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155 lines
7.3 KiB
Markdown
155 lines
7.3 KiB
Markdown
---
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title: "Deploying with Kubernetes"
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---
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# Deploying DataHub with Kubernetes
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## Introduction
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Helm charts for deploying DataHub on a kubernetes cluster is located in
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this [repository](https://github.com/acryldata/datahub-helm). We provide charts for
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deploying [Datahub](https://github.com/acryldata/datahub-helm/tree/master/charts/datahub) and
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it's [dependencies](https://github.com/acryldata/datahub-helm/tree/master/charts/prerequisites)
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(Elasticsearch, optionally Neo4j, MySQL, and Kafka) on a Kubernetes cluster.
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This doc is a guide to deploy an instance of DataHub on a kubernetes cluster using the above charts from scratch.
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## Setup
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1. Set up a kubernetes cluster
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- In a cloud platform of choice like [Amazon EKS](https://aws.amazon.com/eks),
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[Google Kubernetes Engine](https://cloud.google.com/kubernetes-engine),
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and [Azure Kubernetes Service](https://azure.microsoft.com/en-us/services/kubernetes-service/) OR
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- In local environment using [Minikube](https://minikube.sigs.k8s.io/docs/). Note, more than 7GB of RAM is required
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to run Datahub and it's dependencies
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2. Install the following tools:
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- [kubectl](https://kubernetes.io/docs/tasks/tools/) to manage kubernetes resources
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- [helm](https://helm.sh/docs/intro/install/) to deploy the resources based on helm charts. Note, we only support
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Helm 3.
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## Components
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Datahub consists of 4 main components: [GMS](https://docs.datahub.com/docs/metadata-service),
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[MAE Consumer](https://docs.datahub.com/docs/metadata-jobs/mae-consumer-job) (optional),
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[MCE Consumer](https://docs.datahub.com/docs/metadata-jobs/mce-consumer-job) (optional), and
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[Frontend](https://docs.datahub.com/docs/datahub-frontend). Kubernetes deployment for each of the components are
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defined as subcharts under the main
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[Datahub](https://github.com/acryldata/datahub-helm/tree/master/charts/datahub)
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helm chart.
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The main components are powered by 4 external dependencies:
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- Kafka
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- Local DB (MySQL, Postgres, MariaDB)
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- Search Index (Elasticsearch)
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- Graph Index (Supports either Neo4j or Elasticsearch)
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The dependencies must be deployed before deploying Datahub. We created a separate
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[chart](https://github.com/acryldata/datahub-helm/tree/master/charts/prerequisites)
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for deploying the dependencies with example configuration. They could also be deployed separately on-prem or leveraged
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as managed services. To remove your dependency on Neo4j, set enabled to false in
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the [values.yaml](https://github.com/acryldata/datahub-helm/blob/master/charts/prerequisites/values.yaml#L54) for
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prerequisites. Then, override the `graph_service_impl` field in
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the [values.yaml](https://github.com/acryldata/datahub-helm/blob/master/charts/datahub/values.yaml#L63) of datahub
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instead of `neo4j`.
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## Quickstart
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Assuming kubectl context points to the correct kubernetes cluster, first create kubernetes secrets that contain MySQL
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and Neo4j passwords.
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```(shell)
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kubectl create secret generic mysql-secrets --from-literal=mysql-root-password=datahub
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kubectl create secret generic neo4j-secrets --from-literal=neo4j-password=datahub
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```
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The above commands sets the passwords to "datahub" as an example. Change to any password of choice.
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Add datahub helm repo by running the following
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```(shell)
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helm repo add datahub https://helm.datahubproject.io/
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```
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Then, deploy the dependencies by running the following
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```(shell)
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helm install prerequisites datahub/datahub-prerequisites
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```
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Note, the above uses the default configuration
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defined [here](https://github.com/acryldata/datahub-helm/blob/master/charts/prerequisites/values.yaml). You can change
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any of the configuration and deploy by running the following command.
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```(shell)
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helm install prerequisites datahub/datahub-prerequisites --values <<path-to-values-file>>
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```
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Run `kubectl get pods` to check whether all the pods for the dependencies are running. You should get a result similar
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to below.
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```
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NAME READY STATUS RESTARTS AGE
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elasticsearch-master-0 1/1 Running 0 62m
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elasticsearch-master-1 1/1 Running 0 62m
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elasticsearch-master-2 1/1 Running 0 62m
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prerequisites-cp-schema-registry-cf79bfccf-kvjtv 2/2 Running 1 63m
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prerequisites-kafka-0 1/1 Running 2 62m
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prerequisites-mysql-0 1/1 Running 1 62m
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prerequisites-neo4j-community-0 1/1 Running 0 52m
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prerequisites-zookeeper-0 1/1 Running 0 62m
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```
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deploy Datahub by running the following
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```(shell)
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helm install datahub datahub/datahub
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```
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Values in [values.yaml](https://github.com/acryldata/datahub-helm/blob/master/charts/datahub/values.yaml)
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have been preset to point to the dependencies deployed using
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the [prerequisites](https://github.com/acryldata/datahub-helm/tree/master/charts/prerequisites)
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chart with release name "prerequisites". If you deployed the helm chart using a different release name, update the
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quickstart-values.yaml file accordingly before installing.
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Run `kubectl get pods` to check whether all the datahub pods are running. You should get a result similar to below.
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```
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NAME READY STATUS RESTARTS AGE
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datahub-datahub-frontend-84c58df9f7-5bgwx 1/1 Running 0 4m2s
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datahub-datahub-gms-58b676f77c-c6pfx 1/1 Running 0 4m2s
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datahub-datahub-mae-consumer-7b98bf65d-tjbwx 1/1 Running 0 4m3s
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datahub-datahub-mce-consumer-8c57d8587-vjv9m 1/1 Running 0 4m2s
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datahub-elasticsearch-setup-job-8dz6b 0/1 Completed 0 4m50s
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datahub-kafka-setup-job-6blcj 0/1 Completed 0 4m40s
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datahub-mysql-setup-job-b57kc 0/1 Completed 0 4m7s
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elasticsearch-master-0 1/1 Running 0 97m
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elasticsearch-master-1 1/1 Running 0 97m
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elasticsearch-master-2 1/1 Running 0 97m
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prerequisites-cp-schema-registry-cf79bfccf-kvjtv 2/2 Running 1 99m
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prerequisites-kafka-0 1/1 Running 2 97m
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prerequisites-mysql-0 1/1 Running 1 97m
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prerequisites-neo4j-community-0 1/1 Running 0 88m
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prerequisites-zookeeper-0 1/1 Running 0 97m
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```
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You can run the following to expose the frontend locally. Note, you can find the pod name using the command above. In
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this case, the datahub-frontend pod name was `datahub-datahub-frontend-84c58df9f7-5bgwx`.
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```(shell)
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kubectl port-forward <datahub-frontend pod name> 9002:9002
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```
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You should be able to access the frontend via http://localhost:9002.
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Once you confirm that the pods are running well, you can set up ingress for datahub-frontend to expose the 9002 port to
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the public.
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## Other useful commands
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| Command | Description |
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| ---------------------- | ----------------------- |
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| helm uninstall datahub | Remove DataHub |
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| helm ls | List of Helm charts |
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| helm history | Fetch a release history |
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