Deploy a Qdrant vector database on GKE

Autopilot Standard

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

Vector databases are data stores specifically designed to manage and search through large collections of high-dimensional vectors. These vectors represent data like text, images, audio, video or any data that can be numerically encoded. Unlike traditional databases that rely on exact matches, vector databases specialize in finding similar items or identifying patterns within massive datasets. These characteristics make Qdrant a suitable choice for a variety of applications, including neural network or semantic-based matching, faceted search, and more. Qdrant not only functions as a vector database but also as a vector similarity search engine.

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

Benefits

Qdrant offers the following benefits:

• Wide range of libraries for various programming languages and open API to integrate with other services.
• Horizontal scaling, and support for sharding and replication that simplifies scaling and high availability.
• Container and Kubernetes support that enables deployment and management in modern cloud-native environments.
• Flexible payloads with advanced filtering to tailor search criteria precisely.
• Different quantization options and other optimizations to reduce infrastructure costs and improve performance.

Objectives

In this tutorial, you learn how to:

• Plan and deploy GKE infrastructure for Qdrant.
• Deploy the StatefulHA operator to ensure Qdrant high availability.
• Deploy and configure the Qdrant cluster.
• Upload a demo dataset and run a simple search query.
• Collect metrics and run a dashboard.

Deployment architecture

This architecture sets up a fault-tolerant, scalable GKE cluster for Qdrant across multiple availability zones, ensuring uptime and availability with rolling updates and minimal disruption. It includes using the StatefulHA operator for efficient failover management. For more information, see Regional clusters.

Architecture diagram

The following diagram shows a Qdrant cluster running on multiple nodes and zones in a GKE cluster:

In this architecture, the Qdrant StatefulSet is deployed across three nodes in three different zones.

• You can control how GKE distributes Pods across nodes by configuring the required Pod affinity rules and topology spread constraints in the Helm chart values file.
• If one zone fails, GKE reschedules Pods on new nodes based on the recommended configuration.

For data persistence, the architecture in this tutorial has the following characteristics:

• It uses regional SSD disks (customregional-pd StorageClass) for persisting data. We recommend regional SSD disks for databases due to their low latency and high IOPS.
• All disk data is replicated between primary and secondary zones in the region, increasing tolerance to potential zone failures.

Costs

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

• GKE
• Google Cloud Managed Service for Prometheus
• Backup for GKE

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 Resource Manager, Compute Engine, GKE, IAM Service Account Credentials, and Backup for GKE APIs:

   gcloud services enable cloudresourcemanager.googleapis.com compute.googleapis.com container.googleapis.com iamcredentials.googleapis.com gkebackup.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 Resource Manager, Compute Engine, GKE, IAM Service Account Credentials, and Backup for GKE APIs:

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

12. Grant roles to your user account. Run the following command once for each of the following IAM roles: roles/storage.objectViewer, roles/container.admin, roles/iam.serviceAccountAdmin, roles/compute.admin, roles/gkebackup.admin, 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:

   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.

Autopilot

The following diagram shows an Autopilot 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=$(gcloud auth print-access-token)
terraform -chdir=terraform/gke-autopilot init
terraform -chdir=terraform/gke-autopilot apply \
-var project_id=${PROJECT_ID} \
-var region=${REGION} \
-var cluster_prefix=${KUBERNETES_CLUSTER_PREFIX}

The following variables are replaced at runtime:

• GOOGLE_OAUTH_ACCESS_TOKEN: Replaced by an access token retrieved by gcloud auth print-access-token command to authenticate 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 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 qdrant-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}

The following variables are replaced at runtime:

• GOOGLE_OAUTH_ACCESS_TOKEN is replaced by an access token retrieved by gcloud auth print-access-token command to authenticate 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 qdrant-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 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:
     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
    

Upload the dataset and run search queries with Jupyter 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. Qdrant will serve as a search engine, and the Jupyter notebook Pod you create will serve as a client querying the Qdrant database.

1. Create the books-dataset and demo-app ConfigMaps and deploy the Jupyter notebook:

   kubectl create -n qdrant configmap books-dataset --from-file=manifests/04-notebook/dataset.csv
   kubectl create -n qdrant configmap notebook --from-file=manifests/04-notebook/vector-database.ipynb
   kubectl apply -n qdrant -f manifests/04-notebook/jupyter.yaml
   

   • The Secret named qdrant-apikey created earlier is mounted to the client Pod as an environment variable named APIKEY.
   • The books-dataset ConfigMap contains a csv file with book data for the Qdrant collection
   • The notebook ConfigMap contains the Jupyter notebook to create the Qdrant collection from books-dataset.

2. Wait for GKE to start the Jupyter Pod:

   kubectl wait pods -l app=jupyter-notebook --for condition=Ready --timeout=300s -n qdrant
   

3. Open this URL and click the vector-database.ipynb file. Press Run -> Run all cells. Jupyter will execute all the code and perform a search query.

   This query performs a semantic search against your my_books collection in Qdrant, retrieving a maximum of two results with highest match score relevant to your query text.

   It prints each result separated by a line of dashes, in the following format :

   • Title: Title of the book
   • Author: Author of the book
   • Description: As stored in your document's description metadata field
   • Published: Book publication date
   • Score: Qdrant's relevancy score

   The output is similar to the following:

   Title: Romeo and Juliet
   Author: William Shakespeare, Paul Werstine (Editor), Barbara A. Mowat (Editor),
   Paavo Emil Cajander (Translator)
   Description: In Romeo and Juliet, Shakespeare creates a violent world, in which
   two young people fall in love. It is not simply that their families disapprove;
   the Montagues and the Capulets are engaged in a blood feud.In this death-filled
   setting, the movement from love at first sight to the lovers' final union in
   death seems almost inevitable. And yet, this play set in an extraordinary world
   has become the quintessential story of young love. In part because of its exquisite
   language, it is easy to respond as if it were about all young lovers. Published: 01/01/04
   Score: 0.8935013
   -----
   Title: The Unbearable Lightness of Being
   Author: Milan Kundera, Michael Henry Heim (Translator)
   Description: In The Unbearable Lightness of Being, Milan Kundera tells the story
   of a young woman in love with a man torn between his love for her and his incorrigible
   womanizing and one of his mistresses and her humbly faithful lover. This magnificent
   novel juxtaposes geographically distant places, brilliant and playful reflections,
   and a variety of styles, to take its place as perhaps the major achievement of
   one of the world's truly great writers. Published: 10/27/09
   Score: 0.8931863
   -----
   

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.

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=qdrant
   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 Qdrant on GKE marketplace.
• Explore Qdrant open source software.
• Try out the Qdrant operator that offers API keys management, TLS support with certificate management, and backup scheduling.
• Learn about the best practices for deploying databases on GKE.
• Discover solutions for running data-intensive workloads with GKE.