With an interface that is intentionally similar to the [`multiprocessing.Pool`](https://docs.python.org/3/library/multiprocessing.html#module-multiprocessing.pool) class from the standard library, the `TaskPool` provides you such methods as `apply`, `map`, and `starmap` to execute coroutines concurrently as [`asyncio.Task`](https://docs.python.org/3/library/asyncio-task.html#task-object) objects. There is no limitation imposed on what kind of tasks can be run or in what combination, when new ones can be added, or when they can be cancelled.
For a more streamlined use-case, the `SimpleTaskPool` provides an even more intuitive and simple interface at the cost of flexibility.
If you need control over a task pool at runtime, you can launch an asynchronous `ControlServer` to be able to interface with the pool from an outside process or via a network, and stop/start tasks within the pool as you wish.
Generally speaking, a task is added to a pool by providing it with a coroutine function reference as well as the arguments for that function. Here is what that could look like in the most simplified form:
Since one of the main goals of `asyncio-taskpool` is to be able to start/stop tasks dynamically or "on-the-fly", _most_ of the associated methods are non-blocking _most_ of the time. A notable exception is the `gather_and_close` method for awaiting the return of all tasks in the pool. (It is essentially a glorified wrapper around the [`asyncio.gather`](https://docs.python.org/3/library/asyncio-task.html#asyncio.gather) function.)
Install [`coverage`](https://coverage.readthedocs.io/en/latest/) with `pip`, then execute the [`./coverage.sh`](coverage.sh) shell script to run all unit tests and save the coverage report.
`asyncio-taskpool` is licensed under the **GNU LGPL version 3.0** specifically.
The full license texts for the [GNU GPLv3.0](COPYING) and the [GNU LGPLv3.0](COPYING.LESSER) are included in this repository. If not, see https://www.gnu.org/licenses/.
