Coroutine in Python
Prerequisite : Generators
We all are familiar with function which is also known as a subroutine, procedure, subprocess etc. A function is a sequence of instructions packed as a unit to perform a certain task. When the logic of a complex function is divided into several self-contained steps that are themselves functions, then these functions are called helper functions or subroutines.
Subroutines in Python are called by main function which is responsible for coordination the use of these subroutines. Subroutines have single entry point.
Coroutines are generalization of subroutines. They are used for cooperative multitasking where a process voluntarily yield (give away) control periodically or when idle in order to enable multiple applications to be run simultaneously. The difference between coroutine and subroutine is :
- Unlike subroutines, coroutines have many entry points for suspending and resuming execution. Coroutine can suspend its execution and transfer control to other coroutine and can resume again execution from the point it left off.
- Unlike subroutines, there is no main function to call coroutines in particular order and coordinate the results. Coroutines are cooperative that means they link together to form a pipeline. One coroutine may consume input data and send it to other which process it. Finally there may be a coroutine to display result.
Coroutine Vs Thread
Now you might be thinking how coroutine is different from threads, both seems to do same job.
In case of threads, it’s operating system (or run time environment) that switches between threads according to the scheduler. While in case of coroutine, it’s the programmer and programming language which decides when to switch coroutines. Coroutines work cooperatively multi task by suspending and resuming at set points by programmer.
In Python, coroutines are similar to generators but with few extra methods and slight change in how we use yield statement. Generators produce data for iteration while coroutines can also consume data.
In Python 2.5, a slight modification to the yield statement was introduced, now yield can also be used as expression. For example on the right side of the assignment –
line = (yield)
whatever value we send to coroutine is captured and returned by (yield) expression.
A value can be send to the coroutine by send() method. For example, consider this coroutine which print out name having prefix “Dear” in it. We will send names to coroutine using send() method.
Searching prefix:Dear Dear Atul
Execution of Coroutine
Execution of coroutine is similar to the generator. When we call coroutine nothing happens, it runs only in response to the next() and send() method. This can be seen clearly in above example, as only after calling __next__() method, out coroutine starts executing. After this call, execution advances to the first yield expression, now execution pauses and wait for value to be sent to corou object. When first value is sent to it, it checks for prefix and print name if prefix present. After printing name it goes through loop until it encounters name = (yield) expression again.
Closing a Coroutine
Coroutine might run indefinitely, to close coroutine close() method is used. When coroutine is closed it generates GeneratorExit exception which can be catched in usual way. After closing coroutine, if we try to send values, it will raise StopIteration exception. Following is a simple example :
Searching prefix:Dear Dear Atul Closing coroutine!!
Chaining coroutines for creating pipeline
Coroutines can be used to set pipes. We can chain together coroutines and push data through pipe using send() method. A pipe needs :
- An initial source(producer) which derives the whole pipe line. Producer is usually not a coroutine, it’s just a simple method.
- A sink, which is the end point of the pipe. A sink might collect all data and display it.
Following is a simple example of chaining –
I'm sink, i'll print tokens Searching for ing running moving Done with filtering!! Done with printing!
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