The document discusses switching from a relational database model to a graph database model. It begins with an overview of common objections to adopting NoSQL databases due to complexity of data modeling. It then provides a brief introduction to key-value stores, column-based databases, and document databases before arguing that graph databases are well-suited to complex domains. The remainder of the document focuses on explaining the graph database model and how it differs from and improves upon the relational model for managing relationships through index-free adjacency rather than joins. Code examples are provided for creating a simple graph representing customer and address data using the OrientDB graph database.

1. Switching from the
Relational to the
Graph model
Luca Garulli –
Founder and CEO @NuvolaBase Ltd
Author of OrientDB Doc/Graph DB
Nov 23rd 2012 in Oxford, UK
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www.orientechnologies.com

2. 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...
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3. ...what about the model?
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4. What is the NoSQL answer
about managing complex domains?
Key-Value stores ?
Column-Based ?
Document database ?
Graph database !
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5. 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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6. ...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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7. Every developer knows
the Relational Model,
but who knows the
Graph one?
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8. Back to school:
Graph Theory crash course
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9. Basic Graph
All Your
All Your
Likes
Luca
Luca Base
Base
Conference
Conference
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10. Property Graph Model*
Vertices are
directed
Luca
Luca All Your Base
All Your Base
Likes
name: Luca
name: Luca Conference
Conference
surname: Garulli
surname: Garulli since: 2012
company: NuvolaBase
company: NuvolaBase date: Nov 23 2012
date: Nov 23 2012
Vertices and Edges
can have properties
* https://github.com/tinkerpop/blueprints/wiki/Property-Graph-Model
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11. Property Graph Model
Likes
012
since:
2
All Your
All Your
Luca
Luca Base
Base
Speak Conference
Conference
s
ti
abstra tle: «Switch
ct: «Th in
is talk g...»
presen
ts...»
An Edge connects 2
vertices: use multiple
vertices to represents 1-N
and N-M relationships
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12. Property Graph Model
Studies Oxford
Oxford
Luca
Luca
Likes located
FriendOf
All Your Base
All Your Base
Conference
Conference
John
John Organizes
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13. Compliments, this is your diploma in
«Graph Theory»
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14. Now go back
to our domain:
the CRM
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15. Domain: the super minimal CRM
Customer
Customer Address
Address
Registry system
Order system
Order
Order Stock
Stock
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16. 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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17. 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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18. 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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19. 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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20. 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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21. What’s wrong with the
Relational Model?
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22. 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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23. 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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24. 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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25. Index Lookup
is it really that fast?
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26. 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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27. 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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28. 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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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
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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30. 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
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31. 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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32. Oh! This is why
performance of my database
drops down when
it becomes bigger,
and bigger,
and bigger!
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33. Is there a better way to
manage relationships?
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34. “A graph database is any
storage system
that provides
index-free adjacency”
- Marko Rodriguez
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35. How does GraphDB manage
index-free relationships?
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36. an Open Source (Apache licensed)
document-graph NoSQL dbms
supports: transactions, extended-SQL,
Multi-Master replication, etc
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37. OrientDB: traverse a relationship
The Record ID (RID)
is the physical position
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’
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38. 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?!
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39. 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
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40. 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
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41. 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
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42. 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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43. 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
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44. More on query power
orientdb> select sum( orders.total ) from Customer
where name = ‘Luca’
orientdb> traverse friend from Customer while $depth <= 7
orientdb> select from (
traverse friend from Customer while $depth <= 7
) where city.name = ‘Oxford’
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45. 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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46. 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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47. This is your database
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48. Get last customer bought Whisky
select last(orders.customers) from Stock
where name = ‘Whisky’
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49. Get it’s country
select city.country from #34:22
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50. Get orders from that country
select orders from #55:12
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51. NuvolaBase.com
HTTP/REST
HTTP/REST
The first Graph Database as a Service
on the Cloud
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52. Do we have enough time for a demo?
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53. Questions & (maybe) Answers
Luca Garulli
CEO at
Document-Graph NoSQL
Open Source project
Ltd, London UK
www.twitter.com/lgarulli
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54. 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)
3) GraphDB has a query language specialized to
traverse relationships
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55. Let’s move like a
Spider
on the web
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