mq-bridge library

      ┌────── mq-bridge-lib ──────┐
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mq-bridge is an asynchronous message library for Rust. It connects different messaging systems, data stores, and protocols. Unlike a classic bridge that simply forwards messages, mq-bridge acts as a programmable integration layer, allowing for transformation, filtering, routing and event/command handling. It is built on Tokio and supports patterns like retries, dead-letter queues, and message deduplication.

Features

• Supported Backends: Kafka, NATS, AMQP (RabbitMQ), MQTT, MongoDB, SQL Databases (PostgreSQL, MySQL, SQLite via sqlx), HTTP, ZeroMQ, Files, AWS (SQS/SNS), IBM MQ, and in-memory channels.

  Note: IBM MQ is not included in the full feature set. It requires the ibm-mq feature and the IBM MQ Client library. See mqi crate for installation details.

• Configuration: Routes can be defined via YAML, JSON or environment variables.
• Programmable Logic: Inject custom Rust handlers to transform or filter messages in-flight.
• Middleware:
  • Retries: Exponential backoff for transient failures.
  • Dead-Letter Queues (DLQ): Redirect failed messages.
  • Deduplication: Message deduplication using sled.
• Concurrency: Configurable concurrency per route using Tokio.

Philosophy & Focus

mq-bridge is designed as a programmable integration layer. Its primary goal is to decouple your application logic from the underlying messaging infrastructure.

Unlike libraries that enforce specific architectural patterns (like strict CQRS/Event Sourcing domain modeling) or concurrency models (like Actors), mq-bridge remains unopinionated about your domain logic. Instead, it focuses on reliable data movement and protocol abstraction.

Status

This library was created in 2025 and is still kind of new.

It may still be possible that there are issues with

• old or very new versions of broker servers
• specific settings of the brokers
• subscribe / event and response patterns if those are not available natively
• nats, if jetstream support is disabled
• TLS integration, as this also hasn't been tested a lot and is usually non-trivial to set up

When to use mq-bridge

• Hybrid Messaging: Connect systems speaking different protocols (e.g., MQTT to Kafka) without writing custom adapters.
• Infrastructure Abstraction: Write business logic that consumes CanonicalMessages, allowing you to swap the underlying transport (e.g., switching from RabbitMQ to NATS) via configuration.
• Resilient Pipelines: Apply uniform reliability patterns (Retries, DLQ, Deduplication) across all your data flows.
• Database Integration: Easily combine databases with message brokers. Use it to ingest messages into a database (SQL/NoSQL) or to implement the Outbox Pattern by forwarding database rows to a broker.
• Sidecar / Gateway: Deploy as a standalone service to ingest, filter, and route messages before they reach your core services.
• Polyglot Services: Enable communication between services written in different languages (e.g., Node.js, Python, Go) that prefer different protocols (HTTP, gRPC, Kafka, AMQP) by acting as a universal translator and buffer.

When NOT to use mq-bridge

• Stateful Stream Processing: For windowing, joins, or complex aggregations over time, dedicated stream processing engines are more suitable.
• Domain Aggregate Management: If you need a framework to manage the lifecycle, versioning, and replay of domain aggregates (Event Sourcing), use a specialized library. mq-bridge handles the bus, not the entity.
• Specialization: mq-bridge focuses on a subset of messaging patterns like pub/sub and batching, emulating them if not natively supported. If you need very specific features from a messaging library or protocol, the abstraction layer of mq-bridge may prevent you from using them.

Core Concepts

• Route: A named data pipeline that defines a flow from one input to one output.
• Endpoint: A source or sink for messages.
• Middleware: Components that intercept and process messages (e.g., for error handling).
• Handler: A programmatic component for business logic, such as transforming/consuming messages (CommandHandler) or subscribe them (EventHandler).

Endpoint Behavior

mq-bridge endpoints generally default to a Consumer pattern (Queue), where messages are persisted (if supported by the backend) and distributed among workers.

To achieve Subscriber (Pub/Sub) behavior—where messages are broadcast to all active instances—you must configure the specific backend accordingly. There is no global "subscriber mode" toggle; it is determined by the configuration of the endpoint.

Response Mode

The response output endpoint allows sending a reply back to the requester. This is useful for synchronous request-reply patterns (e.g., HTTP-to-NATS-to-HTTP).

• Availability: Only available if the Input endpoint supports request-reply (HTTP, NATS, Memory, MongoDB).
• Configuration: Use response: {} as the output endpoint.
• Caveats:
  • If the input does not support responses (e.g., File, Kafka), the message sent to response will be dropped.
  • Ensure timeouts are configured correctly on the requester side, as the bridge processing time adds latency.
  • Middleware that drops metadata (like correlation_id) may break the response chain.

Usage

There is a separate repository to use mq-bridge as standalone app, for example as docker container that can be configured via yaml or env variables: https://github.com/marcomq/mq-bridge-app

Programmatic Handlers

For implementing business logic, mq-bridge provides a handler layer that is separate from transport-level middleware. This allows you to process messages programmatically.

Raw Handlers

• CommandHandler: A handler for 1-to-1 or 1-to-0 message transformations. It takes a message and can optionally return a new message to be passed down the publisher chain.
• EventHandler: A terminal handler that reads new messages without removing them for other event handlers.

You can chain these handlers with endpoint publishers.

use mq_bridge::traits::Handler;
use mq_bridge::{CanonicalMessage, Handled};
use std::sync::Arc;

// Define a handler that transforms the message payload
let command_handler = |mut msg: CanonicalMessage| async move {
    let new_payload = format!("handled_{}", String::from_utf8_lossy(&msg.payload));
    msg.payload = new_payload.into();
    Ok(Handled::Publish(msg))
};

// Attach the handler to a route
// let route = Route { ... }.with_handler(command_handler);

Typed Handlers

For more structured, type-safe message handling, mq-bridge provides TypeHandler. It deserializes messages into a specific Rust type before passing them to a handler function. This simplifies message processing by eliminating manual parsing and type checking.

Message selection is based on the kind metadata field in the CanonicalMessage.

use mq_bridge::type_handler::TypeHandler;
use mq_bridge::{CanonicalMessage, Handled};
use serde::Deserialize;
use std::sync::Arc;

// 1. Define your message structures
#[derive(Deserialize)]
struct CreateUser {
    id: u32,
    username: String,
}

#[derive(Deserialize)]
struct DeleteUser {
    id: u32,
}

// 2. Create a TypeHandler and register your typed handlers
let typed_handler = TypeHandler::new()
    .add("create_user", |cmd: CreateUser| async move {
        println!("Handling create_user: {}, {}", cmd.id, cmd.username);
        // ... your logic here
        Ok(Handled::Ack)
    })
    .add("delete_user", |cmd: DeleteUser| async move {
        println!("Handling delete_user: {}", cmd.id);
        // ... your logic here
        Ok(Handled::Ack)
    });

// 3. Attach the handler to a route
let route = Route::new(input, output).with_handler(typed_handler);

// 4. To send a message to the route's input, create a publisher for that endpoint.
//    In a real application, you would create this publisher once and reuse it.
let input_publisher = Publisher::new(route.input.clone()).await.unwrap();

// 5. Create a typed command, serialize it, and send it via the publisher.
let command = CreateUser { id: 1, username: "test".to_string() };
let message = msg!(&command, "create_user"); // This sets the `kind` metadata field.
input_publisher.send(message).await.expect("Failed to send message");

// The running route will receive the message, see the `kind: "create_user"` metadata,
// deserialize the payload into a `CreateUser` struct, and pass it to your registered handler.

Programmatic Usage

You can define and run routes directly in Rust code.

use mq_bridge::{models::Endpoint, stop_route, CanonicalMessage, Handled, Route};
use std::sync::{
    atomic::{AtomicBool, Ordering},
    Arc,
};

#[tokio::main]
async fn main() {
    // Define a route from one in-memory channel to another

    // 1. Create a boolean that is changed in the handler
    let success = Arc::new(AtomicBool::new(false));
    let success_clone = success.clone();

    // 2. Define the Handler
    let handler = move |mut msg: CanonicalMessage| {
        success_clone.store(true, Ordering::SeqCst);
        msg.set_payload_str(format!("modified {}", msg.get_payload_str()));
        async move { Ok(Handled::Publish(msg)) }
    };
    // 3. Define Route
    let input = Endpoint::new_memory("route_in", 200);
    let output = Endpoint::new_memory("route_out", 200);
    let route = Route::new(input, output).with_handler(handler);

    // 4. Run (deploys the route in the background)
    route.deploy("test_route").await.unwrap();

    // 5. Inject Data
    let input_channel = route.input.channel().unwrap();
    input_channel
        .send_message("hello".into())
        .await
        .unwrap();

    // 6. Verify
    let mut verifier = route.connect_to_output("verifier").await.unwrap();
    let received = verifier.receive().await.unwrap();
    assert_eq!(received.message.get_payload_str(), "modified hello");
    assert!(success.load(Ordering::SeqCst));

    stop_route("test_route").await;
}

Patterns: Request-Response

mq-bridge supports request-response patterns, essential for building interactive services (e.g., web APIs). This pattern allows a client to send a request and wait for a correlated response. Due to the asynchronous nature of messaging, ensuring the correct response is delivered to the correct requester is critical, especially under concurrent loads.

mq-bridge offers two ways to handle this, with the response output being the most direct and safest for handling concurrency.

The response Output Endpoint (Recommended)

The recommended approach for request-response is to use the dedicated response endpoint in your route's output.

How it works:

1. An input endpoint that supports request-response (like http) receives a request.
2. The message is passed through the route's processing chain. This is where you typically attach a handler to process the request and generate a response payload.
3. The final message is sent to the output.
4. If the output is response: {}, the bridge sends the message back to the original input source, which then sends it as the reply (e.g., as an HTTP response).

This model inherently solves the correlation problem. The response is part of the same execution context as the request, so there's no risk of mixing up responses between different concurrent requests.

Example: MongoDB Request-Response

Consider a scenario where a service writes a request document to MongoDB and waits for a reply. This library picks up the document, processes it via a handler, and writes the result back to a reply collection.

YAML Configuration (mq-bridge.yaml):

mongo_responder:
  input:
    mongodb:
      url: "mongodb://localhost:27017"
      database: "app_db"
      collection: "requests"
  output:
    # The 'response' endpoint sends the processed message back to the 'requests_replies' collection
    # (or whatever reply_to was set to by the sender).
    response: {}

Programmatic Handler Attachment (in Rust): You would then load this configuration and attach a handler to the route's output endpoint in your Rust code.

use mq_bridge::models::{Config, Handled};
use mq_bridge::CanonicalMessage;

async fn run() {
    // 1. Load configuration from YAML
    // let config: Config = serde_yaml_ng::from_str(include_str!("mq-bridge.yaml")).unwrap();
    // let mut route = config.get("api_gateway").unwrap().clone();

    // 2. Define the handler that processes the request
    let handler = |mut msg: CanonicalMessage| async move {
        // Example: echo the request body with a prefix
        let request_body = String::from_utf8_lossy(&msg.payload);
        let response_body = format!("Handled response for: {}", request_body);
        msg.payload = response_body.into();
        Ok(Handled::Publish(msg))
    };

    // 3. Attach the handler to the output endpoint
    // route.output.handler = Some(std::sync::Arc::new(handler));
    
    // 4. Run the route
    // route.deploy("api_gateway").await.unwrap();
}

Patterns: CQRS

mq-bridge is well-suited for implementing Command Query Responsibility Segregation (CQRS). By combining Routes with Typed Handlers, the bridge serves as both the Command Bus and the Event Bus.

• Command Bus: An input source (e.g., HTTP) receives a command. A TypeHandler processes it (Write Model) and optionally emits an event.
• Event Bus: The emitted event is published to a broker (e.g., Kafka). Downstream routes subscribe to these events to update Read Models (Projections).

// 1. Command Handler (Write Side) 
let command_bus = TypeHandler::new()
    .add("submit_order", |cmd: SubmitOrder| async move {
        // Execute business logic, save to DB...
        // Emit event
        let evt = OrderSubmitted { id: cmd.id };
        Ok(Handled::Publish(
            msg!(evt, "order_submitted")
        ))
});

// 2. Event Handler (Read Side / Projection) 
let projection_handler = TypeHandler::new()
    .add("order_submitted", |evt: OrderSubmitted| async move {
        // Update read database / cache
        Ok(Handled::Ack)
}); 

Configuration

All routes and their endpoints are defined via a configuration file (e.g., mq-bridge.yaml), JSON, or environment variables. For a complete reference of all options, middleware, and examples, see the Configuration Guide

Running Tests

The project includes a comprehensive suite of integration and performance tests that require Docker.

To run the performance benchmarks for all supported backends:

cargo test --test integration_test --release -- --ignored --nocapture --test-threads=1

To run the criterion benchmarks:

cargo bench --features "full"

The times are not stable yet, it is therefore recommended to perform the integration performance test if you want to measure throughput.

AI Disclaimer

This library has been widely written with AI assistance. I used Gemini for planning and writing, CodeRabbit for reviews and Copilot/Claude for bugfixing and other small things. While some of the AI output was great, some other output wasn't. I am aware that in year 2026, AI is still not generating perfect code and sometimes even breaks simple stuff. I reviewed all the output code and re-specified it or changed the code manually whan insuficcient. I didn't change the AI code appearance, so you will sometimes still see code that just looks as it is plain from AI and also most of the readme here was actually written by AI. I don't think it is bad practice, to keep the original code and text appearance. I'm not an english native speaker, so the AI output for text is mostly just way better what I could write. For AI code, the readability is usually sufficient, even if it is sometimes much more verbose what I would write in code.

License

mq-bridge is licensed under the MIT License.