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        "author": "Maxim Zudilov",
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To infer project from the environment, use ``bigquery://`` \u2013\u00a0without ``project``\n\nAuthentication\n______________\n\nFollow the `Google Cloud library guide <https://google-cloud-python.readthedocs.io/en/latest/core/auth.html>`_ for authentication. Alternatively, you can provide the path to a service account JSON file in ``create_engine()``:\n\n.. code-block:: python\n\n    engine = create_engine('bigquery://', credentials_path='/path/to/keyfile.json')\n\n\nLocation\n________\n\nTo specify location of your datasets pass ``location`` to ``create_engine()``:\n\n.. code-block:: python\n\n    engine = create_engine('bigquery://project', location=\"asia-northeast1\")\n\n\nTable names\n___________\n\nTo query tables from non-default projects, use the following format for the table name: ``project.dataset.table``, e.g.:\n\n.. code-block:: python\n\n    sample_table = Table('bigquery-public-data.samples.natality')\n\nBatch size\n__________\n\nBy default, ``arraysize`` is set to ``5000``. ``arraysize`` is used to set the batch size for fetching results. To change it, pass ``arraysize`` to ``create_engine()``:\n\n.. code-block:: python\n\n    engine = create_engine('bigquery://project', arraysize=1000)\n\n\nAdding a Default Dataset\n________________________\n\nIf you want to have the ``Client`` use a default dataset, specify it as the \"database\" portion of the connection string.\n\n.. code-block:: python\n\n    engine = create_engine('bigquery://project/dataset')\n\nWhen using a default dataset, don't include the dataset name in the table name, e.g.:\n\n.. code-block:: python\n\n    table = Table('table_name')\n\nNote that specyfing a default dataset doesn't restrict execution of queries to that particular dataset when using raw queries, e.g.:\n\n.. code-block:: python\n\n    # Set default dataset to dataset_a\n    engine = create_engine('bigquery://project/dataset_a')\n\n    # This will still execute and return rows from dataset_b\n    engine.execute('SELECT * FROM dataset_b.table').fetchall()\n\n\nConnection String Parameters\n____________________________\n\nThere are many situations where you can't call ``create_engine`` directly, such as when using tools like `Flask SQLAlchemy <http://flask-sqlalchemy.pocoo.org/2.3/>`_. For situations like these, or for situations where you want the ``Client`` to have a `default_query_job_config <https://googlecloudplatform.github.io/google-cloud-python/latest/bigquery/generated/google.cloud.bigquery.client.Client.html#google.cloud.bigquery.client.Client>`_, you can pass many arguments in the query of the connection string.\n\nThe ``credentials_path``, ``location``, and ``arraysize`` parameters are used by this library, and the rest are used to create a `QueryJobConfig <https://googlecloudplatform.github.io/google-cloud-python/latest/bigquery/generated/google.cloud.bigquery.job.QueryJobConfig.html#google.cloud.bigquery.job.QueryJobConfig>`_\n\nNote that if you want to use query strings, it will be more reliable if you use three slashes, so ``'bigquery:///?a=b'`` will work reliably, but ``'bigquery://?a=b'`` might be interpreted as having a \"database\" of ``?a=b``, depending on the system being used to parse the connection string.\n\nHere are examples of all the supported arguments. Any not present are either for legacy sql (which isn't supported by this library), or are too complex and are not implemented.\n\n.. code-block:: python\n\n    engine = create_engine(\n        'bigquery://some-project/some-dataset' '?'\n        'credentials_path=/some/path/to.json' '&'\n        'location=some-location' '&'\n        'arraysize=1000' '&'\n        'clustering_fields=a,b,c' '&'\n        'create_disposition=CREATE_IF_NEEDED' '&'\n        'destination=different-project.different-dataset.table' '&'\n        'destination_encryption_configuration=some-configuration' '&'\n        'dry_run=true' '&'\n        'labels=a:b,c:d' '&'\n        'maximum_bytes_billed=1000' '&'\n        'priority=INTERACTIVE' '&'\n        'schema_update_options=ALLOW_FIELD_ADDITION,ALLOW_FIELD_RELAXATION' '&'\n        'use_query_cache=true' '&'\n        'write_disposition=WRITE_APPEND'\n    )\n\n\nCreating tables\n_______________\n\nTo add metadata to a table:\n\n.. code-block:: python\n\n    table = Table('mytable', ..., bigquery_description='my table description', bigquery_friendly_name='my table friendly name')\n\nTo add metadata to a column:\n\n.. code-block:: python\n\n    Column('mycolumn', doc='my column description')\n\n\nRequirements\n============\n\nInstall using\n\n- ``pip install pybigquery``\n\n\nTesting\n============\n\nLoad sample tables::\n\n    ./scripts/load_test_data.sh\n\nThis will create a dataset ``test_pybigquery`` with tables named ``sample_one_row`` and ``sample``.\n\nSet up an environment and run tests::\n\n    pyvenv .env\n    source .env/bin/activate\n    pip install -r dev_requirements.txt\n    pytest\n",
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