Being new to GCP, I have a question about which architecture to use in a particular case.
Suppose I have a Django website running on the App engine (flexible environment?). Users upload images to the website. I would like to first use Google Vision API to perform some label detection on the images and then feed the labels and images to a VM with GPU attached (all running on Google cloud), for additional computationally costly job on the images. After the job is completed by the VM, the resulting images are then available for the user to download or sent to the user email.
Because of the relatively large time spent on the VM+GPU side, and because the website will be accessed by users globally, I would like to reduce the overall latency time and pick the most efficient architecture for the job.
My first thought was to:
upload images to Google Cloud Storage;
use GC functions to perform some quick transformations and then call Google Vision API;
pull the resulting labels and transformed images to the VM and make computations on the VM side;
upload finalized images to Google Cloud Storage.
Now, that's a lot of bouncing back and forth between a storage bucket and APP engine plus VM on either side. I was wondering if there is a 1) quicker and 2) more efficient resources-wise way to achieve the same goal.
If your website is accessed globally, your App Engine choice is the wrong one: App Engine can be deployed in only one region, not globally.
For the frontend, I recommend to use Cloud Run instead (or VM, but I don't like VM) and to put a HTTPS load balancer in front of. Like that, the physical latency is reduced.
And, the files must be also store in the closest region, so in Cloud Storage in different region.
And finally, to duplicate the VM/GPU infrastructure in each region (it could be costly, but it's the best way to reduce latency.
Your process is the right one. I recommend you to expose an API on your VM to notify it when a file is ready. You can use the PubSub notification on Cloud Storage to sink the event in PubSub, and then create a push subscription to invoke your VM directly (instead of a cloud functions).
Like that, you remove a component and you perform all your processing on the VM side.
Related
I'm working with a Django web app deployed on Google App Engine flexible environment.
I'm streaming my data while processing requests in my views using bigquery.Client(). But I think it is not the best way to do it. Do I need to delegate this process outside of the view (using pub/sub, tasks, cloud functions etc.? If so, give me a suitable architecture: which GCP product should I use, how to connect, and what to read.
Based on your comment, I could recommend you Cloud Run;
Cloud Run is a serverless container based product. You write a webserver (that handle your POST request), wrap it in a container and deploy it on Cloud Run.
With a brand new feature, named always on the CPU is not throttled after the response sent (the normal behavior). With always on, you keep the full CPU up to the Cloud Run instances off load (usually after 15 minutes, but can be quicker).
The benefit of the feature is the capacity to return immediately the response to the client, and then to continue to process, asynchronously, your data to store in BigQuery (in streaming mode).
I am implementing a dictionary website using App Engine and Cloud Storage. App Engine controls the backend, like user authentication etc., and Cloud Storage is used to store a JSON file for each dictionary entry.
I would like to rate limit how much a user can download in a given time period so they can't bulk download the JSON files and result in a big charge for me. Ideally, the dictionary would display a captcha if a user downloads too much at once, and allow them to keep downloading if they pass the captcha. What is the best way to achieve this?
Is there a specific service for rate limiting based on IP address or authenticated user? Should I do this through App Engine and only access Cloud Storage through App Engine (perhaps slower since it's using some of my dynamic resources to serve static content)? Or is it possible to have the frontend access Cloud Storage and implement the rate limiting on Cloud Storage directly? Is a Cloud bucket the right service for storage, here? And how can I allow search engine indexing bots to bypass the rate limiting?
As explained by Doug Stevenson in this post
"There is no configuration for limiting the volume of downloads for
files stored in Cloud Storage."
and explaining further:
"If you want to limit what end users can do, you will need to route
them through some middleware component that you build that tracks how
they're using your provided API to download files, and restrict what
they can do based on their prior behavior. This is obviously
nontrivial to implement, but it's possible."
I was handed an assignment but I don't know where to start.
The aim is to have 2 piece of code running. One will run in Open stack private cloud and perform the task of indexing two sets of text, with another running in EC2 with the task of matching the two indexed tests.
I want to access them via google app engine.
Ideally, I would like to click a button or perform an action on Google app engine, which then sends a request to Openstack to run the code and retrieve the output of a txt file.
That outputted text files will then be forwarded onto EC2 where the matching will occur and the results sent back to Google App Engine.
My question is, how can I send the files between the systems using REST requests?
FrankN --
EC2, GAE and OpenStack are disparate cloud computing platforms. To integrate them might include, say, using one platform while saving backups to another.
CloudU.Rackspace.com is a vendor-neutral education site about cloud computing (note: It'll take six or so hours to finish it all). This might help.
Disclaimer: I work for Rackspace.
Firstly, not really sure what your requirements are, why your code does or why are you trying to mix cloud providers in that way.
That said, I would suggest taking the upload from GAE and push it to AWS S3 where you can then retrieve and use as you please from EC2.
Not sure what functionality you are trying to get out of OpenStack that is not present in AWS; however, I would suggest building whatever you are building in EC2 first, then duplicate in on OpenStack services to avoid future vendor lock in.
I am currently using google app engine as my mobile application back end. I have a few tasks that can not be performed in the gae environment (mainly image recognition using opencv). My intention is to retain gae and use AWS to perform these specific tasks.
Is there a simple way to pass specific tasks from gae to AWS? E.g. A task queue?
You could either push tasks from GAE towards AWS, or have your AWS instances pull tasks from GAE.
If you push tasks from GAE towards AWS, you could use URLFetch to push your data towards your AWS instances.
If you prefer to have your AWS instances pull tasks from GAE, you could have your GAE instances put their tasks in the GAE Pull Queue, and then have your AWS instances use the Task Queue REST API to lease tasks from the queue.
In either case, the AWS instance could report back the processing result through a simple POST request to your GAE servlets, or through inserting tasks via the abovementioned REST API which would later be leased by your GAE instances. The latter could be useful if you want to control the rate of which your GAE app process the results.
Disclaimer: I'm a lead developer on the AppScale project.
One way that you could go is with AppScale - it's an open source implementation of the App Engine APIs that runs over Amazon EC2 (as well as other clouds). Since it's open source, you could alter the AppServer that we ship with it to enable OpenCV to be used. This would require you to run your App Engine app in AWS, but you could get creative and have a copy of your app running with Google, and have it send Task Queue requests to the version of your app running in AWS only when you need to use the OpenCV libraries.
Have you considered using amazon simple queue service ? http://aws.amazon.com/sqs/
You should be able to add items to the queue from gae using a standard http clint.
Sure. AppEngine has a Task Queue, where you can put in your tasks by simply implementing DeferredTask. In that task you can make requests to AWS.
Your intention to retain the application in GAE and use AWS to perform a few tasks, that can not be performed in the GAE, seems for me a right scenario.
I'd like to share a few ideas along with some resources to answer the main part of your question:
Is there a simple way to pass specific tasks from gae to AWS? E.g. A task queue?
If you need GAE and AWS to perform the task all the time (24/7) then your application will definitely depend on batch schedule or task queue. They are available by GAE.
However if you could arrange to pull the task in GAE and perform by AWG on interval basis (say twice a day of less than an hour each), you may no need to use them as long you can manage the GAE to put the data on Google Cloud Storage (GCS) as public.
For this scenario, you need to setup AWS EC2 Instance for On/Off Schedule and let the instance to run a boot script using cloud-init to collect the data through your domain that pointed to GCS (c.storage.googleapis.com) like so:
wget -q --read-timeout=0.0 --waitretry=5 --tries=400 \\
--background http://your.domain.com/yourfile?q=XXX...
By having the data from GCS, then AWS can perform these specific tasks. Let it fire up GAE to clean the data and put the result back to GCS to be ready to be used as your mobile application back end.
Following are some options to consider:
You should note that not all of the EC2 types are suitable for On/Off Schedule. I recommend to use EC2-VPC/EBS if you want to setup AWS EC2 Instance for On/Off Schedule
You may no need to setup EC2 if you can set AWS Lambda to perform the task without EC2. The cost is cheaper, a task running twice a day for typically less than 3 seconds with memory consumption up to 128MB typically costs less than $0.0004 USD/month
As outcome of rearranging you your application in GAE and set AWG to perform some of the tasks, it might finally rise your billing rates, try to to optimize the instance class in GAE.
Is it possible to route HTTP traffic between google app engine applications without going through the public internet?
For example, if I'm running a Web Service API on one application and want to build a second application on top of it without traffic going through the internet - for performance reasons.
Between separate apps running on different domains? I suspect not.
But you can use backends to do different work behind the scenes:
Backends are special App Engine instances that have no request deadlines, higher memory and CPU limits, and persistent state across requests. They are started automatically by App Engine and can run continously for long periods. Each backend instance has a unique URL to use for requests, and you can load-balance requests across multiple instances.
When I look at the logs between the backend and the front end instances I see IPs like
0.1.0.3
So yes, those communication paths are internal. I'd hazard a guess that as so much of the internet is google you could say requests between different apps might not travel on the public internet.
Logs indicate low latency communication between front and back ends, not under any particular load however. Your milage may vary.
Backends in Python