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HiveMQ Swarm - MQTT Load Testing

Find scalability, performance and reliability problems

"HiveMQ Swarm ... helped us to gain a better understanding of the capacity limitations of our MQTT Broker clusters and how we need to scale the resources available to each cluster as the overall load increases. As a result, we are more confident in the stability of our production clusters and can more accurately forecast capacity and costs."

HiveMQ Swarm Customer

Load testing and reliability testing MQTT systems is an imperative for any business critical IoT solution. HiveMQ Swarm provides the distributed simulation environment to successfully test millions of MQTT clients, millions of MQTT messages and hundreds of thousands MQTT topic names. Finally a tool to check the performance, scalability and reliability of your IoT solution before it is deployed into production.
HiveMQ Swarm Distributed Platform
"HiveMQ Swarm ... helped us to gain a better understanding of the capacity limitations of our MQTT Broker clusters and how we need to scale the resources available to each cluster as the overall load increases. As a result, we are more confident in the stability of our production clusters and can more accurately forecast capacity and costs."

HiveMQ Swarm Customer

HiveMQ Swarm Features

Complete end-to-end testing of your IoT solution. Swarm allows you to discover potential bottlenecks in your system.
Distributed platform that can create millions of unique network connections. Swarm agents can be used to spawn client connections from anywhere in the world.
Simulation of millions of devices, messages and MQTT topics. Scale up and scale down your testing to simulate actual production scenarios.
Develop reusable scenarios that simulate device behaviours. Integrate these scenarios into your continuous development pipeline to ensure continued testing.
Create custom data generators that simulate complex use cases.
Intuitive reports to provide immediate feedback on test results. Pre-defined Grafana dashboard makes it easy to visualize your results but also allows you to customize to your requirements.
Resource friendly and easy deployment to public clouds (AWS, Azure, etc.) and on-premise Kubernetes deployments.

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