This document provides an overview and introduction to graph databases. It begins with a brief history of graph theory and then defines what a graph is from a data perspective - consisting of nodes connected by edges. It describes some key characteristics of graph databases, such as being well-suited for connected, semi-structured data and having index-free adjacency. Examples of applications that benefit from a graph database approach are also listed, including social networks, page ranking, and collaborative filtering. The document then focuses on the Neo4j graph database, outlining its capabilities, who uses it, its architecture both at the network and internal storage levels, and provides an example of how collaborative filtering can be implemented using the Cypher query language in Ne

2. sergeyenin@gmail.com
Sergey
Enin
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3. AGENDA
GRAPH
NOSQL
DBs
APP
NEO4J

4. Graph
Databases
Graph
Databases

5. Graph Databases: HISTORY
Leonhard Euler (1707 –1783)
Swiss mathematician

6. Graph Databases: HISTORY

7. What is
Graph?

8. Graph Databases: GRAPH
A
B
Ver%ce
(Node)
C
Edge
(Rela%onship)
D

9. Graph Databases: WHAT IS IT?

10. Graph Databases: WHAT IS IT?
Joins
VS
Traversals

11. Graph Databases: CHARACTERISTICS
1
Good for semistructured
connected
data
Index
freeadjacency
2
3
The
underlying
storage
The
processing
engine
4

12. APPs

13. ApplicaFon:
social
graphs

14. Application: PAGE RANK

15. APP: Collaborative filtering

17. Neo4J: CHARACTERISTICS
true
ACID
transac%ons;
scales
to
billions
of
nodes
and
rela%onships;
high
speed
querying
through
traversals;
declara%ve
graph
query
language;

18. Neo4J:
who
use
Neo4J: WHO USE
it

19. Neo4J:
who
use
Neo4J: NETWORK ARCHITECTURE
it
Neo4J:
network
architecture

20. Neo4J:
architecture
Neo4J: ARCHITECTURE

21. Neo4J:
architecture
Neo4J: Internal storage
Node
Rela%onship

22. Neo4J:
architecture
Neo4J: Internal storage

23. APP: Collaborative filtering
Neo4J: CF

24. APP: Collaborative filtering
Neo4J: CF

25. Neo4J: CYPHER
MATCH
(p1:Person)-­‐[x:RATED]-­‐>(m:PRODUCT)<-­‐[y:RATED]-­‐
(p2:Person)
WITH
SUM(x.ra%ng
*
y.ra%ng)
AS
xyDotProduct,
SQRT(REDUCE(xDot
=
0,
a
IN
COLLECT(x.ra%ng)
|
xDot
+
a^2))
AS
xLength,
SQRT(REDUCE(yDot
=
0,
b
IN
COLLECT(y.ra%ng)
|
yDot
+
b^2))
AS
yLength,
p1,
p2
CREATE
UNIQUE
(p1)-­‐[s:SIMILARITY]-­‐(p2)
SET
s.similarity
=
xyDotProduct
/
(xLength
*
yLength)

27. sergeyenin@gmail.com
Thank
you!
/pankrat
/sergeyenin