https://github.com/ankitjaadoo/pricing-feeds-search

https://github.com/ankitjaadoo/pricing-feeds-search

Last synced: 9 days ago
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• Host: GitHub
• URL: https://github.com/ankitjaadoo/pricing-feeds-search
• Owner: ankitjaadoo
• License: mit
• Created: 2024-08-18T18:42:15.000Z (3 months ago)
• Default Branch: main
• Last Pushed: 2024-08-18T21:15:41.000Z (3 months ago)
• Last Synced: 2024-08-19T21:31:48.070Z (3 months ago)
• Language: Python
• Size: 768 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# pricing-feeds-search

## Instructions to run the app

- to run the application, git clone the repo.

- run the command `pip install -r requirements.txt`

- change directory to pricing_feeds_app

- run the command `python manage.py runserver`

## Endpoints to be tested:

- `/pricing/upload/` - use the file 'myFile0.csv' provided in the repo to upload.

- `/pricing/search/` - you can search for any of the products from here.

- `/pricing/edit//` - you can edit the product details from here.

## Deployment steps:

1. Install AWS Elastic Beanstalk CLI: `pip install awsebcli`

2. Initialize Elastic Beanstalk Application: `eb init -p python-3.9.13 pricing-manager --region us-west-2`

3. Create an Environment and Deploy: `eb create pricing-manager-env eb deploy`

4. Set Up Static Files (if using S3) within settings.py: 

- STATIC_URL = '/static/'

- STATIC_ROOT = os.path.join(BASE_DIR, 'static')

- For S3

STATICFILES_STORAGE = 'storages.backends.s3boto3.S3Boto3Storage'

### Important Design Decisions

Before diving into the code, let's discuss some key design decisions for this application:

1. Database Design:

- Schema: Define tables for Store, Product, and PriceHistory. Consider using composite keys or foreign keys to link tables.

- Indexing: Ensure proper indexing on fields like Store ID, SKU, Product Name, and Date for optimized search queries.

- Database Choice: Use a relational database like PostgreSQL to handle complex queries and large datasets efficiently.

2. Scalability:

- Database Sharding/Partitioning: If the database grows large, consider sharding or partitioning by country or store ID.

- Load Balancing: Use load balancers to distribute incoming traffic across multiple server instances.

3. Data Validation:

- Validate the uploaded CSV files for correct formatting and data integrity before persisting them in the database.

4. Concurrency Handling:

- Implement locks or use optimistic concurrency control to handle multiple users editing the same records simultaneously.

5. Security:

- Secure endpoints with authentication and authorization.

- Implement data encryption for sensitive information.

- Use HTTPS to secure data in transit.

6. Deployment:

- Consider containerizing the application using Docker for easier deployment and scaling.

- Use CI/CD pipelines for automated testing and deployment.

7. Logging and Monitoring:

- Integrate logging for tracking errors, and user activity, and monitoring performance.

- Use tools like Prometheus and Grafana for real-time monitoring and alerting.