from .base import Flow
from ..clients.mixin import AsyncPostMixin
[docs]class AsyncFlow(AsyncPostMixin, Flow):
:class:`AsyncFlow` is the asynchronous version of the :class:`Flow`. They share the same interface, except
in :class:`AsyncFlow` :meth:`train`, :meth:`index`, :meth:`search` methods are coroutines
(i.e. declared with the async/await syntax), simply calling them will not schedule them to be executed.
To actually run a coroutine, user need to put them in an eventloop, e.g. via ``asyncio.run()``,
:class:`AsyncFlow` can be very useful in
the integration settings, where Jina/Jina Flow is NOT the main logic, but rather served as a part of other program.
In this case, users often do not want to let Jina control the ``asyncio.eventloop``. On contrary, :class:`Flow`
is controlling and wrapping the eventloop internally, making the Flow looks synchronous from outside.
In particular, :class:`AsyncFlow` makes Jina usage in Jupyter Notebook more natural and reliable.
For example, the following code
will use the eventloop that already spawned in Jupyter/ipython to run Jina Flow (instead of creating a new one).
.. highlight:: python
.. code-block:: python
from jina import AsyncFlow
from jina.types.document.generators import from_ndarray
import numpy as np
with AsyncFlow().add() as f:
await f.index(from_ndarray(np.random.random([5, 4])), on_done=print)
Notice that the above code will NOT work in standard Python REPL, as only Jupyter/ipython implements "autoawait".
Asynchronous in REPL: Autoawait
Another example is when using Jina as an integration. Say you have another IO-bounded job ``heavylifting()``, you
can use this feature to schedule Jina ``index()`` and ``heavylifting()`` concurrently.
One can think of :class:`Flow` as Jina-managed eventloop, whereas :class:`AsyncFlow` is self-managed eventloop.