Loading...
- Monitor and analyze web traffic to determine the top 'k' most frequent requests.
- Allow flexible adjustments of both the 'k' value and time intervals.
- Provide query and reporting capabilities for analyzed data.
- Handle large-scale data inflows from web requests.
- Ensure rapid query response times.
- System must be scalable to accommodate varying data inflows.
- High availability and fault tolerance.
- Secure handling of data to comply with privacy regulations.
- Data Inflow: Estimate 10 million requests per hour, which is approximately 2,777 requests per second.
- Storage Requirements: Assume each request record is 1KB; this results in approximately 10GB per hour.
- Data Growth: With hourly data retention, expect around 240GB per day.
Define what APIs are expected from the system...
- Use a time-series database (e.g., InfluxDB) for storing request data with efficient time-query operations.
- Alternatively, use a combination of NoSQL (e.g., DynamoDB) for high velocity writes, and SQL databases for querying aggregated results.
Database Selection:
- Use a time-series database (e.g., InfluxDB) for storing request data with efficient time-query operations.
- Alternatively, use a combination of NoSQL (e.g., DynamoDB) for high velocity writes, and SQL databases for querying aggregated results.
API Architecture:
- Design RESTful APIs:
- `GET /top-k-requests?interval=5min&k=10`: fetch top 10 requests over last 5 minutes.
- `POST /adjust-parameters`: to adjust 'k' and time interval dynamically.
Caching:
- Implement caching layer (e.g., Redis) for frequent top-k results to reduce database load.
Scalability and Reliability:
- Use load balancers to distribute requests to multiple server instances.
- Implement horizontal scaling for application servers.
- Enable data replication and sharding in the database layer for high availability.
Additional System Design Considerations:
- Implement security using HTTPS and API authentication.
- Use monitoring tools (e.g., Grafana) for system health.
- Implement logging for auditing and debugging.
1. Request Ingestion: Web requests enter through a load balancer.
2. Processing Server: Parse and temporarily store requests in a message queue (e.g., Kafka).
3. Data Storage: Save processed requests to a time-series database.
4. Analytics: Periodically compute and cache top-k requests.
5. Client Interaction: API requests query the cache or database for top-k results.
Dig deeper into 2-3 components and explain in detail how they work. For example, how well does each component scale? Any relevant algorithm or data structure you like to use for a component? Also you could draw a diagram using the diagramming tool to enhance your design...
- Latency vs. Consistency: Using a time-series database offers better performance for time-based queries but may sacrifice some consistency guarantees.
- Storage Cost vs. Query Performance: Caching improves retrieval performance but might increase the cost due to additional storage requirements.
- Real-time vs. Batch Processing: Real-time processing offers up-to-date information but is more resource-intensive compared to batch processing insights at periodic intervals.
Try to discuss as many failure scenarios/bottlenecks as possible.
What are some future improvements you would make? How would you mitigate the failure scenario(s) you described above?