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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)), 80 credits / 120 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. , 47 p.
TRITA-ICT-EX, 2013:179
National Category
Computer and Information Science
URN: urn:nbn:se:kth:diva-140708OAI: diva2:692365
Available from: 2014-01-30 Created: 2014-01-30 Last updated: 2014-01-30

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