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PySQL Connector split into connector and sqlalchemy (#444)
* Modified the gitignore file to not have .idea file * [PECO-1803] Splitting the PySql connector into the core and the non core part (#417) * Implemented ColumnQueue to test the fetchall without pyarrow Removed token removed token * order of fields in row corrected * Changed the folder structure and tested the basic setup to work * Refractored the code to make connector to work * Basic Setup of connector, core and sqlalchemy is working * Basic integration of core, connect and sqlalchemy is working * Setup working dynamic change from ColumnQueue to ArrowQueue * Refractored the test code and moved to respective folders * Added the unit test for column_queue Fixed __version__ Fix * venv_main added to git ignore * Added code for merging columnar table * Merging code for columnar * Fixed the retry_close sesssion test issue with logging * Fixed the databricks_sqlalchemy tests and introduced pytest.ini for the sqla_testing * Added pyarrow_test mark on pytest * Fixed databricks.sqlalchemy to databricks_sqlalchemy imports * Added poetry.lock * Added dist folder * Changed the pyproject.toml * Minor Fix * Added the pyarrow skip tag on unit tests and tested their working * Fixed the Decimal and timestamp conversion issue in non arrow pipeline * Removed not required files and reformatted * Fixed test_retry error * Changed the folder structure to src / databricks * Removed the columnar non arrow flow to another PR * Moved the README to the root * removed columnQueue instance * Revmoved databricks_sqlalchemy dependency in core * Changed the pysql_supports_arrow predicate, introduced changes in the pyproject.toml * Ran the black formatter with the original version * Extra .py removed from all the __init__.py files names * Undo formatting check * Check * Check * Check * Check * Check * Check * Check * Check * Check * Check * Check * Check * Check * Check * BIG UPDATE * Refeactor code * Refractor * Fixed versioning * Minor refractoring * Minor refractoring * Changed the folder structure such that sqlalchemy has not reference here * Fixed README.md and CONTRIBUTING.md * Added manual publish * On push trigger added * Manually setting the publish step * Changed versioning in pyproject.toml * Bumped up the version to 4.0.0.b3 and also changed the structure to have pyarrow as optional * Removed the sqlalchemy tests from integration.yml file * [PECO-1803] Print warning message if pyarrow is not installed (#468) Print warning message if pyarrow is not installed Signed-off-by: Jacky Hu <[email protected]> * [PECO-1803] Remove sqlalchemy and update README.md (#469) Remove sqlalchemy and update README.md Signed-off-by: Jacky Hu <[email protected]> * Removed all sqlalchemy related stuff * generated the lock file * Fixed failing tests * removed poetry.lock * Updated the lock file * Fixed poetry numpy 2.2.2 issue * Workflow fixes --------- Signed-off-by: Jacky Hu <[email protected]> Co-authored-by: Jacky Hu <[email protected]>
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‎.github/workflows/code-quality-checks.yml

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#----------------------------------------------
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- name: Run tests
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run: poetry run python -m pytest tests/unit
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run-unit-tests-with-arrow:
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runs-on: ubuntu-latest
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strategy:
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matrix:
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python-version: [ 3.8, 3.9, "3.10", "3.11" ]
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steps:
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#----------------------------------------------
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# check-out repo and set-up python
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#----------------------------------------------
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- name: Check out repository
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uses: actions/checkout@v2
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- name: Set up python ${{ matrix.python-version }}
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id: setup-python
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uses: actions/setup-python@v2
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with:
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python-version: ${{ matrix.python-version }}
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#----------------------------------------------
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# ----- install & configure poetry -----
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#----------------------------------------------
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- name: Install Poetry
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uses: snok/install-poetry@v1
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with:
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virtualenvs-create: true
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virtualenvs-in-project: true
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installer-parallel: true
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#----------------------------------------------
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# load cached venv if cache exists
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#----------------------------------------------
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- name: Load cached venv
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id: cached-poetry-dependencies
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uses: actions/cache@v2
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with:
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path: .venv-pyarrow
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key: venv-pyarrow-${{ runner.os }}-${{ steps.setup-python.outputs.python-version }}-${{ github.event.repository.name }}-${{ hashFiles('**/poetry.lock') }}
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#----------------------------------------------
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# install dependencies if cache does not exist
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#----------------------------------------------
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- name: Install dependencies
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if: steps.cached-poetry-dependencies.outputs.cache-hit != 'true'
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run: poetry install --no-interaction --no-root
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#----------------------------------------------
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# install your root project, if required
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#----------------------------------------------
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- name: Install library
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run: poetry install --no-interaction --all-extras
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#----------------------------------------------
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# run test suite
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#----------------------------------------------
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- name: Run tests
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run: poetry run python -m pytest tests/unit
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check-linting:
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runs-on: ubuntu-latest
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strategy:

‎.github/workflows/integration.yml

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#----------------------------------------------
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- name: Run e2e tests
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run: poetry run python -m pytest tests/e2e
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- name: Run SQL Alchemy tests
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run: poetry run python -m pytest src/databricks/sqlalchemy/test_local

‎.github/workflows/publish-manual.yml

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name: Publish to PyPI Manual [Production]
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# Allow manual triggering of the workflow
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on:
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workflow_dispatch: {}
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jobs:
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publish:
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name: Publish
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runs-on: ubuntu-latest
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steps:
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#----------------------------------------------
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# Step 1: Check out the repository code
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#----------------------------------------------
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- name: Check out repository
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uses: actions/checkout@v2 # Check out the repository to access the code
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#----------------------------------------------
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# Step 2: Set up Python environment
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#----------------------------------------------
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- name: Set up python
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id: setup-python
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uses: actions/setup-python@v2
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with:
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python-version: 3.9 # Specify the Python version to be used
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#----------------------------------------------
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# Step 3: Install and configure Poetry
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#----------------------------------------------
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- name: Install Poetry
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uses: snok/install-poetry@v1 # Install Poetry, the Python package manager
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with:
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virtualenvs-create: true
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virtualenvs-in-project: true
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installer-parallel: true
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# #----------------------------------------------
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# # Step 4: Load cached virtual environment (if available)
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# #----------------------------------------------
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# - name: Load cached venv
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# id: cached-poetry-dependencies
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# uses: actions/cache@v2
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# with:
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# path: .venv # Path to the virtual environment
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# key: venv-${{ runner.os }}-${{ steps.setup-python.outputs.python-version }}-${{ github.event.repository.name }}-${{ hashFiles('**/poetry.lock') }}
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# # Cache key is generated based on OS, Python version, repo name, and the `poetry.lock` file hash
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# #----------------------------------------------
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# # Step 5: Install dependencies if the cache is not found
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# #----------------------------------------------
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# - name: Install dependencies
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# if: steps.cached-poetry-dependencies.outputs.cache-hit != 'true' # Only run if the cache was not hit
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# run: poetry install --no-interaction --no-root # Install dependencies without interaction
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# #----------------------------------------------
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# # Step 6: Update the version to the manually provided version
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# #----------------------------------------------
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# - name: Update pyproject.toml with the specified version
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# run: poetry version ${{ github.event.inputs.version }} # Use the version provided by the user input
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#----------------------------------------------
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# Step 7: Build and publish the first package to PyPI
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#----------------------------------------------
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- name: Build and publish databricks sql connector to PyPI
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working-directory: ./databricks_sql_connector
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run: |
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poetry build
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poetry publish -u __token__ -p ${{ secrets.PROD_PYPI_TOKEN }} # Publish with PyPI token
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#----------------------------------------------
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# Step 7: Build and publish the second package to PyPI
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#----------------------------------------------
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- name: Build and publish databricks sql connector core to PyPI
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working-directory: ./databricks_sql_connector_core
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run: |
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poetry build
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poetry publish -u __token__ -p ${{ secrets.PROD_PYPI_TOKEN }} # Publish with PyPI token

‎.gitignore

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# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
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# and can be added to the global gitignore or merged into this file. For a more nuclear
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# option (not recommended) you can uncomment the following to ignore the entire idea folder.
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#.idea/
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.idea/
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# End of https://www.toptal.com/developers/gitignore/api/python,macos
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‎CHANGELOG.md

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# Release History
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# 4.0.0 (TBD)
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- Split the connector into two separate packages: `databricks-sql-connector` and `databricks-sqlalchemy`. The `databricks-sql-connector` package contains the core functionality of the connector, while the `databricks-sqlalchemy` package contains the SQLAlchemy dialect for the connector.
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- Pyarrow dependency is now optional in `databricks-sql-connector`. Users needing arrow are supposed to explicitly install pyarrow
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# 3.7.0 (2024-12-23)
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- Fix: Incorrect number of rows fetched in inline results when fetching results with FETCH_NEXT orientation (databricks/databricks-sql-python#479 by @jprakash-db)

‎CONTRIBUTING.md

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The suites marked `[not documented]` require additional configuration which will be documented at a later time.
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#### SQLAlchemy dialect tests
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See README.tests.md for details.
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### Code formatting
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‎README.md

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[![PyPI](https://img.shields.io/pypi/v/databricks-sql-connector?style=flat-square)](https://pypi.org/project/databricks-sql-connector/)
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[![Downloads](https://pepy.tech/badge/databricks-sql-connector)](https://pepy.tech/project/databricks-sql-connector)
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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.
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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/).
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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.
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This connector uses Arrow as the data-exchange format, and supports APIs (e.g. `fetchmany_arrow`) 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. [PyArrow](https://arrow.apache.org/docs/python/index.html) is required to enable this and use these APIs, you can install it via `pip install pyarrow` or `pip install databricks-sql-connector[pyarrow]`.
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You are welcome to file an issue here for general use cases. You can also contact Databricks Support [here](help.databricks.com).
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## Quickstart
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Install the library with `pip install databricks-sql-connector`
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### Installing the core library
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Install using `pip install databricks-sql-connector`
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### Installing the core library with PyArrow
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Install using `pip install databricks-sql-connector[pyarrow]`
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```bash
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> to authenticate the target Databricks user account and needs to open the browser for authentication. So it
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> can only run on the user's machine.
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## SQLAlchemy
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Starting from `databricks-sql-connector` version 4.0.0 SQLAlchemy support has been extracted to a new library `databricks-sqlalchemy`.
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- Github repository [databricks-sqlalchemy github](https://github.com/databricks/databricks-sqlalchemy)
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- PyPI [databricks-sqlalchemy pypi](https://pypi.org/project/databricks-sqlalchemy/)
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### Quick SQLAlchemy guide
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Users can now choose between using the SQLAlchemy v1 or SQLAlchemy v2 dialects with the connector core
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- Install the latest SQLAlchemy v1 using `pip install databricks-sqlalchemy~=1.0`
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- Install SQLAlchemy v2 using `pip install databricks-sqlalchemy`
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## Contributing
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‎examples/sqlalchemy.py

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