Neo4j Graph Data Modelling Session - GraphTalk

Neo4j Inc. All rights reserved 2024
2

Summary of Scenarios
Scenario 1:
● Does our problem involve
understanding
relationships between
entities?
Scenario 3:
● Does the problem explore
relationships of varying or
unknown depth?
Scenario 2:
● Does the problem involve
a lot of self-referencing to
the same type of entity?
Scenario 4:
● Does our problem involve
discovering lots of
different routes or paths?
3

Relational Versus Graph Models
4

Path is starting point
What nodes are visited
to find Ann’s residence?
MATCH (p:Person)-[:OWNS]->(r)
WHERE p.name = 'Ann'
RETURN r
Person Person
Location
Residence
MARRIED
LIVES_AT
LIVES_AT
OWNS
name: ‘Dan’
born: 1975
name: ‘Ann’
born: 1977
address: ‘475 Broad Street’
postalCode: 28394
since: 2005-02-14
financed=TRUE
5

1. Anchor node label
Anchor node properties (indexed)
Anchor relationship type
Anchor relationship properties (indexed)
2. Downstream node labels
Downstream relationship types
3. Anchor node/relationship properties
(non-indexed)
4. Downstream node/relationship properties
Hierarchy of Accessibility
Anchor
Node
Downstream
Nodes
For each data object, how much work must Neo4j do to evaluate if this is a “good” path or a “bad” one?
Most accessible
Least processing required
Least accessible
Most processing required
6

Graph data modelling
Generic Graph specific
7
Collaborative effort

Nodes
8

Identify Entities from Questions
Entities are the nouns in the application questions:
1. What ingredients are used in a recipe?
2. Who is married to this person?
● The generic nouns often become labels in the model
● Use domain knowledge deciding how to further group or differentiate entities
9

Best practice: Avoid complex types for
properties
10

Relationships
11

Identify Connections between Entities
Connections are the verbs in the application questions:
● What ingredients are used in a recipe?
● Who is married to this person?
12

Qualifying a relationship
Use properties to describe the weight or quality of the relationship.
13

Graph Modelling patterns
14

Intermediate Nodes
Create intermediate nodes when you need to:
● Connect more than two nodes in a single context
● Relate something to a relationship
IN_ROLE
15

Using Intermediate Nodes
16

Intermediate Nodes: Sharing Context
17

Intermediate Nodes: Sharing Data
Before
After
18

Intermediate Nodes: Organizing Data
19

Linked Lists
Episodes of the Dr. Who series
DO NOT do this (doubly-linked list):
20

Interleaved Linked List
21

Head and Tail of Linked List
Some possible use cases:
● Add episodes as they are broadcast
● Maintain pointer to first and last
episodes
● Find all broadcast episodes
● Find latest broadcast episode
Current item
22

Timeline Tree
23

Using Multiple Structures
24

Using The Timeline Tree
25

Using Intermediate Nodes
26

Using Linked Lists
27

Exercise 1
● Create a graph data
model
29

Exercise 1 Question
“Which airports can I fly to from Las Vegas airport?”
from to airline flightNumber date departure arrival
LAS LAX WN 82 2021-03-01 1715 1820
LAS ABQ WN 500 2021-03-01 1445 1710
Our Data
30

Exercise 1 Instructions
Steps:
1. Identify the entities and relationships based on the question.
Remember: Use the Simplest Model Possible (SMP)!
2. Go to Arrows - https://arrows.app/
3. Create the graph data model using the sample data and the entities and
relationships you have identified.
LAS LAX WN 82 2021-03-01 1715 1820
LAS ABQ WN 500 2021-03-01 1445 1710
Which airports can I fly to from Las Vegas airport?
31

Entities
Answer: Airport
Exercise 1: Solution
Relationships
Answer: FLIES_TO
32

Exercise 1 Solution
Note
No extra properties aside from
the airport ‘code’
The simplest model to answer
the question.
33

Exercise 1 The Model
34

Exercise 1 Solution checks
● Can we answer our question with the model?
﹣ “Which airports can I ﬂy to from Las Vegas airport?”
● Does the model answer other questions?
﹣ How many airports are connected to a given airport?
﹣ How many airports are there?
MATCH (:Airport {code: 'LAS'})-[:FLIES_TO]->(destination:Airport)
RETURN destination.code
35

Exercise 2
● Apply best practices
36

Exercise 2 Question
“What are the origin and destination airports for a
specific flight?”
LAS LAX WN 82 2021-03-01 1715 1820
LAS ABQ WN 500 2021-03-01 1445 1710
Still Our
Data

Question: Given a flight number, find the origin and destination airports.
“What are the origin and destination airports for a specific flight?”
Steps:
1. Identify the (new!) entities and relationships based on the question.
Remember: SMP!
2. Go to Arrows - https://arrows.app/
3. Update the graph data model using the sample data and the entities and
relationships you have identified (think intermediate nodes).
LAS LAX WN 82 2021-03-01 1715 1820
LAS ABQ WN 500 2021-03-01 1445 1710

Relationships
“What are the and airports for a specific flight?”
Entities
“What are the origin and destination for a specific ?”
Exercise 2 Entities and Relationships
Answer: Flight
flight
airports
Answer:
DEPARTS_FROM,
ARRIVES_AT
origin destination

Exercise 2 A solution
Do we
need these?

Exercise 2 Solution checks
● Can we answer our questions with the model?
﹣ “Which airports can I fly to from Las Vegas airport?”
﹣ “What are the origin and destination airports for a specific flight?
MATCH
(:Airport {code: 'LAS'})<-[:DEPARTS_FROM]-(f:Flight),
(f)-[:ARRIVES_AT]->(destination:Airport)
RETURN
destination.code
MATCH
(origin:Airport)<-[:DEPARTS_FROM]-(f:Flight),
(f)-[:ARRIVES_AT]->(destination:Airport)
WHERE
f.flightNumber = '500' AND f.departure = 1445
RETURN
origin.code, destination.code

Exercise 3
● Graph refactoring
45

Exercise 3 Problem
Airports have too many flights in a day
* https://www.heathrow.com/company/about-heathrow/performance/airport-operations/traffic-statistics
- Heathrow ~1,300 flights per day
- Totalling ~474,500 per year
- 4.7 Million over a decade
- Just 1 airport *
Solutions?
• Bigger Machines
• Less Flights
• Accept the time costs
• Remodel
• Add AirportDay intermediate nodes
(We’ve seen it before!)
?
Dense Node

● Open Arrows
● Refactor your model to add AirportDay nodes
?

Thank you!
Questions?
49

Neo4j Graph Data Modelling Session - GraphTalk

More Related Content

Similar to Neo4j Graph Data Modelling Session - GraphTalk

More from Neo4j

Recently uploaded

Neo4j Graph Data Modelling Session - GraphTalk