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Performance and Extensibility Considerations for Graph Queries in the SQLite Graph Extension

1 min read
206 words
SQLite extensions Performance Extensibility

Meet SQLite Graph: an alpha extension that turns SQLite into a graph store with Cypher queries. It's functional but early, sparking questions about performance, indexing, and optimization for CREATE and MATCH on SQLite [1]. It even passes many openCypher tests (70/70 on the CREATE side) [1].

  • Current capabilities – Full CREATE of nodes and relationships with properties, plus chained patterns. MATCH supports relationship patterns and label/type filtering, and there’s virtual table integration to mix SQL and Cypher in real workloads [1].

  • Alpha limitations – Only forward relationships (no bidirectional), no relationship-property filtering in WHERE, no variable-length paths, and no aggregations or ORDER BY in RETURN yet, all of which limit complex graph queries [1].

  • Performance snapshot – 340K nodes/sec inserts, 390K edges/sec, and 180K nodes/sec scans with WHERE filtering, demonstrating strong throughput for an alpha project [1].

  • Extensions impact – As a virtual table extension, memory usage and plan selection will influence how Cypher queries are executed on SQLite Graph [1].

  • Roadmap – Bidirectional relationships, property projection, aggregations, and full Cypher support planned for Q1 2026. Built by Agentflare AI and MIT licensed [1].

Closing thought: alpha progress is real, but indexing strategies and planner behavior will largely determine real-world scalability.

References

[1]
HackerNews

Show HN: SQLite Graph – Graph database with Cypher queries (alpha, but working)

Adds a graph database layer to SQLite with Cypher support; supports CREATE, MATCH, and RETURN via a virtual table extension.

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