---
title: "Kubernetes"
id: kubernetes
slug: "/kubernetes"
description: "Learn how to deploy your Haystack pipelines through Kubernetes."
---
import ClickableImage from "@site/src/components/ClickableImage";
# Kubernetes
Learn how to deploy your Haystack pipelines through Kubernetes.
The best way to get Haystack running as a workload in a container orchestrator like Kubernetes is to create a service to expose one or more [Hayhooks](../hayhooks.mdx) instances.
## Create a Haystack Kubernetes Service using Hayhooks
As a first step, we recommend to create a local [KinD](https://github.com/kubernetes-sigs/kind) or [Minikube](https://github.com/kubernetes/minikube) Kubernetes cluster. You can manage your cluster from CLI, but tools like [k9s](https://k9scli.io/) or [Lens](https://k8slens.dev/) can ease the process.
When done, start with a very simple Kubernetes Service running a single Hayhooks Pod:
```yaml
kind: Pod
apiVersion: v1
metadata:
name: hayhooks
labels:
app: haystack
spec:
containers:
- image: deepset/hayhooks:v0.6.0
name: hayhooks
imagePullPolicy: IfNotPresent
resources:
limits:
memory: "512Mi"
cpu: "500m"
requests:
memory: "256Mi"
cpu: "250m"
---
kind: Service
apiVersion: v1
metadata:
name: haystack-service
spec:
selector:
app: haystack
type: ClusterIP
ports:
# Default port used by the Hayhooks Docker image
- port: 1416
```
After applying the above to an existing Kubernetes cluster, a `hayhooks` Pod will show up as a Service called `haystack-service`.
Note that the `Service` defined above is of type `ClusterIP`. That means it's exposed only _inside_ the Kubernetes cluster. To expose the Hayhooks API to the _outside_ world as well, you need a `NodePort` or `Ingress` resource. As an alternative, it's also possible to use [Port Forwarding](https://kubernetes.io/docs/tasks/access-application-cluster/port-forward-access-application-cluster/) to access the `Service` locally.
To do that, add port `30080` to Host-To-Node Mapping of our KinD cluster. In other words, make sure that the cluster is created with a node configuration similar to the following:
```yaml
kind: Cluster
apiVersion: kind.x-k8s.io/v1alpha4
nodes:
- role: control-plane
# ...
extraPortMappings:
- containerPort: 30080
hostPort: 30080
protocol: TCP
```
Then, create a simple `NodePort` to test if Hayhooks Pod is running correctly:
```yaml
apiVersion: v1
kind: Service
metadata:
name: haystack-nodeport
spec:
selector:
app: haystack
type: NodePort
ports:
- port: 1416
targetPort: 1416
nodePort: 30080
name: http
```
After applying this, `hayhooks` Pod will be accessible on `localhost:30080`.
From here, you should be able to manage pipelines. Remember that it's possible to deploy multiple different pipelines on a single Hayhooks instance. Check the [Hayhooks docs](../hayhooks.mdx) for more details.
## Auto-Run Pipelines at Pod Start
Hayhooks can load Haystack pipelines at startup, making them readily available when the server starts. You can leverage this mechanism to have your pods immediately serve one or more pipelines when they start.
At startup, it will look for deployed pipelines on the path specified at `HAYHOOKS_PIPELINES_DIR`, then load them.
A [deployed pipeline](https://github.com/deepset-ai/hayhooks?tab=readme-ov-file#deploy-a-pipeline) is essentially a directory which must contain a `pipeline_wrapper.py` file and possibly other files. To preload an [example pipeline](https://github.com/deepset-ai/hayhooks/tree/main/examples/pipeline_wrappers/chat_with_website), you need to mount a local folder inside the cluster node, then make it available on Hayhooks Pod as well.
First, ensure that a local folder is mounted correctly on the KinD cluster node at `/data`:
```yaml
kind: Cluster
apiVersion: kind.x-k8s.io/v1alpha4
nodes:
- role: control-plane
# ...
extraMounts:
- hostPath: /path/to/local/pipelines/folder
containerPath: /data
```
Next, make `/data` available as a volume and mount it on Hayhooks Pod. To do that, update your previous Pod configuration to the following:
```yaml
kind: Pod
apiVersion: v1
metadata:
name: hayhooks
labels:
app: haystack
spec:
containers:
- image: deepset/hayhooks:v0.6.0
name: hayhooks
imagePullPolicy: IfNotPresent
command: ["/bin/sh", "-c"]
args:
- |
pip install trafilatura && \
hayhooks run --host 0.0.0.0
volumeMounts:
- name: local-data
mountPath: /mnt/data
env:
- name: HAYHOOKS_PIPELINES_DIR
value: /mnt/data
- name: OPENAI_API_KEY
valueFrom:
secretKeyRef:
name: openai-secret
key: api-key
resources:
limits:
memory: "512Mi"
cpu: "500m"
requests:
memory: "256Mi"
cpu: "250m"
volumes:
- name: local-data
hostPath:
path: /data
type: Directory
```
Note that:
- We changed the Hayhooks container `command` to install `trafilaura` dependency before startup, since it's needed for our [chat_with_website](https://github.com/deepset-ai/hayhooks/tree/main/examples/pipeline_wrappers/chat_with_website) example pipeline. For a real production environment, we recommend creating a custom Hayhooks image as described [here](docker.mdx#customizing-the-haystack-docker-image).
- We make Hayhooks container read `OPENAI_API_KEY` from a Kubernetes Secret.
Before applying this new configuration, create the `openai-secret`:
```yaml
apiVersion: v1
kind: Secret
metadata:
name: openai-secret
type: Opaque
data:
# Replace the placeholder below with the base64 encoded value of your API key
# Generate it using: echo -n $OPENAI_API_KEY | base64
api-key: YOUR_BASE64_ENCODED_API_KEY_HERE
```
After applying this, check your Hayhooks Pod logs, and you'll see that the `chat_with_website` pipelines have already been deployed.
## Roll Out Multiple Pods
Haystack pipelines are usually stateless, which is a perfect use case for distributing the requests to multiple pods running the same set of pipelines. Let's convert the single-Pod configuration to an actual Kubernetes `Deployment`:
```yaml
apiVersion: apps/v1
kind: Deployment
metadata:
name: haystack-deployment
spec:
replicas: 3
selector:
matchLabels:
app: haystack
template:
metadata:
labels:
app: haystack
spec:
initContainers:
- name: install-dependencies
image: python:3.12-slim
workingDir: /mnt/data
command: ["/bin/bash", "-c"]
args:
- |
echo "Installing dependencies..."
pip install trafilatura
echo "Dependencies installed successfully!"
touch /mnt/data/init-complete
volumeMounts:
- name: local-data
mountPath: /mnt/data
resources:
requests:
memory: "64Mi"
cpu: "100m"
limits:
memory: "128Mi"
cpu: "250m"
containers:
- image: deepset/hayhooks:v0.6.0
name: hayhooks
imagePullPolicy: IfNotPresent
command: ["/bin/sh", "-c"]
args:
- |
pip install trafilatura && \
hayhooks run --host 0.0.0.0
ports:
- containerPort: 1416
name: http
volumeMounts:
- name: local-data
mountPath: /mnt/data
env:
- name: HAYHOOKS_PIPELINES_DIR
value: /mnt/data
- name: OPENAI_API_KEY
valueFrom:
secretKeyRef:
name: openai-secret
key: api-key
resources:
requests:
memory: "256Mi"
cpu: "250m"
limits:
memory: "512Mi"
cpu: "500m"
volumes:
- name: local-data
hostPath:
path: /data
type: Directory
```
Implementing the above configuration will create three pods. Each pod will run a different instance of Hayhooks, all serving the same example pipeline provided by the mounted volume in the previous example.
Note that the `NodePort` you created before will now act as a load balancer and will distribute incoming requests to the three Hayhooks Pods.