Deploying the Sourcegraph Cody API is a critical step for integrating advanced code intelligence capabilities into your development environment. This article outlines the deployment workflows for both Docker and Kubernetes, providing a comprehensive guide for DevOps teams and developers.

Prerequisites for Deployment

Before starting the deployment process, ensure you have the following prerequisites in place:

  • Access to the Sourcegraph Cody API source code or Docker image
  • Docker installed on your system for Docker deployments
  • Kubernetes cluster configured with kubectl access
  • Persistent storage solutions for data persistence
  • Proper network configurations and firewalls

Deploying with Docker

Docker provides a straightforward way to deploy the Cody API locally or on a server. Follow these steps:

Pulling the Docker Image

Start by pulling the latest Cody API image from the Docker registry:

docker pull sourcegraph/cody-api:latest

Running the Container

Run the Docker container with appropriate port mappings and environment variables:

docker run -d -p 8080:8080 --name cody-api \\
  -e "CONFIG_VAR=value" \\
  sourcegraph/cody-api:latest

Deploying with Kubernetes

Kubernetes deployment offers scalability and resilience. Follow these steps to deploy Cody API on your cluster:

Creating a Deployment Manifest

Define a deployment YAML file named cody-api-deployment.yaml:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: cody-api-deployment
spec:
  replicas: 3
  selector:
    matchLabels:
      app: cody-api
  template:
    metadata:
      labels:
        app: cody-api
    spec:
      containers:
      - name: cody-api
        image: sourcegraph/cody-api:latest
        ports:
        - containerPort: 8080
        env:
        - name: CONFIG_VAR
          value: "value"

Applying the Deployment

Apply the deployment to your Kubernetes cluster:

kubectl apply -f cody-api-deployment.yaml

Creating a Service

Expose the Cody API deployment with a service:

apiVersion: v1
kind: Service
metadata:
  name: cody-api-service
spec:
  type: LoadBalancer
  ports:
  - port: 80
    targetPort: 8080
  selector:
    app: cody-api

Apply the service configuration:

kubectl apply -f cody-api-service.yaml

Post-Deployment Considerations

After deploying the Cody API, verify its status and ensure it is accessible. Monitor logs and performance metrics regularly. For production environments, consider setting up auto-scaling and persistent storage solutions to handle load and data durability.

Conclusion

Deploying the Sourcegraph Cody API using Docker or Kubernetes allows flexible integration into various development workflows. Choose the deployment method that best fits your infrastructure and scale requirements to enhance your code intelligence capabilities effectively.