0221 Cypher for SQL Professionals

1. Cypher Query Language Michael Hunger @neo4j Sunday, February 24, 13

2. The plan • Why graphs? • How graphs? • Why Cypher? • How Cypher - Cypher & SQL side by side Sunday, February 24, 13

3. Why graphs, why now? Sunday, February 24, 13

4. Why graphs, why now? • Big Data is the trend 9 Y P ' L 97 N W Y P ' L0 7 N W T5K 0 S @ . ; T5K Q S @ . ; Q B B & ⛽ !2 ? ⛽ !2 ? # O- 3⛵ RC - 3⛵ J X %$ RC 8 X %$ "J 8⚾] " ⚽9 ! M DJ ⚾] [ [ ! , ⚽9 E > 1 I 7+ O , > E > U A IR 7 + O > 1 WA W# : 34 S ✈ ✈ R34 S : X T ' 5U L < K T '. I 50 L < H V ☕" I 0 &N . ⛽ K - ⛵ & N A - G☕" ⛽ ⛵ M UA ZG [< % BQ $ ,: 1 MP F ⚾ [< $ ,: 8 ; % BQ ☕4 ✈ F H Z GV 1⚾P ]8 ; F ☕F ✈ 4 H Z GV ] 2 ? E E⚽ @Y ⚽ @Y 2 ? + CD + CD Sunday, February 24, 13

5. Why graphs, why now? • Big Data is the trend P ' L 97 N W Y • NOSQL is the answer T5K 0 S @ . ; Q & ⛽ B O- 3⛵ !2 ? # X %$ J RC 8⚾] " , ⚽9 ! > [ D 1 M UJ I 7 + O >E X # :R34 5 ✈ S H V ☕⛵ G " I 0L < ⛽ U& N A [< Z ☕9 1MP F ⚾ 8 ; $ , 9 Y P ' L 97 N W P '✈ 47 Z GV ] 2 Y F Y L0 H N W @ P ' L0 7 N W 0 ⚽ T 5 K Q S @ .T 5 K + @ . ; Y ? T5K Q S @ . ; B & ⛽ B & ⛽; Q S CD B ⛽ !2 ? # O- 3⛵ !2 ?3⛵ O- !2 ? - 3⛵ J RC 8 J R X %$ # 8 C " X %$ J RC X %$ " ⚽ ! > [ ! 8 1⚾] " , ⚽9 ! M DJ ⚾] [ [ , M9 J ⚾] > E > U A IR 7 + O U E IR A + O > 1 > D 1 W7 , ⚽9 E > I 7+ O W# : 34 S ✈ ✈ 34 S WA ✈ R34 S ' ' 5U L # :" TI 5U L < K T : X T I 50 L < H V ☕" I 0 H V[☕⛵ - . ⛽ K - ⛵ &X < K . 0 N &N -. &N A ⛽ A G☕" ⛽ ⛵ M UA ZG [< % BQ $ ,: 1MP ⚾F ZG; % BM P ]8 [ < % B 8✈ <$ ,: 1QF ⚾ $ , ;: ☕4 ✈ F H Z GV 1⚾P ]8 ; F ☕F ☕F ✈ 4 H Z GV ] 2 4 HE GV ? E ⚽ @Y 2 ? E⚽ CD @Y ⚽ Z C @Y 2 ? + CD Sunday, February 24, 13 + + D

6. T5K Q S @ & ⛽ ; !2 ? & ⛽ B # O- 3⛵ RC O- 3⛵ !2 ? X %$ J # X %$ M DJ ⚾] " , ⚽9 ! > J RC8 [ P ' L 978N] " , ⚽9 ! > IR 7 + ' > E Y WA Y⚾ W 1 [ 9 Y 1 ' > E 7 : W 4 S O L 97 N T ' U ✈ M UJ I . +P D T 5 K 0 S @34; T O L0 # N@' I 5U L 0 7S 3 WA . T &P W Q # :RB 5 5 KX VS T B ; 0 K Q S @. ; Why graphs, why now? ✈ " N 5 < K -. & ⛽ H V X ! I 0 L H<ZG☕⛵ &⚾⛽A 8 [ < % B Q "2 & U ? ⛽ ⛽ RO A & N Q K ⛽ . M - 2 OP ] F $ ,: B ; ! # O- 3⛵ J ZG☕⛵ 1⚾P 3⛵ [ <☕4! QR C 3⛵ X %$ M - 8 ; ⚽FB # ?- C F ,: ✈ 1 $ % H Z GV 2 ? E RC 8 ⚾] " ✈ ⚽9 !#> ⚾ ]" 2 %$[ ! 8 D Y 2 $[ ! , ☕4[ F Z GV > D 1@8 XJ ? ⚽E ⚽ H E J I Y , +9 DJC ] ⚾ @" J ?⚽ X % U ✈ R 7 + O U CWA 7 + OM U 1 A 7 + O 9 E > DJ I 1 S + M ] D ' >E > I , > # : 34 5 R ✈ T 34 S ✈ W3' S R 4 W X V ☕⛵ G " I 0 L X # :" I 50 L X<#K" . I 50 L < K T ⛽ U& N < K - A H[V %⛵ . & H V N :T &N Z ☕ MP F ⚾ $G☕⛽ Q M U , : 9 Y ; LB < A ⛽ ⛵- G9 Y M P [ < % B Q⚾ :☕ U A : - [< % ✈BigH Z GV the trend F 7 1⚾W P8 Z$ F N W 9 ]8 ; F 1 ]8PZ ☕ 0 • ⚽4 Data is @ T 5 ⚽4 H Z GV. ' 2✈ E ? , N P ] ' L☕ 7 1 F F ✈ ; $ , + 9 Y D Y C @Y; 2 04 P ' G. 7 N 2 E K Q S @ B T 5 K? S Z LV @Y W ⚽ H @ Y ? P ' L0 7 is the answer • NOSQL N@ . - 3⛵X & W O⛽ + C& ? D !2 ⛽ + Q T5 B D 0 C ; @ . K Q? S T5K Q S # J %$[ ! # O- 3⛵ & ⛽ !2R C RC X B ; B8; " ⚾] J %$[ ⚽9 8 > "# O- 3⛵ ! !2 ? & •⛽ Big X M !2in complexity DJ ⚾] WA , ⚽9 J >$[ !R C 1 DJ ? O- 3⛵in size and R C 7 + M U E I I U ✈ R , O > 1 X 8 O > E % WA " # J %$[ ! : 34 S 7 +⚾] 5U L # :KM4' ✈ R 7 + O . E , ⚽9' > 8 ⚾] " , ⚽9 # > X < ✈ R3 UJ I TD S 1 T> DJ I 1 U ✈ R 7 + O ZG H > E☕" WA M 0 H V[ :% . #0 L34 5K - V ⛵ I &X G$⛽ - 5U ⛽ N A ⛵ XIQ & " B : N <S #: 34 5 S ☕4 T 'F Z , ;< M A 1⚾P ]8 ☕ H 1⚾P " 8 [ <& B Q ☕ F ☕⛵ I ;U% :L < $ N ,A 0 I 0 L < F H- GV @ ✈ ? H EV V ⛽ & N ✈ K Z ⚽ . 24F ZG G ] MP E [ Z ☕9 $ , V ☕" 9 M P G ⛽ ⛵ UA [< BQ 9 Y ,:P $ % ' CD + ⚽ @Y W FY 1⚾ ✈ D H Z GV F 2? ]8 ; < Z ' L 1⚾ W ]8 ; ☕4 7 F P✈ F 0 Z GV Y L0 7 N W ' C 0 7 N + P L4 ⚽ @Y 2 ? N ? E . C. T 5 K S C @Y;2 T 5 K Q S @ B T 5 K + S @ B ; ⚽ H + Q @B . ; Q D D & ⛽ !& ⛽3⛵ ? !2 ? ⛽ !2 ? # O- 3⛵ 2R C O- RC - 3⛵ J X %$ RC 8 X %$ # 8 "J X %$ J 8⚾] " ⚽9 ! M DJ ⚾] [ ⚽9 ! ⚾ " , ⚽9 ! > [ , M UJ I > [ 1 I 7+ O , > E > U A IR 7 + O D E ]WA + O > E 1 > 1 ✈ R34 S7 WA ✈ R34 S W# : 34 S ✈ ' : X T ' 5U L # :" TI 5U L < K T &X < K . 0 . I 50 L < H V ☕" I 0 H V[☕⛵ - & N . ⛽ K - ⛵ N A ⛽ & N A - G☕" ⛽ ⛵ M UA ZG [< % BQ $ ,: 1MP F ⚾ ZG; % BM P ]8 [ < % B 8✈ <$ ,: 1⚾ QF $ , ;: ☕4 ✈ F H Z GV 1⚾P ]8 ; F ☕F ☕F ✈ 4 H Z GV ] 2 4 HE GV ? E ⚽ @Y 2 ? E⚽ CD @Y ⚽ Z C @Y 2 ? + CD Sunday, February 24, 13 + + D

7. A graph? Yes, a graph Sunday, February 24, 13

8. A graph database... • Mathematical graphs • remember linked lists, trees? • the general-purpose data structure • “A relational database may tell you the average age of everyone in this room, but a graph database will tell you who is most likely to buy you a beer.” Sunday, February 24, 13

9. Some well-known graphs Sunday, February 24, 13

10. Some well-known graphs diamond Sunday, February 24, 13

11. Some well-known graphs diamond butterﬂy Sunday, February 24, 13

12. Some well-known graphs diamond butterﬂy star Sunday, February 24, 13

13. Some well-known graphs diamond butterﬂy star bull Sunday, February 24, 13

14. Some well-known graphs diamond butterﬂy star bull franklin Sunday, February 24, 13

15. Some well-known graphs diamond butterﬂy star bull franklin robertson Sunday, February 24, 13

16. Some well-known graphs diamond butterﬂy star bull franklin robertson horton Sunday, February 24, 13

17. Some well-known graphs diamond butterﬂy star bull franklin robertson horton neo4j Sunday, February 24, 13

18. Sunday, February 24, 13

19. Graphs are everywhere Sunday, February 24, 13

20. The property graph model Sunday, February 24, 13

23. The property graph model :LOVES :LOVES :KNOWS :KNOWS :KNOWS Sunday, February 24, 13

24. The property graph model name: “A” age: 35 :LOVES name: “B” age: 35 :LOVES since: 2012 :KNOWS :KNOWS since: 2008 name: “D” age: 35 name: “C” :KNOWS age: 35 Sunday, February 24, 13

26. You know relational Sunday, February 24, 13

27. You know relational Sunday, February 24, 13

28. You know relational foo Sunday, February 24, 13

29. You know relational foo bar Sunday, February 24, 13

30. You know relational foo foo_bar bar Sunday, February 24, 13

34. You know relational now consider relationships... Sunday, February 24, 13

40. Neo4j is a graph database • Open Source • High Availability • Backed by Neo Technology • In production for 10+ years Sunday, February 24, 13

41. Why Cypher? • Not only scale of size is hard • Scale of complexity is pretty hard as well Sunday, February 24, 13

42. Cypher attributes Sunday, February 24, 13

43. Cypher attributes #1 Declarative Sunday, February 24, 13

44. Cypher attributes #1 Declarative You tell Cypher what you want, not how to get it Sunday, February 24, 13

46. Cypher attributes #2 Expressive Sunday, February 24, 13

47. Cypher attributes #2 Expressive Optimize syntax for reading Sunday, February 24, 13

49. Cypher attributes #3 Pattern Matching Sunday, February 24, 13

50. Cypher attributes #3 Pattern Matching Patterns are easy for your brain Sunday, February 24, 13

52. Cypher attributes #4 Idempotent Sunday, February 24, 13

53. Cypher attributes #4 Idempotent State change should be expressed idempotently Sunday, February 24, 13

54. 2 Databases 5 Queries Sunday, February 24, 13

57. Q1 Give me all persons older than 30 years Sunday, February 24, 13

58. Q1 START n=node:Person("name:*") WHERE n.age>30 RETURN n VS SELECT * FROM Person WHERE age > 30 Sunday, February 24, 13

62. Q2 Show me all actors, and which movies they starred in Sunday, February 24, 13

63. Neo4j is whiteboard friendly Sunday, February 24, 13

64. Neo4j is whiteboard friendly (actor)-[:ACTS_IN]->(movie) Sunday, February 24, 13

65. Neo4j is whiteboard friendly (actor)-[:ACTS_IN]->(movie) Sunday, February 24, 13

66. Q2 START actor=node:Person("name:*") MATCH (actor)-[:ACTS_IN]->(movie) RETURN actor.name, movie.title VS SELECT Person.name, Movie.title FROM Person JOIN Actor on Person.person_id = Actor.person_id JOIN Movie on Movie.movie_id = Actor.movie_id Sunday, February 24, 13

72. Q3 Show me all persons that are both actors and directors Sunday, February 24, 13

73. Q3 START person=node:Person("name:*") WHERE (person)-[:ACTS_IN]->() AND (person)-[:DIRECTED]->() RETURN person.name VS SELECT name FROM Person WHERE person_id IN ( SELECT person_id FROM Actor) AND person_id IN ( SELECT person_id FROM Director) Sunday, February 24, 13

78. Q4 Show me all Lucy’s co-actors Sunday, February 24, 13

79. Q4 START lucy=node:Person(name="Lucy Liu") MATCH lucy-[:ACTS_IN]->movie, co_actor-[:ACTS_IN]->movie RETURN DISTINCT co_actor.name VS SELECT DISTINCT co_actor.name FROM Person lucy JOIN Actor a1 ON lucy.person_id = a1.person_id JOIN Actor a2 ON a1.movie_id = a2.movie_id JOIN Person co_actor ON co_actor.person.id = a2.person_id WHERE lucy.name = "Lucy Liu" Sunday, February 24, 13

84. Q4 START lucy=node:Person(name="Lucy Liu" MATCH lucy-[:ACTS_IN]->movie, co_actor-[:ACTS_IN]->movie RETURN DISTINCT co_actor.name; VS SELECT DISTINCT co_actor.name FROM Person lucy JOIN Actor a1 ON lucy.person_id = a1.person_id JOIN Actor a2 ON a1.movie_id = a2.movie_id JOIN Person co_actor ON co_actor.person.id = a2.person_id WHERE lucy.name = "Lucy Liu" Sunday, February 24, 13

85. Q5 Show me Lucy’s favorite directors Sunday, February 24, 13

86. Q5 START lucy=node:Person(name="Lucy Liu") MATCH lucy-[:ACTS_IN]->movie, director-[:DIRECTED]->movie RETURN director.name, count(*) ORDER BY count(*) desc VS SELECT dir.name, count(*) FROM Person lucy JOIN Actor on Person.person_id = Actor.person_id JOIN Director on Actor.movie_id = Director.movie_id JOIN Person dir on Director.person.id = dir.person_id WHERE lucy.name = "Lucy Liu" GROUP BY dir.name ORDER BY count(*) DESC Sunday, February 24, 13

94. ...fasten your seat belt Dorothy, 'cause Kansas is going bye-bye Sunday, February 24, 13

95. Q4’ Show me all Lucy’s co-actors Sunday, February 24, 13

96. Q4 START lucy=node:Person(name="Lucy Liu") MATCH lucy-[:ACTS_IN]->movie, co_actor-[:ACTS_IN]->movie RETURN DISTINCT co_actor.name Sunday, February 24, 13

97. Q4’ START lucy=node:Person(name="Lucy Liu") MATCH lucy-[:ACTS_IN*2..2]-co_actor RETURN DISTINCT co_actor.name Sunday, February 24, 13

98. Q4’ START lucy=node:Person(name="Lucy Liu") MATCH lucy-[:ACTS_IN*2..2]-co_actor RETURN DISTINCT co_actor.name Sunday, February 24, 13

99. Q6 Bacon Lucy Sunday, February 24, 13

100. START lucy=node:Person(name="Lucy Liu"), kevin=node:Person(name="Kevin Bacon") MATCH p = shortestPath( lucy-[:ACTS_IN*]-kevin ) RETURN EXTRACT(n in NODES(p) : COALESCE(n.name?, n.title?)) Sunday, February 24, 13

103. Q6 START lucy=node:Person(name="Lucy Liu"), kevin=node:Person(name="Kevin Bacon") MATCH p = shortestPath( lucy-[:ACTS_IN*]-kevin ) RETURN EXTRACT(n in NODES(p) : COALESCE(n.name?, n.title?)) Sunday, February 24, 13

107. Create Cypher 2 (the movie) Sunday, February 24, 13

108. START lucy = node:Person(name="Lucy Liu"), david = node:Person(name="David Lynch") CREATE andres={name:"Andrés Taylor"}, cypher2={title:"Cypher 2"}, lucy-[:ACTS_IN {name:"Rita Foster"}]->cypher2, andres-[:ACTS_IN {name:"Manuel"}]->cypher2, david-[:DIRECTED]->cypher2 RETURN cypher2 Sunday, February 24, 13

114. Add genre nodes Sunday, February 24, 13

115. Person :DIRECTED :ACTS_IN Movie Genre :GENRE :IS_GENRE Root Sunday, February 24, 13

116. Movie Movie title:"Highlander" title:"The Matrix" genre: "Action" genre: "Action" :GENRE :GENRE Genre name: "Action" :IS_GENRE Root Sunday, February 24, 13

117. START ref=node(0), movie=node:Movie("title:*") WHERE HAS(movie.genre) CREATE UNIQUE ref<-[:IS_GENRE]->(genre {name:movie.genre}) genre-[:GENRE]->movie Sunday, February 24, 13

121. Rehash Sunday, February 24, 13

122. START designates the start points Sunday, February 24, 13

123. MATCH describes the pattern Sunday, February 24, 13

124. WHERE ﬁlters the result set Sunday, February 24, 13

125. CREATE creates nodes and relationships Sunday, February 24, 13

126. CREATE UNIQUE creates unique patterns Sunday, February 24, 13

127. RETURN creates the result table Sunday, February 24, 13

128. QUESTION ALL THE THINGS! Sunday, February 24, 13

0221 Cypher for SQL Professionals

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