The combination of coroutines and resource acquisition seems like it could have some unintended (or unintuitive) consequences.
The basic question is whether or not something like this works:
def coroutine(): with open(path, 'r') as fh: for line in fh: yield line
Which it does. (You can test it!)
The deeper concern is that with
is supposed to be something an alternative to finally
, where you ensure that a resource is released at the end of the block. Coroutines can suspend and resume execution from within the with
block, so how is the conflict resolved?
For example, if you open a file with read/write both inside and outside a coroutine while the coroutine hasn't yet returned:
def coroutine(): with open('test.txt', 'rw+') as fh: for line in fh: yield line a = coroutine() assert a.next() # Open the filehandle inside the coroutine first. with open('test.txt', 'rw+') as fh: # Then open it outside. for line in fh: print 'Outside coroutine: %r' % repr(line) assert a.next() # Can we still use it?
I was going for write-locked file handle contention in the previous example, but since most OSes allocate filehandles per-process there will be no contention there. (Kudos to @Miles for pointing out the example didn't make too much sense.) Here's my revised example, which shows a real deadlock condition:
import threading lock = threading.Lock() def coroutine(): with lock: yield 'spam' yield 'eggs' generator = coroutine() assert generator.next() with lock: # Deadlock! print 'Outside the coroutine got the lock' assert generator.next()
Yield is a keyword in Python that is used to return from a function without destroying the states of its local variable and when the function is called, the execution starts from the last yield statement. Any function that contains a yield keyword is termed a generator. Hence, yield is what makes a generator.
We should use yield when we want to iterate over a sequence, but don't want to store the entire sequence in memory. Yield are used in Python generators. A generator function is defined like a normal function, but whenever it needs to generate a value, it does so with the yield keyword rather than return.
yield in Python can be used like the return statement in a function. When done so, the function instead of returning the output, it returns a generator that can be iterated upon. You can then iterate through the generator to extract items. Iterating is done using a for loop or simply using the next() function.
The Yield keyword in Python is similar to a return statement used for returning values or objects in Python. However, there is a slight difference. The yield statement returns a generator object to the one who calls the function which contains yield, instead of simply returning a value.
I don't really understand what conflict you're asking about, nor the problem with the example: it's fine to have two coexisting, independent handles to the same file.
One thing I didn't know that I learned in response to your question it that there is a new close() method on generators:
close()
raises a newGeneratorExit
exception inside the generator to terminate the iteration. On receiving this exception, the generator’s code must either raiseGeneratorExit
orStopIteration
.
close()
is called when a generator is garbage-collected, so this means the generator’s code gets one last chance to run before the generator is destroyed. This last chance means thattry...finally
statements in generators can now be guaranteed to work; thefinally
clause will now always get a chance to run. This seems like a minor bit of language trivia, but using generators andtry...finally
is actually necessary in order to implement thewith
statement described by PEP 343.http://docs.python.org/whatsnew/2.5.html#pep-342-new-generator-features
So that handles the situation where a with
statement is used in a generator, but it yields in the middle but never returns—the context manager's __exit__
method will be called when the generator is garbage-collected.
Edit:
With regards to the file handle issue: I sometimes forget that there exist platforms that aren't POSIX-like. :)
As far as locks go, I think Rafał Dowgird hits the head on the nail when he says "You just have to be aware that the generator is just like any other object that holds resources." I don't think the with
statement is really that relevant here, since this function suffers from the same deadlock issues:
def coroutine(): lock.acquire() yield 'spam' yield 'eggs' lock.release() generator = coroutine() generator.next() lock.acquire() # whoops!
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