Multi-hop graph traversal inside SurrealDB

A SurrealDB 3.x tutorial demonstrates how moving multi-hop graph traversal from application-side breadth-first-search loops into the database can reduce network round trips, transferred data, and latency while enabling filtering during each hop. Using a synthetic GraphRAG dataset of 2,000 entities, 16,000 typed edges, and 800 documents, it shows that unpruned three-hop walks can rapidly expand to hundreds of reachable nodes, making cardinality management more important than individual-hop performance. The recommended approach uses recursive SurrealQL traversal with per-edge filters for relation types and confidence, per-node filters for tenant and visibility constraints, depth ranges, and de-duplicated collection of reachable entities; path output can also provide explanations for why results were retrieved. The tutorial discusses safeguards for dense hubs, noting that per-hop edge slicing bounds expansion but may discard valid routes and can return no results when too few edges exist. It further combines graph traversal with reverse document-mention hops for ranked document retrieval and HNSW vector search for hybrid retrieval, while explaining that normal indexes benefit graph entry points but arrow-based traversal uses direct graph-edge scans rather than secondary edge-table indexes. Benchmark results cited show equivalent 14-entity outputs with server-side traversal requiring one round trip and 241 bytes versus six round trips and about 4,905 bytes for application-side BFS, and the tutorial concludes that pruning at every hop, measuring frontier size, and limiting traversal depth are central practices for scalable graph retrieval.

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