A command line interface for Databricks
The Databricks Command Line Interface (CLI) is an open source tool which provides an easy to use interface to the Databricks platform. The CLI is built on top of the Databricks Rest APIs. Currently, the CLI fully implements the DBFS API and the Workspace API.
PLEASE NOTE, this CLI is under active development and is released as an experimental client. This means that interfaces are still subject to change.
If you’re interested in contributing to the project please reach out. In addition, please leave bug reports as issues on our Github project.
- Python Version > 2.7.9
- Python 3 is not supported
To install simply run pip install databricks-cli
In order to upgrade your databricks-cli installation please run pip install --upgrade databricks-cli
AttributeError: 'module' object has no attribute 'PROTOCOL_TLSv1_2'
For compliance reasons, our webapp requires the client to speak TLSV1.2. The built in version of Python for MacOS does not have this version of TLS built in.
To use databricks-cli you should install a version of Python which has ssl.PROTOCOL_TLSv1_2. For MacOS, the easiest way may be to install Python with Homebrew.
Setting Up Authentication
There are two ways to authenticate to Databricks. The first way is to use your username and password pair. To do this run databricks configure and follow the prompts. The second and recommended way is to use an access token generated from Databricks. To configure the CLI to use the access token run databricks configure --token. After following the prompts, your access credentials will be stored in the file ~/.databrickscfg.
Read Token Management for more information about Databricks Access Tokens.
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.
$ 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 `/`. Options: -v, --version -h, --help Show this message and exit. Commands: 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
$ databricks workspace ls /Usersfirstname.lastname@example.org Usage Logs ETL Common Utilities guava-21.0
Importing a local directory of notebooks
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.
$ tree . ├── a.py ├── b.scala ├── c.sql ├── d.R └── e
$ databricks workspace import_dir . /Usersemail@example.com/example ./a.py -> /Usersfirstname.lastname@example.org/example/a ./b.scala -> /Usersemail@example.com/example/b ./c.sql -> /Usersfirstname.lastname@example.org/example/c ./d.R -> /Usersemail@example.com/example/d
$ databricks workspace ls /Usersfirstname.lastname@example.org/example -l NOTEBOOK a PYTHON NOTEBOOK b SCALA NOTEBOOK c SQL NOTEBOOK d R DIRECTORY e
Exporting a workspace directory to the local filesystem
Similarly, it is possible to export a directory of notebooks from the Databricks workspace to the local filesystem. To do this, the command is simply
$ databricks workspace export_dir /Usersemail@example.com/example .
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.
$ 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. Options: -v, --version -h, --help Show this message and exit. Commands: configure 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
dbfs cp test.txt dbfs:/test.txt # Or recursively dbfs cp -r test-dir dbfs:/test-dir
Copying a file from DBFS
dbfs cp dbfs:/test.txt ./test.txt # Or recursively dbfs cp -r dbfs:/test-dir ./test-dir
Aliasing Command Groups
Sometimes it can be inconvenient to prefix each CLI invocation with the name of a command group. Writing databricks workspace ls can be quite verbose! To make the CLI easier to use, you can alias different command groups to shorter commands. For example to shorten databricks workspace ls to dw ls in the Bourne again shell, you can add alias dw="databricks workspace" to the appropriate bash profile. Typically, this file is located at ~/.bash_profile.
# build image docker build -t docker build -t databricks-cli . # run container docker run -it databricks-cli # run command in docker docker run -it databricks-cli fs --help
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