Introduction to graph databases GraphDays
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This document discusses the rise of graph databases and their advantages over traditional databases. It notes that many major websites and consumer-facing technologies are built around graphs that model relationships in data. Graph databases allow for easier modeling of complex networks and queries involving relationships. They also offer vastly superior performance at scale for highly connected datasets compared to SQL and NoSQL alternatives. Adoption of graph databases is growing rapidly as they transform industries and unlock new competitive advantages for businesses through their ability to leverage relationships in data.
![Key-Value
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UNLOCKING THE POTENTIAL
OF RELATIONSHIPS IN DATA](https://image.slidesharecdn.com/3hannfjnrpw3tvksiz9v-signature-fd7eec71328b804f816377f2d41352e0ad727cb468fe0f4fbbdfe84f728f6324-poli-141217104717-conversion-gate02/85/Introduction-to-graph-databases-GraphDays-8-320.jpg)
![The Property Graph Model
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THE PROPERTY GRAPH
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![The Property Graph Model
(:Person {name:"Ann"}) â[:LOVES]-> (:Person {name:"Dan"})
THE PROPERTY GRAPH
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![The Property Graph Model
(:Person {name:"Ann"}) â[:LOVES]-> (:Person {name:"Dan"})
THE PROPERTY GRAPH
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Node Relationship Node](https://image.slidesharecdn.com/3hannfjnrpw3tvksiz9v-signature-fd7eec71328b804f816377f2d41352e0ad727cb468fe0f4fbbdfe84f728f6324-poli-141217104717-conversion-gate02/85/Introduction-to-graph-databases-GraphDays-15-320.jpg)
![The Property Graph Model
(:Person {name:"Ann"}) â[:LOVES]-> (:Person {name:"Dan"})
THE PROPERTY GRAPH
MODEL
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Node Relationship Node
property propertylabel labeltype](https://image.slidesharecdn.com/3hannfjnrpw3tvksiz9v-signature-fd7eec71328b804f816377f2d41352e0ad727cb468fe0f4fbbdfe84f728f6324-poli-141217104717-conversion-gate02/85/Introduction-to-graph-databases-GraphDays-16-320.jpg)
![Cypher
Query: Whom does Ann love?
(:Person {name:"Ann"})â[:LOVES]->(whom)
CYPHER](https://image.slidesharecdn.com/3hannfjnrpw3tvksiz9v-signature-fd7eec71328b804f816377f2d41352e0ad727cb468fe0f4fbbdfe84f728f6324-poli-141217104717-conversion-gate02/85/Introduction-to-graph-databases-GraphDays-17-320.jpg)
![Cypher
Query: Whom does Ann love?
MATCH (:Person {name:"Ann"})â[:LOVES]->(whom)
CYPHER](https://image.slidesharecdn.com/3hannfjnrpw3tvksiz9v-signature-fd7eec71328b804f816377f2d41352e0ad727cb468fe0f4fbbdfe84f728f6324-poli-141217104717-conversion-gate02/85/Introduction-to-graph-databases-GraphDays-18-320.jpg)
![Cypher
Query: Whom does Ann love?
MATCH (:Person {name:"Ann"})â[:LOVES]->(whom)
RETURN whom
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![Under The Hood
MATCH (:Person {name:"Ann"})â[:LOVES]->(whom)RETURN whom
cypher
native graph processing
native storage
UNDER THE HOOD](https://image.slidesharecdn.com/3hannfjnrpw3tvksiz9v-signature-fd7eec71328b804f816377f2d41352e0ad727cb468fe0f4fbbdfe84f728f6324-poli-141217104717-conversion-gate02/85/Introduction-to-graph-databases-GraphDays-21-320.jpg)
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Introduction to graph databases GraphDays
- 1. Graph All The Things Introduction to Graph Databases Neo4j GraphDays 2014 Chicago Philip Rathle VP of Products, Neo4j @prathle #neo4j
- 4. C 34,3%B 38,4%A 3,3% D 3,8% 1,8% 1,8% 1,8% 1,8% 1,8% E 8,1% F 3,9% INDUSTRY TRENDS: GRAPHS TRANSFORMED CONSUMER WEB
- 5. Use of Relationship Information in The Consumer Web INDUSTRY TRENDS: GRAPHS TRANSFORMED CONSUMER WEB
- 6. Use of Relationship Information in The Consumer Web INDUSTRY TRENDS: GRAPHS TRANSFORMED CONSUMER WEB
- 7. Ref: http://www.gartner.com/id=2081316 Interest Graph Payment Graph Intent Graph Mobile Graph Consumer Web Giants Depends on Five Graphs Gartnerâs â5 Graphsâ Social Graph GARTNERâS 5 GRAPHS OF CONSUMER WEB: SUSTAINABLE COMPETITIVE DIFFERENTIATION COMES FROM MASTERING 5 GRAPHS
- 8. Key-Value Graph DB Riak Redis Neo4j membase 0x235C Philip 0xCD21 Neo4j Chicago 0x2014 [PPR,RB,NL] 0x3821 [CHI, SFO, BOS] 0x3890 B75DD108A Column FamilyName UID Members Groups Photo 0x235C Philip PPR CHI, SFO, BOS B75DD108A893A 0xCD21 Neo4j Chicago CHI PPR,RB, NL 218758D88E901 Cassandra HBase Document DB0x235C {name:Philip, UID: PPR, Groups: [CHI,SFO,BOS]} 0xCD21 {name:Neo4j Chicago, UID: PPR, Members:[PPR,RB,NL], where:{city:Chicago, State: IL}} MongoDB CouchDB NI name:Neo4j Chicago, UID: CHI, Photo: 218758D88E901 ABK name:Philip, UID: PPR, Photo: B75DD108A893A MEMBERsince: 2011 UNLOCKING THE POTENTIAL OF RELATIONSHIPS IN DATA
- 9. A GRAPH DATABASE IS PURPOSE-BUILT FOR: When your business depends on Relationships in Data
- 10. The Property Graph ModelTHE PROPERTY GRAPH MODEL
- 11. The Property Graph ModelTHE PROPERTY GRAPH MODEL LovesAnn Dan
- 12. The Property Graph Model Ann DanLoves THE PROPERTY GRAPH MODEL
- 13. The Property Graph Model (Ann) â[:LOVES]-> (Dan) THE PROPERTY GRAPH MODEL Ann DanLoves
- 14. The Property Graph Model (:Person {name:"Ann"}) â[:LOVES]-> (:Person {name:"Dan"}) THE PROPERTY GRAPH MODEL Ann DanLoves
- 15. The Property Graph Model (:Person {name:"Ann"}) â[:LOVES]-> (:Person {name:"Dan"}) THE PROPERTY GRAPH MODEL Ann DanLoves Node Relationship Node
- 16. The Property Graph Model (:Person {name:"Ann"}) â[:LOVES]-> (:Person {name:"Dan"}) THE PROPERTY GRAPH MODEL Ann DanLoves Node Relationship Node property propertylabel labeltype
- 17. Cypher Query: Whom does Ann love? (:Person {name:"Ann"})â[:LOVES]->(whom) CYPHER
- 18. Cypher Query: Whom does Ann love? MATCH (:Person {name:"Ann"})â[:LOVES]->(whom) CYPHER
- 19. Cypher Query: Whom does Ann love? MATCH (:Person {name:"Ann"})â[:LOVES]->(whom) RETURN whom CYPHER
- 20. CypherCYPHER
- 21. Under The Hood MATCH (:Person {name:"Ann"})â[:LOVES]->(whom)RETURN whom cypher native graph processing native storage UNDER THE HOOD
- 22. BUSINESS & PROJECT IMPACT
- 23. #1: EASIER TO UNDERSTANDâš COMPLEX MODELS âFind all sushi restaurants in NYC that my friends likeâ
- 24. âFind all direct reports and how many they manage, up to 3 levels downâ #2: EASIER TO EXPRESSâš COMPLEX QUERIES Example HR Query: MATCH  (boss)-Ââ[:MANAGES*0..3]-Ââ>(sub),        (sub)-Ââ[:MANAGES*1..3]-Ââ>(report)  WHERE  boss.name  =  âJohn  Doeâ  RETURN  sub.name  AS  Subordinate,  count(report)  AS  Total
- 25. (SELECT T.directReportees AS directReportees, sum(T.count) AS count FROM ( SELECT manager.pid AS directReportees, 0 AS count FROM person_reportee manager WHERE manager.pid = (SELECT id FROM person WHERE name = "fName lName") UNION SELECT manager.pid AS directReportees, count(manager.directly_manages) AS count FROM person_reportee manager WHERE manager.pid = (SELECT id FROM person WHERE name = "fName lName") GROUP BY directReportees UNION SELECT manager.pid AS directReportees, count(reportee.directly_manages) AS count FROM person_reportee manager JOIN person_reportee reportee ON manager.directly_manages = reportee.pid WHERE manager.pid = (SELECT id FROM person WHERE name = "fName lName") GROUP BY directReportees UNION SELECT manager.pid AS directReportees, count(L2Reportees.directly_manages) AS count FROM person_reportee manager JOIN person_reportee L1Reportees ON manager.directly_manages = L1Reportees.pid JOIN person_reportee L2Reportees ON L1Reportees.directly_manages = L2Reportees.pid WHERE manager.pid = (SELECT id FROM person WHERE name = "fName lName") GROUP BY directReportees ) AS T GROUP BY directReportees) UNION (SELECT T.directReportees AS directReportees, sum(T.count) AS count FROM ( SELECT manager.directly_manages AS directReportees, 0 AS count FROM person_reportee manager WHERE manager.pid = (SELECT id FROM person WHERE name = "fName lName") UNION SELECT reportee.pid AS directReportees, count(reportee.directly_manages) AS count FROM person_reportee manager JOIN person_reportee reportee ON manager.directly_manages = reportee.pid WHERE manager.pid = (SELECT id FROM person WHERE name = "fName lName") GROUP BY directReportees UNION (continued from previous page...) SELECT depth1Reportees.pid AS directReportees, count(depth2Reportees.directly_manages) AS count FROM person_reportee manager JOIN person_reportee L1Reportees ON manager.directly_manages = L1Reportees.pid JOIN person_reportee L2Reportees ON L1Reportees.directly_manages = L2Reportees.pid WHERE manager.pid = (SELECT id FROM person WHERE name = "fName lName") GROUP BY directReportees ) AS T GROUP BY directReportees) UNION (SELECT T.directReportees AS directReportees, sum(T.count) AS count FROM( SELECT reportee.directly_manages AS directReportees, 0 AS count FROM person_reportee manager JOIN person_reportee reportee ON manager.directly_manages = reportee.pid WHERE manager.pid = (SELECT id FROM person WHERE name = "fName lName") GROUP BY directReportees UNION SELECT L2Reportees.pid AS directReportees, count(L2Reportees.directly_manages) AS count FROM person_reportee manager JOIN person_reportee L1Reportees ON manager.directly_manages = L1Reportees.pid JOIN person_reportee L2Reportees ON L1Reportees.directly_manages = L2Reportees.pid WHERE manager.pid = (SELECT id FROM person WHERE name = "fName lName") GROUP BY directReportees ) AS T GROUP BY directReportees) UNION (SELECT L2Reportees.directly_manages AS directReportees, 0 AS count FROM person_reportee manager JOIN person_reportee L1Reportees ON manager.directly_manages = L1Reportees.pid JOIN person_reportee L2Reportees ON L1Reportees.directly_manages = L2Reportees.pid WHERE manager.pid = (SELECT id FROM person WHERE name = "fName lName") ) SAME QUERY IN SQL (!!)
- 27. RDBMS/Other vs. Native Graph Database Connectedness of Data Set ResponseTime RDBMS / Other NOSQL # Hops: 0-2 Degree: < 3 Size: Thousands Neo4j # Hops: Tens to Hundreds Degree: Thousands+ Size: Billions+ 1000x faster #3: PERFORMANCE
- 28. DATABASE # PEOPLE QUERY TIME (MS) MySQL 1,000 2,000 Neo4j 1,000 2 Neo4j 1,000,000 2
- 29. Business Impact: Move Faster The  whole  design,  development,  QA,  and  release  process  for  CruchBase  Events  was  a  total  of  2  weeks.â  âThe  ability  to  iterate  that  quickly  is  a  mammoth  step  up  for  us.   In  CrunchBase  1.0  (MySQL),  it  probably  would  have  taken  2  months.â  -Ââ  Kurt  Freytag,  CTO  CrunchBase
- 31. âOur  Neo4j  solution  is  literally  thousands  of  times  faster  than  the  prior  MySQL  solution,âš with  queries  that  require  10-Ââ100  times  less  code.â  -Ââ  Volker  Pacher,  Senior  Developer  eBay Business Impact: Run Faster
- 32. Neo Technology, Inc ConïŹdential Real-Time/ OLTP OfïŹine/ Batch Connected Queries Enable Real-Time Analytics
- 33. GRAPHS ARE TRANSFORMING THE WORLD Core industries âš & Use Cases WEB / ISV Financial Services Tele-communications Network &âš Data Center Management Master Data Management Social Geo ?
- 34. Core industries âš & Use Cases WEB / ISV Financial Services Telecommunications Health Care âš & Life Sciences Network &âš Data Center Management Master Data Management Social GEO Finance GRAPHS ARE TRANSFORMING THE WORLD
- 35. Neo Technology, Inc ConïŹdential Core industries âš & Use Cases WEB / ISV Financial Services Telecom- munications Health Care âš & Life Sciences Web Social,âš HR & Recruiting Media & Publishing Energy, Services, Automotive, Govât, Logistics, Education, Gaming, Other Network &âš Data Center Management Master Data Management Social GEO Recomm- endations Identity & Access Mgmt Search & Discovery BI, CRM, Impact Analysis, Fraud Detection, Resource Optimization, etc. Finance Neo4j Adoption Snapshot
- 36. GRAPH DATABASES - THE FASTEST GROWING DBMS CATEGORY Source: http://db-engines.com/en/ranking/graph+dbms!
- 37. 0% 10% 20% 30% 2011 2014 2017 25% 2.5% 0% %ofEnterprisesusingGraphDatabases âForrester estimates that over 25% of enterprises will be using graph databases by 2017â Sources âą Forrester TechRadarâą: Enterprise DBMS, Feb 13 2014 (http://www.forrester.com/TechRadar+Enterprise +DBMS+Q1+2014/fulltext/-/E-RES106801) âą Dataversity Mar 31 2014: âDeconstructing NoSQL:Analysis of a 2013 Survey on the Use, Production and Assessment of NoSQLTechnologies in the Enterpriseâ (http://www.dataversity.net) âą Neo Technology customer base in 2011 and 2014 âą Estimation of other graph vendorsâ customer base in 2011 and 2014 based on best available intelligence â25% of survey respondents said they plan to use Graph databases in the future.â Graph Databases: Powering The Enterprise GRAPH DATABASES - POWERING THE ENTERPRISE
- 38. Ref: Gartner, âIT Market Clock for Database Management Systems, 2014,â September 22, 2014 https://www.gartner.com/doc/2852717/it-market-clock-database-management âGraph analysis is possibly the single most effective competitive differentiator for organizations pursuing data- driven operations and decisions after the design of data capture.â Graph Databases: Can Transform Your Business GRAPH DATABASES - CAN TRANSFORM YOUR BUSINESS
- 39. Summary When your business depends on Relationships in Data SUMMARY