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 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. Here’s an
example:
fn main() { use std::net::IpAddr; let home: IpAddr = "127.0.0.1".parse().unwrap(); }
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 unwrap
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.
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, 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 operations on 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.