`airflow.providers.google.cloud.sensors.bigquery`¶

This module contains Google BigQuery sensors.

Module Contents¶

Classes¶

`BigQueryTableExistenceSensor`	Checks for the existence of a table in Google Bigquery.
`BigQueryTablePartitionExistenceSensor`	Checks for the existence of a partition within a table in Google Bigquery.

class airflow.providers.google.cloud.sensors.bigquery.BigQueryTableExistenceSensor(*, project_id, dataset_id, table_id, gcp_conn_id='google_cloud_default', impersonation_chain=None, deferrable=conf.getboolean('operators', 'default_deferrable', fallback=False), **kwargs)[source]¶

Bases: airflow.sensors.base.BaseSensorOperator

Checks for the existence of a table in Google Bigquery.

Parameters

project_id (str) – The Google cloud project in which to look for the table. The connection supplied to the hook must provide access to the specified project.
dataset_id (str) – The name of the dataset in which to look for the table. storage bucket.
table_id (str) – The name of the table to check the existence of.
gcp_conn_id (str) – (Optional) The connection ID used to connect to Google Cloud.
impersonation_chain (str | collections.abc.Sequence[str] | None) – 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: collections.abc.Sequence[str] = ('project_id', 'dataset_id', 'table_id', 'impersonation_chain')[source]¶

ui_color = '#f0eee4'[source]¶

poke(context)[source]¶

Override when deriving this class.

execute(context)[source]¶

Airflow runs this method on the worker and defers using the trigger.

execute_complete(context, event=None)[source]¶

Act as a callback for when the trigger fires - returns immediately.

Relies on trigger to throw an exception, otherwise it assumes execution was successful.

class airflow.providers.google.cloud.sensors.bigquery.BigQueryTablePartitionExistenceSensor(*, project_id, dataset_id, table_id, partition_id, gcp_conn_id='google_cloud_default', impersonation_chain=None, deferrable=conf.getboolean('operators', 'default_deferrable', fallback=False), **kwargs)[source]¶