gcs_to_local

Data ProcessingStorage


Providers:

Run this DAG

1. Install Astronomer CLISkip if you already have the CLI

2. Initate the project:

3. Copy and paste the code below into a file in the

dags
directory.

4. Add the following to your requirements.txt file:

5. Run the DAG:

# 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.
import os
from airflow import models
from airflow.providers.google.cloud.transfers.gcs_to_local import GCSToLocalFilesystemOperator
from airflow.utils.dates import days_ago
PROJECT_ID = os.environ.get("GCP_PROJECT_ID", "example-id")
BUCKET = os.environ.get("GCP_GCS_BUCKET", "test-gcs-example-bucket")
PATH_TO_REMOTE_FILE = os.environ.get("GCP_GCS_PATH_TO_UPLOAD_FILE", "test-gcs-example-remote.txt")
PATH_TO_LOCAL_FILE = os.environ.get("GCP_GCS_PATH_TO_SAVED_FILE", "test-gcs-example-local.txt")
with models.DAG(
"example_gcs_to_local",
start_date=days_ago(1),
schedule_interval=None,
tags=['example'],
) as dag:
# [START howto_operator_gcs_download_file_task]
download_file = GCSToLocalFilesystemOperator(
task_id="download_file",
object_name=PATH_TO_REMOTE_FILE,
bucket=BUCKET,
filename=PATH_TO_LOCAL_FILE,
)
# [END howto_operator_gcs_download_file_task]