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Deploying Neo4j Database on Google Kubernetes Engine (GKE) using Helm

Posted on 05-13-2025 02:44 PM
and filed in cloud-community-blog
bhardwaju
Staff

Neo4j is a popular graph database that is well-suited for storing and querying connected data. Google Kubernetes Engine (GKE) is a managed Kubernetes service that makes it easy to deploy and manage containerized applications. In this article, we'll walk through the steps involved in deploying a Neo4j cluster on GKE.

What is Google Kubernetes Engine (GKE)?

GKE provides a managed environment for deploying, managing, and scaling your containerized applications using Kubernetes. Kubernetes is an open-source system for automating deployment, scaling, and management of containerized applications. GKE simplifies the process of setting up and maintaining a Kubernetes cluster by handling the underlying infrastructure and providing features like:

  • Automated cluster provisioning: GKE automatically provisions and manages the underlying virtual machines and networking required for your Kubernetes cluster.
  • Scalability and high availability: GKE allows you to easily scale your applications up or down based on demand and ensures high availability through features like auto-scaling and node auto-repair.
  • Integration with other Google Cloud services: GKE integrates seamlessly with other Google Cloud services like Cloud Logging, Cloud Monitoring, and Cloud Storage.

image1.pngWhat are Helm Charts?

Helm is a package manager for Kubernetes that simplifies the deployment and management of applications. Helm Charts are packages of pre-configured Kubernetes resources that can be easily deployed with a single command. They provide a templating engine that allows you to customize the deployment based on your specific needs.

Prerequisites

  • A Google Cloud Project with billing enabled
  • A GKE cluster with at least 3 nodes
  • Basic understanding of Kubernetes concepts like pods, deployments, and services

Configure the Neo4j Helm chart repository

Add the Neo4j Helm chart repository.

helm repo add neo4j https://helm.neo4j.com/neo4j


Update the repository:

helm repo update

Check for the available Neo4j Helm charts

helm search repo neo4j/ --versions | grep 5.26.1

The output should be similar to the following:

neo4j/neo4j                       5.26.1  5.26.1  Neo4j is the world's leading graph database
neo4j/neo4j-admin                 5.26.1  5.26.1  Neo4j is the world's leading graph database
neo4j/neo4j-headless-service      5.26.1  -       Neo4j is the world's leading graph database
neo4j/neo4j-persistent-volume     5.26.1  -       Sets up persistent disks suitable for a Neo4j H...
neo4j/neo4j-reverse-proxy         5.26.1  5.26.1  Sets up an http server and a reverse proxy for ...

Deploy a standalone instance

This quickstart guide walks through the basics of deploying a Neo4j standalone instance to a cloud or a local Kubernetes cluster using the Neo4j Helm chart.

Environment-specific prerequisites

On the GCP console open the Cloud Shell and click on Authorise:

All the shell commands in this guide assume that the GCP Project, compute zone, and region to use have been set using the CLOUDSDK_CORE_PROJECT, CLOUDSDK_COMPUTE_ZONE, and CLOUDSDK_COMPUTE_REGION environment variables, for example:

export CLOUDSDK_CORE_PROJECT="my-neo4j-project"
export CLOUDSDK_COMPUTE_ZONE="europe-west2-a"
export CLOUDSDK_COMPUTE_REGION="europe-west2"

If you do not have a Google Kubernetes Engine (GKE) cluster, you can create a single-node one using:

gcloud container clusters create my-neo4j-gke-cluster --num-nodes=1 --machine-type "e2-standard-2"

e2-standard-2 is the minimum instance type required for running the examples of this startup guide on GKE.

Configure kubectl to use your GKE cluster using:

gcloud container clusters get-credentials my-neo4j-gke-cluster

Fetching cluster endpoint and auth data.
kubeconfig entry generated for my-neo4j-gke-cluster.

Create a values.yaml file

Select the tab as per your Kubernetes environment and using the provided example, create a YAML file for your standalone instance.

neo4j:
  name: my-standalone
  resources:
    cpu: "0.5"
    memory: "2Gi"

  # Uncomment to set the initial password
  #password: "my-initial-password"

  # Uncomment to use enterprise edition
  #edition: "enterprise"
  #acceptLicenseAgreement: "yes"

volumes:
  data:
    mode: "dynamic"
    dynamic:
      # In GKE;
      # * premium-rwo provisions SSD disks (recommended)
      # * standard-rwo provisions balanced SSD-backed disks
      # * standard provisions HDD disks
      storageClassName: premium-rwo

Install a Neo4j standalone instance

Install Neo4j using the deployment values.yaml file, created in Create a value.yaml file, and the neo4j/neo4j Helm chart:

Create a neo4j namespace and configure it to be used in the current context:

kubectl create namespace neo4j
kubectl config set-context --current --namespace=neo4j


Install the Neo4j standalone server:

helm install my-neo4j-release neo4j/neo4j --namespace neo4j -f my-neo4j.values.yaml

Example output

LAST DEPLOYED: Wed Oct 26 15:19:17 2022
NAMESPACE: neo4j
STATUS: deployed
REVISION: 1
TEST SUITE: None
NOTES:
Thank you for installing neo4j.

Your release "my-neo4j-release" has been installed in namespace "neo4j".

The neo4j user's password has been set to "my-password".To view the progress of the rollout try:

  $ kubectl --namespace "neo4j" rollout status --watch --timeout=600s statefulset/my-neo4j-release

Once rollout is complete you can log in to Neo4j at "neo4j://my-neo4j-release.neo4j.svc.cluster.local:7687". Try:

  $ kubectl run --rm -it --namespace "neo4j" --image "neo4j:5.1.0" cypher-shell \
     -- cypher-shell -a "neo4j://my-neo4j-release.neo4j.svc.cluster.local:7687" -u neo4j -p "my-password"

Graphs are everywhere!

Run the kubectl rollout command provided in the output of helm install to watch the Neo4j’s rollout until it is complete.

kubectl rollout status --watch --timeout=600s statefulset/my-neo4j-release

Verify the installation

kubectl get statefulsets

NAME               READY   AGE
my-neo4j-release   1/1     2m11s

Check that the pod is Running:

kubectl get pods

NAME                 READY   STATUS    RESTARTS   AGE
my-neo4j-release-0   1/1     Running   0          16m

Check that the pod logs look OK:

kubectl exec my-neo4j-release-0 -- tail -n50 /logs/neo4j.log

2022-10-26 14:19:51.728+0000 INFO  Command expansion is explicitly enabled for configuration
2022-10-26 14:19:51.733+0000 WARN  Unrecognized setting. No declared setting with name: server.panic.shutdown_on_panic.
2022-10-26 14:19:51.749+0000 INFO  Starting...
2022-10-26 14:19:53.062+0000 INFO  This instance is ServerId{cb9f2f3c} (cb9f2f3c-cd70-40b1-ac8e-13d9c4d26173)
2022-10-26 14:19:54.970+0000 INFO  ======== Neo4j 5.1.0 ========
2022-10-26 14:19:59.528+0000 INFO  Bolt enabled on 0.0.0.0:7687.
2022-10-26 14:20:01.523+0000 INFO  Remote interface available at http://localhost:7474/
2022-10-26 14:20:01.530+0000 INFO  id: EF772BAFBDCD3C4921D00A5707C88D6EDE514915DBCC7134E8704AFA15DC19C8
2022-10-26 14:20:01.530+0000 INFO  name: system
2022-10-26 14:20:01.531+0000 INFO  creationDate: 2022-10-26T14:19:56.631Z
2022-10-26 14:20:01.531+0000 INFO  Started.

Check that the services look OK:

kubectl get services

NAME                                         TYPE           CLUSTER-IP    EXTERNAL-IP      PORT(S)                         AGE
service/my-neo4j-release-lb-neo4j            LoadBalancer   10.36.5.34    34.105.179.172   7474:30288/TCP,7687:30584/TCP   14m
service/kubernetes                           ClusterIP      10.36.0.1                443/TCP                         22h
service/my-neo4j-release                     ClusterIP      10.36.11.18              7687/TCP,7474/TCP               14m
service/my-neo4j-release-admin               ClusterIP      10.36.3.238              6362/TCP,7687/TCP,7474/TCP      14m

Use the external IP of the LoadBalancer to access Neo4j from an application outside the Kubernetes cluster. For more information, see Applications accessing Neo4j from outside Kubernetes.

  1. In a web browser, open the Neo4j Browser at http://EXTERNAL_IP:7474/browser.
  2. Use the automatically-generated password (as printed in the output of the helm install command) or the one you have configured in the my-neo4j.values.yaml file.

Conclusion

Deploying Neo4j on GKE provides a scalable and reliable platform for running your graph database. By using Helm, you can simplify the deployment process and manage your Neo4j cluster with ease.

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