A distributed architecture for MQTT messaging: the case of TBMQ

Abstract This article introduces ThingsBoard Message Queue (TBMQ), a scalable and fault-tolerant Message Queuing Telemetry Transport (MQTT) broker designed to address the challenges of data handling in large-scale Internet of Things (IoT) ecosystems. TBMQ’s distributed architecture enables horizontal scalability, fault tolerance, and reliable data delivery, ensuring high availability and data durability under heavy load conditions. The broker efficiently handles two prevalent communication patterns in big data IoT applications: the fan-in pattern, where a large number of devices generate high volumes of messages for processing, and the fan-out pattern, where a few requests trigger significant outgoing data to multiple devices. Leveraging Apache Kafka (Kafka), TBMQ supports over 100 million concurrent client connections and a throughput exceeding 3 million messages per second. This paper explores the architectural decisions, technical innovations, and strategic use of Kafka, providing insights into building reliable and scalable messaging systems suitable for the demanding requirements of big data in IoT.

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Publication Details

Journal
Journal Of Big Data
Published
2025-09-26
DOI
https://doi.org/10.1186/s40537-025-01271-x
Citations
1
Primary Topic
Gene expression and cancer classification
Type
article
Field-Weighted Citation Impact
0.81
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article

A distributed architecture for MQTT messaging: the case of TBMQ

1 citations
Journal Of Big Data
Gene expression and cancer classification
0.81
article

A distributed architecture for MQTT messaging: the case of TBMQ

article en
1 citations

Abstract

Abstract This article introduces ThingsBoard Message Queue (TBMQ), a scalable and fault-tolerant Message Queuing Telemetry Transport (MQTT) broker designed to address the challenges of data handling in large-scale Internet of Things (IoT) ecosystems. TBMQ’s distributed architecture enables horizontal scalability, fault tolerance, and reliable data delivery, ensuring high availability and data durability under heavy load conditions. The broker efficiently handles two prevalent communication patterns in big data IoT applications: the fan-in pattern, where a large number of devices generate high volumes of messages for processing, and the fan-out pattern, where a few requests trigger significant outgoing data to multiple devices. Leveraging Apache Kafka (Kafka), TBMQ supports over 100 million concurrent client connections and a throughput exceeding 3 million messages per second. This paper explores the architectural decisions, technical innovations, and strategic use of Kafka, providing insights into building reliable and scalable messaging systems suitable for the demanding requirements of big data in IoT.

Journal Of Big DataVol. 12(1)
Pukhov Institute for Modelling in Energy Engineering (UA), Ternopil Ivan Pului National Technical University (UA), Palladin Institute of Biochemistry (UA), Kyiv National Economic University named after Vadym Hetman (UA), National University "Kyiv Aviation Institute" (UA)
Openalex Percentile: Top 20%
Gene expression and cancer classification
0.81
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A distributed architecture for MQTT messaging: the case of TBMQ · Journal Of Big Data (2025) | TGRS Research Map | TGRS