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Github in a Graph
- 1. Github in a Graph with Neo4j Andreas Kollegger @akollegger #neo4j 1
- 2. 1
- 3. How we'll get the Octocat into the Graph 1.Intro to Graphs, Socially 2.Graph Database Essentials 3.Github as a Graph 4.Workshop! 2
- 4. Graphs... 3
- 5. Meet Emil...
- 6. Meet Emil... Emil
- 7. Meet Emil... ๏ from: Sweden Emil
- 8. Meet Emil... ๏ from: Sweden ๏ appearance: blonde and handsome Emil
- 9. Meet Emil... ๏ from: Sweden ๏ appearance: blonde and handsome ๏ personality: super friendly nerd Emil
- 10. Meet Emil... ๏ from: Sweden ๏ appearance: blonde and handsome ๏ personality: super friendly nerd ๏ interests: piano, pickled herring Emil
- 11. Emil has some friends Emil
- 12. Emil has some friends ๏ Kerstin: his sister Emil Kerstin
- 13. Emil has some friends ๏ Kerstin: his sister ๏ Peter: his buddy Peter Emil Kerstin
- 14. Emil has some friends ๏ Kerstin: his sister ๏ Peter: his buddy ๏ Andreas: me! Peter Emil Andreas Kerstin
- 15. His friends introduced more friends Peter Emil Andreas Kerstin
- 16. His friends introduced more friends ๏ Michael: master hacker Michael Peter Emil Andreas Kerstin
- 17. His friends introduced more friends ๏ Michael: master hacker ๏ Johan: technology sage Michael Johan Peter Emil Andreas Kerstin
- 18. His friends introduced more friends ๏ Michael: master hacker ๏ Johan: technology sage ๏ Madelene: polyglot journalist Michael Johan Peter Emil Andreas Madelene Kerstin
- 19. His friends introduced more friends ๏ Michael: master hacker ๏ Johan: technology sage ๏ Madelene: polyglot journalist ๏ Allison: marketing maven Allison Michael Johan Peter Emil Andreas Madelene Kerstin
- 20. So, we have a bunch of people Allison Michael Johan Peter Emil Andreas Madelene Kerstin
- 21. So, we have a bunch of people ๏ how do we know they are friends? Allison Michael Johan Peter Emil Andreas Madelene Kerstin
- 22. So, we have a bunch of people ๏ how do we know they are friends? ๏ either ask each pair: are you friends? Allison Michael Johan Peter Emil Andreas Madelene Kerstin
- 23. So, we have a bunch of people ๏ how do we know they are friends? ๏ either ask each pair: are you friends? ๏ or, we can add explicit connections Allison Michael Johan Peter Emil Andreas Madelene Kerstin
- 24. So, we have a bunch of people ๏ how do we know they are friends? ๏ either ask each pair: are you friends? ๏ or, we can add explicit connections Allison Michael Johan Peter Emil Andreas Madelene Kerstin
- 25. There's a problem here Allison Michael Johan Peter Emil Andreas Madelene Kerstin
- 26. There's a problem here ๏ Emil is awesome Allison Michael Johan Peter Emil Andreas Madelene Kerstin
- 27. There's a problem here ๏ Emil is awesome ๏ What about other relationships? Allison Michael Johan Peter Emil Andreas Madelene Kerstin
- 28. There's a problem here ๏ Emil is awesome ๏ What about other relationships? ๏ We mentioned "introductions" so there must've been at least some pre-existing connections Allison Michael Johan Peter Emil Andreas Madelene Kerstin
- 29. There's a problem here ๏ Emil is awesome ๏ What about other relationships? ๏ We mentioned "introductions" so there must've been at least some pre-existing connections Allison Michael Johan Peter Emil Andreas Madelene Kerstin
- 30. This can continue... Allison Michael Johan Peter Emil Andreas Madelene Kerstin
- 31. This can continue... Allison Michael Johan Peter Emil Anna Andreas Adam Madelene Kerstin
- 32. This can continue... ๏ this is how social networks grow Allison Michael Johan Peter Emil Anna Andreas Adam Madelene Kerstin
- 33. This can continue... ๏ this is how social networks grow ๏ either meet people directly Allison Michael Johan Peter Emil Anna Andreas Adam Madelene Kerstin
- 34. This can continue... ๏ this is how social networks grow ๏ either meet people directly ๏ or be introduced Allison Michael Johan Peter Emil Anna Andreas Adam Madelene Kerstin
- 35. This can be useful Allison Michael Johan Peter Emil Anna Andreas Adam Madelene Kerstin
- 36. This can be useful ๏ professional recommendation (LinkedIn) Allison Michael Johan Peter Emil Anna Andreas Adam Madelene Kerstin
- 37. This can be useful ๏ professional recommendation (LinkedIn) ๏ product recommendation (Amazon) Allison Michael Johan Peter Emil Anna Andreas Adam Madelene Kerstin
- 38. This can be useful ๏ professional recommendation (LinkedIn) ๏ product recommendation (Amazon) ๏ restaurant recommendations (Yelp) Allison Michael Johan Peter Emil Anna Andreas Adam Madelene Kerstin
- 39. This can be useful ๏ professional recommendation (LinkedIn) ๏ product recommendation (Amazon) ๏ restaurant recommendations (Yelp) ๏ same domain, or reaching across domains Allison Michael Johan Peter Emil Anna Andreas Adam Madelene Kerstin
- 40. This is really just data Allison Michael Johan Peter Emil Anna Andreas Adam Madelene Kerstin
- 41. This is really just data ๏ it's just a graph Allison Michael Johan Peter Emil Anna Andreas Adam Madelene Kerstin
- 42. 12
- 43. A graph? 12
- 44. Yes, a graph... 13
- 45. Yes, a graph... ๏ you know the common data structures 13
- 46. Yes, a graph... ๏ you know the common data structures •linked lists, trees, object "graphs" 13
- 47. Yes, a graph... ๏ you know the common data structures •linked lists, trees, object "graphs" ๏ a graph is the general purpose data structure 13
- 48. Yes, a graph... ๏ you know the common data structures •linked lists, trees, object "graphs" ๏ a graph is the general purpose data structure •suitable for any connected data 13
- 49. Yes, a graph... ๏ you know the common data structures •linked lists, trees, object "graphs" ๏ a graph is the general purpose data structure •suitable for any connected data ๏ well-understood patterns and algorithms 13
- 50. Yes, a graph... ๏ you know the common data structures •linked lists, trees, object "graphs" ๏ a graph is the general purpose data structure •suitable for any connected data ๏ well-understood patterns and algorithms •studied since Leonard Euler's 7 Bridges (1736) 13
- 51. Yes, a graph... ๏ you know the common data structures •linked lists, trees, object "graphs" ๏ a graph is the general purpose data structure •suitable for any connected data ๏ well-understood patterns and algorithms •studied since Leonard Euler's 7 Bridges (1736) •Codd's Relational Model (1970) 13
- 52. Yes, a graph... ๏ you know the common data structures •linked lists, trees, object "graphs" ๏ a graph is the general purpose data structure •suitable for any connected data ๏ well-understood patterns and algorithms •studied since Leonard Euler's 7 Bridges (1736) •Codd's Relational Model (1970) •not a new idea, just an idea who's time is now 13
- 53. 14
- 54. Everyone is talking about graphs... 14
- 55. Everyone is talking about graphs... Facebook Open Graph 14
- 56. Everyone is talking about graphs... Facebook Open Graph 14
- 57. Emil's Graph
- 58. Emil's Graph Emil
- 59. Emil's Graph Peter Kerstin Emil Andreas
- 60. Emil's Graph Peter Johan Kerstin Michael Emil Madelene Andreas Allison
- 61. Emil's Graph Peter Johan star Kerstin Michael Emil Madelene Andreas Allison
- 62. Emil's Graph Peter Johan star Kerstin Michael Emil Madelene Andreas Allison
- 63. Emil's Graph Peter Johan star Kerstin Michael Emil Madelene Andreas Allison butterfly
- 64. Emil's Graph Peter Johan star Kerstin Michael Emil Madelene Andreas Allison butterfly
- 65. Emil's Graph Peter Johan diamond star Kerstin Michael Emil Madelene Andreas Allison butterfly
- 66. Emil's Graph Peter Johan diamond star Kerstin Michael Emil Madelene Andreas Allison Anna butterfly Adam
- 67. Emil's Graph Peter Johan diamond star Kerstin Michael Emil Madelene Andreas Allison Anna butterfly bull Adam
- 68. 16
- 69. Well known named graphs 16
- 70. Well known named graphs star 16
- 71. Well known named graphs star butterfly 16
- 72. Well known named graphs star butterfly diamond 16
- 73. Well known named graphs star butterfly diamond bull 16
- 74. Well known named graphs star butterfly diamond bull franklin 16
- 75. Well known named graphs star butterfly diamond bull franklin robertson 16
- 76. Well known named graphs star butterfly diamond bull franklin robertson horton 16
- 77. Well known named graphs star butterfly diamond bull franklin robertson horton hall-janko 16
- 78. 16
- 79. Text 17
- 80. The best known of all possible graphs... Text 17
- 81. 18
- 82. How can you use this? With a Graph Database 18
- 83. A graph database... 19
- 84. A graph database... ๏ optimized for the connections between records 19
- 85. A graph database... ๏ optimized for the connections between records ๏ really, really fast at querying across records 19
- 86. A graph database... ๏ optimized for the connections between records ๏ really, really fast at querying across records ๏ a database: transactional with the usual operations 19
- 87. A graph database... ๏ optimized for the connections between records ๏ really, really fast at querying across records ๏ a database: transactional with the usual operations ๏ “A relational database may tell you the average age of everyone at this workshop, but a graph database will tell you who is most likely to buy you a beer.” 19
- 88. 20
- 89. You know relational 20
- 90. You know relational 20
- 91. You know relational foo 20
- 92. You know relational foo bar 20
- 93. You know relational foo foo_bar bar 20
- 94. You know relational foo foo_bar bar 20
- 95. You know relational foo foo_bar bar 20
- 96. You know relational foo foo_bar bar 20
- 97. You know relational now consider relationships... 20
- 98. You know relational now consider relationships... 20
- 99. You know relational now consider relationships... 20
- 100. You know relational now consider relationships... 20
- 101. You know relational now consider relationships... 20
- 102. You know relational now consider relationships... 20
- 103. 20
- 104. 21
- 105. We're talking about a Property Graph 21
- 106. We're talking about a Property Graph Nodes 21
- 107. We're talking about a Property Graph Nodes Relationships 21
- 108. We're talking about a Property Graph Em Joh il a n knows knows Alli Tob Lar Nodes son ias knows s knows And And knows knows rea rés s knows knows knows Pet Miic Mc knows Ian er knows a a knows knows De Mic lia h ael Relationships Properties (each a key+value) + Indexes (for easy look-ups) 21
- 109. 21
- 110. 22
- 111. And, but, so how do you query this "graph" database? 22
- 112. 23
- 113. Cypher - a graph query language ๏ a pattern-matching query language ๏ declarative grammar with clauses (like SQL) ๏ aggregation, ordering, limits ๏ create, read, update, delete 23
- 114. Cypher - a graph query language ๏ a pattern-matching query language ๏ declarative grammar with clauses (like SQL) ๏ aggregation, ordering, limits ๏ create, read, update, delete // get node 1, traverse 2 steps away start a=node(1) match (a)--()--(c) return c // create a node with a 'name' property CREATE (me {name: 'Andreas'}) return me ๏ more on this later... 23
- 115. Cypher - pattern matching 24
- 116. Cypher - pattern matching 24
- 117. Cypher - pattern matching 24
- 118. Cypher - pattern matching 24
- 119. Cypher - pattern matching 24
- 120. Cypher - pattern matching 24
- 121. Cypher - pattern matching 24
- 122. Cypher - pattern matching syntax 25
- 123. Cypher - pattern matching syntax 25
- 124. Cypher - pattern matching syntax () --> () 25
- 125. Cypher - pattern matching syntax 26
- 126. Cypher - pattern matching syntax A B 26
- 127. Cypher - pattern matching syntax A B (A) --> (B) 26
- 128. Cypher - pattern matching syntax 27
- 129. Cypher - pattern matching syntax A B 27
- 130. Cypher - pattern matching syntax A B (A) -- (B) 27
- 131. Cypher - pattern matching syntax 28
- 132. Cypher - pattern matching syntax LOVES A B 28
- 133. Cypher - pattern matching syntax LOVES A B A -[:LOVES]-> B 28
- 134. Cypher - pattern matching syntax 29
- 135. Cypher - pattern matching syntax A B C 29
- 136. Cypher - pattern matching syntax A B C A --> B --> C 29
- 137. Cypher - pattern matching syntax 30
- 138. Cypher - pattern matching syntax A B C 30
- 139. Cypher - pattern matching syntax A B C A --> B --> C, A --> C 30
- 140. Cypher - pattern matching syntax A B C A --> B --> C, A --> C A --> B --> C <-- A 30
- 141. 31
- 142. Cypher - read clauses 31
- 143. Cypher - read clauses // get node 1, traverse 2 steps away START a=node(1) MATCH (a)--()--(c) RETURN c // get node from an index, return it START a=node:users(login='akollegger') RETURN a // get node from an index, match, filter // with where, then return results START a=node:users(login='akollegger') MATCH (a)-[r]-(b) WHERE b.login='jakewins' RETURN r,b 31
- 144. Cypher - CREATE a node A 32
- 145. Cypher - CREATE a node A // create a person named Andreas CREATE (a {name:'Andreas'}) 32
- 146. Cypher - CREATE relationships 33
- 147. Cypher - CREATE relationships LOVES A B 33
- 148. Cypher - CREATE relationships LOVES A B A -[:LOVES]-> B 33
- 149. Cypher - CREATE relationships LOVES A B A -[:LOVES]-> B // create love between two people START a=node:people(name='Andreas'), b=node:people(name='Anna') CREATE a-[:LOVES]->(b) 33
- 150. Cypher - CREATE UNIQUE data 34
- 151. Cypher - CREATE UNIQUE data LOVES A B 34
- 152. Cypher - CREATE UNIQUE data LOVES A B A -[:LOVES]-> B 34
- 153. Cypher - CREATE UNIQUE data LOVES A B A -[:LOVES]-> B // create love between two people START a=node:people(name='Andreas'), b=node:people(name='Anna') CREATE UNIQUE a-[:LOVES]->(b) 34
- 154. Cypher - CREATE full path 35
- 155. Cypher - CREATE full path LOVES A B 35
- 156. Cypher - CREATE full path LOVES A B A -[:LOVES]-> B 35
- 157. Cypher - CREATE full path LOVES A B A -[:LOVES]-> B // create an entire path at once CREATE p=(a {name:'Andreas'})-[:LOVES]-> (b {name:'Anna'}) return p 35
- 158. Cypher - update a Node A 36
- 159. Cypher - update a Node A // update the nerdiness of Andreas START a=node:people(name='Andreas') SET a.nerdiness = 10 36
- 160. Cypher - DELETE data 37
- 161. Cypher - DELETE data LOVES A B 37
- 162. Cypher - DELETE data LOVES A B A -[:LOVES]-> B 37
- 163. Cypher - DELETE data LOVES A B A -[:LOVES]-> B // Goodbye Anna! remove relationships START a=node:people(name='Anna') MATCH a-[r]-(b) DELETE a,r 37
- 164. Github as a Graph 38
- 165. Github - collaborative coding ๏ Hosting of git repositories ๏ Prominent social aspect • follow other coders • collaborate with other coders • branches and forks • watch repositories • star repositories 39
- 166. Github User Graph - simple User login: "octocat" id: 1 avatar_url: "https://github.com/images/error/octocat_happy.gif" gravatar_id: "somehexcode" url: "https://api.github.com/users/octocat" akollegger follows jakewins name: "monalisa octocat" company: "GitHub" blog: "https://github.com/blog" location: "San Francisco" email: "octocat@github.com" follows hireable: false follows bio: "There once was..." public_repos: 2 nawroth public_gists: 1 followers: 20 following: 0 html_url: "https://github.com/octocat" created_at: "2008-01-14T04:33:35Z" type: "User" details: http://developer.github.com 40
- 167. Github User Graph - complete akollegger follows jakewins follows follows nawroth details: http://developer.github.com 41
- 168. Github User Graph - complete akollegger follows jakewins follows follows nawroth details: http://developer.github.com 41
- 169. Github User Graph - complete heroku member of neo4j mattt follows member of akollegger follows jakewins owns follows follows FEC_GRAPH nawroth follows collaborates on jexp details: http://developer.github.com 41
- 170. Github User Graph - complete heroku member of neo4j mattt follows member of akollegger follows jakewins owns follows follows FEC_GRAPH nawroth follows collaborates on jexp details: http://developer.github.com 41
- 171. Github User Graph - complete heroku member of (user)-[:follows]->(users) neo4j mattt follows member of akollegger follows jakewins owns follows follows FEC_GRAPH nawroth follows collaborates on jexp details: http://developer.github.com 41
- 172. Github User Graph - complete heroku member of (user)-[:follows]->(users) neo4j mattt (user)-[:member_of]->(org) follows member of akollegger follows jakewins owns follows follows FEC_GRAPH nawroth follows collaborates on jexp details: http://developer.github.com 41
- 173. Github User Graph - complete heroku member of (user)-[:follows]->(users) neo4j mattt (user)-[:member_of]->(org) (user)-[:owns]->(repository) member of follows akollegger follows jakewins owns follows follows FEC_GRAPH nawroth follows collaborates on jexp details: http://developer.github.com 41
- 174. Github User Graph - complete heroku member of (user)-[:follows]->(users) neo4j mattt (user)-[:member_of]->(org) (user)-[:owns]->(repository) member of follows (user)-[:collaborates_on]->(repos) akollegger follows jakewins owns follows follows FEC_GRAPH nawroth follows collaborates on jexp details: http://developer.github.com 41
- 175. Workshop 42
- 176. Preparations ๏Get on Github ๏Get Neo4j 43
- 177. What would you like to do? A.Data Import B.Cypher Query Challenge C.Graph Visualization 44
- 178. Ready to Start? Go here... ๏http://graphhub-east.github.com ๏http://graphhub-west.github.com 45
- 179. 46
- 180. and, Thanks :) 46
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