Relational-Core Graph Analytics Querying graphs at SQL scale, and why the node/edge model is a performance tax, not a truer picture of connected data
A paper argues that a columnar relational engine fronted by a graph query language matches or exceeds native graph engines on analytical graph queries and scales past the point where in-memory graph engines fail. It presents ClickGraph and DeltaGraph, systems that translate Cypher directly onto the native relational schema and execute in place on ClickHouse, Databricks, or in-process on lakehouse files, with no import and no separate cluster.
The paper challenges the assumption that graph analytics requires a purpose-built graph engine. It claims that for enterprise analytical workloads, a columnar relational engine with a graph query language can match or exceed native graph engines and scale further. The node/edge property graph is described as a re-encoding of relationships already present in relational tables, making reconstruction at query time pure overhead. ClickGraph and DeltaGraph translate Cypher onto existing relational schemas and execute on ClickHouse, Databricks, or lakehouse files without import or separate clusters. The output is ordinary SQL, allowing query rewriting and engine extension.
The approach eliminates the need for a separate graph database by compiling graph queries to SQL over columnar relational storage. This leverages mature relational optimizers and storage engines, potentially improving scalability and reducing operational complexity. The claim that node/edge models are redundant with relational foreign keys suggests a shift toward unified analytical engines.
Enterprises with existing relational data warehouses may adopt graph query languages without migrating to specialized graph databases, reducing costs and simplifying architecture. This could pressure native graph database vendors in analytical use cases.
Reduces infrastructure costs by avoiding separate graph clusters and data duplication. Enables graph analytics on existing lakehouse/warehouse investments, potentially accelerating time-to-insight for connected-data queries.
Watch for benchmarks comparing ClickGraph/DeltaGraph against native graph engines on large-scale analytical workloads, and for adoption by enterprises using ClickHouse or Databricks. Potential open-source releases or commercial offerings may follow.