Kubernetes

LangStream container images are available on the Github packages registry. A LangStream cluster is made up of the following images:

• datastax/langstream-deployer

• datastax/langstream-control-plane

• datastax/langstream-runtime

• datastax/langstream-api-gateway

• datastax/langstream-cli

Quickstart

To create a LangStream control plane, you will need kubectl, helm cli, and a running K8s cluster with a recent version.

Connect to your GCP cluster:

gcloud container clusters get-credentials <langstream-cluster> --region us-east1 --project <project-name>
Fetching cluster endpoint and auth data.
Kubernetes v1.21.0-alpha+eabdbc0a40fe7efda92e10270f27b0a3485fb743
kubeconfig entry generated for langstream-cluster.

Connect to your Azure cluster:

az account set --subscription <subscription-name>
az aks get-credentials --resource-group k8s-resource-group --name dev
Merged "dev" as current context in /Users/mendon.kissling/.kube/config

Connect to your EKS cluster:

aws eks update-kubeconfig --region="us-east-2" --name="langstream-cluster"
Added new context arn:aws:eks:us-east-2:423019603865:cluster/langstream-cluster to /Users/mendon.kissling/.kube/config

Create a minikube cluster:

minikube start cpu="4"
Done! kubectl is now configured to use "minikube" cluster and "default" namespace by default

Add the LangStream chart repo to your Helm installation and update it to the latest version:

helm repo add langstream https://langstream.ai/charts
helm repo update langstream

External codeStorage component

This component stores the state of LangStream applications in an S3 API-compatible bucket or Azure blob.

Modify the values.yaml file you'll be deploying with to configure the external codeStorage component.

These values can be found in your storage provider's dashboard.

Azure:

codeStorage:
  type: azure
  configuration:
    endpoint: https://<storage-account>.blob.core.windows.net
    container: langstream
    storage-account-name: <storage-account>
    storage-account-key: <storage-account-key>

S3:

codeStorage:
  type: s3
  configuration:
    access-key: <aws-access-key>
    secret-key: <aws-secret-key>

Minio:

codeStorage:
  type: s3
  configuration:
    endpoint: http://minio.minio-dev.svc.cluster.local:9000
    access-key: minioadmin
    secret-key: minioadmin

If you're using GKE Cloud Storage, see Simple migration from Amazon S3 to Cloud Storage for using the Cloud Storage API to interact with an S3 bucket.

To configure a local S3-compatible storage service, such as minio, run:

kubectl apply -f https://raw.githubusercontent.com/LangStream/langstream/main/helm/examples/minio-dev.yaml

Install LangStream

Install the LangStream Helm chart:

helm install langstream \
    -n langstream \
    --create-namespace \
    --values values.yaml \
    langstream/langstream

Result:

NAME: langstream
LAST DEPLOYED: Tue Nov  7 11:38:05 2023
NAMESPACE: langstream
STATUS: deployed
REVISION: 1
TEST SUITE: None

In your Kubernetes cluster, you should see four new pods deploy in the langstream namespace. For more configuration options, see the Helm charts documentation.

Deploy Kafka cluster

Install the Strimzi Kafka operator.

helm repo add strimzi https://strimzi.io/charts/
helm install strimzi-kafka strimzi/strimzi-kafka-operator

Install a persistent Kafka cluster to the langstream namespace.

kubectl apply -f https://strimzi.io/examples/latest/kafka/kafka-persistent-single.yaml -n langstream
kafka.kafka.strimzi.io/my-cluster created

The Strimzi operator will create and watch a single-ZooKeeper Kafka cluster.

In your LangStream application's instance.yaml file, point the bootstrap.servers to your new Kafka bootstrap server's address.

instance:
  streamingCluster:
    type: "kafka"
    configuration:
      admin:
        bootstrap.servers: my-cluster-kafka-bootstrap.langstream.svc.cluster.local:9092

For production deployments, set the bootstrap server address as a secret value for KAFKA_BOOTSTRAP_SERVERS. For more, see Secrets.

Open the control-plane and api-gateway ports

Control plane:

kubectl -n langstream port-forward svc/langstream-control-plane 8090:8090 &

API gateway:

kubectl -n langstream port-forward svc/langstream-api-gateway 8091:8091 &

Alternate port forwarding

You can instead open the control plane and API gateway ports in your Helm chart.

This example uses traefik for ingress.

apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  name: control-plane
  annotations:
    kubernetes.io/ingress.class: "traefik"
spec:
  rules:
    - host: langstream.yourdomain.local  # Replace with your actual domain or host
      http:
        paths:
            backend:
              service:
                name: langstream-control-plane
                port:
                  number: 8090

---
apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  name: api-gateway
  annotations:
    kubernetes.io/ingress.class: "traefik"
spec:
  rules:
    - host: ws.langstream.yourdomain.local  # Replace with your actual domain or host
      http:
        paths:
            backend:
              service:
                name:  langstream-api-gateway
                port:
                  number: 8091

Deploy sample application

Run a sample application to test your new environment. For more on building LangStream applications, see Set up your development environment.

langstream apps deploy test -app sample-app/application -s sample-app/secrets.yaml -i sample-app/instance.yaml
packaging app: /Users/mendon.kissling/Documents/GitHub/LS Application/sample-app/application
app packaged
deploying application: test (1 KB)
application test deployed

To monitor deployment from the CLI, use langstream apps get <app-name>.

langstream-app-setup and langstream-runtime-deployer pods will deploy in the langstream-default namespace in your Kubernetes cluster. When these pods reach a Completed state, your application pod(s) will deploy.

For integrating LangStream and your remote cluster into VSCode workflows, Install the VSCode extension and use the provided starter applications & agent snippets.