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Documentation for this project can be found here:

https://verbose-equals-true.gitlab.io/django-postgres-vue-gitlab-ecs/

Here is an overview of the project architecture, including the CI/CD pipeline and the AWS infrastructure that will be automatically provisioned through the AWS Cloud Development Kit:

(This diagram was created withdraw.io. Here's the link to the a read-only version of the diagram on draw.io:https://drive.google.com/file/d/1gU61zjoW80fCusUcswU1zhEE5VFB1Z5U/view?usp=sharing

1 - GitLab is used to host the source code, test the source code and deploy the application to AWS.

2 - Unit testing (see.gitlab-ci.yml)

2a - Pytest

2b - Jest

2c - Cypress

3 - Deployment phase (see/gitlab-ci/aws/cdk.yml)

3a - Quasar PWA assets are built if there are changes in thequasar directory

3b - AWS Cloud Development Kit (CDK) defines all infrastructure in AWS (4a - 12)

3c - AWS CLI is used to run Fargate tasks through manual GitLab CI jobs

4 - CDK Assets (ECR and S3 buckets that CDK uses internally to manage build assets and artifacts)

4a - Elastic Container Repository is used to manage the Django docker image used in various parts of the application

4b - S3 bucket used to store files associated with CDK and CloudFormation

5 - Route53 is used to route traffic to the CloudFront distribution

6 - CloudFront distribution that serves as the "front desk" of the application. It routes requests to to the correct CloudFront Origin

7 - CloudFront Origin Configurations

7a - S3 bucket for Quasar PWA assets

7b - Application Load Balancer for Django application (/api/,/admin/,/flower/,/ws/,/graphql/)

7c - S3 bucket for Django assets (static files, public media and private media)

8 - Web server and websocket servers

8a - Fargate service running uvicorn process (REST, GraphQL, Django Channels)

8b - Autoscaling Group for Fargate Service that serves Django API

9 - Celery and celery worker autoscaling

9a - Fargate service that is autoscaled between 0 andN Fargate tasks for a given celery queue

9b - Scheduled Event that triggers a Lambda to make a request to Django backend which collects celery queue metrics and published metrics to CloudWatch using boto3

9c - Lambda event the makes a request to/api/celery-metrics/

9d - CloudWatch alarm that is used to scale the Fargate service for a celery queue

9e - Autoscaling group for celery Fargate service

10 - Fargate tasks that run Django management commands such asmigrate andcollectstatic. These are triggered from manual GitLab CI jobs using the AWS CLI (3c)

11 - ElastiCache for Redis, used for Caching, Celery Broker, Channels Layer, etc.

12 - Aurora Postgres Serverless

First, copy.env.template to a new file in the project's root directory called.env. This file will be read bydocker-compose in the next step. Adjust any of the values in this file if needed, or add new variables for any secret information you need to pass to docker-compose (or to docker containers).

docker-compose up

Openhttp://localhost in your browser.

You can specify environment variables for docker-compose by adding an.env file to the root of the project based on.env.template.

To use social sign on in local development, you will need to create an application with the given provider such as GitHub, Google, Facebook, etc.

Go tohttps://github.com/settings/applications/new, and add the following:

• Application Name: A name for the development application, such asMy App Dev
• Homepage URL:http://localhost
• Application description: (optional)
• Authorization callback URLhttp://localhost/auth/github/callback (this route is defined inquasar/src/router/routes.js)

In the.env file, add theClient ID of your GitHub OAuth App as theGITHUB_KEY variable, and add theClient Secret as theGITHUB_SECRET variable.

This project uses VuePress for documentation. To view the documentation site locally, run the following command:

docker-compose -f compose/docs.yml up --build

This will make the docs available athttp://localhost:8082/docs/. Hot-reloading through websockets is supported, so changes will show up as they are saved in your code editor.

With all containers running, run the following commands:

docker exec -it backend bash# cd notebooks/# ../manage.py shell_plus --notebook

or use this single command:

docker exec -it backend bash -c 'cd notebooks && ../manage.py shell_plus --notebook'