Asyncio. Often methods need to run, but we do not need to wait for them. They are not blocking methods. We can run them in the background.
With asyncio, a module in Python 3.5, we can use an event loop to run asynchronous methods. With "yield from" we can run methods in parallel.
An example. This program introduces a simple "logic" method that computes a number. After each iteration it uses the "yield from" syntax to call asyncio.sleep.
Start We use get_event_loop to begin adding methods to run. We create a tasks list with ensure_future calls.
Next We call run_until_complete with the result of gather() to execute all our methods in parallel.
Important The methods would not yield to each other without the "yield from asyncio.sleep" statement.
import asyncio
@asyncio.coroutine
def logic(max):
# This method runs some logic in a loop.# ... The max is specified as an argument.
count = 0
for i in range(1, max):
count += i
count = count / i
# Provide a chance to run other methods.
yield from asyncio.sleep(1)
# Finished.
print("Logic result", max, count)
# Get our event loop.
loop = asyncio.get_event_loop()
# Call logic method four times.
tasks = [
asyncio.ensure_future(logic(5)),
asyncio.ensure_future(logic(20)),
asyncio.ensure_future(logic(10)),
asyncio.ensure_future(logic(1))]
# Run until all logic methods have completed.# ... The sleep call will allow all to run in parallel.
loop.run_until_complete(asyncio.gather(*tasks))
loop.close()Logic result 1 0
Logic result 5 1.375
Logic result 10 1.1274057539682538
Logic result 20 1.0557390762436003
Yield. Having a call to a "yield from" method is critical to having parallel method execution in Python. Sleep() simply does nothing—it pauses the current thread.
But Having asleep call gives other methods a chance to run. The other methods run when asyncio.sleep is called.
Some notes. In a real program, the asyncio.sleep method is still useful. In a long-running method, we can call asyncio.sleep periodically to allow other things to happen.
Some notes, continued. With the basic pattern in this program, we can run tasks in parallel in Python. We can load data from files, compute values in memory, or do anything.
A review. Async programming is a key development in Python 3.5. This feature enables more complex programs to execute—without blocking. So the program remains responsive.
Dot Net Perls is a collection of tested code examples. Pages are continually updated to stay current, with code correctness a top priority.
Sam Allen is passionate about computer languages. In the past, his work has been recommended by Apple and Microsoft and he has studied computers at a selective university in the United States.
This page was last updated on Feb 20, 2024 (edit).