在 python-多线程中创建线程

Question

我有以下线程结构：

               (1)
            /   |   \
          (2)  (3)  (4)
           |    |    |
          (5)  (6)  (7)
           |    |    |
          (8)  (9)  (10)
           |    |    |
          (11) (12) (13)
            \  /     |
            (14)     |
              \     /
                (15)

如你所见，第一个函数启动了三个线程，然后每个都启动了一个新线程。第14个节点是第11个和第12个节点的连接； 15 号是 13 号和 14 号的交点。我实现了前两个级别（节点 1、2、3、4）如下：

self.first()
    list = ['a','b','c']
    with concurrent.futures.ThreadPoolExecutor() as executor:
        results = executor.map(self.do_this, list)
        for result in results:
            print(result)

但是不知道从这里到哪里去。

Answer 1

这是一个像您这样的图的运行器示例。

这个想法是定义一个运行每个任务的函数（do_task 这里），并构建每个任务所需的（直接）依赖关系图。下面的示例 task_deps 反映了上面的图表。

然后 run_graph 函数将使用每个任务 ID 调用 do_task；该函数应该做任何计算结果所需的事情（如果需要，它可以读取任何先前计算的 results）。

run_graph函数最终会return一个{task_id: result}的字典。

下面的代码输出

Scheduling {1}
Scheduling {2, 3, 4}
Scheduling {5, 6, 7}
Scheduling {8, 9, 10}
Scheduling {11, 12, 13}
Scheduling {14}
Scheduling {15}

据推测，这与你的图表从上到下的结构完全对应，和

{1: 'Task 1 completed with result 42',
 2: 'Task 2 completed with result 84',
 3: 'Task 3 completed with result 126',
 4: 'Task 4 completed with result 168',
 5: 'Task 5 completed with result 210',
 6: 'Task 6 completed with result 252',
 7: 'Task 7 completed with result 294',
 8: 'Task 8 completed with result 336',
 9: 'Task 9 completed with result 378',
 10: 'Task 10 completed with result 420',
 11: 'Task 11 completed with result 462',
 12: 'Task 12 completed with result 504',
 13: 'Task 13 completed with result 546',
 14: 'Task 14 completed with result 588',
 15: 'Task 15 completed with result 630'}

import concurrent.futures


def do_task(task_id, results, dependencies):
    # sanity check - this function could use `dependencies` and `results` too
    assert all(dep in results for dep in dependencies)
    return f"Task {task_id} completed with result {task_id * 42}"


def run_graph(task_dependencies, runner):
    # Dict for results for each task.
    results = {}
    # Set of tasks yet to be completed.
    todo = set(task_dependencies)

    with concurrent.futures.ThreadPoolExecutor() as executor:
        # While there are items in the to-do set...
        while todo:
            # ... figure out what we can immediately execute by
            # comparing the dependency set to the result keys we already have
            # (i.e. the complement of the to-do set)
            next_tasks = {
                task_id
                for (task_id, deps) in task_dependencies.items()
                if task_id in todo and set(deps) <= set(results)
            }
            # If there are no next tasks we could schedule, it means the dependency
            # graph is incorrect (or at the very least incompleteable).
            if not next_tasks:
                raise RuntimeError(
                    f"Unable to schedule tasks, bad dependencies? Todo: {todo}"
                )

            print("Scheduling", next_tasks)
            # Submit tasks for execution in parallel. `futures` will be a list of
            # 2-tuples (task_id, future).
            futures = [
                (
                    task_id,
                    executor.submit(
                        runner, task_id, results, task_dependencies[task_id]
                    ),
                )
                for task_id in next_tasks
            ]

            # Loop over the futures, waiting for their results; when a future
            # finishes, save the result value and remove that task from the
            # to-do set.
            for (task_id, future) in futures:
                results[task_id] = future.result()
                todo.remove(task_id)
    # Once the while loop finishes, we have our results.
    return results


if __name__ == "__main__":
    task_deps = {
        1: (),
        2: (1,),
        3: (1,),
        4: (1,),
        5: (2,),
        6: (3,),
        7: (4,),
        8: (5,),
        9: (6,),
        10: (7,),
        11: (8,),
        12: (9,),
        13: (10,),
        14: (11, 12),
        15: (14, 13),
    }

    result = run_graph(task_deps, do_task)
    print(result)

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