets, which are optional because the closure body has only one expression. These are all valid definitions that will produce the same behavior when they’re called. Evaluating the closures is required for add_one_v3 and add_one_v4 to be able to compile because the types will be inferred from their usage. This is similar to let v = Vec::new(); needing either type annotations or values of some type to be inserted into the Vec for Rust to be able to infer the type.

For closure definitions, the compiler will infer one concrete type for each of their parameters and for their return value. For instance, Listing 13-3 shows the definition of a short closure that just returns the value it receives as a parameter. This closure isn’t very useful except for the purposes of this example. Note that we haven’t added any type annotations to the definition. Because there are no type annotations, we can call the closure with any type, which we’ve done here with String the first time. If we then try to call example_closure with an integer, we’ll get an error.

Filename: src/main.rs

fn main() {
    let example_closure = |x| x;

    let s = example_closure(String::from("hello"));
    let n = example_closure(5);
}

Listing 13-3: Attempting to call a closure whose types are inferred with two different types

The compiler gives us this error:

$ cargo run
   Compiling closure-example v0.1.0 (file:///projects/closure-example)
error[E0308]: mismatched types
 --> src/main.rs:5:29
  |
5 |     let n = example_closure(5);
  |                             ^- help: try using a conversion method: `.to_string()`
  |                             |
  |                             expected struct `String`, found integer

For more information about this error, try `rustc --explain E0308`.
error: could not compile `closure-example` due to previous error

The first time we call example_closure with the String value, the compiler infers the type of x and the return type of the closure to be String. Those types are then locked into the closure in example_closure, and we get a type error when we next try to use a different type with the same closure.

Capturing References or Moving Ownership

Closures can capture values from their environment in three ways, which directly map to the three ways a function can take a parameter: borrowing immutably, borrowing mutably, and taking ownership. The closure will decide which of these to use based on what the body of the function does with the captured values.

In Listing 13-4, we define a closure that captures an immutable reference to the vector named list because it only needs an immutable reference to print the value:

Filename: src/main.rs

fn main() {
    let list = vec![1, 2, 3];
    println!("Before defining closure: {:?}", list);

    let only_borrows = || println!("From closure: {:?}", list);

    println!("Before calling closure: {:?}", list);
    only_borrows();
    println!("After calling closure: {:?}", list);
}

Listing 13-4: Defining and calling a closure that captures an immutable reference

This example also illustrates that a variable can bind to a closure definition, and we can later call the closure by using the variable name and parentheses as if the variable name were a function name.

Because we can have multiple immutable references to list at the same time, list is still accessible from the code before the closure definition, after the closure definition but before the closure is called, and after the closure is called. This code compiles, runs, and prints:

$ cargo run
   Compiling closure-example v0.1.0 (file:///projects/closure-example)
    Finished dev [unoptimized + debuginfo] target(s) in 0.43s
     Running `target/debug/closure-example`
Before defining closure: [1, 2, 3]
Before calling closure: [1, 2, 3]
From closure: [1, 2, 3]
After calling closure: [1, 2, 3]

Next, in Listing 13-5, we change the closure body so that it adds an element to the list vector. The closure now captures a mutable reference:

Filename: src/main.rs

fn main() {
    let mut list = vec![1, 2, 3];
    println!("Before defining closure: {:?}", list);

    let mut borrows_mutably = || list.push(7);

    borrows_mutably();
    println!("After calling closure: {:?}", list);
}

Listing 13-5: Defining and calling a closure that captures a mutable reference

This code compiles, runs, and prints:

$ cargo run
   Compiling closure-example v0.1.0 (file:///projects/closure-example)
    Finished dev [unoptimized + debuginfo] target(s) in 0.43s
     Running `target/debug/closure-example`
Before defining closure: [1, 2, 3]
After calling closure: [1, 2, 3, 7]

Note that there’s no longer a println! between the definition and the call of the borrows_mutably closure: when borrows_mutably is defined, it captures a mutable reference to list. We don’t use the closure again after the closure is called, so the mutable borrow ends. Between the closure definition and the closure call, an immutable borrow to print isn’t allowed because no other borrows are allowed when there’s a mutable borrow. Try adding a println! there to see what error message you get!

If you want to force the closure to take ownership of the values it uses in the environment even though the body of the closure doesn’t strictly need ownership, you can use the move keyword before the parameter list. This technique is mostly useful when passing a closure to a new thread to move the data so that it’s owned by the new thread. We’ll have more examples of move closures in Chapter 16 when we talk about concurrency.

Moving Captured Values Out of Closures and the Fn Traits

Once a closure has captured a reference or captured ownership of a value where the closure is defined (thus affecting what, if anything, is moved into the closure), the code in the body of the closure defines what happens to the references or values when the closure is evaluated later (thus affecting what, if anything, is moved out of the closure). A closure body can do any of the following: move a captured value out of the closure, mutate the captured value, neither move nor mutate the value, or capture nothing from the environment to begin with.

The way a closure captures and handles values from the environment affects which traits the closure implements, and traits are how functions and structs can specify what kinds of closures they can use. Closures will automatically implement one, two, or all three of these Fn traits, in an additive fashion:

  1. FnOnce applies to closures that can be called at least once. All closures implement at least this trait, because all closures can be called. A closure that moves captured values out of its body will only implement FnOnce and none of the other Fn traits, because it can only be called once.
  2. FnMut applies to closures that don’t move captured values out of their body, but that might mutate the captured values. These closures can be called more than once.
  3. Fn applies to closures that don’t move captured values out of their body and that don’t mutate captured values, as well as closures that capture nothing from their environment. These closures can be called more than once without mutating their environment, which is important in cases such as calling a closure multiple times concurrently.

Let’s look at the definition of the unwrap_or_else method on Option<T> that we used in Listing 13-6:

impl<T> Option<T> {
    pub fn unwrap_or_else<F>(self, f: F) -> T
    where
        F: FnOnce() -> T
    {
        match self {
            Some(x) => x,
            None => f(),
        }
    }
}

Recall that T is the generic type representing the type of the value in the Some variant of an Option. That type T is also the return type of the unwrap_or_else function: code that calls unwrap_or_else on an Option<String>, for example, will get a String.

Next, notice that the unwrap_or_else function has the additional generic type parameter F. The F type is the type of the parameter named f, which is the closure we provide when calling unwrap_or_else.

The trait bound specified on the generic type F is FnOnce() -> T, which means F must be able to be called at least once, take no arguments, and return a T. Using FnOnce in the trait bound expresses the constraint that unwrap_or_else is only going to call f at most one time. In the body of unwrap_or_else, we can see that if the Option is Some, f won’t be called. If the Option is None, f will be called once. Because all closures implement FnOnce, unwrap_or_else accepts the most different kinds of closures and is as flexible as it can be.

Note: Functions can implement all three of the Fn traits too. If what we want to do doesn’t require capturing a value from the environment, we can use the name of a function rather than a closure where we need something that implements one of the Fn traits. For example, on an Option<Vec<T>> value, we could call unwrap_or_else(Vec::new) to get a new, empty vector if the value is None.

Now let’s look at the standard library method sort_by_key defined on slices, to see how that differs from unwrap_or_else and why sort_by_key uses FnMut instead of FnOnce for the trait bound.

The closure gets one argument, a reference to the current item in the slice being considered, and returns a value of type K that can be ordered. This function is useful when you want to sort a slice by a particular attribute of each item. In Listing 13-x, we have a list of Rectangle instances and we use sort_by_key to order them by their width attribute from low to high:

Filename: src/main.rs

#[derive(Debug)]
struct Rectangle {
    width: u32,
    height: u32,
}

fn main() {
    let mut list = [
        Rectangle { width: 10, height: 1 },
        Rectangle { width: 3, height: 5 },
        Rectangle { width: 7, height: 12 },
    ];

    list.sort_by_key(|r| r.width);
    println!("{:#?}", list);
}

Listing 13-7: Using sort_by_key to order rectangles by width

This code prints:

$ cargo run
   Compiling rectangles v0.1.0 (file:///projects/rectangles)
    Finished dev [unoptimized + debuginfo] target(s) in 0.41s
     Running `target/debug/rectangles`
[
    Rectangle {
        width: 3,
        height: 5,
    },
    Rectangle {
        width: 7,
        height: 12,
    },
    Rectangle {
        width: 10,
        height: 1,
    },
]

The reason sort_by_key is defined to take an FnMut closure is that it calls the closure multiple times: once for each item in the slice. The closure |r| r.width doesn’t capture, mutate, or move out anything from its environment, so it meets the trait bound requirements.

In contrast, Listing 13-8 shows an example of a closure that implements just the FnOnce trait, because it moves a value out of the environment. The compiler won’t let us use this closure with sort_by_key:

Filename: src/main.rs

#[derive(Debug)]
struct Rectangle {
    width: u32,
    height: u32,
}

fn main() {
    let mut list = [
        Rectangle { width: 10, height: 1 },
        Rectangle { width: 3, height: 5 },
        Rectangle { width: 7, height: 12 },
    ];

    let mut sort_operations = vec![];
    let value = String::from("by key called");

    list.sort_by_key(|r| {
        sort_operations.push(value);
        r.width
    });
    println!("{:#?}", list);
}

Listing 13-8: Attempting to use an FnOnce closure with sort_by_key

This is a contrived, convoluted way (that doesn’t work) to try and count the number of times sort_by_key gets called when sorting list. This code attempts to do this counting by pushing value—a String from the closure’s environment—into the sort_operations vector. The closure captures value then moves value out of the closure by transferring ownership of value to the sort_operations vector. This closure can be called once; trying to call it a second time wouldn’t work because value would no longer be in the environment to be pushed into sort_operations again! Therefore, this closure only implements FnOnce. When we try to compile this code, we get this error that value can’t be moved out of the closure because the closure must implement FnMut:

$ cargo run
   Compiling rectangles v0.1.0 (file:///projects/rectangles)
error[E0507]: cannot move out of `value`, a captured variable in an `FnMut` closure
  --> src/main.rs:27:30
   |
24 |       let value = String::from("by key called");
   |           ----- captured outer variable
25 | 
26 |       list.sort_by_key(|r| {
   |  ______________________-
27 | |         sort_operations.push(value);
   | |                              ^^^^^ move occurs because `value` has type `String`, which does not implement the `Copy` trait
28 | |         r.width
29 | |     });
   | |_____- captured by this `FnMut` closure

For more information about this error, try `rustc --explain E0507`.
error: could not compile `rectangles` due to previous error

The error points to the line in the closure body that moves value out of the environment. To fix this, we need to change the closure body so that it doesn’t move values out of the environment. To count the number of times sort_by_key is called, keeping a counter in the environment and incrementing its value in the closure body is a more straightforward way to calculate that. The closure in Listing 13-x works with sort_by_key because it is only capturing a mutable reference to the num_sort_operations counter and can therefore be called more than once:

Filename: src/main.rs

#[derive(Debug)]
struct Rectangle {
    width: u32,
    height: u32,
}

fn main() {
    let mut list = [
        Rectangle { width: 10, height: 1 },
        Rectangle { width: 3, height: 5 },
        Rectangle { width: 7, height: 12 },
    ];

    let mut num_sort_operations = 0;
    list.sort_by_key(|r| {
        num_sort_operations += 1;
        r.width
    });
    println!("{:#?}, sorted in {num_sort_operations} operations", list);
}

Listing 13-9: Using an FnMut closure with sort_by_key is allowed

The Fn traits are important when defining or using functions or types that make use of closures. In the next section, we’ll discuss iterators. Many iterator methods take closure arguments, so keep these closure details in mind as we continue!

Processing a Series of Items with Iterators

The iterator pattern allows you to perform some task on a sequence of items in turn. An iterator is responsible for the logic of iterating over each item and determining when the sequence has finished. When you use iterators, you don’t have to reimplement that logic yourself.

In Rust, iterators are lazy, meaning they have no effect until you call methods that consume the iterator to use it up. For example, the code in Listing 13-10 creates an iterator over the items in the vector v1 by calling the iter method defined on Vec<T>. This code by itself doesn’t do anything useful.

fn main() {
    let v1 = vec![1, 2, 3];

    let v1_iter = v1.iter();
}

Listing 13-10: Creating an iterator

The iterator is stored in the v1_iter variable. Once we’ve created an iterator, we can use it in a variety of ways. In Listing 3-5 in Chapter 3, we iterated over an array using a for loop to execute some code on each of its items. Under the hood this implicitly created and then consumed an iterator, but we glossed over how exactly that works until now.

In the example in Listing 13-11, we separate the creation of the iterator from the use of the iterator in the for loop. When the for loop is called using the iterator in v1_iter, each element in the iterator is used in one iteration of the loop, which prints out each value.

fn main() {
    let v1 = vec![1, 2, 3];

    let v1_iter = v1.iter();

    for val in v1_iter {
        println!("Got: {}", val);
    }
}

Listing 13-11: Using an iterator in a for loop

In languages that don’t have iterators provided by their standard libraries, you would likely write this same functionality by starting a variable at index 0, using that variable to index into the vector to get a value, and incrementing the variable value in a loop until it reached the total number of items in the vector.

Iterators handle all that logic for you, cutting down on repetitive code you could potentially mess up. Iterators give you more flexibility to use the same logic with many different kinds of sequences, not just data structures you can index into, like vectors. Let’s examine how iterators do that.

The Iterator Trait and the next Method

All iterators implement a trait named Iterator that is defined in the standard library. The definition of the trait looks like this:


#![allow(unused)]
fn main() {
pub trait Iterator {
    type Item;

    fn next(&mut self) -> Option<Self::Item>;

    // methods with default implementations elided
}
}

Notice this definition uses some new syntax: type Item and Self::Item, which are defining an associated type with this trait. We’ll talk about associated types in depth in Chapter 19. For now, all you need to know is that this code says implementing the Iterator trait requires that you also define an Item type, and this Item type is used in the return type of the next method. In other words, the Item type will be the type returned from the iterator.

The Iterator trait only requires implementors to define one method: the next method, which returns one item of the iterator at a time wrapped in Some and, when iteration is over, returns None.

We can call the next method on iterators directly; Listing 13-12 demonstrates what values are returned from repeated calls to next on the iterator created from the vector.

Filename: src/lib.rs

#[cfg(test)]
mod tests {
    #[test]
    fn iterator_demonstration() {
        let v1 = vec![1, 2, 3];

        let mut v1_iter = v1.iter();

        assert_eq!(v1_iter.next(), Some(&1));
        assert_eq!(v1_iter.next(), Some(&2));
        assert_eq!(v1_iter.next(), Some(&3));
        assert_eq!(v1_iter.next(), None);
    }
}

Listing 13-12: Calling the next method on an iterator

Note that we needed to make v1_iter mutable: calling the next method on an iterator changes internal state that the iterator uses to keep track of where it is in the sequence. In other words, this code consumes, or uses up, the iterator. Each call to next eats up an item from the iterator. We didn’t need to make v1_iter mutable when we used a for loop because the loop took ownership of v1_iter and made it mutable behind the scenes.

Also note that the values we get from the calls to next are immutable references to the values in the vector. The iter method produces an iterator over immutable references. If we want to create an iterator that takes ownership of v1 and returns owned values, we can call into_iter instead of iter. Similarly, if we want to iterate over mutable references, we can call iter_mut instead of iter.

Methods that Consume the Iterator

The Iterator trait has a number of different methods with default implementations provided by the standard library; you can find out about these methods by looking in the standard library API documentation for the Iterator trait. Some of these methods call the next method in their definition, which is why you’re required to implement the next method when implementing the Iterator trait.

Methods that call next are called consuming adaptors, because calling them uses up the iterator. One example is the sum method, which takes ownership of the iterator and iterates through the items by repeatedly calling next, thus consuming the iterator. As it iterates through, it adds each item to a running total and returns the total when iteration is complete. Listing 13-13 has a test illustrating a use of the sum method:

Filename: src/lib.rs

#[cfg(test)]
mod tests {
    #[test]
    fn iterator_sum() {
        let v1 = vec![1, 2, 3];

        let v1_iter = v1.iter();

        let total: i32 = v1_iter.sum();

        assert_eq!(total, 6);
    }
}

Listing 13-13: Calling the sum method to get the total of all items in the iterator

We aren’t allowed to use v1_iter after the call to sum because sum takes ownership of the iterator we call it on.

Methods that Produce Other Iterators

Iterator adaptors are methods defined on the Iterator trait that don’t consume the iterator. Instead, they produce different iterators by changing some aspect of the original iterator.

Listing 13-17 shows an example of calling the iterator adaptor method map, which takes a closure to call on each item as the items are iterated through. The map method returns a new iterator that produces the modified items. The closure here creates a new iterator in which each item from the vector will be incremented by 1:

Filename: src/main.rs

fn main() {
    let v1: Vec<i32> = vec![1, 2, 3];

    v1.iter().map(|x| x + 1);
}

Listing 13-14: Calling the iterator adaptor map to create a new iterator

However, this code produces a warning:

$ cargo run
   Compiling iterators v0.1.0 (file:///projects/iterators)
warning: unused `Map` that must be used
 --> src/main.rs:4:5
  |
4 |     v1.iter().map(|x| x + 1);
  |     ^^^^^^^^^^^^^^^^^^^^^^^^^
  |
  = note: `#[warn(unused_must_use)]` on by default
  = note: iterators are lazy and do nothing unless consumed

warning: `iterators` (bin "iterators") generated 1 warning
    Finished dev [unoptimized + debuginfo] target(s) in 0.47s
     Running `target/debug/iterators`

The code in Listing 13-14 doesn’t do anything; the closure we’ve specified never gets called. The warning reminds us why: iterator adaptors are lazy, and we need to consume the iterator here.

To fix this warning and consume the iterator, we’ll use the collect method, which we used in Chapter 12 with env::args in Listing 12-1. This method consumes the iterator and collects the resulting values into a collection data type.

In Listing 13-15, we collect the results of iterating over the iterator that’s returned from the call to map into a vector. This vector will end up containing each item from the original vector incremented by 1.

Filename: src/main.rs

fn main() {
    let v1: Vec<i32> = vec![1, 2, 3];

    let v2: Vec<_> = v1.iter().map(|x| x + 1).collect();

    assert_eq!(v2, vec![2, 3, 4]);
}

Listing 13-15: Calling the map method to create a new iterator and then calling the collect method to consume the new iterator and create a vector

Because map takes a closure, we can specify any operation we want to perform on each item. This is a great example of how closures let you customize some behavior while reusing the iteration behavior that the Iterator trait provides.

You can chain multiple calls to iterator adaptors to perform complex actions in a readable way. But because all iterators are lazy, you have to call one of the consuming adaptor methods to get results from calls to iterator adaptors.

Using Closures that Capture Their Environment

Many iterator adapters take closures as arguments, and commonly the closures we’ll specify as arguments to iterator adapters will be closures that capture their environment. For this example, we’ll use the filter method that takes a closure. The closure gets an item from the iterator and returns a Boolean. If the closure returns true, the value will be included in the iteration produced by filter. If the closure returns false, the value won’t be included.

In Listing 13-16, we use filter with a closure that captures the shoe_size variable from its environment to iterate over a collection of Shoe struct instances. It will return only shoes that are the specified size.

Filename: src/lib.rs

#[derive(PartialEq, Debug)]
struct Shoe {
    size: u32,
    style: String,
}

fn shoes_in_size(shoes: Vec<Shoe>, shoe_size: u32) -> Vec<Shoe> {
    shoes.into_iter().filter(|s| s.size == shoe_size).collect()
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn filters_by_size() {
        let shoes = vec![
            Shoe {
                size: 10,
                style: String::from("sneaker"),
            },
            Shoe {
                size: 13,
                style: String::from("sandal"),
            },
            Shoe {
                size: 10,
                style: String::from("boot"),
            },
        ];

        let in_my_size = shoes_in_size(shoes, 10);

        assert_eq!(
            in_my_size,
            vec![
                Shoe {
                    size: 10,
                    style: String::from("sneaker")
                },
                Shoe {
                    size: 10,
                    style: String::from("boot")
                },
            ]
        );
    }
}

Listing 13-16: Using the filter method with a closure that captures shoe_size

The shoes_in_size function takes ownership of a vector of shoes and a shoe size as parameters. It returns a vector containing only shoes of the specified size.

In the body of shoes_in_size, we call into_iter to create an iterator that takes ownership of the vector. Then we call filter to adapt that iterator into a new iterator that only contains elements for which the closure returns true.

The closure captures the shoe_size parameter from the environment and compares the value with each shoe’s size, keeping only shoes of the size specified. Finally, calling collect gathers the values returned by the adapted iterator into a vector that’s returned by the function.

The test shows that when we call shoes_in_size, we get back only shoes that have the same size as the value we specified.

Improving Our I/O Project

With this new knowledge about iterators, we can improve the I/O project in Chapter 12 by using iterators to make places in the code clearer and more concise. Let’s look at how iterators can improve our implementation of the Config::build function and the search function.

Removing a clone Using an Iterator

In Listing 12-6, we added code that took a slice of String values and created an instance of the Config struct by indexing into the slice and cloning the values, allowing the Config struct to own those values. In Listing 13-17, we’ve reproduced the implementation of the Config::build function as it was in Listing 12-23:

Filename: src/lib.rs

use std::env;
use std::error::Error;
use std::fs;

pub struct Config {
    pub query: String,
    pub file_path: String,
    pub ignore_case: bool,
}

impl Config {
    pub fn build(args: &[String]) -> Result<Config, &'static str> {
        if args.len() < 3 {
            return Err("not enough arguments");
        }

        let query = args[1].clone();
        let file_path = args[2].clone();

        let ignore_case = env::var("IGNORE_CASE").is_ok();

        Ok(Config {
            query,
            file_path,
            ignore_case,
        })
    }
}

pub fn run(config: Config) -> Result<(), Box<dyn Error>> {
    let contents = fs::read_to_string(config.file_path)?;

    let results = if config.ignore_case {
        search_case_insensitive(&config.query, &contents)
    } else {
        search(&config.query, &contents)
    };

    for line in results {
        println!("{line}");
    }

    Ok(())
}

pub fn search<'a>(query: &str, contents: &'a str) -> Vec<&'a str> {
    let mut results = Vec::new();

    for line in contents.lines() {
        if line.contains(query) {
            results.push(line);
        }
    }

    results
}

pub fn search_case_insensitive<'a>(
    query: &str,
    contents: &'a str,
) -> Vec<&'a str> {
    let query = query.to_lowercase();
    let mut results = Vec::new();

    for line in contents.lines() {
        if line.to_lowercase().contains(&query) {
            results.push(line);
        }
    }

    results
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn case_sensitive() {
        let query = "duct";
        let contents = "\
Rust:
safe, fast, productive.
Pick three.
Duct tape.";

        assert_eq!(vec!["safe, fast, productive."], search(query, contents));
    }

    #[test]
    fn case_insensitive() {
        let query = "rUsT";
        let contents = "\
Rust:
safe, fast, productive.
Pick three.
Trust me.";

        assert_eq!(
            vec!["Rust:", "Trust me."],
            search_case_insensitive(query, contents)
        );
    }
}

Listing 13-17: Reproduction of the Config::build function from Listing 12-23

At the time, we said not to worry about the inefficient clone calls because we would remove them in the future. Well, that time is now!

We needed clone here because we have a slice with String elements in the parameter args, but the build function doesn’t own args. To return ownership of a Config instance, we had to clone the values from the query and filename fields of Config so the Config instance can own its values.

With our new knowledge about iterators, we can change the build function to take ownership of an iterator as its argument instead of borrowing a slice. We’ll use the iterator functionality instead of the code that checks the length of the slice and indexes into specific locations. This will clarify what the Config::build function is doing because the iterator will access the values.

Once Config::build takes ownership of the iterator and stops using indexing operations that borrow, we can move the String values from the iterator into Config rather than calling clone and making a new allocation.

Using the Returned Iterator Directly

Open your I/O project’s src/main.rs file, which should look like this:

Filename: src/main.rs

use std::env;
use std::process;

use minigrep::Config;

fn main() {
    let args: Vec<String> = env::args().collect();

    let config = Config::build(&args).unwrap_or_else(|err| {
        eprintln!("Problem parsing arguments: {err}");
        process::exit(1);
    });

    // --snip--

    if let Err(e) = minigrep::run(config) {
        eprintln!("Application error: {e}");

        process::exit(1);
    }
}

We’ll first change the start of the main function that we had in Listing 12-24 to the code in Listing 13-18, which this time uses an iterator. This won’t compile until we update Config::build as well.

Filename: src/main.rs

use std::env;
use std::process;

use minigrep::Config;

fn main() {
    let config = Config::build(env::args()).unwrap_or_else(|err| {
        eprintln!("Problem parsing arguments: {err}");
        process::exit(1);
    });

    // --snip--

    if let Err(e) = minigrep::run(config) {
        eprintln!("Application error: {e}");

        process::exit(1);
    }
}

Listing 13-18: Passing the return value of env::args to Config::build

The env::args function returns an iterator! Rather than collecting the iterator values into a vector and then passing a slice to Config::build, now we’re passing ownership of the iterator returned from env::args to Config::build directly.

Next, we need to update the definition of Config::build. In your I/O project’s src/lib.rs file, let’s change the signature of Config::build to look like Listing 13-19. This still won’t compile because we need to update the function body.

Filename: src/lib.rs

use std::env;
use std::error::Error;
use std::fs;

pub struct Config {
    pub query: String,
    pub file_path: String,
    pub ignore_case: bool,
}

impl Config {
    pub fn build(
        mut args: impl Iterator<Item = String>,
    ) -> Result<Config, &'static str> {
        // --snip--
        if args.len() < 3 {
            return Err("not enough arguments");
        }

        let query = args[1].clone();
        let file_path = args[2].clone();

        let ignore_case = env::var("IGNORE_CASE").is_ok();

        Ok(Config {
            query,
            file_path,
            ignore_case,
        })
    }
}

pub fn run(config: Config) -> Result<(), Box<dyn Error>> {
    let contents = fs::read_to_string(config.file_path)?;

    let results = if config.ignore_case {
        search_case_insensitive(&config.query, &contents)
    } else {
        search(&config.query, &contents)
    };

    for line in results {
        println!("{line}");
    }

    Ok(())
}

pub fn search<'a>(query: &str, contents: &'a str) -> Vec<&'a str> {
    let mut results = Vec::new();

    for line in contents.lines() {
        if line.contains(query) {
            results.push(line);
        }
    }

    results
}

pub fn search_case_insensitive<'a>(
    query: &str,
    contents: &'a str,
) -> Vec<&'a str> {
    let query = query.to_lowercase();
    let mut results = Vec::new();

    for line in contents.lines() {
        if line.to_lowercase().contains(&query) {
            results.push(line);
        }
    }

    results
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn case_sensitive() {
        let query = "duct";
        let contents = "\
Rust:
safe, fast, productive.
Pick three.
Duct tape.";

        assert_eq!(vec!["safe, fast, productive."], search(query, contents));
    }

    #[test]
    fn case_insensitive() {
        let query = "rUsT";
        let contents = "\
Rust:
safe, fast, productive.
Pick three.
Trust me.";

        assert_eq!(
            vec!["Rust:", "Trust me."],
            search_case_insensitive(query, contents)
        );
    }
}

Listing 13-19: Updating the signature of Config::build to expect an iterator

The standard library documentation for the env::args function shows that the type of the iterator it returns is std::env::Args, and that type implements the Iterator trait and returns String values.

We’ve updated the signature of the Config::build function so the parameter args has a generic type with the trait bounds impl Iterator<Item = String> instead of &[String]. This usage of the impl Trait syntax we discussed in the “Traits as Parameters” section of Chapter 10 means that args can be any type that implements the Iterator type and returns String items.

Because we’re taking ownership of args and we’ll be mutating args by iterating over it, we can add the mut keyword into the specification of the args parameter to make it mutable.

Using Iterator Trait Methods Instead of Indexing

Next, we’ll fix the body of Config::build. Because args implements the Iterator trait, we know we can call the next method on it! Listing 13-20 updates the code from Listing 12-23 to use the next method:

Filename: src/lib.rs

use std::env;
use std::error::Error;
use std::fs;

pub struct Config {
    pub query: String,
    pub file_path: String,
    pub ignore_case: bool,
}

impl Config {
    pub fn build(
        mut args: impl Iterator<Item = String>,
    ) -> Result<Config, &'static str> {
        args.next();

        let query = match args.next() {
            Some(arg) => arg,
            None => return Err("Didn't get a query string"),
        };

        let file_path = match args.next() {
            Some(arg) => arg,
            None => return Err("Didn't get a file path"),
        };

        let ignore_case = env::var("IGNORE_CASE").is_ok();

        Ok(Config {
            query,
            file_path,
            ignore_case,
        })
    }
}

pub fn run(config: Config) -> Result<(), Box<dyn Error>> {
    let contents = fs::read_to_string(config.file_path)?;

    let results = if config.ignore_case {
        search_case_insensitive(&config.query, &contents)
    } else {
        search(&config.query, &contents)
    };

    for line in results {
        println!("{line}");
    }

    Ok(())
}

pub fn search<'a>(query: &str, contents: &'a str) -> Vec<&'a str> {
    let mut results = Vec::new();

    for line in contents.lines() {
        if line.contains(query) {
            results.push(line);
        }
    }

    results
}

pub fn search_case_insensitive<'a>(
    query: &str,
    contents: &'a str,
) -> Vec<&'a str> {
    let query = query.to_lowercase();
    let mut results = Vec::new();

    for line in contents.lines() {
        if line.to_lowercase().contains(&query) {
            results.push(line);
        }
    }

    results
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn case_sensitive() {
        let query = "duct";
        let contents = "\
Rust:
safe, fast, productive.
Pick three.
Duct tape.";

        assert_eq!(vec!["safe, fast, productive."], search(query, contents));
    }

    #[test]
    fn case_insensitive() {
        let query = "rUsT";
        let contents = "\
Rust:
safe, fast, productive.
Pick three.
Trust me.";

        assert_eq!(
            vec!["Rust:", "Trust me."],
            search_case_insensitive(query, contents)
        );
    }
}

Listing 13-20: Changing the body of Config::build to use iterator methods

Remember that the first value in the return value of env::args is the name of the program. We want to ignore that and get to the next value, so first we call next and do nothing with the return value. Second, we call next to get the value we want to put in the query field of Config. If next returns a Some, we use a match to extract the value. If it returns None, it means not enough arguments were given and we return early with an Err value. We do the same thing for the filename value.

Making Code Clearer with Iterator Adaptors

We can also take advantage of iterators in the search function in our I/O project, which is reproduced here in Listing 13-21 as it was in Listing 12-19:

Filename: src/lib.rs

use std::error::Error;
use std::fs;

pub struct Config {
    pub query: String,
    pub file_path: String,
}

impl Config {
    pub fn build(args: &[String]) -> Result<Config, &'static str> {
        if args.len() < 3 {
            return Err("not enough arguments");
        }

        let query = args[1].clone();
        let file_path = args[2].clone();

        Ok(Config { query, file_path })
    }
}

pub fn run(config: Config) -> Result<(), Box<dyn Error>> {
    let contents = fs::read_to_string(config.file_path)?;

    Ok(())
}

pub fn search<'a>(query: &str, contents: &'a str) -> Vec<&'a str> {
    let mut results = Vec::new();

    for line in contents.lines() {
        if line.contains(query) {
            results.push(line);
        }
    }

    results
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn one_result() {
        let query = "duct";
        let contents = "\
Rust:
safe, fast, productive.
Pick three.";

        assert_eq!(vec!["safe, fast, productive."], search(query, contents));
    }
}

Listing 13-21: The implementation of the search function from Listing 12-19

We can write this code in a more concise way using iterator adaptor methods. Doing so also lets us avoid having a mutable intermediate results vector. The functional programming style prefers to minimize the amount of mutable state to make code clearer. Removing the mutable state might enable a future enhancement to make searching happen in parallel, because we wouldn’t have to manage concurrent access to the results vector. Listing 13-22 shows this change:

Filename: src/lib.rs

use std::env;
use std::error::Error;
use std::fs;

pub struct Config {
    pub query: String,
    pub file_path: String,
    pub ignore_case: bool,
}

impl Config {
    pub fn build(
        mut args: impl Iterator<Item = String>,
    ) -> Result<Config, &'static str> {
        args.next();

        let query = match args.next() {
            Some(arg) => arg,
            None => return Err("Didn't get a query string"),
        };

        let file_path = match args.next() {
            Some(arg) => arg,
            None => return Err("Didn't get a file path"),
        };

        let ignore_case = env::var("IGNORE_CASE").is_ok();

        Ok(Config {
            query,
            file_path,
            ignore_case,
        })
    }
}

pub fn run(config: Config) -> Result<(), Box<dyn Error>> {
    let contents = fs::read_to_string(config.file_path)?;

    let results = if config.ignore_case {
        search_case_insensitive(&config.query, &contents)
    } else {
        search(&config.query, &contents)
    };

    for line in results {
        println!("{line}");
    }

    Ok(())
}

pub fn search<'a>(query: &str, contents: &'a str) -> Vec<&'a str> {
    contents
        .lines()
        .filter(|line| line.contains(query))
        .collect()
}

pub fn search_case_insensitive<'a>(
    query: &str,
    contents: &'a str,
) -> Vec<&'a str> {
    let query = query.to_lowercase();
    let mut results = Vec::new();

    for line in contents.lines() {
        if line.to_lowercase().contains(&query) {
            results.push(line);
        }
    }

    results
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn case_sensitive() {
        let query = "duct";
        let contents = "\
Rust:
safe, fast, productive.
Pick three.
Duct tape.";

        assert_eq!(vec!["safe, fast, productive."], search(query, contents));
    }

    #[test]
    fn case_insensitive() {
        let query = "rUsT";
        let contents = "\
Rust:
safe, fast, productive.
Pick three.
Trust me.";

        assert_eq!(
            vec!["Rust:", "Trust me."],
            search_case_insensitive(query, contents)
        );
    }
}

Listing 13-22: Using iterator adaptor methods in the implementation of the search function

Recall that the purpose of the search function is to return all lines in contents that contain the query. Similar to the filter example in Listing 13-16, this code uses the filter adaptor to keep only the lines that line.contains(query) returns true for. We then collect the matching lines into another vector with collect. Much simpler! Feel free to make the same change to use iterator methods in the search_case_insensitive function as well.

Choosing Between Loops or Iterators

The next logical question is which style you should choose in your own code and why: the original implementation in Listing 13-21 or the version using iterators in Listing 13-22. Most Rust programmers prefer to use the iterator style. It’s a bit tougher to get the hang of at first, but once you get a feel for the various iterator adaptors and what they do, iterators can be easier to understand. Instead of fiddling with the various bits of looping and building new vectors, the code focuses on the high-level objective of the loop. This abstracts away some of the commonplace code so it’s easier to see the concepts that are unique to this code, such as the filtering condition each element in the iterator must pass.

But are the two implementations truly equivalent? The intuitive assumption might be that the more low-level loop will be faster. Let’s talk about performance.

Comparing Performance: Loops vs. Iterators

To determine whether to use loops or iterators, you need to know which implementation is faster: the version of the search function with an explicit for loop or the version with iterators.

We ran a benchmark by loading the entire contents of The Adventures of Sherlock Holmes by Sir Arthur Conan Doyle into a String and looking for the word the in the contents. Here are the results of the benchmark on the version of search using the for loop and the version using iterators:

test bench_search_for  ... bench:  19,620,300 ns/iter (+/- 915,700)
test bench_search_iter ... bench:  19,234,900 ns/iter (+/- 657,200)

The iterator version was slightly faster! We won’t explain the benchmark code here, because the point is not to prove that the two versions are equivalent but to get a general sense of how these two implementations compare performance-wise.

For a more comprehensive benchmark, you should check using various texts of various sizes as the contents, different words and words of different lengths as the query, and all kinds of other variations. The point is this: iterators, although a high-level abstraction, get compiled down to roughly the same code as if you’d written the lower-level code yourself. Iterators are one of Rust’s zero-cost abstractions, by which we mean using the abstraction imposes no additional runtime overhead. This is analogous to how Bjarne Stroustrup, the original designer and implementor of C++, defines zero-overhead in “Foundations of C++” (2012):

In general, C++ implementations obey the zero-overhead principle: What you don’t use, you don’t pay for. And further: What you do use, you couldn’t hand code any better.

As another example, the following code is taken from an audio decoder. The decoding algorithm uses the linear prediction mathematical operation to estimate future values based on a linear function of the previous samples. This code uses an iterator chain to do some math on three variables in scope: a buffer slice of data, an array of 12 coefficients, and an amount by which to shift data in qlp_shift. We’ve declared the variables within this example but not given them any values; although this code doesn’t have much meaning outside of its context, it’s still a concise, real-world example of how Rust translates high-level ideas to low-level code.

let buffer: &mut [i32];
let coefficients: [i64; 12];
let qlp_shift: i16;

for i in 12..buffer.len() {
    let prediction = coefficients.iter()
                                 .zip(&buffer[i - 12..i])
                                 .map(|(&c, &s)| c * s as i64)
                                 .sum::<i64>() >> qlp_shift;
    let delta = buffer[i];
    buffer[i] = prediction as i32 + delta;
}

To calculate the value of prediction, this code iterates through each of the 12 values in coefficients and uses the zip method to pair the coefficient values with the previous 12 values in buffer. Then, for each pair, we multiply the values together, sum all the results, and shift the bits in the sum qlp_shift bits to the right.

Calculations in applications like audio decoders often prioritize performance most highly. Here, we’re creating an iterator, using two adaptors, and then consuming the value. What assembly code would this Rust code compile to? Well, as of this writing, it compiles down to the same assembly you’d write by hand. There’s no loop at all corresponding to the iteration over the values in coefficients: Rust knows that there are 12 iterations, so it “unrolls” the loop. Unrolling is an optimization that removes the overhead of the loop controlling code and instead generates repetitive code for each iteration of the loop.

All of the coefficients get stored in registers, which means accessing the values is very fast. There are no bounds checks on the array access at runtime. All these optimizations that Rust is able to apply make the resulting code extremely efficient. Now that you know this, you can use iterators and closures without fear! They make code seem like it’s higher level but don’t impose a runtime performance penalty for doing so.

Summary

Closures and iterators are Rust features inspired by functional programming language ideas. They contribute to Rust’s capability to clearly express high-level ideas at low-level performance. The implementations of closures and iterators are such that runtime performance is not affected. This is part of Rust’s goal to strive to provide zero-cost abstractions.

Now that we’ve improved the expressiveness of our I/O project, let’s look at some more features of cargo that will help us share the project with the world.

More About Cargo and Crates.io

So far we’ve used only the most basic features of Cargo to build, run, and test our code, but it can do a lot more. In this chapter, we’ll discuss some of its other, more advanced features to show you how to do the following:

  • Customize your build through release profiles
  • Publish libraries on crates.io
  • Organize large projects with workspaces
  • Install binaries from crates.io
  • Extend Cargo using custom commands

Cargo can do even more than the functionality we cover in this chapter, so for a full explanation of all its features, see its documentation.

Customizing Builds with Release Profiles

In Rust, release profiles are predefined and customizable profiles with different configurations that allow a programmer to have more control over various options for compiling code. Each profile is configured independently of the others.

Cargo has two main profiles: the dev profile Cargo uses when you run cargo build and the release profile Cargo uses when you run cargo build --release. The dev profile is defined with good defaults for development, and the release profile has good defaults for release builds.

These profile names might be familiar from the output of your builds:

$ cargo build
    Finished dev [unoptimized + debuginfo] target(s) in 0.0s
$ cargo build --release
    Finished release [optimized] target(s) in 0.0s

The dev and release are these different profiles used by the compiler.

Cargo has default settings for each of the profiles that apply when you haven't explicitly added any [profile.*] sections in the project’s Cargo.toml file. By adding [profile.*] sections for any profile you want to customize, you override any subset of the default settings. For example, here are the default values for the opt-level setting for the dev and release profiles:

Filename: Cargo.toml

[profile.dev]
opt-level = 0

[profile.release]
opt-level = 3

The opt-level setting controls the number of optimizations Rust will apply to your code, with a range of 0 to 3. Applying more optimizations extends compiling time, so if you’re in development and compiling your code often, you’ll want fewer optimizations to compile faster even if the resulting code runs slower. The default opt-level for dev is therefore 0. When you’re ready to release your code, it’s best to spend more time compiling. You’ll only compile in release mode once, but you’ll run the compiled program many times, so release mode trades longer compile time for code that runs faster. That is why the default opt-level for the release profile is 3.

You can override a default setting by adding a different value for it in Cargo.toml. For example, if we want to use optimization level 1 in the development profile, we can add these two lines to our project’s Cargo.toml file:

Filename: Cargo.toml

[profile.dev]
opt-level = 1

This code overrides the default setting of 0. Now when we run cargo build, Cargo will use the defaults for the dev profile plus our customization to opt-level. Because we set opt-level to 1, Cargo will apply more optimizations than the default, but not as many as in a release build.

For the full list of configuration options and defaults for each profile, see Cargo’s documentation.

Publishing a Crate to Crates.io

We’ve used packages from crates.io as dependencies of our project, but you can also share your code with other people by publishing your own packages. The crate registry at crates.io distributes the source code of your packages, so it primarily hosts code that is open source.

Rust and Cargo have features that make your published package easier for people to find and use. We’ll talk about some of these features next and then explain how to publish a package.

Making Useful Documentation Comments

Accurately documenting your packages will help other users know how and when to use them, so it’s worth investing the time to write documentation. In Chapter 3, we discussed how to comment Rust code using two slashes, //. Rust also has a particular kind of comment for documentation, known conveniently as a documentation comment, that will generate HTML documentation. The HTML displays the contents of documentation comments for public API items intended for programmers interested in knowing how to use your crate as opposed to how your crate is implemented.

Documentation comments use three slashes, ///, instead of two and support Markdown notation for formatting the text. Place documentation comments just before the item they’re documenting. Listing 14-1 shows documentation comments for an add_one function in a crate named my_crate.

Filename: src/lib.rs

/// Adds one to the number given.
///
/// # Examples
///
/// ```
/// let arg = 5;
/// let answer = my_crate::add_one(arg);
///
/// assert_eq!(6, answer);
/// ```
pub fn add_one(x: i32) -> i32 {
    x + 1
}

Listing 14-1: A documentation comment for a function

Here, we give a description of what the add_one function does, start a section with the heading Examples, and then provide code that demonstrates how to use the add_one function. We can generate the HTML documentation from this documentation comment by running cargo doc. This command runs the rustdoc tool distributed with Rust and puts the generated HTML documentation in the target/doc directory.

For convenience, running cargo doc --open will build the HTML for your current crate’s documentation (as well as the documentation for all of your crate’s dependencies) and open the result in a web browser. Navigate to the add_one function and you’ll see how the text in the documentation comments is rendered, as shown in Figure 14-1:

Rendered HTML documentation for the `add_one` function of `my_crate`

Figure 14-1: HTML documentation for the add_one function

Commonly Used Sections

We used the # Examples Markdown heading in Listing 14-1 to create a section in the HTML with the title “Examples.” Here are some other sections that crate authors commonly use in their documentation:

  • Panics: The scenarios in which the function being documented could panic. Callers of the function who don’t want their programs to panic should make sure they don’t call the function in these situations.
  • Errors: If the function returns a Result, describing the kinds of errors that might occur and what conditions might cause those errors to be returned can be helpful to callers so they can write code to handle the different kinds of errors in different ways.
  • Safety: If the function is unsafe to call (we discuss unsafety in Chapter 19), there should be a section explaining why the function is unsafe and covering the invariants that the function expects callers to uphold.

Most documentation comments don’t need all of these sections, but this is a good checklist to remind you of the aspects of your code users will be interested in knowing about.

Documentation Comments as Tests

Adding example code blocks in your documentation comments can help demonstrate how to use your library, and doing so has an additional bonus: running cargo test will run the code examples in your documentation as tests! Nothing is better than documentation with examples. But nothing is worse than examples that don’t work because the code has changed since the documentation was written. If we run cargo test with the documentation for the add_one function from Listing 14-1, we will see a section in the test results like this:

   Doc-tests my_crate

running 1 test
test src/lib.rs - add_one (line 5) ... ok

test result: ok. 1 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.27s

Now if we change either the function or the example so the assert_eq! in the example panics and run cargo test again, we’ll see that the doc tests catch that the example and the code are out of sync with each other!

Commenting Contained Items

The style of doc comment //! adds documentation to the item that contains the comments rather than to the items following the comments. We typically use these doc comments inside the crate root file (src/lib.rs by convention) or inside a module to document the crate or the module as a whole.

For example, to add documentation that describes the purpose of the my_crate crate that contains the add_one function, we add documentation comments that start with //! to the beginning of the src/lib.rs file, as shown in Listing 14-2:

Filename: src/lib.rs

//! # My Crate
//!
//! `my_crate` is a collection of utilities to make performing certain
//! calculations more convenient.

/// Adds one to the number given.
// --snip--
///
/// # Examples
///
/// ```
/// let arg = 5;
/// let answer = my_crate::add_one(arg);
///
/// assert_eq!(6, answer);
/// ```
pub fn add_one(x: i32) -> i32 {
    x + 1
}

Listing 14-2: Documentation for the my_crate crate as a whole

Notice there isn’t any code after the last line that begins with //!. Because we started the comments with //! instead of ///, we’re documenting the item that contains this comment rather than an item that follows this comment. In this case, that item is the src/lib.rs file, which is the crate root. These comments describe the entire crate.

When we run cargo doc --open, these comments will display on the front page of the documentation for my_crate above the list of public items in the crate, as shown in Figure 14-2:

Rendered HTML documentation with a comment for the crate as a whole

Figure 14-2: Rendered documentation for my_crate, including the comment describing the crate as a whole

Documentation comments within items are useful for describing crates and modules especially. Use them to explain the overall purpose of the container to help your users understand the crate’s organization.

Exporting a Convenient Public API with pub use

The structure of your public API is a major consideration when publishing a crate. People who use your crate are less familiar with the structure than you are and might have difficulty finding the pieces they want to use if your crate has a large module hierarchy.

In Chapter 7, we covered how to make items public using the pub keyword, and bring items into a scope with the use keyword. However, the structure that makes sense to you while you’re developing a crate might not be very convenient for your users. You might want to organize your structs in a hierarchy containing multiple levels, but then people who want to use a type you’ve defined deep in the hierarchy might have trouble finding out that type exists. They might also be annoyed at having to enter use my_crate::some_module::another_module::UsefulType; rather than use my_crate::UsefulType;.

The good news is that if the structure isn’t convenient for others to use from another library, you don’t have to rearrange your internal organization: instead, you can re-export items to make a public structure that’s different from your private structure by using pub use. Re-exporting takes a public item in one location and makes it public in another location, as if it were defined in the other location instead.

For example, say we made a library named art for modeling artistic concepts. Within this library are two modules: a kinds module containing two enums named PrimaryColor and SecondaryColor and a utils module containing a function named mix, as shown in Listing 14-3:

Filename: src/lib.rs

//! # Art
//!
//! A library for modeling artistic concepts.

pub mod kinds {
    /// The primary colors according to the RYB color model.
    pub enum PrimaryColor {
        Red,
        Yellow,
        Blue,
    }

    /// The secondary colors according to the RYB color model.
    pub enum SecondaryColor {
        Orange,
        Green,
        Purple,
    }
}

pub mod utils {
    use crate::kinds::*;

    /// Combines two primary colors in equal amounts to create
    /// a secondary color.
    pub fn mix(c1: PrimaryColor, c2: PrimaryColor) -> SecondaryColor {
        // --snip--
        unimplemented!();
    }
}

Listing 14-3: An art library with items organized into kinds and utils modules

Figure 14-3 shows what the front page of the documentation for this crate generated by cargo doc would look like:

Rendered documentation for the `art` crate that lists the `kinds` and `utils` modules

Figure 14-3: Front page of the documentation for art that lists the kinds and utils modules

Note that the PrimaryColor and SecondaryColor types aren’t listed on the front page, nor is the mix function. We have to click kinds and utils to see them.

Another crate that depends on this library would need use statements that bring the items from art into scope, specifying the module structure that’s currently defined. Listing 14-4 shows an example of a crate that uses the PrimaryColor and mix items from the art crate:

Filename: src/main.rs

use art::kinds::PrimaryColor;
use art::utils::mix;

fn main() {
    let red = PrimaryColor::Red;
    let yellow = PrimaryColor::Yellow;
    mix(red, yellow);
}

Listing 14-4: A crate using the art crate’s items with its internal structure exported

The author of the code in Listing 14-4, which uses the art crate, had to figure out that PrimaryColor is in the kinds module and mix is in the utils module. The module structure of the art crate is more relevant to developers working on the art crate than to those using it. The internal structure doesn’t contain any useful information for someone trying to understand how to use the art crate, but rather causes confusion because developers who use it have to figure out where to look, and must specify the module names in the use statements.

To remove the internal organization from the public API, we can modify the art crate code in Listing 14-3 to add pub use statements to re-export the items at the top level, as shown in Listing 14-5:

Filename: src/lib.rs

//! # Art
//!
//! A library for modeling artistic concepts.

pub use self::kinds::PrimaryColor;
pub use self::kinds::SecondaryColor;
pub use self::utils::mix;

pub mod kinds {
    // --snip--
    /// The primary colors according to the RYB color model.
    pub enum PrimaryColor {
        Red,
        Yellow,
        Blue,
    }

    /// The secondary colors according to the RYB color model.
    pub enum SecondaryColor {
        Orange,
        Green,
        Purple,
    }
}

pub mod utils {
    // --snip--
    use crate::kinds::*;

    /// Combines two primary colors in equal amounts to create
    /// a secondary color.
    pub fn mix(c1: PrimaryColor, c2: PrimaryColor) -> SecondaryColor {
        SecondaryColor::Orange
    }
}

Listing 14-5: Adding pub use statements to re-export items

The API documentation that cargo doc generates for this crate will now list and link re-exports on the front page, as shown in Figure 14-4, making the PrimaryColor and SecondaryColor types and the mix function easier to find.

Rendered documentation for the `art` crate with the re-exports on the front page

Figure 14-4: The front page of the documentation for art that lists the re-exports

The art crate users can still see and use the internal structure from Listing 14-3 as demonstrated in Listing 14-4, or they can use the more convenient structure in Listing 14-5, as shown in Listing 14-6:

Filename: src/main.rs

use art::mix;
use art::PrimaryColor;

fn main() {
    // --snip--
    let red = PrimaryColor::Red;
    let yellow = PrimaryColor::Yellow;
    mix(red, yellow);
}

Listing 14-6: A program using the re-exported items from the art crate

In cases where there are many nested modules, re-exporting the types at the top level with pub use can make a significant difference in the experience of people who use the crate. Another common use of pub use is to re-export definitions of a dependency in the current crate to make that crate's definitions part of your crate’s public API.

Creating a useful public API structure is more of an art than a science, and you can iterate to find the API that works best for your users. Choosing pub use gives you flexibility in how you structure your crate internally and decouples that internal structure from what you present to your users. Look at some of the code of crates you’ve installed to see if their internal structure differs from their public API.

Setting Up a Crates.io Account

Before you can publish any crates, you need to create an account on crates.io and get an API token. To do so, visit the home page at crates.io and log in via a GitHub account. (The GitHub account is currently a requirement, but the site might support other ways of creating an account in the future.) Once you’re logged in, visit your account settings at https://crates.io/me/ and retrieve your API key. Then run the cargo login command with your API key, like this:

$ cargo login abcdefghijklmnopqrstuvwxyz012345

This command will inform Cargo of your API token and store it locally in ~/.cargo/credentials. Note that this token is a secret: do not share it with anyone else. If you do share it with anyone for any reason, you should revoke it and generate a new token on crates.io.

Adding Metadata to a New Crate

Let’s say you have a crate you want to publish. Before publishing, you’ll need to add some metadata in the [package] section of the crate’s Cargo.toml file.

Your crate will need a unique name. While you’re working on a crate locally, you can name a crate whatever you’d like. However, crate names on crates.io are allocated on a first-come, first-served basis. Once a crate name is taken, no one else can publish a crate with that name. Before attempting to publish a crate, search for the name you want to use. If the name has been used, you will need to find another name and edit the name field in the Cargo.toml file under the [package] section to use the new name for publishing, like so:

Filename: Cargo.toml

[package]
name = "guessing_game"

Even if you’ve chosen a unique name, when you run cargo publish to publish the crate at this point, you’ll get a warning and then an error:

$ cargo publish
    Updating crates.io index
warning: manifest has no description, license, license-file, documentation, homepage or repository.
See https://doc.rust-lang.org/cargo/reference/manifest.html#package-metadata for more info.
--snip--
error: failed to publish to registry at https://crates.io

Caused by:
  the remote server responded with an error: missing or empty metadata fields: description, license. Please see https://doc.rust-lang.org/cargo/reference/manifest.html for how to upload metadata

This errors because you’re missing some crucial information: a description and license are required so people will know what your crate does and under what terms they can use it. In Cargo.toml, add a description that's just a sentence or two, because it will appear with your crate in search results. For the license field, you need to give a license identifier value. The Linux Foundation’s Software Package Data Exchange (SPDX) lists the identifiers you can use for this value. For example, to specify that you’ve licensed your crate using the MIT License, add the MIT identifier:

Filename: Cargo.toml

[package]
name = "guessing_game"
license = "MIT"

If you want to use a license that doesn’t appear in the SPDX, you need to place the text of that license in a file, include the file in your project, and then use license-file to specify the name of that file instead of using the license key.

Guidance on which license is appropriate for your project is beyond the scope of this book. Many people in the Rust community license their projects in the same way as Rust by using a dual license of MIT OR Apache-2.0. This practice demonstrates that you can also specify multiple license identifiers separated by OR to have multiple licenses for your project.

With a unique name, the version, your description, and a license added, the Cargo.toml file for a project that is ready to publish might look like this:

Filename: Cargo.toml

[package]
name = "guessing_game"
version = "0.1.0"
edition = "2021"
description = "A fun game where you guess what number the computer has chosen."
license = "MIT OR Apache-2.0"

[dependencies]

Cargo’s documentation describes other metadata you can specify to ensure others can discover and use your crate more easily.

Publishing to Crates.io

Now that you’ve created an account, saved your API token, chosen a name for your crate, and specified the required metadata, you’re ready to publish! Publishing a crate uploads a specific version to crates.io for others to use.

Be careful, because a publish is permanent. The version can never be overwritten, and the code cannot be deleted. One major goal of crates.io is to act as a permanent archive of code so that builds of all projects that depend on crates from crates.io will continue to work. Allowing version deletions would make fulfilling that goal impossible. However, there is no limit to the number of crate versions you can publish.

Run the cargo publish command again. It should succeed now:

$ cargo publish
    Updating crates.io index
   Packaging guessing_game v0.1.0 (file:///projects/guessing_game)
   Verifying guessing_game v0.1.0 (file:///projects/guessing_game)
   Compiling guessing_game v0.1.0
(file:///projects/guessing_game/target/package/guessing_game-0.1.0)
    Finished dev [unoptimized + debuginfo] target(s) in 0.19s
   Uploading guessing_game v0.1.0 (file:///projects/guessing_game)

Congratulations! You’ve now shared your code with the Rust community, and anyone can easily add your crate as a dependency of their project.

Publishing a New Version of an Existing Crate

When you’ve made changes to your crate and are ready to release a new version, you change the version value specified in your Cargo.toml file and republish. Use the Semantic Versioning rules to decide what an appropriate next version number is based on the kinds of changes you’ve made. Then run cargo publish to upload the new version.

Deprecating Versions from Crates.io with cargo yank

Although you can’t remove previous versions of a crate, you can prevent any future projects from adding them as a new dependency. This is useful when a crate version is broken for one reason or another. In such situations, Cargo supports yanking a crate version.

Yanking a version prevents new projects from depending on that version while allowing all existing projects that depend on it to continue. Essentially, a yank means that all projects with a Cargo.lock will not break, and any future Cargo.lock files generated will not use the yanked version.

To yank a version of a crate, in the directory of the crate that you’ve previously published, run cargo yank and specify which version you want to yank. For example, if we've published a crate named guessing_game version 1.0.1 and we want to yank it, in the project directory for guessing_game we'd run:

$ cargo yank --vers 1.0.1
    Updating crates.io index
        Yank guessing_game:1.0.1

By adding --undo to the command, you can also undo a yank and allow projects to start depending on a version again:

$ cargo yank --vers 1.0.1 --undo
    Updating crates.io index
      Unyank guessing_game_:1.0.1

A yank does not delete any code. It cannot, for example, delete accidentally uploaded secrets. If that happens, you must reset those secrets immediately.

Cargo Workspaces

In Chapter 12, we built a package that included a binary crate and a library crate. As your project develops, you might find that the library crate continues to get bigger and you want to split your package further into multiple library crates. Cargo offers a feature called workspaces that can help manage multiple related packages that are developed in tandem.

Creating a Workspace

A workspace is a set of packages that share the same Cargo.lock and output directory. Let’s make a project using a workspace—we’ll use trivial code so we can concentrate on the structure of the workspace. There are multiple ways to structure a workspace, so we'll just show one common way. We’ll have a workspace containing a binary and two libraries. The binary, which will provide the main functionality, will depend on the two libraries. One library will provide an add_one function, and a second library an add_two function. These three crates will be part of the same workspace. We’ll start by creating a new directory for the workspace:

$ mkdir add
$ cd add

Next, in the add directory, we create the Cargo.toml file that will configure the entire workspace. This file won’t have a [package] section. Instead, it will start with a [workspace] section that will allow us to add members to the workspace by specifying the path to the package with our binary crate; in this case, that path is adder:

Filename: Cargo.toml

[workspace]

members = [
    "adder",
]

Next, we’ll create the adder binary crate by running cargo new within the add directory:

$ cargo new adder
     Created binary (application) `adder` package

At this point, we can build the workspace by running cargo build. The files in your add directory should look like this:

├── Cargo.lock
├── Cargo.toml
├── adder
│   ├── Cargo.toml
│   └── src
│       └── main.rs
└── target

The workspace has one target directory at the top level that the compiled artifacts will be placed into; the adder package doesn’t have its own target directory. Even if we were to run cargo build from inside the adder directory, the compiled artifacts would still end up in add/target rather than add/adder/target. Cargo structures the target directory in a workspace like this because the crates in a workspace are meant to depend on each other. If each crate had its own target directory, each crate would have to recompile each of the other crates in the workspace to place the artifacts in its own target directory. By sharing one target directory, the crates can avoid unnecessary rebuilding.

Creating the Second Package in the Workspace

Next, let’s create another member package in the workspace and call it add_one. Change the top-level Cargo.toml to specify the add_one path in the members list:

Filename: Cargo.toml

[workspace]

members = [
    "adder",
    "add_one",
]

Then generate a new library crate named add_one:

$ cargo new add_one --lib
     Created library `add_one` package

Your add directory should now have these directories and files:

├── Cargo.lock
├── Cargo.toml
├── add_one
│   ├── Cargo.toml
│   └── src
│       └── lib.rs
├── adder
│   ├── Cargo.toml
│   └── src
│       └── main.rs
└── target

In the add_one/src/lib.rs file, let’s add an add_one function:

Filename: add_one/src/lib.rs

pub fn add_one(x: i32) -> i32 {
    x + 1
}

Now we can have the adder package with our binary depend on the add_one package that has our library. First, we’ll need to add a path dependency on add_one to adder/Cargo.toml.

Filename: adder/Cargo.toml

[dependencies]
add_one = { path = "../add_one" }

Cargo doesn’t assume that crates in a workspace will depend on each other, so we need to be explicit about the dependency relationships.

Next, let’s use the add_one function (from the add_one crate) in the adder crate. Open the adder/src/main.rs file and add a use line at the top to bring the new add_one library crate into scope. Then change the main function to call the add_one function, as in Listing 14-7.

Filename: adder/src/main.rs

use add_one;

fn main() {
    let num = 10;
    println!(
        "Hello, world! {num} plus one is {}!",
        add_one::add_one(num)
    );
}

Listing 14-7: Using the add_one library crate from the adder crate

Let’s build the workspace by running cargo build in the top-level add directory!

$ cargo build
   Compiling add_one v0.1.0 (file:///projects/add/add_one)
   Compiling adder v0.1.0 (file:///projects/add/adder)
    Finished dev [unoptimized + debuginfo] target(s) in 0.68s

To run the binary crate from the add directory, we can specify which package in the workspace we want to run by using the -p argument and the package name with cargo run:

$ cargo run -p adder
    Finished dev [unoptimized + debuginfo] target(s) in 0.0s
     Running `target/debug/adder`
Hello, world! 10 plus one is 11!

This runs the code in adder/src/main.rs, which depends on the add_one crate.

Depending on an External Package in a Workspace

Notice that the workspace has only one Cargo.lock file at the top level, rather than having a Cargo.lock in each crate’s directory. This ensures that all crates are using the same version of all dependencies. If we add the rand package to the adder/Cargo.toml and add_one/Cargo.toml files, Cargo will resolve both of those to one version of rand and record that in the one Cargo.lock. Making all crates in the workspace use the same dependencies means the crates will always be compatible with each other. Let’s add the rand crate to the [dependencies] section in the add_one/Cargo.toml file so we can use the rand crate in the add_one crate:

Filename: add_one/Cargo.toml

[dependencies]
rand = "0.8.3"

We can now add use rand; to the add_one/src/lib.rs file, and building the whole workspace by running cargo build in the add directory will bring in and compile the rand crate. We will get one warning because we aren’t referring to the rand we brought into scope:

$ cargo build
    Updating crates.io index
  Downloaded rand v0.8.3
   --snip--
   Compiling rand v0.8.3
   Compiling add_one v0.1.0 (file:///projects/add/add_one)
warning: unused import: `rand`
 --> add_one/src/lib.rs:1:5
  |
1 | use rand;
  |     ^^^^
  |
  = note: `#[warn(unused_imports)]` on by default

warning: 1 warning emitted

   Compiling adder v0.1.0 (file:///projects/add/adder)
    Finished dev [unoptimized + debuginfo] target(s) in 10.18s

The top-level Cargo.lock now contains information about the dependency of add_one on rand. However, even though rand is used somewhere in the workspace, we can’t use it in other crates in the workspace unless we add rand to their Cargo.toml files as well. For example, if we add use rand; to the adder/src/main.rs file for the adder package, we’ll get an error:

$ cargo build
  --snip--
   Compiling adder v0.1.0 (file:///projects/add/adder)
error[E0432]: unresolved import `rand`
 --> adder/src/main.rs:2:5
  |
2 | use rand;
  |     ^^^^ no external crate `rand`

To fix this, edit the Cargo.toml file for the adder package and indicate that rand is a dependency for it as well. Building the adder package will add rand to the list of dependencies for adder in Cargo.lock, but no additional copies of rand will be downloaded. Cargo has ensured that every crate in every package in the workspace using the rand package will be using the same version, saving us space and ensuring that the crates in the workspace will be compatible with each other.

Adding a Test to a Workspace

For another enhancement, let’s add a test of the add_one::add_one function within the add_one crate:

Filename: add_one/src/lib.rs

pub fn add_one(x: i32) -> i32 {
    x + 1
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn it_works() {
        assert_eq!(3, add_one(2));
    }
}

Now run cargo test in the top-level add directory. Running cargo test in a workspace structured like this one will run the tests for all the crates in the workspace:

$ cargo test
   Compiling add_one v0.1.0 (file:///projects/add/add_one)
   Compiling adder v0.1.0 (file:///projects/add/adder)
    Finished test [unoptimized + debuginfo] target(s) in 0.27s
     Running target/debug/deps/add_one-f0253159197f7841

running 1 test
test tests::it_works ... ok

test result: ok. 1 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.00s

     Running target/debug/deps/adder-49979ff40686fa8e

running 0 tests

test result: ok. 0 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.00s

   Doc-tests add_one

running 0 tests

test result: ok. 0 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.00s

The first section of the output shows that the it_works test in the add_one crate passed. The next section shows that zero tests were found in the adder crate, and then the last section shows zero documentation tests were found in the add_one crate.

We can also run tests for one particular crate in a workspace from the top-level directory by using the -p flag and specifying the name of the crate we want to test:

$ cargo test -p add_one
    Finished test [unoptimized + debuginfo] target(s) in 0.00s
     Running target/debug/deps/add_one-b3235fea9a156f74

running 1 test
test tests::it_works ... ok

test result: ok. 1 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.00s

   Doc-tests add_one

running 0 tests

test result: ok. 0 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.00s

This output shows cargo test only ran the tests for the add_one crate and didn’t run the adder crate tests.

If you publish the crates in the workspace to crates.io, each crate in the workspace will need to be published separately. Like cargo test, we can publish a particular crate in our workspace by using the -p flag and specifying the name of the crate we want to publish.

For additional practice, add an add_two crate to this workspace in a similar way as the add_one crate!

As your project grows, consider using a workspace: it’s easier to understand smaller, individual components than one big blob of code. Furthermore, keeping the crates in a workspace can make coordination between crates easier if they are often changed at the same time.

Installing Binaries with cargo install

The cargo install command allows you to install and use binary crates locally. This isn’t intended to replace system packages; it’s meant to be a convenient way for Rust developers to install tools that others have shared on crates.io. Note that you can only install packages that have binary targets. A binary target is the runnable program that is created if the crate has a src/main.rs file or another file specified as a binary, as opposed to a library target that isn’t runnable on its own but is suitable for including within other programs. Usually, crates have information in the README file about whether a crate is a library, has a binary target, or both.

All binaries installed with cargo install are stored in the installation root’s bin folder. If you installed Rust using rustup.rs and don’t have any custom configurations, this directory will be $HOME/.cargo/bin. Ensure that directory is in your $PATH to be able to run programs you’ve installed with cargo install.

For example, in Chapter 12 we mentioned that there’s a Rust implementation of the grep tool called ripgrep for searching files. To install ripgrep, we can run the following:

$ cargo install ripgrep
    Updating crates.io index
  Downloaded ripgrep v11.0.2
  Downloaded 1 crate (243.3 KB) in 0.88s
  Installing ripgrep v11.0.2
--snip--
   Compiling ripgrep v11.0.2
    Finished release [optimized + debuginfo] target(s) in 3m 10s
  Installing ~/.cargo/bin/rg
   Installed package `ripgrep v11.0.2` (executable `rg`)

The second-to-last line of the output shows the location and the name of the installed binary, which in the case of ripgrep is rg. As long as the installation directory is in your $PATH, as mentioned previously, you can then run rg --help and start using a faster, rustier tool for searching files!

Extending Cargo with Custom Commands

Cargo is designed so you can extend it with new subcommands without having to modify Cargo. If a binary in your $PATH is named cargo-something, you can run it as if it was a Cargo subcommand by running cargo something. Custom commands like this are also listed when you run cargo --list. Being able to use cargo install to install extensions and then run them just like the built-in Cargo tools is a super convenient benefit of Cargo’s design!

Summary

Sharing code with Cargo and crates.io is part of what makes the Rust ecosystem useful for many different tasks. Rust’s standard library is small and stable, but crates are easy to share, use, and improve on a timeline different from that of the language. Don’t be shy about sharing code that’s useful to you on crates.io; it’s likely that it will be useful to someone else as well!

Smart Pointers

A pointer is a general concept for a variable that contains an address in memory. This address refers to, or “points at,” some other data. The most common kind of pointer in Rust is a reference, which you learned about in Chapter 4. References are indicated by the & symbol and borrow the value they point to. They don’t have any special capabilities other than referring to data, and have no overhead.

Smart pointers, on the other hand, are data structures that act like a pointer but also have additional metadata and capabilities. The concept of smart pointers isn’t unique to Rust: smart pointers originated in C++ and exist in other languages as well. Rust has a variety of smart pointers defined in the standard library that provide functionality beyond that provided by references. To explore the general concept, we'll look at a couple of different examples of smart pointers, including a reference counting smart pointer type. This pointer enables you to allow data to have multiple owners by keeping track of the number of owners and, when no owners remain, cleaning up the data.

Rust, with its concept of ownership and borrowing, has an additional difference between references and smart pointers: while references only borrow data, in many cases, smart pointers own the data they point to.

Though we didn't call them as much at the time, we’ve already encountered a few smart pointers in this book, including String and Vec<T> in Chapter 8. Both these types count as smart pointers because they own some memory and allow you to manipulate it. They also have metadata and extra capabilities or guarantees. String, for example, stores its capacity as metadata and has the extra ability to ensure its data will always be valid UTF-8.

Smart pointers are usually implemented using structs. Unlike an ordinary struct, smart pointers implement the Deref and Drop traits. The Deref trait allows an instance of the smart pointer struct to behave like a reference so you can write your code to work with either references or smart pointers. The Drop trait allows you to customize the code that's run when an instance of the smart pointer goes out of scope. In this chapter, we’ll discuss both traits and demonstrate why they’re important to smart pointers.

Given that the smart pointer pattern is a general design pattern used frequently in Rust, this chapter won’t cover every existing smart pointer. Many libraries have their own smart pointers, and you can even write your own. We’ll cover the most common smart pointers in the standard library:

  • Box<T> for allocating values on the heap
  • Rc<T>, a reference counting type that enables multiple ownership
  • Ref<T> and RefMut<T>, accessed through RefCell<T>, a type that enforces the borrowing rules at runtime instead of compile time

In addition, we’ll cover the interior mutability pattern where an immutable type exposes an API for mutating an interior value. We’ll also discuss reference cycles: how they can leak memory and how to prevent them.

Let’s dive in!

Using Box<T> to Point to Data on the Heap

The most straightforward smart pointer is a box, whose type is written Box<T>. Boxes allow you to store data on the heap rather than the stack. What remains on the stack is the pointer to the heap data. Refer to Chapter 4 to review the difference between the stack and the heap.

Boxes don’t have performance overhead, other than storing their data on the heap instead of on the stack. But they don’t have many extra capabilities either. You’ll use them most often in these situations:

  • When you have a type whose size can’t be known at compile time and you want to use a value of that type in a context that requires an exact size
  • When you have a large amount of data and you want to transfer ownership but ensure the data won’t be copied when you do so
  • When you want to own a value and you care only that it’s a type that implements a particular trait rather than being of a specific type

We’ll demonstrate the first situation in the “Enabling Recursive Types with Boxes” section. In the second case, transferring ownership of a large amount of data can take a long time because the data is copied around on the stack. To improve performance in this situation, we can store the large amount of data on the heap in a box. Then, only the small amount of pointer data is copied around on the stack, while the data it references stays in one place on the heap. The third case is known as a trait object, and Chapter 17 devotes an entire section, “Using Trait Objects That Allow for Values of Different Types,” just to that topic. So what you learn here you’ll apply again in Chapter 17!

Using a Box<T> to Store Data on the Heap

Before we discuss the heap storage use case for Box<T>, we’ll cover the syntax and how to interact with values stored within a Box<T>.

Listing 15-1 shows how to use a box to store an i32 value on the heap:

Filename: src/main.rs

fn main() {
    let b = Box::new(5);
    println!("b = {}", b);
}

Listing 15-1: Storing an i32 value on the heap using a box

We define the variable b to have the value of a Box that points to the value 5, which is allocated on the heap. This program will print b = 5; in this case, we can access the data in the box similar to how we would if this data were on the stack. Just like any owned value, when a box goes out of scope, as b does at the end of main, it will be deallocated. The deallocation happens both for the box (stored on the stack) and the data it points to (stored on the heap).

Putting a single value on the heap isn’t very useful, so you won’t use boxes by themselves in this way very often. Having values like a single i32 on the stack, where they’re stored by default, is more appropriate in the majority of situations. Let’s look at a case where boxes allow us to define types that we wouldn’t be allowed to if we didn’t have boxes.

Enabling Recursive Types with Boxes

A value of recursive type can have another value of the same type as part of itself. Recursive types pose an issue because at compile time Rust needs to know how much space a type takes up. However, the nesting of values of recursive types could theoretically continue infinitely, so Rust can’t know how much space the value needs. Because boxes have a known size, we can enable recursive types by inserting a box in the recursive type definition.

As an example of a recursive type, let’s explore the cons list. This is a data type commonly found in functional programming languages. The cons list type we’ll define is straightforward except for the recursion; therefore, the concepts in the example we’ll work with will be useful any time you get into more complex situations involving recursive types.

More Information About the Cons List

A cons list is a data structure that comes from the Lisp programming language and its dialects and is made up of nested pairs, and is the Lisp version of a linked list. Its name comes from the cons function (short for “construct function”) in Lisp that constructs a new pair from its two arguments. By calling cons on a pair consisting of a value and another pair, we can construct cons lists made up of recursive pairs.

For example, here's a pseudocode representation of a cons list containing the list 1, 2, 3 with each pair in parentheses:

(1, (2, (3, Nil)))

Each item in a cons list contains two elements: the value of the current item and the next item. The last item in the list contains only a value called Nil without a next item. A cons list is produced by recursively calling the cons function. The canonical name to denote the base case of the recursion is Nil. Note that this is not the same as the “null” or “nil” concept in Chapter 6, which is an invalid or absent value.

The cons list isn’t a commonly used data structure in Rust. Most of the time when you have a list of items in Rust, Vec<T> is a better choice to use. Other, more complex recursive data types are useful in various situations, but by starting with the cons list in this chapter, we can explore how boxes let us define a recursive data type without much distraction.

Listing 15-2 contains an enum definition for a cons list. Note that this code won’t compile yet because the List type doesn’t have a known size, which we’ll demonstrate.

Filename: src/main.rs

enum List {
    Cons(i32, List),
    Nil,
}

fn main() {}

Listing 15-2: The first attempt at defining an enum to represent a cons list data structure of i32 values

Note: We’re implementing a cons list that holds only i32 values for the purposes of this example. We could have implemented it using generics, as we discussed in Chapter 10, to define a cons list type that could store values of any type.

Using the List type to store the list 1, 2, 3 would look like the code in Listing 15-3:

Filename: src/main.rs

enum List {
    Cons(i32, List),
    Nil,
}

use crate::List::{Cons, Nil};

fn main() {
    let list = Cons(1, Cons(2, Cons(3, Nil)));
}

Listing 15-3: Using the List enum to store the list 1, 2, 3

The first Cons value holds 1 and another List value. This List value is another Cons value that holds 2 and another List value. This List value is one more Cons value that holds 3 and a List value, which is finally Nil, the non-recursive variant that signals the end of the list.

If we try to compile the code in Listing 15-3, we get the error shown in Listing 15-4:

$ cargo run
   Compiling cons-list v0.1.0 (file:///projects/cons-list)
error[E0072]: recursive type `List` has infinite size
 --> src/main.rs:1:1
  |
1 | enum List {
  | ^^^^^^^^^ recursive type has infinite size
2 |     Cons(i32, List),
  |               ---- recursive without indirection
  |
help: insert some indirection (e.g., a `Box`, `Rc`, or `&`) to make `List` representable
  |
2 |     Cons(i32, Box<List>),
  |               ++++    +

error[E0391]: cycle detected when computing drop-check constraints for `List`
 --> src/main.rs:1:1
  |
1 | enum List {
  | ^^^^^^^^^
  |
  = note: ...which immediately requires computing drop-check constraints for `List` again
  = note: cycle used when computing dropck types for `Canonical { max_universe: U0, variables: [], value: ParamEnvAnd { param_env: ParamEnv { caller_bounds: [], reveal: UserFacing, constness: NotConst }, value: List } }`

Some errors have detailed explanations: E0072, E0391.
For more information about an error, try `rustc --explain E0072`.
error: could not compile `cons-list` due to 2 previous errors

Listing 15-4: The error we get when attempting to define a recursive enum

The error shows this type “has infinite size.” The reason is that we’ve defined List with a variant that is recursive: it holds another value of itself directly. As a result, Rust can’t figure out how much space it needs to store a List value. Let’s break down why we get this error. First, we'll look at how Rust decides how much space it needs to store a value of a non-recursive type.

Computing the Size of a Non-Recursive Type

Recall the Message enum we defined in Listing 6-2 when we discussed enum definitions in Chapter 6:

enum Message {
    Quit,
    Move { x: i32, y: i32 },
    Write(String),
    ChangeColor(i32, i32, i32),
}

fn main() {}

To determine how much space to allocate for a Message value, Rust goes through each of the variants to see which variant needs the most space. Rust sees that Message::Quit doesn’t need any space, Message::Move needs enough space to store two i32 values, and so forth. Because only one variant will be used, the most space a Message value will need is the space it would take to store the largest of its variants.

Contrast this with what happens when Rust tries to determine how much space a recursive type like the List enum in Listing 15-2 needs. The compiler starts by looking at the Cons variant, which holds a value of type i32 and a value of type List. Therefore, Cons needs an amount of space equal to the size of an i32 plus the size of a List. To figure out how much memory the List type needs, the compiler looks at the variants, starting with the Cons variant. The Cons variant holds a value of type i32 and a value of type List, and this process continues infinitely, as shown in Figure 15-1.

An infinite Cons list

Figure 15-1: An infinite List consisting of infinite Cons variants

Using Box<T> to Get a Recursive Type with a Known Size

Because Rust can’t figure out how much space to allocate for recursively defined types, the compiler gives an error with this helpful suggestion:

help: insert some indirection (e.g., a `Box`, `Rc`, or `&`) to make `List` representable
  |
2 |     Cons(i32, Box<List>),
  |               ^^^^    ^

In this suggestion, “indirection” means that instead of storing a value directly, we should change the data structure to store the value indirectly by storing a pointer to the value instead.

Because a Box<T> is a pointer, Rust always knows how much space a Box<T> needs: a pointer’s size doesn’t change based on the amount of data it’s pointing to. This means we can put a Box<T> inside the Cons variant instead of another List value directly. The Box<T> will point to the next List value that will be on the heap rather than inside the Cons variant. Conceptually, we still have a list, created with lists holding other lists, but this implementation is now more like placing the items next to one another rather than inside one another.

We can change the definition of the List enum in Listing 15-2 and the usage of the List in Listing 15-3 to the code in Listing 15-5, which will compile:

Filename: src/main.rs

enum List {
    Cons(i32, Box<List>),
    Nil,
}

use crate::List::{Cons, Nil};

fn main() {
    let list = Cons(1, Box::new(Cons(2, Box::new(Cons(3, Box::new(Nil))))));
}

Listing 15-5: Definition of List that uses Box<T> in order to have a known size

The Cons variant needs the size of an i32 plus the space to store the box’s pointer data. The Nil variant stores no values, so it needs less space than the Cons variant. We now know that any List value will take up the size of an i32 plus the size of a box’s pointer data. By using a box, we’ve broken the infinite, recursive chain, so the compiler can figure out the size it needs to store a List value. Figure 15-2 shows what the Cons variant looks like now.

A finite Cons list

Figure 15-2: A List that is not infinitely sized because Cons holds a Box

Boxes provide only the indirection and heap allocation; they don’t have any other special capabilities, like those we’ll see with the other smart pointer types. They also don’t have the performance overhead that these special capabilities incur, so they can be useful in cases like the cons list where the indirection is the only feature we need. We’ll look at more use cases for boxes in Chapter 17, too.

The Box<T> type is a smart pointer because it implements the Deref trait, which allows Box<T> values to be treated like references. When a Box<T> value goes out of scope, the heap data that the box is pointing to is cleaned up as well because of the Drop trait implementation. These two traits will be even more important to the functionality provided by the other smart pointer types we’ll discuss in the rest of this chapter. Let’s explore these two traits in more detail.

Treating Smart Pointers Like Regular References with the Deref Trait

Implementing the Deref trait allows you to customize the behavior of the dereference operator * (not to be confused with the multiplication or glob operator). By implementing Deref in such a way that a smart pointer can be treated like a regular reference, you can write code that operates on references and use that code with smart pointers too.

Let’s first look at how the dereference operator works with regular references. Then we’ll try to define a custom type that behaves like Box<T>, and see why the dereference operator doesn’t work like a reference on our newly defined type. We’ll explore how implementing the Deref trait makes it possible for smart pointers to work in ways similar to references. Then we’ll look at Rust’s deref coercion feature and how it lets us work with either references or smart pointers.

Note: there’s one big difference between the MyBox<T> type we’re about to build and the real Box<T>: our version will not store its data on the heap. We are focusing this example on Deref, so where the data is actually stored is less important than the pointer-like behavior.

Following the Pointer to the Value

A regular reference is a type of pointer, and one way to think of a pointer is as an arrow to a value stored somewhere else. In Listing 15-6, we create a reference to an i32 value and then use the dereference operator to follow the reference to the value:

Filename: src/main.rs

fn main() {
    let x = 5;
    let y = &x;

    assert_eq!(5, x);
    assert_eq!(5, *y);
}

Listing 15-6: Using the dereference operator to follow a reference to an i32 value

The variable x holds an i32 value 5. We set y equal to a reference to x. We can assert that x is equal to 5. However, if we want to make an assertion about the value in y, we have to use *y to follow the reference to the value it’s pointing to (hence dereference) so the compiler can compare the actual value. Once we dereference y, we have access to the integer value y is pointing to that we can compare with 5.

If we tried to write assert_eq!(5, y); instead, we would get this compilation error:

$ cargo run
   Compiling deref-example v0.1.0 (file:///projects/deref-example)
error[E0277]: can't compare `{integer}` with `&{integer}`
 --> src/main.rs:6:5
  |
6 |     assert_eq!(5, y);
  |     ^^^^^^^^^^^^^^^^ no implementation for `{integer} == &{integer}`
  |
  = help: the trait `PartialEq<&{integer}>` is not implemented for `{integer}`
  = note: this error originates in the macro `assert_eq` (in Nightly builds, run with -Z macro-backtrace for more info)

For more information about this error, try `rustc --explain E0277`.
error: could not compile `deref-example` due to previous error

Comparing a number and a reference to a number isn’t allowed because they’re different types. We must use the dereference operator to follow the reference to the value it’s pointing to.

Using Box<T> Like a Reference

We can rewrite the code in Listing 15-6 to use a Box<T> instead of a reference; the dereference operator used on the Box<T> in Listing 15-7 functions in the same way as the dereference operator used on the reference in Listing 15-6:

Filename: src/main.rs

fn main() {
    let x = 5;
    let y = Box::new(x);

    assert_eq!(5, x);
    assert_eq!(5, *y);
}

Listing 15-7: Using the dereference operator on a Box<i32>

The main difference between Listing 15-7 and Listing 15-6 is that here we set y to be an instance of a box pointing to a copied value of x rather than a reference pointing to the value of x. In the last assertion, we can use the dereference operator to follow the box’s pointer in the same way that we did when y was a reference. Next, we’ll explore what is special about Box<T> that enables us to use the dereference operator by defining our own box type.

Defining Our Own Smart Pointer

Let’s build a smart pointer similar to the Box<T> type provided by the standard library to experience how smart pointers behave differently from references by default. Then we’ll look at how to add the ability to use the dereference operator.

The Box<T> type is ultimately defined as a tuple struct with one element, so Listing 15-8 defines a MyBox<T> type in the same way. We’ll also define a new function to match the new function defined on Box<T>.

Filename: src/main.rs

struct MyBox<T>(T);

impl<T> MyBox<T> {
    fn new(x: T) -> MyBox<T> {
        MyBox(x)
    }
}

fn main() {}

Listing 15-8: Defining a MyBox<T> type

We define a struct named MyBox and declare a generic parameter T, b