A Harlequin adapter for Databricks.
Project description
harlequin-databricks
A Harlequin adapter for Databricks. Supports connecting to Databricks SQL warehouses or Databricks Runtime (DBR) interactive clusters.
Installation
harlequin-databricks depends on harlequin, so installing this package using any of the methods
below will also install harlequin.
Using uv
The recommended way to install harlequin-databricks is using uv:
uv tool install harlequin-databricks
This command will install harlequin-databricks into an isolated environment and add it to your
PATH so you can easily run the executable.
Alternative installation methods
Alternatively, if you know what you're doing, after installing Python 3.9 or above, install
harlequin-databricks using pip, pipx, poetry, or any other program that can install Python
packages from PyPI. For example:
pip install harlequin-databricks
Connecting to Databricks
To connect to Databricks you are going to need to provide as CLI arguments:
- server-hostname
- http-path
- credentials for one of the following authentication methods:
- a personal access token (PAT)
- a username and password
- an OAuth U2M type
- a service principle client ID and secret for OAuth M2M
Personal Access Token (PAT) authentication:
harlequin -a databricks --server-hostname ***.cloud.databricks.com --http-path /sql/1.0/endpoints/*** --access-token dabpi***
Username and password (basic) authentication:
harlequin -a databricks --server-hostname ***.cloud.databricks.com --http-path /sql/1.0/endpoints/*** --username *** --password ***
OAuth U2M authentication:
For OAuth user-to-machine (U2M) authentication
supply either databricks-oauth or azure-oauth to the --auth-type CLI argument:
harlequin -a databricks --server-hostname ***.cloud.databricks.com --http-path /sql/1.0/endpoints/*** --auth-type databricks-oauth
OAuth M2M authentication:
For OAuth machine-to-machine (M2M) authentication
you need to pip install databricks-sdk as an additional dependency
(databricks-sdk is an optional dependency of
harlequin-databricks) and supply --client-id and --client-secret CLI arguments:
harlequin -a databricks --server-hostname ***.cloud.databricks.com --http-path /sql/1.0/endpoints/*** --client-id *** --client-secret ***
Store an alias for your connection string
We recommend you include an alias for your connection string in your .bash_profile/.zprofile so
you can launch harlequin-databricks with a short command like hdb each time.
Run this command (once) to create the alias:
echo 'alias hdb="harlequin -a databricks --server-hostname ***.cloud.databricks.com --http-path /sql/1.0/endpoints/*** --access-token dabpi***"' >> .bash_profile
Using Unity Catalog and want fast Data Catalog indexing?
Supply the --skip-legacy-indexing command line flag if you do not care about legacy metastores
(e.g. hive_metastore) being indexed in Harlequin's Data Catalog pane.
This flag will skip indexing of old non-Unity Catalog metastores (i.e. they won't appear in the Data Catalog pane with this flag).
Because of the way legacy Databricks metastores works, a separate SQL query is required to fetch the metadata of each table in a legacy metastore. This means indexing them for Harlequin's Data Catalog pane is slow.
Databricks's Unity Catalog upgrade brought Information Schema, which allows harlequin-databricks to fetch metadata for all Unity Catalog assets with only two SQL queries.
So if your Databricks instance is running Unity Catalog, and you no longer care about the legacy
metastores, setting the --skip-legacy-indexing CLI flag is recommended as it will mean
much faster indexing & refreshing of the assets in the Data Catalog pane.
Initialization Scripts
Each time you start Harlequin, it will execute SQL commands from a Databricks initialization script. For example:
USE CATALOG my_catalog;
SET TIME ZONE 'Asia/Tokyo';
DECLARE yesterday DATE DEFAULT CURRENT_DATE - INTERVAL '1' DAY;
Multi-line SQL is allowed, but must be terminated by a semicolon.
Configuring the Script Location
By default, Harlequin will execute the script found at ~/.databricksrc. However, you can provide
a different path using the --init-path option (aliased to -i or -init):
harlequin -a databricks --init-path /path/to/my/script.sql
Disabling Initialization
If you would like to open Harlequin without running the script you have at ~/.databricksrc, you
can either pass a nonexistent path (or /dev/null) to the option above, or start Harlequin with
the --no-init option:
harlequin -a databricks --no-init
Other CLI options:
For more details on other command line options, run:
harlequin --help
For more information, see the harlequin-databricks Docs.
Issues, Contributions and Feature Requests
Please report bugs/issues with this adapter via the GitHub issues page. You are welcome to attempt fixes yourself by forking this repo then opening a PR.
For feature suggestions, please post in the discussions.
Special thanks to...
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file harlequin_databricks-0.6.0.tar.gz.
File metadata
- Download URL: harlequin_databricks-0.6.0.tar.gz
- Upload date:
- Size: 23.0 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: uv/0.7.19
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
25a29e8fe56f6a6ad8e3e15b3b73e630fe367444a87452a708b595b36447bbbd
|
|
| MD5 |
44478d59bef119147bb1ccc41defff10
|
|
| BLAKE2b-256 |
cf2dcbfc24bff3dd48dfcbe0f57326d1f321129e73e42a34b0ef47094836e7fe
|
File details
Details for the file harlequin_databricks-0.6.0-py3-none-any.whl.
File metadata
- Download URL: harlequin_databricks-0.6.0-py3-none-any.whl
- Upload date:
- Size: 24.6 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: uv/0.7.19
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
c392a94fb724e851424033781b2ada05d8fb135d8a749a9e6f588316c50ecca9
|
|
| MD5 |
1e9862b96a32015b10aa51bb729717fe
|
|
| BLAKE2b-256 |
d7ef2ac7ed2ea8f52943031cdca376cee2e74992fca7cae0695bb41c9371fadf
|