Modelling Data in Neo4j (plus a few tips)
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Modelling Data in Neo4j, bidirectional relationships, qualifying relationships with properties vs. relationship types (performance comparison), Neo4j hardware sizing, Cypher vs. Java API
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Modelling Data in Neo4j (plus a few tips)
- 1. GraphAware TM by Michal Bachman plus a few best practices and lessons learned Modelling Data in Neo4j
- 6. GraphAware TM Quick intro 1x mistake 1x experiment Contents
- 7. GraphAware TM Quick intro 1x mistake 1x experiment 1x FAQ Contents
- 8. GraphAware TM Quick intro 1x mistake 1x experiment 1x FAQ 1x case-study Contents
- 10. GraphAware TM Larger Volumes Data Has Changed
- 11. GraphAware TM Larger Volumes Less Structured Data Has Changed
- 12. GraphAware TM Larger Volumes Less Structured More Interconnected Data Has Changed
- 13. GraphAware TM Larger Volumes Less Structured More Interconnected Polygot Persistence Data Has Changed
- 17. GraphAware TM Key-Value Stores Column-Family Stores Document Databases NoSQL
- 18. GraphAware TM Key-Value Stores Column-Family Stores Document Databases Graph Databases NoSQL
- 19. GraphAware TM The first three use aggregate data models, graph databases work with simple records and complex interconnections. Graph Databases
- 25. ipsum name: "Drama" type: "genre" name: "Triller" type: "genre" name: "Pulp Fiction" year: 1994 type: "movie" DIRECTED IS_OF_GENRE name: "Quentin Tarantino" type: "person" name: "Director" type: "occupation" name: "Actor" type: "occupation" IS_OF_GENRE ACTED_IN name: "Samuel L. Jackson" type: "person" IS_A IS_A IS_A ACTED_IN role: "Jules Winnfield" role: "Jimmie Dimmick" GraphAware TM Property Graph
- 26. name: "Drama" type: "genre" name: "Triller" type: "genre" name: "Pulp Fiction" year: 1994 type: "movie" DIRECTED IS_OF_GENRE name: "Quentin Tarantino" type: "person" name: "Director" type: "occupation" name: "Actor" type: "occupation" IS_OF_GENRE ACTED_IN name: "Samuel L. Jackson" type: "person" IS_A IS_A IS_A ACTED_IN role: "Jules Winnfield" role: "Jimmie Dimmick" GraphAware TM Traversal
- 27. GraphAware TM There is no single correct way. Modeling Data as Graphs
- 28. ipsum name: "Drama" type: "genre" name: "Triller" type: "genre" name: "Pulp Fiction" year: 1994 type: "movie" DIRECTED IS_OF_GENRE name: "Quentin Tarantino" type: "person" name: "Director" type: "occupation" name: "Actor" type: "occupation" IS_OF_GENRE ACTED_IN name: "Samuel L. Jackson" type: "person" IS_A IS_A IS_A ACTED_IN role: "Jules Winnfield" role: "Jimmie Dimmick" GraphAware TM One Way
- 29. GraphAware TM name: "Pulp Fiction" year: 1994 type: "movie" genres: "Drama", "Thriller" DIRECTED name: "Quentin Tarantino" type: "person" occupation: "Actor", "Director" ACTED_AS name: "Samuel L. Jackson" type: "person" occupation: "Actor" ACTED_AS name: "Jules Winnfield" type: "role" name: "Jimmie Dimmick" type: "role" CHARACTER_IN CHARACTER_IN Another Way
- 34. DEFEATED Czech Republic Sweden DEFEATED_BY GraphAware TM Ice HockeyIce Hockey (Implied Relationship)
- 35. GraphAware TM In Neo4j, the speed of traversal does not depend on the direction of the relationships being traversed. Traversals
- 37. Node Record in the Node Store (9 bytes), first bit = inUse flag Relationship Record in the Relationship Store (33 bytes), first bit = inUse flag, second bit unused next relationship (35 bits) next property (36 bits) first node (35 bits) second node (35 bits) type (16 bits) first node's previous relationship (35 bits) first node's next relationship (35 bits) second node's first relationship (35 bits) second node's next relationship (35 bits) next property (36 bits) GraphAware TM Neo4j Data Layout
- 38. PARTNERNeo Technology GraphAware PARTNERNeo Technology GraphAware GraphAware TM Company Partnership (Naturally Bidirectional)
- 39. PARTNER Neo Technology GraphAware PARTNER GraphAware TM Company Partnership (Naturally Bidirectional)
- 40. PARTNER Neo Technology GraphAware PARTNER GraphAware TM Company Partnership (Naturally Bidirectional)
- 43. GraphAware TM Neo4j APIs allow developers to completely ignore relationship direction when querying the graph. Why?
- 48. Pulp Fiction Michal RATED rating: 5 Mark Daniela RATED rating: 1 RATED rating: 4 GraphAware TM Qualifying by Properties
- 49. GraphAware TM START pulpFiction=node({id}) MATCH (pulpFiction)<-‐[r:RATED]-‐(fan) WHERE r.rating > 3 RETURN fan Who liked Pulp Fiction? (Cypher)
- 50. GraphAware TM for (Relationship r : pulpFiction.getRelationships(INCOMING, RATED)) { if ((int) r.getProperty("rating") > 3) { Node fan = r.getStartNode(); //do something with it } } Who liked Pulp Fiction? (Java)
- 51. Pulp Fiction Michal LOVED Mark Daniela HATED LIKED GraphAware TM Qualifying by Relationship Type
- 52. GraphAware TM START pulpFiction=node({id}) MATCH (pulpFiction)<-‐[r:LIKED|LOVED]-‐(fan) RETURN fan Who liked Pulp Fiction? (Cypher)
- 53. GraphAware TM for (Relationship r : pF.getRelationships(INCOMING, LIKED, LOVED)) { Node fan = r.getStartNode(); //do something with it } Who liked Pulp Fiction? (Java)
- 54. GraphAware TM
- 55. GraphAware TM
- 59. HDD Record Files Transaction Log Operating System JVM Neo4j Object Cache Core API Other APIs Transaction Management File System Cache Nodes Relationships Properties Relationship Types GraphAware TM Neo4j Architecture
- 60. GraphAware TM > cd data > ls -‐ah Disk Space
- 61. GraphAware TM drwxr-‐xr-‐x 5 bachmanm wheel 170B 19 Oct 12:56 index -‐rw-‐r-‐-‐r-‐-‐ 1 bachmanm wheel 31K 19 Oct 12:56 messages.log -‐rw-‐r-‐-‐r-‐-‐ 1 bachmanm wheel 69B 19 Oct 12:56 neostore -‐rw-‐r-‐-‐r-‐-‐ 1 bachmanm wheel 9B 19 Oct 12:56 neostore.id -‐rw-‐r-‐-‐r-‐-‐ 1 bachmanm wheel 8.8K 19 Oct 12:56 neostore.nodestore.db -‐rw-‐r-‐-‐r-‐-‐ 1 bachmanm wheel 9B 19 Oct 12:56 neostore.nodestore.db.id -‐rw-‐r-‐-‐r-‐-‐ 1 bachmanm wheel 39M 19 Oct 12:56 neostore.propertystore.db -‐rw-‐r-‐-‐r-‐-‐ 1 bachmanm wheel 153B 19 Oct 12:56 neostore.propertystore.db.arrays -‐rw-‐r-‐-‐r-‐-‐ 1 bachmanm wheel 9B 19 Oct 12:56 neostore.propertystore.db.arrays.id -‐rw-‐r-‐-‐r-‐-‐ 1 bachmanm wheel 9B 19 Oct 12:56 neostore.propertystore.db.id -‐rw-‐r-‐-‐r-‐-‐ 1 bachmanm wheel 43B 19 Oct 12:56 neostore.propertystore.db.index -‐rw-‐r-‐-‐r-‐-‐ 1 bachmanm wheel 9B 19 Oct 12:56 neostore.propertystore.db.index.id -‐rw-‐r-‐-‐r-‐-‐ 1 bachmanm wheel 140B 19 Oct 12:56 neostore.propertystore.db.index.keys -‐rw-‐r-‐-‐r-‐-‐ 1 bachmanm wheel 9B 19 Oct 12:56 neostore.propertystore.db.index.keys.id -‐rw-‐r-‐-‐r-‐-‐ 1 bachmanm wheel 154B 19 Oct 12:56 neostore.propertystore.db.strings -‐rw-‐r-‐-‐r-‐-‐ 1 bachmanm wheel 9B 19 Oct 12:56 neostore.propertystore.db.strings.id -‐rw-‐r-‐-‐r-‐-‐ 1 bachmanm wheel 31M 19 Oct 12:56 neostore.relationshipstore.db -‐rw-‐r-‐-‐r-‐-‐ 1 bachmanm wheel 9B 19 Oct 12:56 neostore.relationshipstore.db.id -‐rw-‐r-‐-‐r-‐-‐ 1 bachmanm wheel 38B 19 Oct 12:56 neostore.relationshiptypestore.db -‐rw-‐r-‐-‐r-‐-‐ 1 bachmanm wheel 9B 19 Oct 12:56 neostore.relationshiptypestore.db.id -‐rw-‐r-‐-‐r-‐-‐ 1 bachmanm wheel 140B 19 Oct 12:56 neostore.relationshiptypestore.db.names -‐rw-‐r-‐-‐r-‐-‐ 1 bachmanm wheel 9B 19 Oct 12:56 neostore.relationshiptypestore.db.names.id Disk Space
- 62. GraphAware TM Disk Space node 9B relationship 33B property 41B
- 63. GraphAware TM Disk Space (Example) 1,000 nodes x 9B = = 8.8 kB 1,000,000 rels x 33B = = 31.5 MB 2,010,000 props x 41B = = 78.6 MB TOTAL 110.1 MB
- 64. GraphAware TM How about low level cache? Any guesses? Low Level Cache
- 65. GraphAware TM Same as disk space Low Level Cache
- 66. GraphAware TM High Level Cache node 344B relationship 208B property 116B ...
- 68. GraphAware TM case study Java API vs. Cypher
- 70. User 2 User 1 User 3 TRAVELLED_WITH User 4 TRAVELLED_TOGETHER FRIEND TRAVELLED_WITH weight: 5 weight: 1 weight: 3 weight: 4 GraphAware TM Data Model
- 71. GraphAware TM START from=node:node_auto_index(user_id="{FROM}"), to=node:node_auto_index(user_id="{TO}") MATCH p = from-‐[r*1..5]-‐>to RETURN extract(n in nodes(p) : n.user_id), extract(rel in relationships(p) : rel.weight), extract(rel in relationships(p) : type(rel)) ORDER BY length(p), reduce(totalWeight = 0, rel in relationships(p) : totalWeight + rel.weight) LIMIT 3
- 72. GraphAware TM START from=node:node_auto_index(user_id="{FROM}"), to=node:node_auto_index(user_id="{TO}") MATCH p = from-‐[r*1..5]-‐>to RETURN extract(n in nodes(p) : n.user_id), extract(rel in relationships(p) : rel.weight), extract(rel in relationships(p) : type(rel)) ORDER BY length(p), reduce(totalWeight = 0, rel in relationships(p) : totalWeight + rel.weight) LIMIT 3 > 1 second
- 74. 10 - 20 ms
- 75. GraphAware TM Java API vs. Cypher
- 76. GraphAware TM Cypher is great! Java API vs. Cypher
- 77. GraphAware TM Cypher is great! Cypher is improving Java API vs. Cypher
- 78. GraphAware TM Cypher is great! Cypher is improving But don’t be afraid of writing some Java Java API vs. Cypher