Google system design interview: Design Spotify (with ex-Google EM)

In summary, the most interesting aspect of this interview is the candidate's systematic approach to handling scale and performance bottlenecks, particularly through the implementation of multi-layer caching and the strategic split between relational metadata storage and object storage for audio files.

• Constrain the problem scope early by identifying core use cases to keep the design focused and feasible.
• Perform back-of-the-envelope calculations to estimate storage requirements, data size, and user scale.
• Separate storage by access patterns, using relational databases for mutable metadata and object storage for immutable audio blobs.
• Identify hot content bottlenecks and implement multi-layer caching (device, CDN, application server) to reduce latency and backend load.
• Optimize load balancing metrics for streaming workloads by monitoring network bandwidth and I/O rather than relying solely on CPU usage.
• Apply geo-aware replication strategies to place data closer to regional users and minimize latency.
• Clearly communicate design trade-offs and acknowledge knowledge gaps to maintain transparency and guide the interview collaboratively.