Deploy a Weaviate vector database on GKE

Autopilot Standard

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This tutorial shows you how to deploy a Weaviate vector database cluster on Google Kubernetes Engine (GKE).

Weaviate is an open-source vector database with low-latency performance and basic support for different media types such as text and images. It supports semantic search, question answering, and classification. Weaviate is fully built on Go and it stores both objects and vectors, allowing the use of vector search, keyword search, and a combination of both as a hybrid search. From an infrastructure perspective, Weaviate is a cloud-native and fault-tolerant database. Fault tolerance is delivered by leaderless architecture where each node of the database cluster can serve read and write requests which in turn excludes a single point of failure.

This tutorial is intended for cloud platform administrators and architects, ML engineers, and MLOps (DevOps) professionals interested in deploying vector database clusters on GKE.

Benefits

Weaviate offers the following benefits:

• Libraries for various programming languages and open API to integrate with other services.
• Horizontal scaling.
• A balance between cost-effectiveness and query speed, especially when dealing with large datasets. You can choose how much data is stored in memory versus on disk.

Objectives

In this tutorial, you learn how to:

• Plan and deploy GKE infrastructure for Weaviate.
• Deploy and configure the Weaviate database in a GKE cluster.
• Run a Notebook to generate and store example vector embeddings within your database, and perform vector-based search queries.

Costs

In this document, you use the following billable components of Google Cloud:

• GKE
• Google Cloud Managed Service for Prometheus

To generate a cost estimate based on your projected usage, use the pricing calculator. New Google Cloud users might be eligible for a free trial.

When you finish the tasks that are described in this document, you can avoid continued billing by deleting the resources that you created. For more information, see Clean up.

Before you begin

In this tutorial, you use Cloud Shell to run commands. Cloud Shell is a shell environment for managing resources hosted on Google Cloud. It comes preinstalled with the Google Cloud CLI, kubectl, Helm and Terraform command-line tools. If you don't use Cloud Shell, you must install the Google Cloud CLI.

1. Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.
2. Install the Google Cloud CLI.

3. To initialize the gcloud CLI, run the following command:

   gcloud init

   Note: If you installed the gcloud CLI previously, make sure you have the latest version by running gcloud components update.

4. Create or select a Google Cloud project.

   Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

   • Create a Google Cloud project:

     gcloud projects create PROJECT_ID

     Replace PROJECT_ID with a name for the Google Cloud project you are creating.

   • Select the Google Cloud project that you created:

     gcloud config set project PROJECT_ID

     Replace PROJECT_ID with your Google Cloud project name.

5. Make sure that billing is enabled for your Google Cloud project.

6. Enable the Cloud Resource Manager, Compute Engine, GKE, and IAM Service Account Credentials APIs:

   gcloud services enable cloudresourcemanager.googleapis.com compute.googleapis.com container.googleapis.com iamcredentials.googleapis.com

7. Install the Google Cloud CLI.

8. To initialize the gcloud CLI, run the following command:

   gcloud init

   Note: If you installed the gcloud CLI previously, make sure you have the latest version by running gcloud components update.

9. Create or select a Google Cloud project.

   Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

   • Create a Google Cloud project:

     gcloud projects create PROJECT_ID

     Replace PROJECT_ID with a name for the Google Cloud project you are creating.

   • Select the Google Cloud project that you created:

     gcloud config set project PROJECT_ID

     Replace PROJECT_ID with your Google Cloud project name.

10. Make sure that billing is enabled for your Google Cloud project.

11. Enable the Cloud Resource Manager, Compute Engine, GKE, and IAM Service Account Credentials APIs:

    gcloud services enable cloudresourcemanager.googleapis.com compute.googleapis.com container.googleapis.com iamcredentials.googleapis.com

12. Grant roles to your user account. Run the following command once for each of the following IAM roles: roles/compute.securityAdmin, roles/compute.viewer, roles/container.clusterAdmin, roles/container.admin, roles/iam.serviceAccountAdmin, roles/iam.serviceAccountUser, roles/monitoring.viewer

    gcloud projects add-iam-policy-binding PROJECT_ID --member="user:USER_IDENTIFIER" --role=ROLE

    • Replace PROJECT_ID with your project ID.

    • Replace USER_IDENTIFIER with the identifier for your user account. For example, user:[e-mail].

    • Replace ROLE with each individual role.

Set up your environment

To set up your environment with Cloud Shell, follow these steps:

1. Set environment variables for your project, region, and a Kubernetes cluster resource prefix:

   export PROJECT_ID=PROJECT_ID
   export KUBERNETES_CLUSTER_PREFIX=weaviate
   export REGION=us-central1
   

   Replace PROJECT_ID with your Google Cloud project ID.

   This tutorial uses us-central1 region to create your deployment resources.

2. Check the version of Helm:

   helm version
   

   Update the version if it's older than 3.13:

   curl https://raw.githubusercontent.com/helm/helm/main/scripts/get-helm-3 | bash
   

3. Clone the sample code repository from GitHub:

   git clone https://github.com/GoogleCloudPlatform/kubernetes-engine-samples
   

4. Navigate to the weaviate directory:

   cd kubernetes-engine-samples/databases/weaviate
   

Create your cluster infrastructure

In this section, you run a Terraform script to create a private, highly-available, regional GKE cluster to deploy your Weaviate database.

You can choose to deploy Weaviate using a Standard or Autopilot cluster. Each has its own advantages and different pricing models.

Autopilot

The following diagram shows an Autopilot GKE cluster deployed in the project.

To deploy the cluster infrastructure, run the following commands in the Cloud Shell:

export GOOGLE_OAUTH_ACCESS_TOKEN=$(gcloud auth print-access-token)
terraform -chdir=terraform/gke-autopilot init
terraform -chdir=terraform/gke-autopilot apply \
-var project_id=${PROJECT_ID} \
-var region=${REGION} \
-var cluster_prefix=${KUBERNETES_CLUSTER_PREFIX}

GKE replaces the following variables at runtime:

• GOOGLE_OAUTH_ACCESS_TOKEN uses the gcloud auth print-access-token command to retrieve an access token that authenticates interactions with various Google Cloud APIs
• PROJECT_ID, REGION, and KUBERNETES_CLUSTER_PREFIX are the environment variables defined in the Set up your environment section and assigned to the new relevant variables for the Autopilot cluster you are creating.

When prompted, type yes.

The output is similar to the following:

...
Apply complete! Resources: 9 added, 0 changed, 0 destroyed.

Outputs:

kubectl_connection_command = "gcloud container clusters get-credentials weaviate-cluster --region us-central1"

Terraform creates the following resources:

• A custom VPC network and private subnet for the Kubernetes nodes.
• A Cloud Router to access the internet through Network Address Translation (NAT).
• A private GKE cluster in the us-central1 region.
• A ServiceAccount with logging and monitoring permissions for the cluster.
• Google Cloud Managed Service for Prometheus configuration for cluster monitoring and alerting.

Standard

The following diagram shows a Standard private regional GKE cluster deployed across three different zones.

To deploy the cluster infrastructure, run the following commands in the Cloud Shell:

export GOOGLE_OAUTH_ACCESS_TOKEN=$(gcloud auth print-access-token)
terraform -chdir=terraform/gke-standard init
terraform -chdir=terraform/gke-standard apply \
-var project_id=${PROJECT_ID} \
-var region=${REGION} \
-var cluster_prefix=${KUBERNETES_CLUSTER_PREFIX}

GKE replaces the following variables at runtime:

• GOOGLE_OAUTH_ACCESS_TOKEN uses the gcloud auth print-access-token command to retrieve an access token that authenticates interactions with various Google Cloud APIs.
• PROJECT_ID, REGION, and KUBERNETES_CLUSTER_PREFIX are the environment variables defined in Set up your environment section and assigned to the new relevant variables for the Standard cluster that you are creating.

When prompted, type yes. It might take several minutes for these commands to complete and for the cluster to show a ready status.

The output is similar to the following:

...
Apply complete! Resources: 10 added, 0 changed, 0 destroyed.

Outputs:

kubectl_connection_command = "gcloud container clusters get-credentials weaviate-cluster --region us-central1"

Terraform creates the following resources:

• A custom VPC network and private subnet for the Kubernetes nodes.
• A Cloud Router to access the internet through Network Address Translation (NAT).
• A private GKE cluster in the us-central1 region with autoscaling enabled (one to two nodes per zone).
• A ServiceAccount with logging and monitoring permissions for the cluster.
• Google Cloud Managed Service for Prometheus configuration for cluster monitoring and alerting.

Connect to the cluster

Configure kubectl to fetch credentials and communicate with your new GKE cluster:

gcloud container clusters get-credentials \
    ${KUBERNETES_CLUSTER_PREFIX}-cluster --region ${REGION}

Deploy the Weaviate database to your cluster

To use Helm chart to deploy the Weaviate database to your GKE cluster, follow these steps:

1. Add the Weaviate database Helm Chart repository before you can deploy it on your GKE cluster:

   helm repo add weaviate https://weaviate.github.io/weaviate-helm
   

2. Create the namespace weaviate for the database:

   kubectl create ns weaviate
   

3. Create a secret to store the API key:

   kubectl create secret generic apikeys --from-literal=AUTHENTICATION_APIKEY_ALLOWED_KEYS=$(openssl rand -base64 32) -n weaviate
   

4. Deploy an internal load balancer to access Weaviate from within the virtual network:

   kubectl apply -n weaviate -f manifests/05-ilb/ilb.yaml
   

   The ilb.yaml manifest describes the load balancer service:

   apiVersion: v1
   kind: Service
   metadata:
     annotations:
       #cloud.google.com/neg: '{"ingress": true}'
       networking.gke.io/load-balancer-type: "Internal"
     labels:
       app.kubernetes.io/name: weaviate
     name: weaviate-ilb
   spec:
     ports:
     - name: http
       port: 8080
       protocol: TCP
       targetPort: 8080
     - name: grpc
       port: 50051
       protocol: TCP
       targetPort: 50051
     selector:
       app: weaviate
     type: LoadBalancer

5. Apply the manifest to deploy Weaviate cluster:

   helm upgrade --install "weaviate" weaviate/weaviate \
   --namespace "weaviate" \
   --values ./manifests/01-basic-cluster/weaviate_cluster.yaml
   

   The weaviate_cluster.yaml manifest describes the Deployment:

   initContainers:
     sysctlInitContainer:
       enabled: false
     extraInitContainers: {}
   resources: 
      requests:
        cpu: '1'
        memory: '4Gi'
      limits:
        cpu: '2'
        memory: '4Gi'
   replicas: 3
   storage:
     size: 10Gi
     storageClassName: "premium-rwo"
   service:
     name: weaviate
     ports:
       - name: http
         protocol: TCP
         port: 80
     type: ClusterIP
   grpcService:
     enabled: true
     name: weaviate-grpc
     ports:
       - name: grpc
         protocol: TCP
         port: 50051
     type: ClusterIP
   authentication:
     anonymous_access:
       enabled: false
   authorization:
     admin_list:
       enabled: true
       users:
         - [e-mail]
   modules:
     text2vec-palm:
       enabled: true
   env:
     AUTHENTICATION_APIKEY_ENABLED: 'true'
     AUTHENTICATION_APIKEY_USERS: '[e-mail]'
     PROMETHEUS_MONITORING_ENABLED: true
   envSecrets:
     AUTHENTICATION_APIKEY_ALLOWED_KEYS: apikeys
   tolerations:
     - key: "app.stateful/component"
       operator: "Equal"
       value: "weaviate"
       effect: NoSchedule

   Wait for a few minutes for the Weaviate cluster to fully start.

6. Check the deployment status:

   kubectl get weaviate -n weaviate --watch
   

   The output is similar to following, if the weaviate database is successfully deployed:

   NAME: weaviate
   LAST DEPLOYED: Tue Jun 18 13:15:53 2024
   NAMESPACE: weaviate
   STATUS: deployed
   REVISION: 1
   TEST SUITE: None
   

7. Wait for Kubernetes to start the resources:

   kubectl wait pods -l app.kubernetes.io/name=weaviate --for condition=Ready --timeout=300s -n weaviate
   

Run queries with Vertex AI Colab Enterprise notebook

This section explains how to connect to your Weaviate database using Colab Enterprise. You can use a dedicated runtime template to deploy to the weaviate-vpc, so the notebook can communicate with resources in the GKE cluster.

Create a runtime template

To create a runtime template:

1. In the Google Cloud console, go to the Colab Enterprise Runtime Templates page and make sure your project is selected:

   Go to Runtime Templates

2. Click New Template. The Create new runtime template page appears.

3. In the Runtime basics section:

   • In the Display name field, enter weaviate-connect.
   • In the Region drop-down list, select us-central1. This is the same region as your GKE cluster.

4. In the Configure compute section:

   • In the Machine type drop-down list, select e2-standard-2.
   • In the Disk size field, enter 30.

5. In the Networking and security section:

   • In the Network drop-down list, select the network where your GKE cluster resides.
   • In the Subnetwork drop-down list, select a corresponding subnetwork.
   • Clear the Enable public internet access checkbox.

6. Click Create to finish creating the runtime template. Your runtime template appears in the list on the Runtime templates tab.

Create a runtime

To create a runtime:

1. In the runtime templates list, for the template you just created, click in the Actions column, and then click Create runtime. The Create Vertex AI Runtime pane appears.

2. Click Create to create a runtime based on your template.

3. On the Runtimes tab that opens, wait for the status to transition to Healthy.

Import the notebook

To import the notebook:

1. Go to the Notebooks tab and click Import notebook from URLs.

2. In the Import source select URL.

3. Under Notebook URLs paste the following link: raw.githubusercontent.com/GoogleCloudPlatform/kubernetes-engine-samples/main/databases/weaviate/manifests/02-notebook/vector-database.ipynb

4. Click Import.

Connect to the runtime and run queries

To connect to the runtime and run queries:

1. In the notebook, next to the Connect button, click the Additional connection options. The Connect to Vertex AI Runtime pane appears.

2. Select Connect to a runtime and then select Connect to an existing Runtime.

3. Select the runtime you launched and click Connect.

4. Click the Run cell button to the left of each code cell to run the notebook cells.

The notebook contains code cells and text that describes each code block. Running a code cell executes its commands and displays an output. You can run the cells in order, or run individual cells as needed.

For more information about Vertex AI Colab Enterprise, see Colab Enterprise documentation.

View Prometheus metrics for your cluster

The GKE cluster is configured with Google Cloud Managed Service for Prometheus, which enables collection of metrics in the Prometheus format. This service provides a fully managed solution for monitoring and alerting, allowing for collection, storage, and analysis of metrics from the cluster and its applications.

The following diagram shows how Prometheus collects metrics for your cluster:

The GKE private cluster in the diagram contains the following components:

• Weaviate Pods that expose metrics on the path /metrics and port 2112.
• Prometheus-based collectors that process the metrics from the Weaviate Pods.
• A PodMonitoring resource that sends the metrics to Cloud Monitoring.

To export and view the metrics, follow these steps:

1. Create the PodMonitoring resource to scrape metrics by labelSelector:

   kubectl apply -n weaviate -f manifests/03-prometheus-metrics/pod-monitoring.yaml
   

   The pod-monitoring.yaml manifest describes the PodMonitoring resource:

   apiVersion: monitoring.googleapis.com/v1
   kind: PodMonitoring
   metadata:
     name: weaviate
   spec:
     selector:
       matchLabels:
         app: weaviate
     endpoints:
     - port: 2112
       interval: 30s
       path: /metrics

2. To import a custom Cloud Monitoring dashboard with the configurations defined in dashboard.json:

   gcloud --project "${PROJECT_ID}" monitoring dashboards create --config-from-file monitoring/dashboard.json
   

3. After the command runs successfully, go to the Cloud Monitoring Dashboards:

   Go to Dashboards overview

4. From the list of dashboards, open the Weaviate Overview dashboard. It might take some time to collect and display metrics. The dashboard shows amount of Shards, Vectors and operations latency

Clean up

To avoid incurring charges to your Google Cloud account for the resources used in this tutorial, either delete the project that contains the resources, or keep the project and delete the individual resources.

Delete the project

The easiest way to avoid billing is to delete the project you created for this tutorial.

Caution: Deleting a project has the following effects:

• Everything in the project is deleted. If you used an existing project for the tasks in this document, when you delete it, you also delete any other work you've done in the project.
• Custom project IDs are lost. When you created this project, you might have created a custom project ID that you want to use in the future. To preserve the URLs that use the project ID, such as an appspot.com URL, delete selected resources inside the project instead of deleting the whole project.

If you plan to explore multiple architectures, tutorials, or quickstarts, reusing projects can help you avoid exceeding project quota limits.

Delete a Google Cloud project:

gcloud projects delete PROJECT_ID

If you deleted the project, your clean up is complete. If you didn't delete the project, proceed to delete the individual resources.

Delete individual resources

1. Set environment variables.

   export PROJECT_ID=${PROJECT_ID}
   export KUBERNETES_CLUSTER_PREFIX=weaviate
   export REGION=us-central1
   

2. Run the terraform destroy command:

   export GOOGLE_OAUTH_ACCESS_TOKEN=$(gcloud auth print-access-token)
   terraform  -chdir=terraform/FOLDER destroy \
   -var project_id=${PROJECT_ID} \
   -var region=${REGION} \
   -var cluster_prefix=${KUBERNETES_CLUSTER_PREFIX}
   

   Replace FOLDER with either gke-autopilot or gke-standard, depending on the type of GKE cluster you created.

   When prompted, type yes.

3. Find all unattached disks:

   export disk_list=$(gcloud compute disks list --filter="-users:* AND labels.name=${KUBERNETES_CLUSTER_PREFIX}-cluster" --format "value[separator=|](name,region)")
   

4. Delete the disks:

   for i in $disk_list; do
    disk_name=$(echo $i| cut -d'|' -f1)
    disk_region=$(echo $i| cut -d'|' -f2|sed 's|.*/||')
    echo "Deleting $disk_name"
    gcloud compute disks delete $disk_name --region $disk_region --quiet
   done
   

5. Delete the GitHub repository:

   rm -r ~/kubernetes-engine-samples/
   

What's next

• Explore Weaviate on GKE marketplace.
• Learn about the best practices for deploying databases on GKE.
• Discover solutions for running data-intensive workloads with GKE.