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Neo4j · Cypher · GDS · AuraDB
Native Graph Database

Neo4j, where the graph is the query.

In short: Neo4j is the leading native graph database, storing data as nodes, relationships, and properties rather than tables. Its index-free adjacency makes multi-hop relationship traversals fast, queried with the Cypher language. It powers fraud detection, recommendation engines, knowledge graphs, and identity graphs, with the Graph Data Science library for in-database algorithms.

Native graph storage with index-free adjacency, Cypher query language designed for traversal, Graph Data Science library for in-database algorithms, and AuraDB managed-cloud across AWS, Azure, GCP.

JUSDB_NEO4J_PROD
LIVE

Neo4j 5 · causal cluster

3 Core (Raft) + 2 Read Replicas

Tuned
Queries / sec

0.00k

Traversal p99

1ms

Nodes

0M

Page-cache hit

99.0%

Query Throughput

0.00k QPS

[OK] cypher: query plan uses NodeIndexSeek, 4ms

[INF] index: population on :User(email) 73% complete

[OK] cluster: Raft quorum healthy, 3 core servers

[INF] page-cache: warmup 91%, 84M nodes resident

Representative fleet view · illustrative metrics

0+

Neo4j Clusters Managed

0.99%

Uptime SLA

0×

Traversal Speedup vs SQL JOINs

0%

Avg Cost Savings

Building a graph workload?

  • Fraud detection / identity graph needs multi-hop traversal at production scale — Postgres recursive CTEs are slow, and the team is debating Neo4j Community vs Enterprise plus GDS.
  • Knowledge graph for RAG — retrieval needs to traverse semantic relationships, not just nearest-neighbour vectors, and the architecture call hasn't been made between Neo4j + vectors and a pure vector DB.
  • Cypher learning curve — the team is fluent in SQL and uncertain whether Cypher's graph-first model justifies the ramp time for the workload at hand.

JusDB Neo4j specialists design, deploy, and operate graph workloads. See Neo4j consulting →

Neo4j service paths

What we do

What we build with Neo4j

From graph model design to production GDS pipelines — end-to-end Neo4j expertise.

Native Graph Storage
Index-free adjacency means traversals are O(1) per hop — no JOIN cost growth with depth. The right architecture when relationship paths are the query.
Cypher Query Language
Purpose-built for graph traversal — ASCII-art syntax representing nodes and edges. Five-hop queries that are concise where SQL would be verbose and slow.
Graph Data Science (GDS)
In-database algorithms — PageRank, community detection, pathfinding, similarity, embeddings, ML pipelines — without exporting graph data to external tools.
Knowledge Graphs
RAG architecture where retrieval is graph-traversal — combine Neo4j with vector search for hybrid retrieval, semantic relationships preserved through queries.
Fraud & Identity Graphs
Transaction-account-device graphs for fraud detection, identity-resolution patterns, multi-hop suspicious-pattern queries that relational stores struggle with.
AuraDB Cloud Operations
Managed Neo4j on AWS, Azure, GCP — Professional, Business Critical, and Virtual Dedicated tiers with HA, automated backup, and per-region placement.

Traversal performance

Deep traversals, constant cost per hop

Index-free adjacency keeps each hop O(1) — so five-hop fraud and recommendation queries stay fast where recursive SQL CTEs fall off a cliff. We tune the graph model, indexes, and Cypher to match the workload.

Graph model & relationship-direction design for traversal
Cypher query tuning — PROFILE / EXPLAIN plan analysis
Index and constraint strategy for node lookups
GDS algorithm selection on graph projections
AuraDB tier sizing for the working-set graph

Cypher Performance

After tuning
Full graph scans eliminated0%
Index-backed label lookups0%
Page-cache hit rate0%
Bounded traversal depth0%

50×

Traversal speedup

50%

Cost reduction

Real cases

Queries we've transformed

Missing Index

3,000ms

4ms

Full label scan over 84M :User nodes

The fix

CREATE INDEX FOR (u:User) ON (u.email)

Cartesian Product

9,400ms

27ms

Unbounded variable-length path explosion

The fix

Bounded path: MATCH (a)-[:KNOWS*1..3]-(b)

Full Graph Scan

12,100ms

19ms

Query had no anchor, scanned all nodes

The fix

Anchored traversal on an indexed start node

Causal Cluster ACTIVECore servers (Raft) + Read Replicas

0.00%

Cluster Uptime

<0s

Leader Re-election

0ms

Replica Lag

core-01 · 7687
LEADERONLINE
core-02 · 7687
FOLLOWERONLINE
core-03 · 7687
FOLLOWERONLINE
replica-01 · 7687
READ REPLICAONLINE

High availability

Always on. Cluster-engineered.

Neo4j causal clustering replicates the graph across core members with Raft consensus and read replicas for scale-out reads. A lost core fails over in seconds — real availability, on AuraDB or self-managed.

Causal clustering with Raft consensus across core members
Read replicas for horizontal read scale-out
Automatic leader election & fast failover
AuraDB Business Critical & self-managed HA topologies
Online backup with verified point-in-time restore

Incident response

A runaway-traversal P1, handled in under 15 minutes.

When an unbounded variable-length Cypher path saturates a core member, a named Neo4j engineer responds — not a ticket queue. We diagnose via query logs, bound the traversal online, and add the missing index.

P1 alert → named Neo4j engineer paged in under 15 minutes
Root cause via query.log, PROFILE & cluster metrics
Traversal-bounding & index fix applied online — no downtime
Blameless postmortem with a prevention plan
Live incident replayP1 → resolved · ~14 min
1
00:00Alert fired

Recommendation query p99 > 3s — graph degrading

2
00:03On-call paged

Named engineer in under 15 min, not a ticket queue

3
00:07Root cause

Missing index on :User(email) — full label scan

4
00:11Fix applied

CREATE INDEX + anchored traversal on indexed node

5
00:14Resolved

Lookup 3s → 4ms, p99 cleared — total 14 min

Pre-Migration Assessment

RDBMS / RDF → Neo4j 5

READY
Relational → graph data modeling0%
Data load (neo4j-admin import)0%
Index & constraint creation0%
Cutover readiness0%

Estimated cutover window: < 10 minutes

Migration

Move to Neo4j without the downtime

Relational → Neo4j, or self-managed → AuraDB. We model the graph from the relational schema, bulk-import with neo4j-admin / LOAD CSV, validate traversals against the source, then cut over with confidence.

Relational-to-graph data modeling (nodes, edges, properties)
Bulk import via neo4j-admin import / LOAD CSV
Self-managed → AuraDB managed-cloud migration
AWS, Azure & GCP AuraDB targets with HA tiers

FAQ

Neo4j — common questions

Ready to evaluate Neo4j?

Book a 30-minute scoping call. We'll discuss your workload, the graph-vs-relational tradeoff, and the shape of the right engagement before any statement of work.

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