Samyama：一个统一的图-向量数据库，具有数据库内优化、代理增强和硬件加速
Samyama: A Unified Graph-Vector Database with In-Database Optimization, Agentic Enrichment, and Hardware Acceleration
AI总结：
Samyama是一款统一的图-向量数据库，结合了数据库内优化、代理增强和硬件加速，通过Rust实现高性能和内存安全性。
摘要：
现代数据架构在图数据库、向量存储、分析引擎和优化求解器之间碎片化，导致复杂的ETL管道和同步开销。我们提出了Samyama，一个用Rust编写的高性能图-向量数据库，将这些工作负载整合到一个引擎中。Samyama结合了基于RocksDB的持久存储与版本化- Arena MVCC模型，一个具有35个物理操作符的向量化查询执行器，一个具有计划枚举和谓词下推的基于成本的查询计划器，一个专用的基于CSR的分析引擎，以及原生的RDF/SPARQL支持。系统将22个元启发式优化求解器直接整合到其查询语言中，实现了HNSW向量索引与图RAG能力，并引入了代理增强，通过LLMs实现自主图扩展。企业版增加了通过wgpu的GPU加速、生产级可观测性、时间点恢复以及通过HTTP/2 Raft传输的加固高可用性。我们在通用硬件（Mac Mini M4，16 GB RAM）上的评估显示：吞吐量为255,000节点/秒（CPU）和412,0 -= 000节点/秒（GPU加速）；1百万节点下的Cypher查询每秒115,000次；多跳遍历的延迟减少4.0-4.7倍；1百万节点下的GPU PageRank加速8.2倍；以及100%通过LDBC Graphalytics验证（28/28测试）。这些结果表明，一个统一的图-向量-优化引擎可以在通用硬件上实现有竞争力的性能，同时保持Rust的内存安全保证。
英文摘要：
Modern data architectures fragment across graph databases, vector stores, analytics engines and optimization solvers, forcing ETL between them. We present Samyama, a graph-vector database in Rust that unifies these workloads in one engine: a RocksDB-backed store with MVCC, a vectorized executor, a cost-based planner, a CSR analytics engine, RDF and SPARQL, 22 metaheuristic solvers callable from the query language, HNSW vector indexing, and agentic enrichment that expands a graph via LLMs. It has been run to billion-edge scale: 74.3M nodes and 1.07B edges from four biomedical sources on one machine for $2.50 of spot compute. This version re-measures the system at release v1.8.0 on rented Linux hosts a reader can boot, replacing earlier figures taken on a Mac Mini that nobody could re-run. On a 16-vCPU cloud instance, ingestion reaches 123K-152K nodes/s and PageRank costs 6.7 ms per iteration at ten thousand nodes and 236 ms at a million. The previously reported 8.2x GPU speedup is withdrawn; in its place we measure the CUDA path, which no earlier version measured at all, on a rented NVIDIA A16: parity with the CPU at ten thousand nodes, 1.62x at a hundred thousand and 2.70x at a million, with 1.1-1.35 s of context initialisation on the first call. The earlier Cypher-throughput figure, its claim of near-constant index-driven scaling, and a vector-search figure are withdrawn as unreproducible. openCypher conformance, once estimated at ~90%, is now measured at 99.9% of evaluated TCK scenarios. Against Neo4j 5 and FalkorDB on one host over an identical SF10 extract, 14 of 14 SNB complex reads land within 5x of the best competitor and 7 of 14 are faster.
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