Source code for airflow.providers.google.cloud.operators.datapipeline

#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements.  See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License.  You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied.  See the License for the
# specific language governing permissions and limitations
# under the License.
"""This module contains Google Data Pipelines operators."""
from __future__ import annotations

from typing import TYPE_CHECKING, Sequence

from airflow.exceptions import AirflowException
from airflow.providers.google.cloud.hooks.datapipeline import DEFAULT_DATAPIPELINE_LOCATION, DataPipelineHook
from airflow.providers.google.cloud.operators.cloud_base import GoogleCloudBaseOperator

if TYPE_CHECKING:
    from airflow.utils.context import Context


[docs]class CreateDataPipelineOperator(GoogleCloudBaseOperator):
    """
    Creates a new Data Pipelines instance from the Data Pipelines API.

    :param body: The request body (contains instance of Pipeline). See:
        https://cloud.google.com/dataflow/docs/reference/data-pipelines/rest/v1/projects.locations.pipelines/create#request-body
    :param project_id: The ID of the GCP project that owns the job.
    :param location: The location to direct the Data Pipelines instance to (for example us-central1).
    :param gcp_conn_id: The connection ID to connect to the Google Cloud
        Platform.
    :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).

        .. warning::
            This option requires Apache Beam 2.39.0 or newer.

    Returns the created Data Pipelines instance in JSON representation.
    """

    def __init__(
        self,
        *,
        body: dict,
        project_id: str | None = None,
        location: str = DEFAULT_DATAPIPELINE_LOCATION,
        gcp_conn_id: str = "google_cloud_default",
        impersonation_chain: str | Sequence[str] | None = None,
        **kwargs,
    ) -> None:
        super().__init__(**kwargs)

        self.body = body
        self.project_id = project_id
        self.location = location
        self.gcp_conn_id = gcp_conn_id
        self.impersonation_chain = impersonation_chain
        self.datapipeline_hook: DataPipelineHook | None = None
        self.body["pipelineSources"] = {"airflow": "airflow"}

[docs]    def execute(self, context: Context):
        if self.body is None:
            raise AirflowException(
                "Request Body not given; cannot create a Data Pipeline without the Request Body."
            )
        if self.project_id is None:
            raise AirflowException(
                "Project ID not given; cannot create a Data Pipeline without the Project ID."
            )
        if self.location is None:
            raise AirflowException("location not given; cannot create a Data Pipeline without the location.")

        self.datapipeline_hook = DataPipelineHook(
            gcp_conn_id=self.gcp_conn_id,
            impersonation_chain=self.impersonation_chain,
        )

        self.data_pipeline = self.datapipeline_hook.create_data_pipeline(
            project_id=self.project_id,
            body=self.body,
            location=self.location,
        )
        if self.data_pipeline:
            if "error" in self.data_pipeline:
                raise AirflowException(self.data_pipeline.get("error").get("message"))

        return self.data_pipeline


[docs]class RunDataPipelineOperator(GoogleCloudBaseOperator):
    """
    Runs a Data Pipelines Instance using the Data Pipelines API.

    :param data_pipeline_name:  The display name of the pipeline. In example
        projects/PROJECT_ID/locations/LOCATION_ID/pipelines/PIPELINE_ID it would be the PIPELINE_ID.
    :param project_id: The ID of the GCP project that owns the job.
    :param location: The location to direct the Data Pipelines instance to (for example us-central1).
    :param gcp_conn_id: The connection ID to connect to the Google Cloud
        Platform.

    Returns the created Job in JSON representation.
    """

    def __init__(
        self,
        data_pipeline_name: str,
        project_id: str | None = None,
        location: str = DEFAULT_DATAPIPELINE_LOCATION,
        gcp_conn_id: str = "google_cloud_default",
        **kwargs,
    ) -> None:
        super().__init__(**kwargs)

        self.data_pipeline_name = data_pipeline_name
        self.project_id = project_id
        self.location = location
        self.gcp_conn_id = gcp_conn_id

[docs]    def execute(self, context: Context):
        self.data_pipeline_hook = DataPipelineHook(gcp_conn_id=self.gcp_conn_id)

        if self.data_pipeline_name is None:
            raise AirflowException("Data Pipeline name not given; cannot run unspecified pipeline.")
        if self.project_id is None:
            raise AirflowException("Data Pipeline Project ID not given; cannot run pipeline.")
        if self.location is None:
            raise AirflowException("Data Pipeline location not given; cannot run pipeline.")

        self.response = self.data_pipeline_hook.run_data_pipeline(
            data_pipeline_name=self.data_pipeline_name,
            project_id=self.project_id,
            location=self.location,
        )

        if self.response:
            if "error" in self.response:
                raise AirflowException(self.response.get("error").get("message"))

        return self.response