Function dispatch_queue_batch

graphene/utils/dataloader.py:216–270

(loader, queue)

async def dispatch_queue_batch(loader, queue):
    # Collect all keys to be loaded in this dispatch
    keys = [loaded.key for loaded in queue]

    # Call the provided batch_load_fn for this loader with the loader queue's keys.
    batch_future = loader.batch_load_fn(keys)

    # Assert the expected response from batch_load_fn
    if not batch_future or not iscoroutine(batch_future):
        return failed_dispatch(  # pragma: no cover
            loader,
            queue,
            TypeError(
                (
                    "DataLoader must be constructed with a function which accepts "
                    "Iterable<key> and returns Future<Iterable<value>>, but the function did "
                    "not return a Coroutine: {}."
                ).format(batch_future)
            ),
        )

    try:
        values = await batch_future
        if not isinstance(values, Iterable):
            raise TypeError(  # pragma: no cover
                (
                    "DataLoader must be constructed with a function which accepts "
                    "Iterable<key> and returns Future<Iterable<value>>, but the function did "
                    "not return a Future of a Iterable: {}."
                ).format(values)
            )

        values = list(values)
        if len(values) != len(keys):
            raise TypeError(  # pragma: no cover
                (
                    "DataLoader must be constructed with a function which accepts "
                    "Iterable<key> and returns Future<Iterable<value>>, but the function did "
                    "not return a Future of a Iterable with the same length as the Iterable "
                    "of keys."
                    "\n\nKeys:\n{}"
                    "\n\nValues:\n{}"
                ).format(keys, values)
            )

        # Step through the values, resolving or rejecting each Future in the
        # loaded queue.
        for loaded, value in zip(queue, values):
            if isinstance(value, Exception):
                loaded.future.set_exception(value)
            else:
                loaded.future.set_result(value)

    except Exception as e:
        return failed_dispatch(loader, queue, e)


def failed_dispatch(loader, queue, error):