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Providing a Data Model to the CATS key-value store
KTH, School of Information and Communication Technology (ICT).
2013 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Search or social media giants are no longer the only individuals that face the problems of managing Big Data. Many of today’s applications and services experience sudden bursts in growth, with increased data generation rates, that require storage and analysis support for large amounts of data. Traditional relational database management system (RDBMS) have been adapted to a distributed environment in an effort to make them suitable for Big Data, but they do not scale linearly and tend to obtain little extra performance as they grow in size. On the other hand, solutions built natively for a distributed environment, referred to as “Not only SQL” (NoSQL) provide a limited data model with few possible operations compared to structured query language (SQL). However, providing a data model with more complex, SQL like operations, raises some particular challenges in a distributed environment.

This thesis presents the design of a data model on top of the CATS keyvalue store. The purpose of this data model is to provide support for more complex data, compared to the simple key-value operations currently supported by CATS. Objects containing a number of fields can be stored and retrieved. Secondary indexes on different fields allow the search of objects based on the value of these indexed fields. The thesis also presents mechanisms for colocating data that is used together in order to reduce the latency of operations by exploiting data locality. The ability to dynamically adapt the way data is saved to disk according to different data access patterns can also help to provide faster services. The evaluation of a prototype of the system provides measurements on the overhead associated with the data model compared to the underlying key-value store.

Place, publisher, year, edition, pages
2013. , 55 p.
TRITA-ICT-EX, 2013:179
National Category
Computer and Information Science
URN: urn:nbn:se:kth:diva-177865OAI: diva2:874624
Available from: 2015-12-01 Created: 2015-11-27 Last updated: 2015-12-01Bibliographically approved

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