https://docs.python.org/3/library/concurrent.futures.html#concurrent.futures.Executor
17.4.1. #Executor Objects
class #concurrent.futures.Executor
An abstract class that provides methods to execute calls asynchronously. It should not be used directly, but through its concrete subclasses.
submit(fn, *args, **kwargs)
Schedules the callable, fn, to be executed as fn(*args **kwargs) and returns a Future object representing the execution of the callable.
with ThreadPoolExecutor(max_workers=1) as executor:
future = executor.submit(pow, 323, 1235)
print(future.result())
map(func, *iterables, timeout=None, chunksize=1)
Equivalent to #map(func, *iterables) except func is executed asynchronously and several calls to func may be made concurrently. The returned iterator raises a concurrent.futures.TimeoutError if __next__() is called and the result isn’t available after timeout seconds from the original call to #Executor.map(). timeout can be an int or a float. If timeout is not specified or None, there is no limit to the wait time. If a call raises an exception, then that exception will be raised when its value is retrieved from the iterator. When using ProcessPoolExecutor, this method chops iterables into a number of chunks which it submits to the pool as separate tasks. The (approximate) size of these chunks can be specified by setting chunksize to a positive integer. For very long iterables, using a large value for chunksize can significantly improve performance compared to the default size of 1. With ThreadPoolExecutor, chunksize has no effect.
Changed in version 3.5: Added the chunksize argument.
http://www.giantflyingsaucer.com/blog/?p=5557
In spring 2014 Python 3.4 shipped a provisional package (#asyncio) which according to the docs “provides infrastructure for writing single-threaded #concurrent code using #coroutines, #multiplexing I/O access over #sockets and other resources, running network clients and servers, and other related primitives“. I can’t possibly cover everything in this article but I can introduce some of the things you can do with it. As per my New’s Years resolution I’ll be building these #examples using Python 3.4.2 (Asyncio has been ported back to Python 3.3 now as well).
https://pymotw.com/3/asyncio/executors.html
Combining Coroutines with Threads and Processes
A lot of existing libraries are not ready to be used with #asyncio natively. They may block, or depend on concurrency features not available through the module. It is still possible to use those libraries in an application based on asyncio by using an #executor from #concurrent.futures to run the code either in a separate thread or a separate process.
#Threads
The #run_in_executor() method of the event loop takes an executor instance, a regular callable to invoke, and any arguments to be passed to the callable. It returns a Future that can be used to wait for the function to finish its work and return something. If no executor is passed in, a #ThreadPoolExecutor is created. This example explicitly creates an executor to limit the number of worker threads it will have available.
#Processes
A ProcessPoolExecutor works in much the same way, creating a set of worker #processes instead of threads. Using separate processes requires more system resources, but for computationally-intensive operations it can make sense to run a separate task on each CPU core.
#learn