Deploy a Qdrant vector database on GKE

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:

   For the purpose of this tutorial, use us-central1 region to create your deployment resources.

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

   • Replace PROJECT_ID with your Google Cloud project ID.

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 qdrant directory to start creating deployment resources:

   cd kubernetes-engine-samples/databases/qdrant
   

Create your cluster infrastructure

This section involves running a Terraform script to create a private, highly-available, regional GKE cluster to deploy your Qdrant database.

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

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}

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

Outputs:

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

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}

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

Outputs:

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

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 Qdrant database to your cluster

In this tutorial, you deploy the Qdrant database (in distributed mode) and the Stateful HA operator to your GKE cluster cluster using the Helm chart.

The deployment creates a GKE cluster with the following configuration:

• Three replicas of the Qdrant nodes.
• Tolerations, node affinities, and topology spread constraints are configured to ensure proper distribution across Kubernetes nodes. This leverages the node pools and different availability zones.
• A RePD volume with the SSD disk type is provisioned for data storage.
• A Stateful HA operator is used to manage failover processes and ensure high availability.
• For authentication, the database creates a Kubernetes secret containing the API key.

To use the Helm chart to deploy Qdrant database, follow these steps:

1. Enable the StatefulHA add-on:

   Autopilot

   GKE automatically enables the StatefulHA add-on at cluster creation.

   Standard

   Run the following command:

   gcloud container clusters update ${KUBERNETES_CLUSTER_PREFIX}-cluster \
       --project=${PROJECT_ID} \
       --region=${REGION} \
       --update-addons=StatefulHA=ENABLED
   

   It might take 15 minutes for this command to complete and for the cluster to show a ready status.

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

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

3. Create namespace qdrant for the database:

   kubectl create ns qdrant
   

4. Apply the manifest to create a regional persistent SSD disk StorageClass:

   kubectl apply -n qdrant -f manifests/01-regional-pd/regional-pd.yaml
   

   The regional-pd.yaml manifest describes the persistent SSD disk StorageClass:

   apiVersion: storage.k8s.io/v1
   kind: StorageClass
   allowVolumeExpansion: true
   metadata:
     name: ha-regional
   parameters:
     replication-type: regional-pd
     type: pd-ssd
     availability-class: regional-hard-failover
   provisioner: pd.csi.storage.gke.io
   reclaimPolicy: Retain
   volumeBindingMode: WaitForFirstConsumer

5. Deploy a Kubernetes configmap with a metrics sidecar configuration and a Qdrant cluster by using Helm:

   kubectl apply -n qdrant -f manifests/03-prometheus-metrics/metrics-cm.yaml
   helm install qdrant-database qdrant/qdrant -n qdrant \
   -f manifests/02-values-file/values.yaml
   

   The metrics-cm.yaml manifest describes the metrics sidecar ConfigMap:

   apiVersion: v1
   kind: ConfigMap
   metadata:
     name: nginx-conf
   data:
     default.conf.template: |
       server {
         listen 80;
         location / {
           proxy_pass http://localhost:6333/metrics;
           proxy_http_version 1.1;
           proxy_set_header Host $http_host;
           proxy_set_header api-key ${QDRANT_APIKEY};
           proxy_set_header X-Forwarded-For $remote_addr;
         }
       }

   The values.yaml manifest describes the Qdrant cluster configuration :

   replicaCount: 3
   
   config:
     service:
       enable_tls: false
     cluster:
       enabled: true
     storage:
       optimizers:
         deleted_threshold: 0.5
         vacuum_min_vector_number: 1500
         default_segment_number: 2
         max_segment_size_kb: null
         memmap_threshold_kb: null
         indexing_threshold_kb: 25000
         flush_interval_sec: 5
         max_optimization_threads: 1
   
   livenessProbe:
     enabled: true
     initialDelaySeconds: 60
   
   resources:
     limits:
       cpu: "2"
       memory: 4Gi
     requests:
       cpu: "1"
       memory: 4Gi
   
   tolerations:
     - key: "app.stateful/component"
       operator: "Equal"
       value: "qdrant"
       effect: NoSchedule
   
   affinity:
     nodeAffinity:
       preferredDuringSchedulingIgnoredDuringExecution:
       - weight: 1
         preference:
           matchExpressions:
           - key: "app.stateful/component"
             operator: In
             values:
             - "qdrant"
   
   topologySpreadConstraints:
     - maxSkew: 1
       topologyKey: "topology.kubernetes.io/zone"
       whenUnsatisfiable: ScheduleAnyway
       labelSelector:
         matchLabels:
           app.kubernetes.io/name: qdrant
           app.kubernetes.io/instance: qdrant
   
   podDisruptionBudget:
     enabled: true
     maxUnavailable: 1
   
   persistence:
     accessModes: ["ReadWriteOnce"]
     size: 10Gi
     storageClassName: ha-regional
   
   apiKey: true
   
   sidecarContainers:
     - name: metrics
       image: nginx:1.27
       resources:
         requests:
           memory: "128Mi"
           cpu: "250m"
         limits:
           memory: "128Mi"
           cpu: "500m"
       ports:
       - containerPort: 80
       env:
       - name: QDRANT_APIKEY 
         valueFrom:
           secretKeyRef:
             name: qdrant-database-apikey          
             key: api-key
       volumeMounts:
           - name: nginx-conf
             mountPath: /etc/nginx/templates/default.conf.template
             subPath: default.conf.template
             readOnly: true
   additionalVolumes:
     - name: nginx-conf
       configMap:
         name: nginx-conf
         items:
           - key: default.conf.template
             path: default.conf.template 

   This configuration enables the cluster mode, allowing you to setup a highly available and distributed Qdrant cluster.

6. Add a label to Qdrant statefulset:

   kubectl label statefulset qdrant-database examples.ai.gke.io/source=qdrant-guide -n qdrant
   

7. Deploy an internal load balancer to access your Qdrant database that's running in the same VPC as your GKE cluster:

   kubectl apply -n qdrant -f manifests/02-values-file/ilb.yaml
   

   The ilb.yaml manifest describes the LoadBalancer 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: qdrant
     name: qdrant-ilb
   spec:
     ports:
     - name: http
       port: 6333
       protocol: TCP
       targetPort: 6333
     - name: grpc
       port: 6334
       protocol: TCP
       targetPort: 6334
     selector:
       app: qdrant
       app.kubernetes.io/instance: qdrant-database
     type: LoadBalancer

8. Check the deployment status:

   helm ls -n qdrant
   

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

   NAME    NAMESPACE       REVISION        UPDATED                                 STATUS          CHART           APP VERSION
   qdrant-database  qdrant          1               2024-02-06 20:21:15.737307567 +0000 UTC deployed        qdrant-0.7.6    v1.7.4
   

9. Wait for GKE to start the required workloads:

   kubectl wait pods -l app.kubernetes.io/instance=qdrant-database --for condition=Ready --timeout=300s -n qdrant
   

   This command might take a few minutes to complete successfully.

10. Once GKE starts the workloads, verify that GKE has created the Qdrant workloads:

    kubectl get pod,svc,statefulset,pdb,secret -n qdrant
    

11. Start the HighAvailabilityApplication (HAA) resource for Qdrant:

    kubectl apply -n qdrant -f manifests/01-regional-pd/ha-app.yaml
    

    The ha-app.yaml manifest describes the HighAvailabilityApplication resource:

    kind: HighAvailabilityApplication
    apiVersion: ha.gke.io/v1
    metadata:
      name: qdrant-database
      namespace: qdrant
    spec:
      resourceSelection:
        resourceKind: StatefulSet
      policy:
        storageSettings:
          requireRegionalStorage: true
        failoverSettings:
          forceDeleteStrategy: AfterNodeUnreachable
          afterNodeUnreachable:
            afterNodeUnreachableSeconds: 20 # 60 seconds total

    The following GKE resources are created for the Qdrant cluster:

    • The Qdrant StatefulSet that controls three Pod replicas.
    • A PodDisruptionBudget, ensuring a maximum of one unavailable replica.
    • The qdrant-database Service, exposing the Qdrant port for inbound connections and replication between nodes.
    • The qdrant-database-headless Service, providing the list of running Qdrant Pods.
    • The qdrant-database-apikey Secret, facilitating secure database connection.
    • Stateful HA operator Pod and HighlyAvailableApplication resource, actively monitoring the Qdrant application. The HighlyAvailableApplication resource defines failover rules to apply against Qdrant.

12. To check if the failover rules are applied, describe the resource and confirm Status: Message: Application is protected.

    kubectl describe highavailabilityapplication qdrant-database -n qdrant
    

    The output is similar to the following:

    Status:
    Conditions:
        Last Transition Time:  2023-11-30T09:54:52Z
        Message:               Application is protected
        Observed Generation:   1
        Reason:                ApplicationProtected
        Status:                True
        Type:                  Protected
    

Run queries with Vertex AI Colab Enterprise notebook

Qdrant organizes vectors and payloads in collections. Vector embedding is a technique that represents words or entities as numerical vectors while maintaining their semantic relationships. This is important for similarity searches as it enables finding similarities based on meaning rather than exact matches, making tasks like search and recommendation systems more effective and nuanced.

This section shows you how to upload Vectors into a new Qdrant Collection and run a search queries.

In this example, you use a dataset from a CSV file that contains a list of books in different genres. You create a Colab Enterprise notebook to perform a search query on the Qdrant database.

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 add_box New Template. The Create new runtime template page appears.

3. In the Runtime basics section:

   • In the Display name field, enter qdrant-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 more_vert 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 enter the following link:

   https://raw.githubusercontent.com/GoogleCloudPlatform/kubernetes-engine-samples/refs/heads/main/databases/qdrant/manifests/04-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 arrow_drop_down 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:

• Qdrant Pods that expose metrics on the path / and port 80. These metrics are provided by the sidecar container named metrics.
• Prometheus-based collectors that process the metrics from the Qdrant 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 qdrant -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: qdrant
   spec:
     selector:
       matchLabels:
         app: qdrant
         app.kubernetes.io/instance: qdrant-database
     endpoints:
     - port: 80
       interval: 30s
       path: / 

2. Create a 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 Qdrant Overview dashboard. It might take 1-2 minutes to collect and display metrics.

   The dashboard shows a count of key metrics:

   • Collections
   • Embedded vectors
   • Pending operations
   • Running nodes

Back up your cluster configuration

The Backup for GKE feature lets you schedule regular backups of your entire GKE cluster configuration, including the deployed workloads and their data.

In this tutorial, you configure a backup plan for your GKE cluster to perform backups of all workloads, including Secrets and Volumes, every day at 3 AM. To ensure efficient storage management, backups older than three days would be automatically deleted.

To configure Backup plans, follow these steps:

1. Enable the Backup for GKE feature for your cluster:

   gcloud container clusters update ${KUBERNETES_CLUSTER_PREFIX}-cluster \
   --project=${PROJECT_ID} \
   --region=${REGION} \
   --update-addons=BackupRestore=ENABLED
   

2. Create a backup plan with a daily schedule for all namespaces within the cluster:

   gcloud beta container backup-restore backup-plans create ${KUBERNETES_CLUSTER_PREFIX}-cluster-backup \
   --project=${PROJECT_ID} \
   --location=${REGION} \
   --cluster="projects/${PROJECT_ID}/locations/${REGION}/clusters/${KUBERNETES_CLUSTER_PREFIX}-cluster" \
   --all-namespaces \
   --include-secrets \
   --include-volume-data \
   --cron-schedule="0 3 * * *" \
   --backup-retain-days=3
   

   The command uses the relevant environment variables at runtime.

   The cluster name's format is relative to your project and region as follows:

   projects/PROJECT_ID/locations/REGION/clusters/CLUSTER_NAME
   

   When prompted, type y.The output is similar to the following:

   Create request issued for: [qdrant-cluster-backup]
   Waiting for operation [projects/PROJECT_ID/locations/us-central1/operations/operation-1706528750815-610142ffdc9ac-71be4a05-f61c99fc] to complete...⠹
   

   This operation might take a few minutes to complete successfully. After the execution is complete, the output is similar to the following:

   Created backup plan [qdrant-cluster-backup].
   

3. You can see your newly created backup plan qdrant-cluster-backup listed on the Backup for GKE console.

   Go to Backup for GKE

If you want to restore the saved backup configurations, see Restore a backup.