Paper Info
Title
A Fair Comparison of Message Queuing Systems
Abstract
The production and real-time usage of streaming data bring new challenges for data systems due to huge volume of streaming data and quick response request of applications. Message queuing systems that offer high throughput and low latency play an important role in today's big streaming data processing. There are several popular message queuing systems in production usage and also many in-lab message queuing systems in academia. These systems with different design philosopies have different characteristics. It is non-trivial for a non-expert to choose a suitable system to meet his specific requirement. With this premise, our primary contribution is to provide the community with a fair comparison among message queuing systems, using a standardized comparison metric and reproducible experimental environment. Five typical message queuing systems (including Kafka, RabbitMQ, RocketMQ, ActiveMQ and Pulsar) are evaluated qualitatively (in analysis) and quantitatively (in experimental results). This article also highlights the distinct features of each system and summarizes the best-suited use cases of each system. The fair comparison and the insight analysis provided in this article can help users choose the best-suited message queuing systems.
Year
DOI
Venue
2021
10.1109/ACCESS.2020.3046503
IEEE Access
Keywords
DocType
Volume
Big data,streaming processing,message queuing system
Journal
9
ISSN
Citations 
PageRank 
2169-3536
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Guo Fu100.34
Yanfeng Zhang217015.56
Ge YU31313175.88