Bcn On Rails May2010 On Graph Databases

On Graph Databases

Pere Urbón Bayes
purbon@purbon.com

May of 2010

BcnOnRails May - 2010 - On Graph Databases 1

On Graph Databases

● NoSQL movement.
● Graph databases.
● Pros and cons.
● Use cases.
● Technology overview.
● Example.


NoSQL Movement

● Next Generation of Databases.
● Innovative. (?)
● Open Source. (?)
● Non-Relational.
● Schema-less.
● Distributed.
● Scalable.


NoSQL Movement

● Stores. ● More Stores.
– Document. – Grid database.
– Key/Value. – XML Database.
– Object oriented. – RDF.
– Column. – .....
– Graph database.


NoSQL Movement

● NoSQL is not the holy grail, never forget it.
● Precursors & roots begun at the early 70's.
– Network databases, Charles Bachman 1969.

案ずるより産むが易し。
– Giving birth to a baby is easier than worrying about it.


Graph Databases

● Data strongly related.
– Social networks.
– GIS Systems.
– Transportation.
– Bibliographic.
– File systems.
– ........

GitHub Ruby community by country


Graph Databases

● The Property Graph.
– Labeled.
– Directed.
– Attributed.
– Multigraph.
● Talk about.
– Nodes with types.
– Edges with types.
– Attributes.

Graph Databases

● Graph storage.
– Adjacency Matrix.
– Adjacency List.
– Incidence Matrix.
– Incidence List.
● GraphDB's.
– Bitmaps.
– B+Trees.
– RB Trees.

Graph Databases

Query MySQL OIM DEX

Q1:count 20,38 17,35 0

RDBMS OIM DEX Q2:scan 32,76 174,64 3,14

data 27.36 GB 54 GB 9.69 GB Q3:select 7,34 5,43 0,84

Q4:projection 17,34 43,7 33,19
ratio 10,9 21,51 3,86
overhead Q5:combine 0,74 2,61 0,01
load time 52891 s 17543 s 95579 s
Q6:explode 0,07 202,07 0,01

Q7:values 12,28 20,77 0,01

Q8:hub >3hours >3hours 624,68


Graph Databases


Use cases

● Network analysis.
● Link analysis.
● Graph mining.
● Neural networks.
● Bibliographic search.
● Semantic web.


Use cases

● Algorithmic recruitment with GitHub.
– Centrality: The importance of a vertex within a
graph.
● Betweens: Vertex that occur on many shortest
path have higher centrality.
– O(v^3) without any optimization.

● Another possible choices:
– Closeness: Vertex with a short geodesic distance
to other ones have a high closeness.
● Usually preferred on network analysis.


Graph Databases

● Shortest Paths. ● Centrality.
– BFS/DFS. – Betweenness.
– Dijkstra. – Closeness.
– Floyd-Warshall. – Diameter.
– Ford. – Radius.
● Connectivity. ● Traversals.
– Strongly connected. – BFS/DFS.
– Weakly connected. ● Communities.
● Staining.

Pros and cons

● Data facts. ● Relational model facts.
– Growths – E.F Codd model.
exponentially. – Normalization.
– Hugh – Object-Relational
interdependency impedance
and complexity. mismatch.
– Relationships are – Join's doesn't scale.
important.
– Big tables.
– Structure change
over time. – Denormalization.


Technology overview

● Neo4J: Open source database NoSQL graph.
● Dex: The high performance graph database.
● HyperGraphDB: An IA and semantic web
graph database.
● Infogrid: The Internet Graph database.
● Sones: SaaS dot Net Graph database.
● VertexDB: High performance database server.


Benchmarking

Kernel Scale 15 DEX Neo4j Jena HypergraphDB
K1 Load (s) 7,44 697 141 +24h

K2 Scan edges (s) 0,0010 2,71 0,689
K3 2-hops (s) 0,0120 0,0260 0,443
Kernel DEX Neo4j Jena Hypergr
K4 BC (s) 14,8 8,24 138 aphDB
Scale 20
Db size (MB) 30 17 207 K1 Load (s) 317 32.094 4.560 +24h

K2 Scan 0,005 751 18,6
Graph Database Performance on the edges (s)
HPC Scalable Graph Analysis Benchmark K3 2-hops (s) 0,033 0,0230 0,4580
K4 BC (s) 617 7.027 59.512

Db size (MB) 893 539 6.656


Technology overview


Technology overview
● Neo4J.rb ( JRuby target )
– Active record integration.
– Dynamic and schema free.
– Fast traversal of relationships.
– Transactions with rollbacks support.
– Indexing and querying of ruby objects.
– Massive loaders.
http://wiki.neo4j.org/content/Ruby
– Ruby on Rails integration.
– Accessible throw REST.


Technology overview
Creating nodes Properties
require "rubygems" node = Neo4j::Node.new
require 'neo4j' node[:name] = 'foo'
node[:age] = 123
Neo4j::Transaction.run do node[:hungry] = false
node = Neo4j::Node.new node[4] = 3.14
end node[:age] # => 123

Transactions over blocks Creating relationships

Neo4j::Transaction.run do node1 = Neo4j::Node.new
# neo4j operations goes here node2 = Neo4j::Node.new
end Neo4j::Relationship.new(:friends, node1, node2)

# which is same as
node1.rels.outgoing(:friends) << node2


Technology overview
Accessing relationships
node1.rels.empty? # => false

# The rels method returns an enumeration of relationship objects.
# The nodes method on the relationships returns the nodes instead.
node1.rels.nodes.include?(node2) # => true

node1.rels.first # => the first relationship this node1 has.
node1.rels.nodes.first # => node2 first node of any relationship type
node2.rels.incoming(:friends).nodes.first # => node1 first node of relationship type 'friends'
node2.rels.incoming(:friends).first # => a relationship object between node1 and node2

Properties on Relationships
rel = node1.rels.outgoing(:friends).first

rel[:since] = 1982
node1.rels.first[:since] # => 1982


Example

For the joy of someone, lets play a little with a
graph database.


On Graph Databases

Thanks you!
Pere Urbón Bayes
purbon@purbon.com


Bcn On Rails May2010 On Graph Databases

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