:py:mod:`airflow.providers.google.cloud.operators.bigquery`
===========================================================

.. py:module:: airflow.providers.google.cloud.operators.bigquery

.. autoapi-nested-parse::

   This module contains Google BigQuery operators.


Module Contents
---------------

Classes
~~~~~~~

.. autoapisummary::

   airflow.providers.google.cloud.operators.bigquery.BigQueryUIColors
   airflow.providers.google.cloud.operators.bigquery.BigQueryConsoleLink
   airflow.providers.google.cloud.operators.bigquery.BigQueryConsoleIndexableLink
   airflow.providers.google.cloud.operators.bigquery.BigQueryCheckOperator
   airflow.providers.google.cloud.operators.bigquery.BigQueryValueCheckOperator
   airflow.providers.google.cloud.operators.bigquery.BigQueryIntervalCheckOperator
   airflow.providers.google.cloud.operators.bigquery.BigQueryGetDataOperator
   airflow.providers.google.cloud.operators.bigquery.BigQueryExecuteQueryOperator
   airflow.providers.google.cloud.operators.bigquery.BigQueryCreateEmptyTableOperator
   airflow.providers.google.cloud.operators.bigquery.BigQueryCreateExternalTableOperator
   airflow.providers.google.cloud.operators.bigquery.BigQueryDeleteDatasetOperator
   airflow.providers.google.cloud.operators.bigquery.BigQueryCreateEmptyDatasetOperator
   airflow.providers.google.cloud.operators.bigquery.BigQueryGetDatasetOperator
   airflow.providers.google.cloud.operators.bigquery.BigQueryGetDatasetTablesOperator
   airflow.providers.google.cloud.operators.bigquery.BigQueryPatchDatasetOperator
   airflow.providers.google.cloud.operators.bigquery.BigQueryUpdateTableOperator
   airflow.providers.google.cloud.operators.bigquery.BigQueryUpdateDatasetOperator
   airflow.providers.google.cloud.operators.bigquery.BigQueryDeleteTableOperator
   airflow.providers.google.cloud.operators.bigquery.BigQueryUpsertTableOperator
   airflow.providers.google.cloud.operators.bigquery.BigQueryUpdateTableSchemaOperator
   airflow.providers.google.cloud.operators.bigquery.BigQueryInsertJobOperator


Attributes
~~~~~~~~~~

.. autoapisummary::

   airflow.providers.google.cloud.operators.bigquery.BIGQUERY_JOB_DETAILS_LINK_FMT


.. py:data:: BIGQUERY_JOB_DETAILS_LINK_FMT
   :annotation: = https://console.cloud.google.com/bigquery?j={job_id}

   
.. py:class:: BigQueryUIColors

   Bases: :py:obj:`enum.Enum`

   Hex colors for BigQuery operators

   .. py:attribute:: CHECK
      :annotation: = #C0D7FF

      
   .. py:attribute:: QUERY
      :annotation: = #A1BBFF

      
   .. py:attribute:: TABLE
      :annotation: = #81A0FF

      
   .. py:attribute:: DATASET
      :annotation: = #5F86FF

      
.. py:class:: BigQueryConsoleLink

   Bases: :py:obj:`airflow.models.BaseOperatorLink`

   Helper class for constructing BigQuery link.

   .. py:attribute:: name
      :annotation: = BigQuery Console

      
   .. py:method:: get_link(self, operator, dttm = None, ti_key = None)

      Link to external system.

      Note: The old signature of this function was ``(self, operator, dttm: datetime)``. That is still
      supported at runtime but is deprecated.

      :param operator: airflow operator
      :param ti_key: TaskInstance ID to return link for
      :return: link to external system


.. py:class:: BigQueryConsoleIndexableLink

   Bases: :py:obj:`airflow.models.BaseOperatorLink`

   Helper class for constructing BigQuery link.

   .. py:attribute:: index
      :annotation: :int

      
   .. py:method:: name(self)
      :property:

      Name of the link. This will be the button name on the task UI.

      :return: link name


   .. py:method:: get_link(self, operator, dttm = None, ti_key = None)

      Link to external system.

      Note: The old signature of this function was ``(self, operator, dttm: datetime)``. That is still
      supported at runtime but is deprecated.

      :param operator: airflow operator
      :param ti_key: TaskInstance ID to return link for
      :return: link to external system


.. py:class:: BigQueryCheckOperator(*, sql, gcp_conn_id = 'google_cloud_default', use_legacy_sql = True, location = None, impersonation_chain = None, labels = None, **kwargs)

   Bases: :py:obj:`_BigQueryDbHookMixin`, :py:obj:`airflow.operators.sql.SQLCheckOperator`

   Performs checks against BigQuery. The ``BigQueryCheckOperator`` expects
   a sql query that will return a single row. Each value on that
   first row is evaluated using python ``bool`` casting. If any of the
   values return ``False`` the check is failed and errors out.

   .. seealso::
       For more information on how to use this operator, take a look at the guide:
       :ref:`howto/operator:BigQueryCheckOperator`

   Note that Python bool casting evals the following as ``False``:

   * ``False``
   * ``0``
   * Empty string (``""``)
   * Empty list (``[]``)
   * Empty dictionary or set (``{}``)

   Given a query like ``SELECT COUNT(*) FROM foo``, it will fail only if
   the count ``== 0``. You can craft much more complex query that could,
   for instance, check that the table has the same number of rows as
   the source table upstream, or that the count of today's partition is
   greater than yesterday's partition, or that a set of metrics are less
   than 3 standard deviation for the 7 day average.

   This operator can be used as a data quality check in your pipeline, and
   depending on where you put it in your DAG, you have the choice to
   stop the critical path, preventing from
   publishing dubious data, or on the side and receive email alerts
   without stopping the progress of the DAG.

   :param sql: the sql to be executed
   :param gcp_conn_id: (Optional) The connection ID used to connect to Google Cloud.
   :param use_legacy_sql: Whether to use legacy SQL (true)
       or standard SQL (false).
   :param location: The geographic location of the job. See details at:
       https://cloud.google.com/bigquery/docs/locations#specifying_your_location
   :param impersonation_chain: Optional service account to impersonate using short-term
       credentials, or chained list of accounts required to get the access_token
       of the last account in the list, which will be impersonated in the request.
       If set as a string, the account must grant the originating account
       the Service Account Token Creator IAM role.
       If set as a sequence, the identities from the list must grant
       Service Account Token Creator IAM role to the directly preceding identity, with first
       account from the list granting this role to the originating account (templated).
   :param labels: a dictionary containing labels for the table, passed to BigQuery

   .. py:attribute:: template_fields
      :annotation: :Sequence[str] = ['sql', 'gcp_conn_id', 'impersonation_chain', 'labels']

      
   .. py:attribute:: template_ext
      :annotation: :Sequence[str] = ['.sql']

      
   .. py:attribute:: ui_color
      

.. py:class:: BigQueryValueCheckOperator(*, sql, pass_value, tolerance = None, gcp_conn_id = 'google_cloud_default', use_legacy_sql = True, location = None, impersonation_chain = None, labels = None, **kwargs)

   Bases: :py:obj:`_BigQueryDbHookMixin`, :py:obj:`airflow.operators.sql.SQLValueCheckOperator`

   Performs a simple value check using sql code.

   .. seealso::
       For more information on how to use this operator, take a look at the guide:
       :ref:`howto/operator:BigQueryValueCheckOperator`

   :param sql: the sql to be executed
   :param use_legacy_sql: Whether to use legacy SQL (true)
       or standard SQL (false).
   :param gcp_conn_id: (Optional) The connection ID used to connect to Google Cloud.
   :param location: The geographic location of the job. See details at:
       https://cloud.google.com/bigquery/docs/locations#specifying_your_location
   :param impersonation_chain: Optional service account to impersonate using short-term
       credentials, or chained list of accounts required to get the access_token
       of the last account in the list, which will be impersonated in the request.
       If set as a string, the account must grant the originating account
       the Service Account Token Creator IAM role.
       If set as a sequence, the identities from the list must grant
       Service Account Token Creator IAM role to the directly preceding identity, with first
       account from the list granting this role to the originating account (templated).
   :param labels: a dictionary containing labels for the table, passed to BigQuery

   .. py:attribute:: template_fields
      :annotation: :Sequence[str] = ['sql', 'gcp_conn_id', 'pass_value', 'impersonation_chain', 'labels']

      
   .. py:attribute:: template_ext
      :annotation: :Sequence[str] = ['.sql']

      
   .. py:attribute:: ui_color
      

.. py:class:: BigQueryIntervalCheckOperator(*, table, metrics_thresholds, date_filter_column = 'ds', days_back = -7, gcp_conn_id = 'google_cloud_default', use_legacy_sql = True, location = None, impersonation_chain = None, labels = None, **kwargs)

   Bases: :py:obj:`_BigQueryDbHookMixin`, :py:obj:`airflow.operators.sql.SQLIntervalCheckOperator`

   Checks that the values of metrics given as SQL expressions are within
   a certain tolerance of the ones from days_back before.

   This method constructs a query like so ::

       SELECT {metrics_threshold_dict_key} FROM {table}
       WHERE {date_filter_column}=<date>

   .. seealso::
       For more information on how to use this operator, take a look at the guide:
       :ref:`howto/operator:BigQueryIntervalCheckOperator`

   :param table: the table name
   :param days_back: number of days between ds and the ds we want to check
       against. Defaults to 7 days
   :param metrics_thresholds: a dictionary of ratios indexed by metrics, for
       example 'COUNT(*)': 1.5 would require a 50 percent or less difference
       between the current day, and the prior days_back.
   :param use_legacy_sql: Whether to use legacy SQL (true)
       or standard SQL (false).
   :param gcp_conn_id: (Optional) The connection ID used to connect to Google Cloud.
   :param location: The geographic location of the job. See details at:
       https://cloud.google.com/bigquery/docs/locations#specifying_your_location
   :param impersonation_chain: Optional service account to impersonate using short-term
       credentials, or chained list of accounts required to get the access_token
       of the last account in the list, which will be impersonated in the request.
       If set as a string, the account must grant the originating account
       the Service Account Token Creator IAM role.
       If set as a sequence, the identities from the list must grant
       Service Account Token Creator IAM role to the directly preceding identity, with first
       account from the list granting this role to the originating account (templated).
   :param labels: a dictionary containing labels for the table, passed to BigQuery

   .. py:attribute:: template_fields
      :annotation: :Sequence[str] = ['table', 'gcp_conn_id', 'sql1', 'sql2', 'impersonation_chain', 'labels']

      
   .. py:attribute:: ui_color
      

.. py:class:: BigQueryGetDataOperator(*, dataset_id, table_id, max_results = 100, selected_fields = None, gcp_conn_id = 'google_cloud_default', delegate_to = None, location = None, impersonation_chain = None, **kwargs)

   Bases: :py:obj:`airflow.models.BaseOperator`

   Fetches the data from a BigQuery table (alternatively fetch data for selected columns)
   and returns data in a python list. The number of elements in the returned list will
   be equal to the number of rows fetched. Each element in the list will again be a list
   where element would represent the columns values for that row.

   **Example Result**: ``[['Tony', '10'], ['Mike', '20'], ['Steve', '15']]``

   .. seealso::
       For more information on how to use this operator, take a look at the guide:
       :ref:`howto/operator:BigQueryGetDataOperator`

   .. note::
       If you pass fields to ``selected_fields`` which are in different order than the
       order of columns already in
       BQ table, the data will still be in the order of BQ table.
       For example if the BQ table has 3 columns as
       ``[A,B,C]`` and you pass 'B,A' in the ``selected_fields``
       the data would still be of the form ``'A,B'``.

   **Example**: ::

       get_data = BigQueryGetDataOperator(
           task_id='get_data_from_bq',
           dataset_id='test_dataset',
           table_id='Transaction_partitions',
           max_results=100,
           selected_fields='DATE',
           gcp_conn_id='airflow-conn-id'
       )

   :param dataset_id: The dataset ID of the requested table. (templated)
   :param table_id: The table ID of the requested table. (templated)
   :param max_results: The maximum number of records (rows) to be fetched
       from the table. (templated)
   :param selected_fields: List of fields to return (comma-separated). If
       unspecified, all fields are returned.
   :param gcp_conn_id: (Optional) The connection ID used to connect to Google Cloud.
   :param delegate_to: The account to impersonate using domain-wide delegation of authority,
       if any. For this to work, the service account making the request must have
       domain-wide delegation enabled.
   :param location: The location used for the operation.
   :param impersonation_chain: Optional service account to impersonate using short-term
       credentials, or chained list of accounts required to get the access_token
       of the last account in the list, which will be impersonated in the request.
       If set as a string, the account must grant the originating account
       the Service Account Token Creator IAM role.
       If set as a sequence, the identities from the list must grant
       Service Account Token Creator IAM role to the directly preceding identity, with first
       account from the list granting this role to the originating account (templated).

   .. py:attribute:: template_fields
      :annotation: :Sequence[str] = ['dataset_id', 'table_id', 'max_results', 'selected_fields', 'impersonation_chain']

      
   .. py:attribute:: ui_color
      

   .. py:method:: execute(self, context)

      This is the main method to derive when creating an operator.
      Context is the same dictionary used as when rendering jinja templates.

      Refer to get_template_context for more context.


.. py:class:: BigQueryExecuteQueryOperator(*, sql, destination_dataset_table = None, write_disposition = 'WRITE_EMPTY', allow_large_results = False, flatten_results = None, gcp_conn_id = 'google_cloud_default', delegate_to = None, udf_config = None, use_legacy_sql = True, maximum_billing_tier = None, maximum_bytes_billed = None, create_disposition = 'CREATE_IF_NEEDED', schema_update_options = None, query_params = None, labels = None, priority = 'INTERACTIVE', time_partitioning = None, api_resource_configs = None, cluster_fields = None, location = None, encryption_configuration = None, impersonation_chain = None, **kwargs)

   Bases: :py:obj:`airflow.models.BaseOperator`

   Executes BigQuery SQL queries in a specific BigQuery database.
   This operator does not assert idempotency.

   This operator is deprecated.
   Please use :class:`airflow.providers.google.cloud.operators.bigquery.BigQueryInsertJobOperator`

   :param sql: the SQL code to be executed as a single string, or
       a list of str (sql statements), or a reference to a template file.
       Template references are recognized by str ending in '.sql'
   :param destination_dataset_table: A dotted
       ``(<project>.|<project>:)<dataset>.<table>`` that, if set, will store the results
       of the query. (templated)
   :param write_disposition: Specifies the action that occurs if the destination table
       already exists. (default: 'WRITE_EMPTY')
   :param create_disposition: Specifies whether the job is allowed to create new tables.
       (default: 'CREATE_IF_NEEDED')
   :param allow_large_results: Whether to allow large results.
   :param flatten_results: If true and query uses legacy SQL dialect, flattens
       all nested and repeated fields in the query results. ``allow_large_results``
       must be ``true`` if this is set to ``false``. For standard SQL queries, this
       flag is ignored and results are never flattened.
   :param gcp_conn_id: (Optional) The connection ID used to connect to Google Cloud.
   :param delegate_to: The account to impersonate using domain-wide delegation of authority,
       if any. For this to work, the service account making the request must have
       domain-wide delegation enabled.
   :param udf_config: The User Defined Function configuration for the query.
       See https://cloud.google.com/bigquery/user-defined-functions for details.
   :param use_legacy_sql: Whether to use legacy SQL (true) or standard SQL (false).
   :param maximum_billing_tier: Positive integer that serves as a multiplier
       of the basic price.
       Defaults to None, in which case it uses the value set in the project.
   :param maximum_bytes_billed: Limits the bytes billed for this job.
       Queries that will have bytes billed beyond this limit will fail
       (without incurring a charge). If unspecified, this will be
       set to your project default.
   :param api_resource_configs: a dictionary that contain params
       'configuration' applied for Google BigQuery Jobs API:
       https://cloud.google.com/bigquery/docs/reference/rest/v2/jobs
       for example, {'query': {'useQueryCache': False}}. You could use it
       if you need to provide some params that are not supported by BigQueryOperator
       like args.
   :param schema_update_options: Allows the schema of the destination
       table to be updated as a side effect of the load job.
   :param query_params: a list of dictionary containing query parameter types and
       values, passed to BigQuery. The structure of dictionary should look like
       'queryParameters' in Google BigQuery Jobs API:
       https://cloud.google.com/bigquery/docs/reference/rest/v2/jobs.
       For example, [{ 'name': 'corpus', 'parameterType': { 'type': 'STRING' },
       'parameterValue': { 'value': 'romeoandjuliet' } }]. (templated)
   :param labels: a dictionary containing labels for the job/query,
       passed to BigQuery
   :param priority: Specifies a priority for the query.
       Possible values include INTERACTIVE and BATCH.
       The default value is INTERACTIVE.
   :param time_partitioning: configure optional time partitioning fields i.e.
       partition by field, type and expiration as per API specifications.
   :param cluster_fields: Request that the result of this query be stored sorted
       by one or more columns. BigQuery supports clustering for both partitioned and
       non-partitioned tables. The order of columns given determines the sort order.
   :param location: The geographic location of the job. Required except for
       US and EU. See details at
       https://cloud.google.com/bigquery/docs/locations#specifying_your_location
   :param encryption_configuration: [Optional] Custom encryption configuration (e.g., Cloud KMS keys).
       **Example**: ::

           encryption_configuration = {
               "kmsKeyName": "projects/testp/locations/us/keyRings/test-kr/cryptoKeys/test-key"
           }
   :param impersonation_chain: Optional service account to impersonate using short-term
       credentials, or chained list of accounts required to get the access_token
       of the last account in the list, which will be impersonated in the request.
       If set as a string, the account must grant the originating account
       the Service Account Token Creator IAM role.
       If set as a sequence, the identities from the list must grant
       Service Account Token Creator IAM role to the directly preceding identity, with first
       account from the list granting this role to the originating account (templated).

   .. py:attribute:: template_fields
      :annotation: :Sequence[str] = ['sql', 'destination_dataset_table', 'labels', 'query_params', 'impersonation_chain']

      
   .. py:attribute:: template_ext
      :annotation: :Sequence[str] = ['.sql']

      
   .. py:attribute:: template_fields_renderers
      

   .. py:attribute:: ui_color
      

   .. py:method:: operator_extra_links(self)
      :property:

      Return operator extra links


   .. py:method:: execute(self, context)

      This is the main method to derive when creating an operator.
      Context is the same dictionary used as when rendering jinja templates.

      Refer to get_template_context for more context.


   .. py:method:: on_kill(self)

      Override this method to cleanup subprocesses when a task instance
      gets killed. Any use of the threading, subprocess or multiprocessing
      module within an operator needs to be cleaned up or it will leave
      ghost processes behind.


.. py:class:: BigQueryCreateEmptyTableOperator(*, dataset_id, table_id, table_resource = None, project_id = None, schema_fields = None, gcs_schema_object = None, time_partitioning = None, bigquery_conn_id = 'google_cloud_default', google_cloud_storage_conn_id = 'google_cloud_default', delegate_to = None, labels = None, view = None, materialized_view = None, encryption_configuration = None, location = None, cluster_fields = None, impersonation_chain = None, exists_ok = False, **kwargs)

   Bases: :py:obj:`airflow.models.BaseOperator`

   Creates a new, empty table in the specified BigQuery dataset,
   optionally with schema.

   The schema to be used for the BigQuery table may be specified in one of
   two ways. You may either directly pass the schema fields in, or you may
   point the operator to a Google Cloud Storage object name. The object in
   Google Cloud Storage must be a JSON file with the schema fields in it.
   You can also create a table without schema.

   .. seealso::
       For more information on how to use this operator, take a look at the guide:
       :ref:`howto/operator:BigQueryCreateEmptyTableOperator`

   :param project_id: The project to create the table into. (templated)
   :param dataset_id: The dataset to create the table into. (templated)
   :param table_id: The Name of the table to be created. (templated)
   :param table_resource: Table resource as described in documentation:
       https://cloud.google.com/bigquery/docs/reference/rest/v2/tables#Table
       If provided all other parameters are ignored.
   :param schema_fields: If set, the schema field list as defined here:
       https://cloud.google.com/bigquery/docs/reference/rest/v2/jobs#configuration.load.schema

       **Example**: ::

           schema_fields=[{"name": "emp_name", "type": "STRING", "mode": "REQUIRED"},
                          {"name": "salary", "type": "INTEGER", "mode": "NULLABLE"}]

   :param gcs_schema_object: Full path to the JSON file containing
       schema (templated). For
       example: ``gs://test-bucket/dir1/dir2/employee_schema.json``
   :param time_partitioning: configure optional time partitioning fields i.e.
       partition by field, type and  expiration as per API specifications.

       .. seealso::
           https://cloud.google.com/bigquery/docs/reference/rest/v2/tables#timePartitioning
   :param bigquery_conn_id: [Optional] The connection ID used to connect to Google Cloud and
       interact with the Bigquery service.
   :param google_cloud_storage_conn_id: [Optional] The connection ID used to connect to Google Cloud.
       and interact with the Google Cloud Storage service.
   :param delegate_to: The account to impersonate using domain-wide delegation of authority,
       if any. For this to work, the service account making the request must have
       domain-wide delegation enabled.
   :param labels: a dictionary containing labels for the table, passed to BigQuery

       **Example (with schema JSON in GCS)**: ::

           CreateTable = BigQueryCreateEmptyTableOperator(
               task_id='BigQueryCreateEmptyTableOperator_task',
               dataset_id='ODS',
               table_id='Employees',
               project_id='internal-gcp-project',
               gcs_schema_object='gs://schema-bucket/employee_schema.json',
               bigquery_conn_id='airflow-conn-id',
               google_cloud_storage_conn_id='airflow-conn-id'
           )

       **Corresponding Schema file** (``employee_schema.json``): ::

           [
             {
               "mode": "NULLABLE",
               "name": "emp_name",
               "type": "STRING"
             },
             {
               "mode": "REQUIRED",
               "name": "salary",
               "type": "INTEGER"
             }
           ]

       **Example (with schema in the DAG)**: ::

           CreateTable = BigQueryCreateEmptyTableOperator(
               task_id='BigQueryCreateEmptyTableOperator_task',
               dataset_id='ODS',
               table_id='Employees',
               project_id='internal-gcp-project',
               schema_fields=[{"name": "emp_name", "type": "STRING", "mode": "REQUIRED"},
                              {"name": "salary", "type": "INTEGER", "mode": "NULLABLE"}],
               bigquery_conn_id='airflow-conn-id-account',
               google_cloud_storage_conn_id='airflow-conn-id'
           )
   :param view: [Optional] A dictionary containing definition for the view.
       If set, it will create a view instead of a table:

       .. seealso::
           https://cloud.google.com/bigquery/docs/reference/rest/v2/tables#ViewDefinition
   :param materialized_view: [Optional] The materialized view definition.
   :param encryption_configuration: [Optional] Custom encryption configuration (e.g., Cloud KMS keys).
       **Example**: ::

           encryption_configuration = {
               "kmsKeyName": "projects/testp/locations/us/keyRings/test-kr/cryptoKeys/test-key"
           }
   :param location: The location used for the operation.
   :param cluster_fields: [Optional] The fields used for clustering.
           BigQuery supports clustering for both partitioned and
           non-partitioned tables.

           .. seealso::
               https://cloud.google.com/bigquery/docs/reference/rest/v2/tables#clustering.fields
   :param impersonation_chain: Optional service account to impersonate using short-term
       credentials, or chained list of accounts required to get the access_token
       of the last account in the list, which will be impersonated in the request.
       If set as a string, the account must grant the originating account
       the Service Account Token Creator IAM role.
       If set as a sequence, the identities from the list must grant
       Service Account Token Creator IAM role to the directly preceding identity, with first
       account from the list granting this role to the originating account (templated).
   :param exists_ok: If ``True``, ignore "already exists" errors when creating the table.

   .. py:attribute:: template_fields
      :annotation: :Sequence[str] = ['dataset_id', 'table_id', 'project_id', 'gcs_schema_object', 'labels', 'view',...

      
   .. py:attribute:: template_fields_renderers
      

   .. py:attribute:: ui_color
      

   .. py:attribute:: operator_extra_links
      

   .. py:method:: execute(self, context)

      This is the main method to derive when creating an operator.
      Context is the same dictionary used as when rendering jinja templates.

      Refer to get_template_context for more context.


.. py:class:: BigQueryCreateExternalTableOperator(*, bucket = None, source_objects = None, destination_project_dataset_table = None, table_resource = None, schema_fields = None, schema_object = None, source_format = None, autodetect = False, compression = None, skip_leading_rows = None, field_delimiter = None, max_bad_records = 0, quote_character = None, allow_quoted_newlines = False, allow_jagged_rows = False, bigquery_conn_id = 'google_cloud_default', google_cloud_storage_conn_id = 'google_cloud_default', delegate_to = None, src_fmt_configs = None, labels = None, encryption_configuration = None, location = None, impersonation_chain = None, **kwargs)

   Bases: :py:obj:`airflow.models.BaseOperator`

   Creates a new external table in the dataset with the data from Google Cloud
   Storage.

   The schema to be used for the BigQuery table may be specified in one of
   two ways. You may either directly pass the schema fields in, or you may
   point the operator to a Google Cloud Storage object name. The object in
   Google Cloud Storage must be a JSON file with the schema fields in it.

   .. seealso::
       For more information on how to use this operator, take a look at the guide:
       :ref:`howto/operator:BigQueryCreateExternalTableOperator`

   :param bucket: The bucket to point the external table to. (templated)
   :param source_objects: List of Google Cloud Storage URIs to point
       table to. If source_format is 'DATASTORE_BACKUP', the list must only contain a single URI.
   :param destination_project_dataset_table: The dotted ``(<project>.)<dataset>.<table>``
       BigQuery table to load data into (templated). If ``<project>`` is not included,
       project will be the project defined in the connection json.
   :param schema_fields: If set, the schema field list as defined here:
       https://cloud.google.com/bigquery/docs/reference/rest/v2/jobs#configuration.load.schema

       **Example**: ::

           schema_fields=[{"name": "emp_name", "type": "STRING", "mode": "REQUIRED"},
                          {"name": "salary", "type": "INTEGER", "mode": "NULLABLE"}]

       Should not be set when source_format is 'DATASTORE_BACKUP'.
   :param table_resource: Table resource as described in documentation:
       https://cloud.google.com/bigquery/docs/reference/rest/v2/tables#Table
       If provided all other parameters are ignored. External schema from object will be resolved.
   :param schema_object: If set, a GCS object path pointing to a .json file that
       contains the schema for the table. (templated)
   :param source_format: File format of the data.
   :param autodetect: Try to detect schema and format options automatically.
       The schema_fields and schema_object options will be honored when specified explicitly.
       https://cloud.google.com/bigquery/docs/schema-detect#schema_auto-detection_for_external_data_sources
   :param compression: [Optional] The compression type of the data source.
       Possible values include GZIP and NONE.
       The default value is NONE.
       This setting is ignored for Google Cloud Bigtable,
       Google Cloud Datastore backups and Avro formats.
   :param skip_leading_rows: Number of rows to skip when loading from a CSV.
   :param field_delimiter: The delimiter to use for the CSV.
   :param max_bad_records: The maximum number of bad records that BigQuery can
       ignore when running the job.
   :param quote_character: The value that is used to quote data sections in a CSV file.
   :param allow_quoted_newlines: Whether to allow quoted newlines (true) or not (false).
   :param allow_jagged_rows: Accept rows that are missing trailing optional columns.
       The missing values are treated as nulls. If false, records with missing trailing
       columns are treated as bad records, and if there are too many bad records, an
       invalid error is returned in the job result. Only applicable to CSV, ignored
       for other formats.
   :param bigquery_conn_id: (Optional) The connection ID used to connect to Google Cloud and
       interact with the Bigquery service.
   :param google_cloud_storage_conn_id: (Optional) The connection ID used to connect to Google Cloud
       and interact with the Google Cloud Storage service.
   :param delegate_to: The account to impersonate using domain-wide delegation of authority,
       if any. For this to work, the service account making the request must have
       domain-wide delegation enabled.
   :param src_fmt_configs: configure optional fields specific to the source format
   :param labels: a dictionary containing labels for the table, passed to BigQuery
   :param encryption_configuration: [Optional] Custom encryption configuration (e.g., Cloud KMS keys).
       **Example**: ::

           encryption_configuration = {
               "kmsKeyName": "projects/testp/locations/us/keyRings/test-kr/cryptoKeys/test-key"
           }
   :param location: The location used for the operation.
   :param impersonation_chain: Optional service account to impersonate using short-term
       credentials, or chained list of accounts required to get the access_token
       of the last account in the list, which will be impersonated in the request.
       If set as a string, the account must grant the originating account
       the Service Account Token Creator IAM role.
       If set as a sequence, the identities from the list must grant
       Service Account Token Creator IAM role to the directly preceding identity, with first
       account from the list granting this role to the originating account (templated).

   .. py:attribute:: template_fields
      :annotation: :Sequence[str] = ['bucket', 'source_objects', 'schema_object', 'destination_project_dataset_table', 'labels',...

      
   .. py:attribute:: template_fields_renderers
      

   .. py:attribute:: ui_color
      

   .. py:attribute:: operator_extra_links
      

   .. py:method:: execute(self, context)

      This is the main method to derive when creating an operator.
      Context is the same dictionary used as when rendering jinja templates.

      Refer to get_template_context for more context.


.. py:class:: BigQueryDeleteDatasetOperator(*, dataset_id, project_id = None, delete_contents = False, gcp_conn_id = 'google_cloud_default', delegate_to = None, impersonation_chain = None, **kwargs)

   Bases: :py:obj:`airflow.models.BaseOperator`

   This operator deletes an existing dataset from your Project in Big query.
   https://cloud.google.com/bigquery/docs/reference/rest/v2/datasets/delete

   .. seealso::
       For more information on how to use this operator, take a look at the guide:
       :ref:`howto/operator:BigQueryDeleteDatasetOperator`

   :param project_id: The project id of the dataset.
   :param dataset_id: The dataset to be deleted.
   :param delete_contents: (Optional) Whether to force the deletion even if the dataset is not empty.
       Will delete all tables (if any) in the dataset if set to True.
       Will raise HttpError 400: "{dataset_id} is still in use" if set to False and dataset is not empty.
       The default value is False.
   :param gcp_conn_id: (Optional) The connection ID used to connect to Google Cloud.
   :param delegate_to: The account to impersonate using domain-wide delegation of authority,
       if any. For this to work, the service account making the request must have
       domain-wide delegation enabled.
   :param impersonation_chain: Optional service account to impersonate using short-term
       credentials, or chained list of accounts required to get the access_token
       of the last account in the list, which will be impersonated in the request.
       If set as a string, the account must grant the originating account
       the Service Account Token Creator IAM role.
       If set as a sequence, the identities from the list must grant
       Service Account Token Creator IAM role to the directly preceding identity, with first
       account from the list granting this role to the originating account (templated).

   **Example**: ::

       delete_temp_data = BigQueryDeleteDatasetOperator(
           dataset_id='temp-dataset',
           project_id='temp-project',
           delete_contents=True, # Force the deletion of the dataset as well as its tables (if any).
           gcp_conn_id='_my_gcp_conn_',
           task_id='Deletetemp',
           dag=dag)

   .. py:attribute:: template_fields
      :annotation: :Sequence[str] = ['dataset_id', 'project_id', 'impersonation_chain']

      
   .. py:attribute:: ui_color
      

   .. py:method:: execute(self, context)

      This is the main method to derive when creating an operator.
      Context is the same dictionary used as when rendering jinja templates.

      Refer to get_template_context for more context.


.. py:class:: BigQueryCreateEmptyDatasetOperator(*, dataset_id = None, project_id = None, dataset_reference = None, location = None, gcp_conn_id = 'google_cloud_default', delegate_to = None, impersonation_chain = None, exists_ok = False, **kwargs)

   Bases: :py:obj:`airflow.models.BaseOperator`

   This operator is used to create new dataset for your Project in BigQuery.
   https://cloud.google.com/bigquery/docs/reference/rest/v2/datasets#resource

   .. seealso::
       For more information on how to use this operator, take a look at the guide:
       :ref:`howto/operator:BigQueryCreateEmptyDatasetOperator`

   :param project_id: The name of the project where we want to create the dataset.
   :param dataset_id: The id of dataset. Don't need to provide, if datasetId in dataset_reference.
   :param location: The geographic location where the dataset should reside.
   :param dataset_reference: Dataset reference that could be provided with request body.
       More info:
       https://cloud.google.com/bigquery/docs/reference/rest/v2/datasets#resource
   :param gcp_conn_id: (Optional) The connection ID used to connect to Google Cloud.
   :param delegate_to: The account to impersonate using domain-wide delegation of authority,
       if any. For this to work, the service account making the request must have
       domain-wide delegation enabled.
   :param impersonation_chain: Optional service account to impersonate using short-term
       credentials, or chained list of accounts required to get the access_token
       of the last account in the list, which will be impersonated in the request.
       If set as a string, the account must grant the originating account
       the Service Account Token Creator IAM role.
       If set as a sequence, the identities from the list must grant
       Service Account Token Creator IAM role to the directly preceding identity, with first
       account from the list granting this role to the originating account (templated).
   :param exists_ok: If ``True``, ignore "already exists" errors when creating the dataset.
       **Example**: ::

           create_new_dataset = BigQueryCreateEmptyDatasetOperator(
               dataset_id='new-dataset',
               project_id='my-project',
               dataset_reference={"friendlyName": "New Dataset"}
               gcp_conn_id='_my_gcp_conn_',
               task_id='newDatasetCreator',
               dag=dag)

   .. py:attribute:: template_fields
      :annotation: :Sequence[str] = ['dataset_id', 'project_id', 'dataset_reference', 'impersonation_chain']

      
   .. py:attribute:: template_fields_renderers
      

   .. py:attribute:: ui_color
      

   .. py:attribute:: operator_extra_links
      

   .. py:method:: execute(self, context)

      This is the main method to derive when creating an operator.
      Context is the same dictionary used as when rendering jinja templates.

      Refer to get_template_context for more context.


.. py:class:: BigQueryGetDatasetOperator(*, dataset_id, project_id = None, gcp_conn_id = 'google_cloud_default', delegate_to = None, impersonation_chain = None, **kwargs)

   Bases: :py:obj:`airflow.models.BaseOperator`

   This operator is used to return the dataset specified by dataset_id.

   .. seealso::
       For more information on how to use this operator, take a look at the guide:
       :ref:`howto/operator:BigQueryGetDatasetOperator`

   :param dataset_id: The id of dataset. Don't need to provide,
       if datasetId in dataset_reference.
   :param project_id: The name of the project where we want to create the dataset.
       Don't need to provide, if projectId in dataset_reference.
   :param gcp_conn_id: (Optional) The connection ID used to connect to Google Cloud.
   :param delegate_to: The account to impersonate using domain-wide delegation of authority,
       if any. For this to work, the service account making the request must have
       domain-wide delegation enabled.
   :param impersonation_chain: Optional service account to impersonate using short-term
       credentials, or chained list of accounts required to get the access_token
       of the last account in the list, which will be impersonated in the request.
       If set as a string, the account must grant the originating account
       the Service Account Token Creator IAM role.
       If set as a sequence, the identities from the list must grant
       Service Account Token Creator IAM role to the directly preceding identity, with first
       account from the list granting this role to the originating account (templated).

   :rtype: dataset
       https://cloud.google.com/bigquery/docs/reference/rest/v2/datasets#resource

   .. py:attribute:: template_fields
      :annotation: :Sequence[str] = ['dataset_id', 'project_id', 'impersonation_chain']

      
   .. py:attribute:: ui_color
      

   .. py:attribute:: operator_extra_links
      

   .. py:method:: execute(self, context)

      This is the main method to derive when creating an operator.
      Context is the same dictionary used as when rendering jinja templates.

      Refer to get_template_context for more context.


.. py:class:: BigQueryGetDatasetTablesOperator(*, dataset_id, project_id = None, max_results = None, gcp_conn_id = 'google_cloud_default', delegate_to = None, impersonation_chain = None, **kwargs)

   Bases: :py:obj:`airflow.models.BaseOperator`

   This operator retrieves the list of tables in the specified dataset.

   .. seealso::
       For more information on how to use this operator, take a look at the guide:
       :ref:`howto/operator:BigQueryGetDatasetTablesOperator`

   :param dataset_id: the dataset ID of the requested dataset.
   :param project_id: (Optional) the project of the requested dataset. If None,
       self.project_id will be used.
   :param max_results: (Optional) the maximum number of tables to return.
   :param gcp_conn_id: (Optional) The connection ID used to connect to Google Cloud.
   :param delegate_to: The account to impersonate using domain-wide delegation of authority,
       if any. For this to work, the service account making the request must have
       domain-wide delegation enabled.
   :param impersonation_chain: Optional service account to impersonate using short-term
       credentials, or chained list of accounts required to get the access_token
       of the last account in the list, which will be impersonated in the request.
       If set as a string, the account must grant the originating account
       the Service Account Token Creator IAM role.
       If set as a sequence, the identities from the list must grant
       Service Account Token Creator IAM role to the directly preceding identity, with first
       account from the list granting this role to the originating account (templated).

   .. py:attribute:: template_fields
      :annotation: :Sequence[str] = ['dataset_id', 'project_id', 'impersonation_chain']

      
   .. py:attribute:: ui_color
      

   .. py:method:: execute(self, context)

      This is the main method to derive when creating an operator.
      Context is the same dictionary used as when rendering jinja templates.

      Refer to get_template_context for more context.


.. py:class:: BigQueryPatchDatasetOperator(*, dataset_id, dataset_resource, project_id = None, gcp_conn_id = 'google_cloud_default', delegate_to = None, impersonation_chain = None, **kwargs)

   Bases: :py:obj:`airflow.models.BaseOperator`

   This operator is used to patch dataset for your Project in BigQuery.
   It only replaces fields that are provided in the submitted dataset resource.

   This operator is deprecated.
   Please use :class:`airflow.providers.google.cloud.operators.bigquery.BigQueryUpdateTableOperator`

   :param dataset_id: The id of dataset. Don't need to provide,
       if datasetId in dataset_reference.
   :param dataset_resource: Dataset resource that will be provided with request body.
       https://cloud.google.com/bigquery/docs/reference/rest/v2/datasets#resource
   :param project_id: The name of the project where we want to create the dataset.
       Don't need to provide, if projectId in dataset_reference.
   :param gcp_conn_id: (Optional) The connection ID used to connect to Google Cloud.
   :param delegate_to: The account to impersonate using domain-wide delegation of authority,
       if any. For this to work, the service account making the request must have
       domain-wide delegation enabled.
   :param impersonation_chain: Optional service account to impersonate using short-term
       credentials, or chained list of accounts required to get the access_token
       of the last account in the list, which will be impersonated in the request.
       If set as a string, the account must grant the originating account
       the Service Account Token Creator IAM role.
       If set as a sequence, the identities from the list must grant
       Service Account Token Creator IAM role to the directly preceding identity, with first
       account from the list granting this role to the originating account (templated).

   :rtype: dataset
       https://cloud.google.com/bigquery/docs/reference/rest/v2/datasets#resource

   .. py:attribute:: template_fields
      :annotation: :Sequence[str] = ['dataset_id', 'project_id', 'impersonation_chain']

      
   .. py:attribute:: template_fields_renderers
      

   .. py:attribute:: ui_color
      

   .. py:method:: execute(self, context)

      This is the main method to derive when creating an operator.
      Context is the same dictionary used as when rendering jinja templates.

      Refer to get_template_context for more context.


.. py:class:: BigQueryUpdateTableOperator(*, table_resource, fields = None, dataset_id = None, table_id = None, project_id = None, gcp_conn_id = 'google_cloud_default', delegate_to = None, impersonation_chain = None, **kwargs)

   Bases: :py:obj:`airflow.models.BaseOperator`

   This operator is used to update table for your Project in BigQuery.
   Use ``fields`` to specify which fields of table to update. If a field
   is listed in ``fields`` and is ``None`` in table, it will be deleted.

   .. seealso::
       For more information on how to use this operator, take a look at the guide:
       :ref:`howto/operator:BigQueryUpdateTableOperator`

   :param dataset_id: The id of dataset. Don't need to provide,
       if datasetId in table_reference.
   :param table_id: The id of table. Don't need to provide,
       if tableId in table_reference.
   :param table_resource: Dataset resource that will be provided with request body.
       https://cloud.google.com/bigquery/docs/reference/rest/v2/tables#resource
   :param fields: The fields of ``table`` to change, spelled as the Table
       properties (e.g. "friendly_name").
   :param project_id: The name of the project where we want to create the table.
       Don't need to provide, if projectId in table_reference.
   :param gcp_conn_id: (Optional) The connection ID used to connect to Google Cloud.
   :param delegate_to: The account to impersonate using domain-wide delegation of authority,
       if any. For this to work, the service account making the request must have
       domain-wide delegation enabled.
   :param impersonation_chain: Optional service account to impersonate using short-term
       credentials, or chained list of accounts required to get the access_token
       of the last account in the list, which will be impersonated in the request.
       If set as a string, the account must grant the originating account
       the Service Account Token Creator IAM role.
       If set as a sequence, the identities from the list must grant
       Service Account Token Creator IAM role to the directly preceding identity, with first
       account from the list granting this role to the originating account (templated).

   :rtype: table
       https://cloud.google.com/bigquery/docs/reference/rest/v2/tables#resource

   .. py:attribute:: template_fields
      :annotation: :Sequence[str] = ['dataset_id', 'table_id', 'project_id', 'impersonation_chain']

      
   .. py:attribute:: template_fields_renderers
      

   .. py:attribute:: ui_color
      

   .. py:attribute:: operator_extra_links
      

   .. py:method:: execute(self, context)

      This is the main method to derive when creating an operator.
      Context is the same dictionary used as when rendering jinja templates.

      Refer to get_template_context for more context.


.. py:class:: BigQueryUpdateDatasetOperator(*, dataset_resource, fields = None, dataset_id = None, project_id = None, gcp_conn_id = 'google_cloud_default', delegate_to = None, impersonation_chain = None, **kwargs)

   Bases: :py:obj:`airflow.models.BaseOperator`

   This operator is used to update dataset for your Project in BigQuery.
   Use ``fields`` to specify which fields of dataset to update. If a field
   is listed in ``fields`` and is ``None`` in dataset, it will be deleted.
   If no ``fields`` are provided then all fields of provided ``dataset_resource``
   will be used.

   .. seealso::
       For more information on how to use this operator, take a look at the guide:
       :ref:`howto/operator:BigQueryUpdateDatasetOperator`

   :param dataset_id: The id of dataset. Don't need to provide,
       if datasetId in dataset_reference.
   :param dataset_resource: Dataset resource that will be provided with request body.
       https://cloud.google.com/bigquery/docs/reference/rest/v2/datasets#resource
   :param fields: The properties of dataset to change (e.g. "friendly_name").
   :param project_id: The name of the project where we want to create the dataset.
       Don't need to provide, if projectId in dataset_reference.
   :param gcp_conn_id: (Optional) The connection ID used to connect to Google Cloud.
   :param delegate_to: The account to impersonate using domain-wide delegation of authority,
       if any. For this to work, the service account making the request must have
       domain-wide delegation enabled.
   :param impersonation_chain: Optional service account to impersonate using short-term
       credentials, or chained list of accounts required to get the access_token
       of the last account in the list, which will be impersonated in the request.
       If set as a string, the account must grant the originating account
       the Service Account Token Creator IAM role.
       If set as a sequence, the identities from the list must grant
       Service Account Token Creator IAM role to the directly preceding identity, with first
       account from the list granting this role to the originating account (templated).

   :rtype: dataset
       https://cloud.google.com/bigquery/docs/reference/rest/v2/datasets#resource

   .. py:attribute:: template_fields
      :annotation: :Sequence[str] = ['dataset_id', 'project_id', 'impersonation_chain']

      
   .. py:attribute:: template_fields_renderers
      

   .. py:attribute:: ui_color
      

   .. py:attribute:: operator_extra_links
      

   .. py:method:: execute(self, context)

      This is the main method to derive when creating an operator.
      Context is the same dictionary used as when rendering jinja templates.

      Refer to get_template_context for more context.


.. py:class:: BigQueryDeleteTableOperator(*, deletion_dataset_table, gcp_conn_id = 'google_cloud_default', delegate_to = None, ignore_if_missing = False, location = None, impersonation_chain = None, **kwargs)

   Bases: :py:obj:`airflow.models.BaseOperator`

   Deletes BigQuery tables

   .. seealso::
       For more information on how to use this operator, take a look at the guide:
       :ref:`howto/operator:BigQueryDeleteTableOperator`

   :param deletion_dataset_table: A dotted
       ``(<project>.|<project>:)<dataset>.<table>`` that indicates which table
       will be deleted. (templated)
   :param gcp_conn_id: (Optional) The connection ID used to connect to Google Cloud.
   :param delegate_to: The account to impersonate using domain-wide delegation of authority,
       if any. For this to work, the service account making the request must have
       domain-wide delegation enabled.
   :param ignore_if_missing: if True, then return success even if the
       requested table does not exist.
   :param location: The location used for the operation.
   :param impersonation_chain: Optional service account to impersonate using short-term
       credentials, or chained list of accounts required to get the access_token
       of the last account in the list, which will be impersonated in the request.
       If set as a string, the account must grant the originating account
       the Service Account Token Creator IAM role.
       If set as a sequence, the identities from the list must grant
       Service Account Token Creator IAM role to the directly preceding identity, with first
       account from the list granting this role to the originating account (templated).

   .. py:attribute:: template_fields
      :annotation: :Sequence[str] = ['deletion_dataset_table', 'impersonation_chain']

      
   .. py:attribute:: ui_color
      

   .. py:method:: execute(self, context)

      This is the main method to derive when creating an operator.
      Context is the same dictionary used as when rendering jinja templates.

      Refer to get_template_context for more context.


.. py:class:: BigQueryUpsertTableOperator(*, dataset_id, table_resource, project_id = None, gcp_conn_id = 'google_cloud_default', delegate_to = None, location = None, impersonation_chain = None, **kwargs)

   Bases: :py:obj:`airflow.models.BaseOperator`

   Upsert BigQuery table

   .. seealso::
       For more information on how to use this operator, take a look at the guide:
       :ref:`howto/operator:BigQueryUpsertTableOperator`

   :param dataset_id: A dotted
       ``(<project>.|<project>:)<dataset>`` that indicates which dataset
       will be updated. (templated)
   :param table_resource: a table resource. see
       https://cloud.google.com/bigquery/docs/reference/v2/tables#resource
   :param project_id: The name of the project where we want to update the dataset.
       Don't need to provide, if projectId in dataset_reference.
   :param gcp_conn_id: (Optional) The connection ID used to connect to Google Cloud.
   :param delegate_to: The account to impersonate, if any.
       For this to work, the service account making the request must have domain-wide
       delegation enabled.
   :param location: The location used for the operation.
   :param impersonation_chain: Optional service account to impersonate using short-term
       credentials, or chained list of accounts required to get the access_token
       of the last account in the list, which will be impersonated in the request.
       If set as a string, the account must grant the originating account
       the Service Account Token Creator IAM role.
       If set as a sequence, the identities from the list must grant
       Service Account Token Creator IAM role to the directly preceding identity, with first
       account from the list granting this role to the originating account (templated).

   .. py:attribute:: template_fields
      :annotation: :Sequence[str] = ['dataset_id', 'table_resource', 'impersonation_chain']

      
   .. py:attribute:: template_fields_renderers
      

   .. py:attribute:: ui_color
      

   .. py:attribute:: operator_extra_links
      

   .. py:method:: execute(self, context)

      This is the main method to derive when creating an operator.
      Context is the same dictionary used as when rendering jinja templates.

      Refer to get_template_context for more context.


.. py:class:: BigQueryUpdateTableSchemaOperator(*, schema_fields_updates, dataset_id, table_id, include_policy_tags = False, project_id = None, gcp_conn_id = 'google_cloud_default', delegate_to = None, impersonation_chain = None, **kwargs)

   Bases: :py:obj:`airflow.models.BaseOperator`

   Update BigQuery Table Schema
   Updates fields on a table schema based on contents of the supplied schema_fields_updates
   parameter. The supplied schema does not need to be complete, if the field
   already exists in the schema you only need to supply keys & values for the
   items you want to patch, just ensure the "name" key is set.

   .. seealso::
       For more information on how to use this operator, take a look at the guide:
       :ref:`howto/operator:BigQueryUpdateTableSchemaOperator`

   :param schema_fields_updates: a partial schema resource. see
       https://cloud.google.com/bigquery/docs/reference/rest/v2/tables#TableSchema

   **Example**: ::

       schema_fields_updates=[
           {"name": "emp_name", "description": "Some New Description"},
           {"name": "salary", "policyTags": {'names': ['some_new_policy_tag']},},
           {"name": "departments", "fields": [
               {"name": "name", "description": "Some New Description"},
               {"name": "type", "description": "Some New Description"}
           ]},
       ]

   :param include_policy_tags: (Optional) If set to True policy tags will be included in
       the update request which requires special permissions even if unchanged (default False)
       see https://cloud.google.com/bigquery/docs/column-level-security#roles
   :param dataset_id: A dotted
       ``(<project>.|<project>:)<dataset>`` that indicates which dataset
       will be updated. (templated)
   :param table_id: The table ID of the requested table. (templated)
   :param project_id: The name of the project where we want to update the dataset.
       Don't need to provide, if projectId in dataset_reference.
   :param gcp_conn_id: (Optional) The connection ID used to connect to Google Cloud.
   :param delegate_to: The account to impersonate, if any.
       For this to work, the service account making the request must have domain-wide
       delegation enabled.
   :param location: The location used for the operation.
   :param impersonation_chain: Optional service account to impersonate using short-term
       credentials, or chained list of accounts required to get the access_token
       of the last account in the list, which will be impersonated in the request.
       If set as a string, the account must grant the originating account
       the Service Account Token Creator IAM role.
       If set as a sequence, the identities from the list must grant
       Service Account Token Creator IAM role to the directly preceding identity, with first
       account from the list granting this role to the originating account (templated).

   .. py:attribute:: template_fields
      :annotation: :Sequence[str] = ['schema_fields_updates', 'dataset_id', 'table_id', 'project_id', 'impersonation_chain']

      
   .. py:attribute:: template_fields_renderers
      

   .. py:attribute:: ui_color
      

   .. py:attribute:: operator_extra_links
      

   .. py:method:: execute(self, context)

      This is the main method to derive when creating an operator.
      Context is the same dictionary used as when rendering jinja templates.

      Refer to get_template_context for more context.


.. py:class:: BigQueryInsertJobOperator(configuration, project_id = None, location = None, job_id = None, force_rerun = True, reattach_states = None, gcp_conn_id = 'google_cloud_default', delegate_to = None, impersonation_chain = None, cancel_on_kill = True, result_retry = DEFAULT_RETRY, result_timeout = None, **kwargs)

   Bases: :py:obj:`airflow.models.BaseOperator`

   Executes a BigQuery job. Waits for the job to complete and returns job id.
   This operator work in the following way:

   - it calculates a unique hash of the job using job's configuration or uuid if ``force_rerun`` is True
   - creates ``job_id`` in form of
       ``[provided_job_id | airflow_{dag_id}_{task_id}_{exec_date}]_{uniqueness_suffix}``
   - submits a BigQuery job using the ``job_id``
   - if job with given id already exists then it tries to reattach to the job if its not done and its
       state is in ``reattach_states``. If the job is done the operator will raise ``AirflowException``.

   Using ``force_rerun`` will submit a new job every time without attaching to already existing ones.

   For job definition see here:

       https://cloud.google.com/bigquery/docs/reference/v2/jobs

   .. seealso::
       For more information on how to use this operator, take a look at the guide:
       :ref:`howto/operator:BigQueryInsertJobOperator`


   :param configuration: The configuration parameter maps directly to BigQuery's
       configuration field in the job  object. For more details see
       https://cloud.google.com/bigquery/docs/reference/v2/jobs
   :param job_id: The ID of the job. It will be suffixed with hash of job configuration
       unless ``force_rerun`` is True.
       The ID must contain only letters (a-z, A-Z), numbers (0-9), underscores (_), or
       dashes (-). The maximum length is 1,024 characters. If not provided then uuid will
       be generated.
   :param force_rerun: If True then operator will use hash of uuid as job id suffix
   :param reattach_states: Set of BigQuery job's states in case of which we should reattach
       to the job. Should be other than final states.
   :param project_id: Google Cloud Project where the job is running
   :param location: location the job is running
   :param gcp_conn_id: The connection ID used to connect to Google Cloud.
   :param delegate_to: The account to impersonate using domain-wide delegation of authority,
       if any. For this to work, the service account making the request must have
       domain-wide delegation enabled.
   :param impersonation_chain: Optional service account to impersonate using short-term
       credentials, or chained list of accounts required to get the access_token
       of the last account in the list, which will be impersonated in the request.
       If set as a string, the account must grant the originating account
       the Service Account Token Creator IAM role.
       If set as a sequence, the identities from the list must grant
       Service Account Token Creator IAM role to the directly preceding identity, with first
       account from the list granting this role to the originating account (templated).
   :param cancel_on_kill: Flag which indicates whether cancel the hook's job or not, when on_kill is called
   :param result_retry: How to retry the `result` call that retrieves rows
   :param result_timeout: The number of seconds to wait for `result` method before using `result_retry`

   .. py:attribute:: template_fields
      :annotation: :Sequence[str] = ['configuration', 'job_id', 'impersonation_chain']

      
   .. py:attribute:: template_ext
      :annotation: :Sequence[str] = ['.json', '.sql']

      
   .. py:attribute:: template_fields_renderers
      

   .. py:attribute:: ui_color
      

   .. py:attribute:: operator_extra_links
      

   .. py:method:: prepare_template(self)

      Hook triggered after the templated fields get replaced by their content.

      If you need your operator to alter the content of the file before the
      template is rendered, it should override this method to do so.


   .. py:method:: execute(self, context)

      This is the main method to derive when creating an operator.
      Context is the same dictionary used as when rendering jinja templates.

      Refer to get_template_context for more context.


   .. py:method:: on_kill(self)

      Override this method to cleanup subprocesses when a task instance
      gets killed. Any use of the threading, subprocess or multiprocessing
      module within an operator needs to be cleaned up or it will leave
      ghost processes behind.