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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.
BigQueryTableExistenceAsyncSensor	Checks for the existence of a table in Google Big Query.
BigQueryTableExistencePartitionAsyncSensor	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)

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 | 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: Sequence[str] = ('project_id', 'dataset_id', 'table_id', 'impersonation_chain')

ui_color = '#f0eee4'

poke(context)

Override when deriving this class.

execute(context)

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

execute_complete(context, event=None)

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)

Bases: airflow.sensors.base.BaseSensorOperator

Checks for the existence of a partition within 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.

• partition_id (str) – The name of the partition to check the existence of.

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

• impersonation_chain (str | 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: Sequence[str] = ('project_id', 'dataset_id', 'table_id', 'partition_id', 'impersonation_chain')

ui_color = '#f0eee4'

poke(context)

Override when deriving this class.

execute(context)

Airflow runs this method on the worker and defers using the triggers if deferrable is True.

execute_complete(context, event=None)

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.BigQueryTableExistenceAsyncSensor(**kwargs)

Bases: BigQueryTableExistenceSensor

Checks for the existence of a table in Google Big Query.

This class is deprecated and will be removed in a future release.

Please use airflow.providers.google.cloud.sensors.bigquery.BigQueryTableExistenceSensor and set deferrable attribute to True instead.

Parameters

• project_id – 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 – The name of the dataset in which to look for the table. storage bucket.

• table_id – The name of the table to check the existence of.

• gcp_conn_id – The connection ID used to connect to Google Cloud.

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

• 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).

• polling_interval – The interval in seconds to wait between checks table existence.

class airflow.providers.google.cloud.sensors.bigquery.BigQueryTableExistencePartitionAsyncSensor(**kwargs)

Bases: BigQueryTablePartitionExistenceSensor

Checks for the existence of a partition within a table in Google BigQuery.

This class is deprecated and will be removed in a future release.

Please use airflow.providers.google.cloud.sensors.bigquery.BigQueryTablePartitionExistenceSensor and set deferrable attribute to True instead.

Parameters

• project_id – 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 – The name of the dataset in which to look for the table. storage bucket.

• partition_id – The name of the partition to check the existence of.

• table_id – The name of the table to check the existence of.

• gcp_conn_id – The connection ID used to connect to Google Cloud.

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

• 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).

• poke_interval – The interval in seconds to wait between checks table existence.

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