Using Flow API to Compose Your Jina Workflow

In this section, you will get to know how to construct the flow using different approaches.

Table of Contents

Feature description and expected outcome

In search systems, tasks such as indexing often involve multiple steps: preprocessing, encoding, storing, etc. In Jina’s architecture, each step is implemented by an Executor and wrapped by a Pod. This microservice design makes the whole pipeline flexible and scalable. Accomplishing a task is then linking all these Pods to work together, either sequentially or in parallel; locally or remotely.

The Flow API is a context manager for Pods. Each Flow object corresponds to a real-world task. It helps the user to manage the states and contexts of all Pods required in that task. The Flow API translates a workflow defined in Python code, YAML file, or interactive graph to a runtime backed by multi-thread/process, Kubernetes, Docker Swarm, etc. Users don’t need to worry about where the Pod is running or how the Pods are connected.

Flow is a context manager

Before you start

Make sure you install latest version of Jina via Installation.


Use Flow API in Python

Create a Flow

To create a new Flow:

from jina.flow import Flow

f = Flow()

Flow() accepts some arguments. See jina flow --help or check here for details.

When the arguments given to Flow() cannot be parsed, they are propagated to all the Flow’s Pods for parsing (if they are accepted, see jina pod --help for the list of arguments). For example:

f = Flow(read_only=True)

will set the read_only attribute of all Pods in f to True.

Add Pod into the Flow

To add a Pod to the Flow, simply call .add(), syntax of YAML file can be found here:

f = (Flow().add(name='p1', uses='mypod1.yml')
           .add(name='p2', uses='mypod2.yml', timeout_ready=50000)
           .add(name='p3', uses='mypod3.yml', read_only=True))

This will create a sequential workflow:

gateway -> p1 -> p2 -> p3 -> gateway

The input of each Pod is the output of the last Pod in sequential order. The gateway is the entrypoint of the whole Jina network. The gateway Pod is automatically added to every Flow, of which the output is the first Pod and the input is the last Pod defined in the Flow.

All accepted arguments follow the command line interface of Pod, which can be found in jina pod --help. Just remember to replace the dash - to underscore _ in the name of the argument when referring to it in Python.

Besides the file path, in Flow API uses can accept other types:

Type Example Remark
YAML file path crafter/my.yml
Inline YAML - !Buffer2URI | {mimetype: png} Don't forget - ! in the beginning
The name of an executor listed here NumpyIndexer Only the executors that have full default values can be directly used
Built-in simple executors listed here _clear Always starts with _
Docker image docker://jinahub/pod.encoder.dummy_mwu_encoder:0.0.6-0.9.3 Add docker:// before the image name and set timeout_ready to -1 to avoid timeout error
Add a Containerized Pod into the Flow

To run a Pod in a Docker container, simply specify the image argument:

f = (Flow().add(name='p1')
           .add(name='p2', image='jinaai/hub.examples.mwu_encoder:latest')

This will run p2 in a Docker container equipped with the image jinaai/hub.examples.mwu_encoder:latest. More information on using containerized Pod can be found here.

Add a Remote Pod into the Flow

To run a Pod remotely, simply specify the host and port_expose arguments. For example:

f = (Flow().add(name='p1')
           .add(name='p2', host='', port_expose=53100)

This will start p2 remotely on, whereas p1 and p3 run locally.

To use remote Pods, you need to start a gateway on in advance. More information on using remote Pods can be found here.

Add a Remote Containerized Pod into the Flow

A very useful pattern is to combine the above two features together:

f = (Flow().add(name='p1')
           .add(name='p2', host='', port_expose=53100,

This will start p2 remotely on running a Docker container equipped with the image jinaai/hub.executors.encoders.bidaf:latest. Of course Docker is required on More information on using remote Pods can be found here.

Parallelize the Steps

By default, if you keep adding .add() to a Flow, it will create a sequential workflow chain. You can parallelize some of the steps by using needs argument. For example,

f = (Flow().add(name='p1')
           .add(name='p3', needs='p1'))

This creates a workflow, where p2 and p3 work in parallel with the output of p1.

gateway -> p1 -> p2
              -> p3 -> gateway 

Waiting for Parallel Steps to Finish

In the prior example, the message is returned to the gateway regardless of the status of p2. To wait for multiple parallel steps to finish before continuing, you can do:

f = (Flow().add(name='p1')
           .add(name='p3', needs='p1')
           .join(['p2', 'p3']))

which gives

gateway -> p1 -> p2 ->
            |          | -> wait until both done -> gateway
              -> p3 -> 

Run a Flow

To run a Flow, simply use the with keyword:

f = (Flow().add(name='p1')
           .add(name='p3', needs='p1')
           .join(['p2', 'p3']))

with f:
    # the flow is now running

Though you can manually call the start() method to run the flow, you also need to call the corresponding close() method to release the resource. Using with saves you the trouble, as the resource is automatically released when running out of the scope.

Test Connectivity with Dry Run

You can test the whole workflow with pass. For example:

with f:

Iterate over Pods in the Flow

You can iterate the Pods in a Flow like you would a list. For example:

f = (Flow().add(name='p1')
           .add(name='p3', needs='p1')
           .join(['p2', 'p3']))

for p in
    print(f'name: {p[0]} in: {str(p[1].args.socket_in)} out: {str(p[1].args.socket_out)}')

Note will build the underlying network context but not run the Pods. It is very useful for debugging.

Feed Data to the Flow

You can use .index(), .search() to feed index data and search query to a Flow:

with f:
with f:, top_k=50, on_done=print)
  • input_fn is an Iterator[bytes], each of which corresponds to the representation of a Document with bytes.

  • on_done is the callback function after each request, and takes a Request protobuf as its only input.

A simple input_fn is defined as follows:

def input_fn():
    for _ in range(10):
        yield b's'

# or ...
input_fn = (b's' for _ in range(10))

Please note that the current Flow API does not support using index() and search() together in the same with scope. This is because the workflow of index() and search() are usually different, and you cannot use one workflow for both tasks.

Feed Data to the Flow from Other Clients

If you don’t use Python as a client, or your client and Flow are in different instances, you can keep a Flow running and use a client in another language to connect to it:

import threading

with f:

WARNING: don’t use a while loop to do the waiting, it is extremely inefficient:

with f:
    while True: # <- dont do that
        pass # <- dont do that

Use Flow API in YAML

You can also write a Flow in YAML:

version: '1.0'
    restful: true
  - name: pod0  # notice the change here, name is now an attribute
    method: add  # by default method is always add, available: add, needs, inspect
    uses: _pass
    needs: gateway
  - name: pod1  # notice the change here, name is now an attribute
    method: add  # by default method is always add, available: add, needs, inspect
    uses: _pass
    needs: gateway
  - method: inspect  # add an inspect node on pod1
  - method: needs  # let's try something new in Flow YAML v1: needs
    needs: [pod1, pod0]

You can use enviroment variables with $ in YAML. More information on the Flow YAML Schema can be found here.

Load a Flow from YAML

from jina.flow import Flow
f = Flow.load_config('myflow.yml')

Design a Flow with Dashboard

With Jina Dashboard, you can interactively drag and drop Pods, set their attribute and export to a Flow YAML file.

More information on the dashboard can be found here.