This document discusses Neo4j and how understanding its technology can help solve problems. It begins by explaining how Neo4j differs from traditional databases by using an index-free adjacency model that allows for faster querying of highly connected graph data. It then provides perspectives on Neo4j from an IT portfolio view and technical architecture view. It concludes by discussing how Neo4j can enable connected enterprises by powering applications for analytics, transactions, integration and visualization of graph data.

1. Neo4j: What’s Under the Hood
How knowing this can help you
Philip Rathle
VP of Product Management
@prathle

2. 1. Choose the right technology tool for the job
2. Solve intractable problems: (Business) <--> ( IT)
3. Identify new business opportunities

3. 3
(Perspectives)-[:Shape]->(Understanding)

4. 1. A Historical Perspective

5. Data Management in 1979
Paper Forms
Tiny RAM Spinning Platters
(Low Capacity /
Slow, Sequential IO) RDBMS
Relational Model
The RDBMS Era
Confidential - Neo4j, Inc.

6. Data Management Today
Dynamic Real-World Systems
Abundant
RAM
Flash & IO Co-
Processors
(High-Capacity Storage &
Ultra-Fast Random I/O)
Confidential - Neo4j, Inc.
A New Graph Era Emerging
Neo4j
Property Graph Model
Real-Time
Connected Data

7. 2. An IT Portfolio Perspective

8. 8
TRADITIONAL
DATABASES
Store and retrieve data
Real time storage & retrieval
Up to
3
Max #
of
hops
IT Portfolio Perspective

9. 9
TRADITIONAL
DATABASES
BIG DATA
TECHNOLOGY
Store and retrieve data Aggregate and filter data
Real time storage & retrieval
Long running queries
Aggregation & filtering
Up to
3
Max #
of
hops
1
IT Portfolio Perspective

10. 10
TRADITIONAL
DATABASES
BIG DATA
TECHNOLOGY
Store and retrieve data Aggregate and filter data Connections in data
Real time storage & retrieval Real-Time Connected Insights
Long running queries
Aggregation & filtering
“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
Up to
3
Max #
of
hops
1 Millions
IT Portfolio Perspective

11. Illustration by David Somerville based on the original by Hugh McLeod (@gapingvoid)
RDBMS
&
Aggregate-
Oriented NoSQL
Hadoop /
EDW/
Columnar
RDBMS
|<———————- Graph Database & ———————>|
Graph Compute Engine
(Graph Transactions & Analytics)

12. 3. A Technical Architecture Perspective
Core Technology Differences

13. What Makes Neo4j Different?
Index-Free Adjacency
13

14. Connectedness and Size of Data Set
ResponseTime
Relational and
Other NoSQL
Databases
0 to 2 hops
0 to 3 degrees
Thousands of connections
1000x
Advantage
Tens to hundreds of hops
Thousands of degrees
Billions of connections
Neo4j
“Minutes to
milliseconds”
This Enables:
“Minutes to Milliseconds” Real-Time Query Performance

15. ACID Consistency Non ‘Graph-ACID’ DBMSs
15
Maintains Integrity Over Time
Guaranteed Graph Consistency
Becomes Corrupt Over Time
Not ‘Good Enough’ for Graphs
And is Supported By:
ACID Graph Writes : A Requirement for Graph Transactions

16. What Is Different In Neo4j?
Cypher Query Language
16
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
• More time understanding the answers
• Leaving time to ask the next question
Less time debugging queries:
• More time writing the next piece of code
• Improved quality of overall code base
Code that’s easier to read:
• Faster ramp-up for new project members
• Improved maintainability & troubleshooting

17. 17
Neo4j Graph Database: Foundational Components
1
2
3
4
5
6
Index-Free Adjacency
In memory and on flash/disk
vs
ACID Foundation
Required for safe writes
Full-Stack Clustering
Causal consistency
Language, Drivers, Tooling
Developer Experience,
Graph Efficiency, Type Safety
Graph Engine
Cost-Based Optimizer, Graph
Statistics, Cypher Runtime
Hardware Optimizations
For next-gen infrastructure

18. Neo4j Graph Database: Enterprise Infrastructure
18
Neo4j Security Foundation Multi-Clustering Support for
Global Internet Apps
Rolling Upgrades
Schema Constraints Concurrent/Transactional Write
Performance
Auto Cache Reheating
For Restarts, Restores and Cluster
Expansion
Neo4j 3.4 now supports
rolling upgrades
3.4 3.5
Upgrade older instances while keeping other
members stable and without requiring a restart
of the environment
3.5

19. What Parts of the Organization
Will Use Graphs
19

20. The Connected Enterprise
Consumers of Connected Data
20
AI & Graph Analytics
• Sentiment analysis
• Customer
segmentation
• Machine learning
• Cognitive computing
• Community detection
Transactional Graphs
• Fraud detection
• Real-time recommendations
• Network and IT operations
management
• Knowledge Graphs
• Master Data Management
Discovery & Visualization
• Fraud detection
• Network and IT
operations
• Product information
management
• Risk and portfolio analysis
Data
Scientists
Business
Users
Applications

21. What Neo4j Does:
Enables the Connected Enterprise
21

22. 22
Development &
Administration
Analytics
Tooling
Graph
Analytics
Graph
Transactions
Data Integration
Discovery & VisualizationDrivers & APIs
AI
Neo4j Graph Platform

23. 23
The Neo4j Desktop Platform
Graph Apps for Technologies & End Users
The easiest way to start building Neo4j apps
Includes Neo4j Enterprise for Development, Neo4j Browser, APOC, Graph Algorithms, and more
https://neo4j.com/download

24. The Visual Power of Graphs
24

25. 25
Graph Visualization Options with Neo4j
Neo4j Bloom
Provided by Neo4j
Exclusively optimized for Neo4j
graphs
Deploys easily in Neo4j Desktop
Focused on graph exploration
thru a code-free UI
Near natural language search
Currently caters to data
analysts and graph SMEs
Currently for individual or small
team use
Viz Toolkits
3rd party e.g. vis.js, d3.js, Keylines
Some offer data hooks into
Neo4j, others may require
custom integration
Offer robust APIs for flexible
control of the viz output
Cater to developers who will
create a custom solution, usually
with limited interactivity
Departmental, enterprise or
public use
BI Tools
3rd party e.g. Tableau, Qlik
Not optimized for graph data,
may require a special connector
UI for dashboard and report
creation with many kinds of viz,
in addition to graph viz
Cater to business users and
data analysts
Departmental, cross-
department or enterprise use
Graph Viz Solutions
3rd party, incl. Kineviz,
Graphistry, Linkurious, …
Have to support multiple
graph models and sources
Feature UI for exploration or
APIs for customizing output
and embedding/publishing
Solutions may cater to
business users, analysts or
developers
Small team, departmental or
cross-department use
Little technical expertise Most technically involved
Exploration focused Publishing / Consumption focused
Smaller deployments Larger deployments

26. Graph Analytics:
Graph & ML Algorithms
neo4j.com/
graph-algorithms-
book/
Pathfinding
& Search
Centrality /
Importance
Community
Detection
Link
Prediction
Finds optimal paths
or evaluates route
availability and quality
Determines the
importance of distinct
nodes in the network
Detects group
clustering or partition
options
Evaluates how
alike nodes are
Estimates the likelihood
of nodes forming a
future relationship
Similarity

27. 27
Learn More!

28. Graphs & Cypher in Spark 3.0
SparkCypher & Morpheus

29. Language Standardization
Working towards industry agreement across vendors
29
ISO GQLopenCypher
Supporting Cypher as an
industry-shared language
(since 2015)
Evolving towards a formal
language Standard
https://openCypher.org

30. 30
Neo4j Community & Ecosystem
…and many more!!

31. Thank You!
@prathle
31