Deploy lakeFS on GCP

1. Save the following configuration file as config.yaml:

   ---
   database:
     type: "postgres"
     postgres:
       connection_string: "[DATABASE_CONNECTION_STRING]"
   auth:
     encrypt:
       # replace this with a randomly-generated string:
       secret_key: "[ENCRYPTION_SECRET_KEY]"
   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]
   

2. Download the binary to run on the GCE instance.
3. Run the lakefs binary on the GCE machine:

   lakefs --config config.yaml run
   

   Note: it is preferable to run the binary as a service using systemd or your operating system’s facilities.

To support container-based environments like Google Cloud Run, lakeFS can be configured using environment variables. Here is a docker run command to demonstrate starting lakeFS using Docker:

docker run \
  --name lakefs \
  -p 8000:8000 \
  -e LAKEFS_DATABASE_TYPE="postgres" \
  -e LAKEFS_DATABASE_POSTGRES_CONNECTION_STRING="[DATABASE_CONNECTION_STRING]" \
  -e LAKEFS_AUTH_ENCRYPT_SECRET_KEY="[ENCRYPTION_SECRET_KEY]" \
  -e LAKEFS_BLOCKSTORE_TYPE="gs" \
  treeverse/lakefs:latest run

See the reference for a complete list of environment variables.

You can install lakeFS on Kubernetes using a Helm chart.

To install lakeFS with Helm:

1. Copy the Helm values file relevant for Google Storage:

   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]
   

2. 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 it into Kubernetes secrets.

3. 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 my-lakefs lakefs/lakefs -f conf-values.yaml
   

   my-lakefs is the Helm Release name.

Load balancing

To configure a load balancer to direct requests to the lakeFS servers you can use the LoadBalancer Service type or a Kubernetes Ingress. By default, lakeFS operates on port 8000 and exposes a /_health endpoint that you can use for health checks.

💡 The NGINX Ingress Controller by default limits the client body size to 1 MiB. Some clients use bigger chunks to upload objects - for example, multipart upload to lakeFS using the S3-compatible Gateway or a simple PUT request using the OpenAPI Server. Checkout Nginx documentation for increasing the limit, or an example of Nginx configuration with MinIO.