:py:mod:`airflow.providers.google.cloud.transfers.gcs_to_bigquery`
==================================================================

.. py:module:: airflow.providers.google.cloud.transfers.gcs_to_bigquery

.. autoapi-nested-parse::

   This module contains a Google Cloud Storage to BigQuery operator.


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

Classes
~~~~~~~

.. autoapisummary::

   airflow.providers.google.cloud.transfers.gcs_to_bigquery.GCSToBigQueryOperator


.. py:class:: GCSToBigQueryOperator(*, bucket, source_objects, destination_project_dataset_table, schema_fields=None, schema_object=None, source_format='CSV', compression='NONE', create_disposition='CREATE_IF_NEEDED', skip_leading_rows=0, write_disposition='WRITE_EMPTY', field_delimiter=',', max_bad_records=0, quote_character=None, ignore_unknown_values=False, allow_quoted_newlines=False, allow_jagged_rows=False, encoding='UTF-8', max_id_key=None, gcp_conn_id='google_cloud_default', delegate_to=None, schema_update_options=(), src_fmt_configs=None, external_table=False, time_partitioning=None, cluster_fields=None, autodetect=True, encryption_configuration=None, location=None, impersonation_chain = None, labels=None, description=None, **kwargs)

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

   Loads files from Google Cloud Storage into BigQuery.

   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:GCSToBigQueryOperator`

   :param bucket: The bucket to load from. (templated)
   :param source_objects: String or List of Google Cloud Storage URIs to load from. (templated)
       If source_format is 'DATASTORE_BACKUP', the list must only contain a single URI.
   :param destination_project_dataset_table: The dotted
       ``(<project>.|<project>:)<dataset>.<table>`` BigQuery table to load data into.
       If ``<project>`` is not included, project will be the project defined in
       the connection json. (templated)
   :param schema_fields: If set, the schema field list as defined here:
       https://cloud.google.com/bigquery/docs/reference/v2/jobs#configuration.load
       Should not be set when source_format is 'DATASTORE_BACKUP'.
       Parameter must be defined if 'schema_object' is null and autodetect is False.
   :param schema_object: If set, a GCS object path pointing to a .json file that
       contains the schema for the table. (templated)
       Parameter must be defined if 'schema_fields' is null and autodetect is False.
   :param source_format: File format to export.
   :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 create_disposition: The create disposition if the table doesn't exist.
   :param skip_leading_rows: Number of rows to skip when loading from a CSV.
   :param write_disposition: The write disposition if the table already exists.
   :param field_delimiter: The delimiter to use when loading from a 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 ignore_unknown_values: [Optional] Indicates if BigQuery should allow
       extra values that are not represented in the table schema.
       If true, the extra values are ignored. If false, records with extra columns
       are treated as bad records, and if there are too many bad records, an
       invalid error is returned in the job result.
   :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 encoding: The character encoding of the data. See:
       https://cloud.google.com/bigquery/docs/reference/rest/v2/jobs#configuration.query.tableDefinitions.(key).csvOptions.encoding
       https://cloud.google.com/bigquery/docs/reference/rest/v2/tables#externalDataConfiguration.csvOptions.encoding
   :param max_id_key: If set, the name of a column in the BigQuery table
       that's to be loaded. This will be used to select the MAX value from
       BigQuery after the load occurs. The results will be returned by the
       execute() command, which in turn gets stored in XCom for future
       operators to use. This can be helpful with incremental loads--during
       future executions, you can pick up from the max ID.
   :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 schema_update_options: Allows the schema of the destination
       table to be updated as a side effect of the load job.
   :param src_fmt_configs: configure optional fields specific to the source format
   :param external_table: Flag to specify if the destination table should be
       a BigQuery external table. Default Value is False.
   :param time_partitioning: configure optional time partitioning fields i.e.
       partition by field, type and  expiration as per API specifications.
       Note that 'field' is not available in concurrency with
       dataset.table$partition.
   :param cluster_fields: Request that the result of this load 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.
       Not applicable for external tables.
   :param autodetect: [Optional] Indicates if we should automatically infer the
       options and schema for CSV and JSON sources. (Default: ``True``).
       Parameter must be set to True if 'schema_fields' and 'schema_object' are undefined.
       It is suggested to set to True if table are create outside of Airflow.
   :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: [Optional] 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 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: [Optional] Labels for the BiqQuery table.
   :param description: [Optional] Description for the BigQuery table.

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

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

      
   .. py:attribute:: ui_color
      :annotation: = #f0eee4

      
   .. 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.