Milvus is a distributed vector database that aims to store, index and manage massive embedding vectors. Its ability to efficiently index and search through trillions of vectors makes Milvus a go-to choice for AI and machine learning workloads.
Kubernetes (K8s), on the other hand, excels in managing and scaling containerized applications. It provides features like auto-scaling, self-healing, and load balancing, which are crucial for maintaining high availability and performance in production environments.
- Docker - Ensure Docker is installed on your system.
- Kubernetes - Have a Kubernetes cluster ready. You can use minikube for local development or a cloud provider's Kubernetes service for production environments. For this tutorial, I will use k3d
- Helm - Install Helm, a package manager for Kubernetes, to help you manage Kubernetes applications, you can check our documentation to see how to do that https://milvus.io/docs/install_cluster-helm.md
- Kubectl - Install kubectl, a command-line tool for interacting with Kubernetes clusters, to deploy applications, inspect and manage cluster resources, and view logs.
- K9s - Terminal based UI to interact with your Kubernetes clusters. It makes it easier to navigate, observe and manage your deployed applications in the wild.
- Create your cluster with:
k3d cluster start - Check the status of your K8s cluster:
kubectl cluster-info
- Get the Helm values:
helm show values milvus/milvus > milvus_helm.yaml - Install Milvus on your cluster:
helm upgrade --install --values milvus_helm.yaml milvus milvus/milvus --namespace milvus --create-namespace - Port forward the Milvus proxy to your laptop:
kubectl port-forward svc/my-milvus 19530:19530
Check out the Jupyter Notebook showing you how to insert data in your cluster.
Feel free to check out Milvus, and share your experiences with the community by joining our Discord.