There's a lot of directions you can take this and I think that's kind of the point.
Start by clarifying requirements: what defines a 'specific photo' (exact match, similarity, metadata), stream characteristics (rate, format, ordering), and constraints (latency, throughput, memory). Then propose a scalable architecture that ingests the stream, extracts features, and performs efficient matching using appropriate indexing and filtering techniques.
Pro tip: Emphasize trade-offs between exact and approximate matching, and discuss how to handle concept drift or changing photo definitions over time without reprocessing the entire stream.
Ask questions to understand the photo matching criteria (e.g., exact hash, perceptual hash, object detection), stream volume and velocity, and latency/accuracy requirements.
Outline a scalable ingestion layer (e.g., Kafka, Kinesis) that buffers and partitions the stream for parallel processing, ensuring fault tolerance and backpressure handling.
Describe how to extract relevant features (e.g., perceptual hashes, embeddings) from each photo and build an index (e.g., inverted index, LSH, FAISS) for fast similarity search.
Explain the matching process: for each incoming photo, compute features, query the index, and apply thresholds or verification steps to confirm a match, handling false positives/negatives.
Discuss scaling strategies (sharding, replication), trade-offs between exact vs. approximate matching, memory vs. accuracy, and how to monitor and tune the system.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.