System requirements

Functional:

1. Real-time weather updates.
2. Forecasts (hourly/daily).

Non-Functional:

1. Scalability to handle millions of users.
2. High availability (99.9% uptime).
3. Low-latency responses.
4. Fault tolerance and security.

Capacity estimation

Number of Requests:

• Assume 100 million users with 1% requesting data per minute = 1 million requests per minute (~16,667 requests per second).
• Peak usage could be 10 times this, or 166,670 RPS during severe weather events.

Data Storage:

• Current weather data: For 500,000 locations (cities/regions) with updates every 10 minutes = ~72 GB/day.
• Historical data: Storing 1 year’s worth of weather data at 72 GB/day = ~26 TB/year.

Bandwidth:

• Normal load: 16,667 RPS * 1-5 KB per request = ~16.7 MB/s = ~1.4 TB/day.
• Peak load: 166,670 RPS = ~167 MB/s = ~14.4 TB/day.

API design

GET /weather/current

• Request: GET /weather/current?location={city_name/coordinates}
• Response:

GET /weather/forecast

• Request: GET /weather/forecast?location={city_name/coordinates}&type={hourly/daily}
• Response:

Database design

Data Model:

1. Weather Data Table (Current Weather)
   • LocationID (Primary Key)
   • Temperature
   • Humidity
   • Wind Speed
   • Description
   • Timestamp
2. Forecast Data Table
   • LocationID (Foreign Key)
   • ForecastType (Hourly/Daily)
   • ForecastTime
   • Temperature
   • Condition

High-level design

Components:

1. API Gateway: Receives user requests and forwards them to relevant services.
2. Weather Aggregator Service: Collects weather data from third-party APIs and sensors, processes it, and stores it in the real-time database.
3. Location-Based Weather Service: Returns current weather, forecasts, and historical data.
4. Weather Alert System: Manages user subscriptions and sends notifications.
5. Real-Time Database: Stores processed weather data (DynamoDB/Cassandra).
6. Cache Layer: Redis to cache frequently requested weather data.
7. Historical Data Storage: Stores historical weather data in a cold storage system (e.g., AWS S3)

Request flows

Weather Request Flow:

• Step 1: User sends a request for current weather via the API Gateway.
• Step 2: API Gateway forwards the request to the Location-Based Weather Service.
• Step 3: The service checks the cache for recent weather data. If found, it returns the data.
• Step 4: If not cached, the service queries the real-time database.
• Step 5: Weather data is returned to the user, and it is cached for future requests.

Weather Alert Flow:

• Step 1: User subscribes to weather alerts using the subscription API.
• Step 2: The alert system registers the user and monitors the weather data.
• Step 3: If severe weather conditions are detected, the system sends notifications to subscribed users via email, SMS, or push notifications.

Detailed component design

1.Weather Aggregator Service:

• Scalability: Use multiple instances of this service, each handling a subset of weather sources.
• Algorithms: Rate-limiting and throttling are used to avoid exceeding third-party API limits.

2. Weather Alert System:

• Scalability: The system uses a Pub/Sub model (e.g., Google Pub/Sub) to scale up during extreme weather events.
• Data Structure: A queue is used to process and distribute alerts efficiently

Trade offs/Tech choices

SQL vs. NoSQL: Chose NoSQL (e.g., DynamoDB) for scalability and high throughput with real-time data. NoSQL is ideal for handling high volumes of weather updates.

Cache (Redis): Used to minimize database load and reduce response times for frequently requested data.

Pub/Sub for Alerts: Chose Pub/Sub architecture to decouple the alerting system from the main weather data processing pipeline, allowing it to scale independently.

Failure scenarios/bottlenecks

1. API Rate Limits: External APIs may have limits. Using rate-limiting mechanisms and caching helps mitigate this.
2. Database Failures: Ensure database replication and backups to avoid data loss.
3. Traffic Spikes: Use auto-scaling groups to spin up more instances during traffic surges.

Future improvements

1. Machine Learning Forecasts: Implement machine learning algorithms to enhance prediction accuracy using historical data.
2. Edge Computing: Distribute weather data closer to the users with edge computing nodes to reduce latency.