Neo4j Fundamentals

Today’s first graph 
(:Person { name:”Max De Marzi"} )-[:TEACHES]-> (:Database { name:"Neo4j"} )

Logistics
• WIFI-SSID: IvyRoomGuest Password: no password
• Install Neo4j 3.2.1 from USB or from neo4j.com/download
• Coffee break
• Working together today
• Pair on the exercises and help each other!
• Ask relevant questions immediately: raise your hand if you have problems
• Keep larger questions to the breaks / end
• Use the scratch-pad google doc: bit.ly/_fundamentals
• Join neo4j.com/slack and go to the #training-fundamentals channel
• Please fill out the feedback form: bit.ly/neo-survey (gives a discount code)

What are we going to do today?
• Module I Intro to graphs and Neo4j
• Module III First steps in Cypher
• Module III Hands-on Cypher
• Resources

Module I: Intro to Graphs and Neo4j
• Why graphs?
• Intro to the property graph model
• Querying the graph

The world is a graph – everything is connected
• people, places, events
• companies, markets
• countries, history, politics
• sciences, art, teaching
• technology, networks, machines,  
applications, users
• software, code, dependencies,  
architecture, deployments
• criminals, fraudsters and their behavior

Use Cases
Internal Applications
Master Data Management
Network and  
IT Operations
Fraud Detection
Customer-Facing Applications
Real-Time Recommendations
Graph-Based Search
Identity and  
Access Management

Tom Hanks Hugo Weaving
Cloud Atlas
The Matrix
Lana
Wachowski
ACTED_IN
ACTED_IN ACTED_IN
DIRECTED
DIRECTED
Whiteboard friendliness

name: Tom Hanks
born: 1956
title: Cloud Atlas
released: 2012
title: The Matrix
released: 1999
name: Lana Wachowski
born: 1965
ACTED_IN
roles: Zachry
ACTED_IN
roles: Bill Smoke
DIRECTED
DIRECTED
ACTED_IN
roles: Agent Smith
name: Hugo Weaving
born: 1960
Person
Movie
Movie
Person Director
ActorPerson Actor
Whiteboard friendliness

Neo4j Fundamentals
• Nodes
• Relationships
• Properties
• Labels

CAR
Property Graph Model Components
Nodes
• Represent the objects in the graph
• Can be labeled
PERSON PERSON

CAR
DRIVES
Nodes
•Represent the objects in the graph
•Can be labeled
Relationships
• Relate nodes by type and direction
LOVES
LOVES
LIVES WITH
OW
NS
PERSON PERSON

CAR
DRIVES
name: “Dan”
born: May 29, 1970
twitter: “@dan”
name: “Ann”
born: Dec 5, 1975
since:  
Jan 10, 2011
brand: “Volvo”
model: “V70”
Nodes
•Represent the objects in the graph
•Can be labeled
Relationships
•Relate nodes by type and direction
Properties
• Name-value pairs that can go on
nodes and relationships.
LOVES
LOVES
LIVES WITH
OW
NS
PERSON PERSON

Exercise: Graphs are all around us
Can you identify nodes and relationships in the room?

Summary of the graph building blocks
• Nodes - Entities and complex value types
• Relationships - Connect entities and structure domain
• Properties - Entity attributes, relationship qualities, metadata
• Labels - Group nodes by role

A pattern matching query language made for graphs
21
Cypher

22
• Declarative
• Expressive
• Pattern Matching
Cypher

Pattern in our Graph Model
LOVES
Dan Ann
NODE NODE
Relationship

Cypher: Express Graph Patterns
(:Person { name:"Dan"} ) -[:LOVES]-> (:Person { name:"Ann"} )
LOVES
Dan Ann
LABEL PROPERTY
NODE NODE
LABEL PROPERTY
Relationship

Cypher: CREATE Graph Patterns
CREATE (:Person { name:"Dan"} ) -[:LOVES]-> (:Person { name:"Ann"} )
LOVES
Dan Ann
LABEL PROPERTY
NODE NODE
LABEL PROPERTY
Relationship

Cypher: MATCH Graph Patterns
MATCH (:Person { name:"Dan"} ) -[:LOVES]-> ( whom ) RETURN whom
LOVES
Dan ?
VARIABLE
NODE NODE
LABEL PROPERTY
Relationship

Module II: First steps in Cypher
• ASCII-Art
• Demo
• Constraints
• Let's graph our group!

Module II: First steps in Cypher
We will learn how to:
• represent nodes and relationships in Cypher
• create and find nodes
• add properties
• use constraints and create nodes uniquely
• create relationships
• find patterns
• delete data

Ascii Art: Nodes
() or (n)
• Surrounded with parentheses
• Use an alias to refer to our node later
(n:Label1)
• Specify a Label, starts with a colon :
• Group nodes by roles or types, think of labels as tags
(n:Label {prop: 'value'})
• Nodes can have properties

Ascii Art: Relationships
--> or -[r:TYPE]->
• Wrapped with hyphens & square brackets
• Like labels, a relationship type starts with a colon :
< > Specify the direction of the relationship
-[:KNOWS {since: 2010}]->
• Relationships can have properties too!

Ascii Art: Relationships
(n:Label {prop:'value'})-[:TYPE]->(m:Label)
• mini graph examples that we want to search for
• use variables for later use
(p1:Person {name:'Alice'})-[:KNOWS]->(p2:Person {name:'Bob')

CREATE a person node within the graph to represent yourself! 
 
 
 
 
Label of :Person
Property :name should be 'My Name'
Let's try it - CREATE myself!

CREATE statement:
CREATE (p:Person {name: 'My Name'}) 
RETURN p;
Properties are stored as key-value pairs {key:'value'}
Welcome to the graph

CREATE (p:Person {name: 'My Name'}) 
RETURN p; 
 
Then run the following query
MATCH (p:Person {name: 'My Name'}) 
RETURN p 
 
OOOPS! I created two of me, that's not good.
Let’s try running the create statement again

Unique Constraints
We create unique constraints to:
• ensure uniqueness
• allow fast lookup of nodes which match label-property pairs.

We create unique constraints to:
• ensure uniqueness
 
CREATE CONSTRAINT ON (label:Label) 
ASSERT label.property IS UNIQUE
Unique Constraints

Let’s create a constraint on :Person(name): 
CREATE CONSTRAINT ON (p:Person)
ASSERT p.name IS UNIQUE;
Creating a constraint

Let’s create a constraint on :Person(name): 
 
Unable to create CONSTRAINT ON ( person:Person ) ASSERT person.name IS UNIQUE: 
Multiple nodes with label `Person` have property `name` = 'My Name': 
node(292) 
node(293) 
 
Oops! Our data violates the constraint we’re trying to create.

We’ll first get rid of all the nodes we’ve created:
MATCH (n)
DETACH DELETE n;
 
And create the constraint again: 
 

Let's do it together
• Let’s create a graph of our group together!
• Open your browser to http://l.neo4j.org/trainer
• And type along with me.
• Don't be afraid, this is just a sandbox in a playground.

CREATE statement: 
CREATE (p:Person {name: 'Your Name'}) 
RETURN p 
 
Let's find ourselves: CREATE -> MATCH
MATCH (p:Person {name: 'Your Name'})
RETURN p;
Your turn: CREATE a node for yourself

SET and update properties
Find yourself and SET your city as a property.
SET p.city = 'Your City'
RETURN p;

Let's create ourselves twice. What happens now?
CREATE (p:Person {name: 'Your Name'})
MERGEto the rescue:
• MATCHes the whole pattern to find it
• If not found CREATE the pattern
• Relies on constraint for strong guarantees
MERGE (p:Person {name: 'Your Name'})
RETURN p
MERGE - Find Or Create

Most challenging task of the course:  
Ask your right neighbor for their name. 
Find the pair of you: 
MATCH (p1:Person {name: 'Your Name'}) 
MATCH (p2:Person {name: 'Your Neighbor'}) 
RETURN p1, p2;
Or in one statement:
MATCH (p1:Person {name: 'Your Name'}), 
(p2:Person {name: 'Your Neighbor'})
RETURN p1, p2;
Creating relationships

Create a relationship between you and your neighbor: 
MATCH (p1:Person {name: 'Your Name'})
MATCH (p2:Person {name: 'Your Neighbor'})
CREATE (p1)-[:KNOWS]->(p2);
CREATEing relationships

To avoid duplicate relationships, use MERGE
MERGE (p1:Person {name: 'Your Name'}) 
MERGE (p2:Person {name: 'Your Neighbor'}) 
MERGE (p1)-[:KNOWS]->(p2);
Relationship is created uniquely by
• type
• direction (can be left off)
• properties
MERGEing relationships

Let's clean up and delete ourselves
MATCH (p:Person {name: 'Your Name'}) 
DELETE p;
Oops! We can’t delete a node that still has relationships attached. We
need to delete those as well.
Cleaning up

Let's clean up and delete ourselves
MATCH (p:Person {name: 'Your Name'}) 
DELETE p;
Oops! We can’t delete a node that still has relationships attached. We
need to delete those as well.
 
DETACH DELETE p;
Cleaning up

Open these in your browser. They’ll be useful for the rest of the session:
● Cypher Reference Card 
http://neo4j.com/docs/cypher-refcard/
● Neo4j Developer Pages  
http://neo4j.com/developer/cypher
● Neo4j Documentation  
http://neo4j.com/docs
Useful resources for learning Cypher

Module III: Hands-on with Cypher
• Setup
• Cypher basics
• Aggregates
• Constraints and indexes
• Write queries
• Style and modeling conventions
• Modeling exercise: Movie genres

We will learn how to
• represent nodes and relationships in Cypher
• create and find nodes
• add properties
• use constraints and create nodes uniquely
• create relationships
• find patterns
• delete data

1. Start the server.
2. It should be running on: http://localhost:7474
3. Log-in with default credentials
user: neo4j password: neo4j
4. Choose a new password
We’re good to go!
Make sure you’ve got Neo4j running

:play movies
Type the following command into the query editor: 
 
:play movies

• Change node colors
• Change which node property is displayed
• Run queries with CMD (CTRL) + Enter
• Insert new line with SHIFT + Enter
• Expand the query bar with ESC
• Double-click a node and see what happens!
• Use :clear to clear past results
• CMD (CTRL) + Up Arrow to scroll through past queries
Neo4j Browser 101

Nodes
Nodes are drawn with parentheses. 
()

Relationships
Relationships are drawn with square brackets. 
[]

Patterns
Patterns are drawn by connecting nodes and relationships with hyphens,
optionally specifying a direction with > and < signs. 
 
()-[]-()
()-[]->()
()<-[]-()

The components of a Cypher query
MATCH (m:Movie) 
RETURN m 
 
MATCH and RETURN are Cypher keywords 
m is a variable 
:Movie is a node label

MATCH (p:Person)-[r:ACTED_IN]->(m:Movie) 
RETURN p, r, m 
 
p,r, and m are variables 
:Movie is a node label 
:ACTED_IN is a relationship type

MATCH path = (:Person)-[:ACTED_IN]->(:Movie) 
RETURN path 
 
path is a variable 
:Movie is a node label 
:ACTED_IN is a relationship type

MATCH (m:Movie) 
RETURN m
Graph versus Tabular results

MATCH (m:Movie) 
RETURN m.title, m.released 
 
Properties are accessed with {variable}.{property_key}
Graph versus Tabular results

Case sensitive 
 
Node labels
Relationship types
Property keys
Case insensitive 
 
Cypher keywords 
Case sensitivity

Case sensitive 
 
:Person
:ACTED_IN
name
Case insensitive 
 
MaTcH
return
 
Case sensitivity

Cypher: The Basics
 
:play http://guides.neo4j.com/fundamentals/cypher_the_basics.html

Follow the guides in your browser until you see...

Aggregates
Aggregate queries in Cypher are a little bit different than in SQL as we
don’t need to specify a grouping key.

Aggregates
We implicitly group by any non aggregate fields in the RETURN
statement. 
// implicitly groups by p.name 
MATCH (p:Person)-[:ACTED_IN]->(m:Movie) 
RETURN p.name, count(*) AS movies

Other aggregate functions
There are other aggregate functions as well.  
 
You can see the full list on the Cypher refcard: 
neo4j.com/docs/cypher-refcard/current/

Continue with the guide
Continue with the guide in your browser

:schema
 
:schema

There are three types of unique constraints:
• Unique node property constraint
Unique Constraints

 
ASSERT label.property IS UNIQUE
Unique Constraints

• Node property existence constraint
Unique Constraints

ASSERT EXISTS(label.name)
Unique Constraints

• Relationship property existence constraint
Unique Constraints

• Relationship property existence constraint
CREATE CONSTRAINT ON ()-[rel:REL_TYPE]->() 
ASSERT EXISTS(rel.name)
Unique Constraints

Indexes
We create indexes to:

Indexes
We create indexes to:
 
CREATE INDEX ON :Label(property)

What are these fast lookups?
The following predicates use indexes:
• Equality
• STARTS WITH
• CONTAINS
• ENDS WITH
• Range searches
• (Non-) existence checks

How are indexes used in Neo4j?
Indexes are only used to find the starting points for queries.
Use index scans to look up rows in
tables and join them with rows
from other tables
Use indexes to find the starting
points for a query.
Relational Graph

Cypher: The Basics
 
:play http://guides.neo4j.com/fundamentals/cypher_constraints.html

The CREATE Clause
CREATE (m:Movie {title:'Mystic River', released:2003})
RETURN m

The SET Clause
MATCH (m:Movie {title: 'Mystic River'})
SET m.tagline = 'We bury our sins here, Dave. We wash them clean.'
RETURN m

The CREATE Clause
MATCH (m:Movie {title: 'Mystic River'})
MATCH (p:Person {name: 'Kevin Bacon'})
CREATE (p)-[r:ACTED_IN {roles: ['Sean']}]->(m)
RETURN p, r, m

The MERGE Clause
MERGE (p:Person {name: 'Tom Hanks'})
RETURN p

The MERGE Clause
MERGE (p:Person {name: 'Tom Hanks', oscar: true})
RETURN p

The MERGE Clause
MERGE (p:Person {name: 'Tom Hanks', oscar: true})
RETURN p 
 
There is not a :Person node with name:'Tom Hanks' and oscar:true in the
graph, but there is a :Person node with name:'Tom Hanks'.  
 
What do you think will happen here?

The MERGE Clause
SET p.oscar = true
RETURN p

The MERGE Clause
MERGE (p:Person {name: 'Tom Hanks'})-[:ACTED_IN]
->(m:Movie {title: 'The Terminal'})
RETURN p, m

The MERGE Clause
MERGE (p:Person {name: 'Tom Hanks'})-[:ACTED_IN]
->(m:Movie {title: 'The Terminal'})
RETURN p, m
 
There is not a :Movie node with title:"The Terminal" in the graph, but there
is a :Person node with name:"Tom Hanks".  
 
What do you think will happen here?

MERGE (m:Movie {title: 'The Terminal'})
MERGE (p)-[r:ACTED_IN]->(m)
RETURN p, r, m
The MERGE Clause

MERGE (p:Person {name: 'Your Name'})
ON CREATE SET p.created = timestamp(), p.updated = 0
ON MATCH SET p.updated = p.updated + 1
RETURN p.created, p.updated;
ON CREATE and ON MATCH

Clauses
Cypher clauses should be written in ALL CAPS and on their own line. 
 
MATCH ... 
WHERE ... 
RETURN ...

Functions
Functions should be written in lowerCamelCase. 
 
MATCH path = allShortestPaths() ... 
WHERE size(nodes(path)) ...  
RETURN ...

Keywords
Keywords should be written in ALL CAPS but don’t need to go on their
own line. 
 
MATCH ... 
RETURN collect(DISTINCT n)

Labels
Labels are written in UpperCamelCase. 
 
CREATE(n:Person) 
CREATE(n:GraphDatabase) 
CREATE(n:VeryDescriptiveLabel)

Relationship Types
Relationships are written in UPPERCASE_WITH_UNDERSCORES (to
separate words). 
 
CREATE(n)-[:LOVES]->(m) 
CREATE(n)-[:REALLY_LOVES]->(m) 
CREATE(n)-[:IS_IN_LOVE_WITH]->(m)

Properties are written in lowerCamelCase. 
 
CREATE(n) 
SET n.name = 'Dave' 
 
CREATE(n) 
SET n.firstName = 'Dave' 
 
CREATE(n) 
SET n.fullName = 'Dave Gordon'
Properties

The age old question: should we model them as properties or as nodes? 
 
Adding movie genres
vs

MATCH (m:Movie {title: 'The Matrix'}) 
SET m.genre = ['Action', 'Sci-Fi'] 
RETURN m
Genres as properties

MATCH (m:Movie {title: 'Mystic River'}) 
SET m.genre = ['Action', 'Mystery'] 
RETURN m
 
 
Genres as properties

Accessing a movie’s genres is quick and easy.
MATCH (m:Movie {title:"The Matrix"}) 
RETURN m.genre;
The good side of properties

Finding movies that share genres is painful and we have a disconnected
pattern in the MATCH clause - a sure sign you have a modeling issue.
MATCH (m1:Movie), (m2:Movie) 
WHERE any(x IN m1.genre WHERE x IN m2.genre) 
AND m1 <> m2 
RETURN m1, m2;
The bad side of properties

MATCH (m:Movie {title:"The Matrix"}) 
MERGE (action:Genre {name:"Action"}) 
MERGE (scifi:Genre {name:"Sci-Fi"}) 
MERGE (m)-[:IN_GENRE]->(action) 
MERGE (m)-[:IN_GENRE]->(scifi)
 
 
Genres as nodes

MATCH (m:Movie {title:"Mystic River"}) 
MERGE (action:Genre {name:"Action"}) 
MERGE (mystery:Genre {name:"Mystery"}) 
MERGE (m)-[:IN_GENRE]->(action) 
MERGE (m)-[:IN_GENRE]->(mystery)
Genres as nodes

Finding movies that share genres is a natural graph pattern.
MATCH (m1:Movie)-[:IN_GENRE]->(g:Genre), 
(m2:Movie)-[:IN_GENRE]->(g) 
RETURN m1, m2, g
The good side of nodes

Accessing the genres of movies requires a bit more typing.
MATCH (m:Movie {title:"The Matrix"}), 
(m)-[:IN_GENRE]->(g:Genre) 
RETURN g.name;
The (not too) bad side of nodes

Thank you for your time! 
Please help us improve: bit.ly/neo-survey .
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