What's new in HiveMQ Edge 2026.7

We are excited to announce the release of HiveMQ Edge 2026.7. This release introduces two major features Tag Browsing for bulk device tag operations and Tag Schema for standardized data point schema generation alongside important bug fixes and a security update.

Tag Browsing

What It Is

Configuring protocol adapter tags one at a time is impractical at scale. When a single OPC-UA server exposes hundreds or thousands of variable nodes, manually creating tags for each one is tedious and error-prone. Tag Browsing solves this by letting you scan a device's address space, download the discovered variables as a file, edit the file with your preferred tools, and import it back into HiveMQ Edge to create tags and mappings in bulk. Two new REST API endpoints power this feature.

How It Works

The workflow is file-based by design. After browsing, you download a device tag file containing up to 21 fields per tag — 14 editable columns (tag name, description, MQTT topics, QoS, message expiry, timestamps, and more) and 7 read-only informational columns (node path, namespace URI, node ID, data type, and access level).

You can edit this file using any tool that fits your workflow — a spreadsheet for manual selection, formulas for bulk naming patterns, a Python script for automation, or curl for CI/CD integration. To accept the generated defaults for any field, simply enter an asterisk (*) as a wildcard, making it easy to onboard large numbers of tags at once.

When importing, you choose from five conflict-resolution modes to control how the import interacts with existing tags:

• MERGE_SAFE (default): creates new tags but rejects changes that would overwrite existing ones.
• MERGE_OVERWRITE: creates and updates tags without removing unrelated ones.
• OVERWRITE: performs a full sync, matching the adapter's tags exactly to the file.
• CREATE: asserts that the adapter has no existing tags before importing.
• DELETE: removes the tags specified in the file.

Multi-mapping is also supported: a single tag can publish to multiple MQTT topics by including multiple rows that share the same node ID and tag name.

HiveMQ Edge also includes a dedicated Browse and Import panel in the adapter settings UI, providing point-and-click access to the same capabilities without needing to use the REST API directly. From the panel you can specify a root node ID to scope the browsing, set a maximum recursion depth, and select your preferred output format.

How It Helps

• Scale: Onboard hundreds of tags in minutes instead of hours of manual configuration.
• Flexibility: Work with familiar tools — spreadsheets, scripts, AI tools, or CI/CD pipelines — instead of being locked into a UI-only workflow.
• Safety: Five conflict-resolution modes and exhaustive validation (16 error codes) prevent accidental overwrites or invalid configurations from reaching your adapters.
• Automation: The REST API design makes it straightforward to integrate tag management into automated provisioning and deployment workflows.
• Tag Browsing currently supports OPC-UA adapters. The underlying interface is designed to accommodate additional protocols in future releases.

For full documentation, see Bulk Device Tag Operations.

Tag Schema

What It Is

HiveMQ Edge now generates standardized data point schemas for all protocol adapters. Tag Schema provides a consistent, structured description of the data each tag produces, enabling downstream consumers. Such as Data Hub policies, data transformation pipelines, and MQTT clients; to understand and validate the shape of incoming data automatically.

How It Works

A new standardized schema builder in the Adapter SDK allows each protocol adapter to declare the structure of its data points in a uniform way. Schema generation has been implemented for all major protocol adapters in this release:

• OPC-UA: schemas reflect the rich OPC-UA type system, including data types, access levels, and metadata fields.
• Modbus: schemas describe register-based data layouts with appropriate numeric types.
• S7/ADS: schemas capture PLC data block structures and their corresponding data types.

The generated schemas are fully integrated with HiveMQ Edge's Data Hub, where they can be referenced by data policies for payload validation and transformation.

How It Helps

• Consistency: Every protocol adapter now describes its data in the same schema format, eliminating adapter-specific handling in downstream processing.
• Validation: Data Hub policies can reference these schemas to validate payloads at the edge, catching data quality issues before they propagate to the cloud.
• Interoperability: Standardized schemas make it easier to integrate HiveMQ Edge with external systems that rely on structured data contracts.

Additional Improvements

• Fixed Data Combiner creation regression: Resolved an issue introduced in Edge 2026.6 where creating a new Data Combiner through the frontend would always fail with the error "The Edge broker must be connected to the combiner's sources."
• Security: Bouncycastle CVE-2025-14813: Upgraded Bouncycastle from 1.83 to 1.84 to address a vulnerability in the GOST-R-3413-2015 CTR implementation that affected counter handling beyond 255 blocks.

Advance Notice

Starting April 2026, HiveMQ Edge will be compiled with Java v25 (class file version 69).

This means that Java 25 (or a compatible JDK 25 distribution) will be required to run HiveMQ Edge. Java 25 is a Long-Term Support release, making it a solid foundation for enterprise deployments. For Edge's container distribution, JDK 25 is already the Java Runtime Environment since 2026.2. Note that Java 21 — the previous LTS — reaches the end of permissive licensing in September 2026. Upgrading now avoids last-minute disruption. We encourage users who provide their own runtime environment to plan their JDK upgrades accordingly ahead of the April release.

Java 25 is a Long-Term Support release and brings meaningful concurrency improvements to HiveMQ Edge. Virtual threads — introduced in Java 21 — are now fully supported, and structured concurrency and scoped values — finalized in Java 25 — make the internal management of concurrent tasks safer and more predictable. Together, these features allow HiveMQ Edge to handle more connections with lower resource overhead and lay the groundwork for further performance and reliability improvements in future releases.

Get Started Today

Use the download link to get HiveMQ Edge 2026.7, or find us on GitHub and Docker:

Get started by running

    docker run --name hivemq-edge --pull=always -d -p 1883:1883 -p 8080:8080 hivemq/hivemq-edge
  

Or clone our repository

    git clone git@github.com:hivemq/hivemq-edge.git
  

You may also try out our Helm Chart

    helm repo add hivemq https://hivemq.github.io/helm-charts && helm repo update