The document details the deployment of a voting application on Google App Engine, using a multi-platform design with Docker, Flask, and Redis. It outlines the steps to set up the project, including creating an `app.yaml` file, installing necessary packages, and deploying the app. The deployment results include service status and performance metrics, such as response codes and instance creation.

1. Deploying to
AppEngine

2. ⇒ Whoami
Alex Hayorov @hayorov
We

3. Cats
vs
Dogs
voting

4. Choose Get the results

5. Our applicationArchitecture
• Multiplatform design
https://github.com/hayorov/example-voting-app
• Dockerized
• Hosted in Docker VM

6. Our applicationVoting-app
GET /
voting-appPOST /
vote: a
rpush
voter_id, vote
•Flask (python 2.7-alpine)
•Gunicorn
•Redis (TCP)

7. New project on cloud.google.com
Install Google Cloud Platform SDK (gcloud)
runtime: python27
api_version: 1
threadsafe: true
handlers:
- url: /
script: app.app
- url: /static
static_dir: static
Create app.yaml file

8. ⇒ gcloud app deploy --project catsdogsvote
You are about to deploy the following services:
- catsdogsvote/default/20161120t203231 (from [example-voting-app/vote/app.yaml])
Deploying to URL: [https://catsdogsvote.appspot.com]
Beginning deployment of service [default]...
File upload done.
Updating service [default]...done.
Deployed service [default] to [https://catsdogsvote.appspot.com]
goto https://catsdogsvote.appspot.com

10. Collecting Flask (from -r requirements.txt (line 1))
Using cached Flask-0.11.1.tar.gz
…
Running setup.py install for Flask ... done
Running setup.py install for Redis ... done
Running setup.py install for gunicorn ... done
Successfully installed Flask Jinja2-2.8 MarkupSafe-0.23 Redis Werkzeug click gunicorn itsdangerous-
0.24
⇒ virtualenv ./env && pip install -r requirements.txt #fixme
Create appengine_config.py
from google.appengine.ext import vendor
vendor.add('env/lib/python2.7/site-packages')
⇒ gcloud app deploy --project catsdogsvote

11. Choose Get the results
powered by AppEngine

12. rps-instances/time
t, secs
rps
1k
100
Count: 321612
Load: line(1, 1000, 10m) const(100, 5m)
HTTP codes
Count Percent
200 - OK 65.2% (430490)
500 - Error 32.2% (212734)
??? - Error 2.6% (17306)
workload
300 600
created instances
183
Response time distribution
time, s quantile
12000 100
5000 87
1000 71
500 63
350 41
150 4