49 KiB
+++ title = "Async/Await" weight = 12 path = "async-await" date = 0000-01-01
[extra] chapter = "Interrupts" +++
In this post we explore cooperative multitasking and the async/await feature of Rust. This will make it possible to run multiple concurrent tasks in our kernel. TODO
This blog is openly developed on GitHub. If you have any problems or questions, please open an issue there. You can also leave comments at the bottom. The complete source code for this post can be found in the post-12 branch.
Multitasking
One of the fundamental features of most operating systems is multitasking, which is the ability to execute multiple tasks concurrently. For example, you probably have other programs open while looking at this post, such as a text editor or a terminal window. Even if you have only a single browser window open, there are probably various background tasks for managing your desktop windows, checking for updates, or indexing files.
While it seems like all tasks run in parallel, only a single task can be executed on a CPU core at a time. To create the illusion that the tasks run in parallel, the operating system rapidly switches between active tasks so that each one can make a bit of progress. Since computers are fast, we don't notice these switches most of the time.
While single-core CPUs can only execute a single task at a time, multi-core CPUs can run multiple tasks in a truly parallel way. For example, a CPU with 8 cores can run 8 tasks at the same time. We will explain how to setup multi-core CPUs in a future post. For this post, we will focus on single-core CPUs for simplicity. (It's worth noting that all multi-core CPUs start with only a single active core, so we can treat them as single-core CPUs for now.)
There are two forms of multitasking: Cooperative multitasking requires tasks to regularly give up control of the CPU so that other tasks can make progress. Preemptive multitasking uses operating system capabilities to switch threads at arbitrary points in time by forcibly pausing them. In the following we will explore the two forms of multitasking in more detail and discuss their respective advantages and drawbacks.
Preemptive Multitasking
The idea behind preemptive multitasking is that the operating system controls when to switch tasks. For that, it utilizes the fact that it regains control of the CPU on each interrupt. This makes it possible to switch tasks whenever new input is available to the system. For example, it would be possible to switch tasks when the mouse is moved or a network packet arrives. The operating system can also determine the exact time that a task is allowed to run by configuring a hardware timer to send an interrupt after that time.
The following graphic illustrates the task switching process on a hardware interrupt:
In the first row, the CPU is executing task A1 of program A. All other tasks are paused. In the second row, a hardware interrupt arrives at the CPU. As described in the Hardware Interrupts post, the CPU immediately stops the execution of task A1 and jumps to the interrupt handler defined in the interrupt descriptor table (IDT). Through this interrupt handler, the operating system now has control of the CPU again, which allows it to switch to task B1 instead of continuing task A1.
Saving State
Since tasks are interrupted at arbitrary points in time, they might be in the middle of some calculation. In order to be able to resume them later, the operating system must backup the whole state of the task, including its call stack and the values of all CPU registers. This process is called a context switch.
As the call stack can be very large, the operating system typically sets up a separate call stack for each task instead of backing up the call stack content on each task switch. Such a task with a separate stack is called a thread of execution or thread for short. By using a separate stack for each task, only the register contents need to be saved on a context switch (including the program counter and stack pointer). This approach minimizes the performance overhead of a context switch, which is very important since context switches often occur up to 100 times per second.
Discussion
The main advantage of preemptive multitasking is that the operating system can fully control the allowed execution time of a task. This way, it can guarantee that each task gets a fair share of the CPU time, without the need to trust the tasks to cooperate. This is especially important when running third-party tasks or when multiple users share a system.
The disadvantage of preemption is that each task requires its own stack. Compared to a shared stack, this results in a higher memory usage per task and often limits the number of tasks in the system. Another disadvantage is that the operating system always has to save the complete CPU register state on each task switch, even if the task only used a small subset of the registers.
Preemptive multitasking and threads are fundamental components of an operating system because they make it possible to run untrusted userspace programs. We will discuss these concepts in full detail in future posts. For this post, however, we will focus on cooperative multitasking, which also provides useful capabilities for our kernel.
Cooperative Multitasking
Instead of forcibly pausing running tasks at arbitrary points in time, cooperative multitasking lets each task run until it voluntarily gives up control of the CPU. This allows tasks to pause themselves at convenient points in time, for example when it needs to wait for an I/O operation anyway.
Cooperative multitasking is often used at the language level, for example in form of coroutines or async/await. The idea is that either the programmer or the compiler inserts yield operations into the program, which give up control of the CPU and allow other tasks to run. For example, a yield could be inserted after each iteration of a complex loop.
It is common to combine cooperative multitasking with asynchronous operations. Instead of blocking until an operation is finished and preventing other tasks to run in this time, asynchronous operations return a "not ready" status if the operation is not finished yet. In this case, the waiting task can execute a yield operation to let other tasks run.
Saving State
Since tasks define their pause points themselves, they don't need the operating system to save their state. Instead, they can save exactly the state they need for continuation before they pause themselves, which often results in better performance. For example, a task that just finished a complex computation might only need to backup the final result of the computation since it does not need the intermediate results anymore.
Language-supported implementations of cooperative tasks are often even able to backup up the required parts of the call stack before pausing. As an example, Rust's async/await implementation stores all local variables that are still needed in an automatically generated struct (see below). By backing up the relevant parts of the call stack before pausing, all tasks can share the same call stack, which results in a much smaller memory consumption per task. As a result, it is possible to create an almost arbitrary number of tasks without running out of memory.
Discussion
The drawback of cooperative multitasking is that an uncooperative task can potentially run for an unlimited amount of time. Thus, a malicious or buggy task can prevent other tasks from running and slow down or even block the whole system. For this reason, cooperative multitasking should only be used when all tasks are known to cooperate. As a counterexample, it's not a good idea to make the operating system rely on the cooperation of arbitrary userlevel programs.
However, the strong performance and memory benefits of cooperative multitasking make it a good approach for usage within a program, especially in combination with asynchronous operations. Since an operating system kernel is a performance-critical program that interacts with asynchronous hardware, cooperative multitasking seems like a good approach for concurrency in our kernel.
Async/Await in Rust
The Rust language provides first-class support for cooperative multitasking in form of async/await. Before we can explore what async/await is and how it works, we need to understand how futures and asynchronous programming work in Rust.
Futures
A future represents a value that might not be available yet. This could be for example an integer that is computed by another task or a file that is downloaded from the network. Instead of waiting until the value is available, futures make it possible to continue execution until the value is needed.
Example
The concept of futures is best illustrated with a small example:
This sequence diagram shows a main function that reads a file from the file system and then calls a function foo. This process is repeated two times: Once with a synchronous read_file call and once with an asynchronous async_read_file call.
With the synchronous call, the main function needs to wait until the file is loaded from the file system. Only then it can call the foo function, which requires it to again wait for the result.
With the asynchronous async_read_file call, the file system directly returns a future and loads the file asynchronously in the background. This allows the main function to call foo much earlier, which then runs in parallel with the file load. In this example, the file load even finishes before foo returns, so main can directly work with the file without further waiting after foo returns.
Futures in Rust
In Rust, futures are represented by the Future trait, which looks like this:
pub trait Future {
type Output;
fn poll(self: Pin<&mut Self>, cx: &mut Context) -> Poll<Self::Output>;
}
The associated type Output specfies the type of the asynchronous value. For example, the async_read_file function in the diagram above would return a Future instance with Output set to File.
The poll method allows to check if the value is already available. It returns a Poll enum, which looks like this:
pub enum Poll<T> {
Ready(T),
Pending,
}
When the value is already available (e.g. the file was fully read from disk), it is returned wrapped in the Ready variant. Otherwise, the Pending variant is returned, which signals the caller that the value is not yet available.
The poll method takes two arguments: self: Pin<&mut Self> and cx: &mut Context. The former behaves like a normal &mut self reference, with the difference that the Self value is pinned to its memory location. Understanding Pin and why it is needed is difficult without understanding how async/await works first. We will therefore explain it later in this post.
The purpose of the cx: &mut Context parameter is to pass a Waker instance to the asynchronous task, e.g. the file system load. This Waker allows the asynchronous task to signal that it (or a part of it) is finished, e.g. that the file was loaded from disk. Since the main task knows that it will be notified when the Future is ready, it does not need to call poll over and over again. We will explain this process in more detail later in this post when we implement an own Waker type.
Working with Futures
We now know how futures are defined and understand the basic idea behind the poll method. However, we still don't know how to effectively work with futures. The problem is that futures represent results of asynchronous tasks, which might be not available yet. In practice, however, we often need these values directly for further calculations. So the question is: How can we efficiently retrieve the value of a future when we need it?
Waiting on Futures
One possible answer is to wait until a future becomes ready. This could look something like this:
let future = async_read_file("foo.txt");
let file_content = loop {
match future.poll(…) {
Poll::Ready(value) => break value,
Poll::Pending => {}, // do nothing
}
}
Here we actively wait for the future by calling poll over and over again in a loop. The arguments to poll don't matter here, so we omitted them. While this solution works, it is very inefficient because we keep the CPU busy until the value becomes available.
A more efficient approach could be to block the current thread until the future becomes available. This is of course only possible if you have threads, so this solution does not work for our kernel, at least not yet. Even on systems where blocking is supported, it is often not desired because it turns an asynchronous task into a synchronous task again, thereby inhibiting the potential performance benefits of parallel tasks.
Future Combinators
An alternative to waiting is to use future combinators. Future combinators are functions like map that allow chaining and combining futures together, similar to the functions on Iterator. Instead of waiting on the future, these combinators return a future themselves, which applies the mapping operation on poll.
As an example, a simple string_len combinator for converting Future<Output = String> to a Future<Output = usize could look like this:
struct StringLen<F> {
inner_future: F,
}
impl<F> Future for StringLen<F> where Fut: Future<Output = String> {
type Output = usize;
fn poll(mut self: Pin<&mut Self>, cx: &mut Context<'_>) -> Poll<T> {
match self.inner_future.poll(cx) {
Poll::Ready(s) => Poll::Ready(s.len()),
Poll::Pending => Poll::Pending,
}
}
}
fn string_len(string: impl Future<Output = String>)
-> impl Future<Output = usize>
{
StringLen {
inner_future: string,
}
}
// Usage
fn file_len() -> impl Future<Output = usize> {
let file_content_future = async_read_file("foo.txt");
string_len(file_content_future)
}
This code does not quite work because it does not handle pinning, but it suffices as an example. The basic idea is that the string_len function wraps a given Future instance into a new StringLen struct, which also implements Future. When the wrapped future is polled, it polls the inner future. If the value is not ready yet, Poll::Pending is returned from the wrapped future too. If the value is ready, the string is extracted from the Poll::Ready variant and its length is calculated. Afterwards, it is wrapped in Poll::Ready again and returned.
With this string_len function, we can calculate the length of an asynchronous string without waiting for it. Since the function returns a Future again, the caller can't work directly on the returned value, but needs to use combinator functions again. This way, the whole call graph becomes asynchronous and we can efficiently wait for multiple futures at once at some point, e.g. in the main function.
Manually writing combinator functions is difficult, therefore they are often provided by libraries. While the Rust standard library itself provides no combinator methods yet, the semi-official (and no_std compatible) futures crate does. Its FutureExt trait provides high-level combinator methods such as map or then, which can be used to manipulate the result with arbitrary closures.
Advantages
The big advantage of future combinators is that they keep the operations asynchronous. In combination with asynchronous I/O interfaces, this approach can lead to very high performance. The fact that future combinators are implemented as normal structs with trait implementations allows the compiler to excessively optimize them. For more details, see the Zero-cost futures in Rust post, which announced the addition of futures to the Rust ecosystem.
Drawbacks
While future combinators make it possible to write very efficient code, they can be difficult to use in some situations because of the type system and the closure based interface. For example, consider code like this:
fn example(min_len: usize) -> impl Future<Output = String> {
async_read_file("foo.txt").then(move |content| {
if content.len() < min_len {
Either::Left(async_read_file("bar.txt").map(|s| content + &s))
} else {
Either::Right(future::ready(content))
}
})
}
Here we read the file foo.txt and then use the then combinator to chain a second future based on the file content. If the content length is smaller than the given min_len, we read a different bar.txt file and append it to content using the map combinator. Otherwise we return only the content of foo.txt.
We need to use the move keyword for the closure passed to then because otherwise there would be a lifetime error for min_len. The reason for the Either wrapper is that if and else blocks must always have the same type. Since we return different future types in the blocks, we must use the wrapper type to unify them into a single type. The ready function wraps a value into a future, which is immediately ready. The function is required here because the Either wrapper expects that the wrapped value implements Future.
As you can imagine, this can quickly lead to very complex code for larger projects. It gets especially complicated if borrowing and different lifetimes are involved. For this reason, a lot of work was invested to add support for async/await to Rust, with the goal of making asynchronous code radically simpler to write.
The Async/Await Pattern
The idea behind async/await is to let the programmer write code that looks like normal synchronous code, but is turned into asynchronous code by the compiler. It works based on the two keywords async and await. The async keyword can be used in a function signature to turn a synchronous function into an asynchronous function that returns a future:
async fn foo() -> u32 {
0
}
// the above is roughly translated by the compiler to:
fn foo() -> impl Future<Output = u32> {
future::ready(0)
}
This keyword alone wouldn't be that useful. However, inside async functions, the await keyword can be used to retrieve the asynchronous value of a future:
async fn example(min_len: usize) -> String {
let content = async_read_file("foo.txt").await;
if content.len() < min_len {
content + &async_read_file("bar.txt").await
} else {
content
}
}
This function is a direct translation of the example function above, which used combinator functions. Using the .await operator, we can retrieve the value of a future without needing any closures or Either types. As a result, we can write our code like we write normal synchronous code, with the difference that this is still asynchronous code.
State Machine Transformation
What the compiler does behind this scenes is to transform the body of the async function into a state machine, with each .await call representing a different state. For the above example function, the compiler creates a state machine with the following four states:
Each state represents a different pause point of the function. The "Start" and "End" states represent the function at the beginning and end of its execution. The "Waiting on foo.txt" state represents that the function is currently waiting for the first async_read_file result. Similarly, the "Waiting on bar.txt" state represents the pause point where the function is waiting on the second async_read_file result.
The state machine implements the Future trait by making each poll call a possible state transition:
The diagram uses arrows to represent state switches and diamond shapes to represent alternative ways. For example, if the foo.txt file is not ready, the path marked with "no" is takes and the "Waiting on foo.txt" state is reached. Otherwise, the "yes" path is taken. The small red diamond without caption represents the if content.len() < 100 branch of the example function.
We see that the first poll call starts the function and lets it run until it reaches a future that is not ready yet. If all futures on the path are ready, the function can run till the "End" state, where it returns its result wrapped in Poll::Ready. Otherwise, the state machine enters a waiting state and returns Poll::Pending. On the next poll call, the state machine then starts from the last waiting state and retries the last operation.
Saving State
In order to be able to continue from the last waiting state, the state machine must save it internally. In addition, it must save all the variables that it needs to continue execution on the next poll call. This is where the compiler can really shine: Since it knows which variables are used when, it can automatically generate structs with exactly the variables that are needed.
As an example, the compiler generates the following structs for the above example function:
// The `example` function again so that you don't have to scroll up
async fn example(min_len: usize) -> String {
let content = async_read_file("foo.txt").await;
if content.len() < min_len {
content + &async_read_file("bar.txt").await
} else {
content
}
}
// The compiler-generated state structs:
struct StartState {
min_len: usize,
}
struct WaitingOnFooTxtState {
min_len: usize,
foo_txt_future: impl Future<Output = String>,
}
struct WaitingOnBarTxtState {
content: String,
bar_txt_future: impl Future<Output = String>,
}
struct EndState {}
In the "start" and "Waiting on foo.txt" states, the min_len parameter needs to be stored because it is required for the comparison with content.len() later. The "Waiting on foo.txt" state additionally stores a foo_txt_future, which represents the future returned by the async_read_file call. This future needs to be polled again when the state machine continues, so it needs to be saved.
The "Waiting on bar.txt" state contains the content variable because it is needed for the string concatenation after bar.txt is ready. It also stores a bar_txt_future that represents the in-progress load of bar.txt. The struct does not contain the min_len variable because it is no longer needed after the content.len() comparison. In the "end" state, no variables are stored because the function did already run to completion.
Keep in mind that this is only an example for the code that the compiler could generate. The struct names and the field layout are an implementation detail and might be different.
The Full State Machine Type
While the exact compiler-generated code is an implementation detail, it helps in understanding to imagine how the generated state machine could look for the example function. We already defined the structs representing the different states and containing the required variables. To create a state machine on top of them, we can combine them into an enum:
enum ExampleStateMachine {
Start(StartState),
WaitingOnFooTxt(WaitingOnFooTxtState),
WaitingOnBarTxt(WaitingOnBarTxtState),
End(EndState),
}
We define a separate enum variant for each state and add the corresponding state struct to each variant as a field. To implement the state transitions, the compiler generates an implementation of the Future trait based on the example function:
impl Future for ExampleStateMachine {
type Output = String; // return type of `example`
fn poll(self: Pin<&mut Self>, cx: &mut Context) -> Poll<Self::Output> {
loop {
match self { // TODO: handle pinning
ExampleStateMachine::Start(state) => {…}
ExampleStateMachine::WaitingOnFooTxt(state) => {…}
ExampleStateMachine::WaitingOnFooTxt(state) => {…}
ExampleStateMachine::End(state) => {…}
}
}
}
}
The Output type of the future is String because it's the return type of the example function. To implement the poll function, we use a match statement on the current state inside a loop. The idea is that we switch to the next state as long as possible and use an explicit return Poll::Pending when we can't continue.
For simplicity, we only show simplified code and don't handle pinning, ownership, lifetimes, etc. So this and the following code should be treated as pseudo-code and not used directly. Of course, the real compiler-generated code handles everything correctly, albeit possibly in a different way.
To keep the code excerpts small, we present the code for each match arm separately. Let's begin with the Start state:
ExampleStateMachine::Start(state) => {
// from body of `example`
let foo_txt_future = async_read_file("foo.txt");
// `.await` operation
let state = WaitingOnFooTxtState {
min_len: state.min_len,
foo_txt_future,
};
*self = ExampleStateMachine::WaitingOnFooTxt(state);
}
The state machine is in the Start state when it is right at the beginning of the function. In this case, we execute all the code from the body of the example function until the first .await. To make the code more readable, we introduce a new foo_txt_future to represent the future returned by async_read_file right before the .await. To handle the .await operation, we change the state of self to WaitingOnFooTxt, which includes the construction of the WaitingOnFooTxtState struct.
Since the match self {…} statement is executed in a loop, the execution jumps to the WaitingOnFooTxt arm next:
ExampleStateMachine::WaitingOnFooTxt(state) => {
match state.foo_txt_future.poll(cx) {
Poll::Pending => return Poll::Pending,
Poll::Ready(content) => {
// from body of `example`
if content.len() < state.min_len {
let bar_txt_future = async_read_file("bar.txt");
// `.await` operation
let state = WaitingOnBarTxtState {
content,
bar_txt_future,
};
*self = ExampleStateMachine::WaitingOnBarTxt(state);
} else {
*self = ExampleStateMachine::End(EndState));
return Poll::Ready(content);
}
}
}
}
In this match arm we first call the poll function of the foo_txt_future. If it is not ready, we exit the loop and return Poll::Pending too. Since self stays in the WaitingOnFooTxt state in this case, the next poll call on the state machine will enter the same match arm and retry polling the foo_txt_future.
When the foo_txt_future is ready, we assign the result to the content variable and continue to execute the code of the example function: If content.len() is smaller than the min_len saved in the state struct, the bar.txt file is read asynchronously. We again translate the .await operation into a state change, this time into the WaitingOnBarTxt state. Since we're executing the match inside a loop, the execution directly jumps to the match arm for the new state afterwards, where the bar_txt_future is polled.
In case we enter the else branch, no further .await operation occurs. We reach the end of the function and return content wrapped in Poll::Ready. We also change the current state to the End state.
The code for the WaitingOnBarTxt state looks like this:
ExampleStateMachine::WaitingOnBarTxt(state) => {
match state.bar_txt_future.poll(cx) {
Poll::Pending => return Poll::Pending,
Poll::Ready(bar_txt) => {
*self = ExampleStateMachine::End(EndState));
// from body of `example`
return Poll::Ready(state.content + &bar_txt);
}
}
}
Similar to the WaitingOnFooTxt state, we start by polling the bar_txt_future. If it is still pending, we exit the loop and return Poll::Pending too. Otherwise, we can perform the last operation of the example function: Concatenating the content variable with the result from the future. We update the state machine to the End state and then return the result wrapped in Poll::Ready.
Finally, the code for the End state looks like this:
ExampleStateMachine::End(_) => {
panic!("poll called after Poll::Ready was returned");
}
Futures should not be polled again after they returned Poll::Ready, therefore we panic if poll is called when we are already in the End state.
We now know how the compiler-generated state machine and its implementation of the Future trait could look like. In practice, the compiler generates code in different way. (In case you're interested, the implementation is currently based on generators, but this is only an implementation detail.)
The last piece of the puzzle is the generated code for the example function itself. Remember, the function header was defined like this:
async fn example(min_len: usize) -> String
Since the complete function body is now implemented by the state machine, the only thing that the function needs to do is to initialize the state machine. The generated code for this could look like this:
fn example(min_len: usize) -> ExampleStateMachine {
ExampleStateMachine::Start(StartState {
min_len,
})
}
The function no longer has an async modifier since it now explicitly returns a ExampleStateMachine type, which implements the Future trait. As expected, the state machine is constructed in the Start state and the corresponding state struct is initialized with the min_len parameter.
Note that this function does not start the execution of the state machine. This is a fundamental design decision of Rust's futures: They do nothing until they are polled for the first time.
Pinning
We already stumbled across pinning multiple times in this post. Now is finally the time to explore what pinning is and why it is needed.
Self-Referential Structs
As explained above, the state machine transformation stores the local variables of each pause point in a struct. For small examples like our example function, this was straightforward and did not lead to any problems. However, things become more difficult when variables reference each other. For example, consider this function:
async fn pin_example() -> i32 {
let array = [1, 2, 3];
let element = &array[2];
async_write_file("foo.txt", element.to_string()).await;
*element
}
This function creates a small array with the contents 1, 2, and 3. It then creates a reference to the last array element and stores it in an element variable. Next, it asynchronously writes the number converted to a string to a foo.txt file. Finally, it returns the number referenced by element.
Since the function uses a single await operation, the resulting state machine has three states: start, end, and "waiting on write". The function takes no arguments, so the struct for the start state is empty. Like before, the struct for the end state is empty too because the function is finished at this point. The struct for the "waiting on write" state is more interesting:
struct WaitingOnWriteState {
array: [1, 2, 3],
element: 0x1001a, // address of the last array element
}
We need to store both the array and element variables because element is required for the return type and array is referenced by element. Since element is a reference, it stores a pointer (i.e. a memory address) to the referenced element. We used 0x1001a as an example memory address here. In reality it needs to be the address of the last element of the array field, so it depends on where the struct lives in memory. Structs with such internal pointers are called self-referential structs because they reference themselves from one of their fields.
The Problem with Self-Referential Structs
The internal pointer of our self-referential struct leads to a fundamental problem, which becomes apparent when we look at its memory layout:
The array field starts at address 0x10014 and the element field at address 0x10020. It points to address 0x1001a because the last array element lives at this address. At this point, everything is still fine. However, an issue occurs when we move this struct to a different memory address:
We moved the struct a bit so that it starts at address 0x10024 now. The problem is that the element field still points to address 0x1001a even though the last array element now lives at address 0x1002a. Thus, the pointer is dangling with the result that undefined behavior occurs on the next poll call.
Possible Solutions
There are two fundamental approaches to solve the dangling pointer problem:
- Update the pointer on move: The idea is to update the internal pointer whenever the struct is moved in memory so that it is still valid after the move. Unfortunately, this approach would require extensive changes to Rust that would result in potentially huge performance losses. The reason is that some kind of runtime would need to keep track of the type of all struct fields and check on every move operation whether a pointer update is required.
- Forbid moving the struct: As we saw above, the dangling pointer only occurs when we move the struct in memory. By completely forbidding move operations on self-referential structs, the problem can be also avoided. The big advantage of this approach is that it can be implemented at the type system level without additional runtime costs. The drawback is that it puts the burden of dealing with move operations on possibly self-referential structs on the programmer.
Rust understandably decided for the second solution. For this, the pinning API was proposed in RFC 2349. In the following, we will give a short overview of this API and explain how it works with async/await and futures.
Heap Values
The first observation is that heap allocated values already have a fixed memory address most of the time. They are created using a call to allocate and are not moved in memory until they are freed through a deallocate call again. This is required because a Box<T> is essentially a pointer to the heap memory, so that an address change would make the pointer invalid.
Using heap allocation, we can try to create a self-referential struct:
fn main() {
let mut heap_value = Box::new(SelfReferential {
self_ptr: 0 as *const _,
});
let ptr = &*heap_value as *const SelfReferential;
heap_value.self_ptr = ptr;
println!("heap value at: {:p}", heap_value);
println!("internal reference: {:p}", heap_value.self_ptr);
}
struct SelfReferential {
self_ptr: *const Self,
}
When we execute this, we see that the address of heap value and its internal pointer are equal, which means that the self_ptr field is valid. Since the heap_value variable is only a pointer, moving it (e.g. by passing it to a function) does not change the address, so that the self_ptr stays valid.
However, there is still a way to break this: We can move out of a Box<T> or replace its content:
let stack_value = mem::replace(&mut *heap_value, SelfReferential {
self_ptr: 0 as *const _,
});
println!("value at: {:p}", &stack_value);
println!("internal reference: {:p}", stack_value.self_ptr);
Here we use the mem::replace function to replace the heap allocated value with a new struct instance. This allows us to move the original heap_value to the stack, while the self_ptr field of of the struct still points to the heap. When you try to run the example on the playground, you see that the printed "value at:" and "internal reference:" lines show indeed different pointers.
The fundamental problem is that Box<T> allows us to get a &mut T reference to the heap allocated value. This &mut reference allows us to to use methods like mem::replace or mem::swap to invalidate the heap allocated value. To resolve this problem, we must prevent that &mut references to self-referential structs can be created.
Pin<Box<T>> and Unpin
The pinning API provides a solution to the &mut T problem in form of the Pin wrapper type and the Unpin marker trait. The idea behind these types is to gate all methods of Pin that can be used to get &mut references (e.g. get_mut or deref_mut) on the Unpin trait. The Unpin trait is an auto trait, which is automatically implemented for all types except types that explicitly opt-out. By making self-referential structs opt-out of Unpin, there is no (safe) way to get a &mut T from a Pin<Box<T>> type for them. As a result, their internal self-references are guaranteed to stay valid.
As an example, let's update the SelfReferential type from the above example to opt-out of Unpin:
use core::marker::PhantomPinned;
struct SelfReferential {
self_ptr: *const Self,
_pin: PhantomPinned,
}
We opt-out by adding a second _pin field of type PhantomPinned. This type is a zero-sized marker type whose only purpose is to not implement the Unpin trait. Because of the way auto traits work, a single field that is not Unpin suffices to make the complete struct opt-out of Unpin.
The second step is to change the Box<SelfReferential> type in the example to a Pin<Box<SelfReferential>> type. The easiest way to do this is to use the Box::pin function instead of Box::new for creating the heap allocated value:
let mut heap_value = Box::pin(SelfReferential {
self_ptr: 0 as *const _,
_pin: PhantomPinned,
});
In addition to changing Box::new to Box::pin, we also need to add the new _pin field in the struct initializer. Since PhantomPinned is a zero sized type, we only need its type name to initialize it.
When we try to run our adjusted example now, we see that it no longer works:
error[E0594]: cannot assign to data in a dereference of `std::pin::Pin<std::boxed::Box<SelfReferential>>`
--> src/main.rs:10:5
|
10 | heap_value.self_ptr = ptr;
| ^^^^^^^^^^^^^^^^^^^^^^^^^ cannot assign
|
= help: trait `DerefMut` is required to modify through a dereference, but it is not implemented for `std::pin::Pin<std::boxed::Box<SelfReferential>>`
error[E0596]: cannot borrow data in a dereference of `std::pin::Pin<std::boxed::Box<SelfReferential>>` as mutable
--> src/main.rs:16:36
|
16 | let stack_value = mem::replace(&mut *heap_value, SelfReferential {
| ^^^^^^^^^^^^^^^^ cannot borrow as mutable
|
= help: trait `DerefMut` is required to modify through a dereference, but it is not implemented for `std::pin::Pin<std::boxed::Box<SelfReferential>>`
Both errors occur because the Pin<Box<SelfReferential>> type no longer implements the DerefMut trait. This exactly what we wanted because the DerefMut trait would return a &mut reference, which we want to prevent. This only works because we both opted-out of Unpin and changed Box::new to Box::pin.
The problem now is that the compiler does not only prevent moving the type in line 16, but also forbids to initialize the self_ptr field in line 10. This happens because the compiler can't differentiate between valid and invalid uses of &mut references. To get the initialization working again, we have to use the unsafe get_unchecked_mut method:
// safe because modifying a field doesn't move the whole struct
unsafe {
let mut_ref = Pin::as_mut(&mut heap_value);
Pin::get_unchecked_mut(mut_ref).self_ptr = ptr;
}
The get_unchecked_mut function works on a Pin<&mut T> instead of a Pin<Box<T>>, so we have to use the Pin::as_mut for converting the value before. Then we can set the self_ptr field using the &mut reference returned by get_unchecked_mut.
Now the only error left is the desired error on mem::replace. Remember, this operation tries to move the heap allocated value to stack, which would break the self-reference stored in the self_ptr field. By opting out of Unpin and using Pin<Box<T>>, we can prevent this error and safely work with self-referential structs. Note that the compiler is not able to prove that the creation of the self-reference is safe (yet), so we need to use an unsafe block and verify the correctness ourselves.
Stack Pinning and Pin<&mut T>
In the previous section we learned how to use Pin<Box<T>> to safely create a heap allocated self-referential value. While this approach works fine and is relatively safe (apart from the unsafe construction), the required heap allocation comes with a performance cost. Since Rust always wants to provide zero-cost abstractions when possible, the pinning API also allows to create Pin<&mut T> instances that point to stack allocated values.
Unlike Pin<Box<T>> instances, which have ownership of the wrapped value, Pin<&mut T> instances only temporarily borrow the wrapped value. This makes things more compilicated, as it requires the programmer to ensure additional guarantees themself. Most importantly, a Pin<&mut T> must stay pinned for the whole lifetime of the referenced T, which can be difficult to verify for stack based variables. To help with this, crates like pin-utils exist, but I still wouldn't recommend pinning to the stack unless you really know what you're doing.
For further reading, check out the documentation of the pin module and the Pin::new_unchecked method.
Pinning and Futures
As we already saw in this post, the Future::poll method uses pinning in form of a Pin<&mut Self> parameter:
fn poll(self: Pin<&mut Self>, cx: &mut Context) -> Poll<Self::Output>
The reason that this method takes self: Pin<&mut Self> instead of the normal &mut self is that future instances created from async/await are often self-referential, as we saw above. By wrapping Self into Pin and letting the compiler opt-out of Unpin for self-referentual futures generated from async/await, it is guaranteed that the futures are not moved in memory between poll calls. This ensures that all internal references are still valid.
It is worth noting that moving futures before the first poll call is fine. This is a result of the fact that futures are lazy and do nothing until they're polled for the first time. The start state of the generated state machines therefore only contains the function arguments, but no internal references. In order to call poll, the caller must wrap the future into Pin first, which ensures that the future cannot moved in memory anymore.
Since the Pin<&mut Self> interface is predefined by the Future trait, there is no way to use the safer Pin<Box<Self>> instead. This can make it quite challenging to safely implement Future yourself. For this reason I recommend against implementing Future manually and instead sticking to using async/await and the combinator methods of the futures crate.
In case you're interested in understanding how to safely implement Future yourself, take a look at the relatively short source of the map combinator method of the futures crate and the section about projections and structural pinning of the pin documentation.