Thread and multiprocessing pooling, task processing via asyncio
Python library for modern thread / multiprocessing pooling and task processing via asyncio.
Warning: atasker is not suitable for the lightweight tasks in high-load environments. For such projects it's highly recommended to use lightweight version: neotasker
No matter how your code is written, atasker automatically detects blocking functions and coroutines and launches them in a proper way, in a thread, asynchronous loop or in multiprocessing pool.
Tasks are grouped into pools. If there's no space in pool, task is being placed into waiting queue according to their priority. Pool also has "reserve" for the tasks with priorities "normal" and higher. Tasks with "critical" priority are always executed instantly.
This library is useful if you have a project with many similar tasks which produce approximately equal CPU/memory load, e.g. API responses, scheduled resource state updates etc.
pip3 install atasker
asynchronous programming is a perfect way to make your code fast and reliable
multithreading programming is a perfect way to run blocking code in the background
atasker combines advantages of both ways: atasker tasks run in separate threads however task supervisor and workers are completely asynchronous. But all their public methods are thread-safe.
Why not standard Python thread pool?
- threads in a standard pool don't have priorities
Why not standard asyncio loops?
- compatibility with blocking functions
- async workers
Why not concurrent.futures?
concurrent.futures is a great standard Python library which allows you to execute specified tasks in a pool of workers.
For thread-based tasks, atasker extends concurrent.futures.ThreadPoolExecutor functionality.
atasker method background_task solves the same problem but in slightly different way, adding priorities to the tasks, while atasker workers do absolutely different job:
in concurrent.futures worker is a pool member which executes the single specified task.
in atasker worker is an object, which continuously generates new tasks with the specified interval or on external event, and executes them in thread or multiprocessing pool.
from atasker import task_supervisor # set pool size task_supervisor.set_thread_pool(pool_size=20, reserve_normal=5, reserve_high=5) task_supervisor.start() # ... # start workers, other threads etc. # ... # optionally block current thread task_supervisor.block() # stop from any thread task_supervisor.stop()
from atasker import background_task, TASK_LOW, TASK_HIGH, wait_completed # with annotation @background_task def mytask(): print('I am working in the background!') return 777 task = mytask() # optional result = wait_completed(task) print(task.result) # 777 print(result) # 777 # with manual decoration def mytask2(): print('I am working in the background too!') task = background_task(mytask2, priority=TASK_HIGH)()
# new asyncio loop is automatically created in own thread a1 = task_supervisor.create_aloop('myaloop', default=True) async def calc(a): print(a) await asyncio.sleep(1) print(a * 2) return a * 3 # call from sync code # put coroutine task = background_task(calc)(1) wait_completed(task) # run coroutine and wait for result result = a1.run(calc(1))
from atasker import background_worker, TASK_HIGH @background_worker def worker1(**kwargs): print('I am a simple background worker') @background_worker async def worker_async(**kwargs): print('I am async background worker') @background_worker(interval=1) def worker2(**kwargs): print('I run every second!') @background_worker(queue=True) def worker3(task, **kwargs): print('I run when there is a task in my queue') @background_worker(event=True, priority=TASK_HIGH) def worker4(**kwargs): print('I run when triggered with high priority') worker1.start() worker_async.start() worker2.start() worker3.start() worker4.start() worker3.put_threadsafe('todo1') worker4.trigger_threadsafe() from atasker import BackgroundIntervalWorker class MyWorker(BackgroundIntervalWorker): def run(self, **kwargs): print('I am custom worker class') worker5 = MyWorker(interval=0.1, name='worker5') worker5.start()
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