The document discusses data modeling with graphs. It covers terminology related to relational databases and graph databases, provides examples of modeling entities as nodes and relationships, discusses patterns for modeling common structures, and highlights best practices and anti-patterns to consider. Key takeaways include indexing nodes for starting queries, using relationships to enable queries, reading the graph as sentences, and watching for verb'd nouns when naming relationships.

1. Data modeling
. . . with graphs
@PeterBell
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2. - Terminology
- Examples
- Patterns
- Key takeaways
Agenda
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3. Terminology
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4. relational database
tables
columns
records
foreign keys
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5. Wednesday, November 28, 12

6. neo4j
nodes
relationships
properties
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8. Wednesday, November 28, 12

9. indexes
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11. indexes
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12. traversals
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16. Cypher
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17. OO language - domain objects
Object (graph|relational) mapping
Java domain objects Java domain objects
Hibernate Spring Data Neo4j
SQL Cypher
Tables/rows/columns/FKs Nodes/relationships/properties
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18. Examples
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20. Wednesday, November 28, 12

21. Article
CATEGORIZED_UNDER WRITTEN_BY
ADDED_TO
Category Author
Comment
WRITTEN_BY
FOLLOWS
Commenter
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22. Geographic
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23. Bioinformatic
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24. Patterns
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25. start with a whiteboard
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26. Wednesday, November 28, 12

27. Handling entities
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28. ...provide a familiar and consistent Spring
based programming model while retaining
store specific features and capabilities
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29. Wednesday, November 28, 12

30. Restaurant
- name
- address
- cuisine
- comment
RECOMMENDS_THE
- rating
User
- first_name
- last_name
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33. Restaurant
- name
- address
- cuisine
RECOMMENDS_A - comment
- rating
User
- first_name
- last_name
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34. Restaurant SERVES
- name Cuisine
- address
- comment
RECOMMENDS_THE
- rating
User
- first_name
- last_name
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35. SERVES
Taj Mahal Indian cuisine
- “Great garlic nan and tandoori”
RECOMMENDS
- 4/5
Fred Jones
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36. read sentences from graph
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38. Indexes for starting points
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39. User
- email_address
- first_name
- last_name
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40. Relationships for querying
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41. User
- email_address
- first_name
- last_name
- shipping_state
Select * where shipping_state = ‘Ca’?
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42. User State
- email_address LIVES_IN - name
- first_name
- code
- last_name
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43. Jess
LIVES_IN
California
LIVES_IN
LIVES_IN
Andreas
Andrew
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44. Use meaningful names
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45. Language Country
- language_id
- country_id
- name
- name
- word count
- flag url
- country_id
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46. Language LanguageCountry Country
- language_id - language_id - country_id
- name - country_id - name
- word count - spoken_since - flag url
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47. Language LanguageCountry Country
- language_id - language_id - country_id
- name - country_id - name
- word count - spoken_since - flag url
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48. Language Country
IS_SPOKEN_IN
- name - name
- word count - flag url
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49. Language Country
IS_SPOKEN_IN
- name - name
- word count - flag url
SIMILAR_TO ADJACENT_TO
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50. Anti-Patterns
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51. Hefty nodes
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52. User
- first_name
- ship_address
- last_name
- ship_city
- email_address
- ship_state
- bill_address
- ship_zip
- bill_city
- regular_customer
- bill_state
- bill_zip
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53. BILLS_TO Address
User
- first_name - street_address
- last_name - city
- email_address - state
- regular_customer SHIPS_TO - zip
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54. BILLS_TO
User Address
- first_name - street_address
- last_name SHIPS_TO - city
- email_address - state
- zip
IS_A
Regular customer
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55. Jess
IS_A
Regular
customer IS_A
IS_A
Andreas
Andrew
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56. Missing nodes
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57. EMAILED
Peter Jim
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58. SENT TO
Peter Email Jim
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59. Hot node
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60. Wednesday, November 28, 12

61. Key takeaways
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62. Indexes for starting points
Relationships for queries
Read sentences from the graph
Look out for verb’d nouns
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63. - Indexes for starting points
- Relationships for queries
- Read sentences from the graph
- Look out for verb’d nouns
Data modeling with graphs @PeterBell
Wednesday, November 28, 12