rust/doc/tutorial-tasks.md

% 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 io::println;
use 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 io::println;
# use 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 io::print;
# use 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 task::spawn;
use pipes::{stream, Port, Chan};

let (port, chan): (Port<int>, Chan<int>) = stream();

do spawn |move chan| {
    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 pipes::{stream, Chan, Port};
let (port, chan): (Port<int>, Chan<int>) = 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 task::{spawn};
# use task::spawn;
# use pipes::{stream, Port, Chan};
# fn some_expensive_computation() -> int { 42 }
# let (port, chan) = stream();
do spawn |move chan| {
    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 pipes::{stream, Port, Chan};
# fn some_other_expensive_computation() {}
# let (port, chan) = stream::<int>();
# 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 task::{spawn};
# use pipes::{stream, Port, Chan};
# fn some_expensive_computation() -> int { 42 }
let (port, chan) = stream();

do spawn |move chan| {
    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 task::spawn;
use pipes::{stream, SharedChan};

let (port, chan) = stream();
let chan = SharedChan(move 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 |move child_chan| {
        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 task::spawn;
# use pipes::{stream, Port, Chan};

// 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 |move chan| {
        chan.send(some_expensive_computation(init_val));
    }
    move 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 `assert` 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 task::spawn;
# fn do_some_work() { loop { task::yield() } }
# do task::try {
// Create a child task that fails
do spawn { die!() }

// 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<int,
()>`. `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<int, ()> = do task::try {
    if some_condition() {
        calculate_result()
    } else {
        die!(~"oops!");
    }
};
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 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 dies, it kills the other one.

~~~
# fn sleep_forever() { loop { task::yield() } }
# do task::try {
do task::spawn {
    do task::spawn {
        die!();  // All three tasks will die.
    }
    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 die automatically if one of its child task dies, 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 pipes::{stream, Chan, Port};
# use task::{spawn, try};
# fn sleep_forever() { loop { task::yield() } }
# do task::try {
let (receiver, sender): (Port<int>, Chan<int>) = stream();
do spawn |move receiver| {  // Bidirectionally linked
    // Wait for the supervised child task to exist.
    let message = receiver.recv();
    // Kill both it and the parent task.
    assert message != 42;
}
do try |move sender| {  // 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::<int> {
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();
die!();  // 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
    die!();
}
sleep_for(time1);  // Won't get forced awake
die!();
// 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;
# use pipes::{Port, Chan};
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 std::comm::DuplexStream;
# use pipes::{Port, Chan};
# use task::spawn;
# 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 |move to_child| {
    stringifier(&to_child);
};

from_child.send(22);
assert from_child.recv() == ~"22";

from_child.send(23);
from_child.send(0);

assert from_child.recv() == ~"23";
assert 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.