Deploy an Elasticsearch 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:

   export PROJECT_ID=PROJECT_ID
   export KUBERNETES_CLUSTER_PREFIX=elasticsearch
   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 elasticsearch directory to start creating deployment resources:

   cd kubernetes-engine-samples/databases/elasticsearch
   

Create your cluster infrastructure

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

You can choose to deploy Elasticsearch 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 elasticsearch-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 elasticsearch-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 Elasticsearch database and StatefulHA operator

In this section, you deploy the Elasticsearch database (in cluster mode) and StatefulHA operator to your GKE cluster using the ECK Operator Helm Chart.

The Deployment creates a GKE cluster with the following configuration:

• Three replicas of the Elasticsearch nodes.
• DaemonSet to change virtual memory settings, for optimal Elasticsearch performance.
• Configuration of NodeAffinity and PodAntiAffinity to ensure proper distribution across Kubernetes nodes, optimizing the use of node pools and maximizing availability across different zones.
• A Stateful HA operator that manages failover processes and ensures high availability.
• For authentication, the database creates Kubernetes Secrets with authentication credentials, passwords, and certificates.

To use the Helm chart to deploy the Elasticsearch 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. Create an Elastic Cloud on Kubernetes (ECK) Custom Resource Definition (CRD):

   kubectl apply -f https://download.elastic.co/downloads/eck/2.11.1/crds.yaml
   

3. Deploy the ECK operator:

   kubectl apply -f https://download.elastic.co/downloads/eck/2.11.1/operator.yaml
   

4. Create the namespace elastic for the database:

   kubectl create ns elastic
   

5. Install the HighAvailabilityApplication (HAA) resource, which defines failover rules for Elasticsearch..

   kubectl apply -n elastic -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: elasticsearch-ha-es-main
     namespace: elastic
   spec:
     resourceSelection:
       resourceKind: StatefulSet
     policy:
       storageSettings:
         requireRegionalStorage: false
       failoverSettings:
         forceDeleteStrategy: AfterNodeUnreachable
         afterNodeUnreachable:
           afterNodeUnreachableSeconds: 20 # 60 seconds total

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

   kubectl apply -n elastic -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

7. Deploy the DaemonSet resource to set virtual memory in each node:

   kubectl apply -n elastic -f manifests/02-elasticsearch/mmap-count.yaml
   

   The mmap-count.yaml manifest describes the DaemonSet:

   apiVersion: apps/v1
   kind: DaemonSet
   metadata:
     name: max-map-count-setter
     labels:
       k8s-app: max-map-count-setter
   spec:
     selector:
       matchLabels:
         name: max-map-count-setter
     template:
       metadata:
         labels:
           name: max-map-count-setter
       spec:
         initContainers:
           - name: max-map-count-setter
             image: docker.io/bash:5.2.21
             resources:
               limits:
                 cpu: 100m
                 memory: 32Mi
             securityContext:
               privileged: true
               runAsUser: 0
             command: ['/usr/local/bin/bash', '-e', '-c', 'echo 262144 > /proc/sys/vm/max_map_count']
         containers:
           - name: sleep
             image: docker.io/bash:5.2.21
             command: ['sleep', 'infinity']

8. Apply the manifest to deploy Elasticsearch cluster:

   kubectl apply -n elastic -f manifests/02-elasticsearch/elasticsearch.yaml
   

   The elasticsearch.yaml manifest describes the Deployment:

   apiVersion: elasticsearch.k8s.elastic.co/v1
   kind: Elasticsearch
   metadata:
     name: elasticsearch-ha
   spec:
     version: 8.11.4
     nodeSets:
     - name: main
       count: 3
       volumeClaimTemplates:
       - metadata:
           name: elasticsearch-data 
         spec:
           accessModes:
           - ReadWriteOnce
           resources:
             requests:
               storage: 10Gi
           storageClassName: ha-regional
       config:
       podTemplate:
         metadata:
           labels:
             app.stateful/component: elasticsearch
         spec:
           initContainers:
           - name: max-map-count-check
             command: ['sh', '-c', "while true; do mmc=$(cat /proc/sys/vm/max_map_count); if [ ${mmc} -eq 262144 ]; then exit 0; fi; sleep 1; done"]
           containers:
           - name: metrics
             image: quay.io/prometheuscommunity/elasticsearch-exporter:v1.7.0
             command:
               - /bin/elasticsearch_exporter
               - --es.ssl-skip-verify
               - --es.uri=https://$(ES_USER):$(ES_PASSWORD)@localhost:9200
             securityContext:
               runAsNonRoot: true
               runAsGroup: 10000
               runAsUser: 10000
             resources:
               requests:
                 memory: "128Mi"
                 cpu: "25m"
               limits:
                 memory: "128Mi"
                 cpu: "100m"
             ports:
             - containerPort: 9114
             env:
             - name: ES_USER
               value: "elastic"
             - name: ES_PASSWORD
               valueFrom:
                 secretKeyRef:
                   name: elasticsearch-ha-es-elastic-user
                   key: elastic
           - name: elasticsearch
             resources:
               limits:
                 memory: 4Gi
                 cpu: 1
           affinity:
             nodeAffinity:
               preferredDuringSchedulingIgnoredDuringExecution:
                 - weight: 1
                   preference:
                     matchExpressions:
                     - key: app.stateful/component
                       operator: In
                       values:
                       - elasticsearch
             podAntiAffinity:
               preferredDuringSchedulingIgnoredDuringExecution:
               - weight: 1
                 podAffinityTerm:
                   labelSelector:
                     matchLabels:
                       app.stateful/component: elasticsearch
                   topologyKey: topology.kubernetes.io/zone

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

9. Check the deployment status:

   kubectl get elasticsearch -n elastic --watch
   

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

   NAME               HEALTH   NODES   VERSION   PHASE   AGE
   elasticsearch-ha   green    3       8.11.4    Ready   2m30s
   

   Wait for HEALTH to show as green. Press Ctrl+C to exit the command if needed.

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

    kubectl apply -n elastic -f manifests/02-elasticsearch/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: elasticsearch
      name: elastic-ilb
    spec:
      ports:
      - name: https
        port: 9200
        protocol: TCP
        targetPort: 9200
      selector:
        common.k8s.elastic.co/type: elasticsearch
        elasticsearch.k8s.elastic.co/cluster-name: elasticsearch-ha
      type: LoadBalancer

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

    kubectl describe highavailabilityapplication elasticsearch-ha-es-main -n elastic
    

    The output is similar to following

    Status:
      Conditions:
        Last Transition Time:  2024-02-01T13:27:50Z
        Message:               Application is protected
        Observed Generation:   1
        Reason:                ApplicationProtected
        Status:                True
        Type:                  Protected
    Events:                    <none>
    

12. Once GKE starts the workloads, verify that GKE has created the Elasticsearch workloads:

    kubectl get pod,svc,statefulset,pdb,secret,daemonset -n elastic
    

    The output is similar to the following:

    NAME                             READY   STATUS    RESTARTS   AGE
    pod/elasticsearch-ha-es-main-0   2/2     Running   0          7m16s
    pod/elasticsearch-ha-es-main-1   2/2     Running   0          7m16s
    pod/elasticsearch-ha-es-main-2   2/2     Running   0          7m16s
    pod/max-map-count-setter-28wt9   1/1     Running   0          7m27s
    pod/max-map-count-setter-cflsw   1/1     Running   0          7m27s
    pod/max-map-count-setter-gzq9k   1/1     Running   0          7m27s
    
    NAME                                        TYPE        CLUSTER-IP    EXTERNAL-IP   PORT(S)    AGE
    service/elasticsearch-ha-es-http            ClusterIP   10.52.8.28   <none>        9200/TCP   7m18s
    service/elasticsearch-ha-es-internal-http   ClusterIP   10.52.3.48   <none>        9200/TCP   7m18s
    service/elasticsearch-ha-es-main            ClusterIP   None         <none>        9200/TCP   7m16s
    service/elasticsearch-ha-es-transport       ClusterIP   None         <none>        9300/TCP   7m18s
    
    NAME                                        READY   AGE
    statefulset.apps/elasticsearch-ha-es-main   3/3     7m16s
    
    NAME                                                     MIN AVAILABLE   MAX UNAVAILABLE   ALLOWED DISRUPTIONS   AGE
    poddisruptionbudget.policy/elasticsearch-ha-es-default   2               N/A               1                     7m16s
    
    NAME                                                 TYPE     DATA   AGE
    secret/elasticsearch-ha-es-elastic-user              Opaque   1      7m18s
    secret/elasticsearch-ha-es-file-settings             Opaque   1      7m16s
    secret/elasticsearch-ha-es-http-ca-internal          Opaque   2      7m17s
    secret/elasticsearch-ha-es-http-certs-internal       Opaque   3      7m17s
    secret/elasticsearch-ha-es-http-certs-public         Opaque   2      7m17s
    secret/elasticsearch-ha-es-internal-users            Opaque   4      7m18s
    secret/elasticsearch-ha-es-main-es-config            Opaque   1      7m16s
    secret/elasticsearch-ha-es-main-es-transport-certs   Opaque   7      7m16s
    secret/elasticsearch-ha-es-remote-ca                 Opaque   1      7m16s
    secret/elasticsearch-ha-es-transport-ca-internal     Opaque   2      7m16s
    secret/elasticsearch-ha-es-transport-certs-public    Opaque   1      7m16s
    secret/elasticsearch-ha-es-xpack-file-realm          Opaque   4      7m18s
    
    NAME                                  DESIRED   CURRENT   READY   UP-TO-DATE   AVAILABLE   NODE SELECTOR   AGE
    daemonset.apps/max-map-count-setter   6         6         6       6            6           <none>          13m
    

The following GKE resources are created for the Elasticsearch cluster:

• The Elasticsearch StatefulSet that controls three Pod replicas.
• A DaemonSet to configure virtual memory settings.
• Services to connect to Elasticsearch.
• Secrets with superuser credentials and service-related certificates.
• Stateful HA operator Pod and HighlyAvailableApplication resource, actively monitoring the Elasticsearch application.

Run queries with Vertex AI Colab Enterprise notebook

This section explains how to generate embeddings into Elasticsearch documents and perform semantic search queries using the official Elasticsearch Python client in Colab Enterprise notebook. A document in Elasticsearch is composed of various fields, each paired with its corresponding value.

In this example, you use a dataset from a CSV file that contains a list of books in different genres. Elasticsearch serves as a search engine, and the Pod you create serves as a client querying the Elasticsearch database.

You can use a dedicated runtime template to deploy to the elasticsearch-vpc VPC (Virtual Private Cloud), so the notebook can communicate with resources in your 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 add_box New Template. The Create new runtime template page appears.

3. In the Runtime basics section:

   • In the Display name field, enter elastic-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/main/databases/elasticsearch/manifests/03-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:

• Elasticsearch Pods that expose metrics on the path / and port 9114. These metrics are provided by the sidecar container named metrics that contains the elasticsearch_exporter.
• Prometheus-based collectors that process the metrics from the Elasticsearch Pod.
• A PodMonitoring resource that sends the metrics to Cloud Monitoring.

The cluster configuration defines a sidecar container with metrics exporter in the Prometheus format:

apiVersion: elasticsearch.k8s.elastic.co/v1
kind: Elasticsearch
metadata:
  name: elasticsearch-ha
spec:
  ...
  nodeSets:
  - name: main
    ...
    podTemplate:
      spec:
        containers:
        ...
        - name: metrics
          image: quay.io/prometheuscommunity/elasticsearch-exporter:v1.7.0
          command:
          - /bin/elasticsearch_exporter
          - --es.ssl-skip-verify
          - --es.uri=https://$(ES_USER):$(ES_PASSWORD)@localhost:9200
          ...
          env:
          - name: ES_USER
            value: "elastic"
          - name: ES_PASSWORD
            valueFrom:
            secretKeyRef:
              name: elasticsearch-ha-es-elastic-user
              key: elastic

To export and view the metrics, follow these steps:

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

   kubectl apply -n elastic -f manifests/04-prometheus-metrics/pod-monitoring.yaml
   

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

   apiVersion: monitoring.googleapis.com/v1
   kind: PodMonitoring
   metadata:
     name: elasticsearch
   spec:
     selector:
       matchLabels:
         app.stateful/component: elasticsearch
         elasticsearch.k8s.elastic.co/cluster-name: elasticsearch-ha
     endpoints:
     - port: 9114
       interval: 30s
       path: /metrics

   After a few minutes, the built-in dashboard "Elasticsearch Prometheus Overview" displays.

2. To view more data-related graphs, 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 ElasticSearch Overview dashboard. It might take 1-2 minutes to collect and display metrics.

   The dashboard shows a count of key metrics:

   • Indexes
   • Documents and Shards
   • Pending operations
   • Running nodes with their health statuses

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 are automatically deleted.

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: [elasticsearch-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 [elasticsearch-cluster-backup].
   

3. You can see your newly created backup plan elasticsearch-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.