RDBMS to Graphs

(RDBMS) -[:TO]->(Graph)
An Introduction To Graphs and Neo4j
William Lyon
@lyonwj

William Lyon
Developer Relations Engineer @neo4j
will@neo4j.com
@lyonwj
lyonwj.com

AGENDA
• Use Cases
• SQL Pains
• Building a Neo4j Application
• Moving from RDBMS -> Graph Models
• Walk through an Example
• Creating Data in Graphs
• Querying Data

The Traditional Approach Towards Data

HR-tools Supply Payments Logistics CRM Support
TRADITIONAL DATA STRUCTURE
RDBMS RDBMS RDBMSRDBMS RDBMS RDBMS

SHIFT TOWARDS SYSTEMS OF ENGAGEMENT

Users Engaging With DevicesUsers Engaging With Users Devices Engaging With Devices
SYSTEMS OF ENGAGEMENT

SHIFT TOWARDS SYSTEMS OF ENGAGEMENT
You are here!
Data volume

SYSTEMS OF RECORD
Relational Database Model
Structured
Pre-computed
Based on rigid rules
NoSQL Database Model
Highly Flexible
Real-Time Queries
Highly Contextual

This is data modeled as a graph!

A Graph Is
NODE
NODE
NODE
RELATIONSHIP
RELATIONSHIP
RELATIONSHIP

HAS
AVAILABLE
HOTEL
ROOMS
AVAILABLE

CREATING COMPETITIVE ADVANTAGE
FROM THE USE OF DATA

ACCOUNT
HOLDER 2
ACCOUNT
HOLDER 1
ACCOUNT
HOLDER 3
CREDIT
CARD
BANK
ACCOUNT
BANK
ACCOUNT
BANK
ACCOUNT
ADDRESS
PHONE NUMBER
PHONE NUMBER
SSN 2
UNSECURE LOAN
SSN 2
UNSECURE LOAN
CREDIT
CARD

Speed
“We found Neo4j to be literally thousands of times faster
than our prior MySQL solution, with queries that require
10-100 times less code. Today, Neo4j provides eBay with
functionality that was previously impossible.”
- Volker Pacher, Senior Developer
“Minutes to milliseconds” performance
Queries up to 1000x faster than RDBMS or other NoSQL

A Naturally Adaptive Model
A Query Language Designed
for Connectedness
+
=Agility

Cypher
Typical Complex SQL Join The Same Query using Cypher
MATCH (boss)-[:MANAGES*0..3]->(sub),
(sub)-[:MANAGES*1..3]->(report)
WHERE boss.name = “John Doe”
RETURN sub.name AS Subordinate,  
count(report) AS Total
Project Impact
Less time writing queries
Less time debugging queries
Code that’s easier to read

NEO4j USE CASES
Real Time Recommendations
Master Data Management
Fraud Detection
Identity & Access Management
Graph Based Search
Network & IT-Operations

NEO4j USE CASES
Fraud Detection
Graph Based Search
GRAPH THINKING:
VIEWED
VIEWED
BOUGHT
VIEWED
BOUGHT
BOUGHT
BOUGHT
BOUGHT

“As the current market leader in graph databases,
and with enterprise features for scalability and
availability, Neo4j is the right choice to meet our
demands.” Marcos Wada
Software Developer, Walmart
NEO4j USE CASES
Fraud Detection
Graph Based Search

NEO4j USE CASES
Fraud Detection
Graph Based Search
GRAPH THINKING:
MANAGES
MANAGES
LEADS
REGION
M
ANAG
ES
MANAGES
REGION
LEADS
LEADS
COLLABORATES

Neo4j is the heart of Cisco HMP: used for governance
and single source of truth and a one-stop shop for all
of Cisco’s hierarchies.
NEO4j USE CASES
Fraud Detection
Graph Based Search

NEO4j USE CASES
Fraud Detection
Graph Based Search
GRAPH THINKING:
Solu%on
Support
Case
Support
Case
Knowledge
Base Ar%cle
Message
Knowledge
Base Ar%cle
Knowledge
Base Ar%cle
Neo4j is the heart of Cisco’s Helpdesk Solution too.

NEO4j USE CASES
Fraud Detection
Graph Based Search
GRAPH THINKING:
Fraud Detection
O
PENED_ACCO
UNT
HAS
IS_ISSUED
HAS
LIVES
LIVES
IS_ISSUED
OPENED_ACCOUNT

“Graph databases offer new methods of uncovering
fraud rings and other sophisticated scams with a
high-level of accuracy, and are capable of stopping
advanced fraud scenarios in real-time.”
Gorka Sadowski
Cyber Security Expert
NEO4j USE CASES
Fraud Detection
Graph Based Search

GRAPH THINKING:
Graph Based Search
NEO4j USE CASES
Fraud Detection
Graph Based Search
PUBLISH
INCLUDE
INCLUDE
CREATE
CAPTURE
IN
IN
SOURCE
USES
USES
IN
IN
USES
SOURCE
SOURCE

Uses Neo4j to manage the digital assets inside of its next
generation in-flight entertainment system.
NEO4j USE CASES
Fraud Detection
Graph Based Search

NEO4j USE CASES
Fraud Detection
Graph Based Search
BROWSES
CONNECTS
BRIDGES
ROUTES
POWERS
ROUTES
POWERS
POWERS
HOSTS
QUERIES
GRAPH THINKING:

Uses Neo4j for network topology analysis
for big telco service providers
NEO4j USE CASES
Fraud Detection
Graph Based Search

GRAPH THINKING:
Identity And Access Management
NEO4j USE CASES
Fraud Detection
Graph Based Search
TRUSTS
TRUSTS
ID
ID
AUTHENTICATES
AUTHENTICATES
O
W
NS
OWNS
CAN_READ

UBS was the recipient of the 2014
Graphie Award for “Best Identify And
Access Management App”
NEO4j USE CASES
Fraud Detection
Graph Based Search

Neo4j Adoption by Selected Verticals
SOFTWARE
FINANCIAL
SERVICES
RETAIL
MEDIA &
BROADCASTING
SOCIAL
NETWORKS
TELECOM HEALTHCARE

SQL
Day in the Life of a RDBMS Developer

SELECT
p.name,
c.country, c.leader, p.hair,
u.name, u.pres, u.state
FROM
people p
LEFT JOIN country c ON c.ID=p.country
LEFT JOIN uni u ON p.uni=u.id
WHERE
u.state=‘CT’

JOIN
JOIN
JOIN
JOIN
JOIN
JOIN
JOIN
JOIN
JOIN
JOIN
JOIN
JOIN
JOIN
JOIN
JOIN
JOIN
JOIN
JOIN
JOIN
JOIN
JOIN
JOIN

• Complex to model and store relationships
• Performance degrades with increases in data
• Queries get long and complex
• Maintenance is painful
SQL Pains

• Easy to model and store relationships
• Performance of relationship traversal remains constant with
growth in data size
• Queries are shortened and more readable
• Adding additional properties and relationships can be done on
the ﬂy - no migrations
Graph Gains

Relational Versus Graph Models
Relational Model Graph Model
KNOWS
KNOWS
KNOWS
ANDREAS
TOBIAS
MICA
DELIA
Person FriendPerson-Friend
ANDREAS
DELIA
TOBIAS
MICA

What does this Graph look like?

Property Graph Model
CREATE (:Person { name:“Dan”} ) - [:LOVES]-> (:Person { name:“Ann”} )
LOVES
LABEL PROPERTY
NODE NODE
LABEL PROPERTY

MATCH
(p:Person)-[:WENT_TO]->(u:Uni),
(p)-[:LIVES_IN]->(c:Country),
(u)-[:LED_BY]->(l:Leader),
(u)-[:LOCATED_IN]->(s:State)
WHERE
s.abbr = ‘CT’
RETURN
p.name,
c.country, c.leader, p.hair,
u.name, l.name, s.abbr

How do you use Neo4j?
CREATE MODEL
+
LOAD DATA QUERY DATA

Architectural Options
Data Storage and
Business Rules Execu5on
Data Mining
and Aggrega5on
Applica'on
Graph Database Cluster
Neo4j Neo4j Neo4j
Ad Hoc
Analysis
Bulk Analy'c
Infrastructure
Hadoop, EDW …
Data
Scien'st
End User
Databases
Rela5onal
NoSQL
Hadoop

RDBMS to Graph Options
MIGRATE
ALL DATA
MIGRATE
SUBSET
DUPLICATE
SUBSET
Non-Graph Queries Graph Queries
Graph Queries Non-Graph Queries
All Queries
Rela3onal
Database
Graph
Database
Application
Application
Application
Non Graph
Data
All Data

Northwind - the canonical RDBMS Example

(Simple) JOIN Tables Become Relationships

Attributed JOIN Tables -> Relationships with Properties

Property Graph Model
CREATE (:Employee{ ﬁrstName:“Steven”} ) -[:REPORTS_TO]-> (:Employee{ ﬁrstName:“Andrew”} )
REPORTS_TO
Steven Andrew
LABEL PROPERTY
NODE NODE
LABEL PROPERTY

Who do people report to?
MATCH
(e:Employee)<-[:REPORTS_TO]-(sub:Employee)
RETURN
*

Who do people report to?
MATCH
(e:Employee)<-[:REPORTS_TO]-(sub:Employee)
RETURN
e.employeeID AS managerID,
e.firstName AS managerName,
sub.employeeID AS employeeID,
sub.firstName AS employeeName;

Who does Robert report to?
MATCH
p=(e:Employee)<-[:REPORTS_TO]-(sub:Employee)
WHERE
sub.firstName = ‘Robert’
RETURN
p

What is Robert’s reporting chain?
MATCH
p=(e:Employee)<-[:REPORTS_TO*]-(sub:Employee)
WHERE
sub.firstName = ‘Robert’
RETURN
p

What is Robert’s reporting chain?

Who’s the Big Boss?
MATCH
(e:Employee)
WHERE
NOT (e)-[:REPORTS_TO]->()
RETURN
e.firstName as bigBoss

Product Cross-Selling
MATCH
(choc:Product {productName: 'Chocolade'})
<-[:INCLUDES]-(:Order)<-[:SOLD]-(employee),
(employee)-[:SOLD]->(o2)-[:INCLUDES]->(other:Product)
RETURN
employee.firstName,
other.productName,
COUNT(DISTINCT o2) as count
ORDER BY
count DESC
LIMIT 5;

https://neo4j.com/graphacademy/online-training/

http://network.graphdemos.com/

3 Steps to Creating the Graph
IMPORT NODES CREATE INDEXES IMPORT RELATIONSHIPS

Importing Nodes
// Create customers
USING PERIODIC COMMIT
LOAD CSV WITH HEADERS FROM "https://
raw.githubusercontent.com/neo4j-contrib/developer-resources/
gh-pages/data/northwind/customers.csv" AS row
CREATE (:Customer {companyName: row.CompanyName, customerID:
row.CustomerID, fax: row.Fax, phone: row.Phone});
// Create products
gh-pages/data/northwind/products.csv" AS row
CREATE (:Product {productName: row.ProductName, productID:
row.ProductID, unitPrice: toFloat(row.UnitPrice)});

Importing Nodes
// Create suppliers
gh-pages/data/northwind/suppliers.csv" AS row
CREATE (:Supplier {companyName: row.CompanyName, supplierID:
row.SupplierID});
// Create employees
gh-pages/data/northwind/employees.csv" AS row
CREATE (:Employee {employeeID:row.EmployeeID, firstName:
row.FirstName, lastName: row.LastName, title: row.Title});

Importing Nodes
// Create categories
gh-pages/data/northwind/categories.csv" AS row
CREATE (:Category {categoryID: row.CategoryID, categoryName:
row.CategoryName, description: row.Description});
// Create orders
gh-pages/data/northwind/orders.csv" AS row
MERGE (order:Order {orderID: row.OrderID}) ON CREATE SET
order.shipName = row.ShipName;

Creating Indexes
CREATE INDEX ON :Product(productID);
CREATE INDEX ON :Product(productName);
CREATE INDEX ON :Category(categoryID);
CREATE INDEX ON :Employee(employeeID);
CREATE INDEX ON :Supplier(supplierID);
CREATE INDEX ON :Customer(customerID);
CREATE INDEX ON :Customer(customerName);

Creating Relationships
gh-pages/data/northwind/orders.csv" AS row
MATCH (order:Order {orderID: row.OrderID})
MATCH (customer:Customer {customerID: row.CustomerID})
MERGE (customer)-[:PURCHASED]->(order);
gh-pages/data/northwind/products.csv" AS row
MATCH (product:Product {productID: row.ProductID})
MATCH (supplier:Supplier {supplierID: row.SupplierID})
MERGE (supplier)-[:SUPPLIES]->(product);

LOAD CSV WITH HEADERS FROM "https://raw.githubusercontent.com/neo4j-
contrib/developer-resources/gh-pages/data/northwind/orders.csv" AS row
MERGE (order)-[pu:INCLUDES]->(product)
ON CREATE SET pu.unitPrice = toFloat(row.UnitPrice), pu.quantity =
toFloat(row.Quantity);
LOAD CSV WITH HEADERS FROM "https://raw.githubusercontent.com/neo4j-
contrib/developer-resources/gh-pages/data/northwind/orders.csv" AS row
MATCH (employee:Employee {employeeID: row.EmployeeID})
MERGE (employee)-[:SOLD]->(order);

LOAD CSV WITH HEADERS FROM "https://raw.githubusercontent.com/
neo4j-contrib/developer-resources/gh-pages/data/northwind/
products.csv" AS row
MATCH (category:Category {categoryID: row.CategoryID})
MERGE (product)-[:PART_OF]->(category);
LOAD CSV WITH HEADERS FROM "https://raw.githubusercontent.com/
neo4j-contrib/developer-resources/gh-pages/data/northwind/
employees.csv" AS row
MATCH (employee:Employee {employeeID: row.EmployeeID})
MATCH (manager:Employee {employeeID: row.ReportsTo})
MERGE (employee)-[:REPORTS_TO]->(manager);

High Performance LOADing
neo4j-import
4.58 million things

and their relationships…

Loads in 100 seconds!

https://github.com/neo4j-contrib/neo4j-apoc-procedures

User Defined Procedures
User-defined procedures are written in Java,

deployed into the database, and called from Cypher.
http://neo4j.com/docs/developer-manual/current/#procedures

User-defined Procedures
https://github.com/neo4j-examples/neo4j-procedure-template

Apoc Procedures

THERE’S A
PROCEDURE
FOR THAT

https://neo4j-contrib.github.io/neo4j-apoc-procedures/
#_loading_data_from_web_apis_json_xml_csv

https://github.com/neo4j-contrib/neo4j-apoc-procedures/blob/master/readme.adoc

https://neo4j.com/blog/icij-neo4j-unravel-panama-papers/

https://neo4j.com/blog/ofﬁcial-neo4j-jdbc-driver-3-0/

Resources
http://neo4j.com/developer/
Neo4j Developer Resources

RDBMS to Graphs

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