The document discusses best practices for graph data modeling in Neo4j. It describes different types of modeling including whiteboarding, instance modeling, logical modeling, physical modeling, and tuned modeling. Each type of modeling has a different focus such as conceptual understanding, answering questions, enabling data loading, and optimizing performance. The document provides tips for each modeling type and examples to illustrate graph structures. It also covers topics like relationship types, constraints, indexing, and validating the model.

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© 2022 Neo4j, Inc. All rights reserved.
Graph Data Modeling Best
Practices
Eric Monk,
Principal Solutions Engineer

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What is a Graph Data Model?
The structure and definition of the Nodes, Relationships, and Properties.
Structure = How Labeled Nodes are connected via Relationship Types.
Definition = Labels and Relationship Types used. Assignment of Properties to
Labeled Nodes and Relationship Types along with their data types.
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Explicit Graph Data Modeling is OPTIONAL, but very useful.

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Instance vs Model
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:Person :Person
:KNOWS name: String
age: Integer
Person Node Label
KNOWS Relationship Type
alice
:Person
bob
:Person
:KNOWS name: Bob
age: 42
alice Node with
Person Node Label
KNOWS Relationship
name and age
Property Definitions
Model
name and age
Properties with values
Instance

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We are here:
Data Modeling

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+ More Questions
+ More Queries
+ More Data
Need to harmonize
inputs from multiple
sources
We need to iterate
+ Domain Expertise
+ Performance
Concerns

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Types of Modeling
Whiteboard - graph domain information
Instance Model - graph example data to solve questions
Logical Model - use Node Labels and Relationship Types to define graph
structure + some Property Definition
Physical Model - augment Logical Model with Property Definitions to support
data load + constraints
Tuned Model - account for previous misconceptions and performance
concerns
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Types of Models
Model Type Domain
Expertise
Questions Data Load Cypher
Queries
Performance Constraints /
Indexes
Whiteboard Y Y
Instance
Model
Y Y *
Logical Model Y Y * *
Physical Model Y Y Y Y * Y
Tuned Model Y Y Y Y Y Y
* Partial support

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Types of Models
Model Type Domain
Expertise
Questions Data Load Cypher
Queries
Performance Constraints /
Indexes
Whiteboard Y Y
Instance
Model
Y Y *
Logical Model Y Y * *
Physical Model Y Y Y Y * Y
Tuned Model Y Y Y Y Y Y
* Partial support
Similar level of detail
- slightly different
approaches

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See
Draw your model
visually. Use a tool
such as Arrows to
create and view your
model.
Whiteboard Tips
Know
Use domain relevant
language. Collaborate
with domain experts to
understand the real
world processes the
data represents.
Limit
Initially limit the scope
of your model. Only
include concepts and
relationships relevant to
an initial set of
questions.
Focus on concepts and how they are related.

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Whiteboard Example

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Define
Define properties
relevant to the nodes
and relationships.
Instance Model Tips
Fill
Create example nodes
and relationships with
representative data.
Answer
Trace paths to ensure
questions can be
answered. Write
Cypher if needed.
Representative Data. Focus on answering a question.

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Instance Model Example

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Node, Relationship, or Property?
Item/Action Definition Node Relationship Property
Business noun - Y (1) Y (2)
How things relate - Y
Anchor Starting point Y (3) Y (3)
Traverse Visit matching
relationships and nodes
Y Y
Degree Count of relationships per
node
Y Y (4)
Filter Remove paths by
examining property values
Y
Decorator Return property value Y

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Node, Relationship, or Property? (Notes)
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Business noun - Node
Domain concept
Unique identifier
Avoid supernodes (very high degree nodes)
Business noun - Property
Attribute of a Domain concept
Small set of possible values
Names, quantities, timestamps
Anchor
Both Node Label and Property are used to
Anchor
Degree
Used to access Neo4j count store

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Define
Define properties
relevant to the nodes
and relationships.
Logical Model Tips
Guided
Use node, relationship,
and property guidance
to decide proper graph
structure.
Answer
Trace paths to ensure
questions can be
answered. Write
Cypher if needed.
Informed Modeling. Focus on answering a question.

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Logical Model Example

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Data Discovery
• Identify data sources node and properties in the model
• Getting sample data
• Identify primary keys / node keys
• Can correlate across data sources?
• Does your model map to existing sources? Any gaps?
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Source
Use details from
relevant data sources
to inform your
modeling.
Physical Model Tips
Keys
Determine node keys,
constraints, and
indexes to prepare for
data load.
Load
Load data. Confirm
structures are loaded
properly and data types
are correct.
Enable loading of data. Question(s) should still be answered.

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Physical Model Example

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Iterate
Make adjustments as
needed to tune model
based on additional
data added or new
questions to be
answered.
Tuning Your Model
Performance
Use specific
Relationship Types to
assist traversals.
Ensure constraints and
indexes are used
properly.
Validate
Validate the model to
ensure data is loaded
properly, questions are
answered.
Focus on optimizing the model.

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Relationship Types Example

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images by OokamiKasumi and TransparentJiggly64
https://www.deviantart.com/ookamikasumi/art/Many-Doors-to-Wonderland-166917185 https://www.deviantart.com/transparentjiggly64/art/Champion-Link-Facing-Away-803623424
Room
Imagine a room with
many doors.
Room = node
Door = relationship

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Room
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Treasure
Our adventurer
wants treasure.

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Room
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PATH_TO
PATH_TO
PATH_TO
PATH_TO
PATH_TO
???
Treasure
Uhh…which way to
go?

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NOT!
Treasure
Relationship
traversal NOT
optimal!

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Room
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PATH_TO
_ROOM
PATH_TO
_SHOP
PATH_TO_
MONSTER
PATH_TO
_TREASURE
PATH_TO
_GROOSE
Ahh! I know
which way to go
This WAY!

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Other Optimizations and
Validation

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Tuning Optimizations
• Have specific Relationship Types that lead to distinct Node Types
◦ Avoid the issue of extra traversals + filtering on end Node Labels
• Relationship Locking on Writes
◦ Be cognizant of potential locking issues
◦ Better in 4.3, but could still be an issue
• Avoid traversals through high degree nodes
◦ Cardinality expansion will kill performance and increase memory consumption
• Smart Aggregation
◦ Build intermediate aggregate nodes or use GDS
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Model Validation
• Sanity Checks
◦ Does your model answer your business problem(s)?
◦ Does your model ingest data properly?
• Usability
◦ During testing, does your model introduce usability issues?
• Performance
◦ During testing, does your model perform fast enough?
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Summary
• Whiteboard to get started
• Use Instance modeling and Logical modeling to answer questions
• Use Physical modeling to load data
• Tune your model
• Iterate
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© 2022 Neo4j, Inc. All rights reserved.
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Happy Modeling!
Contact us at
sales@neo4j.com