THESIS
2019
xi, 67 pages : illustrations ; 30 cm
Abstract
With the prevalence of online platforms such as social media, data is generated and accessed with a rapid rate. It is important to design a database management system that is capable of handling high-volume data insertion amd providing answers to queries
efficiently. In this thesis, we introduce Nested B-trees (NB-trees), a data structure that could handle high-volume data insertion with a high insertion rate, and could provide a query performance guarantee which is similar to that of B-trees such that the query performance is asymptotically optimal. Nested B-trees can support insertions at rates higher than LSM-trees, the state-of-the-art data structure for high insertion rate workloads, while improving on their query performance and approaching the query performance of B-trees when c...[
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With the prevalence of online platforms such as social media, data is generated and accessed with a rapid rate. It is important to design a database management system that is capable of handling high-volume data insertion amd providing answers to queries
efficiently. In this thesis, we introduce Nested B-trees (NB-trees), a data structure that could handle high-volume data insertion with a high insertion rate, and could provide a query performance guarantee which is similar to that of B-trees such that the query performance is asymptotically optimal. Nested B-trees can support insertions at rates higher than LSM-trees, the state-of-the-art data structure for high insertion rate workloads, while improving on their query performance and approaching the query performance of B-trees when complemented with Bloom filters. In our experiments, NB-trees could perform queries at least twice as fast as bLSM and LevelDB commonly,
used LSM-tree data stores, while also outperforming them in terms of insertion rate, and performing insertions with much lower delays.
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