MQTT is the go-to protocol for real-time, lightweight communication across millions of devices. From smart homes to industrial IoT, MQTT powers systems where low latency and reliability are non-negotiable.
But here’s the catch: when your app scales, performance bottlenecks often surface where you least expect them—impacting both device-to-cloud communication and user experience.
So how do you make sure your MQTT infrastructure performs at scale?
MQTT (Message Queuing Telemetry Transport) is a lightweight publish-subscribe protocol designed for real-time communication between devices, services, and applications. Originally developed for oil pipeline monitoring, it’s now the backbone of IoT ecosystems, messaging platforms, and automation systems.
MQTT is ideal for scenarios where:
Network bandwidth is limited
Devices frequently disconnect and reconnect
Data must be sent and received with minimal overhead
MQTT clients: Publishers and subscribers that send or receive data
MQTT broker: A central service that routes messages to subscribers based on topics
Topic: A string that defines message routing rules (like home/temperature)
Retained message: A broker feature that stores the last message sent to a topic for new subscribers
QoS levels: Control delivery guarantees across the network
Authentication and TLS encryption: Essential for secure deployments
MQTT is widely supported by open-source software, comes in multiple MQTT versions (including 3.1.1 and 5.0), and supports JSON, binary, and plain text payloads. It’s used for sending data, sharing information, and coordinating actions across thousands of devices.
Most load testing tools are built for HTTP APIs and traditional web traffic. MQTT, being a stateful, event-driven protocol, behaves differently:
Without a purpose-built approach to MQTT load testing, teams are often left guessing:
Unfortunately, these questions are usually answered too late—in production.
Whether you’re building connected devices, live chat, or telemetry platforms, MQTT performance directly affects:
Testing MQTT systems at scale isn’t just about robustness—it’s about reliability, cost efficiency, and avoiding production failures in high-stakes environments.
Companies that skip performance testing for MQTT often end up over-provisioning infrastructure or, worse, firefighting stability issues during customer-facing events.
Gatling is widely known for its powerful load testing engine and test-as-code flexibility. With the MQTT plugin, teams can now simulate thousands of real-time MQTT clients—publishing, subscribing, and managing connections—at scale.
Test-as-code model with full language support (Java, Scala, Kotlin, TypeScript)
Automation-ready via CI/CD integrations
Custom injection profiles to model ramp-up, soak, and spike tests
Real-time dashboards, historical comparisons, and regression alerts
Support for open source tools and third-party brokers
Whether you're testing on-premises deployments, hybrid cloud services, or distributed IoT infrastructures, Gatling makes it easy to stress your system from 0 to thousands of concurrent connections.
Follow our full tutorial to start building and running MQTT tests with Gatling: