Native vs Non-Native Processing

• Native: Engine built for graphs (pointer-based storage). Traversals fly.
• Non-Native: Graph layer on other stores. Simulate edges—slow for depth.

Explaining Native in Depth

Purpose-built, with direct pointers for hops.

Why superior: Real-time on large graphs.

Code Sample (Neo4j native traversal):

MATCH (a)-[*]->(b) RETURN path

flowchart LR
    NodeA["Node A"] -->|pointer| NodeB["Node B"] -->|pointer| NodeC["Node C"]

Explaining Non-Native in Depth

Overlays like SQL graphs; use indexes for simulation.

Why limited: Depth causes join explosions.

Code Sample (SQL graph sim):

SELECT * FROM nodes n JOIN edges e ON n.id = e.from_id

flowchart LR
    Layer["Graph layer"] --> Base["RDBMS or KV store"]

Go native for production—non-native hits walls at scale.

flowchart TD
    A[Native] --> B["Pointers: Fast Hops"]
    A --> C["Optimized Traversals"]
    D["Non-Native"] --> E["Indexes: Extra Layer"]
    D --> F["Slow at Depth"]