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25. Coroutines¶
Coroutines are similar to generators with a few differences. The main differences are:
generators are data producers
coroutines are data consumers
First of all let’s review the generator creation process. We can make generators like this:
def fib():
a, b = 0, 1
while True:
yield a
a, b = b, a+b
We then commonly use it in a for
loop like this:
for i in fib():
print(i)
It is fast and does not put a lot of pressure on memory because it
generates the values on the fly rather than storing them in a list.
Now, if we use yield
in the above example, more generally, we get a
coroutine. Coroutines consume values which are sent to it. A very basic
example would be a grep
alternative in Python:
def grep(pattern):
print("Searching for", pattern)
while True:
line = (yield)
if pattern in line:
print(line)
Wait! What does yield
return? Well we have turned it into a
coroutine. It does not contain any value initially, instead we supply it
values externally. We supply values by using the .send()
method.
Here is an example:
search = grep('coroutine')
next(search)
# Output: Searching for coroutine
search.send("I love you")
search.send("Don't you love me?")
search.send("I love coroutines instead!")
# Output: I love coroutines instead!
The sent values are accessed by yield
. Why did we run next()
? It is
required in order to start the coroutine. Just like generators
, coroutines do not
start the function immediately. Instead they run it in response to the
__next__()
and .send()
methods. Therefore, you have to run
next()
so that the execution advances to the yield
expression.
We can close a coroutine by calling the .close()
method:
search = grep('coroutine')
# ...
search.close()
There is a lot more to coroutines
. I suggest you check out this
awesome
presentation by
David Beazley.