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---
title: "Deploying with Kubernetes"
---
# Deploying Datahub with Kubernetes
## Introduction
[This directory ](https://github.com/linkedin/datahub/tree/master/datahub-kubernetes ) provides
the Kubernetes [Helm ](https://helm.sh/ ) charts for deploying [Datahub ](https://github.com/linkedin/datahub/tree/master/datahub-kubernetes/datahub ) and it's [dependencies ](https://github.com/linkedin/datahub/tree/master/datahub-kubernetes/prerequisites )
(Elasticsearch, Neo4j, MySQL, and Kafka) on a Kubernetes cluster.
## Setup
1. Set up a kubernetes cluster
- In a cloud platform of choice like [Amazon EKS ](https://aws.amazon.com/eks ),
[Google Kubernetes Engine ](https://cloud.google.com/kubernetes-engine ),
and [Azure Kubernetes Service ](https://azure.microsoft.com/en-us/services/kubernetes-service/ ) OR
- In local environment using [Minikube ](https://minikube.sigs.k8s.io/docs/ ).
Note, more than 7GB of RAM is required to run Datahub and it's dependencies
2. Install the following tools:
- [kubectl ](https://kubernetes.io/docs/tasks/tools/ ) to manage kubernetes resources
- [helm ](https://helm.sh/docs/intro/install/ ) to deploy the resources based on helm charts.
Note, we only support Helm 3.
## Components
Datahub consists of 4 main components: [GMS ](https://datahubproject.io/docs/gms ),
[MAE Consumer ](https://datahubproject.io/docs/metadata-jobs/mae-consumer-job ),
[MCE Consumer ](https://datahubproject.io/docs/metadata-jobs/mce-consumer-job ), and
[Frontend ](https://datahubproject.io/docs/datahub-frontend ). Kubernetes deployment
for each of the components are defined as subcharts under the main
[Datahub ](https://github.com/linkedin/datahub/tree/master/datahub-kubernetes/datahub )
helm chart.
The main components are powered by 4 external dependencies:
- Kafka
- Local DB (MySQL, Postgres, MariaDB)
- Search Index (Elasticsearch)
- Graph Index (Supports only Neo4j)
The dependencies must be deployed before deploying Datahub. We created a separate
[chart ](https://github.com/linkedin/datahub/tree/master/datahub-kubernetes/prerequisites )
for deploying the dependencies with example configuration. They could also be deployed
separately on-prem or leveraged as managed services.
## Quickstart
Assuming kubectl context points to the correct kubernetes cluster, first create kubernetes secrets that contain MySQL and Neo4j passwords.
```(shell)
kubectl create secret generic mysql-secrets --from-literal=mysql-root-password=datahub
kubectl create secret generic neo4j-secrets --from-literal=neo4j-password=datahub
```
The above commands sets the passwords to "datahub" as an example. Change to any password of choice.
Second, deploy the dependencies by running the following
```(shell)
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(cd prerequisites & & helm dep update)
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helm install prerequisites prerequisites/
```
Note, after changing the configurations in the values.yaml file, you can run
```(shell)
helm upgrade prerequisites prerequisites/
```
To just redeploy the dependencies impacted by the change.
Run `kubectl get pods` to check whether all the pods for the dependencies are running.
You should get a result similar to below.
```
NAME READY STATUS RESTARTS AGE
elasticsearch-master-0 1/1 Running 0 62m
elasticsearch-master-1 1/1 Running 0 62m
elasticsearch-master-2 1/1 Running 0 62m
prerequisites-cp-schema-registry-cf79bfccf-kvjtv 2/2 Running 1 63m
prerequisites-kafka-0 1/1 Running 2 62m
prerequisites-mysql-0 1/1 Running 1 62m
prerequisites-neo4j-community-0 1/1 Running 0 52m
prerequisites-zookeeper-0 1/1 Running 0 62m
```
deploy Datahub by running the following
```(shell)
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helm install datahub datahub/
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```
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Values in [values.yaml ](https://github.com/linkedin/datahub/tree/master/datahub-kubernetes/datahub/values.yaml )
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have been preset to point to the dependencies deployed using the [prerequisites ](https://github.com/linkedin/datahub/tree/master/datahub-kubernetes/prerequisites )
chart with release name "prerequisites". If you deployed the helm chart using a different release name, update the quickstart-values.yaml file accordingly before installing.
Run `kubectl get pods` to check whether all the datahub pods are running. You should get a result similar to below.
```
NAME READY STATUS RESTARTS AGE
datahub-datahub-frontend-84c58df9f7-5bgwx 1/1 Running 0 4m2s
datahub-datahub-gms-58b676f77c-c6pfx 1/1 Running 0 4m2s
datahub-datahub-mae-consumer-7b98bf65d-tjbwx 1/1 Running 0 4m3s
datahub-datahub-mce-consumer-8c57d8587-vjv9m 1/1 Running 0 4m2s
datahub-elasticsearch-setup-job-8dz6b 0/1 Completed 0 4m50s
datahub-kafka-setup-job-6blcj 0/1 Completed 0 4m40s
datahub-mysql-setup-job-b57kc 0/1 Completed 0 4m7s
elasticsearch-master-0 1/1 Running 0 97m
elasticsearch-master-1 1/1 Running 0 97m
elasticsearch-master-2 1/1 Running 0 97m
prerequisites-cp-schema-registry-cf79bfccf-kvjtv 2/2 Running 1 99m
prerequisites-kafka-0 1/1 Running 2 97m
prerequisites-mysql-0 1/1 Running 1 97m
prerequisites-neo4j-community-0 1/1 Running 0 88m
prerequisites-zookeeper-0 1/1 Running 0 97m
```
You can run the following to expose the frontend locally. Note, you can find the pod name using the command above.
In this case, the datahub-frontend pod name was `datahub-datahub-frontend-84c58df9f7-5bgwx` .
```(shell)
kubectl port-forward < datahub-frontend pod name > 9002:9002
```
You should be able to access the frontend via http://localhost:9002.
Once you confirm that the pods are running well, you can set up ingress for datahub-frontend
to expose the 9002 port to the public.
## Other useful commands
| Command | Description |
|-----|------|
| helm uninstall datahub | Remove DataHub |
| helm ls | List of Helm charts |
| helm history | Fetch a release history |
