{
    "info": {
        "author": "laughingman7743",
        "author_email": "laughingman7743@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "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",
            "Topic :: Database :: Front-Ends"
        ],
        "description": ".. image:: https://img.shields.io/pypi/pyversions/PyAthena.svg\n    :target: https://pypi.python.org/pypi/PyAthena/\n\n.. image:: https://travis-ci.org/laughingman7743/PyAthena.svg?branch=master\n    :target: https://travis-ci.org/laughingman7743/PyAthena\n\n.. image:: https://codecov.io/gh/laughingman7743/PyAthena/branch/master/graph/badge.svg\n    :target: https://codecov.io/gh/laughingman7743/PyAthena\n\n.. image:: https://img.shields.io/pypi/l/PyAthena.svg\n    :target: https://github.com/laughingman7743/PyAthena/blob/master/LICENSE\n\n.. image:: https://img.shields.io/pypi/dm/PyAthena.svg\n    :target: https://pypistats.org/packages/pyathena\n\n\nPyAthena\n========\n\nPyAthena is a Python `DB API 2.0 (PEP 249)`_ compliant client for `Amazon Athena`_.\n\n.. _`DB API 2.0 (PEP 249)`: https://www.python.org/dev/peps/pep-0249/\n.. _`Amazon Athena`: http://docs.aws.amazon.com/athena/latest/APIReference/Welcome.html\n\nlambda-pyathena\n===============\n\nlambda-pyathena is a fork of PyAthena that simply removes boto3 and botocore from the install-requires,\nresulting in an AWS Lambda friendly package.\n\n\nRequirements\n------------\n\n* Python\n\n  - CPython 2,7, 3.5, 3.6, 3.7\n\nInstallation\n------------\n\n.. code:: bash\n\n    $ pip install lambda-pyathena\n\nExtra packages:\n\n+---------------+--------------------------------------+------------------+\n| Package       | Install command                      | Version          |\n+===============+======================================+==================+\n| Pandas        | ``pip install PyAthena[Pandas]``     | >=0.24.0         |\n+---------------+--------------------------------------+------------------+\n| SQLAlchemy    | ``pip install PyAthena[SQLAlchemy]`` | >=1.0.0, <1.3.0  |\n+---------------+--------------------------------------+------------------+\n\nUsage\n-----\n\nBasic usage\n~~~~~~~~~~~\n\n.. code:: python\n\n    from pyathena import connect\n\n    cursor = connect(aws_access_key_id='YOUR_ACCESS_KEY_ID',\n                     aws_secret_access_key='YOUR_SECRET_ACCESS_KEY',\n                     s3_staging_dir='s3://YOUR_S3_BUCKET/path/to/',\n                     region_name='us-west-2').cursor()\n    cursor.execute(\"SELECT * FROM one_row\")\n    print(cursor.description)\n    print(cursor.fetchall())\n\nCursor iteration\n~~~~~~~~~~~~~~~~\n\n.. code:: python\n\n    from pyathena import connect\n\n    cursor = connect(aws_access_key_id='YOUR_ACCESS_KEY_ID',\n                     aws_secret_access_key='YOUR_SECRET_ACCESS_KEY',\n                     s3_staging_dir='s3://YOUR_S3_BUCKET/path/to/',\n                     region_name='us-west-2').cursor()\n    cursor.execute(\"SELECT * FROM many_rows LIMIT 10\")\n    for row in cursor:\n        print(row)\n\nQuery with parameter\n~~~~~~~~~~~~~~~~~~~~\n\nSupported `DB API paramstyle`_ is only ``PyFormat``.\n``PyFormat`` only supports `named placeholders`_ with old ``%`` operator style and parameters specify dictionary format.\n\n.. code:: python\n\n    from pyathena import connect\n\n    cursor = connect(aws_access_key_id='YOUR_ACCESS_KEY_ID',\n                     aws_secret_access_key='YOUR_SECRET_ACCESS_KEY',\n                     s3_staging_dir='s3://YOUR_S3_BUCKET/path/to/',\n                     region_name='us-west-2').cursor()\n    cursor.execute(\"\"\"\n                   SELECT col_string FROM one_row_complex\n                   WHERE col_string = %(param)s\n                   \"\"\", {'param': 'a string'})\n    print(cursor.fetchall())\n\nif ``%`` character is contained in your query, it must be escaped with ``%%`` like the following:\n\n.. code:: sql\n\n    SELECT col_string FROM one_row_complex\n    WHERE col_string = %(param)s OR col_string LIKE 'a%%'\n\n.. _`DB API paramstyle`: https://www.python.org/dev/peps/pep-0249/#paramstyle\n.. _`named placeholders`: https://pyformat.info/#named_placeholders\n\nSQLAlchemy\n~~~~~~~~~~\n\nInstall SQLAlchemy with ``pip install \"SQLAlchemy>=1.0.0, <1.3.0\"`` or ``pip install PyAthena[SQLAlchemy]``.\nSupported SQLAlchemy is 1.0.0 or higher and less than 1.3.0.\n\n.. code:: python\n\n    from urllib.parse import quote_plus  # PY2: from urllib import quote_plus\n    from sqlalchemy.engine import create_engine\n    from sqlalchemy.sql.expression import select\n    from sqlalchemy.sql.functions import func\n    from sqlalchemy.sql.schema import Table, MetaData\n\n    conn_str = 'awsathena+rest://{aws_access_key_id}:{aws_secret_access_key}@athena.{region_name}.amazonaws.com:443/'\\\n               '{schema_name}?s3_staging_dir={s3_staging_dir}'\n    engine = create_engine(conn_str.format(\n        aws_access_key_id=quote_plus('YOUR_ACCESS_KEY_ID'),\n        aws_secret_access_key=quote_plus('YOUR_SECRET_ACCESS_KEY'),\n        region_name='us-west-2',\n        schema_name='default',\n        s3_staging_dir=quote_plus('s3://YOUR_S3_BUCKET/path/to/')))\n    many_rows = Table('many_rows', MetaData(bind=engine), autoload=True)\n    print(select([func.count('*')], from_obj=many_rows).scalar())\n\nThe connection string has the following format:\n\n.. code:: python\n\n    awsathena+rest://{aws_access_key_id}:{aws_secret_access_key}@athena.{region_name}.amazonaws.com:443/{schema_name}?s3_staging_dir={s3_staging_dir}&...\n\nIf you do not specify ``aws_access_key_id`` and ``aws_secret_access_key`` using instance profile or boto3 configuration file:\n\n.. code:: python\n\n    awsathena+rest://:@athena.{region_name}.amazonaws.com:443/{schema_name}?s3_staging_dir={s3_staging_dir}&...\n\nNOTE: ``s3_staging_dir`` requires quote. If ``aws_access_key_id``, ``aws_secret_access_key`` and other parameter contain special characters, quote is also required.\n\nPandas\n~~~~~~\n\nMinimal example for Pandas DataFrame:\n\n.. code:: python\n\n    from pyathena import connect\n    import pandas as pd\n\n    conn = connect(aws_access_key_id='YOUR_ACCESS_KEY_ID',\n                   aws_secret_access_key='YOUR_SECRET_ACCESS_KEY',\n                   s3_staging_dir='s3://YOUR_S3_BUCKET/path/to/',\n                   region_name='us-west-2')\n    df = pd.read_sql(\"SELECT * FROM many_rows\", conn)\n    print(df.head())\n\nAs Pandas DataFrame:\n\n.. code:: python\n\n    from pyathena import connect\n    from pyathena.util import as_pandas\n\n    cursor = connect(aws_access_key_id='YOUR_ACCESS_KEY_ID',\n                     aws_secret_access_key='YOUR_SECRET_ACCESS_KEY',\n                     s3_staging_dir='s3://YOUR_S3_BUCKET/path/to/',\n                     region_name='us-west-2').cursor()\n    cursor.execute(\"SELECT * FROM many_rows\")\n    df = as_pandas(cursor)\n    print(df.describe())\n\nIf you want to use Pandas `DataFrame object`_ directly, you can use `PandasCursor`_.\n\nAsynchronousCursor\n~~~~~~~~~~~~~~~~~~\n\nAsynchronousCursor is a simple implementation using the concurrent.futures package.\nPython 2.7 uses `backport of the concurrent.futures`_ package.\nThis cursor is not `DB API 2.0 (PEP 249)`_ compliant.\n\nYou can use the AsynchronousCursor by specifying the ``cursor_class``\nwith the connect method or connection object.\n\n.. code:: python\n\n    from pyathena import connect\n    from pyathena.async_cursor import AsyncCursor\n\n    cursor = connect(s3_staging_dir='s3://YOUR_S3_BUCKET/path/to/',\n                     region_name='us-west-2',\n                     cursor_class=AsyncCursor).cursor()\n\n.. code:: python\n\n    from pyathena.connection import Connection\n    from pyathena.async_cursor import AsyncCursor\n\n    cursor = Connection(s3_staging_dir='s3://YOUR_S3_BUCKET/path/to/',\n                        region_name='us-west-2',\n                        cursor_class=AsyncCursor).cursor()\n\nIt can also be used by specifying the cursor class when calling the connection object's cursor method.\n\n.. code:: python\n\n    from pyathena import connect\n    from pyathena.async_cursor import AsyncCursor\n\n    cursor = connect(s3_staging_dir='s3://YOUR_S3_BUCKET/path/to/',\n                     region_name='us-west-2').cursor(AsyncCursor)\n\n.. code:: python\n\n    from pyathena.connection import Connection\n    from pyathena.async_cursor import AsyncCursor\n\n    cursor = Connection(s3_staging_dir='s3://YOUR_S3_BUCKET/path/to/',\n                        region_name='us-west-2').cursor(AsyncCursor)\n\nThe default number of workers is 5 or cpu number * 5.\nIf you want to change the number of workers you can specify like the following.\n\n.. code:: python\n\n    from pyathena import connect\n    from pyathena.async_cursor import AsyncCursor\n\n    cursor = connect(s3_staging_dir='s3://YOUR_S3_BUCKET/path/to/',\n                     region_name='us-west-2',\n                     cursor_class=AsyncCursor).cursor(max_workers=10)\n\nThe execute method of the AsynchronousCursor returns the tuple of the query ID and the `future object`_.\n\n.. code:: python\n\n    from pyathena import connect\n    from pyathena.async_cursor import AsyncCursor\n\n    cursor = connect(s3_staging_dir='s3://YOUR_S3_BUCKET/path/to/',\n                     region_name='us-west-2',\n                     cursor_class=AsyncCursor).cursor()\n\n    query_id, future = cursor.execute(\"SELECT * FROM many_rows\")\n\nThe return value of the `future object`_ is an ``AthenaResultSet`` object.\nThis object has an interface that can fetch and iterate query results similar to synchronous cursors.\nIt also has information on the result of query execution.\n\n.. code:: python\n\n    from pyathena import connect\n    from pyathena.async_cursor import AsyncCursor\n\n    cursor = connect(s3_staging_dir='s3://YOUR_S3_BUCKET/path/to/',\n                     region_name='us-west-2',\n                     cursor_class=AsyncCursor).cursor()\n\n    query_id, future = cursor.execute(\"SELECT * FROM many_rows\")\n    result_set = future.result()\n    print(result_set.state)\n    print(result_set.state_change_reason)\n    print(result_set.completion_date_time)\n    print(result_set.submission_date_time)\n    print(result_set.data_scanned_in_bytes)\n    print(result_set.execution_time_in_millis)\n    print(result_set.output_location)\n    print(result_set.description)\n    for row in result_set:\n        print(row)\n\n.. code:: python\n\n    from pyathena import connect\n    from pyathena.async_cursor import AsyncCursor\n\n    cursor = connect(s3_staging_dir='s3://YOUR_S3_BUCKET/path/to/',\n                     region_name='us-west-2',\n                     cursor_class=AsyncCursor).cursor()\n\n    query_id, future = cursor.execute(\"SELECT * FROM many_rows\")\n    result_set = future.result()\n    print(result_set.fetchall())\n\nA query ID is required to cancel a query with the asynchronous cursor.\n\n.. code:: python\n\n    from pyathena import connect\n    from pyathena.async_cursor import AsyncCursor\n\n    cursor = connect(s3_staging_dir='s3://YOUR_S3_BUCKET/path/to/',\n                     region_name='us-west-2',\n                     cursor_class=AsyncCursor).cursor()\n\n    query_id, future = cursor.execute(\"SELECT * FROM many_rows\")\n    cursor.cancel(query_id)\n\nNOTE: The cancel method of the `future object`_ does not cancel the query.\n\n.. _`backport of the concurrent.futures`: https://pypi.python.org/pypi/futures\n.. _`future object`: https://docs.python.org/3/library/concurrent.futures.html#future-objects\n\nPandasCursor\n~~~~~~~~~~~~\n\nPandasCursor directly handles the CSV file of the query execution result output to S3.\nThis cursor is to download the CSV file after executing the query, and then loaded into `DataFrame object`_.\nPerformance is better than fetching data with a cursor.\n\nYou can use the PandasCursor by specifying the ``cursor_class``\nwith the connect method or connection object.\n\n.. code:: python\n\n    from pyathena import connect\n    from pyathena.pandas_cursor import PandasCursor\n\n    cursor = connect(s3_staging_dir='s3://YOUR_S3_BUCKET/path/to/',\n                     region_name='us-west-2',\n                     cursor_class=PandasCursor).cursor()\n\n.. code:: python\n\n    from pyathena.connection import Connection\n    from pyathena.pandas_cursor import PandasCursor\n\n    cursor = Connection(s3_staging_dir='s3://YOUR_S3_BUCKET/path/to/',\n                        region_name='us-west-2',\n                        cursor_class=PandasCursor).cursor()\n\nIt can also be used by specifying the cursor class when calling the connection object's cursor method.\n\n.. code:: python\n\n    from pyathena import connect\n    from pyathena.pandas_cursor import PandasCursor\n\n    cursor = connect(s3_staging_dir='s3://YOUR_S3_BUCKET/path/to/',\n                     region_name='us-west-2').cursor(PandasCursor)\n\n.. code:: python\n\n    from pyathena.connection import Connection\n    from pyathena.pandas_cursor import PandasCursor\n\n    cursor = Connection(s3_staging_dir='s3://YOUR_S3_BUCKET/path/to/',\n                        region_name='us-west-2').cursor(PandasCursor)\n\nThe as_pandas method returns `DataFrame object`_.\n\n.. code:: python\n\n    from pyathena import connect\n    from pyathena.pandas_cursor import PandasCursor\n\n    cursor = connect(s3_staging_dir='s3://YOUR_S3_BUCKET/path/to/',\n                     region_name='us-west-2',\n                     cursor_class=PandasCursor).cursor()\n\n    df = cursor.execute(\"SELECT * FROM many_rows\").as_pandas()\n    print(df.describe())\n    print(df.head())\n\nSupport fetch and iterate query results.\n\n.. code:: python\n\n    from pyathena import connect\n    from pyathena.pandas_cursor import PandasCursor\n\n    cursor = connect(s3_staging_dir='s3://YOUR_S3_BUCKET/path/to/',\n                     region_name='us-west-2',\n                     cursor_class=PandasCursor).cursor()\n\n    cursor.execute(\"SELECT * FROM many_rows\")\n    print(cursor.fetchone())\n    print(cursor.fetchmany())\n    print(cursor.fetchall())\n\n.. code:: python\n\n    from pyathena import connect\n    from pyathena.pandas_cursor import PandasCursor\n\n    cursor = connect(s3_staging_dir='s3://YOUR_S3_BUCKET/path/to/',\n                     region_name='us-west-2',\n                     cursor_class=PandasCursor).cursor()\n\n    cursor.execute(\"SELECT * FROM many_rows\")\n    for row in cursor:\n        print(row)\n\nThe DATE and TIMESTAMP of Athena's data type are returned as `pandas.Timestamp`_ type.\n\n.. code:: python\n\n    from pyathena import connect\n    from pyathena.pandas_cursor import PandasCursor\n\n    cursor = connect(s3_staging_dir='s3://YOUR_S3_BUCKET/path/to/',\n                     region_name='us-west-2',\n                     cursor_class=PandasCursor).cursor()\n\n    cursor.execute(\"SELECT col_timestamp FROM one_row_complex\")\n    print(type(cursor.fetchone()[0]))  # <class 'pandas._libs.tslibs.timestamps.Timestamp'>\n\nExecution information of the query can also be retrieved.\n\n.. code:: python\n\n    from pyathena import connect\n    from pyathena.pandas_cursor import PandasCursor\n\n    cursor = connect(s3_staging_dir='s3://YOUR_S3_BUCKET/path/to/',\n                     region_name='us-west-2',\n                     cursor_class=PandasCursor).cursor()\n\n    cursor.execute(\"SELECT * FROM many_rows\")\n    print(cursor.state)\n    print(cursor.state_change_reason)\n    print(cursor.completion_date_time)\n    print(cursor.submission_date_time)\n    print(cursor.data_scanned_in_bytes)\n    print(cursor.execution_time_in_millis)\n    print(cursor.output_location)\n\nNOTE: PandasCursor handles the CSV file on memory. Pay attention to the memory capacity.\n\n.. _`DataFrame object`: https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html\n.. _`pandas.Timestamp`: https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Timestamp.html\n\nCredentials\n-----------\n\nSupport `Boto3 credentials`_.\n\n.. _`Boto3 credentials`: http://boto3.readthedocs.io/en/latest/guide/configuration.html\n\nAdditional environment variable:\n\n.. code:: bash\n\n    $ export AWS_ATHENA_S3_STAGING_DIR=s3://YOUR_S3_BUCKET/path/to/\n\nTesting\n-------\n\nDepends on the following environment variables:\n\n.. code:: bash\n\n    $ export AWS_ACCESS_KEY_ID=YOUR_ACCESS_KEY_ID\n    $ export AWS_SECRET_ACCESS_KEY=YOUR_SECRET_ACCESS_KEY\n    $ export AWS_DEFAULT_REGION=us-west-2\n    $ export AWS_ATHENA_S3_STAGING_DIR=s3://YOUR_S3_BUCKET/path/to/\n\nRun test\n~~~~~~~~\n\n.. code:: bash\n\n    $ pip install pipenv\n    $ pipenv install --dev\n    $ pipenv run scripts/test_data/upload_test_data.sh\n    $ pipenv run pytest\n    $ pipenv run scripts/test_data/delete_test_data.sh\n\nRun test multiple Python versions\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n.. code:: bash\n\n    $ pip install pipenv\n    $ pipenv install --dev\n    $ pipenv run scripts/test_data/upload_test_data.sh\n    $ pyenv local 3.7.2 3.6.8 3.5.7 2.7.16\n    $ pipenv run tox\n    $ pipenv run scripts/test_data/delete_test_data.sh\n\n\n",
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        "license": "MIT License",
        "maintainer": "",
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        "name": "lambda-pyathena",
        "package_url": "https://pypi.org/project/lambda-pyathena/",
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