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AWS

databricks_mws_workspaces resource

-> Note This resource has an evolving API, which will change in the upcoming versions of the provider in order to simplify user experience.

This resource allows you to set up workspaces in E2 architecture on AWS. Please follow this complete runnable example with new VPC and new workspace setup.

-> Note Initialize provider with alias = "mws" and use provider = databricks.mws for all databricks_mws_* resources. We require all databricks_mws_* resources to be created within its own dedicated terraform module of your environment. Usually this module creates VPC and IAM roles as well. Code that creates workspaces and code that manages workspaces must be in separate terraform modules to avoid common confusion between provider = databricks.mws and provider = databricks.created_workspace. This is why we specify databricks_host and databricks_token outputs, that have to be used in the latter modules:

provider "databricks" {
  host  = module.ai.databricks_host
  token = module.ai.databricks_token
}

Example Usage

Simplest multiworkspace

To get workspace running, you have to configure a couple of things:

  • databricks_mws_credentials - You can share a credentials (cross-account IAM role) configuration ID with multiple workspaces. It is not required to create a new one for each workspace.
  • databricks_mws_storage_configurations - You can share a root S3 bucket with multiple workspaces in a single account. You do not have to create new ones for each workspace. If you share a root S3 bucket for multiple workspaces in an account, data on the root S3 bucket is partitioned into separate directories by workspace.
  • databricks_mws_networks - (optional, but recommended) You can share one customer-managed VPC with multiple workspaces in a single account. You do not have to create a new VPC for each workspace. However, you cannot reuse subnets or security groups with other resources, including other workspaces or non-Databricks resources. If you plan to share one VPC with multiple workspaces, be sure to size your VPC and subnets accordingly. Because a Databricks databricks_mws_networks encapsulates this information, you cannot reuse it across workspaces.
  • databricks_mws_customer_managed_keys - You can share a customer-managed key across workspaces.
variable "databricks_account_id" {
  description = "Account Id that could be found in the bottom left corner of https://accounts.cloud.databricks.com/"
}

provider "databricks" {
  alias = "mws"
  host  = "https://accounts.cloud.databricks.com"
}

// register cross-account ARN
resource "databricks_mws_credentials" "this" {
  provider         = databricks.mws
  account_id       = var.databricks_account_id
  credentials_name = "${var.prefix}-creds"
  role_arn         = var.crossaccount_arn
}

// register root bucket
resource "databricks_mws_storage_configurations" "this" {
  provider                   = databricks.mws
  account_id                 = var.databricks_account_id
  storage_configuration_name = "${var.prefix}-storage"
  bucket_name                = var.root_bucket
}

// register VPC
resource "databricks_mws_networks" "this" {
  provider           = databricks.mws
  account_id         = var.databricks_account_id
  network_name       = "${var.prefix}-network"
  vpc_id             = var.vpc_id
  subnet_ids         = var.subnets_private
  security_group_ids = [var.security_group]
}

// create workspace in given VPC with DBFS on root bucket
resource "databricks_mws_workspaces" "this" {
  provider        = databricks.mws
  account_id      = var.databricks_account_id
  workspace_name  = var.prefix
  deployment_name = var.prefix
  aws_region      = var.region

  credentials_id           = databricks_mws_credentials.this.credentials_id
  storage_configuration_id = databricks_mws_storage_configurations.this.storage_configuration_id
  network_id               = databricks_mws_networks.this.network_id

  token {}
}

output "databricks_token" {
  value     = databricks_mws_workspaces.this.token[0].token_value
  sensitive = true
}

Workspace with Databricks-Managed VPC

VPCs

By default, Databricks creates a VPC in your AWS account for each workspace. Databricks uses it for running clusters in the workspace. Optionally, you can use your VPC for the workspace, using the feature customer-managed VPC. Databricks recommends that you provide your VPC with databricks_mws_networks so that you can configure it according to your organization’s enterprise cloud standards while still conforming to Databricks requirements. You cannot migrate an existing workspace to your VPC. Please see the difference described through IAM policy actions on this page.

variable "databricks_account_id" {
  description = "Account Id that could be found in the bottom left corner of https://accounts.cloud.databricks.com/"
}

resource "random_string" "naming" {
  special = false
  upper   = false
  length  = 6
}

locals {
  prefix = "dltp${random_string.naming.result}"
}

data "databricks_aws_assume_role_policy" "this" {
  external_id = var.databricks_account_id
}

resource "aws_iam_role" "cross_account_role" {
  name               = "${local.prefix}-crossaccount"
  assume_role_policy = data.databricks_aws_assume_role_policy.this.json
  tags               = var.tags
}

data "databricks_aws_crossaccount_policy" "this" {
}

resource "aws_iam_role_policy" "this" {
  name   = "${local.prefix}-policy"
  role   = aws_iam_role.cross_account_role.id
  policy = data.databricks_aws_crossaccount_policy.this.json
}

resource "databricks_mws_credentials" "this" {
  provider         = databricks.mws
  account_id       = var.databricks_account_id
  credentials_name = "${local.prefix}-creds"
  role_arn         = aws_iam_role.cross_account_role.arn
}

resource "aws_s3_bucket" "root_storage_bucket" {
  bucket = "${local.prefix}-rootbucket"
  acl    = "private"
  versioning {
    enabled = false
  }
  force_destroy = true
  tags          = var.tags
}

resource "aws_s3_bucket_public_access_block" "root_storage_bucket" {
  bucket             = aws_s3_bucket.root_storage_bucket.id
  ignore_public_acls = true
}

data "databricks_aws_bucket_policy" "this" {
  bucket = aws_s3_bucket.root_storage_bucket.bucket
}

resource "aws_s3_bucket_policy" "root_bucket_policy" {
  bucket = aws_s3_bucket.root_storage_bucket.id
  policy = data.databricks_aws_bucket_policy.this.json
}

resource "databricks_mws_storage_configurations" "this" {
  provider                   = databricks.mws
  account_id                 = var.databricks_account_id
  storage_configuration_name = "${local.prefix}-storage"
  bucket_name                = aws_s3_bucket.root_storage_bucket.bucket
}

resource "databricks_mws_workspaces" "this" {
  provider        = databricks.mws
  account_id      = var.databricks_account_id
  workspace_name  = local.prefix
  deployment_name = local.prefix
  aws_region      = "us-east-1"

  credentials_id           = databricks_mws_credentials.this.credentials_id
  storage_configuration_id = databricks_mws_storage_configurations.this.storage_configuration_id

  token {}
}

output "databricks_token" {
  value     = databricks_mws_workspaces.this.token[0].token_value
  sensitive = true
}

In order to create a Databricks Workspace that leverages AWS PrivateLink please ensure that you have read and understood the Enable Private Link documentation and then customise the example above with the relevant examples from mws_vpc_endpoint, mws_private_access_settings and mws_networks.

Argument Reference

-> Note All workspaces would be verified to get into runnable state or deleted upon failure. You can only update credentials_id, network_id, and storage_customer_managed_key_id on a running workspace.

The following arguments are available and cannot be changed after workspace is created:

  • account_id - Account Id that could be found in the bottom left corner of Accounts Console.
  • managed_services_customer_managed_key_id - (Optional) customer_managed_key_id from customer managed keys with use_cases set to MANAGED_SERVICES. This is used to encrypt the workspace's notebook and secret data in the control plane.
  • deployment_name - (Optional) part of URL: https://<deployment-name>.cloud.databricks.com
  • workspace_name - name of the workspace, will appear on UI
  • aws_region - AWS region of VPC
  • storage_configuration_id - storage_configuration_id from storage configuration
  • private_access_settings_id - (Optional) Canonical unique identifier of databricks_mws_private_access_settings in Databricks Account

token block

You can specify a token block in the body of workspace resource, so that Terraform manages the refresh of PAT token for the deployment user. The other option is to create databricks_obo_token, though it requires Premium or Enterprise plan enabled as well as more complex setup. Token block exposes token_value, that holds sensitive PAT token and optionally it can accept two arugments:

-> Note Tokens managed by token {} block are recreated when expired.

  • comment - (Optional) Comment, that will appear in "User Settings / Access Tokens" page on Workspace UI. By default it's "Terraform PAT".
  • lifetime_seconds - (Optional) Token expiry lifetime. By default its 2592000 (30 days).

The following arguments could be modified after the workspace is running:

  • network_id - (Optional) network_id from networks. Modifying networks on running workspaces would require three separate terraform apply steps.
  • credentials_id - credentials_id from credentials
  • storage_customer_managed_key_id - (Optional) customer_managed_key_id from customer managed keys with use_cases set to STORAGE. This is used to encrypt the DBFS Storage & Cluster EBS Volumes.

Attribute Reference

In addition to all arguments above, the following attributes are exported:

  • id - Canonical unique identifier for the workspace.
  • workspace_status_message - (String) updates on workspace status
  • workspace_status - (String) workspace status
  • creation_time - (Integer) time when workspace was created
  • workspace_url - (String) URL of the workspace

Import

-> Note Importing this resource is not currently supported.

Timeouts

The timeouts block allows you to specify create, read and update timeouts. It usually takes 5-7 minutes to provision Databricks E2 Workspace and another couple of minutes for your local DNS caches to resolve. Please launch TF_LOG=DEBUG terraform apply whenever you observe timeout issues.

timeouts {
  create = "30m"
  read   = "10m"
  update = "20m
}

You can reset local DNS caches before provisioning new workspaces with one of the following commands:

  • Linux - sudo /etc/init.d/nscd restart
  • Mac OS Sierra, X El Capitan, X Mavericks, X Mountain Lion, or X Lion - sudo killall -HUP mDNSResponder
  • Mac OS X Yosemite - sudo discoveryutil udnsflushcaches
  • Mac OS X Snow Leopard - sudo dscacheutil -flushcache
  • Mac OS X Leopard and below - sudo lookupd -flushcache

Related Resources

The following resources are used in the same context: