This document discusses real-world use cases for graph databases and Neo4j. It describes how graphs can represent data and knowledge better than relational databases. It provides examples of using Cypher to query graphs and discusses how Neo4j is well-suited for knowledge graphs and scale-free graphs. It acknowledges the challenges of scaling graphs and provides recommendations like partitioning, using big hardware, and removing super nodes.

1. Real-World Graph use cases
Graph Days TLV 2017
Assi Barak

2. Fascinated by Graphs
 We think in graphs, not in INNER JOINs
 A great way to represent data and knowledge

3. Disillusioned RDF Practitioner
 Please give me back my properties!
 Reasoners not used in practice
 Some great ideas, though. We should borrow
http://lod-cloud.net/versions/2017-02-20/lod.svg

4. Fascinated by Cypher
 Very expressive
 Human Readable
 Visual

5. Power of Cypher
MATCH (A:Person)
WHERE A.birth_date < '1.1.1970' AND A.death_date != null
RETURN A
UNION
MATCH (A:Person) -[:Offspring]-> (B:Person)
WHERE B.gender = 'male' AND B.birth_date <= '1.1.1950'
RETURN A
“People born before the seventies and
still alive, or people whose father was
born before the fifties”

6. Gremlin is nice, Kind’a
g.v(1).outE('friend').inV().has('age',25).firstName;
No too fond of imperative style

7. Cypher Strikes back
MATCH
(A:Person)-[:Owns]->(B:Dragon),
(A)-[:Offspring]->(C:Person)-[:Owns]->(D:Dragon)-[:Freezes]->(B),
(A)-[:Offspring]->(E:Person)-[:Owns]->(F:Dragon)-[:Freezes]->(B)
WHERE C <> E AND D <> F
RETURN DISTINCT A
“A person who owns a dragon, and has two parents
which each own a dragon that freezed that persons dragon”

8. Using custom traversal via APOC
MATCH (A:Person {name: ‘Rogar Bolton’}), (B:Person {name: 'Robyn Arryn’)
WITH A AS StartNode, B AS EndNode
CALL neo4j.procedures.allSimplePaths(StartNode,EndNode,[‘Freezes’: 2],[‘Dragon’: 4],4) YIELD p as
path
RETURN path
A real world example that I cant really explain

9. Real world 1 - Knowledge Graphs
 Not much to add here
 This is the bread and butter of Neo4j

10. Real world 2 - Scale-Free Graphs
http://mathworld.wolfram.com/Scale-FreeNetwork.html
Very Large Scale-Free graphs appear in Cyber Security, IOT, User-Based analytics and more

11. Scale Free Graphs
 Have a lot of edges
 We would like to ask aggregate queries on
edges
 Cypher custom traversal also comes in
handy
 Several validated scenarios
Match p = (A:Person)-[*1..4]-(B:Person)
WHERE count(p) > 5
RETURN A
“Any person who is linked in up to 4 hops to another
Person, with more that 5 distinct paths”

12. The challenge for scale free graphs
 Well – Scale for one 
 Partitioning graphs is a hard problem, will
mature eventually but no one is there yet
 Neo4j are on the right track
 Some good practices for now

13. Facing the scale challenge
Graph Data Lake
 Most use cases fit into 1TB Memory servers
 This is commodity hardware these days and Neo4j uses it well
 Neo4j overhead is low, graphs are compact
 Build your own partitions if needed
 For huge cases
 Build a Lambda Architecture
 Neo4J can be a great serving speed layer
 Neo4J Transactivity helps here
 Remove Super-nodes
 You don’t usually care about them

14. Big Hardware is coming
 3dx point memory changes the math of memory cost
in the data center
 Intel Optane
 Micron QuantX
 IBM Power8 with CAPI Flash already an interesting
offering
 This is coming to the data center by end 2017, and will
be mainstream by end of 2018
 Memory intensive graphs like Neo4J will benefit from
this immensely
https://arstechnica.com/information-technology/2017/03/intels-first-optane-ssd-375gb-that-you-can-
also-use-as-ram/

15. Visualization
 Vis.js
 Keylines (probably too costly)
 Export results as TDE to tableau BI

16. Final relationship joke..
(neo4j guys asked that I add this)

17. Questions
Assi Barak
Abarak.consulting@gmail.com