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rust/doc/tutorial-tasks.md
Brian Anderson 2f95f7d8de doc: Mention std::par in task tutorial
2012-09-29 19:29:28 -07:00

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% Rust Tasks and Communication Tutorial

Introduction

Rust supports concurrency and parallelism through lightweight tasks. Rust tasks are significantly cheaper to create than traditional threads, with a typical 32-bit system able to run hundreds of thousands simultaneously. Tasks in Rust are what are often referred to as green threads, cooperatively scheduled by the Rust runtime onto a small number of operating system threads.

Tasks provide failure isolation and recovery. When an exception occurs in rust code (either by calling fail explicitly or by otherwise performing an invalid operation) the entire task is destroyed - there is no way to catch an exception as in other languages. Instead tasks may monitor each other to detect when failure has occurred.

Rust tasks have dynamically sized stacks. When a task is first created it starts off with a small amount of stack (in the hundreds to low thousands of bytes, depending on plattform), and more stack is added as needed. A Rust task will never run off the end of the stack as is possible in many other languages, but they do have a stack budget, and if a Rust task exceeds its stack budget then it will fail safely.

Tasks make use of Rust's type system to provide strong memory safety guarantees, disallowing shared mutable state. Communication between tasks is facilitated by the transfer of owned data through the global exchange heap.

This tutorial will explain the basics of tasks and communication in Rust, explore some typical patterns in concurrent Rust code, and finally discuss some of the more exotic synchronization types in the standard library.

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 nice programming 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 the existing core::comm API will be romoved and the user-facing portions of core::pipes will be moved to core::comm. In this tutorial we will discuss pipes and ignore the comm API.

For your reference, these are the standard modules involved in Rust concurrency at the moment.

  • 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 type, for safely sharing immutable data
  • std::par - Some basic tools for implementing parallel algorithms

Spawning a task

Spawning a task is done using the various spawn functions in the module task. Let's begin with the simplest one, task::spawn():

use task::spawn;
use io::println;

let some_value = 22;

do spawn {
    println(~"This executes in the child task.");
    println(fmt!("%d", some_value));
}

The argument to task::spawn() is a unique closure of type fn~(), meaning that it takes no arguments and generates no return value. The effect of task::spawn() is to fire up a child task that will execute the closure in parallel with the creator.

Communication

Now that we have spawned a child task, it would be nice if we could communicate with it. This is done using pipes. Pipes are simply a pair of endpoints, with one for sending messages and another for receiving messages. The easiest way to create a pipe is to use pipes::stream. Imagine we wish to perform two expensive computations in parallel. We might write something like:

use task::spawn;
use pipes::{stream, Port, Chan};

let (chan, port) = 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 walk through this code line-by-line. The first line creates a stream for sending and receiving integers:

# use pipes::stream;
let (chan, port) = stream();

This port is where we will receive the message from the child task once it is complete. The channel will be used by the child to send a message to the port. The next statement actually spawns the child:

# use task::{spawn};
# use comm::{Port, Chan};
# fn some_expensive_computation() -> int { 42 }
# let port = Port();
# let chan = port.chan();
do spawn {
    let result = some_expensive_computation();
    chan.send(result);
}

This child will perform the expensive computation send the result over the channel. (Under the hood, chan was captured by the closure that forms the body of the child task. This capture is allowed because channels are sendable.)

Finally, the parent continues by performing some other expensive computation and then waiting for the child's result to arrive on the port:

# use pipes::{stream, Port, Chan};
# fn some_other_expensive_computation() {}
# let (chan, port) = stream::<int>();
# chan.send(0);
some_other_expensive_computation();
let result = port.recv();

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 it is used.

To see how spawn_conversation() works, we will create a child task that receives uint messages, converts them to a string, and sends the string in response. The child terminates when 0 is received. 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, 10u));
        if value == 0u { 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 strified 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, 10u));
#         if value == 0u { break; }
#     }
# }
# fn main() {

let (from_child, to_child) = DuplexStream();

do spawn || {
    stringifier(&to_child);
};

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

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

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.