CloudDLPDeleteDLPJobOperator

Google

Deletes a long-running DlpJob. This method indicates that the client is no longer interested in the DlpJob result. The job will be cancelled if possible.

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Last Updated: May. 7, 2021

Access Instructions

Install the Google provider package into your Airflow environment.

Import the module into your DAG file and instantiate it with your desired params.

Parameters

dlp_job_idstrThe ID of the DLP job resource to be cancelled.
project_idstr(Optional) Google Cloud project ID where the DLP Instance exists. If set to None or missing, the default project_id from the Google Cloud connection is used.
retrygoogle.api_core.retry.Retry(Optional) A retry object used to retry requests. If None is specified, requests will not be retried.
timeoutfloat(Optional) The amount of time, in seconds, to wait for the request to complete. Note that if retry is specified, the timeout applies to each individual attempt.
metadatasequence[tuple[str, str]]](Optional) Additional metadata that is provided to the method.
gcp_conn_idstr(Optional) The connection ID used to connect to Google Cloud.
impersonation_chainUnion[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).

Documentation

Deletes a long-running DlpJob. This method indicates that the client is no longer interested in the DlpJob result. The job will be cancelled if possible.

See also

For more information on how to use this operator, take a look at the guide: Deleting Job

Example DAGs

Improve this module by creating an example DAG.

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  1. Add an `example_dags` directory to the top-level source of the provider package with an empty `__init__.py` file.
  2. Add your DAG to this directory. Be sure to include a well-written and descriptive docstring
  3. Create a pull request against the source code. Once the package gets released, your DAG will show up on the Registry.

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