© 2022 Neo4j, Inc. All rights reserved.
© 2022 Neo4j, Inc. All rights reserved.
Wednesday, March 22
11:00 EDT 15:00 GMT
16:00 CET 20:30 IST
Intermediate Cypher
& Data Modelling

© 2022 Neo4j, Inc. All rights reserved.
Today we will cover
● Recap: Fundamentals of Graph Databases & Cypher
● How Cypher Queries Work
● Data Modeling Fundamentals
● Advanced Cypher Techniques
● Bring your questions!

© 2022 Neo4j, Inc. All rights reserved.
Follow along with AuraDB Free
● Sign in or Register at console.neo4j.io
● Click the New Instance button
● Choose the Empty instance option
● Copy your password or download your credentials.env file
● When the instance has started, click the Open button to open
Workspace

© 2022 Neo4j, Inc. All rights reserved.
Graph Database
Fundamentals

© 2022 Neo4j, Inc. All rights reserved.
Macbook
Graph Database
Fundamentals
Nodes

© 2022 Neo4j, Inc. All rights reserved.
Macbook
Graph Database
Fundamentals
Nodes
Nodes represent things

© 2022 Neo4j, Inc. All rights reserved.
Macbook
Product
Graph Database
Fundamentals
Nodes
Nodes represent things
Nodes can be identified by
one or more labels

© 2022 Neo4j, Inc. All rights reserved.
Macbook
Product
Graph Database
Fundamentals
Nodes
Nodes represent things
Nodes can be identified by
one or more labels
Fragile

© 2022 Neo4j, Inc. All rights reserved.
Macbook
Product
name: Macbook Pro
price: 2699.00
Graph Database
Fundamentals
Nodes
Nodes represent things
Nodes can be identified by
one or more labels
Nodes can hold properties
as key/value pairs
Fragile

© 2022 Neo4j, Inc. All rights reserved.
Customer
name: Adam
email: adam@neo4j.com
Graph Database
Fundamentals
Relationships
Product
name: Macbook Pro
price: 2699.00

© 2022 Neo4j, Inc. All rights reserved.
Customer
name: Adam
email: adam@neo4j.com
Graph Database
Fundamentals
Relationships
Product
name: Macbook Pro
price: 2699.00

© 2022 Neo4j, Inc. All rights reserved.
Customer
name: Adam
email: adam@neo4j.com
Graph Database
Fundamentals
Relationships
Relationships connect two nodes
Product
name: Macbook Pro
price: 2699.00

© 2022 Neo4j, Inc. All rights reserved.
Customer
name: Adam
email: adam@neo4j.com
Graph Database
Fundamentals
Relationships
Relationships connect two nodes
Relationships have a type
Product
name: Macbook Pro
price: 2699.00
RATED

© 2022 Neo4j, Inc. All rights reserved.
Relationships have a direction
Customer
name: Adam
email: adam@neo4j.com
Graph Database
Fundamentals
Relationships
Relationships connect two nodes
Relationships have a type
Product
name: Macbook Pro
price: 2699.00
RATED

© 2022 Neo4j, Inc. All rights reserved.
Relationships have a direction
Customer
name: Adam
email: adam@neo4j.com
Graph Database
Fundamentals
Relationships
Relationships connect two nodes
Relationships have a type
Product
name: Macbook Pro
price: 2699.00
RATED
stars: 3
createdAt: 2023-03-22
Relationships can hold properties
as key/value pairs

© 2022 Neo4j, Inc. All rights reserved.
Graph Database
Fundamentals
Cypher
Introduction to Cypher
Cypher is a declarative language that allows
you to identify patterns in your data using
an ASCII-art style syntax consisting of
brackets, dashes and arrows.

© 2022 Neo4j, Inc. All rights reserved.
Graph Database
Fundamentals
Cypher
Introduction to Cypher
Cypher is a declarative language that allows
you to identify patterns in your data using
an ASCII-art style syntax consisting of
brackets, dashes and arrows.
Product
RATED
Customer

© 2022 Neo4j, Inc. All rights reserved.
Graph Database
Fundamentals
Cypher
Introduction to Cypher
Cypher is a declarative language that allows
you to identify patterns in your data using
an ASCII-art style syntax consisting of
brackets, dashes and arrows.
Product
RATED
Customer

© 2022 Neo4j, Inc. All rights reserved.
Graph Database
Fundamentals
Cypher
Introduction to Cypher
Cypher is a declarative language that allows
you to identify patterns in your data using
an ASCII-art style syntax consisting of
brackets, dashes and arrows.
Product
RATED
Customer
(c:Customer)-[r:RATED]->(p:Product)

© 2022 Neo4j, Inc. All rights reserved.
Graph Database
Fundamentals
Cypher
Introduction to Cypher
Cypher is a declarative language that allows
you to identify patterns in your data using
an ASCII-art style syntax consisting of
brackets, dashes and arrows.
Product
RATED
Customer
(c:Customer)-[r:RATED]->(p:Product)

© 2022 Neo4j, Inc. All rights reserved.
Graph Database
Fundamentals
Cypher
Introduction to Cypher
Cypher is a declarative language that allows
you to identify patterns in your data using
an ASCII-art style syntax consisting of
brackets, dashes and arrows.
Product
RATED
Customer
(c:Customer)-[r:RATED]->(p:Product)

© 2022 Neo4j, Inc. All rights reserved.
Graph Database
Fundamentals
Cypher
Introduction to Cypher
Cypher is a declarative language that allows
you to identify patterns in your data using
an ASCII-art style syntax consisting of
brackets, dashes and arrows.
Product
RATED
Customer
(c:Customer)-[r:RATED]->(p:Product)

© 2022 Neo4j, Inc. All rights reserved.
Graph Database
Fundamentals
Cypher
Introduction to Cypher
Cypher is a declarative language that allows
you to identify patterns in your data using
an ASCII-art style syntax consisting of
brackets, dashes and arrows.
Product
RATED
Customer
(c:Customer)-[r:RATED]->(p:Product)

© 2022 Neo4j, Inc. All rights reserved.
Graph Database
Fundamentals
Cypher
Introduction to Cypher
Cypher is a declarative language that allows
you to identify patterns in your data using
an ASCII-art style syntax consisting of
brackets, dashes and arrows.
Product
RATED
Customer
(c:Customer)-[r:RATED]->(p:Product)

© 2022 Neo4j, Inc. All rights reserved.
Graph Database
Fundamentals
Cypher
Introduction to Cypher
Cypher is a declarative language that allows
you to identify patterns in your data using
an ASCII-art style syntax consisting of
brackets, dashes and arrows.
Product
RATED
Customer
(c:Customer)-[r:RATED]->(p:Product)

© 2022 Neo4j, Inc. All rights reserved.
name: Adam
email: adam@neo4j.com
Graph Database
Fundamentals
The MATCH clause
Product
name: Macbook Pro
price: 2699.00
RATED
stars: 3
createdAt: 2023-03-22
MATCH (c:Customer)-[r:RATED]->(p:Product)
RETURN c.name AS customer, p.name AS product, r.stars
AS rating
Customer

© 2022 Neo4j, Inc. All rights reserved.
© 2022 Neo4j, Inc. All rights reserved.
MATCH
(c:Customer)-[:PURCHASED]->(o:Order),
(o)-[:ORDERS]->(p:Product),
(p)-[:PART_OF]>(cc:Category),
(cc)<-[:HAS_CHILD*0..3]-(cat:Category)
WHERE c.customerId = “ANTON”
AND cat.name = “Electronics”
RETURN *
The MATCH clause

© 2022 Neo4j, Inc. All rights reserved.
© 2022 Neo4j, Inc. All rights reserved.
MATCH
(c:Customer)-[:PURCHASED]->(o:Order),
(o)-[:ORDERS]->(p:Product),
(p)-[:PART_OF]>(cc:Category),
(cc)<-[:HAS_CHILD*0..3]-(cat:Category)
WHERE c.customerId = “ANTON”
AND cat.name = “Electronics”
RETURN *
The MATCH clause

© 2022 Neo4j, Inc. All rights reserved.
© 2022 Neo4j, Inc. All rights reserved.
MATCH
(c:Customer)-[:PURCHASED]->(o:Order),
(o)-[:ORDERS]->(p:Product),
(p)-[:PART_OF]>(cc:Category),
(cc)<-[:HAS_CHILD*0..3]-(cat:Category)
WHERE c.customerId = “ANTON”
AND cat.name = “Electronics”
RETURN *
The MATCH clause

© 2022 Neo4j, Inc. All rights reserved.
© 2022 Neo4j, Inc. All rights reserved.
MATCH
(c:Customer)-[:PURCHASED]->(o:Order),
(o)-[:ORDERS]->(p:Product),
(p)-[:PART_OF]>(cc:Category),
(cc)<-[:HAS_CHILD*0..3]-(cat:Category)
WHERE c.customerId = “ANTON”
AND cat.name = “Electronics”
RETURN *
The MATCH clause

© 2022 Neo4j, Inc. All rights reserved.
© 2022 Neo4j, Inc. All rights reserved.
MATCH
(c:Customer)-[:PURCHASED]->(o:Order),
(o)-[:ORDERS]->(p:Product),
(p)-[:PART_OF]>(cc:Category),
(cc)<-[:HAS_CHILD*0..3]-(cat:Category)
WHERE c.customerId = “ANTON”
AND cat.name = “Electronics”
RETURN *
The MATCH clause

© 2022 Neo4j, Inc. All rights reserved.
© 2022 Neo4j, Inc. All rights reserved.
MATCH
(c:Customer)-[:PURCHASED]->(o:Order),
(o)-[:ORDERS]->(p:Product),
(p)-[:PART_OF]->(cc:Category),
(cc)<-[:HAS_CHILD*0..3]-(cat:Category)
WHERE c.customerId = “ANTON”
AND cat.name = “Electronics”
RETURN *
The MATCH clause

© 2022 Neo4j, Inc. All rights reserved.
© 2022 Neo4j, Inc. All rights reserved.
MATCH
(c:Customer)-[:PURCHASED]->(o:Order),
(o)-[:ORDERS]->(p:Product),
(p)-[:PART_OF]>(cc:Category),
(cc)<-[:HAS_CHILD*0..3]-(cat:Category)
WHERE c.customerId = “ANTON”
AND cat.name = “Electronics”
RETURN *
The MATCH clause

© 2022 Neo4j, Inc. All rights reserved.
• CREATE - Create a pattern in the graph
• MERGE - Find or create a pattern in the graph
• SET - Add a new label to a node or property to a node or relationship
• REMOVE - Remove a label from a node or property from node or
relationship
• DELETE - Delete a node or relationship from from the graph
Writing to Neo4j

© 2022 Neo4j, Inc. All rights reserved.
AS A
Customer
I WOULD LIKE TO
Register for the website
SO THAT I CAN
Buy my groceries online
A CREATE Example

© 2022 Neo4j, Inc. All rights reserved.
CREATE (c:Customer {
customerID: randomUuid(),
contactName: “Adam Cowley”,
address: “182-194 Union Street”,
city: “London”,
postalCode: “SE1 0LH”,
country: “United Kingdom”
})
A CREATE Example

© 2022 Neo4j, Inc. All rights reserved.
• The CREATE statement is used to create new nodes and relationships
• The MERGE statement will first search for the pattern, and if not found,
will attempt to create it
• You should MERGE when a node or relationship may not exist
• The whole pattern will be created if not found, so it is best to break the
pattern up into smaller pieces
CREATE or MERGE?

© 2022 Neo4j, Inc. All rights reserved.
A MERGE Example
// ❌ BAD
MERGE (p:Product {name: "Beavertown Neck Oil IPA"})
-[:PART_OF]->(cat1:Category {categoryName: "Beverages"})

© 2022 Neo4j, Inc. All rights reserved.
A MERGE Example
// ❌ BAD
MERGE (p:Product {name: "Beavertown Neck Oil IPA"})
-[:PART_OF]->(cat1:Category {categoryName: "Beverages"})
If the category already exists
but the product doesn’t, Neo4j
will attempt to create both
nodes and the relationship

© 2022 Neo4j, Inc. All rights reserved.
A MERGE Example
// ❌ BAD
MERGE (p:Product {name: "Beavertown Neck Oil IPA"})
-[:PART_OF]->(cat1:Category {categoryName: "Beverages"})
// ✅ GOOD
MERGE (cat1:Category {name: “Beverages”})
MERGE (cat2:Category {name: “Alcohol”})
MERGE (p:Product {name: “Beavertown Neck Oil IPA”})
MERGE (p)-[:PART_OF]->(cat1)
MERGE (p)-[:PART_OF]->(cat2)

© 2022 Neo4j, Inc. All rights reserved.
MERGE (p:Product {name: “Beavertown Neck Oil IPA”})
ON CREATE SET p.createdAt = datetime()
ON MATCH SET p:RestockedProduct
SET p.updatedAt = datetime()
REMOVE p:OutOfStock
SETting Properties and Labels

© 2022 Neo4j, Inc. All rights reserved.
MERGE (p:Product {name: “Beavertown Neck Oil IPA”})
ON CREATE SET p.createdAt = datetime()
ON MATCH SET p:RestockedProduct
SET p.updatedAt = datetime()
REMOVE p:OutOfStock
SETting Properties and Labels
Set a property when the node is first created

© 2022 Neo4j, Inc. All rights reserved.
MERGE (p:Product {name: “Beavertown Neck Oil IPA”})
ON CREATE SET p.createdAt = datetime()
ON MATCH SET p:RestockedProduct
SET p.updatedAt = datetime()
REMOVE p:OutOfStock
SETting Properties and Labels
Set a property if the node already exists

© 2022 Neo4j, Inc. All rights reserved.
MERGE (p:Product {name: “Beavertown Neck Oil IPA”})
ON CREATE SET p.createdAt = datetime()
ON MATCH SET p:RestockedProduct
SET p.updatedAt = datetime()
REMOVE p:OutOfStock
SETting Properties and Labels
Set a property regardless

© 2022 Neo4j, Inc. All rights reserved.
MERGE (p:Product {name: “Beavertown Neck Oil IPA”})
ON CREATE SET p.createdAt = datetime()
ON MATCH SET p:RestockedProduct
SET p.updatedAt = datetime()
REMOVE p:OutOfStock
SETting Properties and Labels
The REMOVE keyword will remove a label or property

© 2022 Neo4j, Inc. All rights reserved.
© 2022 Neo4j, Inc. All rights reserved.
Data Modeling

© 2022 Neo4j, Inc. All rights reserved.
© 2022 Neo4j, Inc. All rights reserved.
Derive your data model from your use case
Data Modeling

© 2022 Neo4j, Inc. All rights reserved.
AS A
Customer
I WOULD LIKE TO
Order a new Laptop and accessories
SO THAT I CAN
Build a website using a graph database
Nouns, Verbs and Adjectives

© 2022 Neo4j, Inc. All rights reserved.
AS A
Customer
I WOULD LIKE TO
Order a new Laptop and accessories
SO THAT I CAN
Build a website using a graph database
Nouns, Verbs and Adjectives
Product.

© 2022 Neo4j, Inc. All rights reserved.
AS A
Customer
I WOULD LIKE TO
Order a new Laptop and accessories
SO THAT I CAN
Build a website using a graph database
Nouns, Verbs and Adjectives
Product.

© 2022 Neo4j, Inc. All rights reserved.
Nouns, Verbs and Adjectives
AS A
Customer
I WOULD LIKE TO
Order a new Laptop and accessories
SO THAT I CAN
Build a website using a graph database
Product.

© 2022 Neo4j, Inc. All rights reserved.
Intermediate Nodes

© 2022 Neo4j, Inc. All rights reserved.
Intermediate Nodes
Knowing this fact might be
enough for your use case

© 2022 Neo4j, Inc. All rights reserved.
Intermediate Nodes
Knowing this fact might be
enough for your use case

© 2022 Neo4j, Inc. All rights reserved.
Intermediate Nodes
The order information can now
be linked to multiple nodes
Knowing this fact might be
enough for your use case

© 2022 Neo4j, Inc. All rights reserved.
Property, Relationship or Node - it depends…

© 2022 Neo4j, Inc. All rights reserved.
Property, Relationship or Node - it depends…
{
"customer": {
"CustomerID": "ALFKI",
"CompanyName": "Alfreds Futterkiste",
"ContactName": "Maria Anders",
"ContactTitle": "Sales Representative",
"Phone": "030-0074321",
"Fax": "030-0076545",
"email": "maria.anders@alfreds.de"
},
"address": {
"Address": "Obere Str. 57",
"City": "Berlin",
"Region": null,
"PostalCode": "12209",
"Country": "Germany"
},
"order": {
"OrderID": 10248,
"CustomerID": "ALFKI",
"EmployeeID": 5,
"OrderDate": "1996-07-04T00:00:00",
"RequiredDate": "1996-08-01T00:00:00",
"ShippedDate": "1996-07-16T00:00:00",
"ShipVia": 3,
"Freight": 32.38,
"ShipName": "Alfreds Futterkiste",
"ShipAddress": {
"Address": "Obere Str. 57",
"City": "Berlin",
"Region": null,
"PostalCode": "12209",
"Country": "Germany"
}
}
}

© 2022 Neo4j, Inc. All rights reserved.
● Many databases will duplicate data for
read-time performance
● Address is read-only? Store as a property
● Are billing and shipping addresses saved as
part of the checkout process? Store in a Node
● Are offers served to first-time customers that
are limited by address? Store in a Node
● Is the graph used to optimise delivery routes?
Store as a Node
Property, Relationship or Node - it depends…
{
"customer": {
"CustomerID": "ALFKI",
"CompanyName": "Alfreds Futterkiste",
"ContactName": "Maria Anders",
"ContactTitle": "Sales Representative",
"Phone": "030-0074321",
"Fax": "030-0076545",
"email": "maria.anders@alfreds.de"
},
"address": {
"Address": "Obere Str. 57",
"City": "Berlin",
"Region": null,
"PostalCode": "12209",
"Country": "Germany"
},
"order": {
"OrderID": 10248,
"CustomerID": "ALFKI",
"EmployeeID": 5,
"OrderDate": "1996-07-04T00:00:00",
"RequiredDate": "1996-08-01T00:00:00",
"ShippedDate": "1996-07-16T00:00:00",
"ShipVia": 3,
"Freight": 32.38,
"ShipName": "Alfreds Futterkiste",
"ShipAddress": {
"Address": "Obere Str. 57",
"City": "Berlin",
"Region": null,
"PostalCode": "12209",
"Country": "Germany"
}
}
}

© 2022 Neo4j, Inc. All rights reserved.
© 2022 Neo4j, Inc. All rights reserved.
The easy way, RDBMS to Graph
Data Modeling

© 2022 Neo4j, Inc. All rights reserved.
The Northwind
Database

© 2022 Neo4j, Inc. All rights reserved.
The Northwind
Database

© 2022 Neo4j, Inc. All rights reserved.
Relational
to Graph

© 2022 Neo4j, Inc. All rights reserved.
Relational
to Graph
Table names become
labels, individual records
become nodes

© 2022 Neo4j, Inc. All rights reserved.
Relational
to Graph
Foreign keys become
relationships
(:Supplier)-[:SUPPLIES]->(:Product)
(:Customer)-[:PURCHASED]->(:Order)
(:Shipper)-[:SHIPS]->(:Order)

© 2022 Neo4j, Inc. All rights reserved.
The Northwind
Database
Many-to-Many
Relationships

© 2022 Neo4j, Inc. All rights reserved.
The Northwind
Database
Many-to-Many
Relationships

© 2022 Neo4j, Inc. All rights reserved.
The Northwind
Database
Many-to-Many
Relationships

© 2022 Neo4j, Inc. All rights reserved.
The Northwind
Database
Many-to-Many
Relationships
��

© 2022 Neo4j, Inc. All rights reserved.
The Northwind
Database
Many-to-Many
Relationships
Order
C
O
N
T
A
I
N
S
PRODUCT

© 2022 Neo4j, Inc. All rights reserved.
The Northwind
Database
Many-to-Many
Relationships
Order
C
O
N
T
A
I
N
S
PRODUCT
u
n
i
t
P
r
i
c
e
:
n
u
m
b
e
r
q
u
a
n
t
i
t
y
:
n
u
m
b
e
r
d
i
s
c
o
u
n
t
:
f
l
o
a
t

© 2022 Neo4j, Inc. All rights reserved.
SQL to Cypher
SELECT o.ShipName, o.ShipAddress, o.ShipCity,
o.ShipRegion, o.ShipPostalCode, o.ShipCountry,
o.CustomerID, c.CompanyName AS CustomerName,
c.Address, c.City, c.Region,
c.PostalCode, c.Country,
(e.FirstName + ' ' + e.LastName) AS Salesperson,
o.OrderID, o.OrderDate, o.RequiredDate,
o.ShippedDate, s.CompanyName As ShipperName,
od.ProductID, p.ProductName,
od.UnitPrice, od.Quantity,
od.Discount, o.Freight,
(CONVERT(money,(od.UnitPrice*Quantity*(1-Discount)/100))*100) AS ExtendedPrice
FROM Customers c
INNER JOIN Products p ON p.ProductID = od.ProductID
INNER JOIN Orders o ON c.CustomerID = o.CustomerID
INNER JOIN OrderDetails od ON o.OrderID = od.OrderID
INNER JOIN Employees e ON e.EmployeeID = o.EmployeeID
INNER JOIN Shippers s ON s.ShipperID = o.ShipVia
WHERE c.customerID = "ANTON"
MATCH (c:Customer {customerID: "ANTON"})
-[:PURCHASED]->(o:Order), (o)-[od:ORDERED]->(p:Product),
(p)<-[:SHIPS]-(s:Shipper)
RETURN o.shipName, o.shipAddress, o.shipCity,
o.shipRegion, o.shipPostalCode, o.shipCountry,
c.customerID, c.companyName AS CustomerName,
c.address, c.city, c.region,
c.postalCode, c.country,
e.firstName +" "+ e.lastName AS Salesperson,
o.shippedDate, s.companyName AS ShipperName,
p.productID, p.productName,
od.unitPrice, od.quantity,
od.discount, od.freight,
od.unitPrice * od.quantity
* (1.0-od.discount)/100 * 100 AS ExtendedPrice
https://raw.githubusercontent.com/microsoft/sql-server-samples/master/samples/databases/northwind-pubs/instnwnd.sql

© 2022 Neo4j, Inc. All rights reserved.
SQL to Cypher
MATCH (c:Customer {customerID: "ANTON"})
-[:PURCHASED]->(o:Order), (o)-[od:ORDERED]->(p:Product),
(p)<-[:SHIPS]-(s:Shipper)
RETURN o.shipName, o.shipAddress, o.shipCity,
o.shipRegion, o.shipPostalCode, o.shipCountry,
c.customerID, c.companyName AS CustomerName,
c.address, c.city, c.region,
c.postalCode, c.country,
e.firstName +" "+ e.lastName AS Salesperson,
o.shippedDate, s.companyName AS ShipperName,
p.productID, p.productName,
od.unitPrice, od.quantity,
od.discount, od.freight,
od.unitPrice * od.quantity
* (1.0-od.discount)/100 * 100 AS ExtendedPrice
https://raw.githubusercontent.com/microsoft/sql-server-samples/master/samples/databases/northwind-pubs/instnwnd.sql
SELECT o.ShipName, o.ShipAddress, o.ShipCity,
o.ShipRegion, o.ShipPostalCode, o.ShipCountry,
o.CustomerID, c.CompanyName AS CustomerName,
c.Address, c.City, c.Region,
c.PostalCode, c.Country,
(e.FirstName + ' ' + e.LastName) AS Salesperson,
o.OrderID, o.OrderDate, o.RequiredDate,
o.ShippedDate, s.CompanyName As ShipperName,
od.ProductID, p.ProductName,
od.UnitPrice, od.Quantity,
od.Discount, o.Freight,
(CONVERT(money,(od.UnitPrice*Quantity*(1-Discount)/100))*100) AS ExtendedPrice
FROM Customers c
INNER JOIN Products p ON p.ProductID = od.ProductID
INNER JOIN Orders o ON c.CustomerID = o.CustomerID
INNER JOIN OrderDetails od ON o.OrderID = od.OrderID
INNER JOIN Employees e ON e.EmployeeID = o.EmployeeID
INNER JOIN Shippers s ON s.ShipperID = o.ShipVia
WHERE c.customerID = "ANTON"

© 2022 Neo4j, Inc. All rights reserved.
MATCH (c:Customer {customerID: "ANTON"})
-[:PURCHASED]->(o:Order), (o)-[od:ORDERED]->(p:Product),
(p)<-[:SHIPS]-(s:Shipper)
RETURN o.shipName, o.shipAddress, o.shipCity,
o.shipRegion, o.shipPostalCode, o.shipCountry,
c.customerID, c.companyName AS CustomerName,
c.address, c.city, c.region,
c.postalCode, c.country,
e.firstName +" "+ e.lastName AS Salesperson,
o.shippedDate, s.companyName AS ShipperName,
p.productID, p.productName,
od.unitPrice, od.quantity,
od.discount, od.freight,
od.unitPrice * od.quantity
* (1.0-od.discount)/100 * 100 AS ExtendedPrice
SQL to Cypher
https://raw.githubusercontent.com/microsoft/sql-server-samples/master/samples/databases/northwind-pubs/instnwnd.sql
SELECT o.ShipName, o.ShipAddress, o.ShipCity,
o.ShipRegion, o.ShipPostalCode, o.ShipCountry,
o.CustomerID, c.CompanyName AS CustomerName,
c.Address, c.City, c.Region,
c.PostalCode, c.Country,
(e.FirstName + ' ' + e.LastName) AS Salesperson,
o.OrderID, o.OrderDate, o.RequiredDate,
o.ShippedDate, s.CompanyName As ShipperName,
od.ProductID, p.ProductName,
od.UnitPrice, od.Quantity,
od.Discount, o.Freight,
(CONVERT(money,(od.UnitPrice*Quantity*(1-Discount)/100))*100) AS ExtendedPrice
FROM Customers c
INNER JOIN Products p ON p.ProductID = od.ProductID
INNER JOIN Orders o ON c.CustomerID = o.CustomerID
INNER JOIN OrderDetails od ON o.OrderID = od.OrderID
INNER JOIN Employees e ON e.EmployeeID = o.EmployeeID
INNER JOIN Shippers s ON s.ShipperID = o.ShipVia
WHERE c.customerID = "ANTON"

© 2022 Neo4j, Inc. All rights reserved.
SQL to Cypher
SELECT o.ShipName, o.ShipAddress, o.ShipCity,
o.ShipRegion, o.ShipPostalCode, o.ShipCountry,
o.CustomerID, c.CompanyName AS CustomerName,
c.Address, c.City, c.Region,
c.PostalCode, c.Country,
(e.FirstName + ' ' + e.LastName) AS Salesperson,
o.OrderID, o.OrderDate, o.RequiredDate,
o.ShippedDate, s.CompanyName As ShipperName,
od.ProductID, p.ProductName,
od.UnitPrice, od.Quantity,
od.Discount, o.Freight,
(CONVERT(money,(od.UnitPrice*Quantity*(1-Discount)/100))*100) AS ExtendedPrice
FROM Customers c
INNER JOIN Products p ON p.ProductID = od.ProductID
INNER JOIN Orders o ON c.CustomerID = o.CustomerID
INNER JOIN OrderDetails od ON o.OrderID = od.OrderID
INNER JOIN Employees e ON e.EmployeeID = o.EmployeeID
INNER JOIN Shippers s ON s.ShipperID = o.ShipVia
WHERE c.customerID = "ANTON"
https://raw.githubusercontent.com/microsoft/sql-server-samples/master/samples/databases/northwind-pubs/instnwnd.sql
MATCH (c:Customer {customerID: "ANTON"})
-[:PURCHASED]->(o:Order), (o)-[od:ORDERED]->(p:Product),
(p)<-[:SHIPS]-(s:Shipper)
RETURN o.shipName, o.shipAddress, o.shipCity,
o.shipRegion, o.shipPostalCode, o.shipCountry,
c.customerID, c.companyName AS CustomerName,
c.address, c.city, c.region,
c.postalCode, c.country,
e.firstName +" "+ e.lastName AS Salesperson,
o.shippedDate, s.companyName AS ShipperName,
p.productID, p.productName,
od.unitPrice, od.quantity,
od.discount, od.freight,
od.unitPrice * od.quantity
* (1.0-od.discount)/100 * 100 AS ExtendedPrice

© 2022 Neo4j, Inc. All rights reserved.
SQL to Cypher
SELECT o.ShipName, o.ShipAddress, o.ShipCity,
o.ShipRegion, o.ShipPostalCode, o.ShipCountry,
o.CustomerID, c.CompanyName AS CustomerName,
c.Address, c.City, c.Region,
c.PostalCode, c.Country,
(e.FirstName + ' ' + e.LastName) AS Salesperson,
o.OrderID, o.OrderDate, o.RequiredDate,
o.ShippedDate, s.CompanyName As ShipperName,
od.ProductID, p.ProductName,
od.UnitPrice, od.Quantity,
od.Discount, o.Freight,
(CONVERT(money,(od.UnitPrice*Quantity*(1-Discount)/100))*100) AS ExtendedPrice
FROM Customers c
INNER JOIN Products p ON p.ProductID = od.ProductID
INNER JOIN Orders o ON c.CustomerID = o.CustomerID
INNER JOIN OrderDetails od ON o.OrderID = od.OrderID
INNER JOIN Employees e ON e.EmployeeID = o.EmployeeID
INNER JOIN Shippers s ON s.ShipperID = o.ShipVia
WHERE c.customerID = "ANTON"
https://raw.githubusercontent.com/microsoft/sql-server-samples/master/samples/databases/northwind-pubs/instnwnd.sql
MATCH (c:Customer {customerID: "ANTON"})
-[:PURCHASED]->(o:Order), (o)-[od:ORDERED]->(p:Product),
(p)<-[:SHIPS]-(s:Shipper)
RETURN o.shipName, o.shipAddress, o.shipCity,
o.shipRegion, o.shipPostalCode, o.shipCountry,
c.customerID, c.companyName AS CustomerName,
c.address, c.city, c.region,
c.postalCode, c.country,
e.firstName +" "+ e.lastName AS Salesperson,
o.shippedDate, s.companyName AS ShipperName,
p.productID, p.productName,
od.unitPrice, od.quantity,
od.discount, od.freight,
od.unitPrice * od.quantity
* (1.0-od.discount)/100 * 100 AS ExtendedPrice

© 2022 Neo4j, Inc. All rights reserved.
SQL to Cypher
SELECT o.ShipName, o.ShipAddress, o.ShipCity,
o.ShipRegion, o.ShipPostalCode, o.ShipCountry,
o.CustomerID, c.CompanyName AS CustomerName,
c.Address, c.City, c.Region,
c.PostalCode, c.Country,
(e.FirstName + ' ' + e.LastName) AS Salesperson,
o.OrderID, o.OrderDate, o.RequiredDate,
o.ShippedDate, s.CompanyName As ShipperName,
od.ProductID, p.ProductName,
od.UnitPrice, od.Quantity,
od.Discount, o.Freight,
(CONVERT(money,(od.UnitPrice*Quantity*(1-Discount)/100))*100) AS ExtendedPrice
FROM Customers c
INNER JOIN Products p ON p.ProductID = od.ProductID
INNER JOIN Orders o ON c.CustomerID = o.CustomerID
INNER JOIN OrderDetails od ON o.OrderID = od.OrderID
INNER JOIN Employees e ON e.EmployeeID = o.EmployeeID
INNER JOIN Shippers s ON s.ShipperID = o.ShipVia
WHERE c.customerID = "ANTON"
MATCH (c:Customer {customerID: "ANTON"})
-[:PURCHASED]->(o:Order), (o)-[od:ORDERED]->(p:Product),
(p)<-[:SHIPS]-(s:Shipper)
RETURN o.shipName, o.shipAddress, o.shipCity,
o.shipRegion, o.shipPostalCode, o.shipCountry,
c.customerID, c.companyName AS CustomerName,
c.address, c.city, c.region,
c.postalCode, c.country,
e.firstName +" "+ e.lastName AS Salesperson,
o.shippedDate, s.companyName AS ShipperName,
p.productID, p.productName,
od.unitPrice, od.quantity,
od.discount, od.freight,
od.unitPrice * od.quantity
* (1.0-od.discount)/100 * 100 AS ExtendedPrice
https://raw.githubusercontent.com/microsoft/sql-server-samples/master/samples/databases/northwind-pubs/instnwnd.sql

© 2022 Neo4j, Inc. All rights reserved.
SQL to Cypher
SELECT o.ShipName, o.ShipAddress, o.ShipCity,
o.ShipRegion, o.ShipPostalCode, o.ShipCountry,
o.CustomerID, c.CompanyName AS CustomerName,
c.Address, c.City, c.Region,
c.PostalCode, c.Country,
(e.FirstName + ' ' + e.LastName) AS Salesperson,
o.OrderID, o.OrderDate, o.RequiredDate,
o.ShippedDate, s.CompanyName As ShipperName,
od.ProductID, p.ProductName,
od.UnitPrice, od.Quantity,
od.Discount, o.Freight,
(CONVERT(money,(od.UnitPrice*Quantity*(1-Discount)/100))*100) AS ExtendedPrice
FROM Customers c
INNER JOIN Products p ON p.ProductID = od.ProductID
INNER JOIN Orders o ON c.CustomerID = o.CustomerID
INNER JOIN OrderDetails od ON o.OrderID = od.OrderID
INNER JOIN Employees e ON e.EmployeeID = o.EmployeeID
INNER JOIN Shippers s ON s.ShipperID = o.ShipVia
WHERE c.customerID = "ANTON"
MATCH (c:Customer {customerID: "ANTON"})
-[:PURCHASED]->(o:Order), (o)-[od:ORDERED]->(p:Product),
(p)<-[:SHIPS]-(s:Shipper)
RETURN o.shipName, o.shipAddress, o.shipCity,
o.shipRegion, o.shipPostalCode, o.shipCountry,
c.customerID, c.companyName AS CustomerName,
c.address, c.city, c.region,
c.postalCode, c.country,
e.firstName +" "+ e.lastName AS Salesperson,
o.shippedDate, s.companyName AS ShipperName,
p.productID, p.productName,
od.unitPrice, od.quantity,
od.discount, od.freight,
od.unitPrice * od.quantity
* (1.0-od.discount)/100 * 100 AS ExtendedPrice
https://raw.githubusercontent.com/microsoft/sql-server-samples/master/samples/databases/northwind-pubs/instnwnd.sql

© 2022 Neo4j, Inc. All rights reserved.
SQL to Cypher
SELECT o.ShipName, o.ShipAddress, o.ShipCity,
o.ShipRegion, o.ShipPostalCode, o.ShipCountry,
o.CustomerID, c.CompanyName AS CustomerName,
c.Address, c.City, c.Region,
c.PostalCode, c.Country,
(e.FirstName + ' ' + e.LastName) AS Salesperson,
o.OrderID, o.OrderDate, o.RequiredDate,
o.ShippedDate, s.CompanyName As ShipperName,
od.ProductID, p.ProductName,
od.UnitPrice, od.Quantity,
od.Discount, o.Freight,
(CONVERT(money,(od.UnitPrice*Quantity*(1-Discount)/100))*100) AS ExtendedPrice
FROM Customers c
INNER JOIN Products p ON p.ProductID = od.ProductID
INNER JOIN Orders o ON c.CustomerID = o.CustomerID
INNER JOIN OrderDetails od ON o.OrderID = od.OrderID
INNER JOIN Employees e ON e.EmployeeID = o.EmployeeID
INNER JOIN Shippers s ON s.ShipperID = o.ShipVia
WHERE c.customerID = "ANTON"
MATCH (c:Customer)
-[:PURCHASED]->(o:Order), (o)-[od:ORDERED]->(p:Product),
(p)<-[:SHIPS]-(s:Shipper)
WHERE c.customerID = "ANTON"
RETURN o.shipName, o.shipAddress, o.shipCity,
o.shipRegion, o.shipPostalCode, o.shipCountry,
c.customerID, c.companyName AS CustomerName,
c.address, c.city, c.region,
c.postalCode, c.country,
e.firstName +" "+ e.lastName AS Salesperson,
o.shippedDate, s.companyName AS ShipperName,
p.productID, p.productName,
od.unitPrice, od.quantity,
od.discount, od.freight,
od.unitPrice * od.quantity
* (1.0-od.discount)/100 * 100 AS ExtendedPrice
https://raw.githubusercontent.com/microsoft/sql-server-samples/master/samples/databases/northwind-pubs/instnwnd.sql

© 2022 Neo4j, Inc. All rights reserved.
© 2022 Neo4j, Inc. All rights reserved.
Graph Refactoring

© 2022 Neo4j, Inc. All rights reserved.
● The graph as modeled does not answer all of the use cases.
● A new use case has come up that you must account for in your data
model.
● The Cypher for the use cases does not perform optimally, especially
when the graph scales
Why Refactor?

© 2022 Neo4j, Inc. All rights reserved.
● Create an Index or Constraint
● Adding a new label
● Extracting a property into a Node
● Specific relationship types
Methods of Refactoring

© 2022 Neo4j, Inc. All rights reserved.
© 2022 Neo4j, Inc. All rights reserved.
Adding a label
Graph Refactoring

© 2022 Neo4j, Inc. All rights reserved.
AS A
Sales Representative
I WOULD LIKE TO
Recommend alternatives to discontinued stock
SO THAT I CAN
Ensure Customers have a viable alternative to their favorite products
Adding a label

© 2022 Neo4j, Inc. All rights reserved.
● MATCH all (:Product {discontinued: true}) nodes
● Use the SET keyword to add a new label
● Measure the results
Adding a Discontinued label

© 2022 Neo4j, Inc. All rights reserved.
© 2022 Neo4j, Inc. All rights reserved.
Extracting a property into a Node
Graph Refactoring

© 2022 Neo4j, Inc. All rights reserved.
Refactoring Addresses to Nodes
AS A
Operations Manager
I WOULD LIKE TO
Find customers that live at the same address
SO THAT I CAN
Reduce driver workloads and improve delivery times by optimising delivery
routes

© 2022 Neo4j, Inc. All rights reserved.
● Create a Unique Constraint on (:Address)
● Use a MATCH statement to find Customer address details
● Use a MERGE statement to Find or Create an (:Address) Node
● MERGE a -[:HAS_ADDRESS]-> relationship between (:Customer) and
(:Address) Node
Refactoring Addresses to Nodes

© 2022 Neo4j, Inc. All rights reserved.
© 2022 Neo4j, Inc. All rights reserved.
Specific Relationship Types
Graph Refactoring

© 2022 Neo4j, Inc. All rights reserved.
Specific Relationship Types

© 2022 Neo4j, Inc. All rights reserved.
Refactoring Addresses to Nodes
AS A
Head of Electronic Sales
I WOULD LIKE TO
Find customers who have purchased electricals
SO THAT I CAN
Meet my sales targets by recommending them similar products

© 2022 Neo4j, Inc. All rights reserved.
Specific Relationship Types

© 2022 Neo4j, Inc. All rights reserved.
Specific Relationship Types

© 2022 Neo4j, Inc. All rights reserved.
Specific Relationship Types

© 2022 Neo4j, Inc. All rights reserved.
AS A
Warehouse Manager
I WOULD LIKE TO
Predict which products will be purchased in a given month
SO THAT I CAN
Maintain stock levels and manage customer expectations
Real-time Recommendations

© 2022 Neo4j, Inc. All rights reserved.
MATCH (c:Customer)-[:PURCHASED]->(o:Order)-[:ORDERS]->(p:Product)
RETURN c.customerID, p.productName, [ (p)-[:PART_OF]->(ct) |
ct.categoryName ], count(*) AS count ORDER BY count DESC

© 2022 Neo4j, Inc. All rights reserved.
© 2022 Neo4j, Inc. All rights reserved.
Fun with Cypher

© 2022 Neo4j, Inc. All rights reserved.
● Aggregation functions / min, max, count, collect - employee -> order ->
customer, then sum() to get amount, order by total in descending
● List expression to get products [0..5]
●
● WITH
● Sub Queries - customers other orders
● Who bought biscuits
○ Text search - STARTS WITH / ENDS WITH / CONTAINS
○ Exists patterns - where category isn’t in the biscuit
Reading Data

© 2022 Neo4j, Inc. All rights reserved.
AS A
Sales Manager
I WOULD LIKE TO
Find the most customers who have the highest spend
SO THAT I CAN
Reward their loyalty with personalised offers
Real-time Recommendations

© 2022 Neo4j, Inc. All rights reserved.
AS A
Regional Sales Manager
I WOULD LIKE TO
Identify our most active customers in my territory
SO THAT I CAN
Provide them with a personalised offer
Real-time Recommendations

© 2022 Neo4j, Inc. All rights reserved.
AS A
Warehouse Manager
I WOULD LIKE TO
What customers who purchase biscuits also buy
SO THAT I CAN
Shift a palette of biscuits that are near the end of their shelf life
Real-time Recommendations

© 2022 Neo4j, Inc. All rights reserved.
© 2022 Neo4j, Inc. All rights reserved.
Fun with Cypher

© 2022 Neo4j, Inc. All rights reserved.
AS A
Warehouse Manager
I WOULD LIKE TO
Find a list of
SO THAT I CAN
Reward their loyalty with personalised offers
Real-time Recommendations

© 2022 Neo4j, Inc. All rights reserved.
● Find the top 10 orders by total value
● Use a subquery to find their most purchased
products
○ Aggregation
● Use exits
Real-time Recommendations
WITH
Aggregation functions / min, max,
count, collect
Text search - STARTS WITH /
ENDS WITH / CONTAINS
Exists patterns
Profiling
Pattern comprehension
List expressions
Variable length paths
Sub Queries
Parameters

© 2022 Neo4j, Inc. All rights reserved.

© 2022 Neo4j, Inc. All rights reserved.
AS A
Customer
I WOULD LIKE TO
Receive personalised offers
SO THAT I CAN
Save money on my weekly shop
Real-time Recommendations

© 2022 Neo4j, Inc. All rights reserved.
AS A
Warehouse Manager
I WOULD LIKE TO
Sell excess stock as quickly as possible
SO THAT I CAN
Avoid wastage and increase profits
Real-time Recommendations