This document discusses trends driving the emergence of NoSQL databases and provides an overview of NoSQL. The key trends include: (1) rapidly increasing data set sizes, (2) greater connectivity of data, and (3) more semi-structured and decentralized content. These trends have challenged the performance and architecture of traditional relational databases. NoSQL databases emerged in response and come in four main categories: key-value stores, BigTable clones, document databases, and graph databases. Each has a different data model suited to different types of use cases. Looking ahead, the best approach may be "polyglot persistence" using both SQL and NoSQL solutions.

1. NOSQL 15 minutes of write
performance
#neo4j
Emil Eifrem @emileifrem
CEO, Neo Technology emil@neotechnology.com

2. Trend 1:
data set size
40
2007 Source: IDC 2007

3. 988
Trend 1:
data set size
40
2007 2010 Source: IDC 2007

4. Trend 2: connectedness
Giant
Global
Information connectivity
Graph
(GGG)
Ontologies
RDF
Folksonomies
Tagging
User-
Wikis
generated
content
Blogs
RSS
Hypertext
Text
documents web 1.0 web 2.0 “web 3.0”
1990 2000 2010 2020

5. Trend 3: semi-structure
Individualization of content!
In the salary lists of the 1970s, all elements had
exactly one job
In the salary lists of the 2000s, we need 5 job
columns! Or 8? Or 15?
Trend accelerated by the decentralization of
content generation that is the hallmark of the age
of participation (“web 2.0”)

6. Aside: RDBMS performance
Relational database
Salary List
Performance
Majority of
Webapps
Social network
Semantic
}
Trading
custom
Data complexity

7. Trend 4: architecture
1990s: Database as integration hub

8. Trend 4: architecture
2000s: (Slowly towards...)
Decoupled services with own backend

9. Why NoSQL 2009?
Trend 1: Size.
Trend 2: Connectivity.
Trend 3: Semi-structure.
Trend 4: Architecture.

10. NoSQL
overview

11. First off: the damn name
NoSQL is NOT “Never SQL”
NoSQL is NOT “No To SQL”

12. NOSQL
is simply
Not Only SQL!

13. Four (emerging) NOSQL categories
Key-value stores
Based on Amazon's Dynamo paper
Data model: (global) collection of K-V pairs
Example: Dynomite, Voldemort, Tokyo
BigTable clones
Based on Google's BigTable paper
Data model: big table, column families
Example: Hbase, Hypertable

14. Four (emerging) NOSQL categories
Document databases
Inspired by Lotus Notes
Data model: collections of K-V collections
Example: CouchDB, MongoDB
Graph databases
Inspired by Euler & graph theory
Data model: nodes, rels, K-V on both
Example: AllegroGraph, VertexDB, Neo4j

15. NOSQL data models
Size
Key-value stores
Bigtable clones
Document
databases
Graph databases
Complexity

16. NOSQL data models
Size
Key-value stores
Bigtable clones
Document
databases
Graph databases
(This is still billions of
90% nodes & relationships)
of
use
cases
Complexity

17. Looking ahead: polyglot persistence
SQL && NOSQL

18. http://neotechnology.com