Download as PDF, PPTX
![GraphAware
TM
MATCH
(neo)-‐[:PARTNER]-‐>(partner)
Cypher](https://image.slidesharecdn.com/modelling-131105111009-phpapp01-171229122033/75/Modelling-Data-in-Neo4j-plus-a-few-tips-44-2048.jpg)
![GraphAware
TM
MATCH
(neo)<-‐[:PARTNER]-‐(partner)
Cypher](https://image.slidesharecdn.com/modelling-131105111009-phpapp01-171229122033/75/Modelling-Data-in-Neo4j-plus-a-few-tips-45-2048.jpg)
![GraphAware
TM
MATCH
(neo)-‐[:PARTNER]-‐(partner)
Cypher](https://image.slidesharecdn.com/modelling-131105111009-phpapp01-171229122033/75/Modelling-Data-in-Neo4j-plus-a-few-tips-46-2048.jpg)
![GraphAware
TM
START
pulpFiction=node({id})
MATCH
(pulpFiction)<-‐[r:RATED]-‐(fan)
WHERE
r.rating
>
3
RETURN
fan
Who liked Pulp Fiction? (Cypher)](https://image.slidesharecdn.com/modelling-131105111009-phpapp01-171229122033/75/Modelling-Data-in-Neo4j-plus-a-few-tips-49-2048.jpg)
![GraphAware
TM
START
pulpFiction=node({id})
MATCH
(pulpFiction)<-‐[r:LIKED|LOVED]-‐(fan)
RETURN
fan
Who liked Pulp Fiction? (Cypher)](https://image.slidesharecdn.com/modelling-131105111009-phpapp01-171229122033/75/Modelling-Data-in-Neo4j-plus-a-few-tips-52-2048.jpg)
![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](https://image.slidesharecdn.com/modelling-131105111009-phpapp01-171229122033/75/Modelling-Data-in-Neo4j-plus-a-few-tips-71-2048.jpg)
![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](https://image.slidesharecdn.com/modelling-131105111009-phpapp01-171229122033/75/Modelling-Data-in-Neo4j-plus-a-few-tips-72-2048.jpg)
Uploaded byGraphAware
Modelling Data in Neo4j (plus a few tips)
GraphAware TM discusses best practices for modeling data with Neo4j, emphasizing the advantages of graph databases over traditional data models. It covers topics such as data traversal, relationship directionality, and performance comparisons between Cypher and Java API for database queries. Key lessons include the importance of handling interconnected data and utilizing efficient traversal methods for optimal performance.
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Modelling Data in Neo4j (plus a few tips)
- 1. GraphAware TM by Michal Bachman plusa few best practices and lessons learned Modelling Data in Neo4j
- 2.
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- 5.
- 6. GraphAware TM Quick intro 1x mistake 1xexperiment Contents
- 7. GraphAware TM Quick intro 1x mistake 1xexperiment 1x FAQ Contents
- 8. GraphAware TM Quick intro 1x mistake 1xexperiment 1x FAQ 1x case-study Contents
- 9.
- 10.
- 11.
- 12. GraphAware TM Larger Volumes Less Structured MoreInterconnected Data Has Changed
- 13. GraphAware TM Larger Volumes Less Structured MoreInterconnected Polygot Persistence Data Has Changed
- 14.
- 15.
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- 17. GraphAware TM Key-Value Stores Column-Family Stores DocumentDatabases NoSQL
- 18. GraphAware TM Key-Value Stores Column-Family Stores DocumentDatabases Graph Databases NoSQL
- 19. GraphAware TM The first threeuse aggregate data models, graph databases work with simple records and complex interconnections. Graph Databases
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- 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 nosingle 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
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- 34. DEFEATED Czech Republic Sweden DEFEATED_BY GraphAware TM Ice HockeyIce Hockey(Implied Relationship)
- 35. GraphAware TM In Neo4j, thespeed of traversal does not depend on the direction of the relationships being traversed. Traversals
- 36.
- 37. Node Record inthe 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.
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- 42.
- 43. GraphAware TM Neo4j APIs allowdevelopers to completely ignore relationship direction when querying the graph. Why?
- 44.
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- 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.
- 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)
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- 59. HDD Record Files Transaction Log Operating System JVM Neo4j ObjectCache 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 relationship33B property 41B
- 63. GraphAware TM Disk Space (Example) 1,000nodes 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 lowlevel cache? Any guesses? Low Level Cache
- 65. GraphAware TM Same as diskspace Low Level Cache
- 66. GraphAware TM High Level Cache node344B relationship 208B property 116B ...
- 67.
- 68. GraphAware TM case study Java APIvs. Cypher
- 70. User 2 User 1 User3 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 - 20ms
- 75.
- 76. GraphAware TM Cypher is great! JavaAPI vs. Cypher
- 77. GraphAware TM Cypher is great! Cypheris improving Java API vs. Cypher
- 78. GraphAware TM Cypher is great! Cypheris improving But don’t be afraid of writing some Java Java API vs. Cypher
- 79.