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from __future__ import annotations

from typing import TYPE_CHECKING, Sequence

from import QuickSightHook
from import AwsBaseOperator
from import aws_template_fields

    from airflow.utils.context import Context

[docs]class QuickSightCreateIngestionOperator(AwsBaseOperator[QuickSightHook]): """ Creates and starts a new SPICE ingestion for a dataset; also helps to Refresh existing SPICE datasets. .. seealso:: For more information on how to use this operator, take a look at the guide: :ref:`howto/operator:QuickSightCreateIngestionOperator` :param data_set_id: ID of the dataset used in the ingestion. :param ingestion_id: ID for the ingestion. :param ingestion_type: Type of ingestion. Values Can be INCREMENTAL_REFRESH or FULL_REFRESH. Default FULL_REFRESH. :param wait_for_completion: If wait is set to True, the time interval, in seconds, that the operation waits to check the status of the Amazon QuickSight Ingestion. :param check_interval: if wait is set to be true, this is the time interval in seconds which the operator will check the status of the Amazon QuickSight Ingestion :param aws_conn_id: The Airflow connection used for AWS credentials. If this is ``None`` or empty then the default boto3 behaviour is used. If running Airflow in a distributed manner and aws_conn_id is None or empty, then default boto3 configuration would be used (and must be maintained on each worker node). :param region_name: AWS region_name. If not specified then the default boto3 behaviour is used. :param verify: Whether or not to verify SSL certificates. See: :param botocore_config: Configuration dictionary (key-values) for botocore client. See: """
[docs] aws_hook_class = QuickSightHook
[docs] template_fields: Sequence[str] = aws_template_fields( "data_set_id", "ingestion_id", "ingestion_type", "wait_for_completion", "check_interval", )
[docs] ui_color = "#ffd700"
def __init__( self, data_set_id: str, ingestion_id: str, ingestion_type: str = "FULL_REFRESH", wait_for_completion: bool = True, check_interval: int = 30, **kwargs, ): super().__init__(**kwargs) self.data_set_id = data_set_id self.ingestion_id = ingestion_id self.ingestion_type = ingestion_type self.wait_for_completion = wait_for_completion self.check_interval = check_interval
[docs] def execute(self, context: Context):"Running the Amazon QuickSight SPICE Ingestion on Dataset ID: %s", self.data_set_id) return self.hook.create_ingestion( data_set_id=self.data_set_id, ingestion_id=self.ingestion_id, ingestion_type=self.ingestion_type, wait_for_completion=self.wait_for_completion, check_interval=self.check_interval, )

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