asynq is a library for asynchronous programming in Python with a focus on batching requests to
external services. It also provides seamless interoperability with synchronous code, support for
asynchronous context managers, and tools to make writing and testing asynchronous code easier.
asynq was developed at Quora and is a core component of Quora's architecture. See the original blog
The most important use case for
asynq is batching. For many storage services (e.g., memcache,
redis) it is far faster to make a single request that fetches many keys at once than to make
many requests that each fetch a single key. The
asynq framework makes it easy to write code
that takes advantage of batching without radical changes in code structure from code that does not
For example, synchronous code to retrieve the names of the authors of a list of Quora answers may look like this:
def all_author_names(aids): uids = [author_of_answer(aid) for aid in aids] names = [name_of_user(uid) for uid in uids] return names
Here, each call to
name_of_user would result in a memcache request.
Converted to use
asynq, this code would look like:
@async() def all_author_names(aids): uids = yield [author_of_answer.async(aid) for aid in aids] names = yield [name_of_user.async(uid) for uid in uids] result(names); return
author_of_answer calls will be combined into a single memcache request, as will all of the
Futures are the basic building blocks of
asynq's programming model. The scheduler keeps track
of futures and attempts to schedule them in an efficient way.
asynq uses its own hierarchy of
Future classes, rooted in
asynq.FutureBase. Futures have a
.value() method that computes
their value if necessary and then returns it.
The following are the most important Future classes used in
AsyncTask, a Future representing the execution of an asynchronous function (see below). Normally created by calling
.async()on an asynchronous function.
ConstFuture, a Future whose value is known at creation time. This is useful when you need to pass a Future somewhere, but no computation is actually needed.
BatchItemBase, the building blocks for doing batching. See below for details.
Decorators and asynchronous functions
asynq's asynchronous functions are implemented as Python generator functions. Every time an
asynchronous functions yields one or more Futures, it cedes control the asynq scheduler, which will
resolve the futures that were yielded and continue running the function after the futures have been
Returning a value from an asynchronous function is hard in Python 2 because generators are not
allowed to return a value.
asynq provides the special
result() function that can be used to
return a value from an asynchronous function; it is implemented by raising a custom exception
that is caught by the scheduler. At Quora, we instead use a patched Python 2 binary that does
support returning a value from a generator.
The framework requires usage of the
@async() decorator on all asynchronous functions. This
decorator wraps the generator function so that it can be called like a normal, synchronous function.
It also creates a
.async attribute on the function that allows calling the function
asynchronously. Calling this attribute will return an
AsyncTask object corresponding to the
You can call an asynchronous function synchronously like this:
result = async_fn(a, b)
and asynchronously like this:
result = yield async_fn.async(a, b)
async_fn.async(a, b).value() has the same result as
The decorator has a
pure=True option that disables the
.async attribute and instead makes
the function itself asynchronous, so that calling it returns an
AsyncTask. We recommend to use
this option only in special cases like decorators for asynchronous functions.
asynq also provides an
@async_proxy() decorator for functions that return a Future
directly. Functions decorated with
@async_proxy() look like
@async() functions externally.
An example use case is a function that takes either an asynchronous or a synchronous function,
and calls it accordingly:
@async_proxy() def async_call(fn, *args, **kwargs): if is_async_fn(fn): # Returns an AsyncTask return fn.async(*args, **kwargs) return ConstFuture(fn(*args, **kwargs))
Batching is at the core of what makes
asynq useful. To implement batching, you need to subclass
asynq.BatchBase. The first represents a single entry in a batch
(e.g., a single memcache key to fetch) and the second is responsible for executing the batch when
the scheduler requests it.
Batch items usually do not require much logic beyond registering themselves with the currently
active batch in
__init__. Batches need to override the
which changes the batch that is currently active, and the
_flush method that executes it.
This method should call
.set_value() on all the items in the batch.
An example implementation of batching for memcache is in the
The framework also provides a
DebugBatchItem for testing.
Most users of
asynq should not need to implement batches frequently. At Quora, we use
thousands of asynchronous functions, but only five
asynq provides support for Python context managers that are automatically activated and
deactivated when a particular task is scheduled. This feature is necessary because the scheduler
can schedule tasks in arbitrary order. For example, consider the following code:
@async() def show_warning(): yield do_something_that_creates_a_warning.async() @async() def suppress_warning(): with warnings.catch_warnings(): yield show_warning.async() @async() def caller(): yield show_warning.async(), suppress_warning.async()
This code should show only one warning, because only the second call to
show_warning is within
catch_warnings() context, but depending on how the scheduler happens to execute these
functions, the code that shows the warning may also be executed while
To remedy this problem, you should use an
AsyncContext, which will be automatically paused when
the task that created it is no longer active and resumed when it becomes active again. An
asynq-compatible version of
catch_warnings would look something like this:
class catch_warnings(asynq.AsyncContext): def pause(self): stop_catching_warnings() def resume(self): start_catching_warnings()
asynq scheduler is invoked every time an asynchronous function is called, and it
can invoke arbitrary other active futures, normal Python stack traces become useless in a
sufficiently complicated application built on
asynq. To make debugging easier, the framework
provides the ability to generate a custom
asynq stack trace, which shows how each active
asynchronous function was invoked.
asynq.debug.dump_asynq_stack() method can be used to print this stack, similar to
traceback.print_stack(). The framework also registers a hook to print out the
when an exception happens.
asynq provides a number of additional tools to make it easier to write asynchronous code. Some
of these are in the
asynq.tools module. These tools include:
asynq.async_callcalls a function asynchronously only if it is asynchronous. This can be useful when calling an overridden method that is asynchronous on some child classes but not on others.
asynq.tools.call_with_contextcalls an asynchronous function within the provided context manager. This is helpful in cases where you need to yield multiple tasks at once, but only one needs to be within the context.
asynq.tools.asortedare equivalents of the standard
sortedfunctions that take asynchronous functions as their filter and compare functions.
asynq.tools.acached_per_instancecaches an asynchronous instance method.
asynq.tools.deduplicateprevents multiple simultaneous calls to the same asynchronous function.
asynq.mockmodule is an enhancement to the standard
mockmodule that makes it painless to mock asynchronous functions. Without this module, mocking any asynchronous function will often also require mocking its
.asyncattribute. We recommend using
asynq.mock.patchfor all mocking in projects that use
asynq.generatormodule provides an experimental implementation of asynchronous generators, which can produce a sequence of values while also using
asynq's batching support.
asynq runs on Python 2.7 and Python 3.