A comparison of database management systems DDoS attack robustness

Abstract

A common issue for software companies is that the data size in their database grows larger than their current system can handle. To further complicate matters databases might be forced to process an increasing amount of queries, either due to a growing number of users or due to malicious parties stressing the database with DDoS attacks. One strategy to account for this is to replace a single server database with a database distributed over a cluster to allow scaling the databases’ resources to match varying requirements. This thesis project aims to provide measurements of the robustness during a DDoS attack of two different database management systems, DBMS. One is Microsoft SQL server usually used as a single server database and the distributed database MongoDb. This thesis will try to answer the questions "How is the performance of the DBMS affected by a DDoS attack of varying strength?" and "How does data size affect the DBMS performance?". This has been done by performing two test scenarios. One run on a single server where one Microsoft SQL server instance was compared to three MongoDb instances and one run on a cloud solution comparing two Microsoft SQL Server instances to two MongoDb instances. In both cases, the database instances were hosted on lightweight virtual machines, Docker containers. Results have been generated by measuring response times to these databases while various DDoS attack has been performed on them. The results show that both solutions work well for smaller data sizes while MongoDb is more when the data size grows. Making MongoDb a better choice if the data is expected to continuously grow.