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An SQL database is very flexible since it does not pre compute how
the tables are related (joined). This flexibility comes at a price:
you have to fit the domain model into tables, making synthetic
constructs in the mapping layer necessary
navigating relationships using SQL join operation can be very slow
the mental model of your domain is lost when projecting it into tables and joins
A graph database is a great alternative when you need to navigate
relationship deep or fast. Also, it does not have the impedance
mismatch problem of an SQL database. In Neo4j it’s very natural to map
objects and classes to a graph. This permits you to model the domain
more accurately and provide more useful interaction with the data.
With Neo4j you can do things that are very hard or totally impossible
with an SQL database, examples: recommendation algorithms,geospatial
analysis and deep hierarchies modeling like file systems or product
In this talk we will give an overview of the different ways of using
neo4j: the Rest API, query languages and the native JRuby wrapper. We will
show how neo4j can be used to solve classic graph problems like an
recommendation algorithm. Furthermore, you will also see how natural
and elegant the domain model can be expressed in code by using neo4j
in a Rails 3 application.