claudeforlife/imageprocessor repository overview

⁠About

This mini-project is written to demonstrate an Image management engine built using the following technologies.

Frontend

• Bootstrap4(HTML+ CSS), Jquery

Backend

• Django framework, Redis, Postgres(DB) and Celery
• Nginx(Webserver)

Infrastructure

• Dockerized application deployed on Minikube.

⁠Design Choices and recommendations

⁠Storing images in the DB

Storing images in the DB directly as byte data can work. But overtime, the size of the DB grows out of control. To remedy this situation, we can opt for an object store then in our database we could simply reference the public url of the image.

For this mini-project, we will not deploy an object store on minikube using open source solutions like Minio/Rook. Instead, we could mount a simple persistent volume on disk then use it to store uploaded images.

⁠Click manager

We are required to update the status of each photo in the Image Viewer tab as users scroll to and fro while accepting or rejecting images.

This implies our database writes for every click. This will not scale if you have millions of clicks a second. Alternatively, we can send the individual accept or deny requests as messages then queue them using Celery periodic tasks which will be executed sometime in future.

⁠Drawbacks

• It is good practice to implement the frontend as a standalone application using frameworks such as ReactJs or Vue. For simplicity sake, this exercise will use basic html and css wrapped with bulma(a css framework) with some Javascript implemented with old-school Jquery.

• Only a limited set of backend testing has been done. We should consider implementing frontend and backend tests. See imageProcessor/tests directory for more

• We also need to monitor the entire cluster. This mini-project does not deploy a monitoring stack along side our setup.

• No persistent volume for Redis has been added. For persisting redis config data, we might require to add a storage volume.

⁠How to run this project

⁠Requirements

• Install minikube on a Linux based system following these steps⁠

• From the terminal, start the cluster using minikube start

• Ensure kubectl is installed, if not, follow this manual⁠ to install it.

• To validate that the installation of Kubectl above is complete, run the following command. kubectl version.

• Finally, check the status of your minikube cluster using kubectl cluster-info.

⁠Deploying services

Our mini-project relies on the following services to function; Celery, redis and Postgresql.

In this section, we will describe how to deploy each of these services ontop of Minikube. In the deployment directory, you will find Deployment manifests for each of the above services.

1. Create a service namespace for our stack.

   kubectl create namespace imageprocessor

2. Deploy redis

   In the deployment redis directory, run the following command:

   • kubectl apply -f deployment/redis

   Confirm that you have a running redis deployment in the imageprocessor namespace using kubectl get pods -n imageprocessor

3. Deploy PostgreSQL

   In the deployment postgres directory, run the following command:

   • kubectl apply -f deployment/postgres

1. Deploy Django backend

   In the deployment django directory, run the following commands in order:

   • kubectl apply -f deployment/django/service.yml
   • kubectl apply -f deployment/django/migration.yml
   • kubectl apply -f deployment/django/import.yml
   • kubectl apply -f deployment/django/deployment.yml

Finally, wait and verify that all pods in the imageprocessor namespace are running using: kubectl get pods -n imageprocessor.

⁠Build

This mini-project docker image has been hosted on Dockerhub for ease of use. Alternatively, you can build it locally from the project root directory containing the dockerfile

docker build -t imageprocessor .

⁠Test

This mini-project includes an example test-suite for some models and views.

To run test, in a virtual environment with all dependencies installed, execute the following command:

python manage.py test

⁠Access the application

To access the deployed application, use the following command:

minikube service --url kubernetes-django-service -n imageprocessor

The above command should expose our web-application using a Nodeport. Obtain the URL and place it in a browser page.

To see health status of the Django stack components, visit this url, http://<url-from-above>/healthcheck

Django admin default login credentials are as follows: username: admin password: password

Note:

We use asynchronous tasks to add watermarks to each accepted or rejected image. In a resource limited setup, the celery task might take some time to process and add watermarks to the images in the background.

If your setup is resource limited, then in the image list tab, the new watermarked images will not display immediately after selection.

⁠Screenshots

⁠Minikube cluster status

(base) Claudes-MBP:image_processor ebaneck$ kubectl get pods -A
NAMESPACE        NAME                                   READY   STATUS      RESTARTS   AGE
imageprocessor   celery-beat-57f5f565-f2pbg             1/1     Running     0          101m
imageprocessor   celery-worker-76ddfd5fb7-qx9b5         1/1     Running     0          101m
imageprocessor   django-67cf68fd8c-2vftg                1/1     Running     0          3m38s
imageprocessor   django-db-import-phcxh                 0/1     Completed   0          11m
imageprocessor   django-migrations-622rz                0/1     Completed   0          100m
imageprocessor   postgres-deployment-7544cd96bd-j27q6   1/1     Running     0          101m
imageprocessor   redis-5b46dd97b5-rx8ch                 1/1     Running     0          101m
kube-system      coredns-74ff55c5b-k99p9                1/1     Running     0          108m
kube-system      etcd-minikube                          1/1     Running     0          108m
kube-system      kindnet-7tqn9                          1/1     Running     0          104m
kube-system      kube-apiserver-minikube                1/1     Running     0          108m
kube-system      kube-controller-manager-minikube       1/1     Running     0          108m
kube-system      kube-proxy-xgd5n                       1/1     Running     0          108m
kube-system      kube-scheduler-minikube                1/1     Running     0          108m
kube-system      storage-provisioner                    1/1     Running     1          108m