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What is CAP Theorem

Chapter 8
Handbook of Research on Cloud Infrastructures for Big Data Analytics
CAP theorem has been widely accepted since it was introduced in 2000 as a significant driver of NoSQL technology. CAP theorem can be explained by considering the two nodes on two ends of a partition permitting one node to manipulate data leading to the following three situations. Firstly, since one node has updated the data this will result into inconsistency between the nodes. Under this consideration Consistency is forfeited by picking up AP (availability, partition tolerance). Secondly, for preserving Consistency, the other side of the partition has to act as if it were unavailable, thus forfeiting A (availability). The third possibility that when clients are communicating to preserve Consistency and Availability they must behave in such a way that the failure of a partition on one node should not stop the operation of the other node and paralyze the communications between them i.e. forfeiting P (partition tolerance).
Published in Chapter:
NoSQL Databases
Ganesh Chandra Deka (Ministry of Labour and Employment, India)
Copyright: © 2014 |Pages: 30
DOI: 10.4018/978-1-4666-5864-6.ch008
Abstract
NoSQL databases are designed to meet the huge data storage requirements of cloud computing and big data processing. NoSQL databases have lots of advanced features in addition to the conventional RDBMS features. Hence, the “NoSQL” databases are popularly known as “Not only SQL” databases. A variety of NoSQL databases having different features to deal with exponentially growing data-intensive applications are available with open source and proprietary option. This chapter discusses some of the popular NoSQL databases and their features on the light of CAP theorem.
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Chapter 3
Database Systems for Big Data Storage and Retrieval
Consistency, Availability and Partition tolerance (CAP) are the three primary concerns that determine which data management system is suitable for a given application. The CAP theorem states that it is impossible for any system to achieve all these three features at the same time. For example, to achieve partition tolerance, a system may need to give up consistency or availability. The degree to which these three features are available in a system should be viewed as spanning a spectrum rather than as binary values.
Published in Chapter: Database Systems for Big Data Storage and Retrieval; From: Handbook of Research on Big Data Storage and Visualization Techniques
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