Deploy lakeFS on Azure

Connect to your VM instance using SSH:

1. Create a config.yaml on your VM, with the following parameters:

   ---
   database:
     type: "postgres"
     postgres:
       connection_string: "[DATABASE_CONNECTION_STRING]"
     
   auth:
     encrypt:
       # replace this with a randomly-generated string. Make sure to keep it safe!
       secret_key: "[ENCRYPTION_SECRET_KEY]"
      
   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]
   

2. Download the binary to the VM.

3. Run the lakefs binary:

   lakefs --config config.yaml run
   

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

To support container-based environments, you can configure lakeFS 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="azure" \
  -e LAKEFS_BLOCKSTORE_AZURE_STORAGE_ACCOUNT="[YOUR_STORAGE_ACCOUNT]" \
  -e LAKEFS_BLOCKSTORE_AZURE_STORAGE_ACCESS_KEY="[YOUR_ACCESS_KEY]" \
  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 Azure Blob:

   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 
       #  If 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]
   

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.