MQTT Broker for
Factory Automation

High-performance, database-backed MQTT broker built on Vert.X and Hazelcast.
Store messages persistently, scale horizontally, and integrate with AI models.

Get Started View on GitHub Try Demo Server
✓ MQTT 3.1.1
✓ Multi-Database
✓ OPC UA
✓ Workflows
✓ Dashboard
✓ Clustering
✓ AI Ready

Factory Automation Features

🗄️

Multi-Database Backend

Choose from PostgreSQL, MongoDB, CrateDB, or SQLite. Store retained messages and persistent sessions with enterprise-grade reliability.

🔄

Horizontal Scaling

Built-in Hazelcast clustering enables seamless horizontal scaling. Deploy from production line to enterprise with automatic failover and load balancing.

🤖

AI Integration Ready

Integrated MCP (Model Context Protocol) server allows AI models to query real-time and historical MQTT data directly.

🏭

OPC UA Integration

Native OPC UA client with cluster-aware device management and certificate-based security. Unified archiving ensures industrial data flows through the same central system as MQTT messages. Supports browse paths, node IDs, and wildcard subscriptions for flexible device connectivity.

🏭

WinCC Open Architecture Integration

High-performance bulk transfer from Siemens WinCC Open Architecture SCADA systems. Subscribe to millions of datapoints with a single continuous SQL query leveraging WinCC Open Architecture's powerful dpQueryConnectSingle function. Stream tag values and alerts directly to MQTT topics. Efficient handling of high-volume data changes without per-message overhead.

🏭

WinCC Unified Integration

Modern SCADA integration via GraphQL/WebSocket for Siemens WinCC Unified. Real-time tag value subscriptions with flexible name filters and wildcards. Stream active alarms and alerts with complete alarm details including state, priority, and timestamps. Optional OPC UA quality information for tag values including quality codes and status flags.

🔗

MQTT to MQTT Bridge

Built-in MQTT bridge functionality enables bidirectional message flow between brokers. Forward messages to remote brokers with topic filters and transformations, or consume messages from external MQTT brokers. Perfect for building hierarchical broker architectures and connecting distributed systems without external tools.

🔀

Workflows (Flow Engine)

Visual flow-based programming with JavaScript runtime. Create data processing pipelines with drag-and-drop nodes. Transform, filter, and aggregate MQTT messages in real-time with reusable flow templates and instance-specific configuration.

📊

GraphQL API

Query current topic states and historical data through a modern GraphQL interface with real-time subscriptions support.

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SQL Query Interface

All data stored in central databases can be queried with standard SQL, enabling powerful analytics and reporting.

🔐

Enterprise Security

TLS/SSL support, user authentication with BCrypt, fine-grained ACL rules, and database-level security.

🔍

Topic Browser

Interactive topic tree navigation with search capabilities and message inspection. Browse MQTT topic hierarchies with expandable nodes and view message data in real-time.

🖥️

Web Dashboard

Complete web-based management interface for archive groups, users, and system configuration. Real-time updates without broker restarts.

Flexible Database Backends

MonsterMQ adapts to your infrastructure. Choose the database that fits your needs,
from lightweight SQLite for development to distributed CrateDB for time-series analytics.

PostgreSQL

✓ Full SQL capabilities
✓ Production proven
✓ Cluster support
✓ Advanced analytics

Best for: Production deployments with full SQL requirements

MongoDB

✓ Document store
✓ Flexible schema
✓ Horizontal scaling
✓ High throughput

Best for: NoSQL workloads and flexible data models

CrateDB

CrateDB

✓ Time-series optimized
✓ Distributed SQL
✓ Real-time analytics
✓ IoT scale

Best for: Time-series data and IoT analytics

SQLite

SQLite

✓ Zero configuration
✓ Embedded database
✓ Single file
✓ Fast performance

Best for: Development and single-instance deployments

Kafka

Apache Kafka

✓ Stream processing
✓ Event sourcing
✓ Real-time analytics
✓ Infinite retention

Best for: Stream analytics and event-driven architectures

Hazelcast

Hazelcast

✓ Distributed cache
✓ In-memory speed
✓ Cluster-aware
✓ Automatic replication

Best for: High-speed last values and distributed caching

Database Capability Matrix

Database Session Store Retained Store Message Archive Clustering SQL Queries
PostgreSQL ✓ ✓ ✓ ✓ ✓
MongoDB ✓ ✓ ✓ ✓ ✗
CrateDB ✓ ✓ ✓ ✓ ✓
SQLite ✓ ✓ ✓ ✗ ✓
Apache Kafka ✗ ✗ ✓ ✓ ✗
Hazelcast ✗ ✓ ✗ ✓ ✗

Intelligent Clustering Architecture

Build hierarchical MQTT infrastructures from edge to cloud. MonsterMQ's Hazelcast-based
clustering ensures data flows efficiently without duplication or unnecessary replication.

Enterprise Level

Enterprise Dashboard

Central data lake, AI/ML processing, business intelligence

Topic Filter: Enterprise/+/OEE/+
↑ Aggregated Metrics ↑

Regional Level

Line 1 MonsterMQ
Line 2 MonsterMQ
Line 3 MonsterMQ

Hazelcast Cluster - Aggregation, regional analytics, load balancing

Topic Filter: Enterprise/Line1/#, Enterprise/Line2/#
↑ Selected Data ↑

Edge Level

Machine 1
Machine 2
Machine 3
Machine 4
Machine 5
Machine 6
Machine 7
...

Production machines, sensors, PLCs - Local message processing

Smart Data Flow

Topic filters ensure only relevant data moves between levels, reducing bandwidth and storage costs.

Automatic Failover

Hazelcast clustering provides automatic failover and session migration between cluster nodes.

Central Database

All cluster nodes share a central database, ensuring consistent state and enabling SQL analytics.