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"""This module contains a Google Bigquery sensor."""
from typing import TYPE_CHECKING, Optional, Sequence, Union
from airflow.providers.google.cloud.hooks.bigquery import BigQueryHook
from airflow.sensors.base import BaseSensorOperator
if TYPE_CHECKING:
from airflow.utils.context import Context
[docs]class BigQueryTableExistenceSensor(BaseSensorOperator):
"""
Checks for the existence of a table in Google Bigquery.
:param 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.
:param dataset_id: The name of the dataset in which to look for the table.
storage bucket.
:param table_id: The name of the table to check the existence of.
:param gcp_conn_id: (Optional) The connection ID used to connect to Google Cloud.
: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 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).
"""
[docs] template_fields: Sequence[str] = (
'project_id',
'dataset_id',
'table_id',
'impersonation_chain',
)
def __init__(
self,
*,
project_id: str,
dataset_id: str,
table_id: str,
gcp_conn_id: str = 'google_cloud_default',
delegate_to: Optional[str] = None,
impersonation_chain: Optional[Union[str, Sequence[str]]] = None,
**kwargs,
) -> None:
super().__init__(**kwargs)
self.project_id = project_id
self.dataset_id = dataset_id
self.table_id = table_id
self.gcp_conn_id = gcp_conn_id
self.delegate_to = delegate_to
self.impersonation_chain = impersonation_chain
[docs] def poke(self, context: 'Context') -> bool:
table_uri = f'{self.project_id}:{self.dataset_id}.{self.table_id}'
self.log.info('Sensor checks existence of table: %s', table_uri)
hook = BigQueryHook(
gcp_conn_id=self.gcp_conn_id,
delegate_to=self.delegate_to,
impersonation_chain=self.impersonation_chain,
)
return hook.table_exists(
project_id=self.project_id, dataset_id=self.dataset_id, table_id=self.table_id
)
[docs]class BigQueryTablePartitionExistenceSensor(BaseSensorOperator):
"""
Checks for the existence of a partition within a table in Google Bigquery.
:param 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.
:param dataset_id: The name of the dataset in which to look for the table.
storage bucket.
:param table_id: The name of the table to check the existence of.
:param partition_id: The name of the partition to check the existence of.
:param gcp_conn_id: (Optional) The connection ID used to connect to Google Cloud.
:param delegate_to: The account to impersonate, if any.
For this to work, the service account making the request must
have domain-wide delegation enabled.
: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).
"""
[docs] template_fields: Sequence[str] = (
'project_id',
'dataset_id',
'table_id',
'partition_id',
'impersonation_chain',
)
def __init__(
self,
*,
project_id: str,
dataset_id: str,
table_id: str,
partition_id: str,
gcp_conn_id: str = 'google_cloud_default',
delegate_to: Optional[str] = None,
impersonation_chain: Optional[Union[str, Sequence[str]]] = None,
**kwargs,
) -> None:
super().__init__(**kwargs)
self.project_id = project_id
self.dataset_id = dataset_id
self.table_id = table_id
self.partition_id = partition_id
self.gcp_conn_id = gcp_conn_id
self.delegate_to = delegate_to
self.impersonation_chain = impersonation_chain
[docs] def poke(self, context: 'Context') -> bool:
table_uri = f'{self.project_id}:{self.dataset_id}.{self.table_id}'
self.log.info('Sensor checks existence of partition: "%s" in table: %s', self.partition_id, table_uri)
hook = BigQueryHook(
gcp_conn_id=self.gcp_conn_id,
delegate_to=self.delegate_to,
impersonation_chain=self.impersonation_chain,
)
return hook.table_partition_exists(
project_id=self.project_id,
dataset_id=self.dataset_id,
table_id=self.table_id,
partition_id=self.partition_id,
)