The document discusses Neo4j, a graph database that utilizes a Cypher query language for data storage and management, distinguishing itself from traditional SQL databases. It outlines the structure of graphs, including nodes and edges, and highlights use cases, community support, and visualization tools available for Neo4j. The presentation concludes by emphasizing the efficacy of graphs in modeling various domains and encourages exploration of the Neo4j ecosystem.

1. G R A P H DATA B A S ES

2. W H O A M I ?
• David Simons
• @SwamWithTurtles
• github.com/
SwamWithTurtles
• Technical Lead at Softwire
and part-time hacker
• Statistician in a past life

3. G ra p h i ca l M o d e l l i n g
N e o 4 J : T h e W h at A n d W h y ?
“ Cy p h e r ” Q u e ry La n g u a g e
Le t ’s S e e I t A ct i o n !
T h e G ra p h i n g Eco s y ste m

4. G ra p h i ca l M o d e l l i n g
N e o 4 J : T h e W h at A n d W h y ?
“ Cy p h e r ” Q u e ry La n g u a g e
Le t ’s S e e I t A ct i o n !
T h e G ra p h i n g Eco s y ste m

5. W H AT I S A G R A P H ?
Taken from Jim Webber’s Dr. Who Dataset

6. W H AT I S A G R A P H ?
{ (V, E) : V = [n], E ⊆ V(2) }

7. W H AT I S A G R A P H ?
{ (V, E) : V = [n], E ⊆ V(2) }
Made up of two parts,
“V” and “E”

8. W H AT I S A G R A P H ?
{ (V, E) : V = [n], E ⊆ V(2) }
V is a set of n items

9. W H AT I S A G R A P H ?
Vertex Set

10. W H AT I S A G R A P H ?
{ (V, E) : V = [n], E ⊆ V(2) }
E is made up of pairs
of elements of V
(Ordered and
not necessarily distinct)

11. W H AT I S A G R A P H ?
Edge Set

12. G I V I N G R E A L
W O R L D
M E A N I N G S T O V
A N D E
W H A T I S G R A P H I C A L
M O D E L L I N G ?

13. B R I D G E S AT K Ö N I G S B E R G

14. B R I D G E S AT K Ö N I G S B E R G
V =
bits of land
E =
bridges

15. K E V I N B A C O N
S I X D E G R E E S O F …

20. T H E R E I S N O O P E N
E L E C T I O N D ATA
T H E P R O B L E M

21. E L E C T I O N D ATA

22. E L E C T I O N D ATA

23. E L E C T I O N D ATA
E =
(e.g.) member of, held in,
stood in…
V =
elections, constituencies,
years, politicians and parties

24. G ra p h i ca l M o d e l l i n g
N e o 4 J : T h e W h at A n d W h y ?
“ Cy p h e r ” Q u e ry La n g u a g e
Le t ’s S e e I t A ct i o n !
T h e G ra p h i n g Eco s y ste m

25. W O R L D ’ S L E A D I N G G R A P H
D B :

26. W H E R E D I D I T
C O M E F R O M ?
• First version 2010, v2 came
out December 2013.

27. "embedded, disk-based, fully transactional Java
persistence engine that stores data structured in
graphs rather than in tables"

28. D ATA S T O R A G E

29. D ATA S T O R A G E

30. D ATA
S T O R A G E
• Nodes and edges are all:
• Stored as first-class
objects on the file
system
• “typed”
• Key-value stores

31. C O M M U N I T Y
E D I T I O N
• Free for hacking around in

32. E N T E R P R I S E
E D I T I O N
• Bespoke Prices, but
includes:
• Higher performance
for concurrent
querying
• Clustering
• Hot backups
• Advanced Monitoring

33. O T H E R G R A P H
D ATA B A S E S
• ArangoDB
• OrientDB
• New: Graph Engine

34. W H AT ’ S
W R O N G W I T H
S Q L ?
B U T…

35. N O T H I N G *

36. N O T H I N G *
*If you use it for
the right job

37. D ATA I N T H E
R E L AT I O N S
• “Joins” are first class
objects in the database
that can be queried at no
additional cost
• Certain queries become
trivial (e.g. Joins)

38. P R O T O T Y P I N G
• Easy to see and work with
data
• Schemaless
• Active community with a
lot of libraries

39. N E O 4 J U S E R S

40. C A S E S T U D I E S
Real-time Recommendations

41. C A S E S T U D I E S
Logistics & Delivery
Organisation

42. C A S E S T U D I E S
Online Dating

43. G ra p h i ca l M o d e l l i n g
N e o 4 J : T h e W h at A n d W h y ?
“ Cy p h e r ” Q u e ry La n g u a g e
Le t ’s S e e I t A ct i o n !
T h e G ra p h i n g Eco s y ste m

44. W H AT I S
C Y P H E R ?
• Neo4j’s own query
language
• Declarative
• Designed to be readable
and easy to learn

45. A S C I I A R T S Y N TA X : N O D E S
(n)
(n:Actor)
(n:Actor {name:”Kevin Bacon”})

46. A S C I I A R T S Y N TA X : E D G E S
-[r:starred_in]->
<-[r:starred_in]-
-[r:starred_in]-

47. A S C I I A R T S Y N TA X : E D G E S
(n:Actor)-[r:starred_in]->(m:Movie)

48. A S C I I A R T S Y N TA X : E D G E S
(n:Actor)-[r:starred_in]->(m:Movie)
<-[r:starred_in]-(a:Actor)

49. M AT C H & R E T U R N
MATCH {pattern} RETURN {variables}

50. M AT C H & R E T U R N
MATCH (n:Actor)-[r:starred_in]->(m:Movie)
RETURN n, r, m

51. M AT C H & R E T U R N
MATCH (n:Actor {name: ”Kevin Bacon”})
-[r:starred_in]->(m:Movie)
RETURN m

52. P E R S I S T E N C E
CREATE (n: Actor {name: “David”})
RETURN n

53. P E R S I S T E N C E
MATCH (m:Movie), (a:Actor {name =“David”})
CREATE (a)-[:starred_in]->(m)
RETURN a, m

54. A G G R E G AT I O N
MATCH (n:Actor)<-[:starred_in]-(m:Movie)
RETURN n, sum(m.revenue)

55. L O A D F R O M C S V
LOAD CSV FROM 'foo.csv' AS line
CREATE (:Actor { name: line[1]})

56. G ra p h i ca l M o d e l l i n g
N e o 4 J : T h e W h at A n d W h y ?
“ Cy p h e r ” Q u e ry La n g u a g e
Le t ’s S e e I t A ct i o n !
T h e G ra p h i n g Eco s y ste m

57. G ra p h i ca l M o d e l l i n g
N e o 4 J : T h e W h at A n d W h y ?
“ Cy p h e r ” Q u e ry La n g u a g e
Le t ’s S e e I t A ct i o n !
T h e G ra p h i n g Eco s y ste m

58. D I F F E R E N T
L A N G U A G E
S U P P O R T
• Java
• Spring Data for full
ORM
• Hibernate OGM
• Embedded Java API
• Kundera

59. D I F F E R E N T
L A N G U A G E
S U P P O R T
• .NET - Neo4jClient
• JavaScript - Seraph.js,
node-neo4j
• Clojure - Neocons
• Haskell, Go, PHP and
more…

60. G R A P H E N E D B
• Remote hosting of neo4j
on Heroku, AWS or Azure
• Monitoring, support, back-
ups, scalability

61. V I S U A L I S AT I O N
T O O L S
• Lots of tools out there to
take subgraphs and turn
them into pretty views.

62. V I S U A L I S AT I O N T O O L S : A L C H E M Y J S

63. V I S U A L I S AT I O N T O O L S : L I N K U R I O U S

64. G R A P H A WA R E
• Java libraries that make
developing with graphs
easier:
• “TimeTree”
• “GraphGen”
• “Reco”

65. I N
C O N C L U S I O N …
• Graphs more accurately
model a lot of domains
• Neo4j is a robust and
mature way of storing this
• It’s got a thriving
ecosystem and community
• Go forth and play!

66. A N Y Q U EST I O N S ?
@ Swa m Wi t h Tu rt l e s
s wa m w i t h t u rt l e s . co m