Uploading a File to GAE Blobstore from URL - python

I need to get a video from a URL, for example:
https://zencoder-temp-storage-us-east-1.s3.amazonaws.com/o/20130722/5aacb76fc3fd47715c0329d1235dcccf/4fc612e92131e159abc761f7d54d86b5.mp4?AWSAccessKeyId=AKIAI456JQ76GBU7FECA&Signature=AGgZb1eMr105RXcoQFp8yFFTfFg%3D&Expires=1374614893
and then use Google App Engine to save it to the blobstore (or possibly Google Cloud Storage if the blobstore can't download straight from a URL) is there a simple way to do this? I already have it set up so that I can get a user to upload a video but I am not sure about doing it this way. Is the URLFetch Library what I'm looking for?
Would it be something like:
class UploadHandler(blobstore_handlers.BlobstoreUploadHandler):
def post(self):
#I'm just not sure what to do here, how I can get that 'file' from a URL
upload_files = self.get_uploads('file')
blob_info = upload_files[0]
video = Video(
title = "some title",
video_ref = blob_info.key())
video.put()

Yeah you will need to use urlfetch then use the google cloud storage client library(https://developers.google.com/appengine/docs/python/googlecloudstorageclient/functions#open) to write the results. You need to consider that urlfetch response size is limited to 32mb so you need to split download into 32mb pieces and also there is a 60 seconds user request limit or 10 minute taskqueue limit or the use backends which doesn't have a timeout limit.
You could also directly upload to blobstore or cloudstorage (https://developers.google.com/appengine/docs/python/blobstore/#Uploading_a_Blob) which is handled by special instances that is designed to handle these uploads.

Related

Get Cloud Storage upload response

I am uploading a file to a Cloud Storage bucket using the Python SDK:
from google.cloud import storage
bucket = storage.Client().get_bucket('mybucket')
df = # pandas df to save
csv = df.to_csv(index=False)
output = 'test.csv'
blob = bucket.blob(output)
blob.upload_from_string(csv)
How can I get the response to know if the file was uploaded successfully? I need to log the response to notify the user about the operation.
I tried with:
response = blob.upload_from_string(csv)
but it always return a None object even when the operation has succeded.
You can try with tqdm library.
import os
from google.cloud import storage
from tqdm import tqdm
def upload_function(client, bucket_name, source, dest, content_type=None):
bucket = client.bucket(bucket_name)
blob = bucket.blob(dest)
with open(source, "rb") as in_file:
total_bytes = os.fstat(in_file.fileno()).st_size
with tqdm.wrapattr(in_file, "read", total=total_bytes, miniters=1, desc="upload to %s" % bucket_name) as file_obj:
blob.upload_from_file(file_obj,content_type=content_type,size=total_bytes,
)
return blob
if __name__ == "__main__":
upload_function(storage.Client(), "bucket", "C:\files\", "Cloud:\blob.txt", "text/plain")
Regarding how to get notifications about changes made into the buckets there is a few ways that you could also try:
Using Pub/Sub - This is the recommended way where Pub/Sub notifications send information about changes to objects in your buckets to Pub/Sub, where the information is added to a Pub/Sub topic of your choice in the form of messages. Here you will find an example using python, as in your case, and using other ways as gsutil, other supported languages or REST APIs.
Object change notification with Watchbucket: This will create a notification channel that sends notification events to the given application URL for the given bucket using a gsutil command.
Cloud Functions with Google Cloud Storage Triggers using event-driven functions to handle events from Google Cloud Storage configuring these notifications to trigger in response to various events inside a bucket—object creation, deletion, archiving and metadata updates. Here there is some documentation on how to implement it.
Another way is using Eventarc to build an event-driven architectures, it offers a standardized solution to manage the flow of state changes, called events, between decoupled microservices. Eventarc routes these events to Cloud Run while managing delivery, security, authorization, observability, and error-handling for you. Here there is a guide on how to implement it.
Here you’ll be able to find related post with the same issue and answers:
Using Storage-triggered Cloud Function.
With Object Change Notification and Cloud Pub/Sub Notifications for Cloud Storage.
Answer with a Cloud Pub/Sub topic example.
You can verify if the upload gets any error, then use the exception's response methods:
def upload(blob,content):
try:
blob.upload_from_string(content)
except Exception as e:
status_code = e.response.status_code
status_desc = e.response.json()['error']['message']
else:
status_code = 200
status_desc = 'success'
finally:
return status_code,status_desc
Refs:
https://googleapis.dev/python/google-api-core/latest/_modules/google/api_core/exceptions.html
https://docs.python.org/3/tutorial/errors.html

Google Cloud Storage create_upload_url -- App Engine Flexible Python

On a regular (non-flexible) instance of Google App Engine, you can use the Blobstore API and create a URL to allow a user to upload a file directly into your Blobstore. When it is uploaded, your app engine application is notified of the location of the file and can process it. An example of the python code is:
from google.appengine.ext import blobstore
upload_url = blobstore.create_upload_url('/upload_photo')
See the Blobstore docs.
Switching to Google App Engine Flexible Environment, usage of the Blobstore has been largely replaced by Cloud Storage. In such a case, is there an equivalent of create_upload_url?
My current implementation takes a standard file upload to a python Flask application. Then proceeds with something like:
from flask import request
from google.cloud import storage
uploaded_file = request.files.get('file')
gcs = storage.Client()
bucket = gcs.get_bucket(bucket_name)
blob = bucket.blob(blob_name)
blob.upload_from_string(
uploaded_file.read(),
content_type=uploaded_file.content_type
)
This seems like it is doubling the network load compared with create_upload_url because the file is coming into my app engine instance and then immediately being copied out. So the uploader will be made to wait extra time whilst this is happening. Presumably I will also incur extra App Engine charges for this. Is there a better way?
I have workers that later process the uploaded file, but I tend to download the file from Cloud Storage again in their code because I don't think you can assume that the worker will still have access to a file stored in the instance file system. Therefore I don't get any benefit of having the file uploaded to my instance rather than direct to it's storage location.
I have started using create_resumable_upload_session to create a signed URL that our client side application can upload a file to. Something like:
gcs = storage.Client()
bucket = gcs.get_bucket(BUCKET)
blob = bucket.blob(blob_name)
signed_url = blob.create_resumable_upload_session(content_type=content_type)
Then when the client has successfully uploaded a file to our storage, I subscribe to a Pub/Sub notification of the creation using this Cloud Pub/Sub Notifications for Cloud Storage.
Each blob created with the new Google Cloud Storage Client has a public_url property:
from flask import request
from google.cloud import storage
uploaded_file = request.files.get('file')
gcs = storage.Client()
bucket = gcs.get_bucket(bucket_name)
blob = bucket.blob('blob_name')
blob.upload_from_string(
uploaded_file.read(),
content_type=uploaded_file.content_type
)
url = blob.public_url
--
With the Blobstore, a GAE system handler in your instance takes care of the uploaded file you pass to the upload url created. I'm not sure if it's an issue handling it yourself in your code. If your current approach is problematic, you might want to consider doing the upload client side and not pass the file through App Engine at all. GCS has a REST API and the cloud storage client uses it underneath, so you can read and upload the file directly to GCS on the client side if it's more convenient. There's firebase.google.com/docs/storage/web/upload-files to ease you through the process

How to Define Google Endpoints API File Download Message Endpoint

All the examples I can find on google endpoint api (e.g., tic-tac-toe sample) show strings, integers, enums, etc fields. None of the examples say anything about how to specify document (e.g., image or zip files) uploads or downloads using the API. Is this not possible?
If this is possible, can anyone share a code snippet on how to define google endpoint api on the server to allow downloads and uploads of files? For example, is there a way to set HTTPResponse headers to specify that an endpoint response will serve a zip file? How do we include the zip file in the response?
An example with python or php would be appreciated. If anyone from the endpoints-proto-datastore team is watching this discussion, please say whether or not file downloads are supported in endpoints at the moment. We hate to waste our time trying to figure this out if it is simply impossible. Thanks.
We are seeking a complete example for upload and download. We need to store the key for the uploaded file in our database during upload and retrieve it for download. The client app sends a token that the API needs to use to figure out what file to download. Hence, we would need to store the blob key generated during the upload process in our database. Our database would have the mapping between the token and the blob file's key.
class BlobDataFile(models.Model):
data_code = models.CharField(max_length=10) # Key used by client app to request file
blob_key = models.CharField()
By the way, our app is written in Django 1.7 with a mysql (modeled with models.Model) database. It is infuriating that all the examples for Google App Engine upload I can find is written for a standalone webapp Handlers (no urls.py/views.py solutions could be found anywhere). Hence, building a standalone uploader is as much of a challenge as writing the API code. If your solution has full urls.py/views.py example for uploading files and saving the blob_key in our BlobDataFile, it would be good enough for us.
f you use the blobstore use the get_serving_url function to read the images from url in the client, or use the messages.ByteField in the ResourceContainer and serialize the image with base64.b64decode
#the returned class
class Img(messages.Message):
message = messages.BytesField (1)
#The api class
#endpoints.api(name='helloImg', version='v1')
class HelloImgApi(remote.Service):
ID_RESOURCE = endpoints.ResourceContainer(
message_types.VoidMessage,
id=messages.StringField(1, variant=messages.Variant.STRING))
#endpoints.method(ID_RESOURCE, Img,
path='serveimage/{id}', http_method='GET', #ID is the blobstore key
name='greetings.getImage')
def image_get(self, request):
try:
blob_reader = blobstore.BlobReader(blob_key)
value = blob_reader.read()
return Img(message=value)
except:
raise endpoints.NotFoundException('image %s not found.' %
(request.id,))
APPLICATION = endpoints.api_server([HelloImgApi])
And this is the response (save it in the client with the proper format)
{
"message": "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}
in the client you can do this (in python for continuity)
import base64
myFile = open("mock.jpg", "wb")
img = base64.b64decode(value) #value is the returned string
myFile.write(img)
myFile.close()
Did you try converting the image to base64 string and send it as an argument of your request on the client side?
So you will be able to do that on the server side :
#strArg is the Base64 string sent from the client
img = base64.b64decode(strArg)
filename = 'someFileName.jpg'
with open(filename, 'wb') as f:
f.write(img)
#then you can save the file to your BlobStore

Uploading images to Google App Engine from non-webpage client

I have a Google App Engine application (Python) which needs to allow users to upload images to the blobstore. The client, rather than being a web app, will be native mobile apps. I have the following code to do uploads:
class ImageGetUploadURLHandler(webapp2.RequestHandler):
def get(self):
self.response.headers['Content-Type'] = 'text/plain'
self.response.out.write(blobstore.create_upload_url('/imgupload'))
class ImageUploadHandler(blobstore_handlers.BlobstoreUploadHandler):
def post(self):
imgInfo = self.get_uploads('img')[0]
logging.info('Uploading image %s of size %i'.format(imgInfo.filename, imgInfo.size))
self.redirect('/echo?val=%s' % str(imgInfo.key()))
Where the ImageUploadHandler is mounted on /imgupload, and /echo is a simple handler that echoes back whatever is passed in val as text/plain. As of right now, I'm just using REST testing tools, as I haven't started the mobile apps yet. I did a GET to the ImageGetUploadHandler, and copied the resulting link into the testing application, where I used a POST with a multipart/form-data with an image file under the correct key to the upload URL. When I do, I get the following error:
ValueError: Invalid boundary in multipart form: ''
I can't figure out what's going wrong. Any help much appreciated.

How do I insert video greater than 1MB with gdata.youtube api direct method

I am using the gdata.youtube service to insert a video entry. It is failing with a url fetch error complaining that the file is too large. It is a pretty small video (1.7MB). In an ideal world, there is a natural way to break up the file as necessary and stream it in chunks. Is there an api to do this already.
Here is the code I am using based on the google api tutorial:
my_media_group = gdata.media.Group(
title=gdata.media.Title(text='My Test Movie'),
description=gdata.media.Description(description_type='plain',
text='My description'),
keywords=gdata.media.Keywords(text='cars, funny'),
category=[gdata.media.Category(text='Autos', scheme='http://gdata.youtube.com/schemas/2007/categories.cat', label='Autos')],
player=None
)
where = gdata.geo.Where()
where.set_location((37.0,-122.0))
# create the gdata.youtube.YouTubeVideoEntry to be uploaded
video_entry = gdata.youtube.YouTubeVideoEntry(media=my_media_group,
geo=where)
new_entry = self.client.InsertVideoEntry(video_entry, 'movie.mov')
And here is the error:
RequestTooLargeError: The request to API call urlfetch.Fetch() was too large.
Url fetch has a limit of 1 megabyte for request size. Check the appengine docs for quotas and other limits

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