This document discusses different types of distributed databases. It covers data models like relational, aggregate-oriented, key-value, and document models. It also discusses different distribution models like sharding and replication. Consistency models for distributed databases are explained including eventual consistency and the CAP theorem. Key-value stores are described in more detail as a simple but widely used data model with features like consistency, scaling, and suitable use cases. Specific key-value databases like Redis, Riak, and DynamoDB are mentioned.

1. Distributed Database

2. Data Model
• Relation
• The Dominant Data Model of Last Couple Decade
• Table, Column, Rows
• Cannot Nest One Tuple Within Another
• Aggregate
• Collection of related object that we wish to treat as a unit
• Consistency Unit

3. Relational Data Model

4. Aggregate Data Model

5. • Aggregate-Ignorant
• Relational Data Model
• Graph
• Aggregate-Oriented
• Key-Value
• Document
• Column Family
Data Model Types

6. Distribution Model
• Single Server
• Sharding (Horizontal Scalability)
• Put different data on different node.
• Replication
• Take the same data and copies it over multiple node.

7. Sharding

8. Master-Slave Replication

9. Peer-to-Peer Replication

10. Combining Sharding and Replication

11. Combining Sharding and Replication

12. Consistency

13. Update Consistency
• Write-Write Conflict
• Lost Update
• Pessimistic Consistency
• Optimistic Consistency

14. Read Consistency

15. Replication Inconsistency

16. Relaxing Consistency
• Eventual Consistency
• Quorums
• CAP Theorem
• Consistency
• Availability
• Partition Tolerance

17. CAP Theorem

18. Key-Value
Group A

19. What's Key-Value Store
• Simple Hash Table
• Collection of Key-Value Pair
• Associate Array
• The Key Unique Within Collection

20. Basic Operation
• Insert Pair
• Delete Pair
• Update Value Of Existing Pair
• Find Value Associate With a Particular Key

21. Performance Factor
• The efficiency of the hashing function.
• The design of the keys and the size of the values being stored
and retrieved.
• The distribution of data across partitions.
• The functional patterns that applications follow to store and
retrieve data.

22. Storing All the Data in a Single Bucket

23. Terminology – Oracle vs. Riak

24. Some of The Popular Key-Value Databases
• Redis
• Riak
• Memcache DB
• BerkeleyDB
• HamsterDB
• Amazon DynamoDB
• Project Voldemort

25. Key-Value Store Features
• Consistency
• Consistency is applicable only for operations on a single key
• Eventually Consistent in Distribute Implementation
• Transaction
• Quorum
• Query
• Structure Of Data
• Scaling
• Sharding
• CAP

26. Suitable Use Case
• Storing Session Information
• User Profiles, Preferences
• Shopping Cart Data

27. When Not to Use
• Relation Among Data
• Multioperation Transactions
• Query by Data
• Operations by Sets

28. Distributed, Masterless, Highly-Available Key-Value Store

29. Replication

30. Demo

32. Redis Features
• Extremely Fast!
• Ability to store more than just strings
• Persistence
• Replication
• Built in LUA support

33. Demo