⏰ Expected deployment time: 25 min
lakeFS requires a PostgreSQL database to synchronize actions on your repositories. We will show you how to create a database on Google Cloud SQL, but you can use any PostgreSQL database as long as it’s accessible by your lakeFS installation.
If you already have a database, take note of the connection string and skip to the next step
- Follow the official Google documentation on how to create a PostgreSQL instance. Make sure you’re using PostgreSQL version >= 11.
- On the Users tab in the console, create a user. The lakeFS installation will use it to connect to your database.
- Choose the method by which lakeFS will connect to your database. Google recommends using the SQL Auth Proxy.
Save the following configuration file as
--- 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]
- Download the binary to the GCE instance.
- Run the
lakefsbinary 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
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:
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]
Fill in the missing values and save the file as
conf-values.yaml. For more configuration options, see our Helm chart README.
lakefsConfigparameter is the lakeFS configuration documented here but without sensitive information. Sensitive information like
databaseConnectionStringis given through separate parameters, and the chart will inject it into Kubernetes secrets.
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.
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.
When you first open the lakeFS UI, you will be asked to create an initial admin user.
http://<lakefs-host>/in your browser. If you haven’t set up a load balancer, this will likely be
http://<instance ip address>:8000/
On first use, you’ll be redirected to the setup page:
Follow the steps to create an initial administrator user. Save the credentials you’ve received somewhere safe, you won’t be able to see them again!
- Follow the link and go to the login screen. Use the credentials from the previous step to log in.
- Use the credentials from the previous step to log in
Click Create Repository and choose Blank Repository.
- Under Storage Namespace, enter a path to your desired location on the object store. This is where data written to this repository will be stored.
- Click Create Repository
You should now have a configured repository, ready to use!
Congratulations! Your environment is now ready 🤩