I'm using a combination of the GCS python SDK and google API client to loop through a version-enabled bucket and download specific objects based on metadata.
from google.cloud import storage
from googleapiclient import discovery
from oauth2client.client import GoogleCredentials
def downloadepoch_objects():
request = service.objects().list(
bucket=bucket_name,
versions=True
)
response = request.execute()
for item in response['items']:
if item['metadata']['epoch'] == restore_epoch:
print(item['bucket'])
print(item['name'])
print(item['metadata']['epoch'])
print(item['updated'])
blob = source_bucket.blob(item['name'])
blob.download_to_filename(
'/Users/admin/git/data-processing/{}'.format(item))
downloadepoch_objects()
The above function works properly for a blob that is not within a directory (gs://bucketname/test1.txt) as the item that gets passed in is simply test1.txt. The issue I am running into is when trying to download files from a complex directory tree (gs://bucketname/nfs/media/docs/test1.txt) The item that gets passed is nfs/media/docs/test1.txt. Is it possible to have the.download_to_file() method to create directories if they are not present?
GCS does not have a notion of "directories," although tools like gsutil do a good job of pretending for convenience. If you want all of the objects under the "nfs/media/docs/" path, you can specify that as a prefix, like so:
request = service.objects.list(
bucket=bucket_name,
versions=True,
prefix='nfs/media/docs/', # Only show objects beginning like this
delimiter='/' # Consider this character a directory marker.
)
response = request.execute()
subdirectories = response['prefixes']
objects = response['items']
Because of the prefix
parameter, only objects that begin with 'nfs/media/docs' will be returned in response['items']
. Because of the delimiter
parameter, "subdirectories" will be returned in response['prefixes']
. You can get more details in the Python documentation of the objects.list method .
If you were to use the newer google-cloud Python library , which I'd recommended for new code, the same call would look pretty similar :
from google.cloud import storage
client = storage.Client()
bucket = client.bucket(bucket_name)
iterator = bucket.list_blobs(
versions=True,
prefix='nfs/media/docs/',
delimiter='/'
)
subdirectories = iterator.prefixes
objects = list(iterator)
Below is the working solution. I ended up stripping away the path from the object name and creating the directory structure on the fly. A better way might be as @Brandon Yarbrough suggested using 'prefix + response['prefixes'][0]' but I couldn't quite figure that out. Hope this helps others out.
#!/usr/local/bin/python3
from google.cloud import storage
from googleapiclient import discovery
from oauth2client.client import GoogleCredentials
import json
import os
import pathlib
bucket_name = 'test-bucket'
restore_epoch = '1519189202'
restore_location = '/Users/admin/data/'
credentials = GoogleCredentials.get_application_default()
service = discovery.build('storage', 'v1', credentials=credentials)
storage_client = storage.Client()
source_bucket = storage_client.get_bucket(bucket_name)
def listall_objects():
request = service.objects().list(
bucket=bucket_name,
versions=True
)
response = request.execute()
print(json.dumps(response, indent=2))
def listname_objects():
request = service.objects().list(
bucket=bucket_name,
versions=True
)
response = request.execute()
for item in response['items']:
print(item['name'] + ' Uploaded on: ' + item['updated'] +
' Epoch: ' + item['metadata']['epoch'])
def downloadepoch_objects():
request = service.objects().list(
bucket=bucket_name,
versions=True
)
response = request.execute()
try:
for item in response['items']:
if item['metadata']['epoch'] == restore_epoch:
print('Downloading ' + item['name'] + ' from ' +
item['bucket'] + '; Epoch= ' + item['metadata']['epoch'])
print('Saving to: ' + restore_location)
blob = source_bucket.blob(item['name'])
path = pathlib.Path(restore_location + r'{}'.format(item['name'])).parent
if os.path.isdir(path):
blob.download_to_filename(restore_location + '{}'.format(item['name']))
print('Download complete')
else:
os.mkdir(path)
blob.download_to_filename(restore_location + '{}'.format(item['name']))
print('Download complete')
except Exception:
pass
# listall_objects()
# listname_objects()
downloadepoch_objects()
Following solution worked for me. I am recursively downloading all blobs from a path prefix to a model
directory at the project root, while maintaining the folder structure. Multiple blobs are being downloaded concurrently.
GCS client version google-cloud-storage==1.41.1
import os
from datetime import datetime
from google.cloud import storage
from concurrent.futures import ThreadPoolExecutor
BUCKET_NAME = "ml-model"
def timer(func):
def time_wrapper(*arg, **kwargs):
start = datetime.now()
func(*arg, **kwargs)
diff = datetime.now() - start
logger.info(f"{func.__name__} took {diff.seconds} s and {diff.microseconds//1000} ms")
return time_wrapper
def fetch_environment() -> str:
env = os.environ.get("environment", "staging")
return env
def create_custom_folder(dir_name: str):
if not os.path.exists(dir_name):
os.makedirs(dir_name)
def fetch_gcs_credential_file_path():
return os.environ.get("GCS_CREDENTIAL_FILE_PATH")
class GCS:
def __init__(self):
cred_file_path = fetch_gcs_credential_file_path()
self.client = storage.Client.from_service_account_json(cred_file_path)
self.bucket = self.client.bucket(BUCKET_NAME)
def download_blob(self, blob):
filename = blob.name.replace(self.path_prefix, '')
delimiter_based_splits = filename.split('/')
if len(delimiter_based_splits) > 1:
dir_name = "model/" + "/".join(delimiter_based_splits[: len(delimiter_based_splits)-1])
create_custom_folder(dir_name)
blob.download_to_filename(f"{dir_name}/{delimiter_based_splits[-1]}")
else:
blob.download_to_filename(f"model/" + filename)
@timer
def download_blobs_multithreaded(self, prefix: str):
'''
CREATE FOLDER IF NOT EXISTS
'''
create_custom_folder("model")
blobs = self.bucket.list_blobs(prefix=prefix)
self.path_prefix = prefix
with ThreadPoolExecutor() as executor:
executor.map(self.download_blob, blobs
def download_model():
env = fetch_environment()
folder_path_prefix = f"ml/{env}/{ML_MODEL_NAME}/v1/tf-saved-model/"
gcs = GCS()
gcs.download_blobs_multithreaded(folder_path_prefix)
if __name__ == '__main__':
download_model()
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