Databricks CLI (legacy)

Important

This documentation has been retired and might not be updated.

Databricks recommends that you use Databricks CLI version 0.205 or above instead of the legacy Databricks CLI version 0.18 or below. Databricks CLI version 0.18 or below is not supported by Databricks. For information about Databricks CLI versions 0.205 and above, see What is the Databricks CLI?.

To migrate from Databricks CLI version 0.18 or below to Databricks CLI version 0.205 or above, see Databricks CLI migration.

The legacy Databricks CLI is in an Experimental state. Databricks plans no new feature work for the legacy Databricks CLI at this time.

The legacy Databricks CLI is not supported through Databricks Support channels. To provide feedback, ask questions, and report issues, use the Issues tab in the Command Line Interface for Databricks repository in GitHub.

The legacy Databricks command-line interface (also known as the legacy Databricks CLI) is a utility that provides an easy-to-use interface to automate the Databricks platform from your terminal, command prompt, or automation scripts.

Requirements

  • Python 3 - 3.6 and above

  • Python 2 - 2.7.9 and above

Important

On macOS, the default Python 2 installation does not implement the TLSv1_2 protocol, and running the legacy Databricks CLI with this Python installation results in the error: AttributeError: 'module' object has no attribute 'PROTOCOL_TLSv1_2'. Use Homebrew to install a version of Python that has ssl.PROTOCOL_TLSv1_2.

Set up the CLI

This section describes how to set up the legacy Databricks CLI.

Install or update the CLI

This section describes how to install or update your development machine to run the legacy Databricks CLI.

Install the CLI

Run pip install databricks-cli by using the appropriate version of pip for your Python installation:

pip install databricks-cli

Update the CLI

Run pip install databricks-cli --upgrade by using the appropriate version of pip for your Python installation:

pip install databricks-cli --upgrade

To list the version of the legacy Databricks CLI that is currently installed, run databricks --version:

databricks --version

Set up authentication

Before you can run legacy Databricks CLI commands, you must set up authentication between the legacy Databricks CLI and Databricks. This section describes how to set up authentication for the legacy Databricks CLI.

To authenticate with the legacy Databricks CLI, you can use a Databricks personal access token.

Note

As a security best practice when you authenticate with automated tools, systems, scripts, and apps, Databricks recommends that you use OAuth tokens.

If you use personal access token authentication, Databricks recommends using personal access tokens belonging to service principals instead of workspace users. To create tokens for service principals, see Manage tokens for a service principal.

Set up authentication using a Databricks personal access token

To configure the legacy Databricks CLI to use a personal access token, run the following command:

databricks configure --token

The command begins by issuing the prompt:

Databricks Host (should begin with https://):

Enter your workspace URL, with the format https://<instance-name>.cloud.databricks.com. To get your workspace URL, see Workspace instance names, URLs, and IDs.

The command continues by issuing the prompt to enter your personal access token:

Token:

After you complete the prompts, your access credentials are stored in the file ~/.databrickscfg on Linux or macOS, or %USERPROFILE%\.databrickscfg on Windows. The file contains a default profile entry:

[DEFAULT]
host = <workspace-URL>
token = <personal-access-token>

If the .databrickscfg file already exists, that file’s DEFAULT configuration profile is overwritten with the new data. To create a configuration profile with a different name instead, see Connection profiles.

For CLI 0.8.1 and above, you can change the path of this file by setting the environment variable DATABRICKS_CONFIG_FILE.

export DATABRICKS_CONFIG_FILE=<path-to-file>

setx DATABRICKS_CONFIG_FILE "<path-to-file>" /M

Important

Beginning with CLI 0.17.2, the CLI does not work with a .netrc file. You can have a .netrc file in your environment for other purposes, but the CLI will not use that .netrc file.

CLI 0.8.0 and above supports the following Databricks environment variables:

  • DATABRICKS_HOST

  • DATABRICKS_USERNAME

  • DATABRICKS_PASSWORD

  • DATABRICKS_TOKEN

An environment variable setting takes precedence over the setting in the configuration file.

Test your authentication setup

To check whether you set up authentication correctly, you can run a command such as the following:

databricks fs ls dbfs:/

If successful, this command lists the files and directories in the DBFS root of the workspace that is associated with your DEFAULT profile.

Connection profiles

The legacy Databricks CLI configuration supports multiple connection profiles. The same installation of legacy Databricks CLI can be used to make API calls on multiple Databricks workspaces.

To add a connection profile, specify a unique name for the profile:

databricks configure --token --profile <profile-name>

The .databrickscfg file contains a corresponding profile entry:

[<profile-name>]
host = <workspace-URL>
token = <token>

To use the connection profile:

databricks <group> <command> --profile <profile-name>

If --profile <profile-name> is not specified, the default profile is used. If a default profile is not found, you are prompted to configure the CLI with a default profile.

Test your connection profiles

To check whether you set up any connection profiles correctly, you can run a command such as the following with one of your connection profile names:

databricks fs ls dbfs:/ --profile <profile-name>

If successful, this command lists the files and directories in the DBFS root of the workspace for the specified connection profile. Run this command for each connection profile that you want to test.

To view your available profiles, see your .databrickscfg file.

Use the CLI

This section shows you how to get legacy Databricks CLI help, parse legacy Databricks CLI output, and invoke commands in each command group.

Display CLI command group help

You list the subcommands for any command group by using the --help or -h option. For example, to list the DBFS CLI subcommands:

databricks fs -h

Display CLI subcommand help

You list the help for a subcommand by using the --help or -h option. For example, to list the help for the DBFS copy files subcommand:

databricks fs cp -h

Alias command groups

Sometimes it can be inconvenient to prefix each legacy Databricks CLI invocation with the name of a command group, for example databricks workspace ls in the legacy Databricks CLI. To make the legacy Databricks CLI easier to use, you can alias 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.

Tip

The legacy Databricks CLI already aliases databricks fs to dbfs; databricks fs ls and dbfs ls are equivalent.

Use jq to parse CLI output

Some legacy Databricks CLI commands output the JSON response from the API endpoint. Sometimes it can be useful to parse out parts of the JSON to pipe into other commands. For example, to copy a job definition, you must take the settings field of a get job command and use that as an argument to the create job command. In these cases, we recommend you to use the utility jq.

For example, the following command prints the settings of the job with the ID of 233.

databricks jobs list --output JSON | jq '.jobs[] | select(.job_id == 233) | .settings'

Output:

{
  "name": "Quickstart",
  "new_cluster": {
    "spark_version": "7.5.x-scala2.12",
    "spark_env_vars": {
      "PYSPARK_PYTHON": "/databricks/python3/bin/python3"
    },
    "num_workers": 8,
    ...
  },
  "email_notifications": {},
  "timeout_seconds": 0,
  "notebook_task": {
    "notebook_path": "/Quickstart"
  },
  "max_concurrent_runs": 1
}

As another example, the following command prints only the names and IDs of all available clusters in the workspace:

databricks clusters list --output JSON | jq '[ .clusters[] | { name: .cluster_name, id: .cluster_id } ]'

Output:

[
  {
    "name": "My Cluster 1",
    "id": "1234-567890-grip123"
  },
  {
    "name": "My Cluster 2",
    "id": "2345-678901-patch234"
  }
]

You can install jq for example on macOS by using Homebrew with brew install jq or on Windows by using Chocolatey with choco install jq. For more information on jq, see the jq Manual.

JSON string parameters

String parameters are handled differently depending on your operating system:

You must enclose JSON string parameters in single quotes. For example:

'["20180505", "alantest"]'

You must enclose JSON string parameters in double quotes, and the quote characters inside the string must be preceded by \. For example:

"[\"20180505\", \"alantest\"]"

Troubleshooting

The following sections provide tips for troubleshooting common issues with the legacy Databricks CLI.

Using EOF with databricks configure does not work

For Databricks CLI 0.12.0 and above, using the end of file (EOF) sequence in a script to pass parameters to the databricks configure command does not work. For example, the following script causes Databricks CLI to ignore the parameters, and no error message is thrown:

# Do not do this.
databricksUrl=<workspace-url>
databricksToken=<personal-access-token>

databricks configure --token << EOF
$databricksUrl
$databricksToken
EOF

To fix this issue, do one of the following:

  • Use one of the other programmatic configuration options as described in Set up authentication.

  • Manually add the host and token values to the .databrickscfg file as described in Set up authentication.

  • Downgrade your installation of the Databricks CLI to 0.11.0 or below, and run your script again.