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Increasing resource demands require relational databases to scale. Whilerelational databases are well suited for vertical scaling, specialized hardwarecan be expensive. Conversely, emerging NewSQL and NoSQL data stores aredesigned to scale horizontally. NewSQL databases provide ACID transactionsupport; however, joins are limited to the partition keys, resulting inrestricted query expressiveness. On the other hand, NoSQL databases aredesigned to scale out linearly on commodity hardware; however, they are limitedby slow join performance. Hence, we consider if the NoSQL join performance canbe improved while ensuring ACID semantics and without drastically sacrificingwrite performance, disk utilization and query expressiveness.  This paper presents the Synergy system that leverages schema and workloaddriven mechanism to identify materialized views and a specialized concurrencycontrol system on top of a NoSQL database to enable scalable data managementwith familiar relational conventions. Synergy trades slight write performancedegradation 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 benchmarkshow that, for a database populated with 1M customers, the Synergy systemexhibits a maximum performance improvement of 80.5% as compared to otherevaluated systems.

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