Airflow Summit 2021 is coming July 8-16. Register now!

airflow.providers.google.cloud.operators.bigquery

This module contains Google BigQuery operators.

Module Contents

class airflow.providers.google.cloud.operators.bigquery.BigQueryUIColors[source]

Bases: enum.Enum

Hex colors for BigQuery operators

CHECK = #C0D7FF[source]
QUERY = #A1BBFF[source]
TABLE = #81A0FF[source]
DATASET = #5F86FF[source]

Bases: airflow.models.BaseOperatorLink

Helper class for constructing BigQuery link.

name = BigQuery Console[source]

Bases: airflow.models.BaseOperatorLink

Helper class for constructing BigQuery link.

index :int[source]
name[source]
class airflow.providers.google.cloud.operators.bigquery.BigQueryCheckOperator(*, sql: str, gcp_conn_id: str = 'google_cloud_default', bigquery_conn_id: Optional[str] = None, use_legacy_sql: bool = True, location: Optional[str] = None, impersonation_chain: Optional[Union[str, Sequence[str]]] = None, labels: Optional[dict] = None, **kwargs)[source]

Bases: airflow.providers.google.cloud.operators.bigquery._BigQueryDbHookMixin, 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.

See also

For more information on how to use this operator, take a look at the guide: Check if query result has data

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.

Parameters
  • sql (str) -- the sql to be executed

  • gcp_conn_id (str) -- (Optional) The connection ID used to connect to Google Cloud.

  • bigquery_conn_id (str) -- (Deprecated) The connection ID used to connect to Google Cloud. This parameter has been deprecated. You should pass the gcp_conn_id parameter instead.

  • use_legacy_sql (bool) -- Whether to use legacy SQL (true) or standard SQL (false).

  • location (str) -- The geographic location of the job. See details at: https://cloud.google.com/bigquery/docs/locations#specifying_your_location

  • impersonation_chain (Union[str, Sequence[str]]) -- 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).

  • labels (dict) -- a dictionary containing labels for the table, passed to BigQuery

template_fields = ['sql', 'gcp_conn_id', 'impersonation_chain', 'labels'][source]
template_ext = ['.sql'][source]
ui_color[source]
class airflow.providers.google.cloud.operators.bigquery.BigQueryValueCheckOperator(*, sql: str, pass_value: Any, tolerance: Any = None, gcp_conn_id: str = 'google_cloud_default', bigquery_conn_id: Optional[str] = None, use_legacy_sql: bool = True, location: Optional[str] = None, impersonation_chain: Optional[Union[str, Sequence[str]]] = None, labels: Optional[dict] = None, **kwargs)[source]

Bases: airflow.providers.google.cloud.operators.bigquery._BigQueryDbHookMixin, airflow.operators.sql.SQLValueCheckOperator

Performs a simple value check using sql code.

See also

For more information on how to use this operator, take a look at the guide: Compare query result to pass value

Parameters
  • sql (str) -- the sql to be executed

  • use_legacy_sql (bool) -- Whether to use legacy SQL (true) or standard SQL (false).

  • gcp_conn_id (str) -- (Optional) The connection ID used to connect to Google Cloud.

  • bigquery_conn_id (str) -- (Deprecated) The connection ID used to connect to Google Cloud. This parameter has been deprecated. You should pass the gcp_conn_id parameter instead.

  • location (str) -- The geographic location of the job. See details at: https://cloud.google.com/bigquery/docs/locations#specifying_your_location

  • impersonation_chain (Union[str, Sequence[str]]) -- 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).

  • labels (dict) -- a dictionary containing labels for the table, passed to BigQuery

template_fields = ['sql', 'gcp_conn_id', 'pass_value', 'impersonation_chain', 'labels'][source]
template_ext = ['.sql'][source]
ui_color[source]
class airflow.providers.google.cloud.operators.bigquery.BigQueryIntervalCheckOperator(*, table: str, metrics_thresholds: dict, date_filter_column: str = 'ds', days_back: SupportsAbs[int] = - 7, gcp_conn_id: str = 'google_cloud_default', bigquery_conn_id: Optional[str] = None, use_legacy_sql: bool = True, location: Optional[str] = None, impersonation_chain: Optional[Union[str, Sequence[str]]] = None, labels: Optional[Dict] = None, **kwargs)[source]

Bases: airflow.providers.google.cloud.operators.bigquery._BigQueryDbHookMixin, 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>

See also

For more information on how to use this operator, take a look at the guide: Compare metrics over time

Parameters
  • table (str) -- the table name

  • days_back (int) -- number of days between ds and the ds we want to check against. Defaults to 7 days

  • metrics_thresholds (dict) -- 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.

  • use_legacy_sql (bool) -- Whether to use legacy SQL (true) or standard SQL (false).

  • gcp_conn_id (str) -- (Optional) The connection ID used to connect to Google Cloud.

  • bigquery_conn_id (str) -- (Deprecated) The connection ID used to connect to Google Cloud. This parameter has been deprecated. You should pass the gcp_conn_id parameter instead.

  • location (str) -- The geographic location of the job. See details at: https://cloud.google.com/bigquery/docs/locations#specifying_your_location

  • impersonation_chain (Union[str, Sequence[str]]) -- 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).

  • labels (dict) -- a dictionary containing labels for the table, passed to BigQuery

template_fields = ['table', 'gcp_conn_id', 'sql1', 'sql2', 'impersonation_chain', 'labels'][source]
ui_color[source]
class airflow.providers.google.cloud.operators.bigquery.BigQueryGetDataOperator(*, dataset_id: str, table_id: str, max_results: int = 100, selected_fields: Optional[str] = None, gcp_conn_id: str = 'google_cloud_default', bigquery_conn_id: Optional[str] = None, delegate_to: Optional[str] = None, location: Optional[str] = None, impersonation_chain: Optional[Union[str, Sequence[str]]] = None, **kwargs)[source]

Bases: 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']]

See also

For more information on how to use this operator, take a look at the guide: Fetch data from table

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'
)
Parameters
  • dataset_id (str) -- The dataset ID of the requested table. (templated)

  • table_id (str) -- The table ID of the requested table. (templated)

  • max_results (int) -- The maximum number of records (rows) to be fetched from the table. (templated)

  • selected_fields (str) -- List of fields to return (comma-separated). If unspecified, all fields are returned.

  • gcp_conn_id (str) -- (Optional) The connection ID used to connect to Google Cloud.

  • bigquery_conn_id (str) -- (Deprecated) The connection ID used to connect to Google Cloud. This parameter has been deprecated. You should pass the gcp_conn_id parameter instead.

  • delegate_to (str) -- 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.

  • location (str) -- The location used for the operation.

  • impersonation_chain (Union[str, Sequence[str]]) -- 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).

template_fields = ['dataset_id', 'table_id', 'max_results', 'selected_fields', 'impersonation_chain'][source]
ui_color[source]
execute(self, context)[source]
class airflow.providers.google.cloud.operators.bigquery.BigQueryExecuteQueryOperator(*, sql: Union[str, Iterable], destination_dataset_table: Optional[str] = None, write_disposition: str = 'WRITE_EMPTY', allow_large_results: Optional[bool] = False, flatten_results: Optional[bool] = None, gcp_conn_id: str = 'google_cloud_default', bigquery_conn_id: Optional[str] = None, delegate_to: Optional[str] = None, udf_config: Optional[list] = None, use_legacy_sql: bool = True, maximum_billing_tier: Optional[int] = None, maximum_bytes_billed: Optional[float] = None, create_disposition: str = 'CREATE_IF_NEEDED', schema_update_options: Optional[Union[list, tuple, set]] = None, query_params: Optional[list] = None, labels: Optional[dict] = None, priority: str = 'INTERACTIVE', time_partitioning: Optional[dict] = None, api_resource_configs: Optional[dict] = None, cluster_fields: Optional[List[str]] = None, location: Optional[str] = None, encryption_configuration: Optional[dict] = None, impersonation_chain: Optional[Union[str, Sequence[str]]] = None, **kwargs)[source]

Bases: airflow.models.BaseOperator

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

Parameters
  • sql (Can receive a str representing a sql statement, a list of str (sql statements), or reference to a template file. Template reference are recognized by str ending in '.sql'.) -- the sql code to be executed (templated)

  • destination_dataset_table (str) -- A dotted (<project>.|<project>:)<dataset>.<table> that, if set, will store the results of the query. (templated)

  • write_disposition (str) -- Specifies the action that occurs if the destination table already exists. (default: 'WRITE_EMPTY')

  • create_disposition (str) -- Specifies whether the job is allowed to create new tables. (default: 'CREATE_IF_NEEDED')

  • allow_large_results (bool) -- Whether to allow large results.

  • flatten_results (bool) -- 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.

  • gcp_conn_id (str) -- (Optional) The connection ID used to connect to Google Cloud.

  • bigquery_conn_id (str) -- (Deprecated) The connection ID used to connect to Google Cloud. This parameter has been deprecated. You should pass the gcp_conn_id parameter instead.

  • delegate_to (str) -- 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.

  • udf_config (list) -- The User Defined Function configuration for the query. See https://cloud.google.com/bigquery/user-defined-functions for details.

  • use_legacy_sql (bool) -- Whether to use legacy SQL (true) or standard SQL (false).

  • maximum_billing_tier (int) -- 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.

  • maximum_bytes_billed (float) -- 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.

  • api_resource_configs (dict) -- 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.

  • schema_update_options (Optional[Union[list, tuple, set]]) -- Allows the schema of the destination table to be updated as a side effect of the load job.

  • query_params (list) -- 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)

  • labels (dict) -- a dictionary containing labels for the job/query, passed to BigQuery

  • priority (str) -- Specifies a priority for the query. Possible values include INTERACTIVE and BATCH. The default value is INTERACTIVE.

  • time_partitioning (dict) -- configure optional time partitioning fields i.e. partition by field, type and expiration as per API specifications.

  • cluster_fields (list[str]) -- 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.

  • location (str) -- 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

  • encryption_configuration (dict) --

    [Optional] Custom encryption configuration (e.g., Cloud KMS keys). Example:

    encryption_configuration = {
        "kmsKeyName": "projects/testp/locations/us/keyRings/test-kr/cryptoKeys/test-key"
    }
    

  • impersonation_chain (Union[str, Sequence[str]]) -- 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).

template_fields = ['sql', 'destination_dataset_table', 'labels', 'query_params', 'impersonation_chain'][source]
template_ext = ['.sql'][source]
ui_color[source]

Return operator extra links

execute(self, context)[source]
on_kill(self)[source]
class airflow.providers.google.cloud.operators.bigquery.BigQueryCreateEmptyTableOperator(*, dataset_id: str, table_id: str, table_resource: Optional[Dict[str, Any]] = None, project_id: Optional[str] = None, schema_fields: Optional[List] = None, gcs_schema_object: Optional[str] = None, time_partitioning: Optional[Dict] = None, bigquery_conn_id: str = 'google_cloud_default', google_cloud_storage_conn_id: str = 'google_cloud_default', delegate_to: Optional[str] = None, labels: Optional[Dict] = None, view: Optional[Dict] = None, materialized_view: Optional[Dict] = None, encryption_configuration: Optional[Dict] = None, location: Optional[str] = None, cluster_fields: Optional[List[str]] = None, impersonation_chain: Optional[Union[str, Sequence[str]]] = None, exists_ok: bool = False, **kwargs)[source]

Bases: 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.

See also

For more information on how to use this operator, take a look at the guide: Create native table

Parameters
  • project_id (str) -- The project to create the table into. (templated)

  • dataset_id (str) -- The dataset to create the table into. (templated)

  • table_id (str) -- The Name of the table to be created. (templated)

  • table_resource (Dict[str, Any]) -- Table resource as described in documentation: https://cloud.google.com/bigquery/docs/reference/rest/v2/tables#Table If provided all other parameters are ignored.

  • schema_fields (list) --

    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"}]
    

  • gcs_schema_object (str) -- Full path to the JSON file containing schema (templated). For example: gs://test-bucket/dir1/dir2/employee_schema.json

  • time_partitioning (dict) --

    configure optional time partitioning fields i.e. partition by field, type and expiration as per API specifications.

  • bigquery_conn_id (str) -- [Optional] The connection ID used to connect to Google Cloud and interact with the Bigquery service.

  • google_cloud_storage_conn_id (str) -- [Optional] The connection ID used to connect to Google Cloud. and interact with the Google Cloud Storage service.

  • delegate_to (str) -- 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.

  • labels (dict) --

    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'
    )
    

  • view (dict) --

    [Optional] A dictionary containing definition for the view. If set, it will create a view instead of a table:

  • materialized_view (dict) -- [Optional] The materialized view definition.

  • encryption_configuration (dict) --

    [Optional] Custom encryption configuration (e.g., Cloud KMS keys). Example:

    encryption_configuration = {
        "kmsKeyName": "projects/testp/locations/us/keyRings/test-kr/cryptoKeys/test-key"
    }
    

  • location (str) -- The location used for the operation.

  • cluster_fields (list) --

    [Optional] The fields used for clustering. BigQuery supports clustering for both partitioned and non-partitioned tables.

  • impersonation_chain (Union[str, Sequence[str]]) -- 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).

  • exists_ok (bool) -- If True, ignore "already exists" errors when creating the table.

template_fields = ['dataset_id', 'table_id', 'project_id', 'gcs_schema_object', 'labels', 'view', 'materialized_view', 'impersonation_chain'][source]
template_fields_renderers[source]
ui_color[source]
execute(self, context)[source]
class airflow.providers.google.cloud.operators.bigquery.BigQueryCreateExternalTableOperator(*, bucket: str, source_objects: List, destination_project_dataset_table: str, table_resource: Optional[Dict[str, Any]] = None, schema_fields: Optional[List] = None, schema_object: Optional[str] = None, source_format: str = 'CSV', compression: str = 'NONE', skip_leading_rows: int = 0, field_delimiter: str = ',', max_bad_records: int = 0, quote_character: Optional[str] = None, allow_quoted_newlines: bool = False, allow_jagged_rows: bool = False, bigquery_conn_id: str = 'google_cloud_default', google_cloud_storage_conn_id: str = 'google_cloud_default', delegate_to: Optional[str] = None, src_fmt_configs: Optional[dict] = None, labels: Optional[Dict] = None, encryption_configuration: Optional[Dict] = None, location: Optional[str] = None, impersonation_chain: Optional[Union[str, Sequence[str]]] = None, **kwargs)[source]

Bases: 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.

See also

For more information on how to use this operator, take a look at the guide: Create external table

Parameters
  • bucket (str) -- The bucket to point the external table to. (templated)

  • source_objects (list) -- List of Google Cloud Storage URIs to point table to. If source_format is 'DATASTORE_BACKUP', the list must only contain a single URI.

  • destination_project_dataset_table (str) -- 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.

  • schema_fields (list) --

    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'.

  • table_resource (Dict[str, Any]) -- 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.

  • schema_object (str) -- If set, a GCS object path pointing to a .json file that contains the schema for the table. (templated)

  • source_format (str) -- File format of the data.

  • compression (str) -- [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.

  • skip_leading_rows (int) -- Number of rows to skip when loading from a CSV.

  • field_delimiter (str) -- The delimiter to use for the CSV.

  • max_bad_records (int) -- The maximum number of bad records that BigQuery can ignore when running the job.

  • quote_character (str) -- The value that is used to quote data sections in a CSV file.

  • allow_quoted_newlines (bool) -- Whether to allow quoted newlines (true) or not (false).

  • allow_jagged_rows (bool) -- 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.

  • bigquery_conn_id (str) -- (Optional) The connection ID used to connect to Google Cloud and interact with the Bigquery service.

  • google_cloud_storage_conn_id (str) -- (Optional) The connection ID used to connect to Google Cloud and interact with the Google Cloud Storage service.

  • delegate_to (str) -- 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.

  • src_fmt_configs (dict) -- configure optional fields specific to the source format

  • labels (dict) -- a dictionary containing labels for the table, passed to BigQuery

  • encryption_configuration (dict) --

    [Optional] Custom encryption configuration (e.g., Cloud KMS keys). Example:

    encryption_configuration = {
        "kmsKeyName": "projects/testp/locations/us/keyRings/test-kr/cryptoKeys/test-key"
    }
    

  • location (str) -- The location used for the operation.

  • impersonation_chain (Union[str, Sequence[str]]) -- 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).

template_fields = ['bucket', 'source_objects', 'schema_object', 'destination_project_dataset_table', 'labels', 'table_resource', 'impersonation_chain'][source]
template_fields_renderers[source]
ui_color[source]
execute(self, context)[source]
class airflow.providers.google.cloud.operators.bigquery.BigQueryDeleteDatasetOperator(*, dataset_id: str, project_id: Optional[str] = None, delete_contents: bool = False, gcp_conn_id: str = 'google_cloud_default', bigquery_conn_id: Optional[str] = None, delegate_to: Optional[str] = None, impersonation_chain: Optional[Union[str, Sequence[str]]] = None, **kwargs)[source]

Bases: 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

See also

For more information on how to use this operator, take a look at the guide: Delete dataset

Parameters
  • project_id (str) -- The project id of the dataset.

  • dataset_id (str) -- The dataset to be deleted.

  • delete_contents (bool) -- (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.

  • gcp_conn_id (str) -- (Optional) The connection ID used to connect to Google Cloud.

  • bigquery_conn_id (str) -- (Deprecated) The connection ID used to connect to Google Cloud. This parameter has been deprecated. You should pass the gcp_conn_id parameter instead.

  • delegate_to (str) -- 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.

  • impersonation_chain (Union[str, Sequence[str]]) -- 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)
template_fields = ['dataset_id', 'project_id', 'impersonation_chain'][source]
ui_color[source]
execute(self, context)[source]
class airflow.providers.google.cloud.operators.bigquery.BigQueryCreateEmptyDatasetOperator(*, dataset_id: Optional[str] = None, project_id: Optional[str] = None, dataset_reference: Optional[Dict] = None, location: Optional[str] = None, gcp_conn_id: str = 'google_cloud_default', bigquery_conn_id: Optional[str] = None, delegate_to: Optional[str] = None, impersonation_chain: Optional[Union[str, Sequence[str]]] = None, exists_ok: bool = False, **kwargs)[source]

Bases: 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

See also

For more information on how to use this operator, take a look at the guide: Create dataset

Parameters
  • project_id (str) -- The name of the project where we want to create the dataset.

  • dataset_id (str) -- The id of dataset. Don't need to provide, if datasetId in dataset_reference.

  • location (str) -- The geographic location where the dataset should reside.

  • dataset_reference (dict) -- Dataset reference that could be provided with request body. More info: https://cloud.google.com/bigquery/docs/reference/rest/v2/datasets#resource

  • gcp_conn_id (str) -- (Optional) The connection ID used to connect to Google Cloud.

  • bigquery_conn_id (str) -- (Deprecated) The connection ID used to connect to Google Cloud. This parameter has been deprecated. You should pass the gcp_conn_id parameter instead.

  • delegate_to (str) -- 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.

  • impersonation_chain (Union[str, Sequence[str]]) -- 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).

  • exists_ok -- If True, ignore "already exists" errors when creating the dataset.

template_fields = ['dataset_id', 'project_id', 'dataset_reference', 'impersonation_chain'][source]
template_fields_renderers[source]
ui_color[source]
execute(self, context)[source]
class airflow.providers.google.cloud.operators.bigquery.BigQueryGetDatasetOperator(*, dataset_id: str, project_id: Optional[str] = None, gcp_conn_id: str = 'google_cloud_default', delegate_to: Optional[str] = None, impersonation_chain: Optional[Union[str, Sequence[str]]] = None, **kwargs)[source]

Bases: airflow.models.BaseOperator

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

See also

For more information on how to use this operator, take a look at the guide: Get dataset details

Parameters
  • dataset_id (str) -- The id of dataset. Don't need to provide, if datasetId in dataset_reference.

  • project_id (str) -- The name of the project where we want to create the dataset. Don't need to provide, if projectId in dataset_reference.

  • gcp_conn_id (str) -- (Optional) The connection ID used to connect to Google Cloud.

  • delegate_to (str) -- 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.

  • impersonation_chain (Union[str, Sequence[str]]) -- 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).

Return type

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

template_fields = ['dataset_id', 'project_id', 'impersonation_chain'][source]
ui_color[source]
execute(self, context)[source]
class airflow.providers.google.cloud.operators.bigquery.BigQueryGetDatasetTablesOperator(*, dataset_id: str, project_id: Optional[str] = None, max_results: Optional[int] = None, gcp_conn_id: str = 'google_cloud_default', delegate_to: Optional[str] = None, impersonation_chain: Optional[Union[str, Sequence[str]]] = None, **kwargs)[source]

Bases: airflow.models.BaseOperator

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

See also

For more information on how to use this operator, take a look at the guide: List tables in dataset

Parameters
  • dataset_id (str) -- the dataset ID of the requested dataset.

  • project_id (str) -- (Optional) the project of the requested dataset. If None, self.project_id will be used.

  • max_results (int) -- (Optional) the maximum number of tables to return.

  • gcp_conn_id (str) -- (Optional) The connection ID used to connect to Google Cloud.

  • delegate_to (str) -- 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.

  • impersonation_chain (Union[str, Sequence[str]]) -- 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).

template_fields = ['dataset_id', 'project_id', 'impersonation_chain'][source]
ui_color[source]
execute(self, context)[source]
class airflow.providers.google.cloud.operators.bigquery.BigQueryPatchDatasetOperator(*, dataset_id: str, dataset_resource: dict, project_id: Optional[str] = None, gcp_conn_id: str = 'google_cloud_default', delegate_to: Optional[str] = None, impersonation_chain: Optional[Union[str, Sequence[str]]] = None, **kwargs)[source]

Bases: 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.

See also

For more information on how to use this operator, take a look at the guide: Patch dataset

Parameters
  • dataset_id (str) -- The id of dataset. Don't need to provide, if datasetId in dataset_reference.

  • dataset_resource (dict) -- Dataset resource that will be provided with request body. https://cloud.google.com/bigquery/docs/reference/rest/v2/datasets#resource

  • project_id (str) -- The name of the project where we want to create the dataset. Don't need to provide, if projectId in dataset_reference.

  • gcp_conn_id (str) -- (Optional) The connection ID used to connect to Google Cloud.

  • delegate_to (str) -- 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.

  • impersonation_chain (Union[str, Sequence[str]]) -- 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).

Return type

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

template_fields = ['dataset_id', 'project_id', 'impersonation_chain'][source]
template_fields_renderers[source]
ui_color[source]
execute(self, context)[source]
class airflow.providers.google.cloud.operators.bigquery.BigQueryUpdateTableOperator(*, table_resource: Dict[str, Any], fields: Optional[List[str]] = None, dataset_id: Optional[str] = None, table_id: Optional[str] = None, project_id: Optional[str] = None, gcp_conn_id: str = 'google_cloud_default', delegate_to: Optional[str] = None, impersonation_chain: Optional[Union[str, Sequence[str]]] = None, **kwargs)[source]

Bases: 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.

See also

For more information on how to use this operator, take a look at the guide: Update table

Parameters
  • dataset_id -- The id of dataset. Don't need to provide, if datasetId in table_reference.

  • table_id (str) -- The id of table. Don't need to provide, if tableId in table_reference.

  • table_resource (Dict[str, Any]) -- Dataset resource that will be provided with request body. https://cloud.google.com/bigquery/docs/reference/rest/v2/tables#resource

  • fields (List[str]) -- The fields of table to change, spelled as the Table properties (e.g. "friendly_name").

  • project_id (str) -- The name of the project where we want to create the table. Don't need to provide, if projectId in table_reference.

  • gcp_conn_id (str) -- (Optional) The connection ID used to connect to Google Cloud.

  • delegate_to (str) -- 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.

  • impersonation_chain (Union[str, Sequence[str]]) -- 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).

Return type

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

template_fields = ['dataset_id', 'table_id', 'project_id', 'impersonation_chain'][source]
template_fields_renderers[source]
ui_color[source]
execute(self, context)[source]
class airflow.providers.google.cloud.operators.bigquery.BigQueryUpdateDatasetOperator(*, dataset_resource: Dict[str, Any], fields: Optional[List[str]] = None, dataset_id: Optional[str] = None, project_id: Optional[str] = None, gcp_conn_id: str = 'google_cloud_default', delegate_to: Optional[str] = None, impersonation_chain: Optional[Union[str, Sequence[str]]] = None, **kwargs)[source]

Bases: 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.

See also

For more information on how to use this operator, take a look at the guide: Update dataset

Parameters
  • dataset_id (str) -- The id of dataset. Don't need to provide, if datasetId in dataset_reference.

  • dataset_resource (Dict[str, Any]) -- Dataset resource that will be provided with request body. https://cloud.google.com/bigquery/docs/reference/rest/v2/datasets#resource

  • fields (Sequence[str]) -- The properties of dataset to change (e.g. "friendly_name").

  • project_id (str) -- The name of the project where we want to create the dataset. Don't need to provide, if projectId in dataset_reference.

  • gcp_conn_id (str) -- (Optional) The connection ID used to connect to Google Cloud.

  • delegate_to (str) -- 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.

  • impersonation_chain (Union[str, Sequence[str]]) -- 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).

Return type

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

template_fields = ['dataset_id', 'project_id', 'impersonation_chain'][source]
template_fields_renderers[source]
ui_color[source]
execute(self, context)[source]
class airflow.providers.google.cloud.operators.bigquery.BigQueryDeleteTableOperator(*, deletion_dataset_table: str, gcp_conn_id: str = 'google_cloud_default', bigquery_conn_id: Optional[str] = None, delegate_to: Optional[str] = None, ignore_if_missing: bool = False, location: Optional[str] = None, impersonation_chain: Optional[Union[str, Sequence[str]]] = None, **kwargs)[source]

Bases: airflow.models.BaseOperator

Deletes BigQuery tables

See also

For more information on how to use this operator, take a look at the guide: Delete table

Parameters
  • deletion_dataset_table (str) -- A dotted (<project>.|<project>:)<dataset>.<table> that indicates which table will be deleted. (templated)

  • gcp_conn_id (str) -- (Optional) The connection ID used to connect to Google Cloud.

  • bigquery_conn_id (str) -- (Deprecated) The connection ID used to connect to Google Cloud. This parameter has been deprecated. You should pass the gcp_conn_id parameter instead.

  • delegate_to (str) -- 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.

  • ignore_if_missing (bool) -- if True, then return success even if the requested table does not exist.

  • location (str) -- The location used for the operation.

  • impersonation_chain (Union[str, Sequence[str]]) -- 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).

template_fields = ['deletion_dataset_table', 'impersonation_chain'][source]
ui_color[source]
execute(self, context)[source]
class airflow.providers.google.cloud.operators.bigquery.BigQueryUpsertTableOperator(*, dataset_id: str, table_resource: dict, project_id: Optional[str] = None, gcp_conn_id: str = 'google_cloud_default', bigquery_conn_id: Optional[str] = None, delegate_to: Optional[str] = None, location: Optional[str] = None, impersonation_chain: Optional[Union[str, Sequence[str]]] = None, **kwargs)[source]

Bases: airflow.models.BaseOperator

Upsert BigQuery table

See also

For more information on how to use this operator, take a look at the guide: Upsert table

Parameters
  • dataset_id (str) -- A dotted (<project>.|<project>:)<dataset> that indicates which dataset will be updated. (templated)

  • table_resource (dict) -- a table resource. see https://cloud.google.com/bigquery/docs/reference/v2/tables#resource

  • project_id (str) -- The name of the project where we want to update the dataset. Don't need to provide, if projectId in dataset_reference.

  • gcp_conn_id (str) -- (Optional) The connection ID used to connect to Google Cloud.

  • bigquery_conn_id (str) -- (Deprecated) The connection ID used to connect to Google Cloud. This parameter has been deprecated. You should pass the gcp_conn_id parameter instead.

  • delegate_to (str) -- The account to impersonate, if any. For this to work, the service account making the request must have domain-wide delegation enabled.

  • location (str) -- The location used for the operation.

  • impersonation_chain (Union[str, Sequence[str]]) -- 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).

template_fields = ['dataset_id', 'table_resource', 'impersonation_chain'][source]
template_fields_renderers[source]
ui_color[source]
execute(self, context)[source]
class airflow.providers.google.cloud.operators.bigquery.BigQueryUpdateTableSchemaOperator(*, schema_fields_updates: List[Dict[str, Any]], include_policy_tags: Optional[bool] = False, dataset_id: Optional[str] = None, table_id: Optional[str] = None, project_id: Optional[str] = None, gcp_conn_id: str = 'google_cloud_default', delegate_to: Optional[str] = None, impersonation_chain: Optional[Union[str, Sequence[str]]] = None, **kwargs)[source]

Bases: 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.

See also

For more information on how to use this operator, take a look at the guide: Update table schema

Parameters

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"}
    ]},
]
Parameters
  • include_policy_tags (bool) -- (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

  • dataset_id (str) -- A dotted (<project>.|<project>:)<dataset> that indicates which dataset will be updated. (templated)

  • table_id (str) -- The table ID of the requested table. (templated)

  • project_id (str) -- The name of the project where we want to update the dataset. Don't need to provide, if projectId in dataset_reference.

  • gcp_conn_id (str) -- (Optional) The connection ID used to connect to Google Cloud.

  • bigquery_conn_id (str) -- (Deprecated) The connection ID used to connect to Google Cloud. This parameter has been deprecated. You should pass the gcp_conn_id parameter instead.

  • delegate_to (str) -- The account to impersonate, if any. For this to work, the service account making the request must have domain-wide delegation enabled.

  • location (str) -- The location used for the operation.

  • impersonation_chain (Union[str, Sequence[str]]) -- 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).

template_fields = ['schema_fields_updates', 'dataset_id', 'table_id', 'project_id', 'impersonation_chain'][source]
template_fields_renderers[source]
ui_color[source]
execute(self, context)[source]
class airflow.providers.google.cloud.operators.bigquery.BigQueryInsertJobOperator(configuration: Dict[str, Any], project_id: Optional[str] = None, location: Optional[str] = None, job_id: Optional[str] = None, force_rerun: bool = True, reattach_states: Optional[Set[str]] = None, gcp_conn_id: str = 'google_cloud_default', delegate_to: Optional[str] = None, impersonation_chain: Optional[Union[str, Sequence[str]]] = None, cancel_on_kill: bool = True, **kwargs)[source]

Bases: 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:

See also

For more information on how to use this operator, take a look at the guide: Execute BigQuery jobs

Parameters
  • configuration (Dict[str, Any]) -- 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

  • job_id (str) -- 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.

  • force_rerun (bool) -- If True then operator will use hash of uuid as job id suffix

  • reattach_states -- Set of BigQuery job's states in case of which we should reattach to the job. Should be other than final states.

  • project_id (str) -- Google Cloud Project where the job is running

  • location (str) -- location the job is running

  • gcp_conn_id (str) -- The connection ID used to connect to Google Cloud.

  • delegate_to (str) -- 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.

  • impersonation_chain (Union[str, Sequence[str]]) -- 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).

  • cancel_on_kill (bool) -- Flag which indicates whether cancel the hook's job or not, when on_kill is called

template_fields = ['configuration', 'job_id', 'impersonation_chain'][source]
template_ext = ['.json'][source]
template_fields_renderers[source]
ui_color[source]
prepare_template(self)[source]
execute(self, context: Any)[source]
on_kill(self)[source]

Was this entry helpful?