ghislain fourny big data fall 2019 · 2020-01-31 · ghislain fourny big data fall 2019 14. graph...

Ghislain Fourny

Big Data Fall 2019

14. Graph Databases

pinkyone / 123RF Stock Photo tovovan / 123RF Stock Photo1

Why graph databases?

2

The NoSQL paradigms

foo

bar

foobar

Key-value stores

Triple stores

Column stores Document stores

3

Relational databases...

4


Entity

Entity

Relationship

5


Entity

Entity

Relationship

have

expensivejoins!

6


... are not that

efficient

at relationships!

7

We already know how to partly solve this

though

3NF

0NF

8

We already know how to partly solve this

though

3NF

0NF

... but it has its

limits,too!

9

Traversals...

10

Traversals...

... translate into

multiple joins!11

Reverse traversals...

12

Reverse traversals...

... need

even more indices!13

Traversals...

... translate into

multiple joins!

what if links

would be more

"direct"?

14

Index-free adjacency

15

Graphs

16

Graphs: ingredients

Nodes Edges

17

Graphs: nodes

18

Graphs: edges

19

Graphs: directed graph

20

Graphs: undirected graph

21

Graph representation: adjacency list

A

CB

Node Edges

A [ ]

B [ A, C ]

C [ A ]

22

Graph representation: adjacency matrix

A

CB

A B C

A 0 1 1

B 0 0 0

C 0 1 0

23

Graph representation: incidence matrix

A

CB

1 2 3

A 1 1 0

B -1 0 -1

C 0 -1 1

Edges

Nodes

24

Labeled property graphs: ingredients

Nodes Edges Properties Labels

25

Property graph

26

Properties

Name: Einstein

First name: Albert

Profession: Physicist

27

Labeled graph

28

Labels on nodes

29

Names (labels) on relationships

A

A

A

A

A

B

B

B

A

A

30

Labeled property graph

31

Node with properties and label

Person

Name: Einstein

First name: Albert

Profession: Physicist

In Switzerland

32

Graph database

33

Graph databases: families

Property Graph Triple stores (RDF)

34

Graph databases: native or not

Native Graph DatabaseGraph stored as

RDBMS, document

store, ...

Source Target Name

Alice Bob knows

Eve Bob eavesdrop

Eve Alice eavesdrop

35

RDF

36

Triple-based graph

37

RDF: one triple

ETH Zürich SwitzerlandIs located in

Subject Property Object

38

IRI

http://www.ethz.ch/#school

http://www.example.com/Switzerland

39

Literal

includes XML Schema types!

Foo 2012-12-16

3.1415926535

40

Blank Node

ETH ZürichIs built on ground

Switzerland

Is subset of

41

What can appear where?


IRI

Literal

Blank

node

42

Generalized Graphs


IRI

Literal

Blank

node

43

Syntax

44

RDF Formats

§ RDF/XML

§ Turtle

§ JSON-LD

§ RDFa

§ N-Triples

45

RDF/XML

<rdf:RDF

xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"

xmlns:geo="http://www.example.com/geography#">

<rdf:Description rdf:about="http://www.ethz.ch/#self">

<geo:isLocatedIn

rdf:resource="http://www.example.com/Switzerland"/>

<geo:population>20000</geo:population>

</rdf:Description>

</rdf:RDF>

46

RDF/XML: Subject

<rdf:RDF




<geo:isLocatedIn



</rdf:Description>

</rdf:RDF>

47

RDF/XML: Property

<rdf:RDF




<geo:isLocatedIn



</rdf:Description>

</rdf:RDF>

48

RDF/XML: Object

<rdf:RDF




<geo:isLocatedIn



</rdf:Description>

</rdf:RDF>

49

RDF/XML

<rdf:RDF




<geo:isLocatedIn



</rdf:Description>

</rdf:RDF>http://www.example.com/geography#isLocatedIn

50

RDF/XML

<rdf:RDF




<geo:isLocatedIn


<rdf:type

rdf:resource="http://www.example.com/geography#school"/>


</rdf:Description>

</rdf:RDF>51

RDF/XML

<rdf:RDF




<geo:isLocatedIn


<rdf:type

rdf:resource="http://www.example.com/geography#school"/>


</rdf:Description>

</rdf:RDF>52

RDF/XML

<rdf:RDF



<geo:school rdf:about="http://www.ethz.ch/#self">

<geo:isLocatedIn



</geo:school>

</rdf:RDF>

53

JSON-LD

{

"@context": {

"rdf": "http://www.w3.org/1999/02/22-rdf-syntax-ns#",

"geo": "http://www.example.com/geography#"

},

"@id" : "http://www.ethz.ch/#self",

"rdf:type": "geo:school",

"geo:isLocatedIn": "http://www.example.com/Switzerland",

"geo:population" : 8000000

}

54

Turtle

@prefix geo: <http://www.example.com/geography#> .

@prefix countries: <http://www.example.com/> .

@prefix eth: <http://www.ethz.ch/#> .

eth:self geo:isLocated countries:Switzerland .

eth:self geo:population 8000000 .

55

Turtle




eth:self geo:isLocated countries:Switzerland ;


56

Turtle




eth:self geo:isLocated countries:Switzerland,

eth:self geo:isLocated countries:Europe ;


57

Querying

58

Querying paradigms

Classical

declarative

querying

Query

by

example

?

59

Two languages

Cypher SPARQL

ETH ZürichSwitzerlandIs located in









60

Two languages

Cypher SPARQL










61

Querying labeled property graphs by example

62


63


64


65


66


AA

A

B

B

B

B

B

B

B

AB

A

B

67


AA

A

B

B

B

B

B

B

B

AB

A

B

68

Cypher pattern

AA

A

B

B

B

B

B

B

B

AB

(alpha)-[:A]->(beta)-[:B]->(gamma)

alpha

betagamma

69

Cypher pattern: anchoring a label

AA

A

B

B

B

B

B

B

B

AB

(alpha)

-[:A]->(beta:yellow)

-[:B]->(gamma)

alpha

betagamma

yellow

70

Cypher pattern: filtering a property

AA

A

B

B

B

B

B

B

B

AB

(alpha {name: 'Einstein' })

-[:A]->(beta)

-[:B]->(gamma)

alpha

betagamma

name: Einstein

71

Cypher pattern: anchoring and filtering

AA

A

B

B

B

B

B

B

B

AB

(alpha)

-[:A]->(beta)

-[:B]->(gamma: blue {name: 'ETH'})

alpha

betagamma

name: ETH

blue

72

Cypher pattern: right to left

AA

A

B

B

B

B

B

B

B

AB

(alpha)

-[:A]->(beta)

-[:B]->(gamma)<-[:B]-(delta)

alpha

betagamma

delta

73

Cypher pattern: variable repetition

AA

A

B

B

B

B

B

B

B

AB

(alpha)

-[:A]->(beta)

-[:B]->(gamma)<-[:B]-(delta)

-[:B]->(alpha)

alpha

betagamma

delta

74

Cypher pattern: variable length path

AA

A

B

B

B

B

B

B

B

AB

(alpha)

-[*1..4]->(beta)

alpha

beta

75

Cypher pattern: MATCH clause

MATCH (alpha {name: 'Einstein' })-[:A]->(beta)-[:B]->(gamma)

76

Cypher pattern: MATCH clause


RETURN gamma

77

Cypher pattern: WHERE clause


RETURN gamma

MATCH (alpha)-[:A]->(beta)-[:B]->(gamma)

WHERE alpha.name = 'Einstein'

RETURN gamma

78

Cypher pattern: CREATE clause

CREATE (einstein:Scientist {name: 'Einstein', first: 'Albert' }),

(eth:University {name: 'ETH Zurich' }),

(einstein)-[:VISITED]->(eth)

79

Other clauses

WITH

DELETE

MERGE

FOREACH

SET

UNION

START

80

Two languages

Cypher SPARQL










81

Querying RDF: SPARQL

PREFIX geo: <http://www.example.com/geography#>

PREFIX countries: <http://www.example.com/>

SELECT ?s

WHERE { ?s geo:isLocatedIn countries:Switzerland }

82

SPARQL



SELECT ?s


83

SPARQL



SELECT ?s


84

SPARQL



SELECT ?s

WHERE {

?s geo:isLocatedIn countries:Switzerland .

?s :deliversDiplom :bachelor .

}

85

SPARQL



SELECT ?s

WHERE {

?s geo:isLocatedIn ?c .

?c geo:isInContinent geo:America .

}

86

SPARQL



SELECT ?s

WHERE {

?s geo:isLocatedIn countries:Switzerland.

?s :deliversDiplom :bachelor

}

LIMIT 10

87

SPARQL



SELECT ?s ?name

WHERE {

?s geo:isLocatedIn countries:Switzerland .

?s :deliversDiplom :bachelor .

?s :hasName ?name .

}

ORDER BY ?name

LIMIT 10

88

Architecture (Neo4j)

89

No sharding

90

joins

Graph databases

Document stores

don't like

shardsdon't like

91

Why? Fast traversal

92

Master-slave architecture

Slave

Master

Slave Slave Slave Slave Slave93

Data replication

Slave

Master

Slave Slave94

Data replication

Slave

Master

Slave Slave

Synchronization

95

Data replication (full)

Slave

Master

Slave Slave

Synchronization

96

Read scale-up

Slave97

Writes

Write to the master Write to a slave

or

98

Caching and pages

Fixed-size records

Index-free adjacency

99

Label storage

Person

Jedi

Geek

Person Jedi Geek

100

Properties storage

name first-name Albert

name: Einstein

first-name: Albert

Einstein

101

Relationship storage

A

A

102


A

A

103


A

A

104


A

105


A

106


A

107


A

108


A

109


A

110


A

111


A

112


A

113


A A

114


A A

115


A A

116


A B

A

B

Source

Targets-previous s-next

t-previous t-next

117

Typical sizes

Node: 9 bytes

Relationship: 33 bytes

Relationship name: 5 bytes

Property: 33 bytes

118

Semantics

119

RDF has no semantics

Schwiiz SchoggiChuchichäschtli

120

RDF Schema

Class Property

121

Classes

rdfs:Resource

rdfs:Class

rdf:Property

rdfs:Literal

rdfs:DataType

rdf:HTML

rdf:XMLLiteral

122

Properties

rdf:type

rdfs:label

rdfs:comment

rdfs:range

rdfs:domain

rdfs:subPropertyOf

rdfs:subClassOf

On any resources

On properties

On classes

123

Self-awareness

rdfs:Resource rdfs:Resourcerdf:type

124

Self-awareness

rdfs:Class rdfs:Resourcerdf:subClassOf

125

Self-awareness

rdf:type rdfs:Classrdf:range

126

Self-awareness

rdf:subClassOf rdfs:Propertyrdf:type

127

Simple Entailment (RDF semantics)

I

E

I(E)=true 128

OWL

129

OWL

(In principle) standalone

(Much) More powerful than RDF(S)

130

OWL

<xml/>131

OWL and description logic / AI

132

Entailment (and Syllogisms)

All men are mortal.

Socrates is a man.

Therefore,

Socrates is mortal.

Major

Minor

Conclusion

133

More graph databases...

134

Trees...

135

... and Graphs

136

ghislain fourny big data fall 2019 · 2020-01-31 · ghislain fourny big data fall 2019 14. graph...

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