My presentation about introduction to graph database.

1. Graph Databases
Introduction & Concepts
Vinoth Kannan
vinoth.kannan@widas.de
1

2. Agenda
Overview of NoSQL
What is a Graph Database
Concept
Some Use Cases
Conclusion
2

3. Overview of NoSQL
NoSQL
Not Only SQL
3

4. Types of NoSQL
Key Value Stores
Column Family
Document Databases
Graph Databases
4

5. Key-Value Store
Types of NoSQL
Based on Amazon’s Dynamo platform: Highly
Available Key-Value Store
Data Model:
Global key-value mapping
Big scalable HashMap
Highly fault tolerant
Examples:
Redis, Riak, Voldemort, Tokyo
5

6. Column Family
NoSQL Types
Based on BigTable: Google’s Distributed Storage
System for Structured Data
Data Model:
A big table, with column families
Map Reduce for querying/processing
Every row can have its own Schema
Examples:
HBase, HyperTable, Cassandra
6

7. Document Databases
NoSQL Types
Based on Lotus Notes
Data Model:
A collection of documents
A document is a key value collection
Index-centric, lots of map-reduce
Examples:
CouchDB, MongoDB
7

8. Graph Databases
NoSQL Types
Based on Euler & Graph Theory
Data Model:
Nodes and Relationships
Examples:
Neo4j, OrientDB, InfiniteGraph, AllegroGraph, Titan
8

9. NoSQL Performace
Complexity vs Size
………………..
Graph
Store
Data Complexity
Document
Store
CF Store
K-V
Store
RDBMS
Data Size
9

10. What is a Graph?
An abstract representation of a set of objects where
some pairs are connected by links.
Name
Object (Vertex, Node)
Link (Edge, Arc, Relationship)

11. Different Types of Graphs
Graph Type
Undirected Graph
Directed Graph
Pseudo Graph
Multi Graph
Hyper Graph
Diagram

12. Different Types of Graphs
Graph Type
Weighted Graph
Labeled Graph
Property Graph
Diagram

13. What is a Graph Database?
A database with an explicit graph structure
Each node knows its adjacent nodes
Even as the number of nodes increases, the cost of a
local step (or hop) remains the same
Plus an Index for lookups
Transactional based

14. Compared to Relational Databases
Optimized for aggregation
Optimized for connections

15. Compared to Key Value Stores
Optimized for simple look-ups
Optimized for traversing connected
data

16. Compared to Key Value Stores
Optimized for “trees” of data
Optimized for seeing the forest and
the trees, and the branches, and the
trunks

17. Friends Recommendation
Wondered How ?
17

18. Graph Databases
Basic Concepts – Social Data
Name= “Elena”
Name= “Vinoth”
City= “PF “
Name= “Emanuel”
Name= “Joachim”
3
FRIEND
1
6
12
FRIEND
RELATED
Since : 2012
2
Name= “Thomas”
City= “Wimsheim
9
”
Name= “Y”
18

19. Graph Search Feature of FB
Wondered How ?
19

20. Graph Databases
Basic Concepts – Connection based
Name= “Elena”
Name= “Vinoth”
City= “PF
”
Name= “WIDAS”
3
1
6
FRIEND
Since : 2012
2
Name= “Thomas”
City= “Wimsheim
”
20

21. Graph Databases
Basic Concepts – Spatial Data
Name= “Stuttgart Hbf”
Lat = 48.460
Lon = 9.1040
Name= “WIDAS”
Lat = 48.510
Lon = 8.790
Name= “…..”
Lat = 41.000
Lon = 9.840
distance: 24 km
3
ROAD
1
ROAD
6
12
distance: 51 km
ROAD
distance: 12 km
2
Name= “Pforzheim Cafe”
Lat = 48.530
Lon = 8.420
9
21

22. Power of Graph Database
Social Data
+
Spatial Data
22

23. Graph Databases
Basic Concepts – Social and Spatial Data
Name= “Stuttgart”
Lat = 41.000
Lon = 40.840
Name= “WIDAS”
Lat = 41.000
Lon = 40.840
Name= Thomas
Travel_rating = expert
distance: 24 km
3
Name= Elena
Travel_rating = novice
FRIENDS
1
ROAD
6
12
distance: 51 km
distance: 12 km
2
Name= “Pforzheim”
Lat = 41.000
Lon = 40.840
23

24. Some Use Cases
Highly connected data (social networks)
Recommendations (e-commerce)
Path Finding (how do I know you?)
Anamoly Detection (Financial Services)

25. FDS System with GraphDB
Name= “Vinoth”
IBAN= “DE1234
Name= “Xing Lee”
Country = “China”
IBAN = “XXXXXX”
”
Name= “ATM@Romania”
Lat = 41.000
Lon = 40.840
TRANSFERS
3
6
1
amount: € 4500
LIVES
2
Name= “Pforzheim”
Lat = 41.000
Lon = 40.840
MARKED
9
Name= “Blacklist”
25

26. Thank you!