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[TOC]

# Fearless Concurrency

Handling concurrent programming safely and efficiently is another of Rust’s
major goals. *Concurrent programming*, where different parts of a program
execute independently, and *parallel programming*, where different parts of a
program execute at the same time, are becoming increasingly important as more
computers take advantage of their multiple processors. Historically,
programming in these contexts has been difficult and error prone: Rust hopes to
change that.

<!-- Concurrent programming isn't necessarily helped by having multiple
processors. How I've been teaching it is to distinguish the two by their
workload: concurrent programming serves the needs of I/O-bound workloads and
parallel programming serves the needs of CPU-bound workloads. If you give
CPU bound workloads more CPUs, you have the opportunity to possibly go faster
(assuming sufficient parallelism in the code). For I/O-bound workloads,
rather than the need to have multiple processors, you need to be able to
get as many I/O requests in flight and being processed as you can. This
allows more I/O requests, and as a result better throughput/response time
on those I/O requests.

We could introduce these concepts and then simplify like we do in a bit to
say that the design considerations of Rust allow both concurrency and
parallelism to be done safely (...and for the remainder of the chapter talk
about those design considerations rather than the specifics for either
concurrency or parallelism) /JT -->
<!-- I really don't want to get in the weeds on this because there are many
other books and resources about concurrency and parallelism because these
concepts aren't Rust specific. I want this to feel accessible to programmers
who have never even considered whether their programs are I/O or CPU bound,
because those are the types of programmers we want to empower (and make them
feel empowered to create concurrent and/or parallel code) through Rust. So I'm
deliberately choosing not to change anything here. /Carol -->

Initially, the Rust team thought that ensuring memory safety and preventing
concurrency problems were two separate challenges to be solved with different
methods. Over time, the team discovered that the ownership and type systems are
a powerful set of tools to help manage memory safety *and* concurrency
problems! By leveraging ownership and type checking, many concurrency errors
are compile-time errors in Rust rather than runtime errors. Therefore, rather
than making you spend lots of time trying to reproduce the exact circumstances
under which a runtime concurrency bug occurs, incorrect code will refuse to
compile and present an error explaining the problem. As a result, you can fix
your code while you’re working on it rather than potentially after it has been
shipped to production. We’ve nicknamed this aspect of Rust *fearless*
*concurrency*. Fearless concurrency allows you to write code that is free of
subtle bugs and is easy to refactor without introducing new bugs.

> Note: For simplicity’s sake, we’ll refer to many of the problems as
> *concurrent* rather than being more precise by saying *concurrent and/or
> parallel*. If this book were about concurrency and/or parallelism, we’d be
> more specific. For this chapter, please mentally substitute *concurrent
> and/or parallel* whenever we use *concurrent*.

Many languages are dogmatic about the solutions they offer for handling
concurrent problems. For example, Erlang has elegant functionality for
message-passing concurrency but has only obscure ways to share state between
threads. Supporting only a subset of possible solutions is a reasonable
strategy for higher-level languages, because a higher-level language promises
benefits from giving up some control to gain abstractions. However, lower-level
languages are expected to provide the solution with the best performance in any
given situation and have fewer abstractions over the hardware. Therefore, Rust
offers a variety of tools for modeling problems in whatever way is appropriate
for your situation and requirements.

Here are the topics we’ll cover in this chapter:

* How to create threads to run multiple pieces of code at the same time
* *Message-passing* concurrency, where channels send messages between threads
* *Shared-state* concurrency, where multiple threads have access to some piece
  of data
* The `Sync` and `Send` traits, which extend Rust’s concurrency guarantees to
  user-defined types as well as types provided by the standard library

## Using Threads to Run Code Simultaneously

In most current operating systems, an executed program’s code is run in a
*process*, and the operating system will manage multiple processes at once.
Within a program, you can also have independent parts that run simultaneously.
The features that run these independent parts are called *threads*. For
example, a web server could have multiple threads so that it could respond to
more than one request at the same time.

Splitting the computation in your program into multiple threads to run multiple
tasks at the same time can improve performance, but it also adds complexity.
Because threads can run simultaneously, there’s no inherent guarantee about the
order in which parts of your code on different threads will run. This can lead
to problems, such as:

* Race conditions, where threads are accessing data or resources in an
  inconsistent order
* Deadlocks, where two threads are waiting for each other, preventing both
  threads from continuing
* Bugs that happen only in certain situations and are hard to reproduce and fix
  reliably

Rust attempts to mitigate the negative effects of using threads, but
programming in a multithreaded context still takes careful thought and requires
a code structure that is different from that in programs running in a single
thread.

Programming languages implement threads in a few different ways, and many
operating systems provide an API the language can call for creating new
threads. The Rust standard library uses a *1:1* model of thread implementation,
whereby a program uses one operating system thread per one language thread.
There are crates that implement other models of threading that make different
tradeoffs to the 1:1 model.

### Creating a New Thread with `spawn`

To create a new thread, we call the `thread::spawn` function and pass it a
closure (we talked about closures in Chapter 13) containing the code we want to
run in the new thread. The example in Listing 16-1 prints some text from a main
thread and other text from a new thread:

Filename: src/main.rs

```
use std::thread;
use std::time::Duration;

fn main() {
    thread::spawn(|| {
        for i in 1..10 {
            println!("hi number {} from the spawned thread!", i);
            thread::sleep(Duration::from_millis(1));
        }
    });

    for i in 1..5 {
        println!("hi number {} from the main thread!", i);
        thread::sleep(Duration::from_millis(1));
    }
}
```

Listing 16-1: Creating a new thread to print one thing while the main thread
prints something else

Note that when the main thread of a Rust program completes, all spawned threads
are shut down, whether or not they have finished running. The output from this
program might be a little different every time, but it will look similar to the
following:

```
hi number 1 from the main thread!
hi number 1 from the spawned thread!
hi number 2 from the main thread!
hi number 2 from the spawned thread!
hi number 3 from the main thread!
hi number 3 from the spawned thread!
hi number 4 from the main thread!
hi number 4 from the spawned thread!
hi number 5 from the spawned thread!
```

The calls to `thread::sleep` force a thread to stop its execution for a short
duration, allowing a different thread to run. The threads will probably take
turns, but that isn’t guaranteed: it depends on how your operating system
schedules the threads. In this run, the main thread printed first, even though
the print statement from the spawned thread appears first in the code. And even
though we told the spawned thread to print until `i` is 9, it only got to 5
before the main thread shut down.

If you run this code and only see output from the main thread, or don’t see any
overlap, try increasing the numbers in the ranges to create more opportunities
for the operating system to switch between the threads.

### Waiting for All Threads to Finish Using `join` Handles

The code in Listing 16-1 not only stops the spawned thread prematurely most of
the time due to the main thread ending, but because there is no guarantee on
the order in which threads run, we also can’t guarantee that the spawned thread
will get to run at all!

We can fix the problem of the spawned thread not running or ending prematurely
by saving the return value of `thread::spawn` in a variable. The return type of
`thread::spawn` is `JoinHandle`. A `JoinHandle` is an owned value that, when we
call the `join` method on it, will wait for its thread to finish. Listing 16-2
shows how to use the `JoinHandle` of the thread we created in Listing 16-1 and
call `join` to make sure the spawned thread finishes before `main` exits:

Filename: src/main.rs

```
use std::thread;
use std::time::Duration;

fn main() {
    let handle = thread::spawn(|| {
        for i in 1..10 {
            println!("hi number {} from the spawned thread!", i);
            thread::sleep(Duration::from_millis(1));
        }
    });

    for i in 1..5 {
        println!("hi number {} from the main thread!", i);
        thread::sleep(Duration::from_millis(1));
    }

    handle.join().unwrap();
}
```

Listing 16-2: Saving a `JoinHandle` from `thread::spawn` to guarantee the
thread is run to completion

Calling `join` on the handle blocks the thread currently running until the
thread represented by the handle terminates. *Blocking* a thread means that
thread is prevented from performing work or exiting. Because we’ve put the call
to `join` after the main thread’s `for` loop, running Listing 16-2 should
produce output similar to this:

```
hi number 1 from the main thread!
hi number 2 from the main thread!
hi number 1 from the spawned thread!
hi number 3 from the main thread!
hi number 2 from the spawned thread!
hi number 4 from the main thread!
hi number 3 from the spawned thread!
hi number 4 from the spawned thread!
hi number 5 from the spawned thread!
hi number 6 from the spawned thread!
hi number 7 from the spawned thread!
hi number 8 from the spawned thread!
hi number 9 from the spawned thread!
```

The two threads continue alternating, but the main thread waits because of the
call to `handle.join()` and does not end until the spawned thread is finished.

But let’s see what happens when we instead move `handle.join()` before the
`for` loop in `main`, like this:

Filename: src/main.rs

```
use std::thread;
use std::time::Duration;

fn main() {
    let handle = thread::spawn(|| {
        for i in 1..10 {
            println!("hi number {} from the spawned thread!", i);
            thread::sleep(Duration::from_millis(1));
        }
    });

    handle.join().unwrap();

    for i in 1..5 {
        println!("hi number {} from the main thread!", i);
        thread::sleep(Duration::from_millis(1));
    }
}
```

The main thread will wait for the spawned thread to finish and then run its
`for` loop, so the output won’t be interleaved anymore, as shown here:

```
hi number 1 from the spawned thread!
hi number 2 from the spawned thread!
hi number 3 from the spawned thread!
hi number 4 from the spawned thread!
hi number 5 from the spawned thread!
hi number 6 from the spawned thread!
hi number 7 from the spawned thread!
hi number 8 from the spawned thread!
hi number 9 from the spawned thread!
hi number 1 from the main thread!
hi number 2 from the main thread!
hi number 3 from the main thread!
hi number 4 from the main thread!
```

Small details, such as where `join` is called, can affect whether or not your
threads run at the same time.

### Using `move` Closures with Threads

We'll often use the `move` keyword with closures passed to `thread::spawn`
because the closure will then take ownership of the values it uses from the
environment, thus transferring ownership of those values from one thread to
another. In the “Capturing the Environment with Closures” section of Chapter
13, we discussed `move` in the context of closures. Now, we’ll concentrate more
on the interaction between `move` and `thread::spawn`.

Notice in Listing 16-1 that the closure we pass to `thread::spawn` takes no
arguments: we’re not using any data from the main thread in the spawned
thread’s code. To use data from the main thread in the spawned thread, the
spawned thread’s closure must capture the values it needs. Listing 16-3 shows
an attempt to create a vector in the main thread and use it in the spawned
thread. However, this won’t yet work, as you’ll see in a moment.

Filename: src/main.rs

```
use std::thread;

fn main() {
    let v = vec![1, 2, 3];

    let handle = thread::spawn(|| {
        println!("Here's a vector: {:?}", v);
    });

    handle.join().unwrap();
}
```

Listing 16-3: Attempting to use a vector created by the main thread in another
thread

The closure uses `v`, so it will capture `v` and make it part of the closure’s
environment. Because `thread::spawn` runs this closure in a new thread, we
should be able to access `v` inside that new thread. But when we compile this
example, we get the following error:

```
error[E0373]: closure may outlive the current function, but it borrows `v`, which is owned by the current function
 --> src/main.rs:6:32
  |
6 |     let handle = thread::spawn(|| {
  |                                ^^ may outlive borrowed value `v`
7 |         println!("Here's a vector: {:?}", v);
  |                                           - `v` is borrowed here
  |
note: function requires argument type to outlive `'static`
 --> src/main.rs:6:18
  |
6 |       let handle = thread::spawn(|| {
  |  __________________^
7 | |         println!("Here's a vector: {:?}", v);
8 | |     });
  | |______^
help: to force the closure to take ownership of `v` (and any other referenced variables), use the `move` keyword
  |
6 |     let handle = thread::spawn(move || {
  |                                ++++
```

Rust *infers* how to capture `v`, and because `println!` only needs a reference
to `v`, the closure tries to borrow `v`. However, there’s a problem: Rust can’t
tell how long the spawned thread will run, so it doesn’t know if the reference
to `v` will always be valid.

Listing 16-4 provides a scenario that’s more likely to have a reference to `v`
that won’t be valid:

Filename: src/main.rs

```
use std::thread;

fn main() {
    let v = vec![1, 2, 3];

    let handle = thread::spawn(|| {
        println!("Here's a vector: {:?}", v);
    });

    drop(v); // oh no!

    handle.join().unwrap();
}
```

Listing 16-4: A thread with a closure that attempts to capture a reference to
`v` from a main thread that drops `v`

If Rust allowed us to run this code, there’s a possibility the spawned thread
would be immediately put in the background without running at all. The spawned
thread has a reference to `v` inside, but the main thread immediately drops
`v`, using the `drop` function we discussed in Chapter 15. Then, when the
spawned thread starts to execute, `v` is no longer valid, so a reference to it
is also invalid. Oh no!

To fix the compiler error in Listing 16-3, we can use the error message’s
advice:

```
help: to force the closure to take ownership of `v` (and any other referenced variables), use the `move` keyword
  |
6 |     let handle = thread::spawn(move || {
  |                                ++++
```

By adding the `move` keyword before the closure, we force the closure to take
ownership of the values it’s using rather than allowing Rust to infer that it
should borrow the values. The modification to Listing 16-3 shown in Listing
16-5 will compile and run as we intend:

Filename: src/main.rs

```
use std::thread;

fn main() {
    let v = vec![1, 2, 3];

    let handle = thread::spawn(move || {
        println!("Here's a vector: {:?}", v);
    });

    handle.join().unwrap();
}
```

Listing 16-5: Using the `move` keyword to force a closure to take ownership of
the values it uses

We might be tempted to try the same thing to fix the code in Listing 16-4 where
the main thread called `drop` by using a `move` closure. However, this fix will
not work because what Listing 16-4 is trying to do is disallowed for a
different reason. If we added `move` to the closure, we would move `v` into the
closure’s environment, and we could no longer call `drop` on it in the main
thread. We would get this compiler error instead:

```
error[E0382]: use of moved value: `v`
  --> src/main.rs:10:10
   |
4  |     let v = vec![1, 2, 3];
   |         - move occurs because `v` has type `Vec<i32>`, which does not implement the `Copy` trait
5  |
6  |     let handle = thread::spawn(move || {
   |                                ------- value moved into closure here
7  |         println!("Here's a vector: {:?}", v);
   |                                           - variable moved due to use in closure
...
10 |     drop(v); // oh no!
   |          ^ value used here after move
```

Rust’s ownership rules have saved us again! We got an error from the code in
Listing 16-3 because Rust was being conservative and only borrowing `v` for the
thread, which meant the main thread could theoretically invalidate the spawned
thread’s reference. By telling Rust to move ownership of `v` to the spawned
thread, we’re guaranteeing Rust that the main thread won’t use `v` anymore. If
we change Listing 16-4 in the same way, we’re then violating the ownership
rules when we try to use `v` in the main thread. The `move` keyword overrides
Rust’s conservative default of borrowing; it doesn’t let us violate the
ownership rules.

With a basic understanding of threads and the thread API, let’s look at what we
can *do* with threads.

## Using Message Passing to Transfer Data Between Threads

One increasingly popular approach to ensuring safe concurrency is *message
passing*, where threads or actors communicate by sending each other messages
containing data. Here’s the idea in a slogan from the Go language
documentation at *https://golang.org/doc/effective_go.html#concurrency*:
“Do not communicate by sharing memory; instead, share memory by communicating.”

<!-- are they communicating to decide which thread should be running, or by
"communicate" do we just mean sharing data? /LC -->
<!-- Just sharing data. Is there something that should be clarified here? I'm
not sure what to do because this paragraph doesn't mention deciding which
thread should be running, it only mentions sharing data, so I'm not sure where
the possible confusion is coming from. /Carol -->
<!-- JT, if this will be already obvious to a reader, no changes needed. I just
wanted to ensure there was no potential confusion around what is being
communicated /LC -->
<!-- I like that we want to give a shout-out to Go's thinking process when
we align, though I made a bit of a face reading the quote. "Share memory" is a
such a loaded concept that I think people might stumble a bit over the play on
the technical words.

Funnily the next line following that quote in the Go book is:

"This approach can be taken too far." :D
/JT -->
<!-- I think this means JT is fine leaving this the way it is! /Carol -->

To accomplish message-sending concurrency, Rust's standard library provides an
implementation of *channels*. A channel is a general programming concept by
which data is sent from one thread to another.

You can imagine a channel in programming as being like a directional channel of
water, such as a stream or a river. If you put something like a rubber duck
into a river, it will travel downstream to the end of the waterway.

A channel has two halves: a transmitter and a receiver. The transmitter half is
the upstream location where you put rubber ducks into the river, and the
receiver half is where the rubber duck ends up downstream. One part of your
code calls methods on the transmitter with the data you want to send, and
another part checks the receiving end for arriving messages. A channel is said
to be *closed* if either the transmitter or receiver half is dropped.

Here, we’ll work up to a program that has one thread to generate values and
send them down a channel, and another thread that will receive the values and
print them out. We’ll be sending simple values between threads using a channel
to illustrate the feature. Once you’re familiar with the technique, you could
use channels for any threads that needs to communicate between each other, such
as a chat system or a system where many threads perform parts of a calculation
and send the parts to one thread that aggregates the results.

First, in Listing 16-6, we’ll create a channel but not do anything with it.
Note that this won’t compile yet because Rust can’t tell what type of values we
want to send over the channel.

Filename: src/main.rs

```
use std::sync::mpsc;

fn main() {
    let (tx, rx) = mpsc::channel();
}
```

Listing 16-6: Creating a channel and assigning the two halves to `tx` and `rx`

We create a new channel using the `mpsc::channel` function; `mpsc` stands for
*multiple producer, single consumer*. In short, the way Rust’s standard library
implements channels means a channel can have multiple *sending* ends that
produce values but only one *receiving* end that consumes those values. Imagine
multiple streams flowing together into one big river: everything sent down any
of the streams will end up in one river at the end. We’ll start with a single
producer for now, but we’ll add multiple producers when we get this example
working.

The `mpsc::channel` function returns a tuple, the first element of which is the
sending end--the transmitter--and the second element is the receiving end--the
receiver. The abbreviations `tx` and `rx` are traditionally used in many fields
for *transmitter* and *receiver* respectively, so we name our variables as such
to indicate each end. We’re using a `let` statement with a pattern that
destructures the tuples; we’ll discuss the use of patterns in `let` statements
and destructuring in Chapter 18. For now, know that using a `let` statement
this way is a convenient approach to extract the pieces of the tuple returned
by `mpsc::channel`.

Let’s move the transmitting end into a spawned thread and have it send one
string so the spawned thread is communicating with the main thread, as shown in
Listing 16-7. This is like putting a rubber duck in the river upstream or
sending a chat message from one thread to another.

Filename: src/main.rs

```
use std::sync::mpsc;
use std::thread;

fn main() {
    let (tx, rx) = mpsc::channel();

    thread::spawn(move || {
        let val = String::from("hi");
        tx.send(val).unwrap();
    });
}
```

Listing 16-7: Moving `tx` to a spawned thread and sending “hi”

Again, we’re using `thread::spawn` to create a new thread and then using `move`
to move `tx` into the closure so the spawned thread owns `tx`. The spawned
thread needs to own the transmitter to be able to send messages through the
channel.

The transmitter has a `send` method that takes the value we want to send.
The `send` method returns a `Result<T, E>` type, so if the receiver has
already been dropped and there’s nowhere to send a value, the send operation
will return an error. In this example, we’re calling `unwrap` to panic in case
of an error. But in a real application, we would handle it properly: return to
Chapter 9 to review strategies for proper error handling.

In Listing 16-8, we’ll get the value from the receiver in the main thread. This
is like retrieving the rubber duck from the water at the end of the river or
receiving a chat message.

Filename: src/main.rs

```
use std::sync::mpsc;
use std::thread;

fn main() {
    let (tx, rx) = mpsc::channel();

    thread::spawn(move || {
        let val = String::from("hi");
        tx.send(val).unwrap();
    });

    let received = rx.recv().unwrap();
    println!("Got: {}", received);
}
```

Listing 16-8: Receiving the value “hi” in the main thread and printing it

The receiver has two useful methods: `recv` and `try_recv`. We’re using `recv`,
short for *receive*, which will block the main thread’s execution and wait
until a value is sent down the channel. Once a value is sent, `recv` will
return it in a `Result<T, E>`. When the transmitter closes, `recv` will return
an error to signal that no more values will be coming.

The `try_recv` method doesn’t block, but will instead return a `Result<T, E>`
immediately: an `Ok` value holding a message if one is available and an `Err`
value if there aren’t any messages this time. Using `try_recv` is useful if
this thread has other work to do while waiting for messages: we could write a
loop that calls `try_recv` every so often, handles a message if one is
available, and otherwise does other work for a little while until checking
again.

We’ve used `recv` in this example for simplicity; we don’t have any other work
for the main thread to do other than wait for messages, so blocking the main
thread is appropriate.

When we run the code in Listing 16-8, we’ll see the value printed from the main
thread:

```
Got: hi
```

Perfect!

### Channels and Ownership Transference

The ownership rules play a vital role in message sending because they help you
write safe, concurrent code. Preventing errors in concurrent programming is the
advantage of thinking about ownership throughout your Rust programs. Let’s do
an experiment to show how channels and ownership work together to prevent
problems: we’ll try to use a `val` value in the spawned thread *after* we’ve
sent it down the channel. Try compiling the code in Listing 16-9 to see why
this code isn’t allowed:

Filename: src/main.rs

```
use std::sync::mpsc;
use std::thread;

fn main() {
    let (tx, rx) = mpsc::channel();

    thread::spawn(move || {
        let val = String::from("hi");
        tx.send(val).unwrap();
        println!("val is {}", val);
    });

    let received = rx.recv().unwrap();
    println!("Got: {}", received);
}
```

Listing 16-9: Attempting to use `val` after we’ve sent it down the channel

Here, we try to print `val` after we’ve sent it down the channel via `tx.send`.
Allowing this would be a bad idea: once the value has been sent to another
thread, that thread could modify or drop it before we try to use the value
again. Potentially, the other thread’s modifications could cause errors or
unexpected results due to inconsistent or nonexistent data. However, Rust gives
us an error if we try to compile the code in Listing 16-9:

```
error[E0382]: borrow of moved value: `val`
  --> src/main.rs:10:31
   |
8  |         let val = String::from("hi");
   |             --- move occurs because `val` has type `String`, which does not implement the `Copy` trait
9  |         tx.send(val).unwrap();
   |                 --- value moved here
10 |         println!("val is {}", val);
   |                               ^^^ value borrowed here after move
```

Our concurrency mistake has caused a compile time error. The `send` function
takes ownership of its parameter, and when the value is moved, the receiver
takes ownership of it. This stops us from accidentally using the value again
after sending it; the ownership system checks that everything is okay.

### Sending Multiple Values and Seeing the Receiver Waiting

The code in Listing 16-8 compiled and ran, but it didn’t clearly show us that
two separate threads were talking to each other over the channel. In Listing
16-10 we’ve made some modifications that will prove the code in Listing 16-8 is
running concurrently: the spawned thread will now send multiple messages and
pause for a second between each message.

Filename: src/main.rs

```
use std::sync::mpsc;
use std::thread;
use std::time::Duration;

fn main() {
    let (tx, rx) = mpsc::channel();

    thread::spawn(move || {
        let vals = vec![
            String::from("hi"),
            String::from("from"),
            String::from("the"),
            String::from("thread"),
        ];

        for val in vals {
            tx.send(val).unwrap();
            thread::sleep(Duration::from_secs(1));
        }
    });

    for received in rx {
        println!("Got: {}", received);
    }
}
```

Listing 16-10: Sending multiple messages and pausing between each

This time, the spawned thread has a vector of strings that we want to send to
the main thread. We iterate over them, sending each individually, and pause
between each by calling the `thread::sleep` function with a `Duration` value of
1 second.

In the main thread, we’re not calling the `recv` function explicitly anymore:
instead, we’re treating `rx` as an iterator. For each value received, we’re
printing it. When the channel is closed, iteration will end.

When running the code in Listing 16-10, you should see the following output
with a 1-second pause in between each line:

```
Got: hi
Got: from
Got: the
Got: thread
```

Because we don’t have any code that pauses or delays in the `for` loop in the
main thread, we can tell that the main thread is waiting to receive values from
the spawned thread.

### Creating Multiple Producers by Cloning the Transmitter

Earlier we mentioned that `mpsc` was an acronym for *multiple producer,
single consumer*. Let’s put `mpsc` to use and expand the code in Listing 16-10
to create multiple threads that all send values to the same receiver. We can do
so by cloning the transmitter, as shown in Listing 16-11:

Filename: src/main.rs

```
    // --snip--

    let (tx, rx) = mpsc::channel();

    let tx1 = tx.clone();
    thread::spawn(move || {
        let vals = vec![
            String::from("hi"),
            String::from("from"),
            String::from("the"),
            String::from("thread"),
        ];

        for val in vals {
            tx1.send(val).unwrap();
            thread::sleep(Duration::from_secs(1));
        }
    });

    thread::spawn(move || {
        let vals = vec![
            String::from("more"),
            String::from("messages"),
            String::from("for"),
            String::from("you"),
        ];

        for val in vals {
            tx.send(val).unwrap();
            thread::sleep(Duration::from_secs(1));
        }
    });

    for received in rx {
        println!("Got: {}", received);
    }

    // --snip--
```

Listing 16-11: Sending multiple messages from multiple producers

This time, before we create the first spawned thread, we call `clone` on the
transmitter. This will give us a new transmitter we can pass to the first
spawned thread. We pass the original transmitter to a second spawned thread.
This gives us two threads, each sending different messages to the one receiver.

When you run the code, your output should look something like this:

```
Got: hi
Got: more
Got: from
Got: messages
Got: for
Got: the
Got: thread
Got: you
```

You might see the values in another order, depending on your system. This is
what makes concurrency interesting as well as difficult. If you experiment with
`thread::sleep`, giving it various values in the different threads, each run
will be more nondeterministic and create different output each time.

Now that we’ve looked at how channels work, let’s look at a different method of
concurrency.

## Shared-State Concurrency

Message passing is a fine way of handling concurrency, but it’s not the only
one. Another method would be for multiple threads to access the same shared
data. Consider this part of the slogan from the Go language documentation
again: “do not communicate by sharing memory.”

<!-- NB: if we decide to do anything with the Go quote above, we also
reference it here.
/JT -->
<!-- Also not changing anything here. /Carol -->

What would communicating by sharing memory look like? In addition, why would
message-passing enthusiasts caution not to use memory sharing?

In a way, channels in any programming language are similar to single ownership,
because once you transfer a value down a channel, you should no longer use that
value. Shared memory concurrency is like multiple ownership: multiple threads
can access the same memory location at the same time. As you saw in Chapter 15,
where smart pointers made multiple ownership possible, multiple ownership can
add complexity because these different owners need managing. Rust’s type system
and ownership rules greatly assist in getting this management correct. For an
example, let’s look at mutexes, one of the more common concurrency primitives
for shared memory.

### Using Mutexes to Allow Access to Data from One Thread at a Time

*Mutex* is an abbreviation for *mutual exclusion*, as in, a mutex allows only
one thread to access some data at any given time. To access the data in a
mutex, a thread must first signal that it wants access by asking to acquire the
mutex’s *lock*. The lock is a data structure that is part of the mutex that
keeps track of who currently has exclusive access to the data. Therefore, the
mutex is described as *guarding* the data it holds via the locking system.

Mutexes have a reputation for being difficult to use because you have to
remember two rules:

* You must attempt to acquire the lock before using the data.
* When you’re done with the data that the mutex guards, you must unlock the
  data so other threads can acquire the lock.

For a real-world metaphor for a mutex, imagine a panel discussion at a
conference with only one microphone. Before a panelist can speak, they have to
ask or signal that they want to use the microphone. When they get the
microphone, they can talk for as long as they want to and then hand the
microphone to the next panelist who requests to speak. If a panelist forgets to
hand the microphone off when they’re finished with it, no one else is able to
speak. If management of the shared microphone goes wrong, the panel won’t work
as planned!

Management of mutexes can be incredibly tricky to get right, which is why so
many people are enthusiastic about channels. However, thanks to Rust’s type
system and ownership rules, you can’t get locking and unlocking wrong.

#### The API of `Mutex<T>`

As an example of how to use a mutex, let’s start by using a mutex in a
single-threaded context, as shown in Listing 16-12:

Filename: src/main.rs

```
use std::sync::Mutex;

fn main() {
    [1] let m = Mutex::new(5);

    {
        [2] let mut num = m.lock().unwrap();
        [3] *num = 6;
    [4] }

    [5] println!("m = {:?}", m);
}
```

Listing 16-12: Exploring the API of `Mutex<T>` in a single-threaded context for
simplicity

As with many types, we create a `Mutex<T>` using the associated function `new`
[1]. To access the data inside the mutex, we use the `lock` method to acquire
the lock [2]. This call will block the current thread so it can’t do any work
until it’s our turn to have the lock.

The call to `lock` would fail if another thread holding the lock panicked. In
that case, no one would ever be able to get the lock, so we’ve chosen to
`unwrap` and have this thread panic if we’re in that situation.

After we’ve acquired the lock, we can treat the return value, named `num` in
this case, as a mutable reference to the data inside. The type system ensures
that we acquire a lock before using the value in `m`. The type of `m` is
`Mutex<i32>`, not `i32`, so we *must* call `lock` to be able to use the `i32`
value. We can’t forget; the type system won’t let us access the inner `i32`
otherwise.

As you might suspect, `Mutex<T>` is a smart pointer. More accurately, the call
to `lock` *returns* a smart pointer called `MutexGuard`, wrapped in a
`LockResult` that we handled with the call to `unwrap`. The `MutexGuard` smart
pointer implements `Deref` to point at our inner data; the smart pointer also
has a `Drop` implementation that releases the lock automatically when a
`MutexGuard` goes out of scope, which happens at the end of the inner scope
[4]. As a result, we don’t risk forgetting to release the lock and blocking the
mutex from being used by other threads, because the lock release happens
automatically.

After dropping the lock, we can print the mutex value and see that we were able
to change the inner `i32` to 6 [5].

#### Sharing a `Mutex<T>` Between Multiple Threads

Now, let’s try to share a value between multiple threads using `Mutex<T>`.
We’ll spin up 10 threads and have them each increment a counter value by 1, so
the counter goes from 0 to 10. The next example in Listing 16-13 will have
a compiler error, and we’ll use that error to learn more about using
`Mutex<T>` and how Rust helps us use it correctly.

Filename: src/main.rs

```
use std::sync::Mutex;
use std::thread;

fn main() {
    [1] let counter = Mutex::new(0);
    let mut handles = vec![];

    [2] for _ in 0..10 {
        [3] let handle = thread::spawn(move || {
            [4] let mut num = counter.lock().unwrap();

            [5] *num += 1;
        });
        [6] handles.push(handle);
    }

    for handle in handles {
        [7] handle.join().unwrap();
    }

    [8] println!("Result: {}", *counter.lock().unwrap());
}
```

Listing 16-13: Ten threads each increment a counter guarded by a `Mutex<T>`

We create a `counter` variable to hold an `i32` inside a `Mutex<T>` [1], as we
did in Listing 16-12. Next, we create 10 threads by iterating over a range of
numbers [2]. We use `thread::spawn` and give all the threads the same closure:
one that moves the counter into the thread [3], acquires a lock on the
`Mutex<T>` by calling the `lock` method [4], and then adds 1 to the value in
the mutex [5]. When a thread finishes running its closure, `num` will go out of
scope and release the lock so another thread can acquire it.

In the main thread, we collect all the join handles [6]. Then, as we did in
Listing 16-2, we call `join` on each handle to make sure all the threads finish
[7]. At that point, the main thread will acquire the lock and print the result
of this program [8].

We hinted that this example wouldn’t compile. Now let’s find out why!

```
error[E0382]: use of moved value: `counter`
  --> src/main.rs:9:36
   |
5  |     let counter = Mutex::new(0);
   |         ------- move occurs because `counter` has type `Mutex<i32>`, which does not implement the `Copy` trait
...
9  |         let handle = thread::spawn(move || {
   |                                    ^^^^^^^ value moved into closure here, in previous iteration of loop
10 |             let mut num = counter.lock().unwrap();
   |                           ------- use occurs due to use in closure
```

The error message states that the `counter` value was moved in the previous
iteration of the loop. Rust is telling us that we can’t move the ownership
of lock `counter` into multiple threads. Let’s fix the compiler error with a
multiple-ownership method we discussed in Chapter 15.

#### Multiple Ownership with Multiple Threads

In Chapter 15, we gave a value multiple owners by using the smart pointer
`Rc<T>` to create a reference counted value. Let’s do the same here and see
what happens. We’ll wrap the `Mutex<T>` in `Rc<T>` in Listing 16-14 and clone
the `Rc<T>` before moving ownership to the thread.

Filename: src/main.rs

```
use std::rc::Rc;
use std::sync::Mutex;
use std::thread;

fn main() {
    let counter = Rc::new(Mutex::new(0));
    let mut handles = vec![];

    for _ in 0..10 {
        let counter = Rc::clone(&counter);
        let handle = thread::spawn(move || {
            let mut num = counter.lock().unwrap();

            *num += 1;
        });
        handles.push(handle);
    }

    for handle in handles {
        handle.join().unwrap();
    }

    println!("Result: {}", *counter.lock().unwrap());
}
```

Listing 16-14: Attempting to use `Rc<T>` to allow multiple threads to own the
`Mutex<T>`

Once again, we compile and get... different errors! The compiler is teaching us
a lot.

```
[1] error[E0277]: `Rc<Mutex<i32>>` cannot be sent between threads safely
   --> src/main.rs:11:22
    |
11  |           let handle = thread::spawn(move || {
    |  ______________________^^^^^^^^^^^^^_-
    | |                      |
    | |                      `Rc<Mutex<i32>>` cannot be sent between threads safely
12  | |             let mut num = counter.lock().unwrap();
13  | |
14  | |             *num += 1;
15  | |         });
    | |_________- within this `[closure@src/main.rs:11:36: 15:10]`
    |
[2] = help: within `[closure@src/main.rs:11:36: 15:10]`, the trait `Send` is not implemented for `Rc<Mutex<i32>>`
    = note: required because it appears within the type `[closure@src/main.rs:11:36: 15:10]`
note: required by a bound in `spawn`
```

Wow, that error message is very wordy! Here’s the important part to focus on:
`` `Rc<Mutex<i32>>` cannot be sent between threads safely `` [1]. The compiler
is also telling us the reason why: ``the trait `Send` is not implemented for
`Rc<Mutex<i32>>` `` [2]. We’ll talk about `Send` in the next section: it’s one
of the traits that ensures the types we use with threads are meant for use in
concurrent situations.

Unfortunately, `Rc<T>` is not safe to share across threads. When `Rc<T>`
manages the reference count, it adds to the count for each call to `clone` and
subtracts from the count when each clone is dropped. But it doesn’t use any
concurrency primitives to make sure that changes to the count can’t be
interrupted by another thread. This could lead to wrong counts—subtle bugs that
could in turn lead to memory leaks or a value being dropped before we’re done
with it. What we need is a type exactly like `Rc<T>` but one that makes changes
to the reference count in a thread-safe way.

#### Atomic Reference Counting with `Arc<T>`

Fortunately, `Arc<T>` *is* a type like `Rc<T>` that is safe to use in
concurrent situations. The *a* stands for *atomic*, meaning it’s an *atomically
reference counted* type. Atomics are an additional kind of concurrency
primitive that we won’t cover in detail here: see the standard library
documentation for `std::sync::atomic` for more details. At this point, you just
need to know that atomics work like primitive types but are safe to share
across threads.

You might then wonder why all primitive types aren’t atomic and why standard
library types aren’t implemented to use `Arc<T>` by default. The reason is that
thread safety comes with a performance penalty that you only want to pay when
you really need to. If you’re just performing operations on values within a
single thread, your code can run faster if it doesn’t have to enforce the
guarantees atomics provide.

Let’s return to our example: `Arc<T>` and `Rc<T>` have the same API, so we fix
our program by changing the `use` line, the call to `new`, and the call to
`clone`. The code in Listing 16-15 will finally compile and run:

Filename: src/main.rs

```
use std::sync::{Arc, Mutex};
use std::thread;

fn main() {
    let counter = Arc::new(Mutex::new(0));
    let mut handles = vec![];

    for _ in 0..10 {
        let counter = Arc::clone(&counter);
        let handle = thread::spawn(move || {
            let mut num = counter.lock().unwrap();

            *num += 1;
        });
        handles.push(handle);
    }

    for handle in handles {
        handle.join().unwrap();
    }

    println!("Result: {}", *counter.lock().unwrap());
}
```

Listing 16-15: Using an `Arc<T>` to wrap the `Mutex<T>` to be able to share
ownership across multiple threads

This code will print the following:

```
Result: 10
```

We did it! We counted from 0 to 10, which may not seem very impressive, but it
did teach us a lot about `Mutex<T>` and thread safety. You could also use this
program’s structure to do more complicated operations than just incrementing a
counter. Using this strategy, you can divide a calculation into independent
parts, split those parts across threads, and then use a `Mutex<T>` to have each
thread update the final result with its part.

Note that if you are doing simple numerical operations, there are types simpler
than `Mutex<T>` types provided by the `std::sync::atomic` module of the
standard library. These types provide safe, concurrent, atomic access to
primitive types. We chose to use `Mutex<T>` with a primitive type for this
example so we could concentrate on how `Mutex<T>` works.

<!-- Do we want to mention that for simple counters we have simpler types in
the standard library? (eg, AtomicI64 for the above)
/JT -->
<!-- Done! /Carol-->

### Similarities Between `RefCell<T>`/`Rc<T>` and `Mutex<T>`/`Arc<T>`

You might have noticed that `counter` is immutable but we could get a mutable
reference to the value inside it; this means `Mutex<T>` provides interior
mutability, as the `Cell` family does. In the same way we used `RefCell<T>` in
Chapter 15 to allow us to mutate contents inside an `Rc<T>`, we use `Mutex<T>`
to mutate contents inside an `Arc<T>`.

Another detail to note is that Rust can’t protect you from all kinds of logic
errors when you use `Mutex<T>`. Recall in Chapter 15 that using `Rc<T>` came
with the risk of creating reference cycles, where two `Rc<T>` values refer to
each other, causing memory leaks. Similarly, `Mutex<T>` comes with the risk of
creating *deadlocks*. These occur when an operation needs to lock two resources
and two threads have each acquired one of the locks, causing them to wait for
each other forever. If you’re interested in deadlocks, try creating a Rust
program that has a deadlock; then research deadlock mitigation strategies for
mutexes in any language and have a go at implementing them in Rust. The
standard library API documentation for `Mutex<T>` and `MutexGuard` offers
useful information.

We’ll round out this chapter by talking about the `Send` and `Sync` traits and
how we can use them with custom types.

## Extensible Concurrency with the `Sync` and `Send` Traits

Interestingly, the Rust language has *very* few concurrency features. Almost
every concurrency feature we’ve talked about so far in this chapter has been
part of the standard library, not the language. Your options for handling
concurrency are not limited to the language or the standard library; you can
write your own concurrency features or use those written by others.

However, two concurrency concepts are embedded in the language: the
`std::marker` traits `Sync` and `Send`.

### Allowing Transference of Ownership Between Threads with `Send`

The `Send` marker trait indicates that ownership of values of the type
implementing `Send` can be transferred between threads. Almost every Rust type
is `Send`, but there are some exceptions, including `Rc<T>`: this cannot be
`Send` because if you cloned an `Rc<T>` value and tried to transfer ownership
of the clone to another thread, both threads might update the reference count
at the same time. For this reason, `Rc<T>` is implemented for use in
single-threaded situations where you don’t want to pay the thread-safe
performance penalty.

Therefore, Rust’s type system and trait bounds ensure that you can never
accidentally send an `Rc<T>` value across threads unsafely. When we tried to do
this in Listing 16-14, we got the error `the trait Send is not implemented for
Rc<Mutex<i32>>`. When we switched to `Arc<T>`, which is `Send`, the code
compiled.

Any type composed entirely of `Send` types is automatically marked as `Send` as
well. Almost all primitive types are `Send`, aside from raw pointers, which
we’ll discuss in Chapter 19.

### Allowing Access from Multiple Threads with `Sync`

The `Sync` marker trait indicates that it is safe for the type implementing
`Sync` to be referenced from multiple threads. In other words, any type `T` is
`Sync` if `&T` (an immutable reference to `T`) is `Send`, meaning the reference
can be sent safely to another thread. Similar to `Send`, primitive types are
`Sync`, and types composed entirely of types that are `Sync` are also `Sync`.

The smart pointer `Rc<T>` is also not `Sync` for the same reasons that it’s not
`Send`. The `RefCell<T>` type (which we talked about in Chapter 15) and the
family of related `Cell<T>` types are not `Sync`. The implementation of borrow
checking that `RefCell<T>` does at runtime is not thread-safe. The smart
pointer `Mutex<T>` is `Sync` and can be used to share access with multiple
threads as you saw in the “Sharing a `Mutex<T>` Between Multiple
Threads” section.

### Implementing `Send` and `Sync` Manually Is Unsafe

Because types that are made up of `Send` and `Sync` traits are automatically
also `Send` and `Sync`, we don’t have to implement those traits manually. As
marker traits, they don’t even have any methods to implement. They’re just
useful for enforcing invariants related to concurrency.

Manually implementing these traits involves implementing unsafe Rust code.
We’ll talk about using unsafe Rust code in Chapter 19; for now, the important
information is that building new concurrent types not made up of `Send` and
`Sync` parts requires careful thought to uphold the safety guarantees. “The
Rustonomicon” at *https://doc.rust-lang.org/stable/nomicon/* has more
information about these guarantees and how to uphold them.

## Summary

This isn’t the last you’ll see of concurrency in this book: the project in
Chapter 20 will use the concepts in this chapter in a more realistic situation
than the smaller examples discussed here.

As mentioned earlier, because very little of how Rust handles concurrency is
part of the language, many concurrency solutions are implemented as crates.
These evolve more quickly than the standard library, so be sure to search
online for the current, state-of-the-art crates to use in multithreaded
situations.

The Rust standard library provides channels for message passing and smart
pointer types, such as `Mutex<T>` and `Arc<T>`, that are safe to use in
concurrent contexts. The type system and the borrow checker ensure that the
code using these solutions won’t end up with data races or invalid references.
Once you get your code to compile, you can rest assured that it will happily
run on multiple threads without the kinds of hard-to-track-down bugs common in
other languages. Concurrent programming is no longer a concept to be afraid of:
go forth and make your programs concurrent, fearlessly!

Next, we’ll talk about idiomatic ways to model problems and structure solutions
as your Rust programs get bigger. In addition, we’ll discuss how Rust’s idioms
relate to those you might be familiar with from object-oriented programming.
