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Design a scalable video search system
Design a Scalable Video Search System
Context
You are tasked with designing an end-to-end multimodal retrieval system that supports both:
- Text-to-video search: Users enter a text query and receive the most relevant video segments.
- Video-to-video search: Users provide a reference video and receive visually or semantically similar video segments.
Assume the system must handle a catalog that grows to billions of video clips/segments, serve global users, and adhere to strict latency, cost, and safety requirements.
Requirements
Model Architecture
- Encoders: Design encoders for text and video frames/clips (e.g., dual encoders).
- Temporal Aggregation: Implement temporal aggregation for video (frame-to-clip, clip-to-video).
- Embedding Space: Define a shared embedding space, similarity function, and scoring mechanism.
- Training Objectives: Specify pretraining/fine-tuning objectives (e.g., contrastive, multi-task) and negative sampling strategies.
- Long Videos: Address handling of long videos through techniques like clip sampling and segment-level indexing.
- Re-ranking (Optional): Consider cross-encoder re-ranking for top-K results.
Infrastructure
- Feature Extraction: Design offline and online feature extraction pipelines.
- Ingestion & Enrichment: Implement ingestion and metadata enrichment processes (e.g., ASR, OCR, tags).
- Vector Store: Design indexing in a vector store (e.g., HNSW/IVF, PQ/OPQ), including sharding and replication strategies.
- Storage Layout: Define storage layout for thumbnails and short preview clips.
- Query Routing: Implement query routing and aggregation across shards.
- Performance: Address latency and cost targets, including caching strategies.
- Scalability: Ensure scalability to billions of clips/segments.
Monitoring, Safety, and Evaluation
- System Monitoring: Implement system and model monitoring (e.g., SLOs, drift, recall).
- Content Safety: Address abuse and content safety concerns (e.g., NSFW, spam, PII).
- Evaluation: Define evaluation via offline metrics and online A/B tests.
- Model Updates: Support relevance feedback and incremental model updates.
AI interview prep
Core dilemma
Balancing high-quality, multimodal semantic search relevance over billions of dynamic video segments with strict low-latency, global availability, and cost-efficiency constraints.
Mid-level signals3
Senior signals3
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 a scalable video search system
Design a Scalable Video Search System
Context
You are tasked with designing an end-to-end multimodal retrieval system that supports both:
- Text-to-video search: Users enter a text query and receive the most relevant video segments.
- Video-to-video search: Users provide a reference video and receive visually or semantically similar video segments.
Assume the system must handle a catalog that grows to billions of video clips/segments, serve global users, and adhere to strict latency, cost, and safety requirements.
Requirements
Model Architecture
- Encoders: Design encoders for text and video frames/clips (e.g., dual encoders).
- Temporal Aggregation: Implement temporal aggregation for video (frame-to-clip, clip-to-video).
- Embedding Space: Define a shared embedding space, similarity function, and scoring mechanism.
- Training Objectives: Specify pretraining/fine-tuning objectives (e.g., contrastive, multi-task) and negative sampling strategies.
- Long Videos: Address handling of long videos through techniques like clip sampling and segment-level indexing.
- Re-ranking (Optional): Consider cross-encoder re-ranking for top-K results.
Infrastructure
- Feature Extraction: Design offline and online feature extraction pipelines.
- Ingestion & Enrichment: Implement ingestion and metadata enrichment processes (e.g., ASR, OCR, tags).
- Vector Store: Design indexing in a vector store (e.g., HNSW/IVF, PQ/OPQ), including sharding and replication strategies.
- Storage Layout: Define storage layout for thumbnails and short preview clips.
- Query Routing: Implement query routing and aggregation across shards.
- Performance: Address latency and cost targets, including caching strategies.
- Scalability: Ensure scalability to billions of clips/segments.
Monitoring, Safety, and Evaluation
- System Monitoring: Implement system and model monitoring (e.g., SLOs, drift, recall).
- Content Safety: Address abuse and content safety concerns (e.g., NSFW, spam, PII).
- Evaluation: Define evaluation via offline metrics and online A/B tests.
- Model Updates: Support relevance feedback and incremental model updates.