IoT Observability: What is it and how does it help?
Introduction to IoT Observability
Increasingly, IoT applications are getting deployed in highly distributed structures that incorporate millions of devices and can span the globe. The messages exchanged within this distributed structure must transit through multiple components, including MQTT brokers.
However, there is a gap. Most MQTT brokers on the market currently can’t gather metadata on requests/messages continuously and reliably. This inability creates gaps in tracking that ultimately impact the service level objectives for the teams.
DevOps engineers and software development teams must have the ability to perceive each detail (deep visibility) throughout these distributed structures to meet service-level requirements that customers expect. IoT Observability can solve this problem.
IoT Observability is a method that defines how users (engineers and developers) get deep visibility (at a granular level) - into the key components and metrics of their IoT applications.
IoT Observability enables users to:
Actively debug their IoT applications quickly because they have precise insights.
Improve their IoT applications by identifying critical issues quickly and solving them before they snowball into larger problems.
Develop a deep understanding of how their IoT application works in the broader distributed structure.
IoT Observability Vs. Monitoring
Now that we know what IoT Observability is, let’s explore the difference between it and Monitoring.
| IoT Observability — (Monitoring, Logging, Tracing) | Monitoring |
|---|---|
| Helps users get visibility at a granular level - into the key components and metrics of their IoT applications. | Monitoring is arguably a subset of observability. |
| It allows teams to actively debug their systems by providing the latest information in a simple format. | Monitoring uses telemetry data to watch and understand the health and performance of your applications. |
| Teams can analyze all available outputs in real time to understand the inner state of their systems. | Monitoring uses a set of logs and metrics that allow you to understand how you utilized your app, see its growth, and develop a deeper understanding of your application’s functionality. |
HiveMQ Distributed Tracing Extension — End-to-end IoT Observability for the Multi-cloud
HiveMQ is ultra-flexible so that it can integrate with virtually every existing enterprise system, such as IoT Observability, Monitoring, Message Processing, Databases, Security, and more.
HiveMQ is a platform-agnostic solution that gives its customers the flexibility to adopt a single or multi-cloud strategy via its enterprise extensions. The HiveMQ extension framework provides an open API that allows developers to create custom extensions for their specific infrastructure.
OpenTelemetry underpins HiveMQ’s Distributed Tracing Extension, allowing teams to follow messages through multiple complex systems. It gives a high-level overview of a message’s journey so teams analyzing issues can isolate potential problems, identify affected systems, and troubleshoot them before a major problem can metastasize.
Let’s explore how the HiveMQ platform enables IoT Observability in all of the big three cloud providers.
IoT Observability in Azure, AWS, and Google Cloud
The HiveMQ platform can integrate with all three big cloud platforms via its highly flexible extensions. This integration allows customers to ingest data into existing data pipelines and take advantage of all Cloud offers (Data Analytics, Database storage, Data Explorer, ML & AI, etc.).
Combined, the extension and the broker allow customers to benefit from all the capabilities of a full-featured MQTT broker as well - (Support for all MQTT protocol versions (MQTT 3.x, MQTT 5.x), all three QoS levels, Sparkplug support, and more).
HiveMQ’s Distributed Tracing extension can run in IoT environments that use Azure, AWS or Google Cloud.
Distributed Tracing allows teams to trace messages through multiple, complex systems. By analyzing a message’s journey, teams can isolate potential problems and then look into identified systems to solve these problems.
Learn what else the Distributed Tracing Extension can do for engineering and site reliability teams.
How Does OpenTelemetry Enhance IoT Observability?
HiveMQ’s Distributed Tracing Extension has native support of OpenTelemetry.
OpenTelemetry is an open-source observability framework. The framework includes tools, APIs, and SDKs that help instrument systems to generate, collect, and export telemetry data.
OpenTelemetry records always include primary fields like a transaction ID and timestamp, but they can also include business-relevant information (e.g. Order or Stock number).
The OTel framework standardizes telemetry data. When an Application Monitoring Tool (e.g., Data Dog, Honeycomb.io) receives data, it makes the information observable and displays it in an easy-to-read form. Teams can then see how their IoT applications relate to each other and explain why things aren’t working as expected.
Conclusion
To summarize, IoT observability is a relatively new concept gaining popularity because engineers and developers realize the genuine value of:
Having a deep sense of their IoT applications,
Developing an in-depth understanding of how these applications work inside the wider IoT environment.
Knowing where to start looking when errors occur.
Contact our team to learn more about how your team/company can leverage our industry-leading solution and the extensions that enable high flexibility.
Nasir Qureshi
Nasir Qureshi is a Senior Product Marketing Manager at HiveMQ. With a passion for working on disruptive technology products, Nasir has helped SaaS companies in their hyper-growth journey for over 3 years now. He holds an MBA from California State University with a major in Technology and Data Management. His interests include IoT devices, networking, data security, and privacy.
