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 paper 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 paper can help users choose the best-suited message queuing systems.INDEX TERMS big data, streaming processing, message queuing system
Multinational enterprises conduct global business that has a demand for geo-distributed transactional databases. Existing state-of-the-art databases adopt a sharded master-follower replication architecture. However, the single-master serving mode incurs massive cross-region writes from clients, and the sharded architecture requires multiple round-trip acknowledgments (e.g., 2PC) to ensure atomicity for cross-shard transactions. These limitations drive us to seek yet another design choice. In this paper, we propose a strongly consistent OLTP database GeoGauss with full replica multi-master architecture. To efficiently merge the updates from different master nodes, we propose a multi-master OCC that unifies data replication and concurrent transaction processing. By leveraging an epoch-based delta state merge rule and the optimistic asynchronous execution, GeoGauss ensures strong consistency with light-coordinated protocol and allows more concurrency with weak isolation, which are sufficient to meet our needs. Our geo-distributed experimental results show that GeoGauss achieves 7.06X higher throughput and 17.41X lower latency than the state-of-the-art geo-distributed database CockroachDB on the TPC-C benchmark.
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