字节跳动发布 USDB 优化推荐系统存储架构
字节跳动推出统一序列存储系统 USDB，针对推荐模型中“小更新、大读取”的存储不对称问题。通过级联 LSM 压缩、混合列式格式及聚合下推等技术，USDB 在降低 42.1% 基础设施成本的同时，将平均写入延迟降低 85.8%，并显著提升长历史序列的读取吞吐量。
报道正文
Abstract 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.