Design a Stock Price Alert Notification System

Problem Statement

Design a stock price alert notification system similar to a Robinhood-style watchlist alert feature. At minimum, the system should support:

• Creating alerts like "notify me when AAPL goes above $150".
• Deleting alerts.
• Listing all alerts for a user.
• Notifying users when an alert condition is met.

Constraints

• Assume there are millions of users and thousands of stocks.
• Assume the system should handle a high volume of alert creations and deletions.
• Assume the system should handle a high volume of stock price updates.
• Assume the system should handle a high volume of notifications.

Examples

1. User creates an alert: "notify me when AAPL goes above $150".
2. User deletes an alert: "delete alert for AAPL".
3. User lists all alerts: "list all alerts".
4. User receives a notification: "AAPL is now above $150".

Hints

1. Consider using a pub/sub model for notifications.
2. Consider using a time-series database for stock prices.
3. Consider using caching for frequent stock price queries.
4. Consider using a distributed system for scalability.

Solution

1. Data Model: Create a table Alerts with columns id, user_id, stock_symbol, price_threshold, created_at, and deleted_at.

2. API Endpoints:

   • POST /alerts: Create a new alert.
   • DELETE /alerts/{id}: Delete an existing alert.
   • GET /alerts: List all alerts for a user.

3. Notification Service: Use a pub/sub model to notify users when an alert condition is met.

4. Stock Price Updates: Use a time-series database to store stock prices and update them in real-time.

5. Caching: Cache frequent stock price queries to reduce database load.

6. Scalability: Use a distributed system to handle high volume of requests and data.

Additional Considerations

• Rate Limiting: Implement rate limiting to prevent abuse of the API.
• Monitoring: Monitor the system for performance and reliability.
• Security: Ensure secure access to user data and API endpoints.

This problem statement and solution outline were compiled from various sources, including the DarkInterview URL provided and other relevant web searches.