The volumes of data which Big Data applications have to process are constantly increasing. This requires for the development of highly scalable systems. Microservices is considered as one of the solutions to deal with the scalability problem. However, the literature on practices for building scalable data-intensive systems is still lacking. This thesis aims to investigate and present the benefits and drawbacks of using microservices architecture in big data systems. Moreover, it presents other practices used to increase scalability. It includes containerization, shared-nothing architecture, data sharding, load balancing, clustering, and stateless design. Finally, an experiment comparing the performance of a monolithic application and a microservices-based application was performed. The results show that with increasing amount of load microservices perform better than the monolith. However, to cope with the constantly increasing amount of data, additional techniques should be used together with microservices.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:lnu-88822 |
| Date | January 2018 |
| Creators | Remeika, Mantas, Urbanavicius, Jovydas |
| Publisher | Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM) |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
Page generated in 0.0022 seconds