{
    "info": {
        "author": "Christopher Flynn",
        "author_email": "crf204@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Programming Language :: Python :: Implementation :: CPython",
            "Topic :: Database",
            "Topic :: Database :: Front-Ends"
        ],
        "description": "databricks-dbapi\n================\n\n|pypi| |pyversions|\n\n.. |pypi| image:: https://img.shields.io/pypi/v/databricks-dbapi.svg\n    :target: https://pypi.python.org/pypi/databricks-dbapi\n\n.. |pyversions| image:: https://img.shields.io/pypi/pyversions/databricks-dbapi.svg\n    :target: https://pypi.python.org/pypi/databricks-dbapi\n\nA thin wrapper around `pyhive <https://github.com/dropbox/PyHive>`_ for creating a `DBAPI <https://www.python.org/dev/peps/pep-0249/>`_ connection to an interactive Databricks cluster.\n\nAlso provides a SQLAlchemy Dialect for Databricks interactive clusters.\n\nInstallation\n------------\n\nInstall using pip:\n\n.. code-block:: bash\n\n    pip install databricks-dbapi\n\n\nFor SQLAlchemy support install with:\n\n.. code-block:: bash\n\n    pip install databricks-dbapi[sqlalchemy]\n\nUsage\n-----\n\nThe ``connect()`` function returns a ``pyhive`` Hive connection object, which internally wraps a ``thrift`` connection.\n\nUsing a Databricks API token (recommended):\n\n.. code-block:: python\n\n    import os\n\n    from databricks_dbapi import databricks\n\n\n    token = os.environ[\"DATABRICKS_TOKEN\"]\n    host = os.environ[\"DATABRICKS_HOST\"]\n    cluster = os.environ[\"DATABRICKS_CLUSTER\"]\n\n\n    connection = databricks.connect(\n        host=host,\n        cluster=cluster,\n        token=token,\n    )\n    cursor = connection.cursor()\n\n    cursor.execute(\"SELECT * FROM some_table LIMIT 100\")\n\n    print(cursor.fetchone())\n    print(cursor.fetchall())\n\n\nUsing your username and password (not recommended):\n\n.. code-block:: python\n\n    import os\n\n    from databricks_dbapi import databricks\n\n\n    user = os.environ[\"DATABRICKS_USER\"]\n    password = os.environ[\"DATABRICKS_PASSWORD\"]\n    host = os.environ[\"DATABRICKS_HOST\"]\n    cluster = os.environ[\"DATABRICKS_CLUSTER\"]\n\n\n    connection = databricks.connect(\n        host=host,\n        cluster=cluster,\n        user=user,\n        password=password\n    )\n    cursor = connection.cursor()\n\n    cursor.execute(\"SELECT * FROM some_table LIMIT 100\")\n\n    print(cursor.fetchone())\n    print(cursor.fetchall())\n\n\nConnecting on Azure platform, or with ``http_path``:\n\n.. code-block:: python\n\n    import os\n\n    from databricks_dbapi import databricks\n\n\n    token = os.environ[\"DATABRICKS_TOKEN\"]\n    host = os.environ[\"DATABRICKS_HOST\"]\n    http_path = os.environ[\"DATABRICKS_HTTP_PATH\"]\n\n\n    connection = databricks.connect(\n        host=host,\n        http_path=http_path,\n        token=token,\n    )\n    cursor = connection.cursor()\n\n    cursor.execute(\"SELECT * FROM some_table LIMIT 100\")\n\n    print(cursor.fetchone())\n    print(cursor.fetchall())\n\n\nThe ``pyhive`` connection also provides async functionality:\n\n.. code-block:: python\n\n    import os\n\n    from databricks_dbapi import databricks\n    from TCLIService.ttypes import TOperationState\n\n\n    token = os.environ[\"DATABRICKS_TOKEN\"]\n    host = os.environ[\"DATABRICKS_HOST\"]\n    cluster = os.environ[\"DATABRICKS_CLUSTER\"]\n\n\n    connection = databricks.connect(\n        host=host,\n        cluster=cluster,\n        token=token,\n    )\n    cursor = connection.cursor()\n\n    cursor.execute(\"SELECT * FROM some_table LIMIT 100\", async_=True)\n\n    status = cursor.poll().operationState\n    while status in (TOperationState.INITIALIZED_STATE, TOperationState.RUNNING_STATE):\n        logs = cursor.fetch_logs()\n        for message in logs:\n            print(message)\n\n        # If needed, an asynchronous query can be cancelled at any time with:\n        # cursor.cancel()\n\n        status = cursor.poll().operationState\n\n    print(cursor.fetchall())\n\n\n\nSQLAlchemy\n----------\n\nOnce the ``databricks-dbapi`` package is installed, the ``databricks+pyhive`` dialect/driver will be registered to SQLAlchemy. Fill in the required information when passing the engine URL.\n\n.. code-block:: python\n\n    from sqlalchemy import *\n    from sqlalchemy.engine import create_engine\n    from sqlalchemy.schema import *\n\n\n    # Standard Databricks with user + password\n    # provide user, password, company name for url, database name, cluster name\n    engine = create_engine(\n        \"databricks+pyhive://<user>:<password>@<companyname>.cloud.databricks.com:443/<database>\",\n        connect_args={\"cluster\": \"<cluster>\"}\n    )\n\n    # Standard Databricks with token\n    # provide token, company name for url, database name, cluster name\n    engine = create_engine(\n        \"databricks+pyhive://token:<databricks_token>@<companyname>.cloud.databricks.com:443/<database>\",\n        connect_args={\"cluster\": \"<cluster>\"}\n    )\n\n    # Azure Databricks with user + password\n    # provide user, password, region for url, database name, http_path (with cluster name)\n    engine = create_engine(\n        \"databricks+pyhive://<user>:<password>@<region>.azuredatabricks.net:443/<database>\",\n        connect_args={\"http_path\": \"<azure_databricks_http_path>\"}\n    )\n\n    # Azure Databricks with token\n    # provide token, region for url, database name, http_path (with cluster name)\n    engine = create_engine(\n        \"databricks+pyhive://token:<databrickstoken>@<region>.azuredatabricks.net:443/<database>\",\n        connect_args={\"http_path\": \"<azure_databricks_http_path>\"}\n    )\n\n\n    logs = Table(\"my_table\", MetaData(bind=engine), autoload=True)\n    print(select([func.count(\"*\")], from_obj=logs).scalar())\n\n\nRefer to the following documentation for more details on hostname, cluster name, and http path:\n\n* `Databricks <https://docs.databricks.com/user-guide/bi/jdbc-odbc-bi.html>`_\n* `Azure Databricks <https://docs.azuredatabricks.net/user-guide/bi/jdbc-odbc-bi.html>`_\n\n\nRelated\n-------\n\n* `pyhive <https://github.com/dropbox/PyHive>`_\n* `thrift <https://github.com/apache/thrift/tree/master/lib/py>`_\n\n",
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        "keywords": "databricks,hive,dbapi,sqlalchemy,dialect",
        "license": "MIT",
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        "name": "databricks-dbapi",
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        "summary": "A DBAPI 2.0 interface and SQLAlchemy dialect for Databricks interactive clusters.",
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