This document provides an overview and comparison of NoSQL databases. It discusses key-value stores, column family databases, document databases, and graph databases. For each type, it describes the data model, examples of databases that use that model, and pros and cons. It also covers topics like querying capabilities, concurrency control, partitioning, and replication across NoSQL databases. The document aims to help evaluate which NoSQL database is best suited based on features and use case.

1. NoSQL Evaluation
By:
Karthik Kamath G
karthikkmth50@gmail.com
Bens John
bensjohn723@gmail.com

2. Contents
O Introduction
O Features
O RDBMS
O Data Models.
O Query Possibilities.
O Concurrency control.
O Partioning
O Replication and consistency.

3. Introduction
O SQL= Traditional Relational Database.
O NoSQL = No traditional Relational
Database.
O No SQL != Do not use Structured Query
Language.
O NoSQL = Not only SQL.
O Not every data management/ analysis
problem is solved using traditional
RDBMS.
O BIG Data.

4. Features
O Convenient
O Multi-User
O Safe
O Persistent
O Reliable
O Massive
O Efficient

5. Relational DB
O Adding or removing a feature to a blog is
not possible without system unavailability.
O Due to their normalized data model and
ACID support RDBMS is not useful in
Web2.0 domains, because joins and locks
influence performance in distributed
systems negatively.
O These databases are typically based on
consistency instead of availability.
O Replication techniques are limited .

6. NoSQL providers

7. Key-Value Stores
O Data is addressed by a unique key.
O Values are isolated from each other.
O Schema Free – New values can be added
O Data/values are opaque to the system.
O Example :
Voldemart, Redis, Membase.

8. O Pros :
Simple Data Model.
Scalable.
O Cons:
Create your own “Foreign Keys”.
Poor for complex data.

9. Column Family
O Based on BIGTABLE : Google’s
distributed storage system for structured
data.
O Arbitrary no. of key value pairs can be
stored within rows.
O Relationship to be implemented by App
Logic.
O Columns can be grouped to form column
families
O Examples :
HBase, Cassandra, Hyper table

10. O Hbase & Hypertext – Open source
implementations.
O Cassandra – Additional super column.
O Multiple versions of the same data are
stored in chronological order.

11. O Pros :
Scalable.
O Cons :
Poor for interconnected data.

12. Document Databases
O Data Model :
collection of documents.
document is a key value collection.
within a document keys should be
unique.
JSON: Java Script Object Notation
O Example :
CouchDB, MongoDB.

13. O Pros:
Simple, Powerful Data Model.
Scalable.
O Cons:
Poor for interconnected Data.
Query model limits to keys and
values.

14. Graph Databases
O Data Model :
Nodes and Relationships.
O Examples :
neo4j, Orient DB.
O FlockDB –Twitter-One way relationship.
O Location Based system, Navigation
systems which uses complex relations.

15. O Pros :
Powerful Data Model.
Easy to query.
Friend of a friend Problem is solved.

16. Query Possibilities
For Key Value pairs:
O Key based put, get and delete operations.
O Membase offers REST API.
For Document Databases
O Document stores offer much richer APIs.
O Operations like and,or and between can be
used
O MongoDB supports additional operations like
count and distinct.
O Riak offers functionalities to traverse links
between documents easily.
O UnQL Project

17. For Column Family
O Provide range queries and some
operations like "in", "and/or" and regular
expression.
O Even if every column family store offers a
SQL like query language in order to
provide a more convenient user
interaction, only row keys and indexed
values can be considered in where-
clauses, as well.

18. For graph database
O SPARQL is a popular, declarative query
language with a very simple syntax
providing graph pattern matching.
O Gremlin is an imperative programming
language used to perform graph
traversals based on XPA TH.

19. Concurrency Control
O Traditional databases use pessimistic
consistency strategies with exclusive
access on a dataset.
O Multiversion concurrency control
(MVCC) relaxes strict consistency in
favor of performance.
O In order to cope with two or more
conflicting write operations, every process
stores, additional to the new value, a link
to the version the process read before.

20. Partitioning
O The first strategy distributes datasets by the
range of their keys.
O In order to find a certain key, clients have to
contact the routing server for getting the
partition table.
O The second one is by Consistent Hashing.
O Neighbored keys are distributed randomly
across the cluster.
O Graph algorithms can help identifying
hotspots of strongly connected nodes in the
graph schema.

21. Replication and Consistency
O BASE systems.
O Availability at the cost of consistency.
O Can be inconsistent, but the system
must be high available and high
performant at all time.
O NoSQL systems are not only full ACID or
full BASE systems.
O Big Data is the only store, which supports
full consistency and replication natively.

22. Conclusion
O Choose the proper data model.
O Queries.
O Key value should be used for simple and
fast operations.
O Column DB for large data.
O Graph DB for entities and relationship.
O Document DB offers flexible Data model.

23. Thank
You.