Deciding which database management system (DBMS) to use has perhaps never been harder. In recent years there has been an explosive growth of new types of database management systems that address different issues and performs well for different scenarios. This thesis is an improving case study of an Event Persistence Solution for the Eiffel Framework, which is a framework used for achieving traceability in very-large-scale systems development. The purpose of this thesis is to investigate whether it is possible to improve the performance of the Eiffel Event Persistence Solution by changing from MongoDB, to Elasticsearch or ArangoDB. Experiments were conducted to measure the request throughput for 4 types of requests. As a prerequisite to measuring the performance, support for the different DBMSs and the possibility to change between them was implemented. The results showed that Elasticsearch performed better than MongoDB in terms of nested-document-search as well as for graph-traversal operations. ArangoDB had even better performance for graph-traversal operations but had an inadequate performance for nested-document-search. / 决定使用哪个数据库管理系统（DBMS）可能从未如此困难过。近年来，新型数据库管理系统呈现爆炸式增长，它们解决了不同的问题，并在不同的情境中表现出优异性能。本论文是针对Eiffel框架的事件持久性解决方案的改进案例研究，该框架被用于实现超大规模系统开发中的可追溯性。本文的目的是研究是否可以通过摒弃MongoDB并改用Elasticsearch或ArangoDB来提高Eiffel事件持久性解决方案的性能。为测量4种类型的请求的请求吞吐量进行了实验。作为衡量性能的前提条件，实施了对不同数据库管理系统（可在这些系统之间进行更换）的支持。结果表明，Elasticsearch在嵌套文档搜索和图形遍历操作方面的性能均优于MongoDB。 ArangoDB在图形遍历操作方面具有比前者更好的性能，但在嵌套文档搜索方面的性能不佳。

Arbetet jämför fyra olika NoSQL-databaser med fokus på tidseffektivitet. De fyra databaserna är MongoDB, RavenDB, ArangoDB och Couchbase. Studien består av en benchmark för att mäta tidseffektiviteten av de fyra databaserna och en litteraturstudie av hur tidseffektiviteten påverkas av optimeringslösningar. Tillsammans bidrar dessa metoder till en slutsats från båda perspektiven då de kompletterar varandra och ger en grund för resultatets betydelse. Arbetets grund ligger i ett tidigare examensarbete som går ut på att jämföra en SQL-databas mot en NoSQL-databas med en benchmark. Resultatet av studien visar att för de flesta databaser så ökar söktiden för en query i korrelation med ökningen av query-komplexiteten, och att tidseffektiviteten mellan de olika databaserna varierar vid sökningar med hög komplexitet. Framtida arbeten som kan baseras på denna studie är att göra en liknande benchmark på ett dataset som är större eller att en annan typ av databas används.

There is a large amount of data that needs to be stored today. Handling so much data efficiently is important as minor performance differences can have significant effects on large systems. Knowing how a certain database management system performs is important for companies and organizations to decide which database management system to use. There is currently a gap in the research regarding performance differences between different database management systems. We conducted a study that compares the average query response time of PostgreSQL, MongoDB, ArangoDB and HBase. We also compared the performance between using a single thread and using multiple threads. We compared how they perform with a dataset size and operation count of 10 000, 100 000, and 1 000 000 with insert, update and read queries. The results show that PostgreSQL has the lowest average query response when doing read queries and that MongoDB has the lowest average query response when doing insert and update queries. The results also showed a significant performance gain from using multiple threads instead of using a single thread.

This study conducted an experiment that compares the multi-model NoSQL DBMS ArangoDB with other NoSQL DBMS, in terms of the average response time of queries. The DBMS compared in this experiment are the following: Redis, MongoDB, Couchbase, and OrientDB. The hypothesis that is answered in this study is the following: “There is a significant difference between ArangoDB, OrientDB, Couchbase, Redis, MongoDB in terms of the average response time of queries”. This is examined by comparing the average response time of 1 000, 100 000, and 1 000 000 queries between these database systems. The results show that ArangoDB performs worse compared to the other DBMS. Examples of future work include using additional DBMS in the same experiment and replacing ArangoDB with another multi-model DBMS to decide whether such a DBMS, in general, performs worse than single-model DBMS.