Assi Barak

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