tinyio 0.1.4

A tiny event loop for Python.

tinyio

A tiny (~300 lines) event loop for Python

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 yield rather than await, but the behaviour is the same. Await another coroutine with yield coro. Await on multiple with yield [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
  

Installation

pip install tinyio

Documentation

Loops

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.

You can safely yield the same coroutine multiple times, e.g. perhaps four coroutines have a diamond dependency pattern, with two coroutines each depending on a single shared one.

Threading

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]

Sleeping

This is tinyio.sleep(delay_in_seconds), which is a coroutine you can yield on.

Error propagation

If any coroutine raises an error, then:

1. All coroutines across the entire loop will have tinyio.CancelledError raised in them (from whatever yield point they are currently waiting at).
2. Any functions ran in threads via tinyio.run_in_thread will also have tinyio.CancelledError raised in the thread.
3. The original error is raised out of loop.run(...). This behaviour can be configured (e.g. to collect errors into a BaseExceptionGroup) by setting loop.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.

Batteries-included

We ship batteries-included with the usual collection of standard operations.

Click to expand

tinyio.add_done_callback        tinyio.Semaphore
tinyio.AsCompleted              tinyio.ThreadPool
tinyio.Barrier                  tinyio.timeout
tinyio.Event                    tinyio.TimeoutError
tinyio.Lock

---

• tinyio.add_done_callback(coro, success_callback)

  Used as yield {tinyio.add_done_callback(coro, success_callback)}.

  This wraps coro so that success_callback(out) is called on its output once it completes. Note the {...} above, indicating calling this in nonblocking fashion (otherwise you could just directly call the callbacks yourself).

---

• tinyio.AsCompleted({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 = tinyio.AsCompleted({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 can yield on. Once value many 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(): check the value of the flag.
  • .set(): set the flag to True.
  • .clear(): set the flag to False.
  • .wait(), which is a coroutine you can yield on. This will unblock if the internal flag is True. (Typically this 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 most value coroutines 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 yield on, used as output, success = yield tinyio.timeout(coro, timeout_in_seconds).

  This runs coro for at most timeout_in_seconds. If it succeeds in that time then the pair (output, True) is returned . Else this will return (None, False), and coro will be halted by raising tinyio.TimeoutError inside it.

---

• tinyio.ThreadPool(max_threads)

  This is equivalent to making multiple tinyio.run_in_thread calls, but will limit the number of threads to at most max_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 can yield on. This is equivalent to yield tinyio.run_in_thread(fn, *args, **kwargs).
  • .map(fn, xs), which is a coroutine you can yield on. This is equivalent to yield [tinyio.run_in_thread(fn, x) for x in xs].

---

FAQ

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. :)