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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