# Benchmarking Message Queues
## 8 Conclusions and Future Works  
In this study, we conducted an extensive benchmarking analysis to evaluate the performance of four popular message queue systems: Redis, ActiveMQ Artemis, RabbitMQ, and Apache Kafka. Our goal was to gain deep insights into the strengths, limitations, and relative performance of each technology. To ensure consistent and unbiased evaluations, we employed the OpenMessaging Benchmark tool developed by The Linux Foundation. We focused on two key metrics: latency and throughput, to compare the message queue systems. The results of our study provide valuable insights for practitioners working with message queues. In terms of latency, Redis emerged as the top performer, exhibiting an exceptional performance with consistently low latency. Apache Kafka closely followed Redis, with a negligible difference of approximately 4 milliseconds. ActiveMQ Artemis outperformed RabbitMQ for lower-throughput scenarios, showcasing its strengths in specific use cases. However, as the throughput increased, the latency of ActiveMQ Artemis escalated significantly, surpassing that of RabbitMQ. Regarding throughput, Apache Kafka demonstrated remarkable capabilities, securing the first position by a considerable margin. RabbitMQ secured the second spot, displayinga commendable throughput performance. Redis followed closely behind, although its throughput capabilities were comparatively lower than RabbitMQ and Apache Kafka. ActiveMQ Artemis performed the least efficiently in terms of throughput, highlighting its limitations in handling high-volume message traffic. These findings provide practitioners with valuable guidance in selecting the most suitable message queue system based on their specific requirements. It is important to consider not only the raw performance metrics but also the trade-offs and considerations associated with each technology. Additional factors such as ease of use, community support, and integration capabilities should also be taken into account to make informed decisions. In conclusion, this benchmarking study contributes to the body of knowledge in the field of message queue systems by providing a comprehensive evaluation of Redis, ActiveMQ Artemis, RabbitMQ, and Apache Kafka. The insights gained from this research can guide practitioners in selecting the most appropriate message queue technology for their use cases, considering factors such as latency, throughput, and other relevant considerations. In future research, it would be valuable to expand upon the findings presented in this paper. Firstly, exploring additional performance metrics beyond latency and throughput would provide a more comprehensive understanding of the strengths and weaknesses of Redis, ActiveMQ Artemis, RabbitMQ, and Apache Kafka. This could include factors such as persistence, scalability, fault tolerance, and resource utilization. Additionally, conducting experiments with larger and more diverse workloads, spanning various use case scenarios, would further validate and refine the performance comparisons. Moreover, investigating the impact of different deployment architectures, such as cloud-based or containerized environments, on the message queues' performance would provide insights into their adaptability and suitability in modern computing infrastructures. Lastly, considering the integration and interoperability aspects of these message queues with other technologies and frameworks commonly used in distributed systems would be beneficial for practitioners seeking to leverage them in real-world applications. Conceptualization, R.M. and T.C. ; methodology, R.M. ; software, R.M. and M.S.H.C. ; validation, R.M. ; formal analysis, R.M. and M.S.H.C. ; investigation, R.M. ; resources, R.M. ; data curation, R.M. and M.S.H.C. ; writing--original draft preparation, R.M. and M.A.A. ; writing--review and editing, R.M., M.A.A. and T.C. ; visualization, R.M. and M.S.H.C. ; supervision, R.M. and T.C. ; project administration, R.M. and T.C. ; funding acquisition, T.C.