File storages, such as videos, images etc might occupy the most proportion of most brand servers you might be using. Furthermore, cloud storage capability interaction with business applications is one of the most important items to assess if the cloud storage is excellent or not.
Google Cloud storage pricing per GB is not pricey comparably. And its integration capability with applications are super flexible and friendly. Thus, in this piece, I would walk through Google cloud storage CRUD. By the end of this piece, you can refer and apply these methods to set up applications with Google cloud storage
Table of Contents: Ultimate Guide to Google Cloud Storage CRUD using Python
- Create a GCP Bucket
- Upload Files
- Rename files
- Update files
- Delete files
- Create folders
- Delete folders
- Enable public access to the GCS folders
- Fetch Files Hosted in Google Cloud Storage
- Google Storage PlayBook
Create a Bucket
Buckets in GCP are basic containers where you can store data in the cloud. The objects you store in the cloud are contained or stored in buckets. You cannot use buckets the same way you use directories or folders. The creation process is a bit more restrictive. You also cannot delete buckets. So if you like to store and contain data in GCP for web apps, machine learning purposes etc, creating a bucket is the 1st step. Here is the sample code as follows:
from google.cloud import storage
def create_bucket(bucket_name):
credentials = service_account.Credentials.from_service_account_info(json dict)
bucket = client.create_bucket(bucket_name, location='US-EAST1')
return f"Bucket {bucket.name} created."
bucket_name = "Buyfromlo Bucket"
create_bucket(bucket_name)Upload Files
Below is the sample code to continue the bucket creation along with uploading files using Python scripts.
from google.cloud import storage
def upload_file(bucket_name):
credentials = service_account.Credentials.from_service_account_info(json dict)
bucket = client.bucket(bucket_name)
file_name = 'my-file.txt'
blob = bucket.blob(file_name)
with open(file_name, 'rb') as f:
acontents = f.read()
blob.upload_from_string(contents)
return f'File {file_name} uploaded to {blob.public_url}'
bucket_name = "handsoncloud-new-bucket"
upload_file(bucket_name)Rename files
Naming is one of the most important processes on file access, notably when you deal with a large bunch of datasets like in Machine learning. Here is the sample code as follows how to automatically name the file using Python
bucket.rename_blob(blob, new_file_name)Update files
metadata = {'description': 'This file metadata is updated via HandsOnCloud Tutorial'}
blob.metadata = metadata
blob.patch()
print(f'Metadata for file {file_name} updated.')Delete files
blob = bucket.blob(file_name)
blob.delete()
print(f'File {file_name} deleted.')Create Folders
folder_name = 'Buyfromlo Images'
folder = bucket.blob(folder_name)
folder.upload_from_string('')
print(f'Folder {folder_name} created.')Delete Folders
GCP runs a pay-as-you-go model which implies it doesn’t have any upfront cost, and it can hugely facilitate business to reduce regular recurring fixed cost. Being said that, it’s a monthly charging model. For instance, if you have 5GB data contained always-on in the GCP, it still costs your recurring fees. Therefore, accordingly you need to know how to delete unuseful data for the purpose to avoid wasting dollars.
Here is the sample code as follows:
folder_name = 'buyfromlo'
folder = bucket.blob(folder_name)
folder.delete()
print(f'Folder {folder_name} deleted.')Enable public access to the GCS folders
By default the new files and dataset created and stored on GCP are not open to the public. That implies your app or your script can’t access the file. You must activate and enable public access. Fortunately the way is very straightforward and easy. Here is the code sample as follows:
from google.cloud import storage
from typing import List
def make_bucket_public(bucket_name: str, members: List[str] = ["allUsers"]):
credentials = service_account.Credentials.from_service_account_info(json dict)
bucket = client.bucket(bucket_name)
policy = bucket.get_iam_policy(requested_policy_version=3)
policy.bindings.append({"role": "roles/storage.objectViewer", "members":. members})
bucket.set_iam_policy(policy)
return f"Bucket {bucket.name} is now publicly readable"Fetch Files Hosted in Google Cloud Storage
For more details regarding fetching dataset contained in Google Cloud Storage, please refer to the article as follows:
https://www.easy2digital.com/automation/data/chapter-78-fetching-media-files-using-google-cloud-storage-and-python/
Google Storage Playbook.
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