By Philip Rathle

1. Graph All The Things
Introduction to Graph Databases
Neo4j GraphDays 2014
Chicago
Philip Rathle
VP of Products, Neo4j
@prathle
#neo4j

4. C
34,3%B
38,4%A
3,3%
D
3,8%
1,8%
1,8%
1,8%
1,8%
1,8%
E
8,1%
F
3,9%
INDUSTRY TRENDS:
GRAPHS TRANSFORMED CONSUMER WEB

5. Use of Relationship Information in
The Consumer Web
INDUSTRY TRENDS:
GRAPHS TRANSFORMED CONSUMER WEB

6. Use of Relationship Information in
The Consumer Web
INDUSTRY TRENDS:
GRAPHS TRANSFORMED CONSUMER WEB

7. Ref: http://www.gartner.com/id=2081316
Interest Graph
Payment Graph
Intent Graph
Mobile Graph
Consumer Web Giants Depends on Five Graphs
Gartner’s “5 Graphs”
Social Graph
GARTNER’S 5 GRAPHS OF CONSUMER WEB:
SUSTAINABLE COMPETITIVE DIFFERENTIATION
COMES FROM MASTERING 5 GRAPHS

8. Key-Value
Graph DB
Riak
Redis
Neo4j
membase
0x235C Philip
0xCD21 Neo4j Chicago
0x2014 [PPR,RB,NL]
0x3821 [CHI, SFO, BOS]
0x3890 B75DD108A
Column FamilyName UID Members Groups Photo
0x235C Philip PPR CHI, SFO,
BOS B75DD108A893A
0xCD21 Neo4j
Chicago
CHI PPR,RB,
NL
218758D88E901
Cassandra
HBase
Document DB0x235C {name:Philip, UID: PPR, Groups: [CHI,SFO,BOS]}
0xCD21
{name:Neo4j Chicago, UID: PPR, Members:[PPR,RB,NL],
where:{city:Chicago, State: IL}}
MongoDB
CouchDB
NI
name:Neo4j Chicago, UID: CHI,
Photo: 218758D88E901
ABK
name:Philip, UID: PPR,
Photo: B75DD108A893A
MEMBERsince: 2011
UNLOCKING THE POTENTIAL
OF RELATIONSHIPS IN DATA

9. A GRAPH DATABASE IS
PURPOSE-BUILT FOR:
When your business depends on Relationships in Data

10. The Property Graph ModelTHE PROPERTY GRAPH
MODEL

11. The Property Graph ModelTHE PROPERTY GRAPH
MODEL
LovesAnn Dan

12. The Property Graph Model
Ann DanLoves
THE PROPERTY GRAPH
MODEL

13. The Property Graph Model
(Ann) –[:LOVES]-> (Dan)
THE PROPERTY GRAPH
MODEL
Ann DanLoves

14. The Property Graph Model
(:Person {name:"Ann"}) –[:LOVES]-> (:Person {name:"Dan"})
THE PROPERTY GRAPH
MODEL
Ann DanLoves

15. The Property Graph Model
(:Person {name:"Ann"}) –[:LOVES]-> (:Person {name:"Dan"})
THE PROPERTY GRAPH
MODEL
Ann DanLoves
Node Relationship Node

16. The Property Graph Model
(:Person {name:"Ann"}) –[:LOVES]-> (:Person {name:"Dan"})
THE PROPERTY GRAPH
MODEL
Ann DanLoves
Node Relationship Node
property propertylabel labeltype

17. Cypher
Query: Whom does Ann love?
(:Person {name:"Ann"})–[:LOVES]->(whom)
CYPHER

18. Cypher
Query: Whom does Ann love?
MATCH (:Person {name:"Ann"})–[:LOVES]->(whom)
CYPHER

19. Cypher
Query: Whom does Ann love?
MATCH (:Person {name:"Ann"})–[:LOVES]->(whom)
RETURN whom
CYPHER

20. CypherCYPHER

21. Under The Hood
MATCH (:Person {name:"Ann"})–[:LOVES]->(whom)RETURN whom
cypher
native graph processing
native storage
UNDER THE HOOD

22. BUSINESS & PROJECT IMPACT

23. #1: EASIER TO UNDERSTAND
COMPLEX MODELS
“Find all sushi restaurants in NYC that
my friends like”

24. “Find all direct reports and how many they
manage, up to 3 levels down”
#2: EASIER TO EXPRESS
COMPLEX QUERIES
Example HR Query:
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

25. (SELECT T.directReportees AS directReportees, sum(T.count) AS count
FROM (
SELECT manager.pid AS directReportees, 0 AS count
FROM person_reportee manager
WHERE manager.pid = (SELECT id FROM person WHERE name = "fName lName")
UNION
SELECT manager.pid AS directReportees, count(manager.directly_manages) AS count
FROM person_reportee manager
WHERE manager.pid = (SELECT id FROM person WHERE name = "fName lName")
GROUP BY directReportees
UNION
SELECT manager.pid AS directReportees, count(reportee.directly_manages) AS count
FROM person_reportee manager
JOIN person_reportee reportee
ON manager.directly_manages = reportee.pid
WHERE manager.pid = (SELECT id FROM person WHERE name = "fName lName")
GROUP BY directReportees
UNION
SELECT manager.pid AS directReportees, count(L2Reportees.directly_manages) AS count
FROM person_reportee manager
JOIN person_reportee L1Reportees
ON manager.directly_manages = L1Reportees.pid
JOIN person_reportee L2Reportees
ON L1Reportees.directly_manages = L2Reportees.pid
WHERE manager.pid = (SELECT id FROM person WHERE name = "fName lName")
GROUP BY directReportees
) AS T
GROUP BY directReportees)
UNION
(SELECT T.directReportees AS directReportees, sum(T.count) AS count
FROM (
SELECT manager.directly_manages AS directReportees, 0 AS count
FROM person_reportee manager
WHERE manager.pid = (SELECT id FROM person WHERE name = "fName lName")
UNION
SELECT reportee.pid AS directReportees, count(reportee.directly_manages) AS count
FROM person_reportee manager
JOIN person_reportee reportee
ON manager.directly_manages = reportee.pid
WHERE manager.pid = (SELECT id FROM person WHERE name = "fName lName")
GROUP BY directReportees
UNION
(continued from previous page...)
SELECT depth1Reportees.pid AS directReportees,
count(depth2Reportees.directly_manages) AS count
FROM person_reportee manager
JOIN person_reportee L1Reportees
ON manager.directly_manages = L1Reportees.pid
JOIN person_reportee L2Reportees
ON L1Reportees.directly_manages = L2Reportees.pid
WHERE manager.pid = (SELECT id FROM person WHERE name = "fName lName")
GROUP BY directReportees
) AS T
GROUP BY directReportees)
UNION
(SELECT T.directReportees AS directReportees, sum(T.count) AS count
FROM(
SELECT reportee.directly_manages AS directReportees, 0 AS count
FROM person_reportee manager
JOIN person_reportee reportee
ON manager.directly_manages = reportee.pid
WHERE manager.pid = (SELECT id FROM person WHERE name = "fName lName")
GROUP BY directReportees
UNION
SELECT L2Reportees.pid AS directReportees, count(L2Reportees.directly_manages) AS
count
FROM person_reportee manager
JOIN person_reportee L1Reportees
ON manager.directly_manages = L1Reportees.pid
JOIN person_reportee L2Reportees
ON L1Reportees.directly_manages = L2Reportees.pid
WHERE manager.pid = (SELECT id FROM person WHERE name = "fName lName")
GROUP BY directReportees
) AS T
GROUP BY directReportees)
UNION
(SELECT L2Reportees.directly_manages AS directReportees, 0 AS count
FROM person_reportee manager
JOIN person_reportee L1Reportees
ON manager.directly_manages = L1Reportees.pid
JOIN person_reportee L2Reportees
ON L1Reportees.directly_manages = L2Reportees.pid
WHERE manager.pid = (SELECT id FROM person WHERE name = "fName lName")
)
SAME QUERY IN SQL (!!)

26. PERFORMANCE AT SCALE

27. RDBMS/Other vs. Native Graph Database
Connectedness of Data Set
ResponseTime
RDBMS / Other NOSQL
# Hops: 0-2
Degree: < 3
Size: Thousands
Neo4j
# Hops: Tens to Hundreds
Degree: Thousands+
Size: Billions+
1000x faster
#3: PERFORMANCE

28. DATABASE # PEOPLE
QUERY TIME
(MS)
MySQL 1,000 2,000
Neo4j 1,000 2
Neo4j 1,000,000 2

29. Business Impact: Move Faster
The
whole
design,
development,
QA,
and
release
process
for
CruchBase
Events
was
a
total
of
2
weeks.”
“The
ability
to
iterate
that
quickly
is
a
mammoth
step
up
for
us.
In
CrunchBase
1.0
(MySQL),
it
probably
would
have
taken
2
months.”
-­‐
Kurt
Freytag,
CTO
CrunchBase

30. Total Dollar
Amount
Transaction
Count
Investigate
Investigate
Business Impact: Invent Faster

31. “Our
Neo4j
solution
is
literally
thousands
of
times
faster
than
the
prior
MySQL
solution,
with
queries
that
require
10-­‐100
times
less
code.”
-­‐
Volker
Pacher,
Senior
Developer
eBay
Business Impact: Run Faster

32. Neo Technology, Inc Confidential
Real-Time/
OLTP
Offline/
Batch
Connected Queries Enable
Real-Time Analytics

33. GRAPHS ARE TRANSFORMING
THE WORLD
Core industries
& Use Cases
WEB / ISV Financial Services Tele-communications
Network &
Data Center
Management
Master Data
Management
Social
Geo
?

34. Core industries
& Use Cases
WEB / ISV Financial Services Telecommunications
Health Care
& Life Sciences
Network &
Data Center
Management
Master Data
Management
Social
GEO
Finance
GRAPHS ARE TRANSFORMING
THE WORLD

35. Neo Technology, Inc Confidential
Core
industries
& Use Cases
WEB / ISV
Financial
Services
Telecom-
munications
Health Care
& Life
Sciences
Web Social,
HR &
Recruiting
Media &
Publishing
Energy, Services,
Automotive, Gov’t,
Logistics, Education,
Gaming, Other
Network &
Data Center
Management
Master Data
Management
Social
GEO
Recomm-
endations
Identity &
Access
Mgmt
Search &
Discovery
BI, CRM, Impact
Analysis, Fraud
Detection,
Resource
Optimization, etc.
Finance
Neo4j Adoption Snapshot

36. GRAPH DATABASES - THE FASTEST
GROWING DBMS CATEGORY
Source: http://db-engines.com/en/ranking/graph+dbms!

37. 0%
10%
20%
30%
2011 2014 2017
25%
2.5%
0%
%ofEnterprisesusingGraphDatabases
“Forrester estimates that over 25% of
enterprises will be using graph
databases by 2017”
Sources
• Forrester TechRadar™: Enterprise DBMS, Feb 13 2014 (http://www.forrester.com/TechRadar+Enterprise
+DBMS+Q1+2014/fulltext/-/E-RES106801)
• Dataversity Mar 31 2014: “Deconstructing NoSQL:Analysis of a 2013 Survey on the Use, Production and Assessment
of NoSQLTechnologies in the Enterprise” (http://www.dataversity.net)
• Neo Technology customer base in 2011 and 2014
• Estimation of other graph vendors’ customer base in 2011 and 2014 based on best available intelligence
“25% of survey respondents said
they plan to use Graph databases in
the future.”
Graph Databases:
Powering The Enterprise
GRAPH DATABASES - POWERING
THE ENTERPRISE

38. Ref: Gartner, ‘IT Market Clock for Database Management Systems, 2014,’ September 22, 2014
https://www.gartner.com/doc/2852717/it-market-clock-database-management
“Graph analysis is possibly the
single most effective
competitive differentiator for
organizations pursuing data-
driven operations and
decisions after the design of
data capture.”
Graph Databases:
Can Transform Your Business
GRAPH DATABASES - CAN
TRANSFORM YOUR BUSINESS

39. Summary
When your business depends on Relationships in Data
SUMMARY

40. Your Mission:
Connect.