发表机构
ClickGraph; DeltaGraph（ClickGraph; DeltaGraph）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出ClickGraph与DeltaGraph系统，将Cypher转换为原生关系模式在关系引擎执行，性能远超Neo4j等原生图引擎，突破内存图引擎的扩展限制，证明关系系统可高效处理企业图分析工作负载。
AI 中文摘要
长久以来存在一种固有假设：图分析需要专用的图引擎，而关系系统不适合处理关联数据。但我们针对企业实际运行的工作负载提出相反观点：以图查询语言为前端的列式关系引擎，在分析型图查询上可达到或超越原生图引擎的性能，且关键优势在于其扩展性远超内存图引擎失效的临界点。我们进一步指出，节点/边属性图并非关联数据更忠实的模型，而是对关系表中已明确存在的关系的重新编码；在查询时重构这些关系完全是额外开销。我们提出ClickGraph及其Databricks方言的同类系统DeltaGraph，这两个系统可将Cypher直接转换为原生关系模式——即已存在的表、列和外键——并直接在ClickHouse、Databricks或湖仓文件的进程内执行，无需导入数据或单独集群。由于输出是普通SQL，性能不佳的查询有开放的优化空间：可重写查询，也可扩展引擎本身。我们用同行系统已发表的基准测试支撑该论点，其中列式引擎的性能比Neo4j高2至4个数量级，还通过LDBC社交网络基准测试套件的可复现测量结果提供佐证。
英文摘要
A durable assumption holds that graph analytics requires a purpose-built graph engine, and that relational systems are ill-suited to connected data. We argue the opposite for the workloads enterprises actually run. A columnar relational engine fronted by a graph query language matches or exceeds native graph engines on analytical graph queries, and - decisively - scales past the point where in-memory graph engines fail. We further argue that the node/edge property graph is not a more faithful model of connected data but a re-encoding of relationships that already exist explicitly in relational tables; reconstructing them at query time is pure overhead. We present ClickGraph and its Databricks-dialect sibling DeltaGraph, systems that translate Cypher directly onto the native relational schema - the tables, columns, and foreign keys as they already exist - and execute in place on ClickHouse, Databricks, or in-process on lakehouse files, with no import and no separate cluster. Because the output is ordinary SQL, an underperforming query is an open optimization surface: it can be rewritten, and the engine itself extended. We support the argument with a peer system's own published benchmark, in which a columnar engine outruns Neo4j by two-to-four orders of magnitude, and with reproducible measurements across the LDBC Social Network Benchmark suite.
