04 · graph

Nine algorithms, shipped.

Not a graph database that stored your edges and stopped there. PageRank converges. All-simple-paths bidirectional is 5–20× faster on real graphs. Per-hop EXPLAIN ANALYZE shows you what every step cost.

01 Multi-hop traversal

Friends-of-friends, transitive closure to depth N.

02 Filtered traversal

Per-hop WHERE predicate narrows the frontier.

03 Variable-length paths *1..N

MATCH (a)-[:KNOWS*1..3]->(b) - bounded recursion.

04 Shortest-path BFS

Source-to-target with explicit hop count.

05 Connected components

Union-Find. Community detection without an extra library.

06 Triangle enumeration

Clustering coefficient. Motif counting.

07 All simple paths (forward)

Bounded by (depth, max_paths). Discrete option enumeration.

08 All simple paths (bidirectional)

Same answer, 5–20× faster on real graphs.

09 PageRank

Power-method iteration. Influence ranking that converges.

explain analyze · per-hop

Every relation step, every row count, every millisecond.

Graph queries fan out fast - one wrong predicate at depth 4 multiplies your work by orders of magnitude. EXPLAIN ANALYZE reports the frontier size, edges considered, and time per hop in a single response so you can see where the cost actually went.

{
  "hops": [
    { "depth": 1, "frontier": 12,    "edges": 38,    "ms": 0.4 },
    { "depth": 2, "frontier": 184,   "edges": 612,   "ms": 1.1 },
    { "depth": 3, "frontier": 1942,  "edges": 6204,  "ms": 4.8 },
    { "depth": 4, "frontier": 8773,  "edges": 28891, "ms": 19.7 }
  ],
  "total_ms": 26.0
}

algorithms shipped

< 5 ms

per-hop traversal (small frontier)

5–20×

bidirectional vs forward all-paths

deferred

under evalGremlin / Cypher dialect shim. Today's surface is REST + JSON.
post-1.0OLAP-style PageRank at >10B edges. Single-region today; multi-region is a 1.x conversation.

Read the graph docs, then try it.

Graph docs → Architecture Try it