Streams
So far in this chapter, we have mostly stuck to individual futures. The one big
exception was the async channel we used. Recall how we used the receiver for our
async channel in the “Message Passing” earlier in the chapter.
The async recv
method produces a sequence of items over time. This is an
instance of a much more general pattern, often called a stream.
A sequence of items is something we’ve seen before, when we looked at the
Iterator
trait in Chapter 13. There are two differences between iterators and
the async channel receiver, though. The first is the element of time: iterators
are synchronous, while the channel receiver is asynchronous. The second is the
API. When working directly with an Iterator
, we call its synchronous next
method. With the trpl::Receiver
stream in particular, we called an
asynchronous recv
method instead. These APIs otherwise feel very similar.
That similarity isn’t a coincidence. A stream is similar to an asynchronous
form of iteration. Whereas the trpl::Receiver
specifically waits to receive
messages, though, the general-purpose stream API is much more general: it
provides the next item the way Iterator
does, but asynchronously. The
similarity between iterators and streams in Rust means we can actually create a
stream from any iterator. As with an iterator, we can work with a stream by
calling its next
method and then awaiting the output, as in Listing 17-30.
We start with an array of numbers, which we convert to an iterator and then call
map
on to double all the values. Then we convert the iterator into a stream
using the trpl::stream_from_iter
function. Then we loop over the items in the
stream as they arrive with the while let
loop.
Unfortunately, when we try to run the code, it doesn’t compile. Instead, as we
can see in the output, it reports that there is no next
method available.
error[E0599]: no method named `next` found for struct `Iter` in the current scope
--> src/main.rs:10:40
|
10 | while let Some(value) = stream.next().await {
| ^^^^
|
= note: the full type name has been written to 'file:///projects/async_await/target/debug/deps/async_await-9de943556a6001b8.long-type-1281356139287206597.txt'
= note: consider using `--verbose` to print the full type name to the console
= help: items from traits can only be used if the trait is in scope
help: the following traits which provide `next` are implemented but not in scope; perhaps you want to import one of them
|
1 + use crate::trpl::StreamExt;
|
1 + use futures_util::stream::stream::StreamExt;
|
1 + use std::iter::Iterator;
|
1 + use std::str::pattern::Searcher;
|
help: there is a method `try_next` with a similar name
|
10 | while let Some(value) = stream.try_next().await {
| ~~~~~~~~
As the output suggests, the reason for the compiler error is that we need the
right trait in scope to be able to use the next
method. Given our discussion
so far, you might reasonably expect that to be Stream
, but the trait we need
here is actually StreamExt
. The Ext
there is for “extension”: this is a
common pattern in the Rust community for extending one trait with another.
Why do we need StreamExt
instead of Stream
, and what does the Stream
trait
itself do? Briefly, the answer is that throughout the Rust ecosystem, the
Stream
trait defines a low-level interface which effectively combines the
Iterator
and Future
traits. The StreamExt
trait supplies a higher-level
set of APIs on top of Stream
, including the next
method as well as other
utility methods similar to those provided by the Iterator
trait. We’ll return
to the Stream
and StreamExt
traits in a bit more detail at the end of the
chapter. For now, this is enough to let us keep moving.
The fix to the compiler error is to add a use
statement for trpl::StreamExt
,
as in Listing 17-31.
With all those pieces put together, this code works the way we want! What’s
more, now that we have StreamExt
in scope, we can use all of its utility
methods, just as with iterators. For example, in Listing 17-32, we use the
filter
method to filter out everything but multiples of three and five.
Of course, this isn’t very interesting. We could do that with normal iterators and without any async at all. So let’s look at some of the other things we can do which are unique to streams.
Composing Streams
Many concepts are naturally represented as streams: items becoming available in a queue, or working with more data than can fit in a computer’s memory by only pulling chunks of it from the file system at a time, or data arriving over the network over time. Because streams are futures, we can use them with any other kind of future, too, and we can combine them in interesting ways. For example, we can batch up events to avoid triggering too many network calls, set timeouts on sequences of long-running operations, or throttle user interface events to avoid doing needless work.
Let’s start by building a little stream of messages, as a stand-in for a stream
of data we might see from a WebSocket or another real-time communication
protocol. In Listing 17-33, we create a function get_messages
which returns
impl Stream<Item = String>
. For its implementation, we create an async
channel, loop over the first ten letters of the English alphabet, and send them
across the channel.
We also use a new type: ReceiverStream
, which converts the rx
receiver from
the trpl::channel
into a Stream
with a next
method. Back in main
, we use
a while let
loop to print all the messages from the stream.
When we run this code, we get exactly the results we would expect:
Message: 'a'
Message: 'b'
Message: 'c'
Message: 'd'
Message: 'e'
Message: 'f'
Message: 'g'
Message: 'h'
Message: 'i'
Message: 'j'
We could do this with the regular Receiver
API, or even the regular Iterator
API, though. Let’s add something that requires streams: adding a timeout
which applies to every item in the stream, and a delay on the items we emit.
In Listing 17-34, we start by adding a timeout to the stream with the timeout
method, which comes from the StreamExt
trait. Then we update the body of the
while let
loop, because the stream now returns a Result
. The Ok
variant
indicates a message arrived in time; the Err
variant indicates that the
timeout elapsed before any message arrived. We match
on that result and either
print the message when we receive it successfully, or print a notice about the
timeout. Finally, notice that we pin the messages after applying the timeout to
them, because the timeout helper produces a stream which needs to be pinned to
be polled.
However, because there are no delays between messages, this timeout does not
change the behavior of the program. Let’s add a variable delay to the messages
we send. In get_messages
, we use the enumerate
iterator method with the
messages
array so that we can get the index of each item we are sending along
with the item itself. Then we apply a 100 millisecond delay to even-index items
and a 300 millisecond delay to odd-index items, to simulate the different delays
we might see from a stream of messages in the real world. Because our timeout is
for 200 milliseconds, this should affect half of the messages.
To sleep between messages in the get_messages
function without blocking, we
need to use async. However, we can’t make get_messages
itself into an async
function, because then we’d return a Future<Output = Stream<Item = String>>
instead of a Stream<Item = String>>
. The caller would have to await
get_messages
itself to get access to the stream. But remember: everything in a
given future happens linearly; concurrency happens between futures. Awaiting
get_messages
would require it to send all the messages, including sleeping
between sending each message, before returning the receiver stream. As a result,
the timeout would end up useless. There would be no delays in the stream itself:
the delays would all happen before the stream was even available.
Instead, we leave get_messages
as a regular function which returns a stream,
and spawn a task to handle the async sleep
calls.
Note: calling spawn_task
in this way works because we already set up our
runtime. Calling this particular implementation of spawn_task
without
first setting up a runtime will cause a panic. Other implementations choose
different tradeoffs: they might spawn a new runtime and so avoid the panic but
end up with a bit of extra overhead, or simply not provide a standalone way to
spawn tasks without reference to a runtime. You should make sure you know what
tradeoff your runtime has chosen and write your code accordingly!
Now our code has a much more interesting result! Between every other pair of
messages, we see an error reported: Problem: Elapsed(())
.
Message: 'a'
Problem: Elapsed(())
Message: 'b'
Message: 'c'
Problem: Elapsed(())
Message: 'd'
Message: 'e'
Problem: Elapsed(())
Message: 'f'
Message: 'g'
Problem: Elapsed(())
Message: 'h'
Message: 'i'
Problem: Elapsed(())
Message: 'j'
The timeout doesn’t prevent the messages from arriving in the end—we still get all of the original messages. This is because our channel is unbounded: it can hold as many messages as we can fit in memory. If the message doesn’t arrive before the timeout, our stream handler will account for that, but when it polls the stream again, the message may now have arrived.
You can get different behavior if needed by using other kinds of channels, or other kinds of streams more generally. Let’s see one of those in practice in our final example for this section, by combining a stream of time intervals with this stream of messages.
Merging Streams
First, let’s create another stream, which will emit an item every millisecond if
we let it run directly. For simplicity, we can use the sleep
function to send
a message on a delay, and combine it with the same approach of creating a stream
from a channel we used in get_messages
. The difference is that this time,
we’re going to send back the count of intervals which has elapsed, so the return
type will be impl Stream<Item = u32>
, and we can call the function
get_intervals
.
In Listing 17-36, we start by defining a count
in the task. (We could define
it outside the task, too, but it is clearer to limit the scope of any given
variable.) Then we create an infinite loop. Each iteration of the loop
asynchronously sleeps for one millisecond, increments the count, and then sends
it over the channel. Because this is all wrapped in the task created by
spawn_task
, all of it will get cleaned up along with the runtime, including
the infinite loop.
This kind of infinite loop, which only ends when the whole runtime gets torn down, is fairly common in async Rust: many programs need to keep running indefinitely. With async, this doesn’t block anything else, as long as there is at least one await point in each iteration through the loop.
Back in our main function’s async block, we start by calling get_intervals
.
Then we merge the messages
and intervals
streams with the merge
method,
which combines multiple streams into one stream that produces items from any of
the source streams as soon as the items are available, without imposing any
particular ordering. Finally, we loop over that combined stream instead of over
messages
(Listing 17-37).
At this point, neither messages
nor intervals
needs to be pinned or mutable,
because both will be combined into the single merged
stream. However, this
call to merge
does not compile! (Neither does the next
call in the while let
loop, but we’ll come back to that after fixing this.) The two streams
have different types. The messages
stream has the type Timeout<impl Stream<Item = String>>
, where Timeout
is the type which implements Stream
for a timeout
call. Meanwhile, the intervals
stream has the type impl Stream<Item = u32>
. To merge these two streams, we need to transform one of
them to match the other.
In Listing 17-38, we rework the intervals
stream, because messages
is
already in the basic format we want and has to handle timeout errors. First, we
can use the map
helper method to transform the intervals
into a string.
Second, we need to match the Timeout
from messages
. Because we don’t
actually want a timeout for intervals
, though, we can just create a timeout
which is longer than the other durations we are using. Here, we create a
10-second timeout with Duration::from_secs(10)
. Finally, we need to make
stream
mutable, so that the while let
loop’s next
calls can iterate
through the stream, and pin it so that it’s safe to do so.
That gets us almost to where we need to be. Everything type checks. If you run this, though, there will be two problems. First, it will never stop! You’ll need to stop it with ctrl-c. Second, the messages from the English alphabet will be buried in the midst of all the interval counter messages:
--snip--
Interval: 38
Interval: 39
Interval: 40
Message: 'a'
Interval: 41
Interval: 42
Interval: 43
--snip--
Listing 17-39 shows one way to solve these last two problems. First, we use the
throttle
method on the intervals
stream, so that it doesn’t overwhelm the
messages
stream. Throttling is a way of limiting the rate at which a function
will be called—or, in this case, how often the stream will be polled. Once every
hundred milliseconds should do, because that is in the same ballpark as how
often our messages arrive.
To limit the number of items we will accept from a stream, we can use the take
method. We apply it to the merged stream, because we want to limit the final
output, not just one stream or the other.
Now when we run the program, it stops after pulling twenty items from the
stream, and the intervals don’t overwhelm the messages. We also don’t get
Interval: 100
or Interval: 200
or so on, but instead get Interval: 1
,
Interval: 2
, and so on—even though we have a source stream which can
produce an event every millisecond. That’s because the throttle
call
produces a new stream, wrapping the original stream, so that the original
stream only gets polled at the throttle rate, not its own “native” rate. We
don’t have a bunch of unhandled interval messages we’re choosing to
ignore. Instead, we never produce those interval messages in the first place!
This is the inherent “laziness” of Rust’s futures at work again, allowing us to
choose our performance characteristics.
Interval: 1
Message: 'a'
Interval: 2
Interval: 3
Problem: Elapsed(())
Interval: 4
Message: 'b'
Interval: 5
Message: 'c'
Interval: 6
Interval: 7
Problem: Elapsed(())
Interval: 8
Message: 'd'
Interval: 9
Message: 'e'
Interval: 10
Interval: 11
Problem: Elapsed(())
Interval: 12
There’s one last thing we need to handle: errors! With both of these
channel-based streams, the send
calls could fail when the other side of the
channel closes—and that’s just a matter of how the runtime executes the futures
which make up the stream. Up until now we have ignored this by calling unwrap
,
but in a well-behaved app, we should explicitly handle the error, at minimum by
ending the loop so we don’t try to send any more messages! Listing 17-40 shows
a simple error strategy: print the issue and then break
from the loops. As
usual, the correct way to handle a message send error will vary—just make sure
you have a strategy.
Now that we’ve seen a bunch of async in practice, let’s take a step back and
dig into a few of the details of how Future
, Stream
, and the other key
traits which Rust uses to make async work.