Ok let’s face it. Clock speeds no longer govern the pace at which computer processors improve. Instead we see increased transistor density and higher core counts. Translating to software terms, this means that code won’t run faster, but more of it can run in parallel.
Although making good use of our new-found silicon real estate requires improvements in software, a lot of programming languages have already started down this path by adding features that help with parallel execution. In fact, they’ve been there for years waiting for us to take advantage.
So why don’t we? A good engineer always has an ear to the ground, listening for the latest trends in his industry, so let’s take a look at what Python is building for us.
Python enables parallelism through both the threading and the multiprocessing libraries. Yet it wasn’t until the 3.4 branch that it gave us the asyncio library to help with single-threaded concurrency. This addition was key in providing a more convincing final push to start swapping over from version 2.
The asyncio package allows us to define coroutines. These are code blocks that have the ability of yielding execution to other blocks. They run inside an event loop which iterates through the scheduled tasks and executes them one by one. A task switch occurs when it reaches an
await statement or when the current task completes.
Task execution itself happens the same as in a single-threaded system. Meaning, this is not an implementation of parallelism, it’s actually closer to multithreading. We can perceive the concurrency in situations where a block of code depends on external actions.
This illusion is possible because the block can yield execution while it waits, making anything that depends on external IO, like network or disk storage, a great candidate. When the IO completes, the coroutine receives an interrupt and can proceed with execution. In the meantime, other tasks execute.
The asyncio event loop can also serve as a task scheduler. Both asynchronous and blocking functions can queue up their execution as needed.
Task represents callable blocks of code designed for asynchronous execution within event loops. They execute single-threaded, but can run in parallel through loops on different threads.
Prefixing a function definition with the
async keyword turns it into an asynchronous coroutine. Though the task itself will not exist until it’s added to a loop. This is usually implicit when calling most loop methods, but
asyncio.ensure_future(your_coroutine) is the more direct mechanism.
To denote an operation or instruction that can yield execution, we use the
await keyword. Although it’s only available within a coroutine block and causes a syntax error if used anywhere else.
Please note that the async keyword was not implemented until Python version 3.5. So when working with older versions, use the
@asyncio.coroutine decorator and
yield from keywords instead.
In order to execute a task, we need a reference to the event loop in which to run it. Using
loop = asyncio.get_event_loop() gives us the current loop in our execution thread. Now it’s a matter of calling
loop.run_forever() to have it do some work.
Let’s look at a short example to illustrate a few points. I strongly encourage you to open an interpreter and follow along:
async def do_some_work(x):
print("Waiting " + str(x))
loop = asyncio.get_event_loop()
Here we defined
do_some_work() as a coroutine that waits on the results of external workload. The workload is simulated through
Running the code may be surprising. Did you expect
run_until_complete to be a blocking call? Remember that we’re using the event loop from the current thread to execute the task. We’ll discuss alternatives in more detail later. So for now, the important part is to understand that while execution blocks, the
await keyword still enables concurrency.
For a better picture, let’s change our test code a bit and look at executing tasks in batches:
tasks = [asyncio.ensure_future(do_some_work(2)),
asyncio.gather() function enables results aggregation. It waits for several tasks in the same thread to complete and puts the results in a list.
The main observation here is that both function calls did not execute in sequence. It did not wait 2 seconds, then 5, for a total of 7 seconds. Instead it started to wait 2s, then moved on to the next item which started to wait 5s, returning when the longer task completed, for a total of 5s. Feel free to add more print statements to the base function if it helps visualize.
This means that we can put long running tasks with awaitable code in an execution batch, then ask Python to run them in parallel and wait until they all complete. If you plan it right, this will be faster than running in sequence.
Think of it as an alternative to the
threading package where after spinning up a number of
Threads, we wait for them to complete with
.join(). The major difference is that there’s less overhead incurred than creating a new thread for each function.
Of course, it’s always good to point out that your millage may vary based on the task at hand. If you’re doing compute-heavy work, with little or no time waiting, then the only benefit you get is the grouping of code into logical batches.
What if instead of doing everything in the current thread, we spawn a separate Thread to do the work for us.
from threading import Thread
new_loop = asyncio.new_event_loop()
t = Thread(target=start_loop, args=(new_loop,))
Notice that this time we created a new event loop through
asyncio.new_event_loop(). The idea is to spawn a new thread, pass it that new loop and then call thread-safe functions (discussed later) to schedule work.
The advantage of this method is that work executed by the other event loop will not block execution in the current thread. Thereby allowing the main thread to manage the work, and enabling a new category of execution mechanisms.
Using the thread and event loop from the previous code block, we can easily get work done with the
call_at() methods. They are able to run regular function code blocks (those not defined as coroutines) in an event loop.
However, it’s best to use their
_threadsafe alternatives. Let’s see how that looks:
print("More work %s" % x)
print("Finished more work %s" % x)
Now we’re talking! Executing this code does not block the main interpreter, allowing us to give it more work. Since the work executes in order, we now essentially have a task queue.
We just went to multi-threaded execution of single-threaded code, but isn’t concurrency part of what we get with asyncio? Sure it is! That loop on the worker thread is still async, so let’s enable parallelism by giving it awaitable coroutines.
Doing so is a matter of using
asyncio.run_coroutine_threadsafe(), as seen bellow:
These instructions illustrate how python is going about execution. The first call to
more_work blocks for 20 seconds, while the calls to
do_some_work execute in parallel immediately after
A common situation these days is to send notifications as a result of a task or event. This is usually simple, but talking to an email server to submit a new message can take time, and so can crafting the email itself.
There are many scenarios where we don’t have the luxury of waiting around for tasks to complete. Where doing so provides no benefit to the end user. A prime example being a request for a password reset, or a webhook event that triggers repository builds and emails the results.
The recommended practice so far has been to use a task queuing system like
celery, on top of a message queue server like
rabbitmq to schedule the work. I’m here to tell you that for small things that can easily execute from another thread of your main application, it’s not a bad idea to just use asyncio. The pattern being fairly similar to the code examples we’ve seen so far:
from threading import Thread
"""Generate and send the notification email"""
# Do some work to get email body
message = ...
# Connect to the server
server = smtplib.SMTP("smtp.gmail.com:587")
# Send the email
server.sendmail(from_addr, email, message)
"""Switch to new event loop and run forever"""
# Create the new loop and worker thread
worker_loop = asyncio.new_event_loop()
worker = Thread(target=star_email_worker, args=(worker_loop,))
# Start the thread
# Assume a Flask restful interface endpoint
"""Request notification email"""
Here we assume a Flask web API with an endpoint mounted at
/notify in which to request a notification email of some sort.
send_notification is not a coroutine, so each email will be a blocking call. The worker thread’s event loop will serve as the queue in which to track the outgoing emails.
Why are the SMTP calls synchronous you wonder? Well, while this is a good example of what should be awaitable IO, I’m not aware of an asynchronous SMTP library at the moment. Feel free to substitute with an
run_coroutine_threadsafe, if you do find one.
Here’s an example of batching HTTP requests that run concurrently to several servers, while waiting for responses before processing. I expect it to be useful for those of you that do a lot of scraping, as well as a quick intro to the
async def fetch(url):
"""Perform an HTTP GET to the URL and print the response"""
response = await aiohttp.request('GET', url)
return await response.text()
# Get a reference to the event loop
loop = asyncio.get_event_loop()
# Create the batch of requests we wish to execute
requests = [asyncio.ensure_future(fetch("https://github.com")),
# Run the batch
responses = loop.run_until_complete(asyncio.gather(*requests))
# Examine responses
for resp in responses:
Fairly straightforward, it’s a matter of grouping the work in a list of tasks and using
run_until_complete to get the responses back. This can easily change to use a separate thread in which to make requests, where it would be simple to add all the URLs through the thread-safe methods described previously.
I want to note that the
requests library has asynchronous support through
gevent, but I haven’t done the work to figure out how that can tie into
asyncio. In contrast, I’m not aware of
asyncio plans for the popular scraping framework
scrapy, but I assume they’re working on it.
If at any point you find yourself wanting to stop an infinite event loop, or want to cancel tasks that haven’t completed, I tend to use a
KeyboardInterrupt exception clause to trigger cancellation as shown below. Although the same can be accomplished by using the
signal module and registering a handler for
# Canceling pending tasks and stopping the loop
# Stopping the loop
# Received Ctrl+C
This time we’re introducing the use of
Task.all_tasks() to generate a list of all currently running or scheduled tasks. When coupled with
gather() we can send the
cancel() command to each one and have them all stop executing or remove them from the queue.
Please note that due to signaling deficiencies in Windows, if the loop is empty, the keyboard interrupt is never triggered. A workaround for this situation is to queue a task that sleeps for several seconds. This guarantees that if the interrupt arrives while the task sleeps, the loop will notice when it wakes.
Asynchronous programming can be very confusing. I must confess that I started with some base assumptions that turned out to be wrong. It wasn’t until I dove deeper into it that I realized what’s really going on.
I hope this served as a good introduction to asyncio event loops and tasks, as well as their possible uses. I know there are plenty of other articles out there, but I wanted to make something that tied things to some real world examples. If you have any questions or comments, feel free to drop them below and I’ll help as best I can.
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