| Name | databricks-sql-connector JSON |
| Version |
3.1.2
JSON |
| download |
| home_page | None |
| Summary | Databricks SQL Connector for Python |
| upload_time | 2024-04-18 17:09:33 |
| maintainer | None |
| docs_url | None |
| author | Databricks |
| requires_python | <4.0.0,>=3.8.0 |
| license | Apache-2.0 |
| keywords |
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| VCS |
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| bugtrack_url |
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| requirements |
No requirements were recorded.
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No Travis.
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No coveralls.
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# Databricks SQL Connector for Python
[](https://pypi.org/project/databricks-sql-connector/)
[](https://pepy.tech/project/databricks-sql-connector)
The Databricks SQL Connector for Python allows you to develop Python applications that connect to Databricks clusters and SQL warehouses. It is a Thrift-based client with no dependencies on ODBC or JDBC. It conforms to the [Python DB API 2.0 specification](https://www.python.org/dev/peps/pep-0249/) and exposes a [SQLAlchemy](https://www.sqlalchemy.org/) dialect for use with tools like `pandas` and `alembic` which use SQLAlchemy to execute DDL. Use `pip install databricks-sql-connector[sqlalchemy]` to install with SQLAlchemy's dependencies. `pip install databricks-sql-connector[alembic]` will install alembic's dependencies.
This connector uses Arrow as the data-exchange format, and supports APIs to directly fetch Arrow tables. Arrow tables are wrapped in the `ArrowQueue` class to provide a natural API to get several rows at a time.
You are welcome to file an issue here for general use cases. You can also contact Databricks Support [here](help.databricks.com).
## Requirements
Python 3.8 or above is required.
## Documentation
For the latest documentation, see
- [Databricks](https://docs.databricks.com/dev-tools/python-sql-connector.html)
- [Azure Databricks](https://docs.microsoft.com/en-us/azure/databricks/dev-tools/python-sql-connector)
## Quickstart
Install the library with `pip install databricks-sql-connector`
Note: Don't hard-code authentication secrets into your Python. Use environment variables
```bash
export DATABRICKS_HOST=********.databricks.com
export DATABRICKS_HTTP_PATH=/sql/1.0/endpoints/****************
export DATABRICKS_TOKEN=dapi********************************
```
Example usage:
```python
import os
from databricks import sql
host = os.getenv("DATABRICKS_HOST")
http_path = os.getenv("DATABRICKS_HTTP_PATH")
access_token = os.getenv("DATABRICKS_TOKEN")
connection = sql.connect(
server_hostname=host,
http_path=http_path,
access_token=access_token)
cursor = connection.cursor()
cursor.execute('SELECT :param `p`, * FROM RANGE(10)', {"param": "foo"})
result = cursor.fetchall()
for row in result:
print(row)
cursor.close()
connection.close()
```
In the above example:
- `server-hostname` is the Databricks instance host name.
- `http-path` is the HTTP Path either to a Databricks SQL endpoint (e.g. /sql/1.0/endpoints/1234567890abcdef),
or to a Databricks Runtime interactive cluster (e.g. /sql/protocolv1/o/1234567890123456/1234-123456-slid123)
- `personal-access-token` is the Databricks Personal Access Token for the account that will execute commands and queries
## Contributing
See [CONTRIBUTING.md](CONTRIBUTING.md)
## License
[Apache License 2.0](LICENSE)
Raw data
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"description": "# Databricks SQL Connector for Python\n\n[](https://pypi.org/project/databricks-sql-connector/)\n[](https://pepy.tech/project/databricks-sql-connector)\n\nThe Databricks SQL Connector for Python allows you to develop Python applications that connect to Databricks clusters and SQL warehouses. It is a Thrift-based client with no dependencies on ODBC or JDBC. It conforms to the [Python DB API 2.0 specification](https://www.python.org/dev/peps/pep-0249/) and exposes a [SQLAlchemy](https://www.sqlalchemy.org/) dialect for use with tools like `pandas` and `alembic` which use SQLAlchemy to execute DDL. Use `pip install databricks-sql-connector[sqlalchemy]` to install with SQLAlchemy's dependencies. `pip install databricks-sql-connector[alembic]` will install alembic's dependencies.\n\nThis connector uses Arrow as the data-exchange format, and supports APIs to directly fetch Arrow tables. Arrow tables are wrapped in the `ArrowQueue` class to provide a natural API to get several rows at a time.\n\nYou are welcome to file an issue here for general use cases. You can also contact Databricks Support [here](help.databricks.com).\n\n## Requirements\n\nPython 3.8 or above is required.\n\n## Documentation\n\nFor the latest documentation, see\n\n- [Databricks](https://docs.databricks.com/dev-tools/python-sql-connector.html)\n- [Azure Databricks](https://docs.microsoft.com/en-us/azure/databricks/dev-tools/python-sql-connector)\n\n## Quickstart\n\nInstall the library with `pip install databricks-sql-connector`\n\nNote: Don't hard-code authentication secrets into your Python. Use environment variables\n\n```bash\nexport DATABRICKS_HOST=********.databricks.com\nexport DATABRICKS_HTTP_PATH=/sql/1.0/endpoints/****************\nexport DATABRICKS_TOKEN=dapi********************************\n```\n\nExample usage:\n```python\nimport os\nfrom databricks import sql\n\nhost = os.getenv(\"DATABRICKS_HOST\")\nhttp_path = os.getenv(\"DATABRICKS_HTTP_PATH\")\naccess_token = os.getenv(\"DATABRICKS_TOKEN\")\n\nconnection = sql.connect(\n server_hostname=host,\n http_path=http_path,\n access_token=access_token)\n\ncursor = connection.cursor()\ncursor.execute('SELECT :param `p`, * FROM RANGE(10)', {\"param\": \"foo\"})\nresult = cursor.fetchall()\nfor row in result:\n print(row)\n\ncursor.close()\nconnection.close()\n```\n\nIn the above example:\n- `server-hostname` is the Databricks instance host name.\n- `http-path` is the HTTP Path either to a Databricks SQL endpoint (e.g. /sql/1.0/endpoints/1234567890abcdef),\nor to a Databricks Runtime interactive cluster (e.g. /sql/protocolv1/o/1234567890123456/1234-123456-slid123)\n- `personal-access-token` is the Databricks Personal Access Token for the account that will execute commands and queries\n\n\n## Contributing\n\nSee [CONTRIBUTING.md](CONTRIBUTING.md)\n\n## License\n\n[Apache License 2.0](LICENSE)\n",
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