Hybrid Query Benchmarks¶

This benchmark focuses on the query shape that matters most for NornicDB's positioning: semantic retrieval followed by graph expansion in the same engine.

The goal is not to claim a universal leaderboard result. The goal is to show what happens when vector search and one-hop graph traversal share one execution path instead of being stitched together across multiple systems.

Summary¶

• Vector-only queries stayed in sub-millisecond to low-millisecond territory locally, depending on transport.
• Vector + one-hop graph traversal added a small incremental cost locally.
• Remote latency tracked client-to-server RTT, which means end-to-end latency became network-bound rather than database-bound.

Test Setup¶

Local environment:

• Apple M3 Max
• 64 GB RAM
• Native macOS installer

Remote environment:

• GCP
• 8 vCPU
• 32 GB RAM

Local Results¶

Remote Results¶

Client-to-server latency was about 110 ms.

The practical result is straightforward: once local compute for hybrid retrieval is in low single-digit milliseconds, network RTT dominates the user-visible latency budget.

Why This Matters¶

Most systems make this query shape a composition problem:

1. embed the query
2. call a vector store
3. move the results into a graph store or application layer
4. expand neighbors and shape the result there

NornicDB keeps that inside one execution engine. The benchmark does not prove every workload is constant-time, but it does show that shallow hybrid retrieval can stay tight enough locally that deployment topology matters more than extra database-side micro-optimizations.

Caveats¶

• These are single-node measurements.
• The dataset is not billion-scale.
• Remote throughput is latency-bound, not compute-bound.
• These numbers are useful for query-shape comparison, not as a blanket claim for every graph or vector workload.

Verification Queries¶

Vector-only:

curl -s -u "$NORNIC_USERNAME:$NORNIC_PASSWORD" "$ENDPOINT" \
  -H "Content-Type: application/json" -H "Accept: application/json" \
  -d '{
    "statements":[
      {
        "statement":"CALL db.index.vector.queryNodes('\''idx_original_text'\'', $topK, $text) YIELD node, score RETURN node.originalText AS originalText, score ORDER BY score DESC LIMIT $topK",
        "parameters":{"text":"get it delivered","topK":5},
        "resultDataContents":["row"]
      }
    ]
  }'

Vector + one-hop graph traversal:

curl -s -u "$NORNIC_USERNAME:$NORNIC_PASSWORD" "$ENDPOINT" \
  -H "Content-Type: application/json" -H "Accept: application/json" \
  -d '{
    "statements":[
      {
        "statement":"CALL db.index.vector.queryNodes('\''idx_original_text'\'', $topK, $text) YIELD node, score MATCH (node:OriginalText)-[:TRANSLATES_TO]->(t:TranslatedText) WHERE t.language = $targetLang RETURN node.originalText AS originalText, score, t.language AS language, coalesce(t.auditedText, t.translatedText) AS translatedText ORDER BY score DESC, language LIMIT $topK",
        "parameters":{"text":"get it delivered","topK":5,"targetLang":"es"},
        "resultDataContents":["row"]
      }
    ]
  }'

Related Reading¶

• Benchmarks vs Neo4j
• Graph-RAG: Typical Distributed vs NornicDB In-Memory
• Canonical Graph + Mutation Log Guide