Intro to Graph Databases and Neo4j (SV JUG Apr, 2014)

Neo Technology, Inc Conﬁdential
Emil Eifrem
emil@neotechnology.com
@emileifrem
#neo4j
Graph All Teh Things!!!11
An Introduction to Graph Databases and Neo4j
SiliconValley JUG, 2014
Thursday, April 17, 14

Agenda
• Graphs Are Eating The World
• Wait! What Is A Graph Anyway?
• Use Cases

WARNING!
ALL I’M OFFERING IS THE TRUTH

Victims

Graphs Are Eating
The World

Graphs Are Eating The World

“Graph analysis is the true killer app for Big Data.”
http://blogs.forrester.com/james_kobielus/11-12-19-the_year_ahead_in_big_data_big_cool_new_stuff_looms_large

“[I]t is arguable that graph databases will have a
bigger impact on the database landscape than
Hadoop or its competitors.”
http://www.bloorresearch.com/blog/IM-Blog/2012/5/graph-databases-nosql.html

http://www.asterdata.com/resources/video-discovery-platform.phpttp://www.zdnet.com/teradata-aster-gets-graph-database-hdfs-compatible-ﬁle-store-7000021667/

http://www.forrester.com/TechRadar+Enterprise+DBMS+Q1+2014/fulltext/-/E-RES106801
“Forrester estimates that over 25% of enterprises
will be using graph databases by 2017”

*Source: http://db-engines.com/en/ranking

Finance
Accenture

What Is A Graph,
Anyway?

Graphs!
Dancing With
Michael Jackson
Eating Brains

Dancing With
Michael Jackson
Eating Brains
Not really.
These are Charts!

What Is “Graph Search,” Anyway?

Cypher
LOVES
A B
Graph PatternsASCII art
MATCH (A) -[:LOVES]-> (B)
WHERE A.name = "A"
RETURN B

MATCH (me:Person)-[:IS_FRIEND_OF]->(friend:Person),
(friend)-[:LIKES]->(restaurant),
(restaurant)-[:LOCATED_IN]->(newyork:City),
(restaurant)-[:SERVES]->(sushi:Cuisine)
WHERE me.name = 'Emil' AND newyork.location='New York' AND
sushi.cuisine='Sushi'
RETURN restaurant.name
http://maxdemarzi.com/?s=facebook

“Find all direct reports and how many they manage, up to 3 levels down”
Example HR Query (using SQL)

*“Find all direct reports and how many they manage, up to 3 levels down”
(SELECT T.directReportees AS directReportees, sum(T.count) AS count
FROM (
SELECT manager.pid AS directReportees, 0 AS count
FROM person_reportee manager
WHERE manager.pid = (SELECT id FROM person WHERE name = "fName lName")
UNION
SELECT manager.pid AS directReportees, count(manager.directly_manages) AS count
GROUP BY directReportees
UNION
SELECT manager.pid AS directReportees, count(reportee.directly_manages) AS count
JOIN person_reportee reportee
ON manager.directly_manages = reportee.pid
UNION
SELECT manager.pid AS directReportees, count(L2Reportees.directly_manages) AS count
JOIN person_reportee L1Reportees
ON manager.directly_manages = L1Reportees.pid
ON L1Reportees.directly_manages = L2Reportees.pid
) AS T
GROUP BY directReportees)
UNION
FROM (
SELECT manager.directly_manages AS directReportees, 0 AS count
UNION
SELECT reportee.pid AS directReportees, count(reportee.directly_manages) AS count
UNION
(continued from previous page...)
SELECT depth1Reportees.pid AS directReportees,
count(depth2Reportees.directly_manages) AS count
) AS T
UNION
FROM(
SELECT reportee.directly_manages AS directReportees, 0 AS count
UNION
SELECT L2Reportees.pid AS directReportees, count(L2Reportees.directly_manages) AS
count
) AS T
UNION
(SELECT L2Reportees.directly_manages AS directReportees, 0 AS count
)
Example HR Query (using SQL)

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
Same Query in Cypher
*“Find all direct reports and how many they manage, up to 3 levels down”

What About Performance?
Database # persons query time
MySQL
Neo4j
Neo4j
1,000
๏a sample social graph
•with ~1,000 persons
๏average 50 friends per person
๏pathExists(a,b) limited to depth 4
๏caches warmed up to eliminate disk I/O

MySQL
Neo4j
Neo4j
1,000 2,000 ms

MySQL
Neo4j
Neo4j
1,000 2,000 ms
1,000 2 ms

MySQL
Neo4j
Neo4j
1,000 2,000 ms
1,000 2 ms
1,000,000

MySQL
Neo4j
Neo4j
1,000 2,000 ms
1,000 2 ms
1,000,000 2 ms

“Our
Neo4j
solution
is
literally
thousands
of
times

faster
than
the
prior
MySQL
solution,
with
queries
that
require
10-‐100
times
less
code.”
-‐
Volker
Pacher,
Senior
Developer
eBay

Real-Time/
OLTP
Ofﬂine/
Batch
Connected Data

Use Cases

Network Impact Analysis

Network Management Example
(Network Graph)

Network Management - Create
CREATE
! (crm {name:"CRM"}),
! (dbvm {name:"Database VM"}),
! (www {name:"Public Website"}),
! (wwwvm {name:"Webserver VM"}),
! (srv1 {name:"Server 1"}),
! (san {name:"SAN"}),
! (srv2 {name:"Server 2"}),
! (crm)-[:DEPENDS_ON]->(dbvm),
! (dbvm)-[:DEPENDS_ON]->(srv2),
! (srv2)-[:DEPENDS_ON]->(san),
! (www)-[:DEPENDS_ON]->(dbvm),
! (www)-[:DEPENDS_ON]->(wwwvm),
! (wwwvm)-[:DEPENDS_ON]->(srv1),
! (srv1)-[:DEPENDS_ON]->(san)
Practical Cypher

Network Management - Impact Analysis
// Server 1 Outage
MATCH (n)<-[:DEPENDS_ON*]-(upstream)
WHERE n.name = "Server 1"
RETURN upstream
Practical Cypher
upstream
{name:"Webserver VM"}
{name:"Public Website"}

Network Management - Dependency Analysis
// Public website dependencies
MATCH (n)-[:DEPENDS_ON*]->(downstream)
WHERE n.name = "Public Website"
RETURN downstream
Practical Cypher
downstream
{name:"Database VM"}
{name:"Server 2"}
{name:"SAN"}
{name:"Webserver VM"}
{name:"Server 1"}

Network Management - Statistics
// Most depended on component
MATCH (n)<-[:DEPENDS_ON*]-(dependent)
RETURN n,
count(DISTINCT dependent)
AS dependents
ORDER BY dependents DESC
LIMIT 1
Practical Cypher
n dependents
{name:"SAN"} 6

Route Finding

Recommendations

Logistics

Access Control

Fraud Detection

Securities and Debt

Basically: Graph All The Things!!!1

Gartner’s “5 Graphs”
Social Graph
Ref: http://www.gartner.com/id=2081316
Interest Graph
Payment Graph
Intent Graph
Mobile Graph

• Network Graph
(e.g. Network Dependency Analysis, Network Inventory, etc.)
• Social Graph
(mobile apps, social recommendations, collaboration)
• Call Graph
(creating inferred social graph, churn reduction, etc.)
• Master Data Graph
(org & product hierarchy, data governance, IAM)
• Help Desk Graph
(enterprise collaboration)
5 Graphs of Telco

• Payment Graph
(e.g. Fraud Detection, Credit Risk Analysis, Chargebacks...)
• Customer Graph
(org drillthru, product recommendations, mobile payments, etc.)
• Entitlement Graph
(identity & access management, authorization)
• Asset Graph
(portfolio analytics, risk management, market & sentiment
analysis, compliance)
(enterprise collaboration, corporate hierarchy, data governance)
5 Graphs of Finance
Finance
Finance

• Provider Graph
(e.g. referrals, patient management, research)
• Patient Graph
(support communities, doctor recommendations, clinical trials)
• Bioinformatic Graph
(drug research, genetic screening, bioengineering, etc.)
(biological master data, evolutionary taxonomy,
the access control graph, etc.)
• Treatment Graph
(collaborative medicine, clinical trials, etc.)
5 Graphs of Health Care

Graph Databases
The Definitive Book on Graph Databases
Available as a free PDF download at
graphdatabases.com!

Brown
Bag
Lunch
By request only!
• you bring 10+ colleagues
• you provide a room with a projector + screen
• we bring a bag lunch
• we introduce Neo4j to your team
in 45 min + 15 min for Q&A
Schedule your Neo4j Intro now!

• The premier source for training on Graph
Databases and Neo4j
• Join us for the ‘All You Can Graph Day’ on May 9 in
Palo Alto!
• Sign up at graphacademy.com

• Wednesday, Oct. 22 - graphconnect.com
• Only conference focused on graphDBs
and applications powered by graphs
• Register now for $99 Alpha Geek Passes
GRAPHCONNECT
SF 2014

• 4/24 - GraphPUB Silicon Valley at Tied House
• 5/1 - Uncovering Invisible Relationships with
a GraphDB at Medium
• 5/6 - GraphPANEL Silicon Valley at AOL
• And more at: meetup.com/graphdb-sf
UPCOMING EVENTS
GRAPHPUB
NEO4J
20 14

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Intro to Graph Databases and Neo4j (SV JUG Apr, 2014)

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Intro to Graph Databases and Neo4j (SV JUG Apr, 2014)