Deploy on Kubernetes#

This how-to will go through deploying a simple Flow using Kubernetes, customizing the Kubernetes configuration to your needs, and scaling Executors using replicas and shards.

Deploying a Flow in Kubernetes is the recommended way to use Jina in production.

Since a Flow is composed of Executors which can run in different runtimes depending on how you deploy the Flow, Kubernetes can easily take over the lifetime management of Executors.

Hint

This guide is designed for users who want to manually deploy a Jina project on Kubernetes.

Check out Jina AI Cloud Hosting if you want a one-click solution to deploy and host Jina, leveraging a cloud-native stack of Kubernetes, Prometheus and Grafana, without worrying about provisioning.

Preliminaries#

To follow this how-to, you need access to a Kubernetes cluster.

You can either set up minikube, or use one of many managed Kubernetes solutions in the cloud:

You need to install Linkerd in your K8s cluster. To use Linkerd, install the Linkerd CLI and its control plane in your cluster. This automatically sets up and manages the service mesh proxies when you deploy the Flow.

To understand why you need to install a service mesh like Linkerd refer to this section

Deploy a simple Flow#

By simple in this context we mean a Flow without replicated or sharded Executors - you can see how to use those in Kubernetes later on.

For now, define a Flow, either in YAML or directly in Python, as we do here:

from jina import Flow

f = (
    Flow(port=8080)
    .add(name='encoder', uses='jinaai+docker://jina-ai/CLIPEncoder')
    .add(
        name='indexer',
        uses='jinaai+docker://jina-ai/AnnLiteIndexer',
        uses_with={'dim': 512},
    )
)

You can essentially define any Flow of your liking. Just ensure that all Executors are containerized, either by using ‘jinahub+docker’, or by containerizing your local Executors.

The example Flow here simply encodes and indexes text or image data using two Executors from Executor Hub.

Next, generate Kubernetes YAML configs from the Flow. It’s good practice to define a new Kubernetes namespace for that purpose:

f.to_kubernetes_yaml('./k8s_flow', k8s_namespace='custom-namespace')

The following file structure will be generated - don’t worry if it’s slightly different – there can be changes from one Jina version to another:

.
└── k8s_flow
    ├── gateway
    │   └── gateway.yml
    └── encoder
    │   └── encoder.yml
    └── indexer
        └── indexer.yml

You can inspect these files to see how Flow concepts are mapped to Kubernetes entities. And as always, feel free to modify these files as you see fit for your use case.

Caution: Executor YAML configurations

As a general rule, the configuration files produced by to_kubernets_yaml() should run out of the box, and if you strictly follow this how-to they will.

However, there is an exception to this: If you use a local dockerized Executor, and this Executors configuration is stored in a file other than config.yaml, you will have to adapt this Executor’s Kubernetes YAML. To do this, open the file and replace config.yaml with the actual path to the Executor configuration.

This is because when a Flow contains a Docker image, it can’t see what Executor configuration was used to create that image. Since all of our tutorials use config.yaml for that purpose, the Flow uses this as a best guess. Please adapt this if you named your Executor configuration file differently.

Next you can actually apply these configuration files to your cluster, using kubectl. This launches all Flow microservices.

First, create the namespace you defined earlier:

kubectl create namespace custom-namespace

Now, deploy this Flow to your cluster:

kubectl apply -R -f ./k8s_flow

Check that the Pods were created:

kubectl get pods -n custom-namespace

NAME                              READY   STATUS    RESTARTS   AGE
encoder-8b5575cb9-bh2x8           1/1     Running   0          60m
gateway-7df8765bd9-xf5tf          1/1     Running   0          60m
indexer-8f676fc9d-4fh52           1/1     Running   0          60m
indexer-head-6fcc679d95-8mrm6     1/1     Running   0          60m

Note that the Jina gateway was deployed with name gateway-7df8765bd9-xf5tf.

Once you see that all the Deployments in the Flow are ready, you can start indexing documents:

import portforward

from jina.clients import Client
from docarray import DocumentArray

with portforward.forward('custom-namespace', 'gateway-7df8765bd9-xf5tf', 8080, 8080):
    client = Client(host='localhost', port=8080)
    client.show_progress = True
    docs = client.post(
        '/index',
        inputs=DocumentArray.from_files('./imgs/*.png').apply(
            lambda d: d.convert_uri_to_datauri()
        ),
    )

    print(f' Indexed documents: {len(docs)}')

Deploy Flow with shards and replicas#

After your service mesh is installed, your cluster is ready to run a Flow with scaled Executors. You can adapt the Flow from above to work with two replicas for the encoder, and two shards for the indexer:

from jina import Flow

f = (
    Flow(port=8080)
    .add(name='encoder', uses='jinaai+docker://jina-ai/CLIPEncoder', replicas=2)
    .add(
        name='indexer',
        uses='jinaai+docker://jina-ai/ANNLiteIndexer',
        uses_with={'dim': 512},
        shards=2,
    )
)

Again, you can generate your Kubernetes configuration:

f.to_kubernetes_yaml('./k8s_flow', k8s_namespace='custom-namespace')

Now you should see the following file structure:

.
└── k8s_flow
    ├── gateway
    │   └── gateway.yml
    └── encoder
    │   └─ encoder.yml
    └── indexer
        ├── indexer-0.yml
        ├── indexer-1.yml
        └── indexer-head.yml

Apply your configuration like usual:

Hint: Cluster cleanup

If you already have the simple Flow from the first example running on your cluster, make sure to delete it using kubectl delete -R -f ./k8s_flow.

kubectl apply -R -f ./k8s_flow

Exposing the Flow#

The previous examples use port-forwarding to index documents to the Flow. In real world applications, you may want to expose your service to make it reachable by users so that you can serve search requests.

Caution

Exposing the Flow only works if the environment of your Kubernetes cluster supports External Loadbalancers.

Once the Flow is deployed, you can expose a service:

kubectl expose deployment gateway --name=gateway-exposed --type LoadBalancer --port 80 --target-port 8080 -n custom-namespace
sleep 60 # wait until the external ip is configured

Export the external IP address. This is needed for the client when sending Documents to the Flow in the next section.

export EXTERNAL_IP=`kubectl get service gateway-exposed -n custom-namespace -o=jsonpath='{.status.loadBalancer.ingress[0].ip}'`

Client#

The client:

  • Sends an image to the exposed Flow on $EXTERNAL_IP

  • Retrieves the matches from the Flow.

  • Prints the uris of the closest matches.

You should configure your Client to connect to the Flow via the external IP address as follows:

import os
from jina.clients import Client

host = os.environ['EXTERNAL_IP']
port = 80

client = Client(host=host, port=port)

client.show_progress = True
docs = DocumentArray.from_files("./imgs/*.png").apply(
    lambda d: d.convert_uri_to_datauri()
)
queried_docs = client.post("/search", inputs=docs)

matches = queried_docs[0].matches
print(f"Matched documents: {len(matches)}")

Update your Executor in Kubernetes#

In Kubernetes, you can update your Executors by patching the Deployment corresponding to your Executor.

For instance, in the example above, you can change the CLIPEncoder’s batch_size parameter by changing the content of the Deployment inside the executor.yml dumped by .to_kubernetes_yaml.

You need to add --uses_with and pass the batch size argument to it. This is passed to the container inside the Deployment:

    spec:
      containers:
      - args:
        - executor
        - --name
        - encoder
        - --k8s-namespace
        - custom-namespace
        - --uses
        - config.yml
        - --port
        - '8080'
        - --uses-metas
        - '{}'
        - --uses-with
        - '{"batch_size": 64}'
        - --native
        command:
        - jina

After doing so, re-apply your configuration so the new Executor will be deployed without affecting the other unchanged Deployments:

kubectl apply -R -f ./k8s_flow

Other patching options

In Kubernetes Executors are ordinary Deployments, so you can use other patching options provided by Kubernetes:

  • kubectl replace to replace an Executor using a complete configuration file

  • kubectl patch to patch an Executor using only a partial configuration file

  • kubectl edit to edit an Executor configuration on the fly in your editor

You can find more information about these commands in the official Kubernetes documentation.

Key takeaways#

In short, there are just three key steps to deploy a Jina Flow on Kubernetes:

  1. Use f.to_kubernetes_yaml() to generate Kubernetes configuration files from a Jina Flow object.

  2. Apply the generated file via kubectl(Modify the generated files if necessary)

  3. Expose your Flow outside the K8s cluster

See also#