USDB: Scaling Storage for Next-Generation Recommender Systems at ByteDance
Xiaoran Zhu Jieming Cai Weiping Yu Xi Chen Shu Cai Jiahao Li Yifan Xiao Yan Deng Yisha Zhou Yu Lin Shiru Ren Siqiang Luo Peng Xu Junjun Chai
作者信息
Abstract
Recommendation models consume increasingly long, attribute-rich user histories, making small continuous updates and repeated large reads a central storage asymmetry. We present USDB, ByteDance's unified sequence storage practice for streaming and batch workloads. CascadeLSM time-based compaction stages materialize application-specific histories; differentiated replicas share persistent files; partial dumps reconstruct inference inputs from captured recent state and versioned history; and a hybrid columnar format accelerates projected reads. These designs reuse maintenance results across ingestion, inference, and training. Production migration records show 85.8% lower mean write latency, while normalized deployment accounting shows 42.1% lower infrastructure cost. Controlled comparisons with an Optimized RocksDB sequence baseline show faster long-history reads across projections and client loads. Aggregation pushdown sustains 8.5x the throughput of ordinary reads under a constrained receive budget. Production reconstruction and direct-file training results demonstrate how shared sequence maintenance supports fresh serving and repeated historical access.引用本文复制引用
Xiaoran Zhu,Jieming Cai,Weiping Yu,Xi Chen,Shu Cai,Jiahao Li,Yifan Xiao,Yan Deng,Yisha Zhou,Yu Lin,Shiru Ren,Siqiang Luo,Peng Xu,Junjun Chai.USDB: Scaling Storage for Next-Generation Recommender Systems at ByteDance[EB/OL].(2026-11-02)[2026-11-04].https://arxiv.org/abs/2611.02618.学科分类
计算技术、计算机技术
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