The document discusses switching from a relational database model to a graph database model. It provides an overview of graph databases, including how they represent relationships through edges and vertices, support properties on both, and can model one-to-one, one-to-many, and many-to-many relationships without additional tables. It also notes that graph databases provide index-free adjacency, avoiding the performance issues of JOIN operations in relational databases for traversing relationships.

1. Switching from the
Relational to the
Graph model
Luca Garulli –
Founder and CEO @NuvolaBase Ltd
Author of OrientDB Doc/Graph DB
Nov 29th 2012 in Riga, Latvia
(c) Luca Garulli Licensed under a Creative Commons Attribution-NoDerivs 3.0 Unported License Page 1
www.orientechnologies.com

2. Agenda
12:30
“Switching from the Relational to the Graph model”
13:30
Lunch time!
(c) Luca Garulli Licensed under a Creative Commons Attribution-NoDerivs 3.0 Unported License Page 2

3. Agenda
12:30 You’re here!
“Switching from the Relational to the Graph model”
13:30
Lunch time!
(c) Luca Garulli Licensed under a Creative Commons Attribution-NoDerivs 3.0 Unported License Page 3

4. One of the main resistances of
RDBMS users to pass to a NoSQL product
are related to the
complexity of the model:
Ok, NoSQL products are super for
BigData and BigScale
but...
(c) Luca Garulli Licensed under a Creative Commons Attribution-NoDerivs 3.0 Unported License Page 4

5. ...what about the model?
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6. What is the NoSQL answer
about managing complex domains?
Key-Value stores ?
Column-Based ?
Document database ?
Graph database !
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7. CAUTION!
This presentation will not use a
social like domain with
the classic paradigm of
friend-of-friendN
where the graph databases
are already widely used...
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8. ...But rather we will explore how
to think «graphically» with one of the
most common domains in the
enterprise world:
The old-classic CRM* domain
* today in 99% of the cases a RDBMS is used
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9. Every developer knows
the Relational Model,
but who knows the
Graph one?
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10. Back to school:
Graph Theory crash course
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11. Basic Graph
JavaDay
JavaDay
Likes 2012
2012
Luca
Luca
Riga
Riga
Conference
Conference
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12. Property Graph Model*
Vertices are
directed
JavaDay 2012
JavaDay 2012
Luca
Luca Riga
Likes Riga
name: Luca
name: Luca
surname: Garulli
surname: Garulli since: 2012 Conference
Conference
company: NuvolaBase
company: NuvolaBase
date: Nov 29 2012
date: Nov 29 2012
Vertices and Edges
can have properties
* https://github.com/tinkerpop/blueprints/wiki/Property-Graph-Model
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13. Property Graph Model
Likes
since:
2012 JavaDay
JavaDay
2012
2012
Luca
Luca
Speak Riga
Riga
s Conference
ti
abstra tle: «Switch
Conference
ct: «Th in
is talk g...»
presen
ts...»
An Edge connects 2
vertices: use multiple edges
to represents 1-N and N-M
relationships
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14. Property Graph Model
Studies Oxford
Oxford
Luca
Luca
Likes located
FriendOf
JavaDay 2012
JavaDay 2012
Riga
Riga
John
John Organizes Conference
Conference
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15. Compliments, this is your diploma in
«Graph Theory»
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16. Now go back
to our domain:
the CRM
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17. Domain: the super minimal CRM
Customer
Customer Address
Address
Registry system
Order system
Order
Order Stock
Stock
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18. Domain: the super minimal CRM
Customer
Customer Address
Address
How does
Relational DBMS
Registry system
manage relationships?
Order system
Order
Order Stock
Stock
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19. Relational World: 1-1 Relationships
Primary key Primary key
Customer Address
Id Name Address Id Location
Foreign key
10 Luca 34 34 Rome
11 Mike 44 44 London
34 John 54 54 Oxford
56 Mark 66 66 New Mexico
88 Steve 68 68 Palo Alto
JOIN Customer.Address -> Address.Id
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20. Relational World: 1-N Relationships
Customer Address
Id Name Id Customer Location
10 Luca 24 10 Rome
11 Mike 33 10 London
34 John 44 34 Oxford
56 Mark 66 56 Cologne
88 Steve 68 88 Palo Alto
Inverse JOIN Address.Customer -> Customer.Id
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21. Relational World: N-M Relationships
Customer CustomerAddress Address
Id Name Id Address Id Location
10 Luca 10 24 24 Rome
11 Mike 10 33 33 London
34 John 11 44 44 Oxford
56 Mark 66 Cologne
88 Steve 68 Palo Alto
Additional table with 2 JOINs
(1) CustomerAddress.Id -> Customer.Id and
(2) CustomerAddress.Address -> Address.Id
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22. Relational World: N-M Relationships
Customer CustomerAddress Address
Id Name Id Address Id Location
10 Luca 10 24 24 Rome
11 Mike 10 33 33 London
34 John 11 44 44 Oxford
56 Mark 66 Cologne
88 Steve 68 Palo Alto
Additional table with 2 JOINs
(1) CustomerAddress.Id -> Customer.Id and
(2) CustomerAddress.Address -> Address.Id
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23. What’s wrong with the
Relational Model?
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24. The JOIN is the evil!
Customer CustomerAddress Address
Id Name Id Address Id Location
10 Luca 10 24 24 Rome
11 Mike 10 33 33 London
34 John 34 24 44 Oxford
56 Mark 66 Cologne
88 Steve 68 Palo Alto
These are all JOINs executed
everytime you traverse a
relationship!
relationship
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25. A JOIN means searching for a key in
another table
The first rule to improve performance
is indexing all the keys
Index speeds up searches, but slows down
insert, updates and deletes
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26. So in the best case a JOIN is a lookup
into an index
This is done per single join!
If you traverse hundreds of relationships
you’re executing hundreds of JOINs
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27. Index Lookup
is it really that fast?
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28. Index Lookup: how does it works?
A-Z
A-L M-Z
Think to an
Address Book
where we have to find
the Luca’s phone
number
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29. Index Lookup: how does it works?
A-Z
A-L M-Z
A-L M-Z
A-D E-L M-R S-Z
Index algorithms are all
similar and based on
balanced trees
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30. Index Lookup: how does it works?
A-Z
A-L M-Z
A-L M-Z
A-D E-L M-R S-Z
A-D E-L
A-B C-D E-G H-L
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31. Index Lookup: how does it works?
A-Z
A-L M-Z
A-L M-Z
A-D E-L M-R S-Z
A-D E-L
A-B C-D E-G H-L
E-G H-L
E-F G H-J K-L
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32. Index Lookup: how does it works?
A-Z
A-L M-Z
A-L M-Z
Found!
A-D E-L M-R S-Z
This lookup took 5
A-D E-L steps and grows
A-B C-D E-G H-L
up with the index
E-G H-L size!
E-F G H-J K-L
Luca
(c) Luca Garulli Licensed under a Creative Commons Attribution-NoDerivs 3.0 Unported License Page 32

33. An index lookup is executed
for each JOIN
Querying more tables can easily
produce millions of JOINs/Lookups!
Here the rule: more entries
= more lookup steps = slower JOIN
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34. Oh! This is why
performance of my database
drops down when
it becomes bigger,
and bigger,
and bigger!
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35. Is there a better way to
manage relationships?
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36. “A graph database is any
storage system
that provides
index-free adjacency”
- Marko Rodriguez
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37. How does GraphDB manage
index-free relationships?
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38. an Open Source (Apache licensed)
document-graph NoSQL dbms
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39. Ø config
download, unzip, run!
cut & paste the db directory
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40. 150,000 records per second
(flat records, no index, on commodity hw)
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41. Schema-less
schema is not mandatory, relaxed model,
collect heterogeneous documents all together
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42. Schema-full
schema with constraints on fields and validation rules
Customer.age > 17
Customer.address not null
Customer.surname is mandatory
Customer.email matches 'b[A-Z0-9._%+-]+@[A-Z0-9.-]+.[A-Z]{2,4}b'
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43. Schema-mixed
schema with mandatory and optional fields + constraints
the best of schema-less and schema-full modes
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44. ACID Transactions
db.begin();
try{
// your code
...
db.commit();
} catch( Exception e ) {
db.rollback();
}
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45. Complex types
native support for collections, maps (key/value)
and embedded documents
no more additional tables to handle them
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46. SQL
select * from employee where name like '%Jay%' and status=0
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47. runs
Java
everywhere is available JRE1.6+
®
robust engine
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48. Language bindings
Java as native
JRuby, PHP, C, C++, Scala, .NET,
Ruby, Clojure, Node.js,
Python, Javascript and more!
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49. JPA (partial)
public class Customer {
@Id
private Object id;
private String name;
private String surname;
}
db.save( new Customer() );
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50. Born for the Internet
Supports natively HTTP/RESTful protocol
Documents are transferred in JSON
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51. MVRB-Tree index
the best of B+Tree and RB-Tree
fast on browsing, low insertion cost
it's a new algorithm!
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52. Security
users and roles, encrypted passwords
fine grain privileges
(similar to what RDBMSs offer)
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53. Cache
You can avoid using 3°party caches
like Memcached
2 Levels of cache:
Level1: Database level, 1 per thread
Level2: Storage level, 1 per JVM
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54. Inheritance
OGraphVertex (V)
Person Vehicle
Address : Address brand : BRANDS
Customer Provider
totSold : float totBuyed : float
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55. Polymorphic SQL Query
OGraphVertex (V)
Person Vehicle
Address : Address brand : BRANDS
select * from Person
where city.name = 'Rome‘
Queries are polymorphics
Customer Provider and subclasses of Person can be
totSold : float totBuyed : float part of result set
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56. Let’s go back
to the Graph Stuff
How does OrientDB
manage relationships?
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57. OrientDB: traverse a relationship
The Record ID (RID)
is the physical position
RID = #13:35
RID = #13:35 RID = #13:100
RID = #13:100
Luca
Luca Rome
Rome
label : :‘Customer’
label ‘Customer’ label = ‘Address’
label = ‘Address’
name : :‘Luca’
name ‘Luca’ name = ‘Rome’
name = ‘Rome’
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58. OrientDB: traverse a relationship
The Edge’s RID is saved
inside both vertices, as
«out» and «in»
RID = #13:35
RID = #13:35 RID = #13:100
RID = #13:100
RID = #14:54
RID = #14:54
Lives
Luca
Luca Rome
Rome
out: [#13:35]
out: [#13:35]
in: [#13:100]
in: [#13:100]
out ::[#14:54] Label : :‘Lives’
Label ‘Lives’ in: [#14:54]
out [#14:54] in: [#14:54]
label : :‘Customer’
label ‘Customer’ label = ‘Address’
label = ‘Address’
name : :‘Luca’
name ‘Luca’ name = ‘Rome’
name = ‘Rome’
(c) Luca Garulli Licensed under a Creative Commons Attribution-NoDerivs 3.0 Unported License Page 58

59. OrientDB: traverse a relationship
RID = #13:35
RID = #13:35 RID = #13:100
RID = #13:100
RID = #14:54
RID = #14:54
Lives
Luca
Luca Rome
Rome
out: [#13:35]
out: [#13:35]
in: [#13:100]
in: [#13:100]
out ::[#14:54] Label : :‘Lives’
Label ‘Lives’ in: [#14:54]
out [#14:54] in: [#14:54]
label : :‘Customer’
label ‘Customer’ label = ‘Address’
label = ‘Address’
name : :‘Luca’
name ‘Luca’ name = ‘Rome’
name = ‘Rome’
(c) Luca Garulli Licensed under a Creative Commons Attribution-NoDerivs 3.0 Unported License Page 59

60. OrientDB: traverse a relationship
RID = #13:35
RID = #13:35 RID = #13:100
RID = #13:100
RID = #14:54
RID = #14:54
Lives
Luca
Luca Rome
Rome
out: [#13:35]
out: [#13:35]
in: [#13:100]
in: [#13:100]
out ::[#14:54] Label : :‘Lives’
Label ‘Lives’ in: [#14:54]
out [#14:54] in: [#14:54]
label : :‘Customer’
label ‘Customer’ label = ‘Address’
label = ‘Address’
name : :‘Luca’
name ‘Luca’ name = ‘Rome’
name = ‘Rome’
(c) Luca Garulli Licensed under a Creative Commons Attribution-NoDerivs 3.0 Unported License Page 60

61. GraphDB handles relationships as a
physical LINK to the record
assigned when the edge is created
on the other side
RDBMS computes the
relationship every time you query a database
Is not that crazy?!
(c) Luca Garulli Licensed under a Creative Commons Attribution-NoDerivs 3.0 Unported License Page 61

62. This means jumping from a
O(log N) algorithm to a near O(1)
traversing cost is not more affected
by database size!
This is huge in the BigData age
(c) Luca Garulli Licensed under a Creative Commons Attribution-NoDerivs 3.0 Unported License Page 62

63. OrientDB in the Blueprints micro-benchmark,
on common hw, with a hot cache,
traverses 29,6 Millions
of records in less than 5 seconds
about 6 Millions of nodes traversed per sec!
Do not try this at home
with a RDBMS*!
*unless you live in the Google’s server farm
(c) Luca Garulli Licensed under a Creative Commons Attribution-NoDerivs 3.0 Unported License Page 63

64. Create the graph in SQL
$luca> cd bin
$luca> ./console.sh
OrientDB console v.1.3.0-SNAPSHOT (www.orientdb.org)
Type 'help' to display all the commands supported.
orientdb> create vertex Customer set name = ‘Luca’
Created vertex #13:35 in 0.03 secs
orientdb> create vertex Address set name = ‘Rome’
Created vertex #13:100 in 0.02 secs
orientdb> create edge Lives from #13:35 to #13:100
Created edge #14:54 in 0.02 secs
(c) Luca Garulli Licensed under a Creative Commons Attribution-NoDerivs 3.0 Unported License Page 64

65. Create the graph in Java
OGraphDatabase graph = new OGraphDatabase("local:/tmp/db/graph”);
ODocument luca = graph.createVertex(“Customer");
luca.field(“name", “Luca");
ODocument rome = graph.createVertex(“Address”);
rome.field(“name", “Rome”);
ODocument edge = graph.createEdge(luca, rome, “Lives”);
edge.save();
graph.close();
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66. Query the graph in SQL
orientdb> select in.out from Address where name = ‘Rome’
---+------+---------|--------------------+--------------------+--------+
#| RID |@class |label |out |in |
---+------+---------+--------------------+--------------------+--------+
0| 13:35|Customer |Luca |[#14:54] | |
---+------+---------+--------------------+--------------------+--------+
1 item(s) found. Query executed in 0.007 sec(s).
Incoming vertices
(c) Luca Garulli Licensed under a Creative Commons Attribution-NoDerivs 3.0 Unported License Page 66

67. More on query power
orientdb> select sum( out.in[@class=‘Order’].total ) from Customer
where name = ‘Luca’
orientdb> traverse Customer.out, Friend.in from Customer
while $depth <= 7
orientdb> select from (
traverse Customer.out, Friend.in from Customer
while $depth <= 7
) where @class=‘Customer’ and city.name = ‘Riga’
(c) Luca Garulli Licensed under a Creative Commons Attribution-NoDerivs 3.0 Unported License Page 67

68. Query vs traversal
Once you’ve a well connected database
in the form of a Super Graph you can
cross records instead of query them!
All you need is some root vertices
where to start traversing
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69. Query vs traversal
Special
Special
Customers
Customers Stocks
Stocks
Customers
Customers
Luca
Luca John
John Sylvia
Sylvia
White
White
This is a Soap
Soap
root vertex Order
Order Order
Order
2332
2332 8834
8834
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70. This is your database
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71. Get last customer bought Whisky
select last(out.in[@class=‘Order’].out.in[@class=‘Customer’]) from Stock
where name = ‘Whisky’
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72. Get it’s country
select out.in[@class=‘city’] from #34:22
Riga, Latvia
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73. Get orders from that country
select in.out[@class=‘Customer’] from #55:12
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74. NuvolaBase.com
HTTP/REST
HTTP/REST
The first Graph Database as a Service
on the Cloud
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75. Do we have enough time for live demo?
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76. Questions & (maybe) Answers
Luca Garulli
CEO at
Document-Graph NoSQL
Open Source project
Ltd, London UK
www.twitter.com/lgarulli
Conclusion as last ->
(c) Luca Garulli Licensed under a Creative Commons Attribution-NoDerivs 3.0 Unported License Page 76

77. Summary
1)JOIN is heavy, specially on large databases
2)GraphDB uses LINK as
direct pointers to records:
times from O(log)N to near O(1)
= ready for the BigData
3) GraphDB has a query language specialized to
traverse relationships
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78. Let’s move like a
Spider
on the web
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