airflow.providers.google.cloud.sensors.bigquery

This module contains a Google Bigquery sensor.

Module Contents

class airflow.providers.google.cloud.sensors.bigquery.BigQueryTableExistenceSensor(*, project_id: str, dataset_id: str, table_id: str, bigquery_conn_id: str = 'google_cloud_default', delegate_to: Optional[str] = None, impersonation_chain: Optional[Union[str, Sequence[str]]] = None, **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.

  • bigquery_conn_id (str) – The connection ID to use when connecting to Google BigQuery.

  • 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 = ['project_id', 'dataset_id', 'table_id', 'impersonation_chain'][source]
ui_color = #f0eee4[source]
poke(self, context: dict)[source]
class airflow.providers.google.cloud.sensors.bigquery.BigQueryTablePartitionExistenceSensor(*, project_id: str, dataset_id: str, table_id: str, partition_id: str, bigquery_conn_id: str = 'google_cloud_default', delegate_to: Optional[str] = None, impersonation_chain: Optional[Union[str, Sequence[str]]] = None, **kwargs)[source]

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.

  • bigquery_conn_id (str) – The connection ID to use when connecting to Google BigQuery.

  • 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.

  • 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 = ['project_id', 'dataset_id', 'table_id', 'partition_id', 'impersonation_chain'][source]
ui_color = #f0eee4[source]
poke(self, context: dict)[source]

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