The development of database systems is moving more and more towards handling large quantities of data, also known as Big Data. New database systems have been developed both within NoSQL and the newer system architecture called NewSQL used to tackle the problems that arise with Big Data. NewSQL is becoming more popular due to its ability to handle the security behind data transactions and simultaneously offer scalability. This has led to database systems constantly being optimized. This report goes into detail on how the handling of datatypes affect how database systems are built based on their architecture and code base.  An experiment was conducted on two NewSQL databases, VoltDB and CockroachDB. The purpose of the experiment was to find out how datatypes can be optimized based on time differences in datatypes and then present causes to those time differences. There were clear time differences which showed what datatypes were fastest. When analyzing the codebase and architecture it became clear that VoltDB handles datatypes when compiling while CockroachDB does so during execution.  Another potential reason is that the time differences have to do with their differences in architecture.  This report has, with the available resources, started to build the foundation for a better standard in how datatypes can be implemented in code to increase the performances of database systems. More research is required due to time constraints that can strengthen and expand the results reached in this paper. / Utvecklingen av databassystem rör sig alltmer mot att hantera stora datamängder, även kallat Big Data. Nya databassystem har utvecklats både inom NoSQL och det nyare NewSQL för att tackla problemen med Big Data. För att hantera både säkerheten bakom databastransaktioner och samtidigt behålla skalbarheten så börjar NewSQL klättra alltmer i databasvärlden. Det har lett till att databassystem effektiviseras så mycket som möjligt. Denna rapport går in i detalj på hur hantering av vissa datatyper påverkas av hur databassystem är uppbyggda baserat på deras arkitektur och kodbas. För att ta reda på hur hantering av datatyper kan effektiviseras utfördes ett experiment på NewSQL databaserna VoltDB och CockroachDB för att hitta tidsskillnader för datatyper och sedan ta fram orsaker till dessa skillnader. Det fanns klara tidsskillnader för vilka datatyper som var snabbast och vid analys av kodbas och arkitektur framgick det att VoltDB hanterar datatyper vid kompilering medan CockroachDB gör det vid exekvering. Dessutom ser deras arkitekturer olika ut vilket kan ytterliggare bidra till tidsskillnaderna. Med de resurser som varit tillgängliga har denna rapport påbörjat ett arbete till att lägga en grund för att ta fram en bättre standard till hur datatyper kan implementeras i kod för att öka prestandan i databassystem. På grund av de tidsbegränsningar som funnits behöver mer forskning tas fram som kan stärka och utvidga resultatet som uppnåtts i detta arbete.

This study conducted an experiment on NoSQL and NewSQL database management systems where the average throughput of Cassandra, CockroachDB, MongoDB, and VoltDB was compared using five workloads composed of different proportions of read and update queries. How much these different workload compositions affect throughput for each individual database management system was also investigated. The results showed that VoltDB had the highest throughput overall, and its throughput was affected the least by the workloads’ composition. MongoDB had similar high throughput consistency across workloads but at a much lower throughput level, and its throughput was affected much more by the workload compositions than VoltDB. Cassandra had extremely high throughput for 100 percent update workloads,even beating VoltDB in certain cases, but showed underwhelming results for all other workloads. CockroachDB’s throughput was by far the worst at workloads that had any update queries, but was comparable and sometimes even better than Cassandra and MongoDB with 100 percent read workloads. CockroachDB’s throughput proved to be the most affected by the query composition of workloads.

There has been a surge of new databases in recent years. Applications today create a higher demand on database performance than ever before. Main-memory databases have come into the market quite recently and they are just now catching a lot of interest from many different directions. Main-memory databases are a type of database that stores all of its data in the primary memory. They provide a big increase in performance to a lot of different applications. This work evaluates the difference in performance between two chosen candidates. To represent main memory databases we chose VoltDB and to represent traditional databases we chose MySQL. We have performed several tests on those two databases. We point out differences in functionality, performance and design choices. We want to create a reference where anyone that considers changing from a traditional database to a main memory database, can find support for their decision. What are the advantages and what are the disadvantages of using a main-memory database, and when should we switch from our old database to a newer technology.