Ever used asyncio and wished you hadn't?
tinyio is a dead-simple event loop for Python, born out of my frustration with trying to get robust error handling with asyncio. (I'm not the only one running into its sharp corners: link1, link2.)
This is an alternative for the simple use-cases, where you just need an event loop, and want to crash the whole thing if anything goes wrong. (Raising an exception in every coroutine so it can clean up its resources.)
import tinyio
def slow_add_one(x: int):
yield tinyio.sleep(1)
return x + 1
def foo():
four, five = yield [slow_add_one(3), slow_add_one(4)]
return four, five
loop = tinyio.Loop()
out = loop.run(foo())
assert out == (4, 5)- Somewhat unusually, our syntax uses
yieldrather thanawait, but the behaviour is the same. Await another coroutine withyield coro. Await on multiple withyield [coro1, coro2, ...](a 'gather' in asyncio terminology; a 'nursery' in trio terminology). - An error in one coroutine will cancel all coroutines across the entire event loop.
- If the erroring coroutine is sequentially depended on by a chain of other coroutines, then we chain their tracebacks for easier debugging.
- Errors propagate to and from synchronous operations ran in threads.
- Can nest tinyio loops inside each other, none of this one-per-thread business.
- Ludicrously simple. No need for futures, tasks, etc. Here's the entirety of the day-to-day API:
tinyio.Loop tinyio.run_in_thread tinyio.sleep tinyio.CancelledError
pip install tinyio
Create a loop with tinyio.Loop(). It has a single method, .run(coro), which consumes a coroutine, and which returns the output of that coroutine.
Coroutines can yield four possible things:
yield: yield nothing, this just pauses and gives other coroutines a chance to run.yield coro: wait on a single coroutine, in which case we'll resume with the output of that coroutine once it is available.yield [coro1, coro2, coro3]: wait on multiple coroutines by putting them in a list, and resume with a list of outputs once all have completed. This is what asyncio calls a 'gather' or 'TaskGroup', and what trio calls a 'nursery'.yield {coro1, coro2, coro3}: schedule one or more coroutines but do not wait on their result - they will run independently in the background.
If you yield on the same coroutine multiple times (e.g. in a diamond dependency pattern) then the coroutine will be scheduled once, and on completion all dependees will receive its output. (You can even do this if the coroutine has already finished: yield on it to retrieve its output.)
Blocking functions can be ran in threads using tinyio.run_in_thread(fn, *args, **kwargs), which gives a coroutine you can yield on. Example:
import time, tinyio
def slow_blocking_add_one(x: int) -> int:
time.sleep(1)
return x + 1
def foo(x: int):
out = yield [tinyio.run_in_thread(slow_blocking_add_one, x) for _ in range(3)]
return out
loop = tinyio.Loop()
out = loop.run(foo(x=1)) # runs in one second, not three
assert out == [2, 2, 2]This is tinyio.sleep(delay_in_seconds), which is a coroutine you can yield on.
If any coroutine raises an error, then:
- All coroutines across the entire loop will have
tinyio.CancelledErrorraised in them (from whateveryieldpoint they are currently waiting at). - Any functions ran in threads via
tinyio.run_in_threadwill also havetinyio.CancelledErrorraised in the thread. - The original error is raised out of
loop.run(...). This behaviour can be configured (e.g. to collect errors into aBaseExceptionGroup) by settingloop.run(..., exception_group=None/False/True).
This gives every coroutine a chance to shut down gracefully. Debuggers like patdb offer the ability to navigate across exceptions in an exception group, allowing you to inspect the state of all coroutines that were related to the error.
We ship batteries-included with the usual collection of standard operations.
Click to expand
tinyio.as_completed tinyio.Semaphore
tinyio.Barrier tinyio.ThreadPool
tinyio.Event tinyio.timeout
tinyio.Lock tinyio.TimeoutError-
tinyio.as_completed({coro1, coro2, ...})This schedules multiple coroutines in the background (like
yield {coro1, coro2, ...}), and then offers their results in the order they complete.This is iterated over in the following way, using its
.done()and.get()methods:def main(): iterator = yield tinyio.as_completed({coro1, coro2, coro3}) while not iterator.done(): x = yield iterator.get()
-
tinyio.Barrier(value)This has a single method
barrier.wait(), which is a coroutine you canyieldon. Oncevaluemany coroutines have yielded on this method then it will unblock.
-
tinyio.Event()This is a wrapper around a boolean flag, initialised with
False. This has the following methods:.is_set(): return the value of the flag..set(): set the flag toTrue..clear(): set the flag toFalse..wait(timeout_in_seconds=None), which is a coroutine you canyieldon. This will unblock if the internal flag isTrueor iftimeout_in_secondsseconds pass. (Typically the former is accomplished by calling.set()from another coroutine or from a thread.)
-
tinyio.Lock()This is just a convenience for
tinyio.Semaphore(value=1), see below.
-
tinyio.Semaphore(value)This manages an internal counter that is initialised at
value, is decremented when entering a region, and incremented when exiting. This blocks if this counter is at zero. In this way, at mostvaluecoroutines may acquire the semaphore at a time.This is used as:
semaphore = Semaphore(value) ... with (yield semaphore()): ...
-
tinyio.timeout(coro, timeout_in_seconds)This is a coroutine you can
yieldon, used asoutput, success = yield tinyio.timeout(coro, timeout_in_seconds).This runs
corofor at mosttimeout_in_seconds. If it succeeds in that time then the pair(output, True)is returned . Else this will return(None, False), andcorowill be halted by raisingtinyio.TimeoutErrorinside it.
-
tinyio.ThreadPool(max_threads)This is equivalent to making multiple
tinyio.run_in_threadcalls, but will limit the number of threads to at mostmax_threads. Additional work after that will block until a thread becomes available.This has two methods:
.run_in_thread(fn, *args, **kwargs), which is a coroutine you canyieldon. This is equivalent toyield tinyio.run_in_thread(fn, *args, **kwargs)..map(fn, xs), which is a coroutine you canyieldon. This is equivalent toyield [tinyio.run_in_thread(fn, x) for x in xs].
Why yield - why not await like is normally seen for coroutines?
The reason is that await does not offer a suspension point to an event loop (it just calls __await__ and maybe that offers a suspension point), so if we wanted to use that syntax then we'd need to replace yield coro with something like await tinyio.Task(coro). The traditional syntax is not worth the extra class.
I have a function I want to be a coroutine, but it has zero yield statements, so it is just a normal function?
You can distinguish it from a normal Python function by putting if False: yield somewhere inside its body. Another common trick is to put a yield statement after the final return statement. Bit ugly but oh well.
vs asyncio or trio?.
I wasted a lot of time trying to get correct error propagation with asyncio, trying to reason whether my tasks would be cleaned up correctly or not (edge-triggered vs level-triggered etc etc). trio is excellent but still has a one-loop-per-thread rule, and doesn't propagate cancellations to/from threads. These points inspired me to try writing my own.
tinyio has the following unique features, and as such may be the right choice if any of the following are must-haves for you:
- the propagation of errors to/from threads;
- no one-loop-per-thread rule;
- simple+robust error semantics (crash the whole loop if anything goes wrong);
- tiny, hackable, codebase.
However conversely, tinyio does not offer the ability to schedule work on the event loop whilst cleaning up from errors.
If none of the bullet points are must-haves for you, or if needing the event loop during cleanup is a dealbreaker, then either trio or asyncio are likely to be better choices. :)