% Rust Tasks and Communication Tutorial # Introduction The designers of Rust designed the language from the ground up to support pervasive and safe concurrency through lightweight, memory-isolated tasks and message passing. Rust tasks are not the same as traditional threads: rather, they are more like _green threads_. The Rust runtime system schedules tasks cooperatively onto a small number of operating system threads. Because tasks are significantly cheaper to create than traditional threads, Rust can create hundreds of thousands of concurrent tasks on a typical 32-bit system. Tasks provide failure isolation and recovery. When an exception occurs in Rust code (as a result of an explicit call to `fail!()`, 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 for failure. Rust tasks have dynamically sized stacks. A task begins its life with a small amount of stack space (currently in the low thousands of bytes, depending on platform), and acquires more stack as needed. Unlike in languages such as C, a Rust task cannot run off the end of the stack. However, tasks do have a stack budget. If a Rust task exceeds its stack budget, then it will fail safely: with a checked exception. Tasks use Rust's type system to provide strong memory safety guarantees. In particular, the type system guarantees that tasks cannot share mutable state with each other. Tasks communicate with each other by transferring _owned_ data through the global _exchange heap_. This tutorial explains the basics of tasks and communication in Rust, explores some typical patterns in concurrent Rust code, and finally discusses some of the more unusual synchronization types in the standard library. > ***Warning:*** This tutorial is incomplete ## A note about the libraries While Rust's type system provides the building blocks needed for safe and efficient tasks, all of the task functionality itself is implemented in the core and standard libraries, which are still under development and do not always present a consistent interface. In particular, there are currently two independent modules that provide a message passing interface to Rust code: `core::comm` and `core::pipes`. `core::comm` is an older, less efficient system that is being phased out in favor of `pipes`. At some point, we will remove the existing `core::comm` API and move the user-facing portions of `core::pipes` to `core::comm`. In this tutorial, we discuss `pipes` and ignore the `comm` API. For your reference, these are the standard modules involved in Rust concurrency at this writing. * [`core::task`] - All code relating to tasks and task scheduling * [`core::comm`] - The deprecated message passing API * [`core::pipes`] - The new message passing infrastructure and API * [`std::comm`] - Higher level messaging types based on `core::pipes` * [`std::sync`] - More exotic synchronization tools, including locks * [`std::arc`] - The ARC (atomic reference counted) type, for safely sharing immutable data * [`std::par`] - Some basic tools for implementing parallel algorithms [`core::task`]: core/task.html [`core::comm`]: core/comm.html [`core::pipes`]: core/pipes.html [`std::comm`]: std/comm.html [`std::sync`]: std/sync.html [`std::arc`]: std/arc.html [`std::par`]: std/par.html # Basics The programming interface for creating and managing tasks lives in the `task` module of the `core` library, and is thus available to all Rust code by default. 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 core::io::println; use core::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); // Print something more profound in a different task using a lambda expression spawn( || println("I am also running in a different task!") ); // The canonical way to spawn is using `do` notation do spawn { println("I too am running in a different task!"); } ~~~~ In Rust, there is nothing special about creating tasks: 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, _owned types_. The language leaves the implementation details to the core library. The `spawn` function has a very simple type signature: `fn spawn(f: ~fn())`. Because it accepts only owned closures, and owned closures contain only owned data, `spawn` can safely move the entire closure and all its associated state into an entirely different task for execution. Like any closure, the function passed to `spawn` may capture an environment that it carries across tasks. ~~~ # use core::io::println; # use core::task::spawn; # fn generate_task_number() -> int { 0 } // Generate some state locally let child_task_number = generate_task_number(); do spawn { // Capture it in the remote task println(fmt!("I am child number %d", child_task_number)); } ~~~ By default, the scheduler multiplexes tasks across the available cores, running in parallel. Thus, on a multicore machine, running the following code should interleave the output in vaguely random order. ~~~ # use core::io::print; # use core::task::spawn; for int::range(0, 20) |child_task_number| { do spawn { print(fmt!("I am child number %d\n", child_task_number)); } } ~~~ ## Communication Now that we have spawned a new task, it would be nice if we could communicate with it. Recall that Rust does not have shared mutable state, so one task may not manipulate variables owned by another task. Instead we use *pipes*. A pipe is simply a pair of endpoints: one for sending messages and another for receiving messages. Pipes are low-level communication building-blocks and so come in a variety of forms, each one appropriate for a different use case. In what follows, we cover the most commonly used varieties. The simplest way to create a pipe is to use the `pipes::stream` function to create a `(Port, Chan)` pair. In Rust parlance, a *channel* is a sending endpoint of a pipe, and a *port* is the receiving endpoint. Consider the following example of calculating two results concurrently: ~~~~ use core::task::spawn; use core::comm::{stream, Port, Chan}; let (port, chan): (Port, Chan) = stream(); do spawn || { let result = some_expensive_computation(); chan.send(result); } some_other_expensive_computation(); let result = port.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`, `(chan, port)`, is an example of a *destructuring let*: the pattern separates a tuple into its component parts). ~~~~ # use core::comm::{stream, Chan, Port}; let (port, chan): (Port, Chan) = stream(); ~~~~ The child task will use the channel to send data to the parent task, which will wait to receive the data on the port. The next statement spawns the child task. ~~~~ # use core::task::spawn; # use core::comm::stream; # fn some_expensive_computation() -> int { 42 } # let (port, chan) = stream(); do spawn || { let result = some_expensive_computation(); chan.send(result); } ~~~~ Notice that the creation of the task closure transfers `chan` to the child task implicitly: the closure captures `chan` in its environment. Both `Chan` and `Port` 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 port: ~~~~ # use core::comm::{stream}; # fn some_other_expensive_computation() {} # let (port, chan) = stream::(); # chan.send(0); some_other_expensive_computation(); let result = port.recv(); ~~~~ The `Port` and `Chan` pair created by `stream` enables efficient communication between a single sender and a single receiver, but multiple senders cannot use a single `Chan`, and multiple receivers cannot use a single `Port`. What if our example needed to compute multiple results across a number of tasks? The following program is ill-typed: ~~~ {.xfail-test} # use core::task::{spawn}; # use core::comm::{stream, Port, Chan}; # fn some_expensive_computation() -> int { 42 } let (port, chan) = stream(); do spawn { chan.send(some_expensive_computation()); } // ERROR! The previous spawn statement already owns the channel, // so the compiler will not allow it to be captured again do spawn { chan.send(some_expensive_computation()); } ~~~ Instead we can use a `SharedChan`, a type that allows a single `Chan` to be shared by multiple senders. ~~~ # use core::task::spawn; use core::comm::{stream, SharedChan}; let (port, chan) = stream(); let chan = SharedChan(chan); for uint::range(0, 3) |init_val| { // Create a new channel handle to distribute to the child task let child_chan = chan.clone(); do spawn { child_chan.send(some_expensive_computation(init_val)); } } let result = port.recv() + port.recv() + port.recv(); # fn some_expensive_computation(_i: uint) -> int { 42 } ~~~ Here we transfer ownership of the channel into a new `SharedChan` value. Like `Chan`, `SharedChan` is a non-copyable, owned type (sometimes also referred to as an *affine* or *linear* type). Unlike with `Chan`, though, the programmer may duplicate a `SharedChan`, with the `clone()` method. A cloned `SharedChan` produces a new handle to the same channel, allowing multiple tasks to send data to a single port. Between `spawn`, `stream` and `SharedChan`, we have enough tools to implement many useful concurrency patterns. Note that the above `SharedChan` example is somewhat contrived since you could also simply use three `stream` pairs, but it serves to illustrate the point. For reference, written with multiple streams, it might look like the example below. ~~~ # use core::task::spawn; # use core::comm::stream; // Create a vector of ports, one for each child task let ports = do vec::from_fn(3) |init_val| { let (port, chan) = stream(); do spawn { chan.send(some_expensive_computation(init_val)); } port }; // Wait on each port, accumulating the results let result = ports.foldl(0, |accum, port| *accum + port.recv() ); # fn some_expensive_computation(_i: uint) -> int { 42 } ~~~ # Handling task failure Rust has a built-in mechanism for raising exceptions. The `fail!()` macro (which can also be written with an error string as an argument: `fail!( ~reason)`) and the `fail_unless!` construct (which effectively calls `fail!()` 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 fails, there is no way to "catch" the exception. All tasks are, by default, _linked_ to each other. That means that the fates of all tasks are intertwined: if one fails, so do all the others. ~~~ # use core::task::spawn; # fn do_some_work() { loop { task::yield() } } # do task::try { // Create a child task that fails do spawn { fail!() } // This will also fail because the task we spawned failed do_some_work(); # }; ~~~ While it isn't possible for a task to recover from failure, tasks may notify each other of failure. The simplest way of handling task failure is with the `try` function, which is similar to `spawn`, but immediately blocks waiting for the child task to finish. `try` returns a value of type `Result`. `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). ~~~ # fn some_condition() -> bool { false } # fn calculate_result() -> int { 0 } let result: Result = do task::try { if some_condition() { calculate_result() } else { fail!(~"oops!"); } }; fail_unless!(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 fails, `try` will return an `Error` result. [`Result`]: core/result.html > ***Note:*** A failed task does not currently produce a useful error > value (`try` always returns `Err(())`). In the > future, it may be possible for tasks to intercept the value passed to > `fail!()`. TODO: Need discussion of `future_result` in order to make failure modes useful. But not all failure is 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 failure at a certain boundary (perhaps a small piece of input from the outside world, which you happen to be processing in parallel, is malformed and its processing task can't proceed). Hence, you will need different _linked failure modes_. ## Failure modes By default, task failure is _bidirectionally linked_, which means that if either task fails, it kills the other one. ~~~ # fn sleep_forever() { loop { task::yield() } } # do task::try { do task::spawn { do task::spawn { fail!(); // All three tasks will fail. } sleep_forever(); // Will get woken up by force, then fail } sleep_forever(); // Will get woken up by force, then fail # }; ~~~ If you want parent tasks to be able to kill their children, but do not want a parent to fail automatically if one of its child task fails, you can call `task::spawn_supervised` for _unidirectionally linked_ failure. The function `task::try`, which we saw previously, uses `spawn_supervised` internally, with additional logic to wait for the child task to finish before returning. Hence: ~~~ # use core::comm::{stream, Chan, Port}; # use core::task::{spawn, try}; # fn sleep_forever() { loop { task::yield() } } # do task::try { let (receiver, sender): (Port, Chan) = stream(); do spawn { // Bidirectionally linked // Wait for the supervised child task to exist. let message = receiver.recv(); // Kill both it and the parent task. fail_unless!(message != 42); } do try { // Unidirectionally linked sender.send(42); sleep_forever(); // Will get woken up by force } // Flow never reaches here -- parent task was killed too. # }; ~~~ Supervised failure is useful in any situation where one task manages multiple fallible child tasks, and the parent task can recover if any child fails. On the other hand, if the _parent_ (supervisor) fails, then there is nothing the children can do to recover, so they should also fail. Supervised task failure propagates across multiple generations even if an intermediate generation has already exited: ~~~ # fn sleep_forever() { loop { task::yield() } } # fn wait_for_a_while() { for 1000.times { task::yield() } } # do task::try:: { do task::spawn_supervised { do task::spawn_supervised { sleep_forever(); // Will get woken up by force, then fail } // Intermediate task immediately exits } wait_for_a_while(); fail!(); // Will kill grandchild even if child has already exited # }; ~~~ Finally, tasks can be configured to not propagate failure to each other at all, using `task::spawn_unlinked` for _isolated failure_. ~~~ # fn random() -> uint { 100 } # fn sleep_for(i: uint) { for i.times { task::yield() } } # do task::try::<()> { let (time1, time2) = (random(), random()); do task::spawn_unlinked { sleep_for(time2); // Won't get forced awake fail!(); } sleep_for(time1); // Won't get forced awake fail!(); // It will take MAX(time1,time2) for the program to finish. # }; ~~~ ## Creating a task with a bi-directional communication path A very common thing to do is to spawn a child task where the parent and child both need to exchange messages with each other. The function `std::comm::DuplexStream()` supports this pattern. We'll look briefly at how to use it. To see how `DuplexStream()` works, we will create a child task that repeatedly receives a `uint` message, converts it to a string, and sends the string in response. The child terminates when it receives `0`. Here is the function that implements the child task: ~~~~ # use std::comm::DuplexStream; fn stringifier(channel: &DuplexStream<~str, uint>) { let mut value: uint; loop { value = channel.recv(); channel.send(uint::to_str(value)); if value == 0 { break; } } } ~~~~ The implementation of `DuplexStream` supports both sending and receiving. The `stringifier` function takes a `DuplexStream` that can send strings (the first type parameter) and receive `uint` messages (the second type parameter). The body itself simply loops, reading from the channel and then sending its response back. The actual response itself is simply the stringified version of the received value, `uint::to_str(value)`. Here is the code for the parent task: ~~~~ # use core::task::spawn; # use std::comm::DuplexStream; # fn stringifier(channel: &DuplexStream<~str, uint>) { # let mut value: uint; # loop { # value = channel.recv(); # channel.send(uint::to_str(value)); # if value == 0u { break; } # } # } # fn main() { let (from_child, to_child) = DuplexStream(); do spawn { stringifier(&to_child); }; from_child.send(22); fail_unless!(from_child.recv() == ~"22"); from_child.send(23); from_child.send(0); fail_unless!(from_child.recv() == ~"23"); fail_unless!(from_child.recv() == ~"0"); # } ~~~~ The parent task first calls `DuplexStream` to create a pair of bidirectional endpoints. It then uses `task::spawn` to create the child task, which captures one end of the communication channel. As a result, both parent and child can send and receive data to and from the other.