Tutorial¶
Welcome to the Trio tutorial! Trio is a modern Python library for writing asynchronous applications – that is, programs that want to do multiple things at the same time with parallelized I/O, like a web spider that fetches lots of pages in parallel, a web server juggling lots of simultaneous downloads… that sort of thing. Here we’ll try to give a gentle introduction to asynchronous programming with Trio.
We assume that you’re familiar with Python in general, but don’t worry
– we don’t assume you know anything about asynchronous programming or
Python’s new async/await
feature.
Also, unlike many async/await
tutorials, we assume that your goal
is to use Trio to write interesting programs, so we won’t go into
the nitty-gritty details of how async/await
is implemented inside
the Python interpreter. The word “coroutine” is never mentioned. The
fact is, you really don’t need to know any of that stuff unless you
want to implement a library like Trio, so we leave it out (though
we’ll throw in a few links for those who want to dig deeper).
Okay, ready? Let’s get started.
Before you begin¶
Make sure you’re using Python 3.8 or newer.
python3 -m pip install --upgrade trio
(or on Windows, maybepy -3 -m pip install --upgrade trio
– details)Can you
import trio
? If so then you’re good to go!
If you get lost or confused…¶
…then we want to know! We have a friendly chat channel, you can ask questions using the “python-trio” tag on StackOverflow, or just file a bug (if our documentation is confusing, that’s our fault, and we want to fix it!).
Async functions¶
Python 3.5 added a major new feature: async functions. Using Trio is all about writing async functions, so let’s start there.
An async function is defined like a normal function, except you write
async def
instead of def
:
# A regular function
def regular_double(x):
return 2 * x
# An async function
async def async_double(x):
return 2 * x
“Async” is short for “asynchronous”; we’ll sometimes refer to regular
functions like regular_double
as “synchronous functions”, to
distinguish them from async functions.
From a user’s point of view, there are two differences between an async function and a regular function:
To call an async function, you have to use the
await
keyword. So instead of writingregular_double(3)
, you writeawait async_double(3)
.You can’t use the
await
keyword inside the body of a regular function. If you try it, you’ll get a syntax error:def print_double(x): print(await async_double(x)) # <-- SyntaxError here
But inside an async function,
await
is allowed:async def print_double(x): print(await async_double(x)) # <-- OK!
Now, let’s think about the consequences here: if you need await
to
call an async function, and only async functions can use
await
… here’s a little table:
If a function like this |
wants to call a function like this |
is it gonna happen? |
---|---|---|
sync |
sync |
✓ |
sync |
async |
NOPE |
async |
sync |
✓ |
async |
async |
✓ |
So in summary: As a user, the entire advantage of async functions over regular functions is that async functions have a superpower: they can call other async functions.
This immediately raises two questions: how, and why? Specifically:
When your Python program starts up, it’s running regular old sync code. So there’s a chicken-and-the-egg problem: once we’re running an async function we can call other async functions, but how do we call that first async function?
And, if the only reason to write an async function is that it can call other async functions, why on earth would we ever use them in the first place? I mean, as superpowers go this seems a bit pointless. Wouldn’t it be simpler to just… not use any async functions at all?
This is where an async library like Trio comes in. It provides two things:
A runner function, which is a special synchronous function that takes and calls an asynchronous function. In Trio, this is
trio.run
:So that answers the “how” part.
A bunch of useful async functions – in particular, functions for doing I/O. So that answers the “why”: these functions are async, and they’re useful, so if you want to use them, you have to write async code. If you think keeping track of these
async
andawait
things is annoying, then too bad – you’ve got no choice in the matter! (Well, OK, you could just not use Trio. That’s a legitimate option. But it turns out that theasync/await
stuff is actually a good thing, for reasons we’ll discuss a little bit later.)Here’s an example function that uses
trio.sleep()
. (trio.sleep()
is liketime.sleep()
, but with more async.)import trio async def double_sleep(x): await trio.sleep(2 * x) trio.run(double_sleep, 3) # does nothing for 6 seconds then returns
So it turns out our async_double
function is actually a bad
example. I mean, it works, it’s fine, there’s nothing wrong with it,
but it’s pointless: it could just as easily be written as a regular
function, and it would be more useful that way. double_sleep
is a
much more typical example: we have to make it async, because it calls
another async function. The end result is a kind of async sandwich,
with Trio on both sides and our code in the middle:
trio.run -> double_sleep -> trio.sleep
This “sandwich” structure is typical for async code; in general, it looks like:
trio.run -> [async function] -> ... -> [async function] -> trio.whatever
It’s exactly the functions on the path between trio.run()
and
trio.whatever
that have to be async. Trio provides the async
bread, and then your code makes up the async sandwich’s tasty async
filling. Other functions (e.g., helpers you call along the way) should
generally be regular, non-async functions.
Warning: don’t forget that await
!¶
Now would be a good time to open up a Python prompt and experiment a
little with writing simple async functions and running them with
trio.run
.
At some point in this process, you’ll probably write some code like
this, that tries to call an async function but leaves out the
await
:
import time
import trio
async def broken_double_sleep(x):
print("*yawn* Going to sleep")
start_time = time.perf_counter()
# Whoops, we forgot the 'await'!
trio.sleep(2 * x)
sleep_time = time.perf_counter() - start_time
print(f"Woke up after {sleep_time:.2f} seconds, feeling well rested!")
trio.run(broken_double_sleep, 3)
You might think that Python would raise an error here, like it does
for other kinds of mistakes we sometimes make when calling a
function. Like, if we forgot to pass trio.sleep()
its required
argument, then we would get a nice TypeError
saying so. But
unfortunately, if you forget an await
, you don’t get that. What
you actually get is:
>>> trio.run(broken_double_sleep, 3)
*yawn* Going to sleep
Woke up after 0.00 seconds, feeling well rested!
__main__:4: RuntimeWarning: coroutine 'sleep' was never awaited
>>>
This is clearly broken – 0.00 seconds is not long enough to feel well
rested! Yet the code acts like it succeeded – no exception was
raised. The only clue that something went wrong is that it prints
RuntimeWarning: coroutine 'sleep' was never awaited
. Also, the
exact place where the warning is printed might vary, because it
depends on the whims of the garbage collector. If you’re using PyPy,
you might not even get a warning at all until the next GC collection
runs:
# On PyPy:
>>> trio.run(broken_double_sleep, 3)
*yawn* Going to sleep
Woke up after 0.00 seconds, feeling well rested!
>>> # what the ... ?? not even a warning!
>>> # but forcing a garbage collection gives us a warning:
>>> import gc
>>> gc.collect()
/home/njs/pypy-3.8-nightly/lib-python/3/importlib/_bootstrap.py:191: RuntimeWarning: coroutine 'sleep' was never awaited
if _module_locks.get(name) is wr: # XXX PyPy fix?
0
(If you can’t see the warning above, try scrolling right.)
Forgetting an await
like this is an incredibly common
mistake. You will mess this up. Everyone does. And Python will not
help you as much as you’d hope 😞. The key thing to remember is: if
you see the magic words RuntimeWarning: coroutine '...' was never
awaited
, then this always means that you made the mistake of
leaving out an await
somewhere, and you should ignore all the
other error messages you see and go fix that first, because there’s a
good chance the other stuff is just collateral damage. I’m not even
sure what all that other junk in the PyPy output is. Fortunately I
don’t need to know, I just need to fix my function!
(“I thought you said you weren’t going to mention coroutines!” Yes, well, I didn’t mention coroutines, Python did. Take it up with Guido! But seriously, this is unfortunately a place where the internal implementation details do leak out a bit.)
Why does this happen? In Trio, every time we use await
it’s to
call an async function, and every time we call an async function we
use await
. But Python’s trying to keep its options open for other
libraries that are ahem a little less organized about things. So
while for our purposes we can think of await trio.sleep(...)
as a
single piece of syntax, Python thinks of it as two things: first a
function call that returns this weird “coroutine” object:
>>> trio.sleep(3)
<coroutine object sleep at 0x7f5ac77be6d0>
and then that object gets passed to await
, which actually runs the
function. So if you forget await
, then two bad things happen: your
function doesn’t actually get called, and you get a “coroutine” object
where you might have been expecting something else, like a number:
>>> async_double(3) + 1
TypeError: unsupported operand type(s) for +: 'coroutine' and 'int'
If you didn’t already mess this up naturally, then give it a try on
purpose: try writing some code with a missing await
, or an extra
await
, and see what you get. This way you’ll be prepared for when
it happens to you for real.
And remember: watch out for RuntimeWarning: coroutine '...' was
never awaited
; it means you need to find and fix your missing
await
.
Okay, let’s see something cool already¶
So now we’ve started using Trio, but so far all we’ve learned to do is
write functions that print things and sleep for various lengths of
time. Interesting enough, but we could just as easily have done that
with time.sleep()
. async/await
is useless!
Well, not really. Trio has one more trick up its sleeve, that makes async functions more powerful than regular functions: it can run multiple async functions at the same time. Here’s an example:
1# tasks-intro.py
2
3import trio
4
5
6async def child1():
7 print(" child1: started! sleeping now...")
8 await trio.sleep(1)
9 print(" child1: exiting!")
10
11
12async def child2():
13 print(" child2: started! sleeping now...")
14 await trio.sleep(1)
15 print(" child2: exiting!")
16
17
18async def parent():
19 print("parent: started!")
20 async with trio.open_nursery() as nursery:
21 print("parent: spawning child1...")
22 nursery.start_soon(child1)
23
24 print("parent: spawning child2...")
25 nursery.start_soon(child2)
26
27 print("parent: waiting for children to finish...")
28 # -- we exit the nursery block here --
29 print("parent: all done!")
30
31
32trio.run(parent)
There’s a lot going on in here, so we’ll take it one step at a
time. In the first part, we define two async functions child1
and
child2
. These should look familiar from the last section:
6async def child1():
7 print(" child1: started! sleeping now...")
8 await trio.sleep(1)
9 print(" child1: exiting!")
10
11
12async def child2():
13 print(" child2: started! sleeping now...")
14 await trio.sleep(1)
15 print(" child2: exiting!")
Next, we define parent
as an async function that’s going to call
child1
and child2
concurrently:
18async def parent():
19 print("parent: started!")
20 async with trio.open_nursery() as nursery:
21 print("parent: spawning child1...")
22 nursery.start_soon(child1)
23
24 print("parent: spawning child2...")
25 nursery.start_soon(child2)
26
27 print("parent: waiting for children to finish...")
28 # -- we exit the nursery block here --
29 print("parent: all done!")
It does this by using a mysterious async with
statement to create
a “nursery”, and then “spawns” child1
and child2
into the
nursery.
Let’s start with this async with
thing. It’s actually pretty
simple. In regular Python, a statement like with someobj: ...
instructs the interpreter to call someobj.__enter__()
at the
beginning of the block, and to call someobj.__exit__()
at the end
of the block. We call someobj
a “context manager”. An async
with
does exactly the same thing, except that where a regular
with
statement calls regular methods, an async with
statement
calls async methods: at the start of the block it does await
someobj.__aenter__()
and at that end of the block it does await
someobj.__aexit__()
. In this case we call someobj
an “async
context manager”. So in short: with
blocks are a shorthand for
calling some functions, and since with async/await Python now has two
kinds of functions, it also needs two kinds of with
blocks. That’s
all there is to it! If you understand async functions, then you
understand async with
.
Note
This example doesn’t use them, but while we’re here we might as
well mention the one other piece of new syntax that async/await
added: async for
. It’s basically the same idea as async
with
versus with
: An async for
loop is just like a
for
loop, except that where a for
loop does
iterator.__next__()
to fetch the next item, an async for
does await async_iterator.__anext__()
. Now you understand all
of async/await. Basically just remember that it involves making
sandwiches and sticking the word “async” in front of everything,
and you’ll do fine.
Now that we understand async with
, let’s look at parent
again:
18async def parent():
19 print("parent: started!")
20 async with trio.open_nursery() as nursery:
21 print("parent: spawning child1...")
22 nursery.start_soon(child1)
23
24 print("parent: spawning child2...")
25 nursery.start_soon(child2)
26
27 print("parent: waiting for children to finish...")
28 # -- we exit the nursery block here --
29 print("parent: all done!")
There are only 4 lines of code that really do anything here. On line
20, we use trio.open_nursery()
to get a “nursery” object, and
then inside the async with
block we call nursery.start_soon
twice,
on lines 22 and 25. There are actually two ways to call an async
function: the first one is the one we already saw, using await
async_fn()
; the new one is nursery.start_soon(async_fn)
: it asks Trio
to start running this async function, but then returns immediately
without waiting for the function to finish. So after our two calls to
nursery.start_soon
, child1
and child2
are now running in the
background. And then at line 28, the commented line, we hit the end of
the async with
block, and the nursery’s __aexit__
function
runs. What this does is force parent
to stop here and wait for all
the children in the nursery to exit. This is why you have to use
async with
to get a nursery: it gives us a way to make sure that
the child calls can’t run away and get lost. One reason this is
important is that if there’s a bug or other problem in one of the
children, and it raises an exception, then it lets us propagate that
exception into the parent; in many other frameworks, exceptions like
this are just discarded. Trio never discards exceptions.
Ok! Let’s try running it and see what we get:
parent: started!
parent: spawning child1...
parent: spawning child2...
parent: waiting for children to finish...
child2: started! sleeping now...
child1: started! sleeping now...
[... 1 second passes ...]
child1: exiting!
child2: exiting!
parent: all done!
(Your output might have the order of the “started” and/or “exiting” lines swapped compared to mine.)
Notice that child1
and child2
both start together and then
both exit together. And, even though we made two calls to
trio.sleep(1)
, the program finished in just one second total.
So it looks like child1
and child2
really are running at the
same time!
Now, if you’re familiar with programming using threads, this might look familiar – and that’s intentional. But it’s important to realize that there are no threads here. All of this is happening in a single thread. To remind ourselves of this, we use slightly different terminology: instead of spawning two “threads”, we say that we spawned two “tasks”. There are two differences between tasks and threads: (1) many tasks can take turns running on a single thread, and (2) with threads, the Python interpreter/operating system can switch which thread is running whenever they feel like it; with tasks, we can only switch at certain designated places we call “checkpoints”. In the next section, we’ll dig into what this means.
Task switching illustrated¶
The big idea behind async/await-based libraries like Trio is to run lots of tasks simultaneously on a single thread by switching between them at appropriate places – so for example, if we’re implementing a web server, then one task could be sending an HTTP response at the same time as another task is waiting for new connections. If all you want to do is use Trio, then you don’t need to understand all the nitty-gritty detail of how this switching works – but it’s very useful to have at least a general intuition about what Trio is doing “under the hood” when your code is executing. To help build that intuition, let’s look more closely at how Trio ran our example from the last section.
Fortunately, Trio provides a rich set of tools for inspecting
and debugging your programs. Here we want to watch
trio.run()
at work, which we can do by writing a class we’ll
call Tracer
, which implements Trio’s Instrument
interface. Its job is to log various events as they happen:
class Tracer(trio.abc.Instrument):
def before_run(self):
print("!!! run started")
def _print_with_task(self, msg, task):
# repr(task) is perhaps more useful than task.name in general,
# but in context of a tutorial the extra noise is unhelpful.
print(f"{msg}: {task.name}")
def task_spawned(self, task):
self._print_with_task("### new task spawned", task)
def task_scheduled(self, task):
self._print_with_task("### task scheduled", task)
def before_task_step(self, task):
self._print_with_task(">>> about to run one step of task", task)
def after_task_step(self, task):
self._print_with_task("<<< task step finished", task)
def task_exited(self, task):
self._print_with_task("### task exited", task)
def before_io_wait(self, timeout):
if timeout:
print(f"### waiting for I/O for up to {timeout} seconds")
else:
print("### doing a quick check for I/O")
self._sleep_time = trio.current_time()
def after_io_wait(self, timeout):
duration = trio.current_time() - self._sleep_time
print(f"### finished I/O check (took {duration} seconds)")
def after_run(self):
print("!!! run finished")
Then we re-run our example program from the previous section, but this
time we pass trio.run()
a Tracer
object:
trio.run(parent, instruments=[Tracer()])
This generates a lot of output, so we’ll go through it one step at a time.
First, there’s a bit of chatter while Trio gets ready to run our
code. Most of this is irrelevant to us for now, but in the middle you
can see that Trio has created a task for the __main__.parent
function, and “scheduled” it (i.e., made a note that it should be run
soon):
$ python3 tutorial/tasks-with-trace.py
!!! run started
### new task spawned: <init>
### task scheduled: <init>
### doing a quick check for I/O
### finished I/O check (took 1.1122087016701698e-05 seconds)
>>> about to run one step of task: <init>
### new task spawned: <call soon task>
### task scheduled: <call soon task>
### new task spawned: __main__.parent
### task scheduled: __main__.parent
<<< task step finished: <init>
### doing a quick check for I/O
### finished I/O check (took 6.4980704337358475e-06 seconds)
Once the initial housekeeping is done, Trio starts running the
parent
function, and you can see parent
creating the two child
tasks. Then it hits the end of the async with
block, and pauses:
>>> about to run one step of task: __main__.parent
parent: started!
parent: spawning child1...
### new task spawned: __main__.child1
### task scheduled: __main__.child1
parent: spawning child2...
### new task spawned: __main__.child2
### task scheduled: __main__.child2
parent: waiting for children to finish...
<<< task step finished: __main__.parent
Control then goes back to trio.run()
, which logs a bit more
internal chatter:
>>> about to run one step of task: <call soon task>
<<< task step finished: <call soon task>
### doing a quick check for I/O
### finished I/O check (took 5.476875230669975e-06 seconds)
And then gives the two child tasks a chance to run:
>>> about to run one step of task: __main__.child2
child2 started! sleeping now...
<<< task step finished: __main__.child2
>>> about to run one step of task: __main__.child1
child1: started! sleeping now...
<<< task step finished: __main__.child1
Each task runs until it hits the call to trio.sleep()
, and then
suddenly we’re back in trio.run()
deciding what to run next. How
does this happen? The secret is that trio.run()
and
trio.sleep()
work together to make it happen: trio.sleep()
has access to some special magic that lets it pause itself,
so it sends a note to trio.run()
requesting to be
woken again after 1 second, and then suspends the task. And once the
task is suspended, Python gives control back to trio.run()
,
which decides what to do next. (If this sounds similar to the way that
generators can suspend execution by doing a yield
, then that’s not
a coincidence: inside the Python interpreter, there’s a lot of overlap
between the implementation of generators and async functions.)
Note
You might wonder whether you can mix-and-match primitives from
different async libraries. For example, could we use
trio.run()
together with asyncio.sleep()
? The answer is
no, we can’t, and the paragraph above explains why: the two sides
of our async sandwich have a private language they use to talk to
each other, and different libraries use different languages. So if
you try to call asyncio.sleep()
from inside a
trio.run()
, then Trio will get very confused indeed and
probably blow up in some dramatic way.
Only async functions have access to the special magic for suspending a
task, so only async functions can cause the program to switch to a
different task. What this means is that if a call doesn’t have an await
on it, then you know that it can’t be a place where your task will
be suspended. This makes tasks much easier to reason about than
threads, because there are far fewer ways that tasks can be
interleaved with each other and stomp on each others’ state. (For
example, in Trio a statement like a += 1
is always atomic – even
if a
is some arbitrarily complicated custom object!) Trio also
makes some further guarantees beyond that, but
that’s the big one.
And now you also know why parent
had to use an async with
to
open the nursery: if we had used a regular with
block, then it
wouldn’t have been able to pause at the end and wait for the children
to finish; we need our cleanup function to be async, which is exactly
what async with
gives us.
Now, back to our execution point. To recap: at this point parent
is waiting on child1
and child2
, and both children are
sleeping. So trio.run()
checks its notes, and sees that there’s
nothing to be done until those sleeps finish – unless possibly some
external I/O event comes in. If that happened, then it might give us
something to do. Of course we aren’t doing any I/O here so it won’t
happen, but in other situations it could. So next it calls an
operating system primitive to put the whole process to sleep:
### waiting for I/O for up to 0.9999009938910604 seconds
And in fact no I/O does arrive, so one second later we wake up again,
and Trio checks its notes again. At this point it checks the current
time, compares it to the notes that trio.sleep()
sent saying
when the two child tasks should be woken up again, and realizes
that they’ve slept for long enough, so it schedules them to run soon:
### finished I/O check (took 1.0006483688484877 seconds)
### task scheduled: __main__.child1
### task scheduled: __main__.child2
And then the children get to run, and this time they run to
completion. Remember how parent
is waiting for them to finish?
Notice how parent
gets scheduled when the first child exits:
>>> about to run one step of task: __main__.child1
child1: exiting!
### task scheduled: __main__.parent
### task exited: __main__.child1
<<< task step finished: __main__.child1
>>> about to run one step of task: __main__.child2
child2 exiting!
### task exited: __main__.child2
<<< task step finished: __main__.child2
Then, after another check for I/O, parent
wakes up. The nursery
cleanup code notices that all its children have exited, and lets the
nursery block finish. And then parent
makes a final print and
exits:
### doing a quick check for I/O
### finished I/O check (took 9.045004844665527e-06 seconds)
>>> about to run one step of task: __main__.parent
parent: all done!
### task scheduled: <init>
### task exited: __main__.parent
<<< task step finished: __main__.parent
And finally, after a bit more internal bookkeeping, trio.run()
exits too:
### doing a quick check for I/O
### finished I/O check (took 5.996786057949066e-06 seconds)
>>> about to run one step of task: <init>
### task scheduled: <call soon task>
### task scheduled: <init>
<<< task step finished: <init>
### doing a quick check for I/O
### finished I/O check (took 6.258022040128708e-06 seconds)
>>> about to run one step of task: <call soon task>
### task exited: <call soon task>
<<< task step finished: <call soon task>
>>> about to run one step of task: <init>
### task exited: <init>
<<< task step finished: <init>
!!! run finished
You made it!
That was a lot of text, but again, you don’t need to understand everything here to use Trio – in fact, Trio goes to great lengths to make each task feel like it executes in a simple, linear way. (Just like your operating system goes to great lengths to make it feel like your single-threaded code executes in a simple linear way, even though under the covers the operating system juggles between different threads and processes in essentially the same way Trio does.) But it is useful to have a rough model in your head of how the code you write is actually executed, and – most importantly – the consequences of that for parallelism.
Alternatively, if this has just whetted your appetite and you want to
know more about how async/await
works internally, then this blog
post
is a good deep dive, or check out this great walkthrough to see
how to build a simple async I/O framework from the ground up.
A kinder, gentler GIL¶
Speaking of parallelism – let’s zoom out for a moment and talk about how async/await compares to other ways of handling concurrency in Python.
As we’ve already noted, Trio tasks are conceptually rather similar to
Python’s built-in threads, as provided by the threading
module. And in all common Python implementations, threads have a
famous limitation: the Global Interpreter Lock, or “GIL” for
short. The GIL means that even if you use multiple threads, your code
still (mostly) ends up running on a single core. People tend to find
this frustrating.
But from Trio’s point of view, the problem with the GIL isn’t that it restricts parallelism. Of course it would be nice if Python had better options for taking advantage of multiple cores, but that’s an extremely difficult problem to solve, and in the meantime there are lots of problems where a single core is totally adequate – or where if it isn’t, then process-level or machine-level parallelism works fine.
No, the problem with the GIL is that it’s a lousy deal: we give up on using multiple cores, and in exchange we get… almost all the same challenges and mind-bending bugs that come with real parallel programming, and – to add insult to injury – pretty poor scalability. Threads in Python just aren’t that appealing.
Trio doesn’t make your code run on multiple cores; in fact, as we saw
above, it’s baked into Trio’s design that when it has multiple tasks,
they take turns, so at each moment only one of them is actively running.
We’re not so much overcoming the GIL as embracing it. But if you’re
willing to accept that, plus a bit of extra work to put these new
async
and await
keywords in the right places, then in exchange
you get:
Excellent scalability: Trio can run 10,000+ tasks simultaneously without breaking a sweat, so long as their total CPU demands don’t exceed what a single core can provide. (This is common in, for example, network servers that have lots of clients connected, but only a few active at any given time.)
Fancy features: most threading systems are implemented in C and restricted to whatever features the operating system provides. In Trio our logic is all in Python, which makes it possible to implement powerful and ergonomic features like Trio’s cancellation system.
Code that’s easier to reason about: the
await
keyword means that potential task-switching points are explicitly marked within each function. This can make Trio code dramatically easier to reason about than the equivalent program using threads.
Certainly it’s not appropriate for every app… but there are a lot of situations where the trade-offs here look pretty appealing.
There is one downside that’s important to keep in mind, though. Making checkpoints explicit gives you more control over how your tasks can be interleaved – but with great power comes great responsibility. With threads, the runtime environment is responsible for making sure that each thread gets its fair share of running time. With Trio, if some task runs off and does stuff for seconds on end without executing a checkpoint, then… all your other tasks will just have to wait.
Here’s an example of how this can go wrong. Take our example
from above, and replace the calls to
trio.sleep()
with calls to time.sleep()
. If we run our
modified program, we’ll see something like:
parent: started!
parent: spawning child1...
parent: spawning child2...
parent: waiting for children to finish...
child2 started! sleeping now...
[... pauses for 1 second ...]
child2 exiting!
child1: started! sleeping now...
[... pauses for 1 second ...]
child1: exiting!
parent: all done!
One of the major reasons why Trio has such a rich instrumentation API is to make it possible to write debugging tools to catch issues like this.
Networking with Trio¶
Now let’s take what we’ve learned and use it to do some I/O, which is where async/await really shines.
The traditional toy application for demonstrating network APIs is an “echo server”: a program that awaits arbitrary data from remote clients, and then sends that same data right back. (Probably a more relevant example these days would be an application that does lots of concurrent HTTP requests, but for that you need an HTTP library such as asks, so we’ll stick with the echo server tradition.)
In this tutorial, we present both ends of the pipe: the client, and the server. The client periodically sends data to the server, and displays its answers. The server awaits connections; when a client connects, it recopies the received data back on the pipe.
An echo client¶
To start with, here’s an example echo client, i.e., the program that will send some data at our echo server and get responses back:
1# echo-client.py
2
3import sys
4import trio
5
6# arbitrary, but:
7# - must be in between 1024 and 65535
8# - can't be in use by some other program on your computer
9# - must match what we set in our echo server
10PORT = 12345
11
12
13async def sender(client_stream):
14 print("sender: started!")
15 while True:
16 data = b"async can sometimes be confusing, but I believe in you!"
17 print(f"sender: sending {data!r}")
18 await client_stream.send_all(data)
19 await trio.sleep(1)
20
21
22async def receiver(client_stream):
23 print("receiver: started!")
24 async for data in client_stream:
25 print(f"receiver: got data {data!r}")
26 print("receiver: connection closed")
27 sys.exit()
28
29
30async def parent():
31 print(f"parent: connecting to 127.0.0.1:{PORT}")
32 client_stream = await trio.open_tcp_stream("127.0.0.1", PORT)
33 async with client_stream:
34 async with trio.open_nursery() as nursery:
35 print("parent: spawning sender...")
36 nursery.start_soon(sender, client_stream)
37
38 print("parent: spawning receiver...")
39 nursery.start_soon(receiver, client_stream)
40
41
42trio.run(parent)
Note that this code will not work without a TCP server such as the one we’ll implement below.
The overall structure here should be familiar, because it’s just like
our last example: we have a
parent task, which spawns two child tasks to do the actual work, and
then at the end of the async with
block it switches into full-time
parenting mode while waiting for them to finish. But now instead of
just calling trio.sleep()
, the children use some of Trio’s
networking APIs.
Let’s look at the parent first:
30async def parent():
31 print(f"parent: connecting to 127.0.0.1:{PORT}")
32 client_stream = await trio.open_tcp_stream("127.0.0.1", PORT)
33 async with client_stream:
34 async with trio.open_nursery() as nursery:
35 print("parent: spawning sender...")
36 nursery.start_soon(sender, client_stream)
37
38 print("parent: spawning receiver...")
39 nursery.start_soon(receiver, client_stream)
First we call trio.open_tcp_stream()
to make a TCP connection to
the server. 127.0.0.1
is a magic IP address meaning “the computer
I’m running on”, so this connects us to whatever program on the local
computer is using PORT
as its contact point. This function returns
an object implementing Trio’s Stream
interface,
which gives us methods to send and receive bytes, and to close the
connection when we’re done. We use an async with
block to make
sure that we do close the connection – not a big deal in a toy example
like this, but it’s a good habit to get into, and Trio is designed to
make with
and async with
blocks easy to use.
Finally, we start up two child tasks, and pass each of them a
reference to the stream. (This is also a good example of how
nursery.start_soon
lets you pass positional arguments to the
spawned function.)
Our first task’s job is to send data to the server:
13async def sender(client_stream):
14 print("sender: started!")
15 while True:
16 data = b"async can sometimes be confusing, but I believe in you!"
17 print(f"sender: sending {data!r}")
18 await client_stream.send_all(data)
19 await trio.sleep(1)
It uses a loop that alternates between calling await
client_stream.send_all(...)
to send some data (this is the method
you use for sending data on any kind of Trio stream), and then
sleeping for a second to avoid making the output scroll by too fast on
your terminal.
And the second task’s job is to process the data the server sends back:
22async def receiver(client_stream):
23 print("receiver: started!")
24 async for data in client_stream:
25 print(f"receiver: got data {data!r}")
26 print("receiver: connection closed")
27 sys.exit()
It uses an async for
loop to fetch data from the server.
Alternatively, it could use receive_some
,
which is the opposite of send_all
, but using
async for
saves some boilerplate.
And now we’re ready to look at the server.
An echo server¶
As usual, let’s look at the whole thing first, and then we’ll discuss the pieces:
1# echo-server.py
2
3import trio
4from itertools import count
5
6# Port is arbitrary, but:
7# - must be in between 1024 and 65535
8# - can't be in use by some other program on your computer
9# - must match what we set in our echo client
10PORT = 12345
11
12CONNECTION_COUNTER = count()
13
14
15async def echo_server(server_stream):
16 # Assign each connection a unique number to make our debug prints easier
17 # to understand when there are multiple simultaneous connections.
18 ident = next(CONNECTION_COUNTER)
19 print(f"echo_server {ident}: started")
20 try:
21 async for data in server_stream:
22 print(f"echo_server {ident}: received data {data!r}")
23 await server_stream.send_all(data)
24 print(f"echo_server {ident}: connection closed")
25 # FIXME: add discussion of (Base)ExceptionGroup to the tutorial, and use
26 # exceptiongroup.catch() here. (Not important in this case, but important
27 # if the server code uses nurseries internally.)
28 except Exception as exc:
29 # Unhandled exceptions will propagate into our parent and take
30 # down the whole program. If the exception is KeyboardInterrupt,
31 # that's what we want, but otherwise maybe not...
32 print(f"echo_server {ident}: crashed: {exc!r}")
33
34
35async def main():
36 await trio.serve_tcp(echo_server, PORT)
37
38
39# We could also just write 'trio.run(trio.serve_tcp, echo_server, PORT)', but real
40# programs almost always end up doing other stuff too and then we'd have to go
41# back and factor it out into a separate function anyway. So it's simplest to
42# just make it a standalone function from the beginning.
43trio.run(main)
Let’s start with main
, which is just one line long:
35async def main():
36 await trio.serve_tcp(echo_server, PORT)
What this does is call serve_tcp()
, which is a convenience
function Trio provides that runs forever (or at least until you hit
control-C or otherwise cancel it). This function does several helpful
things:
It creates a nursery internally, so that our server will be able to handle multiple connections at the same time.
It listens for incoming TCP connections on the specified
PORT
.Whenever a connection arrives, it starts a new task running the function we pass (in this example it’s
echo_server
), and passes it a stream representing that connection.When each task exits, it makes sure to close the corresponding connection. (That’s why you don’t see any
async with server_stream
in the server –serve_tcp()
takes care of this for us.)
So serve_tcp()
is pretty handy! This part works pretty much the
same for any server, whether it’s an echo server, HTTP server, SSH
server, or whatever, so it makes sense to bundle it all up together in
a helper function like this.
Now let’s look at echo_server
, which handles each client
connection – so if there are multiple clients, there might be multiple
calls to echo_server
running at the same time. This is where we
implement our server’s “echo” behavior. This should be pretty
straightforward to understand, because it uses the same stream
functions we saw in the last section:
15async def echo_server(server_stream):
16 # Assign each connection a unique number to make our debug prints easier
17 # to understand when there are multiple simultaneous connections.
18 ident = next(CONNECTION_COUNTER)
19 print(f"echo_server {ident}: started")
20 try:
21 async for data in server_stream:
22 print(f"echo_server {ident}: received data {data!r}")
23 await server_stream.send_all(data)
24 print(f"echo_server {ident}: connection closed")
25 # FIXME: add discussion of (Base)ExceptionGroup to the tutorial, and use
26 # exceptiongroup.catch() here. (Not important in this case, but important
27 # if the server code uses nurseries internally.)
28 except Exception as exc:
29 # Unhandled exceptions will propagate into our parent and take
30 # down the whole program. If the exception is KeyboardInterrupt,
31 # that's what we want, but otherwise maybe not...
32 print(f"echo_server {ident}: crashed: {exc!r}")
The argument server_stream
is provided by serve_tcp()
, and
is the other end of the connection we made in the client: so the data
that the client passes to send_all
will come out here. Then we
have a try
block discussed below, and finally the server loop
which alternates between reading some data from the socket and then
sending it back out again (unless the socket was closed, in which case
we quit).
So what’s that try
block for? Remember that in Trio, like Python
in general, exceptions keep propagating until they’re caught. Here we
think it’s plausible there might be unexpected exceptions, and we want
to isolate that to making just this one task crash, without taking
down the whole program. For example, if the client closes the
connection at the wrong moment then it’s possible this code will end
up calling send_all
on a closed connection and get a
BrokenResourceError
; that’s unfortunate, and in a more serious
program we might want to handle it more explicitly, but it doesn’t
indicate a problem for any other connections. On the other hand, if
the exception is something like a KeyboardInterrupt
, we do
want that to propagate out into the parent task and cause the whole
program to exit. To express this, we use a try
block with an
except Exception:
handler.
In general, Trio leaves it up to you to decide whether and how you want to handle exceptions, just like Python in general.
Try it out¶
Open a few terminals, run echo-server.py
in one, run
echo-client.py
in another, and watch the messages scroll by! When
you get bored, you can exit by hitting control-C.
Some things to try:
Open several terminals, and run multiple clients at the same time, all talking to the same server.
See how the server reacts when you hit control-C on the client.
See how the client reacts when you hit control-C on the server.
Flow control in our echo client and server¶
Here’s a question you might be wondering about: why does our client use two separate tasks for sending and receiving, instead of a single task that alternates between them – like the server has? For example, our client could use a single task like:
# Can you spot the two problems with this code?
async def send_and_receive(client_stream):
while True:
data = ...
await client_stream.send_all(data)
received = await client_stream.receive_some()
if not received:
sys.exit()
await trio.sleep(1)
It turns out there are two problems with this – one minor and one
major. Both relate to flow control. The minor problem is that when we
call receive_some
here we’re not waiting for all the data to be
available; receive_some
returns as soon as any data is available. If
data
is small, then our operating systems / network / server will
probably keep it all together in a single chunk, but there’s no
guarantee. If the server sends hello
then we might get hello
,
or he
llo
, or h
e
l
l
o
, or … bottom
line, any time we’re expecting more than one byte of data, we have to
be prepared to call receive_some
multiple times.
And where this would go especially wrong is if we find ourselves in
the situation where data
is big enough that it passes some
internal threshold, and the operating system or network decide to
always break it up into multiple pieces. Now on each pass through the
loop, we send len(data)
bytes, but read less than that. The result
is something like a memory leak: we’ll end up with more and more data
backed up in the network, until eventually something breaks.
Note
If you’re curious how things break, then you can use
receive_some
's optional argument to put
a limit on how many bytes you read each time, and see what happens.
We could fix this by keeping track of how much data we’re expecting at
each moment, and then keep calling receive_some
until we get it all:
This is a bit cumbersome, but it would solve this problem.
There’s another problem, though, that’s deeper. We’re still
alternating between sending and receiving. Notice that when we send
data, we use await
: this means that sending can potentially
block. Why does this happen? Any data that we send goes first into
an operating system buffer, and from there onto the network, and then
another operating system buffer on the receiving computer, before the
receiving program finally calls receive_some
to take the data out
of these buffers. If we call send_all
with a small amount of data,
then it goes into these buffers and send_all
returns immediately.
But if we send enough data fast enough, eventually the buffers fill
up, and send_all
will block until the remote side calls
receive_some
and frees up some space.
Now let’s think about this from the server’s point of view. Each time
it calls receive_some
, it gets some data that it needs to send
back. And until it sends it back, the data that is sitting around takes up
memory. Computers have finite amounts of RAM, so if our server is well
behaved then at some point it needs to stop calling receive_some
until it gets rid of some of the old data by doing its own call to
send_all
. So for the server, really the only viable option is to
alternate between receiving and sending.
But we need to remember that it’s not just the client’s call to
send_all
that might block: the server’s call to send_all
can
also get into a situation where it blocks until the client calls
receive_some
. So if the server is waiting for send_all
to
finish before it calls receive_some
, and our client also waits for
send_all
to finish before it calls receive_some
,… we have a
problem! The client won’t call receive_some
until the server has
called receive_some
, and the server won’t call receive_some
until the client has called receive_some
. If our client is written
to alternate between sending and receiving, and the chunk of data it’s
trying to send is large enough (e.g. 10 megabytes will probably do it
in most configurations), then the two processes will deadlock.
Moral: Trio gives you powerful tools to manage sequential and
concurrent execution. In this example we saw that the server needs
send
and receive_some
to alternate in sequence, while the
client needs them to run concurrently, and both were straightforward
to implement. But when you’re implementing network code like this then
it’s important to think carefully about flow control and buffering,
because it’s up to you to choose the right execution mode!
Other popular async libraries like Twisted and asyncio
tend to paper over
these kinds of issues by throwing in unbounded buffers everywhere.
This can avoid deadlocks, but can introduce its own problems and in
particular can make it difficult to keep memory usage and latency
under control.
While both approaches have their advantages, Trio takes the position
that it’s better to expose the underlying problem as directly as
possible and provide good tools to confront it head-on.
Note
If you want to try and make the deadlock happen on purpose to see
for yourself, and you’re using Windows, then you might need to
split the send_all
call up into two calls that each send half of
the data. This is because Windows has a somewhat unusual way of
handling buffering.
When things go wrong: timeouts, cancellation and exceptions in concurrent tasks¶
TODO: give an example using fail_after()
TODO: explain Cancelled
TODO: explain how cancellation is also used when one child raises an exception
TODO: maybe a brief discussion of KeyboardInterrupt
handling?