Bcn On Rails May2010 On Graph Databases
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Bcn On Rails May2010 On Graph Databases

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Short introduction to graph databases at Bcn On Rails May 2010.

Short introduction to graph databases at Bcn On Rails May 2010.

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Bcn On Rails May2010 On Graph Databases Bcn On Rails May2010 On Graph Databases Presentation Transcript

  • 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. BcnOnRails May - 2010 - On Graph Databases 2
  • NoSQL Movement ● Next Generation of Databases. ● Innovative. (?) ● Open Source. (?) ● Non-Relational. ● Schema-less. ● Distributed. ● Scalable. BcnOnRails May - 2010 - On Graph Databases 3 View slide
  • NoSQL Movement ● Stores. ● More Stores. – Document. – Grid database. – Key/Value. – XML Database. – Object oriented. – RDF. – Column. – ..... – Graph database. BcnOnRails May - 2010 - On Graph Databases 4 View slide
  • 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. BcnOnRails May - 2010 - On Graph Databases 5
  • Graph Databases ● Data strongly related. – Social networks. – GIS Systems. – Transportation. – Bibliographic. – File systems. – ........ GitHub Ruby community by country BcnOnRails May - 2010 - On Graph Databases 6
  • Graph Databases ● The Property Graph. – Labeled. – Directed. – Attributed. – Multigraph. ● Talk about. – Nodes with types. – Edges with types. – Attributes. BcnOnRails May - 2010 - On Graph Databases 7
  • Graph Databases ● Graph storage. – Adjacency Matrix. – Adjacency List. – Incidence Matrix. – Incidence List. ● GraphDB's. – Bitmaps. – B+Trees. – RB Trees. BcnOnRails May - 2010 - On Graph Databases 8
  • 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 BcnOnRails May - 2010 - On Graph Databases 9
  • Graph Databases BcnOnRails May - 2010 - On Graph Databases 10
  • Use cases ● Network analysis. ● Link analysis. ● Graph mining. ● Neural networks. ● Bibliographic search. ● Semantic web. BcnOnRails May - 2010 - On Graph Databases 11
  • 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. BcnOnRails May - 2010 - On Graph Databases 12
  • 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. BcnOnRails May - 2010 - On Graph Databases 13
  • 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. BcnOnRails May - 2010 - On Graph Databases 14
  • 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. BcnOnRails May - 2010 - On Graph Databases 15
  • 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 BcnOnRails May - 2010 - On Graph Databases 16
  • Technology overview BcnOnRails May - 2010 - On Graph Databases 17
  • 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. BcnOnRails May - 2010 - On Graph Databases 18
  • 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 BcnOnRails May - 2010 - On Graph Databases 19
  • 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 BcnOnRails May - 2010 - On Graph Databases 20
  • Example For the joy of someone, lets play a little with a graph database. BcnOnRails May - 2010 - On Graph Databases 21
  • On Graph Databases Thanks you! Pere Urbón Bayes purbon@purbon.com BcnOnRails May - 2010 - On Graph Databases 22