Using synchronization primitives

Synchronization primitives are objects that are used by tasks to communicate and coordinate with each other. They are useful for things like distributing workload, notifying other tasks and guarding access to shared resources.

Semaphores

Semaphores are used for limiting access to a shared resource. A semaphore starts with a maximum value, which is decremented each time the semaphore is acquired by a task and incremented when it is released. If the value drops to zero, any attempt to acquire the semaphore will block until another task frees it.

Example:

from anyio import create_task_group, create_semaphore, sleep, run


async def use_resource(tasknum, semaphore):
    async with semaphore:
        print('Task number', tasknum, 'is now working with the shared resource')
        await sleep(1)


async def main():
    semaphore = create_semaphore(2)
    async with create_task_group() as tg:
        for num in range(10):
            await tg.spawn(use_resource, num, semaphore)

run(main)

Locks

Locks are used to guard shared resources to ensure sole access to a single task at once. They function much like semaphores with a maximum value of 1.

Example:

from anyio import create_task_group, create_lock, sleep, run


async def use_resource(tasknum, lock):
    async with lock:
        print('Task number', tasknum, 'is now working with the shared resource')
        await sleep(1)


async def main():
    lock = create_lock()
    async with create_task_group() as tg:
        for num in range(4):
            await tg.spawn(use_resource, num, lock)

run(main)

Events

Events are used to notify tasks that something they’ve been waiting to happen has happened. An event object can have multiple listeners and they are all notified when the event is triggered. Events can also be reused by clearing the triggered state.

Example:

from anyio import create_task_group, create_event, run


async def notify(event):
    await event.set()


async def main():
    event = create_event()
    async with create_task_group() as tg:
        await tg.spawn(notify, event)
        await event.wait()
        print('Received notification!')

run(main)

Conditions

A condition is basically a combination of an event and a lock. It first acquires a lock and then waits for a notification from the event. Once the condition receives a notification, it releases the lock. The notifying task can also choose to wake up more than one listener at once, or even all of them.

Example:

from anyio import create_task_group, create_condition, sleep, run


async def listen(tasknum, condition):
    async with condition:
        await condition.wait()
        print('Woke up task number', tasknum)


async def main():
    condition = create_condition()
    async with create_task_group() as tg:
        for tasknum in range(6):
            await tg.spawn(listen, tasknum, condition)

        await sleep(1)
        async with condition:
            await condition.notify(1)

        await sleep(1)
        async with condition:
            await condition.notify(2)

        await sleep(1)
        async with condition:
            await condition.notify_all()

run(main)

Queues

Queues are used to send objects between tasks. Queues have two central concepts:

• Producers add things to the queue

• Consumers take things from the queue

When an item is inserted into the queue, it will be given to the next consumer that tries to get an item from the queue. Each item is only ever given to a single consumer.

Queues have a maximum capacity which is determined on creation and cannot be changed later. When the queue is full, any attempt to put an item to it will block until a consumer retrieves an item from the queue. If you wish to avoid blocking on either operation, you can use the full() and empty() methods to find out about either condition.

Example:

from anyio import create_task_group, create_queue, sleep, run


async def produce(queue):
    for number in range(10):
        await queue.put(number)
        await sleep(1)


async def main():
    queue = create_queue(100)
    async with create_task_group() as tg:
        await tg.spawn(produce, queue)
        while True:
            number = await queue.get()
            print(number)
            if number == 9:
                break

run(main)

Capacity limiters

Capacity limiters are like semaphores except that a single borrower (the current task by default) can only hold a single token at a time. It is also possible to borrow a token on behalf of any arbitrary object, so long as that object is hashable.

Example:

from anyio import create_task_group, create_capacity_limiter, sleep, run


async def use_resource(tasknum, limiter):
    async with limiter:
        print('Task number', tasknum, 'is now working with the shared resource')
        await sleep(1)


async def main():
    limiter = create_capacity_limiter(2)
    async with create_task_group() as tg:
        for num in range(10):
            await tg.spawn(use_resource, num, limiter)

run(main)

To adjust the number of total tokens, you can use the set_total_tokens() method.