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Asynchronous Flow¶

Synchronous from outside, Jina runs asynchronously underneath: it manages the eventloop(s) for scheduling the jobs. In some scenario, user wants more control over the eventloop, then AsyncFlow comes to use. In the example below, Jina is part of the integration where another heavy-lifting job is running concurrently:

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#Run this command to install Jina in this notebook 
!pip install jina

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import asyncio
from jina import AsyncFlow
from jina.types.document.generators import from_ndarray


async def run_async_flow_5s():  # WaitDriver pause 5s makes total roundtrip ~5s
    with AsyncFlow().add(uses='- !WaitDriver {}') as f:
        await f.index(from_ndarray(numpy.random.random([5, 4])), on_done=print)

async def heavylifting():  # total roundtrip takes ~5s
    print('heavylifting other io-bound jobs, e.g. download, upload, file io')
    await asyncio.sleep(5)
    print('heavylifting done after 5s')

async def concurrent_main():  # about 5s; but some dispatch cost, can't be just 5s, usually at <7s
    await asyncio.gather(run_async_flow_5s(), heavylifting())

AsyncFlow is very useful when using Jina inside Jupyter Notebook. As Jupyter/ipython already manages an eventloop and thanks to autoawait, the following code can run out-of-the-box in Jupyter:

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from jina.types.document.generators import from_ndarray
from jina import AsyncFlow
import numpy

with AsyncFlow().add() as f:
    f.index(from_ndarray(numpy.random.random([5, 4])), on_done=print)