Agenda

• CAP?
• What kind of a creature Graph DB?
• Graph DB vs relational DB
• Is it a panacea? Downsides
• Neo4J
• Cypher vs SQL
• Is it applicable? Real Use cases
• Demo

CAP theorem

• Consistency - all nodes see the same data at the same time

Brewer'€™s theorem

• Availability - every request receives a response about whether it succeeded or failed

 

• Partition tolerance - the system continues to operate despite arbitrary partitioning due to network failures

What is a graph?

G = (V, E)

Graph database

Graph database

• If you would need to represent a system with all data of every movie ever made, how would you model it?

• You need to cover movie names, actors, directors, budget, dates, costs, location, ratings, shows, tickets...

Graph DB vs Relational DB

Graph DB vs Relational DB

Graph DB vs Relational DB

• Chaotic data model. Irregualar, complex structure
• Highly complex relationships between entities
• Relationships are very different
• Check only relationships of the node, not the whole database

Examples

• Neo4J (Java, .Net, Python, JavaScript)
• AllegroGraph (C#, C, Java, Lisp, Python)
• Oracle Spatial and Graph ( Java, PI/SQL)
• AngoDB(C, C++, JavaScript)

Neo4J

• JVM Based
• Billions of entities 
• Support ACID
• Using Cypher

Cypher

Functions like:     WHERE, ORDER BY, SKIP LIMIT, AND, p.unitPrice > 10

Select and Return Records

SQL

SELECT p.*

FROM products as p;

Cypher

MATCH (p:Product)

RETURN p;

Field Access, Ordering and Paging

SQL

SELECT p.ProductName, p.UnitPrice

FROM products as p

ORDER BY p.UnitPrice DESC

LIMIT 10;

Cypher

MATCH (p:Product)

RETURN p.productName, p.unitPrice

ORDER BY p.unitPrice DESC

LIMIT 10

Downsides

• Use a lot of space
• Relational DB is much faster when operating a huge number of records
• Licence is expensive

Demo

Thank you!