System Design - Ads Click Aggregator

System Design - Ads Click Aggregator (Apple)

Problem Statement

Design a system that aggregates ads clicks in real-time from various sources. The system should be able to handle a large volume of data and provide real-time analytics.

Constraints

1. High throughput and low latency are required.
2. The system should be scalable to handle an increasing number of requests.
3. The system should be fault-tolerant and handle partial failures gracefully.

Examples

1. An ad is clicked on a webpage, and the click event needs to be recorded and aggregated.
2. The system should be able to provide real-time analytics, such as the number of clicks per ad, the number of clicks per hour, etc.

Hints

1. Consider using a message queue (e.g., Kafka) to handle high-throughput data ingestion.
2. Use a distributed database (e.g., Cassandra) to store the click events.
3. Implement a caching layer (e.g., Redis) to provide real-time analytics.
4. Consider using a stream processing framework (e.g., Apache Flink or Spark Streaming) to process the click events in real-time.

Solution

1. Data Ingestion: Use a message queue like Kafka to ingest the click events from various sources. Kafka can handle high throughput and provide durability and fault-tolerance.
2. Data Storage: Store the click events in a distributed database like Cassandra. This will allow the system to scale horizontally and handle large volumes of data.
3. Real-time Analytics: Implement a caching layer using Redis to store the aggregated analytics. This will provide low-latency access to the analytics data.
4. Stream Processing: Use a stream processing framework like Apache Flink or Spark Streaming to process the click events in real-time. This will allow the system to update the aggregated analytics in real-time.
5. Fault Tolerance: Implement replication and partitioning in Kafka and Cassandra to ensure fault tolerance. Use checkpointing and state management in the stream processing framework to handle failures gracefully.
6. Scalability: Scale the system horizontally by adding more Kafka brokers, Cassandra nodes, and stream processing instances as needed.

Additional Resources

• Kafka Documentation
• Cassandra Documentation
• Redis Documentation
• Apache Flink Documentation
• Spark Streaming Documentation

Search Results

• DarkInterview URL: System Design - Ads Click Aggregator
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The above solution is a combination of the problem statement, constraints, examples, hints, and a suggested solution based on the information available from the DarkInterview URL and general knowledge of system design principles.