Asynchronous task scheduler and runner for Python
Project description
Task scheduler based on asyncio. Allows max-concurrency and granularity to be specified. Execution results are printed as a Gantt chart.
Sample Usage
from asynctasks.executor import Executor from asynctasks.executionplan import ExecutionPlan import asyncio #create an array of tasks tasks_dict_array = [{"dependencies": [], "name": "task0"}, {"dependencies": [0], "name": "task1"}, {"dependencies": [1], "name": "task2"}, {"dependencies": [0], "name": "task3"}, {"dependencies": [], "name": "task4"}, {"dependencies": [], "name": "task5"}] #define an async function that should be executed for each task async def print_task(loop, task): print("Executing task {}".format(task)) await asyncio.sleep(0.1) #create an execution plan for the tasks plan = ExecutionPlan().from_dict_array(tasks_dict_array) print("\nBEFORE EXECUTION\n{}".format(plan)) #execute the plan with print_task function and max_concurrency as 2 Executor(plan, 2, 0.01, print_task).trigger_execution() print("\nAFTER EXECUTION\n{}".format(plan.as_gantt()))
Sample Output
BEFORE EXECUTION task0 Ready task1 task2 task3 task4 Ready task5 Ready Executing task {'dependencies': [], 'name': 'task0', 'start_time': 1497528994.1246588} Executing task {'dependencies': [], 'name': 'task4', 'start_time': 1497528994.1246588} Executing task {'dependencies': [0], 'name': 'task1', 'start_time': 1497528994.236074} Executing task {'dependencies': [0], 'name': 'task3', 'start_time': 1497528994.2370954} Executing task {'dependencies': [1], 'name': 'task2', 'start_time': 1497528994.340769} Executing task {'dependencies': [], 'name': 'task5', 'start_time': 1497528994.3417716} AFTER EXECUTION .................................................................................................... task0 ............................... task1 ............................. task2 ................................ task3 ............................. task4 ............................... task5 ...............................
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