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Design an Ads Audience Targeting System
Design a robust and scalable system for advertisers to manage and utilize custom audience lists for ad targeting. This system must enable advertisers to upload large user lists and then either include or exclude these specific users when serving advertisements.
Functional Requirements
- Audience Management: Advertisers must be able to create audiences and upload membership data at a massive scale, supporting lists ranging from millions to billions of user identifiers.
- Identifier Support: The system should support common user identifiers, including hashed email addresses, hashed phone numbers, mobile advertising IDs (MAIDs), and internal user IDs.
- Targeting Logic: Audiences can be associated with ad groups or campaigns, functioning as either an "include" list (target only these users) or an "exclude" list (do not target these users).
- Real-time Ad Serving Lookup: During the ad serving process, given a user request, the system must rapidly determine which audiences the user belongs to (or matches) to effectively filter eligible ads.
- Lifecycle Management: The system must support incremental updates (adding or removing users from an audience), audience expiration, and complete audience deletion.
Non-Functional Requirements
- Low Latency Serving: The ad serving path requires extremely low latency, typically within a single-digit millisecond budget for audience lookup.
- High Ingestion Throughput: The system must handle high throughput for ingesting large volumes of audience data.
- Privacy and Security: Implement robust measures for data privacy and security, including encryption of sensitive data, strict access control, comprehensive audit logs, and defined data retention policies.
- Multi-tenancy: Ensure strong isolation between data and operations of different advertisers.
Deliverables
Your design explanation should cover the following aspects:
- APIs and Data Flow: Describe the APIs and the end-to-end data flow from audience upload, through processing, to final storage.
- Storage and Indexing: Detail your approach for storing and indexing audience membership data to facilitate efficient lookup during ad serving.
- Scalability and Monitoring: Explain how you would scale both the ingestion and serving components of the system, and how you would monitor its correctness and performance.
- Architectural Tradeoffs: Discuss key architectural tradeoffs, such as the choice between Bloom filters, key-value lookups, or inverted indexes for membership checks, and the implications of using batch versus streaming processing for data ingestion.
AI interview prep
Core dilemma
Balancing the need for extremely low-latency, high-volume lookups during ad serving against the challenges of ingesting, processing, and storing massive, frequently updated audience data (billions of identifiers), all while maintaining data consistency, privacy, and multi-tenancy.
Mid-level signals3
Senior signals4
Red flags3
3 tradeoffs and 3 stress tests locked
Interview prep
Unlock the interviewer X-Ray rubric, tradeoff matrix, stress tests, clarifying questions, and key concepts.
Design an Ads Audience Targeting System
Design a robust and scalable system for advertisers to manage and utilize custom audience lists for ad targeting. This system must enable advertisers to upload large user lists and then either include or exclude these specific users when serving advertisements.
Functional Requirements
- Audience Management: Advertisers must be able to create audiences and upload membership data at a massive scale, supporting lists ranging from millions to billions of user identifiers.
- Identifier Support: The system should support common user identifiers, including hashed email addresses, hashed phone numbers, mobile advertising IDs (MAIDs), and internal user IDs.
- Targeting Logic: Audiences can be associated with ad groups or campaigns, functioning as either an "include" list (target only these users) or an "exclude" list (do not target these users).
- Real-time Ad Serving Lookup: During the ad serving process, given a user request, the system must rapidly determine which audiences the user belongs to (or matches) to effectively filter eligible ads.
- Lifecycle Management: The system must support incremental updates (adding or removing users from an audience), audience expiration, and complete audience deletion.
Non-Functional Requirements
- Low Latency Serving: The ad serving path requires extremely low latency, typically within a single-digit millisecond budget for audience lookup.
- High Ingestion Throughput: The system must handle high throughput for ingesting large volumes of audience data.
- Privacy and Security: Implement robust measures for data privacy and security, including encryption of sensitive data, strict access control, comprehensive audit logs, and defined data retention policies.
- Multi-tenancy: Ensure strong isolation between data and operations of different advertisers.
Deliverables
Your design explanation should cover the following aspects:
- APIs and Data Flow: Describe the APIs and the end-to-end data flow from audience upload, through processing, to final storage.
- Storage and Indexing: Detail your approach for storing and indexing audience membership data to facilitate efficient lookup during ad serving.
- Scalability and Monitoring: Explain how you would scale both the ingestion and serving components of the system, and how you would monitor its correctness and performance.
- Architectural Tradeoffs: Discuss key architectural tradeoffs, such as the choice between Bloom filters, key-value lookups, or inverted indexes for membership checks, and the implications of using batch versus streaming processing for data ingestion.