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      A Comparative Analysis of Materialized Views Selection and Concurrency Control Mechanisms in NoSQL Databases

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          Abstract

          Increasing resource demands require relational databases to scale. While relational databases are well suited for vertical scaling, specialized hardware can be expensive. Conversely, emerging NewSQL and NoSQL data stores are designed to scale horizontally. NewSQL databases provide ACID transaction support; however, joins are limited to the partition keys, resulting in restricted query expressiveness. On the other hand, NoSQL databases are designed to scale out linearly on commodity hardware; however, they are limited by slow join performance. Hence, we consider if the NoSQL join performance can be improved while ensuring ACID semantics and without drastically sacrificing write performance, disk utilization and query expressiveness. This paper presents the Synergy system that leverages schema and workload driven mechanism to identify materialized views and a specialized concurrency control system on top of a NoSQL database to enable scalable data management with familiar relational conventions. Synergy trades slight write performance degradation and increased disk utilization for faster join performance (compared to standard NoSQL databases) and improved query expressiveness (compared to NewSQL databases). Experimental results using the TPC-W benchmark show that, for a database populated with 1M customers, the Synergy system exhibits a maximum performance improvement of 80.5% as compared to other evaluated systems.

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          Brewer's conjecture and the feasibility of consistent, available, partition-tolerant web services

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              Efficiently updating materialized views

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                Author and article information

                Journal
                04 October 2017
                Article
                1710.01792

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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