HiveMQ Cloud Introduces Metrics and Troubleshooting in Cloud Console

Maintaining operational reliability and performance is essential in the world of IoT. HiveMQ Cloud’s Cloud Console now includes advanced Metrics and Troubleshooting features, giving you the tools to monitor efficiently and resolve issues effectively. These two, together with existing tracing functionality (available only in the Enterprise plan,) form the three pillars of observability needed in an IoT data platform.

What’s New in HiveMQ Cloud?

We have introduced two major improvements to the user experience aimed at helping you monitor, diagnose, and troubleshoot your HiveMQ Cloud deployment easily. Additionally, a new user interface enhancement is now available, allowing you to easily access logging information for your HiveMQ Cloud cluster directly from the HiveMQ Cloud console. We have also released a list of Metrics for your HiveMQ Cloud cluster that helps you track client connections, message delivery, and key business metrics. These are available for both our Self-Service Starter Plan as well as our Enterprise customers. 

HiveMQ Cloud Metrics Overview

HiveMQ Cloud Metrics provides comprehensive visibility into your IoT deployment’s performance. By tracking key metrics, users can:

• Monitor message throughput and delivery performance.

• Track client connections and disconnections in real time.

• Analyze subscription and topic usage.

With detailed insights into cluster performance, users can proactively identify potential bottlenecks and optimize resource utilization.

Key metrics available in the Cloud Console can be found here.

The provided metrics help with the following use cases:  

• Monitoring & Alerting: Set up dashboards and alerts (e.g., Grafana, Prometheus) to watch for unusual spikes in disconnects, dropped messages, or queue sizes.

• Capacity Planning: Use session, subscription, and queue size metrics to forecast future resource needs as you add more devices or expand data usage.

• Troubleshooting & Root Cause Analysis: Drill down into dropped messages by category to rapidly identify configuration or scaling issues.

• Extension Health: Keep an eye on Kafka and Pub/Sub extension metrics to ensure reliable data forwarding to downstream systems.

• Policy & Behavior Validation: Confirm that the correct devices are covered by the right behaviors or rules, and ensure no device data is “slipping through the cracks.”

HiveMQ Cloud Troubleshooting Overview

HiveMQ Cloud Troubleshooting enables users to quickly understand and resolve issues. With features like log filtering and detailed event tracking, users can:

• Gain insights into operational events within their HiveMQ Cloud cluster.

• Filter log data by any string input such as client ID, timestamp, event type, and log content.

• Diagnose and address issues with precision.

The structured presentation of log data ensures clarity and usability:

The screenshot shows the Troubleshooting tab on your Starter or Enterprise Cloud console. In our example, we can see the logs depicting a successful authentication and authorization of an MQTT device using credentials.  

In the troubleshooting tab, the logs are visible while you are on the page—historical logs are not retained in this view. For a historical analysis, you can connect your logs to a sink of your choice. 

Combining Metrics and Troubleshooting for Operational Excellence

HiveMQ Cloud’s Metrics and Troubleshooting features work together to provide a holistic view of IoT operations. Metrics allow users to identify anomalies or performance degradation while the Troubleshooting tab offers an easy-to-use tool for investigating and resolving these issues.

For example:

• Spike in disconnections: Use metrics to identify a sudden increase in client disconnections. Leverage troubleshooting logs to analyze connection attempts and pinpoint the root cause.

• A sudden increase in queued messages: Use the available metrics such as hivemq_cloud_messages_client_queued_count to understand if the messages are queuing up over time. You can also observe hivemq_cloud_networking_connections_current. If it has decreased at the same time, you might suspect some clients have gone offline, causing message backlogs. If you suspect messages are getting stuck or dropped, you can configure a trace recording, to dive deep (Enterprise only).  

Conclusion

The addition of Metrics and Troubleshooting to HiveMQ Cloud empowers users to maintain high availability and reliability in their IoT deployments. These features provide actionable insights and tools to ensure seamless operations and swift issue resolution. Along with existing trace recording functionality, these form the three pillars of observability that are needed for operational excellence.