The future is here and the future are Graph Databases! Have a lot of interconnected data dat you want to extract value and meaning from it? Having too many joins that are running too slow? Do you want to do Real-Time Recommendations? Read this!

1. Still using MySQL?
Maybe you should reconsider
Radu-Sebastian Amarie
Co-Founder @ Softbinator
Head of Engineering @ Findie.me
radu@softbinator.ro
#85

2. So let’s start!

3. Data trends. (Data is trending)

4. “Every 2 days we create as much information
as we did up to 2003”
– Eric Schmidt, Google

5. Data is more connected.
• Text (content)
• HyperText (added pointers)
• RSS (joined those pointers)
• Blogs (added pingbacks)
• Tagging (grouped related data)
• RDF (described connected data)
• GGG (content + pointers + relationships +
descriptions)

6. Data is much more
connected.
< Email address
similarity between
users from a
Subscriber list on
Mailchimp
You can read more here:
http://blog.mailchimp.com/digging-deeper-
into-wavelength-and-egp-data-finding-
interest-clusters-in-mailchimps-network/

7. Data is more Semi-Structured:
Think IMDb
How would you model the data of all the Movies ever
made?

8. Movies / Details (Title / Description / Storyline) / Cast
(and roles and names and relationship to other
characters) / Crew (positions: Producers / Director /
Director of Photography and 113 other roles) / Plot
Keywords / Taglines / Genres / Motion Picture Ratings
/ Sites / Countries / Countries Filmed In / Languages /
Dates / Budgets / Companies / Credits / Technical
Specs / Trivia / Goofs / Quotes / Reviews / Message
Boards / Ratings / Links to other ratings like
Metascore from MetaCritic / And all the relationships
between all the individual data.

9. What are the current options?

10. Top 25 of 275
More info here:
http://db-engines.com/en/ranking

11. Are any solutions trending?

13. How do we represent this data?

14. How do we represent this data?
Relational Database

15. Graph
DatabaseRelational Database
GOOD FOR:
Well-understood data structures that doesn’t
change too frequently
Known problems involving discrete parts of
the data, or minimal connectivity
GOOD FOR:
Dynamic systems where data topology is difficult to
predict
Dynamic requirements that evolve with the business
Problems where data relationships contribute
meaning & value
How do we represent this data?

16. So, how do you model a graph?

17. So, how do you model a graph?
Mihaela loves Cezar

18. Mihaela loves Cezar
NODE NODE
RELATIONSHIP
LOVES

19. Relationships are directional
NODE NODE
RELATIONSHIP
LOVES
LOVES

20. Detailed property graph
LOVES
LOVES
LIVES WITH
name: ”Mihaela”
born: 639878400
twitterID: miky92
name: ”Kim”
born: 622328400
type: “ROTEM K2”
nickname: “Black Panther”
since: 692328400

21. Labeled property graph
LOVES
LOVES
LIVES WITH
name: ”Mihaela”
born: 639878400
twitterID: miky92
name: ”Kim”
born: 622328400
type: “ROTEM K2”
nickname: “Black Panther”
:Person :Person
:Vechicle:Car since: 692328400

22. Mapping to language
VERB
VERB
VERB
Adjective
Adjective
Adjective
Adjective
Adjective
Adjective
Adjective
Adjective
:Noun :Noun
:Noun Adjective

23. What can a GraphDB contain?
NODES:
• The objects in the graph
• Can have key-value properties
• Can be labeled
RELATIONSHIPS:
• Relate Node by type and
direction
• Can have key-value properties

24. How do you query a graph?
By finding patterns.

25. Great, but can I draw patterns?
Haha, sure!

26. How?
Using ASCII ART!!
A --> B --> C; A --> C;
A --> B --> C --> A;

27. (A) -[:LOVES]->(B)

28. (a {twitterID: ‘miky92’}) -[:LOVES]->(b)

29. MATCH
(a {twitterID: ‘miky92’}) -[:LOVES]->(b)
RETURN b;

30. MATCH
(a {twitterID: ‘miky92’}) -[:LOVES]->(b)
RETURN b.name; //Kim

31. Relational vs Graph

32. PICK YOUR JOINS!

33. MATCH (u:User {id: 1})-[:HAS_SKILL]->(s:Skill) RETURN s
SELECT skills.*, user_skill.*
FROM users
JOIN user_skill ON users.id = user_skill.user_id
JOIN skills ON user_skill.skill_id = skill.id
WHERE users.id = 1

34. Speed!!
“We found Neo4j to be literally thousands of times faster
than our prior MySQL solution, with queries that require
10 - 100 times less code. Today, Neo4j provides eBay with
functionality that was previously impossible.”
- Volker Pacher, Senior Developer
“Minutes to milliseconds” performance
Queries up to 1000x faster than RDBMS or other NoSQL

35. TheSameQueryusing
Cypher
MATCH (boss)-[:MANAGES*0..3]->(sub),
(sub)-[:MANAGES*1..3]->(report)
WHERE boss.name = “John Doe”
RETURN sub.name AS Subordinate,
count(report) AS Total
Project Impact
Less time writing queries
• More time understanding the answers
• Leaving time to ask the next question
Less time debugging queries:
• More time writing the next piece of code
• Improved quality of overall code base
Code that’s easier to read:
• Faster ramp-up for new project members
• Improved maintainability &
troubleshooting

36. Old
Project
Database
Structure

37. Just a
part…

38. Blue node is a Movie – Ticker
www. findie.me/ticker
(awesome movie)

39. MOOREEE!!!!
We’ve (Ștefan actually)
Imported
(:Wikidata)–[:INTO]-> (:Neo4j)
And remade all the relationships for them to make sense.

40. EXAMPLE:
The universe...
… limited to 500

42. Real-Time Recommendation
Super simple example:
Sushi restaurants in New York that my friends like

46. Awesome community support
& Drivers:
.NET / Java / Spring / JavaScript / Python / Ruby / PHP / R / Go / C/C++

47. Recap
Neo4j is Great.
1. When you have a large social-driven project in which your data topology
is difficult to predict.
2. You data is very interconnected and you need that to get extra meaning
& value.
3. Your application evolves rapidly
4. You want to be fast and write queries easily (Cypher became openCypher
in partnership with Oracle and Spark)
5. You want to be able to get recommendations directly from the Database.

48. Questions?

49. Thanks!
Radu-Sebastian Amarie
Co-Founder @ Softbinator
Head of Engineering @ Findie.me
radu@softbinator.ro

50. Thanks to…
(for inspiration)
• Michael Hunger with http://www.slideshare.net/jexp/geekout-publish
• William Lyon with http://www.slideshare.net/neo4j/intro-to-neo4j-and-graph-
databases
• William Lyon again with http://www.slideshare.net/neo4j/introducing-neo4j-30
• Max de Marzi with http://www.slideshare.net/maxdemarzi/introduction-to-
graph-databases-12735789