ByteDance publishes USDB paper on scaling storage for next-gen recommender systems
1equalsequals1 · hn · 2026-11-04
ByteDance released the USDB paper on arXiv, detailing its storage architecture for scaling next-generation recommender systems, which must serve massive embedding and feature data with strict throughput, latency and scalability demands for both training and online serving.
Worth a read for anyone interested in large-scale recommendation infrastructure and training system design.
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