An Introduction to Neo4j Graph database, Cypher query language. Comparison between Neo4j and Graph Database

1. Neo4j
Graph Database

2. Overview
● Introduction to Neo4j
● Cypher Query Language
● Neo4j Language Integration
● Neo4j vs Relational Database (RDBMS)
● Drawbacks
● Domain Applications
● Questions??

3. Introduction to Neo4j
● Open source graph database
● Implemented in Java
● Database that uses graph structures with nodes, relationships/edges and
properties to store data
● Both nodes and relationships can have properties.

4. Neo4j Basics
The Labeled Property Graph Model

5. Definitions
Nodes
● Nodes are the main data elements
● Nodes are connected to other nodes via
relationships/edges
● Nodes can have one or more properties
(i.e., attributes stored as key/value pairs)
● Nodes have one or more labels that
describes its role in the graph
● Example: Person nodes vs Car nodes
Relationships/Edges
● Relationships connect two nodes
● Relationships are directional
● Nodes can have multiple, even recursive
relationships
● Relationships can have one or more
properties (i.e., attributes stored as
key/value pairs)

6. Properties
● Properties are named values where the
name (or key) is a string
● Properties can be indexed and
constrained
● Composite indexes can be created from
multiple properties
Labels
● Labels are used to group nodes into sets
● A node may have multiple labels
● Labels are indexed to accelerate finding
nodes in the graph
● Native label indexes are optimized for
speed

7. Cypher Query Language
● Neo4j's Cypher language is purpose built for working with graph data.
● Uses intuitive patterns to describe graph data
● Declarative, describing what to find, not how to find it

8. Cypher Syntax overview
Creating a node
CREATE (ee:Person{ name: "Emil", from: "Sweden", klout: 99 })
● CREATE clause to create data
● () parentheses to indicate a node
● ee:Person a variable 'ee' and label 'Person' for the new node
● {} brackets to add properties to the node

9. General Match Query structure
MATCH (node:Label) RETURN node, node.property
MATCH (node1:Label1)->(node2:Label2)
WHERE node1.propertyA = {value}
RETURN node2.propertyA, node2.propertyB

10. Expressing relationships/edges in Cypher

11. General syntax for expressing relationships
MATCH (n1:Label1)-[rel:TYPE]->(n2:Label2)
WHERE rel.property > {value}
RETURN rel.property, type(rel)

12. Common Patterns
● friend-of-a-friend (user)-[:KNOWS]-(friend)-[:KNOWS]-(foaf)
● shortest path: path = shortestPath( (user)-[:KNOWS*..5]-
(other) )
● collaborative filtering/recommendation engine
(user)-[:PURCHASED]->(product)<-[:PURCHASED]-()-
[:PURCHASED]->(otherProduct)

13. Neo4j Integration

14. Neo4j vs Relational Databases(RDBMS)
Graph storage structure with
index-free adjacency (Native
graph processing) results in
faster transactions and
processing for data
relationships.
Storage in fixed, predefined
tables with rows and columns
with connected data often
disjointed between tables,
crippling query efficiency.
Data Storage
Neo4j RDBMS

15. Flexible, "whiteboard-friendly"
data model with no mismatch
between logical and physical
model. Data types and sources
can be added or changed at
any time, leading to dramatically
shorter development times and
true agile iteration.
Database model must be
developed with modelers and
translated from a logical model
to a physical one. Since data
types and sources must be
known ahead of time, any
changes require weeks of
downtime for implementation.
Data Modelling
Neo4j RDBMS

16. Cypher: A native graph query
language that provides the most
efficient and expressive way to
describe relationship queries.
SQL: A query language that
increases in complexity with the
number of JOINs needed for
connected data queries.
Query Language
Neo4j RDBMS

17. Graph processing ensures low
latency and real-time
performance, regardless of the
number or depth of
relationships.
Data processing performance
suffers with the number and
depth of JOINs (or relationships
queried).
Query Performance
Neo4j RDBMS

18. Drawbacks of Neo4j
● If data is mostly tabular with not much relationship between the data, neo4j
does not fare well
● Sharding is not supported. That means whole dataset has to be on ONE
server. Only vertical scaling possible If more capacity is required.

19. Domain Applications
● Knowledge Graph
● Social Network
● Real-Time Recommendation Engines
● Internet of Things (IOT)
● Fraud Detection
● Network & IT Operations
● Identity & Access Management (IAM)
● Geospatial Computing
● Genealogy ( study of families and the tracing of their lineages and history)

20. Notable Customers of Neo4j
● EBay (Logistics)
● Walmart (Recommendations)
● Airbnb (Data Analytics)
● NASA (Knowledge Graph)
● Medium (Social Graph and Recommendations)
● LinkedIn China (Social Graph)
● Nulli Identity Management (IAM)

21. Questions??...

22. References
● https://neo4j.com/product
● https://neo4j.com/developer/cypher-query-language/
● https://neo4j.com/blog/why-graph-databases-are-the-future/
● https://neo4j.com/customers/
● https://dzone.com/articles/why-are-native-graph-databases-more-efficient-
than
● http://na-
sjl.marketo.com/rs/neotechnology/images/Understanding%20Neo4j%20Scala
bility(2).pdf
● https://github.com/erabug/wikigraph