Several application domains deal with the management of massive data volumes and thousands of OLTP transactions per second. Traditional relational databases cannot cope with these requirements. NewSQL is a new generation of databases that provides both high scalability and availability and ACID properties support. Besides, it is a promising solution to handle these application data management needs. Although data partitioning is an essential feature for tuning relational databases, stills an open issue for NewSQL systems. In this paper, we propose a hybrid partitioning approach for NewSQL databases that allows the user to define the vertical and horizontal data partitions. In order to determine what site will store each data fragment, we propose a hash function that considers schema information and data access statistics. Our experimental evaluation compares our hybrid VoltDB version against the standard VoltDB. The results highlight that our strategy increases the number of single-site transactions from 37% to 76%.

Several applications require to handle large and heterogeneous data volumes as well as thousands of OLTP transactions per second. Traditional relational databases are not suitable for these requirements. On the other hand, NoSQL databases are able to deal with Big Data, but lacks the support to the traditional ACID properties. NewSQL is a new class of databases that combines the support to OLTP transactions of relational databases with the high availability and scalability of NoSQL databases. However, few works in the literature explore the differences among different NewSQL solutions. In this paper, we discuss the main features of the most prominent NewSQL products, besides we present benchmarking results for analyzing their performance. We believe that both analysis can be useful as a guide to a future choice of NewSQL technologies.

Today, many applications produce and manipulate a large volume of data, the so-called Big Data. Traditional databases (DB), like relational databases, are not suitable to Big Data management. In order to solve this problem, a new category of DB has been proposed, the so-called NoSQL DB. NoSQL DB have different data models, as well as different access methods which are not usually compatible with the SQL language. In this context, approaches have been proposed for providing mapping of relational DB schemata and operations to equivalent ones in NoSQL DB to deal with large relational data sets in the cloud, focusing on scalability and availability. However, these approaches map relational DB only to a single NoSQL data model and, sometimes, to a specific NoSQL DB product. This paper presents SQLtoKeyNoSQL, a layer able to translate relational schemata as well as SQL commands to equivalent schemata and access methods to any key-oriented NoSQL DB (document-oriented, key-value and column-oriented). We present the architecture of our layer focusing on our mapping strategies, as well as some preliminary experiments that evaluate the impact of SQLtoKeyNoSQL as a solution for transparent mapping of relational DB to key-based NoSQL DB.