Microservice: Product Catalog of Smile Sales e-commerce

This microservice provides a public API for users to interact with product information.

A Brief Overview Of The Features

The microservice offers functionalities like:

• Product Listing: Displays a list of products, potentially filtered by facets.
• Autocomplete Search: Enables efficient product search with suggestions based on user input.
• Faceted Search: Allows users to refine their search using dynamic facets that adapt based on the chosen category, search input, and previously selected facets.
• Product Details: Provides detailed information about specific products, including variations (the same products, but with small differences such as color, size, etc.).
• Product Categories: Lists available product categories.
• Deals and Events: Highlights special offers and events related to products.

Technology Stack

Programming Languages

Frameworks

Testing Framework

Databases

Setup

1. Clone repository with:

git clone https://github.com/GhostMEn20034/SMILE_SALES_product_microservice.git

2. Create .env file:

on Windows (PowerShell), run:

New-Item -Path ".env" -ItemType "File"

on Unix or MacOS, run:

touch .env

3. Open any editor and paste the next variables:

mongodb_url=YourMongoDbURL
redis_cache_url=YourRedisUrl
atlas_search_product_index_name=some_index_name # The name of the search index for product search in your MongoDB cluster
atlas_search_search_terms_index_name=other_index_name # The name of the search index for search terms autocomplete in your MongoDB cluster
allowed_origins='["http://localhost:3000"]' # List of allowed origins (REMOVE SINGLE QUOTES IF YOU GONNA RUN THE APP AS DOCKER IMAGE)
relevance_threshold=0.22 # Determines minimum relevance score of search item to be not excluded

Running The app

If you want to run this app you have two options:

• Run it with python command
• Run it as Docker Image

1. Running with python command

1.1 Create Python's virtual environment:

python -m venv venv

1.2. Activate the virtual environment:

on Windows (PowerShell), run:

.\venv\Scripts\activate.ps1

on Unix or MacOS, run:

source venv/bin/activate

1.3 Install all dependencies:

pip install -r requirements.txt

1.4 export all .env values as environment variables, you can do that with command:

export $(xargs < .env)

1.5 Enter the command to run the app:

uvicorn src.main:app --host 0.0.0.0 --port 8000

2. Running as Docker Image

2.1 Build the docker image

docker build -f DockerfileLocal -t your-image-name:latest .

if you want to run the container in the production environment, build the image with the command:

docker build -f DockerfileProd -t your-image-name:latest .

2.2 Run the built image

docker run -d -p 8000:8000 --env-file ./.env your-image-name:latest

Testing

Before running tests, you need to export the environment variable that indicates we run the app in the test mode:

export TEST_MODE=1

Note: It's highly recommended to run tests with temporary MongoDB instance that don't store any data. Because if you forget to set TEST_MODE=1, tests will populate data in the database where you store data, and delete all data after that. To replace your MongoDB instance, just replace the mongodb_url value in .env file with the temporary instance's url.

To run tests use command:

pytest /tests