Source code for google.cloud.bigquery.magics

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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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"""IPython Magics

.. function:: %%bigquery

    IPython cell magic to run a query and display the result as a DataFrame

    .. code-block:: python

        %%bigquery [<destination_var>] [--project <project>] [--use_legacy_sql]
                   [--verbose] [--params <params>]
        <query>

    Parameters:

    * ``<destination_var>`` (optional, line argument):
        variable to store the query results. The results are not displayed if
        this parameter is used.
    * ``--project <project>`` (optional, line argument):
        Project to use for running the query. Defaults to the context
        :attr:`~google.cloud.bigquery.magics.Context.project`.
    * ``--use_bqstorage_api`` (optional, line argument):
        Downloads the DataFrame using the BigQuery Storage API. To use this
        option, install the ``google-cloud-bigquery-storage`` and ``fastavro``
        packages, and `enable the BigQuery Storage API
        <https://console.cloud.google.com/apis/library/bigquerystorage.googleapis.com>`_.
    * ``--use_legacy_sql`` (optional, line argument):
        Runs the query using Legacy SQL syntax. Defaults to Standard SQL if
        this argument not used.
    * ``--verbose`` (optional, line argument):
        If this flag is used, information including the query job ID and the
        amount of time for the query to complete will not be cleared after the
        query is finished. By default, this information will be displayed but
        will be cleared after the query is finished.
    * ``--params <params>`` (optional, line argument):
        If present, the argument following the ``--params`` flag must be
        either:

        * :class:`str` - A JSON string representation of a dictionary in the
          format ``{"param_name": "param_value"}`` (ex. ``{"num": 17}``). Use
          of the parameter in the query should be indicated with
          ``@param_name``. See ``In[5]`` in the Examples section below.

        * :class:`dict` reference - A reference to a ``dict`` in the format
          ``{"param_name": "param_value"}``, where the value types must be JSON
          serializable. The variable reference is indicated by a ``$`` before
          the variable name (ex. ``$my_dict_var``). See ``In[6]`` and ``In[7]``
          in the Examples section below.
    * ``<query>`` (required, cell argument):
        SQL query to run.

    Returns:
        A :class:`pandas.DataFrame` with the query results.

    .. note::
        All queries run using this magic will run using the context
        :attr:`~google.cloud.bigquery.magics.Context.credentials`.

    Examples:
        The following examples can be run in an IPython notebook after loading
        the bigquery IPython extension (see ``In[1]``) and setting up
        Application Default Credentials.

    .. code-block:: none

        In [1]: %load_ext google.cloud.bigquery

        In [2]: %%bigquery
           ...: SELECT name, SUM(number) as count
           ...: FROM `bigquery-public-data.usa_names.usa_1910_current`
           ...: GROUP BY name
           ...: ORDER BY count DESC
           ...: LIMIT 3

        Out[2]:       name    count
           ...: -------------------
           ...: 0    James  4987296
           ...: 1     John  4866302
           ...: 2   Robert  4738204

        In [3]: %%bigquery df --project my-alternate-project --verbose
           ...: SELECT name, SUM(number) as count
           ...: FROM `bigquery-public-data.usa_names.usa_1910_current`
           ...: WHERE gender = 'F'
           ...: GROUP BY name
           ...: ORDER BY count DESC
           ...: LIMIT 3
        Executing query with job ID: bf633912-af2c-4780-b568-5d868058632b
        Query executing: 2.61s
        Query complete after 2.92s

        In [4]: df

        Out[4]:          name    count
           ...: ----------------------
           ...: 0        Mary  3736239
           ...: 1    Patricia  1568495
           ...: 2   Elizabeth  1519946

        In [5]: %%bigquery --params {"num": 17}
           ...: SELECT @num AS num

        Out[5]:     num
           ...: -------
           ...: 0    17

        In [6]: params = {"num": 17}

        In [7]: %%bigquery --params $params
           ...: SELECT @num AS num

        Out[7]:     num
           ...: -------
           ...: 0    17
"""

from __future__ import print_function

import ast
import sys
import time
from concurrent import futures

try:
    import IPython
    from IPython import display
    from IPython.core import magic_arguments
except ImportError:  # pragma: NO COVER
    raise ImportError("This module can only be loaded in IPython.")

from google.api_core import client_info
import google.auth
from google.cloud import bigquery
from google.cloud.bigquery.dbapi import _helpers
import six


IPYTHON_USER_AGENT = "ipython-{}".format(IPython.__version__)


[docs]class Context(object): """Storage for objects to be used throughout an IPython notebook session. A Context object is initialized when the ``magics`` module is imported, and can be found at ``google.cloud.bigquery.magics.context``. """ def __init__(self): self._credentials = None self._project = None self._connection = None self._use_bqstorage_api = None self._default_query_job_config = bigquery.QueryJobConfig() @property def credentials(self): """google.auth.credentials.Credentials: Credentials to use for queries performed through IPython magics Note: These credentials do not need to be explicitly defined if you are using Application Default Credentials. If you are not using Application Default Credentials, manually construct a :class:`google.auth.credentials.Credentials` object and set it as the context credentials as demonstrated in the example below. See `auth docs`_ for more information on obtaining credentials. Example: Manually setting the context credentials: >>> from google.cloud.bigquery import magics >>> from google.oauth2 import service_account >>> credentials = (service_account ... .Credentials.from_service_account_file( ... '/path/to/key.json')) >>> magics.context.credentials = credentials .. _auth docs: http://google-auth.readthedocs.io /en/latest/user-guide.html#obtaining-credentials """ if self._credentials is None: self._credentials, _ = google.auth.default() return self._credentials @credentials.setter def credentials(self, value): self._credentials = value @property def project(self): """str: Default project to use for queries performed through IPython magics Note: The project does not need to be explicitly defined if you have an environment default project set. If you do not have a default project set in your environment, manually assign the project as demonstrated in the example below. Example: Manually setting the context project: >>> from google.cloud.bigquery import magics >>> magics.context.project = 'my-project' """ if self._project is None: _, self._project = google.auth.default() return self._project @project.setter def project(self, value): self._project = value @property def use_bqstorage_api(self): """bool: [Beta] Set to True to use the BigQuery Storage API to download query results To use this option, install the ``google-cloud-bigquery-storage`` and ``fastavro`` packages, and `enable the BigQuery Storage API <https://console.cloud.google.com/apis/library/bigquerystorage.googleapis.com>`_. """ return self._use_bqstorage_api @use_bqstorage_api.setter def use_bqstorage_api(self, value): self._use_bqstorage_api = value @property def default_query_job_config(self): """google.cloud.bigquery.job.QueryJobConfig: Default job configuration for queries. The context's :class:`~google.cloud.bigquery.job.QueryJobConfig` is used for queries. Some properties can be overridden with arguments to the magics. Example: Manually setting the default value for ``maximum_bytes_billed`` to 100 MB: >>> from google.cloud.bigquery import magics >>> magics.context.default_query_job_config.maximum_bytes_billed = 100000000 """ return self._default_query_job_config @default_query_job_config.setter def default_query_job_config(self, value): self._default_query_job_config = value
context = Context() def _run_query(client, query, job_config=None): """Runs a query while printing status updates Args: client (google.cloud.bigquery.client.Client): Client to bundle configuration needed for API requests. query (str): SQL query to be executed. Defaults to the standard SQL dialect. Use the ``job_config`` parameter to change dialects. job_config (google.cloud.bigquery.job.QueryJobConfig, optional): Extra configuration options for the job. Returns: google.cloud.bigquery.job.QueryJob: the query job created Example: >>> client = bigquery.Client() >>> _run_query(client, "SELECT 17") Executing query with job ID: bf633912-af2c-4780-b568-5d868058632b Query executing: 1.66s Query complete after 2.07s 'bf633912-af2c-4780-b568-5d868058632b' """ start_time = time.time() query_job = client.query(query, job_config=job_config) if job_config and job_config.dry_run: return query_job print("Executing query with job ID: {}".format(query_job.job_id)) while True: print("\rQuery executing: {:0.2f}s".format(time.time() - start_time), end="") try: query_job.result(timeout=0.5) break except futures.TimeoutError: continue print("\nQuery complete after {:0.2f}s".format(time.time() - start_time)) return query_job @magic_arguments.magic_arguments() @magic_arguments.argument( "destination_var", nargs="?", help=("If provided, save the output to this variable instead of displaying it."), ) @magic_arguments.argument( "--project", type=str, default=None, help=("Project to use for executing this query. Defaults to the context project."), ) @magic_arguments.argument( "--maximum_bytes_billed", default=None, help=( "maximum_bytes_billed to use for executing this query. Defaults to " "the context default_query_job_config.maximum_bytes_billed." ), ) @magic_arguments.argument( "--dry_run", action="store_true", default=False, help=( "Sets query to be a dry run to estimate costs. " "Defaults to executing the query instead of dry run if this argument is not used." ), ) @magic_arguments.argument( "--use_legacy_sql", action="store_true", default=False, help=( "Sets query to use Legacy SQL instead of Standard SQL. Defaults to " "Standard SQL if this argument is not used." ), ) @magic_arguments.argument( "--use_bqstorage_api", action="store_true", default=False, help=( "[Beta] Use the BigQuery Storage API to download large query results. " "To use this option, install the google-cloud-bigquery-storage and " "fastavro packages, and enable the BigQuery Storage API." ), ) @magic_arguments.argument( "--verbose", action="store_true", default=False, help=( "If set, print verbose output, including the query job ID and the " "amount of time for the query to finish. By default, this " "information will be displayed as the query runs, but will be " "cleared after the query is finished." ), ) @magic_arguments.argument( "--params", nargs="+", default=None, help=( "Parameters to format the query string. If present, the --params " "flag should be followed by a string representation of a dictionary " "in the format {'param_name': 'param_value'} (ex. {\"num\": 17}), " "or a reference to a dictionary in the same format. The dictionary " "reference can be made by including a '$' before the variable " "name (ex. $my_dict_var)." ), ) def _cell_magic(line, query): """Underlying function for bigquery cell magic Note: This function contains the underlying logic for the 'bigquery' cell magic. This function is not meant to be called directly. Args: line (str): "%%bigquery" followed by arguments as required query (str): SQL query to run Returns: pandas.DataFrame: the query results. """ args = magic_arguments.parse_argstring(_cell_magic, line) params = [] if args.params is not None: try: params = _helpers.to_query_parameters( ast.literal_eval("".join(args.params)) ) except Exception: raise SyntaxError( "--params is not a correctly formatted JSON string or a JSON " "serializable dictionary" ) project = args.project or context.project client = bigquery.Client( project=project, credentials=context.credentials, default_query_job_config=context.default_query_job_config, client_info=client_info.ClientInfo(user_agent=IPYTHON_USER_AGENT), ) if context._connection: client._connection = context._connection bqstorage_client = _make_bqstorage_client( args.use_bqstorage_api or context.use_bqstorage_api, context.credentials ) job_config = bigquery.job.QueryJobConfig() job_config.query_parameters = params job_config.use_legacy_sql = args.use_legacy_sql job_config.dry_run = args.dry_run if args.maximum_bytes_billed == "None": job_config.maximum_bytes_billed = 0 elif args.maximum_bytes_billed is not None: value = int(args.maximum_bytes_billed) job_config.maximum_bytes_billed = value error = None try: query_job = _run_query(client, query, job_config) except Exception as ex: error = str(ex) if not args.verbose: display.clear_output() if error: if args.destination_var: print( "Could not save output to variable '{}'.".format(args.destination_var), file=sys.stderr, ) print("\nERROR:\n", error, file=sys.stderr) return if args.dry_run and args.destination_var: IPython.get_ipython().push({args.destination_var: query_job}) return elif args.dry_run: print( "Query validated. This query will process {} bytes.".format( query_job.total_bytes_processed ) ) return query_job result = query_job.to_dataframe(bqstorage_client=bqstorage_client) if args.destination_var: IPython.get_ipython().push({args.destination_var: result}) else: return result def _make_bqstorage_client(use_bqstorage_api, credentials): if not use_bqstorage_api: return None try: from google.cloud import bigquery_storage_v1beta1 except ImportError as err: customized_error = ImportError( "Install the google-cloud-bigquery-storage and pyarrow packages " "to use the BigQuery Storage API." ) six.raise_from(customized_error, err) try: from google.api_core.gapic_v1 import client_info as gapic_client_info except ImportError as err: customized_error = ImportError( "Install the grpcio package to use the BigQuery Storage API." ) six.raise_from(customized_error, err) return bigquery_storage_v1beta1.BigQueryStorageClient( credentials=credentials, client_info=gapic_client_info.ClientInfo(user_agent=IPYTHON_USER_AGENT), )