Databricks Sync (dbSync)

NOTE: For a more extensive and maintained cross-workload migration solution, please use the Databricks Terraform Exporter, which creates Infrastructure-as-a-Code replicas for the entire manually-configured Databricks Workspaces.

Introduction

Databricks Sync is an object synchronization tool to backup, restore, and sync Databricks workspaces.

This package uses credentials from the Databricks CLI

Table of Contents

1. Introduction
2. Documentation
   • Setup
   • Usage
   • FAQ
   • Contributing
3. Quickstart
   • Next Steps
   • Common Commands
   • Backend Instructions
   • Docker Instructions
   • Aliasing
   • Support Matrix for Import and Export Operations
4. Project Support
5. Building the Project
6. Deploying / Installing the Project
7. Releasing the Project
8. Using the Project

Documentation

See the Databricks Sync Documentation for information.

Instructions to install Databricks Sync can be found here.

Quickstart

Next steps:

• Configure YAML file.
• Export object permissions and import them to the target with the object
• Add examples for different scenarios:
  • Backup and Restore
  • Disaster Recovery Sync
  • Batch modification (will require Terraform Object Import support)

Common commands

$ databricks-sync init my-export-config

$ databricks-sync  export \
    --profile <db cli profile> \
    --git-ssh-url [email protected]:..../.....git \
    -c ....test.yaml

optional flags:
    -v DEBUG
    --dry-run
    --dask
    --branch # support new main name convention

$ GIT_PYTHON_TRACE=full databricks-sync import \
    -g [email protected]:.../....git \
    --profile dr_tagert \
    --databricks-object-type cluster_policy \
    --artifact-dir ..../dir \
    --plan \
    --skip-refresh \
    --revision ....

Control the databricks provider version by using:

export DATABRICKS_TERRAFORM_PROVIDER_VERSION="<version here>"

Backend Instructions (Storing terraform state in azure blob or aws s3)

When importing you are able to store and manage your state using blob or s3. You can do this by using the --backend-file. This --backend-file will take a file path to the back end file. You can name the file backend.tf. This backend file will use azure blob or aws s3 to manage the state file. To authenticate to either you will use environment variables.

Please use ARM_SAS_TOKEN or ARM_ACCESS_KEY for sas token and account access key respectively for azure blob. Please use AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY for the key and secret for the s3 bucket. Please go the following links in regards to policies and permissions. If you want to make the region dynamic you can use AWS_DEFAULT_REGION.

1. Storing state in aws s3: https://www.terraform.io/docs/backends/types/s3.html
2. Storing state in azure blob (only azure blob is support as it supports locking): https://www.terraform.io/docs/backends/types/azurerm.html

Docker instructions

These set of instructions are to use docker to build and use the CLI. It avoids the need to have golang, Terraform, the databricks-terraform-provider to get this to run. If you do want to work on this tool please install the prior listed tools to get this to work.

To install this tool please run the following command:

$ docker build -t databricks-sync:latest .

Aliasing

How our alias command works:

This script creates 3 volume mounts with docker of which two are read only.

1. We mount $PWD or present working directory to /usr/src/databricks-sync as that is the working directory. This allows you to manipulate files in the local working directory on your host machine.
2. The second mount is mounting ~/.databrickscfg to /root as that is the home directory of the container. This mount is read only.
3. The third mount is mounting ~/.ssh folder to the /root/.ssh folder. This is so the script can fetch your private keys in a read only fashion for accessing the git repository. This is also a read only mount.

alias dbt='docker run -it --rm --name docker-databricks-sync --env-file <(env | grep -e "[ARM|TF_VAR]") -v "$PWD":/usr/src/databricks-sync -v ~/.databrickscfg:/root/.databrickscfg:ro -v ~/.ssh:/root/.ssh:ro -w /usr/src/databricks-sync databricks-sync'

Support Matrix for Import and Export Operations:

Component	Export to HCL	Import to Workspace	Comments
	User Objects
cluster policy	✅	✅
cluster	✅	✅
dbfs file	✅	✅
instance pool	✅	✅
instance profile	✅	✅
job	✅	✅
multi task job	⬜	⬜
repos	⬜	⬜
notebook	✅	✅
	Administrator Setup
aws s3 mount	⬜️	⬜️
azure adls gen1 mount	⬜️	⬜️
azure adls gen2 mount	⬜️	⬜️
azure blob mount	⬜️	⬜️
secret	✅	✅
secret acl	✅	✅
secret scope	✅	✅
metastore tables	⬜️	⬜️
metastore table ACLs	⬜️	⬜️
	Users Management
group	✅	✅
group instance profile	✅	✅
group member	✅	✅
scim user	✅	✅

Project Support

Please note that all projects in the /databrickslabs github account are provided for your exploration only, and are not formally supported by Databricks with Service Level Agreements (SLAs). They are provided AS-IS and we do not make any guarantees of any kind. Please do not submit a support ticket relating to any issues arising from the use of these projects.

Any issues discovered through the use of this project should be filed as GitHub Issues on the Repo. They will be reviewed as time permits, but there are no formal SLAs for support.