We just added a simple scripting feature to MonsterMQ.com.

Instead of building a MonsterMQ workflow for every small transformation, you can now create simple JavaScript scripts that run directly in the broker. For many use cases, that is much easier: subscribe to one or more input topics, process the data, publish the result.

And it goes beyond MQTT topics: scripts can also use database connections to read and write data directly from PostgreSQL, MySQL, Neo4J, making it possible to enrich, correlate, or persist data directly.

There is also AI generation support built in. Just describe what you want, for example: “take the JSON payload of the trigger topic and publish every single JSON item to a separate topic on output/expand/<item>” and the script gets generated for you.

Personally, I think this makes the MonsterMQ workflows unnecessary. For a lot of broker-side automation, a small script is simpler and easier to understand, especially when AI can help generate it.

#MQTT #MonsterMQ

A new experimental feature has landed in MonsterMQ: It is acting now as a Kafka Broker, so a Kafka Client can subscribe (and publish, if allowed) to streams. Streams are mapped to MQTT topics. So MQTT topic value changes are going into those streams.

Before anyone asks: No, this is not a replacement for Apache Kafka. 🙂

The queueing is currently backed by databases such as PostgreSQL, MongoDB, and SQLite, so it won’t compete with Kafka in terms of throughput and scalability.

But for many smaller and medium-sized use cases, it brings streaming concepts directly into the broker:

• 🔹 MQTT and Kafka-style messaging in one server
• 🔹 Persistent queues stored in a database
• 🔹 Simple deployment without additional infrastructure
• 🔹 Easy integration with the existing MonsterMQ ecosystem

As always, this is a first draft – not yet in the version or docker image!

Feedback is welcome!

👉 MonsterMQ.com

🔥 MonsterMQ now speaks Redis. MQTT clients and Redis clients connect to the same broker – and to the same piece of storage.

👉 Publish a value via MQTT. Read it with a Redis client. Update it via Redis. It arrives as an MQTT message.

Same data. Same broker. Two protocols.

Disclaimer: not all Redis functions are already implemented.

🔗 monstermq.com

Database connections for archives can now be configured at runtime directly in the dashboard – no restart needed.

👉 This is especially important for MonsterMQ-Edge running on a Unified Comfort Panel – you can now configure archiving online, without touching the panel (MMQ config file).

The result: your data from WinCC Unified Panels can be easily archived centrally to PostgreSQL or MongoDB, configured and managed remotely.

Or forward the data to a central full MonsterMQ instances to collect the data from all your panels.

And here’s the bigger picture: MonsterMQ and MonsterMQ-Edge share the same dashboard, same GraphQL interface. One central place to manage both – from the full broker down to the tiny monster running on your panel. 😅

Let me know if you want it as an Edge App, which can be deployed on the Panel. But remember: you need an Edge License to run Edge on the Panel.

MonsterMQ-Edge still has limited functionality compared to the full broker – but it’s growing.

🔗 monstermq.com

#MonsterMQ #WinCCUnified #EdgeComputing #MQTT #OpenSource

It turns your existing panels to a MQTT enabled device!

MonsterMQ-Edge is a lightweight MQTT broker – the Docker image is just 36MB. I have poured it into a SIEMENS Industrial Edge App…

Here’s what it does on the panel:

• 👉 Connects to the WinCC Unified Runtime
• 👉 Configurable tag publishing with wildcard support
• 👉 Configurable from a central MonsterMQ dashboard
• 👉 Store and forward to other MQTT brokers
• 👉 Archive data to Postgres or MongoDB
• 👉 … more will come …

Btw.: you can do the same with the full version of MonsterMQ with WinCC Open Architecture or with WinCC Unified running on the PC.

🔗 monstermq.com 🔥 SIEMENS

Disclaimer: experimental state. But it is cool to see a tiny Monster running on a SIEMENS Panel.

#MonsterMQ #WinCCUnified #EdgeComputing #MQTT #OpenSource

Built in Go, based on another open-source project (Mochi MQTT), implemented to expose the same GraphQL interface as the full MonsterMQ broker and having the same storage backend (SQLite, Postgres, MongoDB).

What does that mean in practice? You can run a lightweight MonsterMQ instance at the edge and monitor and configure it from the same MonsterMQ dashboard and having the data in the same storage format. No separate tooling needed.

Current state:

• 👉 Single binary 25M
• 👉 In memory last value storage
• 👉 Archiver for SQLite, PostgreSQL and MongoDB.
• 👉 MQTT Bridge available to pub/sub from/to other brokers.
• 👉 Backend storage options: SQLite, Postgres or MongoDB
• 👉 Same GraphQL interface – compatible with the existing dashboard

Very early stage, and just an experiment for now. What do you think about it?

🔗 monstermq.com

#MonsterMQ #MqttClaw #MQTT #Edge #EdgeComputing #Go