REST API which resizes images using flask, celery worker and a redis broker

docker-compose up

This would spin up three containers:

• image_resize_api @ localhost:5000
• redis_broker @ localhost:6379
• celery_worker

• POST http://localhost:5000/api/v1/resize

  Headers Content-Type: application/json

  Request Body

    ```
    {
        "imageData": "base64 encoded image"
    }
    ``` 
  

  Response

    ```
    {
        "success": true,
        "token": "Type-5 UUID(same as task-id)"
    }
    ``` 
  

  Example -

  REQUEST: curl http://localhost:5000/api/v1/resize -d @request.json -H "Content-Type: application/json"

  RESPONSE: {"success":true,"token":"bce0e926-f6b4-4e7d-ada3-0548d0a1c2b6"}

• GET http://localhost:5000/api/v1/resize/<token>

  Response

    ```
    {
        "status": SUCESSS,
        "resized_image_url": "image_url"
    }
    ```
  

  Example - REQUEST: curl http://localhost:5000/api/v1/resize/bce0e926-f6b4-4e7d-ada3-0548d0a1c2b6

  RESPONSE: {"resized_image_url":"72a77200-18e4-4df1-992b-d4f80c26c739.jpg","status":"SUCCESS"}

Before running the tests please install the following dependencies in your local environment or you may also activate a fresh virtual environment.

• pip install -r requirements.txt
• pip install nose

Provides nosetests command to discover and run tests.

Run the tests using: nosetests tests/test_api.py

Please make sure as to start the server using docker-compose up before running them.

One of the three tests may fail only the first time after starting the server due to some latency from redis.(if THP support is enabled in your kernel.)

The repo has the following structure:

• api/
  • api/factory.py responsible for app and worker instantiation, each of them can be instantiated independently thus allowing loose coupling
  • api/controller/routes.py defines the endpoints
  • api/controller/tasks.py defines Celery tasks
• tests/
  • tests/test_api.py contains all the tests for the apis
• app.py is the entry point for starting the Flask app server
• worker.py is the entry point for the Celery worker
• requirements.txt is the list of python dependencies for pip
• docker-compose.yml all the service containers
• Dockerfile is the image for the app & celery worker

• flask - micro web application framework, makes getting started quick and easy. Using the inbuilt server too.
• pillow - python imaging library, used for resizing the image
• celery - distributed task queue used for asynchronous task processing
• redis - used as a message broker as well as the result backend for celery to communicate with the flask server.
• mock - used to inject/mock testing configuration while instantiating the app.

• To implement the long-running tasks in application's work flow to resize images we should handle these tasks in the background, outside the basic application flow as a separate worker process.
• As an image is sent via a request to our flask server, it passes the task of resizing off to a task queue and immediately sends back a token i.e same as the task-id as a response back to the client.
• Flask server connects to the Redis message broker and the message broker talks to the Celery worker.Celery communicates via messages, usually using a broker to mediate between clients and workers.
• Celery worker then identifies the methods decorated with @celery.task and runs them asynchronously updating the status of tasks to redis container also used as a result backend.

• The current application doesn't supports different configurations for different environment configurations like development, staging, production etc. We could implement it using a separate config.py module which would define these properties for different environments.
• The current also lacks error handling, logging modes and handling the corner cases (example- image sent as the request is too large or of size zero or corrupted) which is a must for a production grade application. Similarily
• There is no persistent data storage used by the application, so we could use something like MySQL/PostgreSQL to persist task related data. We might also need some tool to manage the migrations.
• The application also lacks basic authentication/authorization which could be implemented. APIs should validate requests to contain basic security headers/keys.
• Currently the celery worker is running as a superuser inside the container which can be avoided by configuring it to not run as the root user.

• There are no independent unit tests written thus currently we need the start the server running to test the entire flow of the API and its integration with the task-queue.
• Tests also lack to check the corner cases as defined above, which needs to be included.
• To unit test the asynchronous tasks we can run those methods synchronously.

• Although Flask has a built-in web server, it’s not suitable for production and needs to be put behind a real web server able to communicate with Flask through a WSGI protocol. A common choice for that is Gunicorn— a Python WSGI HTTP server.
• While being an HTTP web server, Gunicorn, is not suited to face the web. That’s why we need Nginx as a reverse proxy and to serve static files. In case we need to scale up our application to multiple servers, Nginx will take care of load balancing as well.
• Also need to enable SSL module for nginx to configure HTTPS.
• Currently, the resized images are created in a shared docker volume, assigned while creating the services. In a real-life application scenario, we'd require to upload these images to a object storage service(example- S3, Google cloud storage) and access them via a CDN.

• Monitoring of celery workers is also not implemented, for which we could use Flower. Flower also supports basic HTTP auth, oAuth and can also be used to manage the worker pool size and auto scale settings.
• The current application lacks continuous integration and continuous deployment.