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As part of Unified Analytics Platform, Databricks Workspace along with Databricks File System (DBFS) are critical components that facilitate collaboration among data scientists and data engineers: Databricks Workspace manages users’ notebooks, whereas DBFS manages files; both have REST API endpoints to manage notebooks and files respectively.

While the REST APIs are principally designed for general programmatic use, ad-hoc tasks of exploring and manipulating desired content is not that easy with REST APIs without writing repetitive boilerplate code. So to simplify this task for Databricks developers, we have implemented an easy command line interface that interacts with Databricks workspaces and filesystem APIs.

In part 1 of our blog aimed at Databricks developers, we outline some use cases where you can employ a command line interface to interact with Databricks workspaces and manipulate files and folders.

Usage Scenarios

Checking in notebooks to version control systems (VCS)

One of the most common usages of the Databricks CLI is to enable an alternative integration point to VCS.

To check notebooks into VCS, you need a canonical text-based file for the notebook. With the workspace APIs, you can export and import notebooks to this canonical text-based file format. The Databricks CLI builds on this idea further by wrapping these APIs into an easy to use command line interface with support for recursive import and export.

For example, consider a scenario with two users’ workspace and a production workspace: Alice with workspace A, Bob with workspace B, and a production workspace P with notebooks that are run through Databricks Job Scheduler. After developing code in her workspace A, Alice may export her code with databricks workspace export_dir to her git repository and initiate a pull request. Bob can then review and approve the PR, after which Alice can merge her changes to the master. This merge will trigger a Continuous Delivery job in which production workspace P will initiate a databricks workspace import_dir, bringing all new changes into production.

Copying small datasets to DBFS

Another use case for the CLI is importing small datasets to DBFS.

For example:

  • (Recursively) copying datasets/files between local file system and DBFS


To begin, install the CLI by running the following command on your local machine.
pip install --upgrade databricks-cli

Note that the Databricks CLI currently cannot run with Python 3.

After installation is complete, the next step is to provide authentication information to the CLI. The first and recommended way is to use an access token generated from Databricks. To do this run databricks configure --token. A second way is to use your username and password pair. To do this run databricks configure and follow the prompts. After following the prompts, your access credentials will be stored in the file ~/.databrickscfg.

Workspace CLI examples

The implemented commands for the Workspace CLI can be listed by running databricks workspace -h. Commands are run by appending them to databricks workspace. To make it easier to use the workspace CLI, feel free to alias databricks workspace to something shorter. For more information reference Aliasing Command Groups and Workspace API.

$ databricks workspace -h
Usage: databricks workspace [OPTIONS] COMMAND [ARGS]...

  Utility to interact with the Databricks Workspace. Workspace paths must be
  absolute and be prefixed with `/`.

  -v, --version
  -h, --help     Show this message and exit.

  delete      Deletes objects from the Databricks...
  export      Exports a file from the Databricks workspace...
  export_dir  Recursively exports a directory from the...
  import      Imports a file from local to the Databricks...
  import_dir  Recursively imports a directory from local to...
  list        List objects in the Databricks Workspace
  ls          List objects in the Databricks Workspace
  mkdirs      Make directories in the Databricks Workspace.
  rm          Deletes objects from the Databricks...

Listing Workspace Files

Exporting a workspace directory to the local filesystem

The databricks workspace export_dir command will recursively export a directory from the Databricks workspace to the local filesystem. Only notebooks are exported and when exported, the notebooks will have the appropriate extensions (.scala, .py, .sql, .R) appended to their names).

Importing a local directory of notebooks

Similarly, the databricks workspace import_dir command will recursively import a directory from the local filesystem to the Databricks workspace. Only directories and files with the extensions of .scala, .py, .sql, .r, .R are imported. When imported, these extensions will be stripped off the name of the notebook.

To overwrite existing notebooks at the target path, the flag -o must be added.

DBFS CLI Examples

The implemented commands for the DBFS CLI can be listed by running databricks fs -h. Commands are run by appending them to databricks fs and all dbfs paths should be prefixed with dbfs:/. To make the command less verbose, we’ve gone ahead and aliased dbfs to databricks fs. For more information reference DBFS API.

$ databricks fs -h
Usage: databricks fs [OPTIONS] COMMAND [ARGS]...

  Utility to interact with DBFS. DBFS paths are all prefixed
  with dbfs:/. Local paths can be absolute or local.

  -v, --version
  -h, --help     Show this message and exit.

  cp         Copy files to and from DBFS.
  ls         List files in DBFS.
  mkdirs     Make directories in DBFS.
  mv         Moves a file between two DBFS paths.
  rm         Remove files from dbfs.

Copying a file to DBFS

It’s possible to copy files from your localhost to DBFS both file by file and recursively. For example to copy a CSV to DBFS, you can run the following command.

For recursive copying, add the -r flag.

Copying a file from DBFS

Similarly, it is possible to copy files from DBFS back to the local filesystem.

What's Next

We’re actively developing new features for the Databricks CLI for developers. The next item immediately on our roadmap is to support cluster and jobs APIs endpoints. We will cover in the second part of this blog series.

In the spirit of our open source Apache Spark heritage, the source code for the CLI is released on Github. If you have feedback on a feature, please leave an issue on our Github project.

Try this out today on Databricks.

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