% The Rust Tasks and Communication Guide
NOTE This guide is badly out of date an needs to be rewritten.
Rust provides safe concurrent abstractions through a number of core library primitives. This guide will describe the concurrency model in Rust, how it relates to the Rust type system, and introduce the fundamental library abstractions for constructing concurrent programs.
Tasks provide failure isolation and recovery. When a fatal error occurs in Rust
code as a result of an explicit call to panic!(), an assertion failure, or
another invalid operation, the runtime system destroys the entire task. Unlike
in languages such as Java and C++, there is no way to catch an exception.
Instead, tasks may monitor each other to see if they panic.
Tasks use Rust's type system to provide strong memory safety guarantees. In particular, the type system guarantees that tasks cannot induce a data race from shared mutable state.
At its simplest, creating a task is a matter of calling the spawn function
with a closure argument. spawn executes the closure in the new task.
# use std::task::spawn;
// Print something profound in a different task using a named function
fn print_message() { println!("I am running in a different task!"); }
spawn(print_message);
// Alternatively, use a `move ||` expression instead of a named function.
// `||` expressions evaluate to an unnamed closure. The `move` keyword
// indicates that the closure should take ownership of any variables it
// touches.
spawn(move || println!("I am also running in a different task!"));
In Rust, a task is not a concept that appears in the language semantics. Instead, Rust's type system provides all the tools necessary to implement safe concurrency: particularly, ownership. The language leaves the implementation details to the standard library.
The spawn function has the type signature: fn spawn<F:FnOnce()+Send>(f: F). This indicates that it takes as
argument a closure (of type F) that it will run exactly once. This
closure is limited to capturing Send-able data from its environment
(that is, data which is deeply owned). Limiting the closure to Send
ensures that spawn can safely move the entire closure and all its
associated state into an entirely different task for execution.
# use std::task::spawn;
# fn generate_task_number() -> int { 0 }
// Generate some state locally
let child_task_number = generate_task_number();
spawn(move || {
// Capture it in the remote task. The `move` keyword indicates
// that this closure should move `child_task_number` into its
// environment, rather than capturing a reference into the
// enclosing stack frame.
println!("I am child number {}", child_task_number);
});
Now that we have spawned a new task, it would be nice if we could communicate with it. For this, we use channels. A channel is simply a pair of endpoints: one for sending messages and another for receiving messages.
The simplest way to create a channel is to use the channel function to create a
(Sender, Receiver) pair. In Rust parlance, a sender is a sending endpoint
of a channel, and a receiver is the receiving endpoint. Consider the following
example of calculating two results concurrently:
# use std::task::spawn;
let (tx, rx): (Sender<int>, Receiver<int>) = channel();
spawn(move || {
let result = some_expensive_computation();
tx.send(result);
});
some_other_expensive_computation();
let result = rx.recv();
# fn some_expensive_computation() -> int { 42 }
# fn some_other_expensive_computation() {}
Let's examine this example in detail. First, the let statement creates a
stream for sending and receiving integers (the left-hand side of the let,
(tx, rx), is an example of a destructuring let: the pattern separates a tuple
into its component parts).
let (tx, rx): (Sender<int>, Receiver<int>) = channel();
The child task will use the sender to send data to the parent task, which will wait to receive the data on the receiver. The next statement spawns the child task.
# use std::task::spawn;
# fn some_expensive_computation() -> int { 42 }
# let (tx, rx) = channel();
spawn(move || {
let result = some_expensive_computation();
tx.send(result);
});
Notice that the creation of the task closure transfers tx to the child task
implicitly: the closure captures tx in its environment. Both Sender and
Receiver are sendable types and may be captured into tasks or otherwise
transferred between them. In the example, the child task runs an expensive
computation, then sends the result over the captured channel.
Finally, the parent continues with some other expensive computation, then waits for the child's result to arrive on the receiver:
# fn some_other_expensive_computation() {}
# let (tx, rx) = channel::<int>();
# tx.send(0);
some_other_expensive_computation();
let result = rx.recv();
The Sender and Receiver pair created by channel enables efficient
communication between a single sender and a single receiver, but multiple
senders cannot use a single Sender value, and multiple receivers cannot use a
single Receiver value. What if our example needed to compute multiple
results across a number of tasks? The following program is ill-typed:
# fn some_expensive_computation() -> int { 42 }
let (tx, rx) = channel();
spawn(move || {
tx.send(some_expensive_computation());
});
// ERROR! The previous spawn statement already owns the sender,
// so the compiler will not allow it to be captured again
spawn(move || {
tx.send(some_expensive_computation());
});
Instead we can clone the tx, which allows for multiple senders.
let (tx, rx) = channel();
for init_val in range(0u, 3) {
// Create a new channel handle to distribute to the child task
let child_tx = tx.clone();
spawn(move || {
child_tx.send(some_expensive_computation(init_val));
});
}
let result = rx.recv() + rx.recv() + rx.recv();
# fn some_expensive_computation(_i: uint) -> int { 42 }
Cloning a Sender produces a new handle to the same channel, allowing multiple
tasks to send data to a single receiver. It upgrades the channel internally in
order to allow this functionality, which means that channels that are not
cloned can avoid the overhead required to handle multiple senders. But this
fact has no bearing on the channel's usage: the upgrade is transparent.
Note that the above cloning example is somewhat contrived since you could also
simply use three Sender pairs, but it serves to illustrate the point. For
reference, written with multiple streams, it might look like the example below.
# use std::task::spawn;
// Create a vector of ports, one for each child task
let rxs = Vec::from_fn(3, |init_val| {
let (tx, rx) = channel();
spawn(move || {
tx.send(some_expensive_computation(init_val));
});
rx
});
// Wait on each port, accumulating the results
let result = rxs.iter().fold(0, |accum, rx| accum + rx.recv() );
# fn some_expensive_computation(_i: uint) -> int { 42 }
With sync::Future, rust has a mechanism for requesting a computation and
getting the result later.
The basic example below illustrates this.
use std::sync::Future;
# fn main() {
# fn make_a_sandwich() {};
fn fib(n: u64) -> u64 {
// lengthy computation returning an uint
12586269025
}
let mut delayed_fib = Future::spawn(move || fib(50));
make_a_sandwich();
println!("fib(50) = {}", delayed_fib.get())
# }
The call to future::spawn immediately returns a future object regardless of
how long it takes to run fib(50). You can then make yourself a sandwich while
the computation of fib is running. The result of the execution of the method
is obtained by calling get on the future. This call will block until the
value is available (i.e. the computation is complete). Note that the future
needs to be mutable so that it can save the result for next time get is
called.
Here is another example showing how futures allow you to background computations. The workload will be distributed on the available cores.
# use std::num::Float;
# use std::sync::Future;
fn partial_sum(start: uint) -> f64 {
let mut local_sum = 0f64;
for num in range(start*100000, (start+1)*100000) {
local_sum += (num as f64 + 1.0).powf(-2.0);
}
local_sum
}
fn main() {
let mut futures = Vec::from_fn(200, |ind| Future::spawn(move || partial_sum(ind)));
let mut final_res = 0f64;
for ft in futures.iter_mut() {
final_res += ft.get();
}
println!("π^2/6 is not far from : {}", final_res);
}
To share data between tasks, a first approach would be to only use channel as we have seen previously. A copy of the data to share would then be made for each task. In some cases, this would add up to a significant amount of wasted memory and would require copying the same data more than necessary.
To tackle this issue, one can use an Atomically Reference Counted wrapper
(Arc) as implemented in the sync library of Rust. With an Arc, the data
will no longer be copied for each task. The Arc acts as a reference to the
shared data and only this reference is shared and cloned.
Here is a small example showing how to use Arcs. We wish to run concurrently several computations on a single large vector of floats. Each task needs the full vector to perform its duty.
use std::num::Float;
use std::rand;
use std::sync::Arc;
fn pnorm(nums: &[f64], p: uint) -> f64 {
nums.iter().fold(0.0, |a, b| a + b.powf(p as f64)).powf(1.0 / (p as f64))
}
fn main() {
let numbers = Vec::from_fn(1000000, |_| rand::random::<f64>());
let numbers_arc = Arc::new(numbers);
for num in range(1u, 10) {
let task_numbers = numbers_arc.clone();
spawn(move || {
println!("{}-norm = {}", num, pnorm(task_numbers.as_slice(), num));
});
}
}
The function pnorm performs a simple computation on the vector (it computes
the sum of its items at the power given as argument and takes the inverse power
of this value). The Arc on the vector is created by the line:
# use std::rand;
# use std::sync::Arc;
# fn main() {
# let numbers = Vec::from_fn(1000000, |_| rand::random::<f64>());
let numbers_arc = Arc::new(numbers);
# }
and a clone is captured for each task via a procedure. This only copies the wrapper and not its contents. Within the task's procedure, the captured Arc reference can be used as a shared reference to the underlying vector as if it were local.
# use std::rand;
# use std::sync::Arc;
# fn pnorm(nums: &[f64], p: uint) -> f64 { 4.0 }
# fn main() {
# let numbers=Vec::from_fn(1000000, |_| rand::random::<f64>());
# let numbers_arc = Arc::new(numbers);
# let num = 4;
let task_numbers = numbers_arc.clone();
spawn(move || {
// Capture task_numbers and use it as if it was the underlying vector
println!("{}-norm = {}", num, pnorm(task_numbers.as_slice(), num));
});
# }
Rust has a built-in mechanism for raising exceptions. The panic!() macro
(which can also be written with an error string as an argument: panic!( ~reason)) and the assert! construct (which effectively calls panic!() if a
boolean expression is false) are both ways to raise exceptions. When a task
raises an exception, the task unwinds its stack—running destructors and
freeing memory along the way—and then exits. Unlike exceptions in C++,
exceptions in Rust are unrecoverable within a single task: once a task panics,
there is no way to "catch" the exception.
While it isn't possible for a task to recover from panicking, tasks may notify
each other if they panic. The simplest way of handling a panic is with the
try function, which is similar to spawn, but immediately blocks and waits
for the child task to finish. try returns a value of type
Result<T, Box<Any + Send>>. Result is an enum type with two variants:
Ok and Err. In this case, because the type arguments to Result are int
and (), callers can pattern-match on a result to check whether it's an Ok
result with an int field (representing a successful result) or an Err result
(representing termination with an error).
# use std::thread::Thread;
# fn some_condition() -> bool { false }
# fn calculate_result() -> int { 0 }
let result: Result<int, Box<std::any::Any + Send>> = Thread::spawn(move || {
if some_condition() {
calculate_result()
} else {
panic!("oops!");
}
}).join();
assert!(result.is_err());
Unlike spawn, the function spawned using try may return a value, which
try will dutifully propagate back to the caller in a Result enum. If the
child task terminates successfully, try will return an Ok result; if the
child task panics, try will return an Error result.
Note: A panicked task does not currently produce a useful error value (
tryalways returnsErr(())). In the future, it may be possible for tasks to intercept the value passed topanic!().
But not all panics are created equal. In some cases you might need to abort the entire program (perhaps you're writing an assert which, if it trips, indicates an unrecoverable logic error); in other cases you might want to contain the panic at a certain boundary (perhaps a small piece of input from the outside world, which you happen to be processing in parallel, is malformed such that the processing task cannot proceed).