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Neo4j is a graph database. It is an embedded, disk-based, fully transactional Java persistence engine that stores data structured in graphs rather than in tables. A graph (mathematical lingo for a network) is a flexible data structure that allows a more agile and rapid style of development.
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Graph Databasesand Neo4j
Tobias IvarssonHacker @ Neo Technology
twitter: @thobe / #neo4jemail: [email protected]: http://www.neo4j.org/web: http://www.thobe.org/
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username fullname registration speaker payment
mtiberg Michael Tiberg null no 0
thobe Tobias Ivarsson 2010-04-07 yes 0
joe John Doe 2010-02-05 no 700
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AttendeesWe all know the relational model.
It has been predominant for a long time.
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username fullname registration speaker payment
mtiberg Michael Tiberg null no 0
thobe Tobias Ivarsson 2010-04-07 yes 0
joe John Doe 2010-02-05 no 700
... ... ... ... ...
Attendees
username latitude longitude title publish
thobe 55°36'47.70"N 12°58'34.50"E Malmö yes
joe 37°49'36.00"N 122°25'22.00"W San Francisco
no
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Location
The relational model has a few problems, such as:•poor support for sparse data•modifying the data model is almost exclusively done through adding tables
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username fullname registration speaker payment
mtiberg Michael Tiberg null no 0
thobe Tobias Ivarsson 2010-04-07 yes 0
joe John Doe 2010-02-05 no 700
... ... ... ... ...
Attendees
username latitude longitude title publish
thobe 55°36'47.70"N 12°58'34.50"E Malmö yes
joe 37°49'36.00"N 122°25'22.00"W San Francisco
no
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Location
id title time room ...
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Sessions
session user
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After a while, modeling complex relationships leads to complicated schemas
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CF
G B
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Most of the emerging database technologies are concerned with scaling to huge amounts of data and massive load.They do so by making data opaque and distribute elements based on key.
Most focus on scaling to large numbers
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E D
CF
G B
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Most of the emerging database technologies are concerned with scaling to huge amounts of data and massive load.They do so by making data opaque and distribute elements based on key.
Most focus on scaling to large numbers
5
E D
CF
G B
A
Most of the emerging database technologies are concerned with scaling to huge amounts of data and massive load.They do so by making data opaque and distribute elements based on key.
Most focus on scaling to large numbers
5
E D
CF
G B
A
Most of the emerging database technologies are concerned with scaling to huge amounts of data and massive load.They do so by making data opaque and distribute elements based on key.
Most focus on scaling to large numbers
5
E D
CF
G B
A
Most of the emerging database technologies are concerned with scaling to huge amounts of data and massive load.They do so by making data opaque and distribute elements based on key.
Most focus on scaling to large numbers
Scaling to size vs. Scaling to complexity
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Size
Complexity
Key/Value stores
Bigtable clones
Document databases
Graph databases
Scaling to size vs. Scaling to complexity
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Size
Complexity
Key/Value stores
Bigtable clones
Document databases
Graph databases
> 90% of use cases
Billions of nodesand relationships
The Property Graph data model
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•Nodes•Relationships between Nodes•Relationships have Labels•Relationships are directed, but traversed at equal speed in both directions•The semantics of the direction is up to the application (LIVES WITH is reflexive, LOVES is not)•Nodes have key-value properties•Relationships have key-value properties
The Property Graph data model
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•Nodes•Relationships between Nodes•Relationships have Labels•Relationships are directed, but traversed at equal speed in both directions•The semantics of the direction is up to the application (LIVES WITH is reflexive, LOVES is not)•Nodes have key-value properties•Relationships have key-value properties
The Property Graph data model
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•Nodes•Relationships between Nodes•Relationships have Labels•Relationships are directed, but traversed at equal speed in both directions•The semantics of the direction is up to the application (LIVES WITH is reflexive, LOVES is not)•Nodes have key-value properties•Relationships have key-value properties
The Property Graph data model
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LIVES WITHLOVES
OWNSDRIVES
•Nodes•Relationships between Nodes•Relationships have Labels•Relationships are directed, but traversed at equal speed in both directions•The semantics of the direction is up to the application (LIVES WITH is reflexive, LOVES is not)•Nodes have key-value properties•Relationships have key-value properties
The Property Graph data model
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LIVES WITHLOVES
OWNSDRIVES
LOVES
•Nodes•Relationships between Nodes•Relationships have Labels•Relationships are directed, but traversed at equal speed in both directions•The semantics of the direction is up to the application (LIVES WITH is reflexive, LOVES is not)•Nodes have key-value properties•Relationships have key-value properties
The Property Graph data model
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LIVES WITHLOVES
OWNSDRIVES
LOVESname: “James”age: 32twitter: “@spam”
name: “Mary”age: 35
brand: “Volvo”model: “V70”
•Nodes•Relationships between Nodes•Relationships have Labels•Relationships are directed, but traversed at equal speed in both directions•The semantics of the direction is up to the application (LIVES WITH is reflexive, LOVES is not)•Nodes have key-value properties•Relationships have key-value properties
The Property Graph data model
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LIVES WITHLOVES
OWNSDRIVES
LOVESname: “James”age: 32twitter: “@spam”
name: “Mary”age: 35
brand: “Volvo”model: “V70”
item type: “car”
•Nodes•Relationships between Nodes•Relationships have Labels•Relationships are directed, but traversed at equal speed in both directions•The semantics of the direction is up to the application (LIVES WITH is reflexive, LOVES is not)•Nodes have key-value properties•Relationships have key-value properties
Graphs are whiteboard friendly
8Image credits: Tobias Ivarsson
An application domain model outlined on a whiteboard or piece of paper would be translated to an ER-diagram, then normalized to fit a Relational Database.With a Graph Database the model from the whiteboard is implemented directly.
Graphs are whiteboard friendly
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1
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1
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1*
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Image credits: Tobias Ivarsson
An application domain model outlined on a whiteboard or piece of paper would be translated to an ER-diagram, then normalized to fit a Relational Database.With a Graph Database the model from the whiteboard is implemented directly.
Graphs are whiteboard friendly
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thobe
Wardrobe Strength
Joe project blog
Image credits: Tobias Ivarsson
An application domain model outlined on a whiteboard or piece of paper would be translated to an ER-diagram, then normalized to fit a Relational Database.With a Graph Database the model from the whiteboard is implemented directly.
Hello Joe
Neo4j performance analysis
Modularizing Jython
What is Neo4j?๏Neo4j is a Graph Database
•Non-relational (“#nosql”), transactional (ACID), embedded
•Data is stored as a Graph / Network
‣Nodes and Relationships with properties
‣“Property Graph” or “edge-labeled multidigraph”
๏Neo4j is Open Source / Free (as in speech) Software
•AGPLv3
•Commercial (“dual license”) license available
‣Free (as in beer) for first server installation
‣Inexpensive (as in startup-friendly) when you grow 9
Prices are available at http://neotechnology.com/
Contact us if you have questions and/or special license needs (e.g. if you want an evaluation license)
More about Neo4j๏Neo4j is stable
• In 24/7 operation since 2003
๏Neo4j is in active development
•Neo Technology received VC funding October 2009
๏Neo4j delivers high performance graph operations
• traverses 1’000’000+ relationships / secondon commodity hardware
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http://neotechnology.com
Path exists in social network๏Each person has on average 50 friends
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Database # persons query timeRelational databaseNeo4j Graph DatabaseNeo4j Graph Database
1 000 2 000 ms1 000 2 ms
1 000 000 2 ms
Tobias
Emil
JohanPeter
Path exists in social network๏Each person has on average 50 friends
12
Database # persons query timeRelational databaseNeo4j Graph DatabaseNeo4j Graph Database
1 000 2 000 ms1 000 2 ms
1 000 000 2 ms
Tobias
Emil
JohanPeter
Path exists in social network๏Each person has on average 50 friends
12
Database # persons query timeRelational databaseNeo4j Graph DatabaseNeo4j Graph Database
1 000 2 000 ms1 000 2 ms
1 000 000 2 ms
Tobias
Emil
JohanPeter
Path exists in social network๏Each person has on average 50 friends
12
Database # persons query timeRelational databaseNeo4j Graph DatabaseNeo4j Graph Database
1 000 2 000 ms1 000 2 ms
1 000 000 2 ms
Tobias
Emil
JohanPeter
