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Generate readmes for most service samples [(googleapis#599)](GoogleCloudPlatform/python-docs-samples#599)
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‎samples/snippets/README.md

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‎samples/snippets/README.rst

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.. This file is automatically generated. Do not edit this file directly.
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Google BigQuery Python Samples
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===============================================================================
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This directory contains samples for Google BigQuery. `Google BigQuery`_ is Google's fully managed, petabyte scale, low cost analytics data warehouse. BigQuery is NoOps—there is no infrastructure to manage and you don't need a database administrator—so you can focus on analyzing data to find meaningful insights, use familiar SQL, and take advantage of our pay-as-you-go model.
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.. _Google BigQuery: https://cloud.google.com/bigquery/docs
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Setup
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-------------------------------------------------------------------------------
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Authentication
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++++++++++++++
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Authentication is typically done through `Application Default Credentials`_,
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which means you do not have to change the code to authenticate as long as
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your environment has credentials. You have a few options for setting up
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authentication:
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#. When running locally, use the `Google Cloud SDK`_
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.. code-block:: bash
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gcloud beta auth application-default login
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#. When running on App Engine or Compute Engine, credentials are already
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set-up. However, you may need to configure your Compute Engine instance
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with `additional scopes`_.
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#. You can create a `Service Account key file`_. This file can be used to
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authenticate to Google Cloud Platform services from any environment. To use
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the file, set the ``GOOGLE_APPLICATION_CREDENTIALS`` environment variable to
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the path to the key file, for example:
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.. code-block:: bash
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export GOOGLE_APPLICATION_CREDENTIALS=/path/to/service_account.json
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.. _Application Default Credentials: https://cloud.google.com/docs/authentication#getting_credentials_for_server-centric_flow
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.. _additional scopes: https://cloud.google.com/compute/docs/authentication#using
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.. _Service Account key file: https://developers.google.com/identity/protocols/OAuth2ServiceAccount#creatinganaccount
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Install Dependencies
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++++++++++++++++++++
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#. Install `pip`_ and `virtualenv`_ if you do not already have them.
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#. Create a virtualenv. Samples are compatible with Python 2.7 and 3.4+.
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.. code-block:: bash
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$ virtualenv env
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$ source env/bin/activate
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#. Install the dependencies needed to run the samples.
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.. code-block:: bash
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$ pip install -r requirements.txt
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.. _pip: https://pip.pypa.io/
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.. _virtualenv: https://virtualenv.pypa.io/
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Samples
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-------------------------------------------------------------------------------
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Quickstart
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+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
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To run this sample:
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.. code-block:: bash
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$ python quickstart.py
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Sync query
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+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
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To run this sample:
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.. code-block:: bash
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$ python sync_query.py
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usage: sync_query.py [-h] query
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Command-line application to perform synchronous queries in BigQuery.
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For more information, see the README.md under /bigquery.
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Example invocation:
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$ python sync_query.py \
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'SELECT corpus FROM `publicdata.samples.shakespeare` GROUP BY corpus'
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positional arguments:
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query BigQuery SQL Query.
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optional arguments:
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-h, --help show this help message and exit
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Async query
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+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
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To run this sample:
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.. code-block:: bash
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$ python async_query.py
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usage: async_query.py [-h] query
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Command-line application to perform asynchronous queries in BigQuery.
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For more information, see the README.md under /bigquery.
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Example invocation:
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$ python async_query.py 'SELECT corpus FROM `publicdata.samples.shakespeare` GROUP BY corpus'
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positional arguments:
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query BigQuery SQL Query.
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optional arguments:
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-h, --help show this help message and exit
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Snippets
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+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
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To run this sample:
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.. code-block:: bash
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$ python snippets.py
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usage: snippets.py [-h] [--project PROJECT]
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{list-datasets,list-tables,create-table,list-rows,copy-table,delete-table}
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...
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Samples that demonstrate basic operations in the BigQuery API.
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For more information, see the README.md under /bigquery.
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Example invocation:
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$ python snippets.py list-datasets
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The dataset and table should already exist.
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positional arguments:
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{list-datasets,list-tables,create-table,list-rows,copy-table,delete-table}
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list-datasets Lists all datasets in a given project. If no project
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is specified, then the currently active project is
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used
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list-tables Lists all of the tables in a given dataset. If no
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project is specified, then the currently active
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project is used.
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create-table Creates a simple table in the given dataset. If no
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project is specified, then the currently active
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project is used.
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list-rows Prints rows in the given table. Will print 25 rows at
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most for brevity as tables can contain large amounts
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of rows. If no project is specified, then the
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currently active project is used.
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copy-table Copies a table. If no project is specified, then the
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currently active project is used.
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delete-table Deletes a table in a given dataset. If no project is
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specified, then the currently active project is used.
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optional arguments:
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-h, --help show this help message and exit
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--project PROJECT
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Load data from a file
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+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
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To run this sample:
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.. code-block:: bash
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$ python load_data_from_file.py
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usage: load_data_from_file.py [-h] dataset_name table_name source_file_name
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Loads data into BigQuery from a local file.
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For more information, see the README.md under /bigquery.
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Example invocation:
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$ python load_data_from_file.py example_dataset example_table example-data.csv
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The dataset and table should already exist.
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positional arguments:
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dataset_name
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table_name
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source_file_name Path to a .csv file to upload.
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optional arguments:
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-h, --help show this help message and exit
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Load data from Cloud Storage
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+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
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To run this sample:
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.. code-block:: bash
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$ python load_data_from_gcs.py
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usage: load_data_from_gcs.py [-h] dataset_name table_name source
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Loads data into BigQuery from an object in Google Cloud Storage.
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For more information, see the README.md under /bigquery.
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Example invocation:
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$ python load_data_from_gcs.py example_dataset example_table gs://example-bucket/example-data.csv
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The dataset and table should already exist.
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positional arguments:
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dataset_name
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table_name
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source The Google Cloud Storage object to load. Must be in the format
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gs://bucket_name/object_name
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optional arguments:
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-h, --help show this help message and exit
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Load streaming data
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+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
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To run this sample:
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.. code-block:: bash
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$ python stream_data.py
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usage: stream_data.py [-h] dataset_name table_name json_data
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Loads a single row of data directly into BigQuery.
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For more information, see the README.md under /bigquery.
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Example invocation:
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$ python stream_data.py example_dataset example_table '["Gandalf", 2000]'
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The dataset and table should already exist.
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positional arguments:
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dataset_name
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table_name
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json_data The row to load into BigQuery as an array in JSON format.
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optional arguments:
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-h, --help show this help message and exit
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Export data to Cloud Storage
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+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
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To run this sample:
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.. code-block:: bash
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$ python export_data_to_gcs.py
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usage: export_data_to_gcs.py [-h] dataset_name table_name destination
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Exports data from BigQuery to an object in Google Cloud Storage.
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For more information, see the README.md under /bigquery.
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Example invocation:
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$ python export_data_to_gcs.py example_dataset example_table gs://example-bucket/example-data.csv
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The dataset and table should already exist.
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positional arguments:
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dataset_name
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table_name
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destination The desintation Google Cloud Storage object.Must be in the
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format gs://bucket_name/object_name
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optional arguments:
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-h, --help show this help message and exit
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The client library
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-------------------------------------------------------------------------------
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This sample uses the `Google Cloud Client Library for Python`_.
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You can read the documentation for more details on API usage and use GitHub
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to `browse the source`_ and `report issues`_.
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.. Google Cloud Client Library for Python:
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https://googlecloudplatform.github.io/google-cloud-python/
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.. browse the source:
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https://github.com/GoogleCloudPlatform/google-cloud-python
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.. report issues:
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https://github.com/GoogleCloudPlatform/google-cloud-python/issues
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.. _Google Cloud SDK: https://cloud.google.com/sdk/

‎samples/snippets/README.rst.in

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# This file is used to generate README.rst
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product:
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name: Google BigQuery
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short_name: BigQuery
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url: https://cloud.google.com/bigquery/docs
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description: >
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`Google BigQuery`_ is Google's fully managed, petabyte scale, low cost
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analytics data warehouse. BigQuery is NoOps—there is no infrastructure to
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manage and you don't need a database administrator—so you can focus on
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analyzing data to find meaningful insights, use familiar SQL, and take
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advantage of our pay-as-you-go model.
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setup:
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- auth
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- install_deps
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samples:
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- name: Quickstart
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file: quickstart.py
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- name: Sync query
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file: sync_query.py
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show_help: true
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- name: Async query
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file: async_query.py
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show_help: true
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- name: Snippets
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file: snippets.py
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show_help: true
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- name: Load data from a file
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file: load_data_from_file.py
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show_help: true
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- name: Load data from Cloud Storage
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file: load_data_from_gcs.py
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show_help: true
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- name: Load streaming data
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file: stream_data.py
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show_help: true
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- name: Export data to Cloud Storage
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file: export_data_to_gcs.py
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show_help: true
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cloud_client_library: true

‎samples/snippets/async_query.py

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For more information, see the README.md under /bigquery.
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Example invocation:
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$ python async_query.py \
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$ python async_query.py \\
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'SELECT corpus FROM `publicdata.samples.shakespeare` GROUP BY corpus'
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"""
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