Client enables you to send Documents to a running Flow. Same as Gateway, Client supports four networking protocols: gRPC, HTTP, WebSocket and GraphQL with/without TLS.

You may have observed two styles of using a Client in the docs:

from jina import Flow

f = Flow()

with f:'/')
from jina import Client

c = Client(...)  # must match the Flow setup'/')

The implicit style is easier in debugging and local development, as you don’t need to specify the host, port and protocol of the Flow. However, it makes very strong assumptions on (1) one Flow only corresponds to one client (2) the Flow is running on the same machine as the Client. For those reasons, explicit style is recommended for production use.


If you want to connect to your Flow from a programming language other than Python, please follow the third party client documentation.


To connect to a Flow started by:

from jina import Flow

with Flow(port=1234, protocol='grpc') as f:
────────────────────────── 🎉 Flow is ready to serve! ──────────────────────────
╭────────────── 🔗 Endpoint ───────────────╮
│  ⛓      Protocol                   GRPC  │
│  🏠        Local   │
│  🔒      Private  │
│  🌍       Public  │

The Client has to specify the followings parameters to match the Flow and how it was set up:

  • the protocol it needs to use to communicate with the Flow

  • the host and the port as exposed by the Flow

  • if it needs to use TLS encryption (to connect to a Flow that has been configured to use TLS in combination with gRPC, http, or websocket)


Default port The default port for the Client is 80 unless you are using TLS encryption it will be 443

You can define these parameters by passing a valid URI scheme as part of the host argument:

from jina import Client

from jina import Client


Equivalently, you can pass each relevant parameter as a keyword argument:

from jina import Client

Client(host='my.awesome.flow', port=1234, protocol='http')
Client(host='my.awesome.flow', port=1234, protocol='websocket')
Client(host='my.awesome.flow', port=1234, protocol='grpc')
from jina import Client

Client(host='my.awesome.flow', port=1234, protocol='http', tls=True)
Client(host='my.awesome.flow', port=1234, protocol='websocket', tls=True)
Client(host='my.awesome.flow', port=1234, protocol='grpc', tls=True)

You can also use a mix of both:

from jina import Client

Client(host='https://my.awesome.flow', port=1234)
Client(host='my.awesome.flow:1234', protocol='http', tls=True)


You can’t define these parameters both by keyword argument and by host scheme - you can’t have two sources of truth. Example: the following code will raise an exception:

from jina import Client

Client(host='https://my.awesome.flow:1234', port=4321)


We apply RLock to avoid this gRPC issue, so that grpc clients can be used in a multi-threaded environment.

What you should do, is to rely on asynchronous programming or multi-processing rather than multi-threading. For instance, if you’re building a web server, you can introduce multi-processing based parallelism to your app using gunicorn: gunicorn main:app --workers 4 --worker-class uvicorn.workers.UvicornWorker ...

Client API#

When using docarray>=0,30, you specify the schema that you expect the Deployment or Flow to return. You can pass the return type by using the return_type parameter in the method:

from jina import Client
from docarray import DocList, BaseDoc

class InputDoc(BaseDoc):
    text: str = ''

class OutputDoc(BaseDoc):
    tags: Dict[str, int] = {}

c = Client(host='https://my.awesome.flow:1234', port=4321)

Enable compression#

If the communication to the Gateway is via gRPC, you can pass compression parameter to post() to benefit from gRPC compression methods.

The supported choices are: None, gzip and deflate.

from jina import Client

client = Client(), compression='Gzip')

Note that this setting is only effective the communication between the client and the Flow’s gateway.

One can also specify the compression of the internal communication as described here.

Test readiness of the server#

You can check the readiness from the client:

from jina import Deployment

dep = Deployment(port=12345)

with dep:
from jina import Client

client = Client(port=12345)
from jina import Flow

f = Flow(port=12345).add()

with f:
from jina import Client

client = Client(port=12345)

Simple profiling of the latency#

Before sending any real data, you can test the connectivity and network latency by calling the profiling() method:

from jina import Client

c = Client(host='grpc://my.awesome.flow:1234')
 Roundtrip  24ms  100% 
├──  Client-server network  17ms  71% 
└──  Server  7ms  29% 
    ├──  Gateway-executors network  0ms  0% 
    ├──  executor0  5ms  71% 
    └──  executor1  2ms  29% 

Logging configuration#

Similar to the Flow logging configuration, the jina.Client also accepts the log_config argument. The Client can be configured as below:

from jina import Client

client = Client(log_config='./logging.json.yml')

If the Flow is configured with custom logging, the argument will be forwarded to the implicit client.

from jina import Flow

f = Flow(log_config='./logging.json.yml')

with f:
    # the implicit client automatically uses the log_config from the Flow for consistency'/')