Rajatsharma07 / REST-API-Task

Services to Build and Push Docker images to the DockerHub Registry

REST API to Build & Push the docker image from input Dockerfile

This project takes Dockerfile as input through the File Upload web page, builds the image from the Dockerfile, and pushes this image to the DockerHub registry. It is composed of two background jobs, namely build_job and push_job. The push_job depends on the successful completion of the build_job and is only triggered afterwards.

The APIs are created using Python Flask framework. It uses Redis Queue (RQ) which is a Python library for queueing jobs and processing them in the background with workers.

API Endpoints

• http://127.0.0.1:5000/ [GET Request] Base URL: It just displays the welcome message.
• http://127.0.0.1:5000/build_push_image [GET/POST Request] HTML webpage to upload the Dockerfile.
• http://127.0.0.1:5000/job_status?id= [GET Request] - It checks the current status of the triggered jobs by providing the job id as input via querystring.

NOTE: The port number may change depending on the execution method - from the terminal or via the IDE itself.

Dependencies

• RQ requires Redis >= 3.0.0 and we have to make sure Redis server is installed and running successfully.

• Environment creation (apienv): Create a conda/python environment with Python version 3.8.10 and higher and install all required dependencies specified in the requirements.txt file with pip.

• Make sure Docker is installed and running successfully.

NOTE: Tested on MAC OS, there may be problems while running Redis Server on Windows.

Script Parameters

• USERNAME="rajatsharma07" (DockerHub username)
• PASSWORD="xxxxxx" (DockerHub password)
• image_name="rajatsharma07/data_scientists_builds" (username/repository name)
• image_tag="V1.1" (Image versioning)
• no_cache="True" (build image from scratch)

Steps to execute the code

1. Activate apienv environment by running: conda activate apienv.
2. Start the RQ worker: In a separate terminal, follow the step-1 and then run the command rq worker from the root directory of the project.
3. Unit tests can be run before the main services are executed. They are located in the test.py module.
4. Start the services by executing ./run.sh script by providing the appropriate script parameters mentioned above.

NOTE: Services can also be started by running the Python module run.py - but this requires commenting the os.environment parameters and uncommenting the input parameters defined in the __ init __.py module.

Logging

The logs are created in the root directory with the file name: __ init __.log.

Todos and Improvements

1. Dockerize Server
2. Adding script parameters to the file upload webpage
3. Add Swagger documentation
4. Use NGINX for deployment in production environment