Databricks Terraform provider

HashiCorp Terraform is a popular open source tool for creating safe and predictable cloud infrastructure across several cloud providers. You can use the Databricks Terraform provider to manage your Databricks workspaces and the associated cloud infrastructure using a flexible, powerful tool. The goal of the Databricks Terraform provider is to support all Databricks REST APIs, supporting automation of the most complicated aspects of deploying and managing your data platforms. Databricks customers are using the Databricks Terraform provider to deploy and manage clusters and jobs and to configure data access. You use the Databricks Terraform provider to provision Databricks workspaces as well as the Google Cloud Platform Provider to provision required Google Cloud Platform resources for these workspaces.

Getting started

In this section, you install and configure requirements to use Terraform and the Databricks Terraform provider on your local development machine. You then configure Terraform authentication. Following this section, this article provides a sample configuration that you can experiment with to provision a Databricks notebook, cluster, and a job to run the notebook on the cluster in an existing Databricks workspace.

Requirements

  1. You must have the Terraform CLI. See Download Terraform on the Terraform website.

  2. You must have a Terraform project. In your terminal, create an empty directory and then switch to it. (Each separate set of Terraform configuration files must be in its own directory, which is called a Terraform project.) For example: mkdir terraform_demo && cd terraform_demo.

    mkdir terraform_demo && cd terraform_demo
    

    Include Terraform configurations for your project in one or more configuration files in your Terraform project. For information about the configuration file syntax, see Terraform Language Documentation on the Terraform website.

  3. You must add to your Terraform project a dependency for the Databricks Terraform provider. Add the following to one of the configuration files in your Terraform project:

    terraform {
      required_providers {
        databricks = {
          source = "databricks/databricks"
        }
      }
    }
    
  4. You must configure authentication for your Terraform project. See Authentication in the Databricks Terraform provider documentation.

Sample configuration

This section provides a sample configuration that you can experiment with to provision a Databricks notebook, a cluster, and a job to run the notebook on the cluster, in an existing Databricks workspace. It assumes that you have already set up the requirements, as well as created a Terraform project and configured the project with Terraform authentication as described in the previous section.

  1. Create a file named me.tf in your Terraform project, and add the following code. This file gets information about the current user (you):

    # Retrieve information about the current user.
    data "databricks_current_user" "me" {}
    
  2. Create another file named notebook.tf, and add the following code. This file represents the notebook.

    variable "notebook_subdirectory" {
      description = "A name for the subdirectory to store the notebook."
      type        = string
      default     = "Terraform"
    }
    
    variable "notebook_filename" {
      description = "The notebook's filename."
      type        = string
    }
    
    variable "notebook_language" {
      description = "The language of the notebook."
      type        = string
    }
    
    resource "databricks_notebook" "this" {
      path     = "${data.databricks_current_user.me.home}/${var.notebook_subdirectory}/${var.notebook_filename}"
      language = var.notebook_language
      source   = "./${var.notebook_filename}"
    }
    
    output "notebook_url" {
     value = databricks_notebook.this.url
    }
    
  3. Create another file named notebook.auto.tfvars, and add the following code. This file specifies the notebook’s properties.

    notebook_subdirectory = "Terraform"
    notebook_filename     = "notebook-getting-started.py"
    notebook_language     = "PYTHON"
    
  4. Create another file named notebook-getting-started.py, and add the following code. This file represents the notebook’s contents.

    display(spark.range(10))
    
  5. Create another file named cluster.tf, and add the following code. This file represents the cluster.

    variable "cluster_name" {
      description = "A name for the cluster."
      type        = string
      default     = "My Cluster"
    }
    
    variable "cluster_autotermination_minutes" {
      description = "How many minutes before automatically terminating due to inactivity."
      type        = number
      default     = 60
    }
    
    variable "cluster_num_workers" {
      description = "The number of workers."
      type        = number
      default     = 1
    }
    
    # Create the cluster with the "smallest" amount
    # of resources allowed.
    data "databricks_node_type" "smallest" {
      local_disk = true
    }
    
    # Use the latest Databricks Runtime
    # Long Term Support (LTS) version.
    data "databricks_spark_version" "latest_lts" {
      long_term_support = true
    }
    
    resource "databricks_cluster" "this" {
      cluster_name            = var.cluster_name
      node_type_id            = data.databricks_node_type.smallest.id
      spark_version           = data.databricks_spark_version.latest_lts.id
      autotermination_minutes = var.cluster_autotermination_minutes
      num_workers             = var.cluster_num_workers
    }
    
    output "cluster_url" {
     value = databricks_cluster.this.url
    }
    
  6. Create another file named cluster.auto.tfvars, and add the following code. This file specifies the cluster’s properties.

    cluster_name                    = "My Cluster"
    cluster_autotermination_minutes = 60
    cluster_num_workers             = 1
    
  7. Create another file named job.tf, and add the following code. This file represents the job that runs the notebook on the cluster.

    variable "job_name" {
      description = "A name for the job."
      type        = string
      default     = "My Job"
    }
    
    resource "databricks_job" "this" {
      name = var.job_name
      existing_cluster_id = databricks_cluster.this.cluster_id
      notebook_task {
        notebook_path = databricks_notebook.this.path
      }
      email_notifications {
        on_success = [ data.databricks_current_user.me.user_name ]
        on_failure = [ data.databricks_current_user.me.user_name ]
      }
    }
    
    output "job_url" {
      value = databricks_job.this.url
    }
    
  8. Create another file named job.auto.tfvars, and add the following code. This file specifies the jobs’s properties.

    job_name = "My Job"
    
  9. Run terraform plan. If there are any errors, fix them, and then run the command again.

  10. Run terraform apply.

  11. Verify that the notebook, cluster, and job were created: in the output of the terraform apply command, find the URLs for notebook_url, cluster_url, and job_url, and go to them.

  12. Run the job: on the Jobs page, click Run Now. After the job finishes, check your email inbox.

  13. When you are done with this sample, delete the notebook, cluster, and job from the Databricks workspace by running terraform destroy.

    Note

    For more information about the terraform plan, terraform apply, and terraform destroy commands, see Terraform CLI Documentation in the Terraform documentation.

  14. Verify that the notebook, cluster, and job were deleted: refresh the notebook, cluster, and Jobs pages to each display a message that the resource cannot be found.

Next steps

Manage workspace resources for a Databricks workspace.

Troubleshooting

Note

For Terraform-specific support, see the Latest Terraform topics on the HashiCorp Discuss website. For issues specific to the Databricks Terraform Provider, see Issues in the databrickslabs/terraform-provider-databricks GitHub repository.

Error: Failed to install provider

Issue: If you did not check in a terraform.lock.hcl file to your version control system, and you run the terraform init command, the following message appears: Failed to install provider. Additional output may include a message similar to the following:

Error while installing databrickslabs/databricks: v1.0.0: checksum list has no SHA-256 hash for "https://github.com/databricks/terraform-provider-databricks/releases/download/v1.0.0/terraform-provider-databricks_1.0.0_darwin_amd64.zip"

Cause: Your Terraform configurations reference outdated Databricks Terraform providers.

Solution:

  1. Replace databrickslabs/databricks with databricks/databricks in all of your .tf files.

    To automate these replacements, run the following Python command from the parent folder that contains the .tf files to update:

    python3 -c "$(curl -Ls https://dbricks.co/updtfns)"
    
  2. Run the following Terraform command and then approve the changes when prompted:

    terraform state replace-provider databrickslabs/databricks databricks/databricks
    

    For information about this command, see Command: state replace-provider in the Terraform documentation.

  3. Verify the changes by running the following Terraform command:

    terraform init
    

Error: Failed to query available provider packages

Issue: If you did not check in a terraform.lock.hcl file to your version control system, and you run the terraform init command, the following message appears: Failed to query available provider packages.

Cause: Your Terraform configurations reference outdated Databricks Terraform providers.

Solution: Follow the solution instructions in Error: Failed to install provider.

Additional resources