Extra Topics to Consider for Your CTV/OTT Ad Delivery System Design
While we've comprehensively explored the core functionalities of your CTV/OTT ad delivery system design, there are some additional areas you might want to consider to showcase your well-rounded system design expertise during the interview. These topics are inspired by the MyMLTech reading list (https://www.myyltech.com/advance-your-career-with-ai/preparing-for-system-design-interview/reading-and-viewing-list)
Machine Learning System Design Deep Dive:
Large Scale Data Serving: How would you design a system to efficiently serve large machine learning models in production for real-time ad selection or recommendation? Consider techniques like sharding, model compression, and distributed inference.
Feature Store Design: Explore the concept of a feature store - a centralized repository for machine learning features. How would you design a feature store to ensure efficient feature retrieval and management for your ad delivery system?
Model Monitoring and Alerting: In a production environment, how would you monitor the performance of your machine learning models serving ads? Discuss strategies for identifying potential issues like data drift or performance degradation, and triggering alerts for timely intervention.
Scalability and Beyond:
While we covered horizontal and vertical scaling strategies, here are some additional points to consider:
Database Sharding: How would you implement database sharding to horizontally scale your relational database and handle the growing data volume of user interactions and ad campaign information?
Cache Invalidation Strategies: As data in your backend system changes (e.g., ad campaign updates), how would you ensure cache consistency to prevent serving outdated information through API calls?
Microservices Architecture Considerations:
API Gateway Design: If your system utilizes a microservices architecture, how would you design an API Gateway to serve as a single entry point for all API requests? Discuss considerations for scalability, security, and observability within the API Gateway.
NoSQL Options for Specific Needs:
While we focused on data warehousing with relational databases, consider these NoSQL options for specific use cases:
Document Databases: When schema flexibility is crucial, explore how document databases like MongoDB could be utilized for storing user data or ad campaign details.
Graph Databases: If modeling relationships between entities is important (e.g., user viewing history and recommended ads), how might you leverage a graph database like Neo4j?
Key-Value Stores: For caching frequently accessed data or enabling high-performance data retrieval (e.g., user preferences for ad formats), consider the role of key-value stores like Redis in your system design.
Remember, the relevance of these additional topics depends on the specific requirements of your CTV/OTT ad delivery system and the focus of the interview.
Tailoring Your Preparation:
Interview Focus: Is the interview geared towards general system design principles or designing for machine learning workloads? Prepare accordingly, highlighting relevant expertise.
Depth vs. Breadth: While demonstrating a strong grasp of core design concepts is essential, be prepared to delve deeper into specific technologies or design patterns if prompted.
By considering these extra topics and tailoring your preparation to the specific interview, you'll be well-positioned to tackle a wider range of system design questions related to your CTV/OTT ad delivery system or similar data-driven applications.