No SQL DATABASE Description about 4 no sql database.pptx

Description about 4 no sql database

1. No SQL DATABASES Prof. M. S. Kavitha . Department of Computer science and Engineering

2. CO2 Apply the knowledge of Schemaless database, NoSQL , and distribution models to solve complex database design challenges using the open-source tool Cassandra

3. Application of NoSQL Databases Mostly used in social media sites inorder to store massive amount of data Explosion of storage needs in large websites such as Google, Yahoo Rise of cloud-based solutions such as Amazon S3 Shift to dynamically-typed data with the frequent schema changes Open source community

4. RDBMS RDBMS : Relational Database Management Systems Relation :A Relation in 2D table with the following features Name Attributes Tuples

5. Drawbacks of RDBMS Relational database software is expensive. Complex software means expensive hardware. Requires skilled knowledge to implement. Certain applications are slower processing. Increases vulnerability. More difficult to recover if data is lost. Seen as a poor representation of the real world.

6. Comparison of NoSQL With RDBMS NoSQL Database Relational Database NoSQL Database supports a very simple query language. Relational Database supports a powerful query language. NoSQL Database has no fixed schema. Relational Database has a fixed schema. NoSQL Database is only eventually consistent. Relational Database follows acid properties. (Atomicity, Consistency, Isolation, and Durability) NoSQL databases don't support transactions (support only simple transactions). Relational Database supports transactions (also complex transactions with joins). NoSQL Database is used to handle data coming in high velocity. Relational Database is used to handle data coming in low velocity. The NoSQL?s data arrive from many locations. Data in relational database arrive from one or few locations. NoSQL database can manage structured, unstructured and semi-structured data. Relational database manages only structured data. NoSQL databases have no single point of failure. Relational databases have a single point of failure with failover. NoSQL databases can handle big data or data in a very high volume . NoSQL databases are used to handle moderate volume of data. NoSQL has decentralized structure. Relational database has centralized structure. NoSQL database gives both read and write scalability. Relational database gives read scalability only. NoSQL database is deployed in horizontal fashion. Relation database is deployed in vertical fashion.

7. NoSQL Databases • Stands for Not Only SQL . • Having non-relational flat file database. • May not require fixed table schema. • Horizontally scalable-easily add more information. • Avoid JOIN operation. • Relaxation of ACID properties. • Distributed in nature .

8. ACID properties of a transaction ACID-  atomicity, consistency, isolation, and durability. Together, these ACID properties ensure that a set of database operations (grouped together in a transaction) leave the database in a valid state even in the event of unexpected errors.

9. ARCHITECTURE of NOSQL • Several NOSQL systems use a Distributed architecture like Master/Slave, Master/Master , Masterless . • Based on Distributed Hash Tables. • Easily scale out by adding more servers & failure of a server can be tolerated. • Provide full ACID guarantee by adding a supplementary middleware layer.

10. NOSQL DATA MODELS NOSQL databases can be categorized according to their data model into the following four categories: • Key-Value-stores • Document-stores • Graph Databases • Big Table-Column Implementation

11. KEY VALUE STORE • Simplest form of NoSQL store- Each key is mapped to a value containing arbitrary data . • This store has no knowledge of the contents of its payload and simply delivers the data to the application . • Mainly used to encapsulate the information . • Key-value stores is a very simple query model, usually consisting of set, get, and delete primitives . • Main applications based on this store: Redis Level DB Memcache DB

12. KEY VALUE STORE

13. DOCUMENT STORE • Key-document stores map a key to some document that contains structured information. • They store lists and dictionaries, which can be embedded recursively inside one-another . • Freedom and complexity of document stores are two key points: Developers have a lot of freedom in modeling their documents Application-based query logic can become complex. • Main applications based on this store: • Mongo DB • Couch DB • Riak

14. DOCUMENT STORE

15. GRAPH DATABASE  • To avoid JOIN operation in RDBMS, Graph Databases are used. • Graph Database is modeled using three basic building blocks: • Node as vertex Relationship as edge Property as attribute • Graph theory has seen a great usefulness and relevance in many problems across various domains . • Main applications based on this store: • Neo4J • InfoGrid • HyperGraphDB

16. GRAPH DATABASE

17. COLUMN BASED IMPLEMENTATION • In this Model, a key identifies a row, which contains data stored in one or more Column Families. • Within a Column Family, each row can contain multiple columns. • The values within each column are timestamped , so that several versions of a row-column mapping can live within a Column Family. • The model naturally supports sparse column placement. • It is particularly good at modeling historical data with timestamps. • Main applications based on this store: • Cassandra • HBase • Hyper Table

18. COLUMN BASED IMPLEMENTATION