Approaching graph db
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People like graphs. In nowadays they use facebook social graph search to find ex-girlfriend/boyfriends of their sweet hearts, or to search for a new love. Moreover - companies use graphs to evaluate ...

People like graphs. In nowadays they use facebook social graph search to find ex-girlfriend/boyfriends of their sweet hearts, or to search for a new love. Moreover - companies use graphs to evaluate the internal communication effectiveness or to design the enterprise network scheme. In all those tasks the simple questions arise - what type of data storage should be used to solve the problem in the most effective and easy? Graph databases!

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Approaching graph db Presentation Transcript

  • 1. Approaching Graph databases
  • 2. Sergey  Enin   So#ware  Engineering  Team  Leader   EPAM,  Minsk  
  • 3. AGENDA NOSQL   GRAPH   DBs   APP   NEO4J  
  • 4. NoSQL
  • 5. NoSQL Not Only SQL Using  specific  toolset  for  specific  problem   NoSQL  model  is  different  from  SQL  model   NoSQL  databases  is  a  special  purpose  Database    
  • 6. (No)SQL: CLASSIFICATION Database   Data  model   Strengths   Weaks   MySQL   RelaBonal   E-­‐R  data  model   Low  flexibility   Redis   Key-­‐value   High   performance   and  scalability   Low  funcKonality   MongoDB   Document   High   performance   Variable  flexibility   Cassandra   Column   High   performance   and  scalability   Low  funcKonality   Neo4j   Graph   High   High  complexity   performance  
  • 7. Graph  Databases   Graph Databases
  • 8. Graph Databases: HISTORY Leonhard Euler (1707 –1783) Swiss mathematician
  • 9. Graph Databases: HISTORY
  • 10. What is Graph?
  • 11. Graph Databases: GRAPH A   B   VerKce   (Node)   C   Edge   (RelaKonship)   D  
  • 12. Graph Databases: WHAT IS IT?
  • 13. Graph Databases: WHAT IS IT? Joins   VS   Traversals  
  • 14. Graph Databases: CHARACTERISTICS 1 Good for semistructured connected data Index freeadjacency 2 3 The underlying storage The processing engine 4
  • 15. APPs
  • 16. ApplicaBon:  social  graphs  
  • 17. Application: PAGE RANK
  • 18. APP: Collaborative filtering
  • 19. Neo4J: CHARACTERISTICS   true  ACID  transacKons;     scales  to  billions  of  nodes  and  relaKonships;     high  speed  querying  through  traversals;     declaraKve  graph  query  language;  
  • 20. Neo4J:   who   use   Neo4J: WHO USE it  
  • 21. Neo4J:   who   use   Neo4J: NETWORK ARCHITECTURE it   Neo4J:  network  architecture  
  • 22. Neo4J:  architecture   Neo4J: ARCHITECTURE
  • 23. Neo4J:  architecture   Neo4J: Internal storage Node   RelaKonship  
  • 24. Neo4J:  architecture   Neo4J: Internal storage
  • 25. Neo4J: CYPHER
  • 26. Neo4J: CYPHER START      usa=node:mb_fulltext(name="United  States"),    gb=node:mb_fulltext(name="United  Kingdom")   MATCH      (usa:Country),  (gb:Country),    (arKst:ArKst)-­‐[:FROM_AREA]-­‐(usa),    (arKst:ArKst)-­‐[:RECORDING_CONTRACT]-­‐(l:Label),    (label)-­‐[:FROM_AREA]-­‐(gb)   RETURN      arKst,label,usa,gb  
  • 27. Thank You Sergey Enin Software Engineering Team Leader Sergey_Enin@epam.com! tygrysminsk! sergeyenin! pankrat! sergeyenin.com/sec2014!