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Deploy lakeFS on Kubernetes

Database

lakeFS requires a PostgreSQL database to synchronize actions on your repositories. This section assumes you already have a PostgreSQL database accessible from your Kubernetes cluster. Instructions for creating the database can be found on the deployment instructions for AWS, Azure and GCP.

Prerequisites

Users that require S3 access using virtual host addressing should configure an S3 Gateway domain.

Installing on Kubernetes

lakeFS can be easily installed on Kubernetes using a Helm chart. To install lakeFS with Helm:

  1. Copy the Helm values file relevant to your storage provider:
secrets:
    # replace DATABASE_CONNECTION_STRING with the connection string of the database you created in a previous step.
    # e.g. postgres://postgres:myPassword@my-lakefs-db.rds.amazonaws.com:5432/lakefs
    databaseConnectionString: [DATABASE_CONNECTION_STRING]
    # replace this with a randomly-generated string
    authEncryptSecretKey: [ENCRYPTION_SECRET_KEY]
lakefsConfig: |
    blockstore:
      type: s3
      s3:
        region: us-east-1 # optional, fallback in case discover from bucket is not supported
secrets:
    # replace DATABASE_CONNECTION_STRING with the connection string of the database you created in a previous step.
    # e.g.: postgres://postgres:myPassword@localhost/postgres:5432
    databaseConnectionString: [DATABASE_CONNECTION_STRING]
    # replace this with a randomly-generated string
    authEncryptSecretKey: [ENCRYPTION_SECRET_KEY]
lakefsConfig: |
    blockstore:
      type: gs
    # Uncomment the following lines to give lakeFS access to your buckets using a service account:
    # gs:
    #   credentials_json: [YOUR SERVICE ACCOUNT JSON STRING]

Notes for running lakeFS on GKE

  • To connect to your database, you need to use one of the ways of connecting GKE to Cloud SQL.
  • To give lakeFS access to your bucket, you can start the cluster in storage-rw mode. Alternatively, you can use a service account JSON string by uncommenting the gs.credentials_json property in the following yaml.
secrets:
    # replace this with the connection string of the database you created in a previous step:
    databaseConnectionString: [DATABASE_CONNECTION_STRING]
    # replace this with a randomly-generated string
    authEncryptSecretKey: [ENCRYPTION_SECRET_KEY]
lakefsConfig: |
    blockstore:
      type: azure
      azure:
        auth_method: msi # msi for active directory, access-key for access key 
     #  In case you chose to authenticate via access key unmark the following rows and insert the values from the previous step 
     #  storage_account: [your storage account]
     #  storage_access_key: [your access key]
  1. Fill in the missing values and save the file as conf-values.yaml. For more configuration options, see our Helm chart README.

    The lakefsConfig parameter is the lakeFS configuration documented here, but without sensitive information. Sensitive information like databaseConnectionString is given through separate parameters, and the chart will inject them into Kubernetes secrets.

  2. In the directory where you created conf-values.yaml, run the following commands:

     # Add the lakeFS repository
     helm repo add lakefs https://charts.lakefs.io
     # Deploy lakeFS
     helm install example-lakefs lakefs/lakefs -f conf-values.yaml
    

    example-lakefs is the Helm Release name.

You should give your Kubernetes nodes access to all buckets/containers you intend to use lakeFS with. If you can’t provide such access, lakeFS can be configured to use an AWS key-pair, an Azure access key, or a Google Cloud credentials file to authenticate (part of the lakefsConfig YAML below).

Load balancing

You should have a load balancer direct requests to the lakeFS server. Options to do so include a Kubernetes Service of type LoadBalancer, or a Kubernetes Ingress. By default, lakeFS operates on port 8000, and exposes a /_health endpoint which you can use for health checks.

Next Steps

Your next step is to prepare your storage. If you already have a storage bucket/container, you are ready to create your first lakeFS repository.