USDB：為 ByteDance 下一代推薦系統擴充套件儲存方案
USDB: Scaling Storage for Next-Generation Recommender Systems at ByteDance
重點導讀
USDB 是 ByteDance 推出的統一序列儲存方案，專為流式與批次工作負載設計。
- 核心設計：CascadeLSM 時間基 compaction、分層複製、部分轉儲與混合列式格式，整合進入 ingestion、推理與訓練。
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正文
Authors: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
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.
| Comments: | 17 pages, 18 figures, 5 tables |
| Subjects: | Databases (cs.DB) |
| Cite as: | arXiv:2611.02618 [cs.DB] |
| (or arXiv:2611.02618v1 [cs.DB] for this version) | |
| https://doi.org/10.48550/arXiv.2611.02618 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Weiping Yu Mr. [view email]
[v1]
Mon, 2 Nov 2026 06:45:03 UTC (432 KB)
來源： Hacker News (official RSS) (arxiv.org)
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