This thesis is based on measuring how Relational Database Management Systems utilizing data-at-rest encryption with varying AES key lengths impact the performance in terms of transaction throughput of operations through the process of a controlled experiment. By measuring the effect through a series of load tests followed by statistical analysis, the impact of adopting a specific data-at-rest encryption algorithm could be displayed. The results gathered from this experiment were measured regarding the average transactional throughput of SQL operations. An OLTP workload in the benchmarking tool HammerDB was used to generate a transactional workload. This, in turn, was used to perform load tests on SQL databases encrypted with different AES-key lengths. The data gathered from these tests then underwent statistical analysis to either keep or reject the stated hypotheses. The statistical analysis performed on the different versions of the AES-algorithm showed no significant difference in terms of transaction throughput concerning the results gathered from the load tests on MariaDB. However, statistically, significant differences are proven to exist when running the same tests on MySQL. These results answered our research question, "Is there a significant difference in transaction throughput between the AES-128, AES-192, and AES-256 algorithms used to encrypt data-at-rest in MySQL and MariaDB?". The conclusion is that the statistical evidence suggests a significant difference in transactional throughput between AES algorithms in MySQL but not in MariaDB. This conclusion led us to investigate further transactional database performance between MySQL and MariaDB, where a specific type of transaction is measured to determine if there was a difference in performance between the databases themselves using the same encryption algorithm. The statistical evidence confirmed that MariaDB vastly outperformed MySQL in transactional throughput.

Graph traversals are a basic but fundamental ingredient for a variety of graph algorithms and graph-oriented queries. To achieve the best possible query performance, they need to be implemented at the core of a database management system that aims at storing, manipulating, and querying graph data. Increasingly, modern business applications demand native graph query and processing capabilities for enterprise-critical operations on data stored in relational database management systems. In this paper we propose an extensible graph traversal framework (GRAPHITE) as a central graph processing component on a common storage engine inside a relational database management system. We study the influence of the graph topology on the execution time of graph traversals and derive two traversal algorithm implementations specialized for different graph topologies and traversal queries. We conduct extensive experiments on GRAPHITE for a large variety of real-world graph data sets and input configurations. Our experiments show that the proposed traversal algorithms differ by up to two orders of magnitude for different input configurations and therefore demonstrate the need for a versatile framework to efficiently process graph traversals on a wide range of different graph topologies and types of queries. Finally, we highlight that the query performance of our traversal implementations is competitive with those of two native graph database management systems.