1. Reporter: Haiping Wang WAMDM Cloud Group Mail:lulang1022@yahoo.com.cn

2. Outline Why NoSQL? Four trends History What is NoSQL? Definition Three fundamental theories NoSQL categories RDBMS vs. NoSQL

3. Trend1:data set size Rapid Increase of Data 57% every year (IDC2007)  Double every 1.5 years 988EB (1EB=1024PB) data will be produced in 2010 (IDC)  18 million times of all info in books

4. Trend2:Information connectivity

5. Trend3: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. RDBMS performance

7. Trend4:architecture changes

8. NoSQL history The term NoSQL was first used in 1998 Reintroduced in early 2009 by Eric Evans Hot in 2009

9. Outline Why NoSQL? Four trends History What is NoSQL? Definition Three fundamental theories NoSQL categories RDBMS vs. NoSQL

10. Definition From http://nosql-database.org/ Original intention modern web-scale databases Characteristics non-relational, Distributed open-source horizontal scalable schema-free easy replication support simple API eventually consistent / BASE (not ACID) Others… From Wikipedia loosely defined class of non- relational data stores not require fixed table schemas Avoid join operations Scale horizontally NoSQL is NOT Only SQL

11. Fundamental theories CAP BASE AP Eventual consistency Causal consistency Read-your-writes consistency Session consistency Monotonic read consistency Monotonic write consistency

12. Outline Why NoSQL? Four trends History What is NoSQL? Definition Three fundamental theories NoSQL categories RDBMS vs. NoSQL

13. NoSQL categories Key-value stores Based on DHTs / 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. 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. Key-value stores Key Value... name_€#_Stella mood_€#_Happy birthdate%/// 135465645) … dog_12

16. Bigtable clones

17. Document databases Key document dog_12 { type: “Dog”, name: “Stella”, mood: “Happy”, birthdate: 2007-04-01 }

18. Graph databases

19. RDBMS vs. NoSQL Strong consistency vs. Eventual consistency Big dataset vs. HUGE datasets Scaling is possible vs. Scaling is easy SQL vs. Map-Reduce Good availability vs. Very high availability

21. Thank you!!!