mq-bridge library

      ┌────── mq-bridge-lib ──────┐
──────┴───────────────────────────┴──────
            crossing streams

mq-bridge is an asynchronous message library for Rust. It connects message brokers, databases, files, HTTP/WebSocket endpoints, and in-memory channels behind one small set of traits.

It is not only a forwarder. A route can transform, filter, fan out, retry, rate-limit, deduplicate, or turn a request into a response before the message reaches the next system. The core is built on Tokio and keeps the transport details at the edge, so application code can mostly work with CanonicalMessages and handlers.

Architecture

See ARCHITECTURE.md for a detailed overview of the internal design, extensibility, and usage patterns.

Usage Types:

1. Event Handler (TypedHandler): Communicate between applications using strongly-typed message handlers, optionally with response support.
2. Compute Handler: Generally receive and process messages with a custom handler
3. Direct Endpoint Usage: Use publish / publish_batch and receive / receive_batch directly on endpoints. This mode requires manual commit, batch sequencing, and concurrency handling.

For implementation details and quick start examples for each usage type, see the Architecture Guide.

Features

• Supported Backends: Kafka, NATS, AMQP (RabbitMQ), MQTT, MongoDB, SQL Databases (PostgreSQL, MySQL, SQLite via sqlx), HTTP, WebSocket, 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.
• Batching: Every endpoint uses the same send_batch / receive_batch shape. Routes default to single-message batches, but can switch to larger batches with batch_size.
• Middleware:
  • Retries: Exponential backoff for transient failures.
  • Dead-Letter Queues (DLQ): Redirect failed messages.
  • Deduplication: Message deduplication using sled.
  • Limiter: Best-effort throughput limiting in messages per second, including batch-aware pacing.
  • Cookie Jar: Persist and re-inject HTTP-style cookies or selected metadata across requests.
• Concurrency: Configurable concurrency per route using Tokio.

Philosophy & Focus

The project has one main bias: move data reliably without forcing the rest of the application to care too much about the transport.

That means mq-bridge tries to keep the boring parts boring. Kafka offsets, RabbitMQ nacks, HTTP responses, MongoDB polling, WebSocket frames, and file rows are all different in real life, but route code should still be able to receive a batch, process it, publish it, and commit it.

Batching is a big part of that design. Every endpoint is optimized around batch-shaped APIs, even when the backend itself only has a single-message primitive. Batching is disabled by default (batch_size: 1) because it is the safest behavior and easiest to reason about. When throughput matters, increasing batch_size is usually the first knob to try. For example via batch_size: 128 in yaml or .with_batch_size(128) for routes.

The error handling follows the same idea. Batch publishing can report partial success, retryable failures, and non-retryable failures. Route commits are sequenced so cumulative-ack brokers do not accidentally acknowledge later messages before earlier batches are resolved. In other words: batching is not just a performance trick bolted onto the side; ack/nack behavior and retry/DLQ handling were built to work with it.

What it does not try to be: a domain framework, an actor runtime, or a full stream processor. You can build CQRS-ish flows with it, but the library cares more about transport, routing, and delivery behavior than about prescribing your domain model.

Status

This library was created in 2025 and is still fairly 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 without JetStream
• TLS setups, which are usually non-trivial and have just been tested automatically, but not in detail.

Automated integration and performance tests cover all supported endpoints, including queue and subscriber modes, request-reply (where supported), and protocol-specific behaviors. See the backend feature table below for details on configuration and protocol support.

The following table tracks which endpoints are already used actively in other projects for events. Send me a message or create an issue if you use another endpoint in production:

Endpoint	Manual Test
Kafka	✅
MongoDB	✅
HTTP	✅
IBM MQ	✅
Retry Middleware	✅
DLQ Middleware	✅

All endpoints have automated integration tests and did not show data loss during simple in-flight broker restarts.

Test Notes

• NATS: Automated tests are only run with JetStream enabled. Other NATS modes are not covered by integration tests.
• MongoDB: The reply pattern was only tested in an automated test and is not yet used in projects; because it uses emulation that wait for messages, it may cause severe issues if timeouts are not configured correctly.
• Performance Tests: These are generally executed in non-subscriber (queue) mode for all endpoints.
• Request-Reply: Only tested for endpoints that natively support or emulate it (see backend table below for details). Endpoints like SQLx, Files, AWS, IBM MQ, and Sled do not support request-reply and are not tested for this pattern.
• Subscriber Mode: You may also completely emulate a subscriber mode, if the subscribers are static, by performing a fanout and manually create an endpoint for each target.

When to use mq-bridge

• Hybrid Messaging: Connect systems speaking different protocols (e.g., MQTT to Kafka) without writing a custom adapter for every pair.
• Batch-heavy Pipelines: Increase throughput by moving messages in batches while keeping per-message ack/nack decisions.
• Infrastructure Abstraction: Write business logic against CanonicalMessages and swap the underlying transport later.
• Resilient Pipelines: Apply retry, DLQ, deduplication, limiter, and cookie/session behavior consistently around endpoints.
• Database Integration: Combine databases with message brokers, for example by ingesting messages into SQL/MongoDB or forwarding outbox rows to a broker.
• Sidecar / Gateway: Run the bridge beside another service to ingest, filter, and route messages before they reach the core application.

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.
• Protocol-Specific Power Features: mq-bridge intentionally exposes a common subset: publish/consume, pub/sub where possible, request-reply where possible, batching, middleware, and ack/nack handling. If your application depends on highly specific broker features, using that broker's native client directly may be better.

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).

Backend Features & Configuration

mq-bridge endpoints generally default to a Consumer pattern (Queue), where messages are persisted and distributed among workers. To achieve Subscriber (Pub/Sub) behavior, specific configuration is required.

The table below summarizes the capabilities and configuration for each backend:

Backend	Subscriber Config (Pub/Sub)	Request-Reply	Nack Support
AMQP	Set subscribe_mode: true	Emulated (Property)	Yes (Basic.nack)
AWS	N/A (Use SNS)	No	Yes (Visibility Timeout)
File	Set mode: subscribe	No	Simulated (In-Memory)
gRPC	N/A	No	No
HTTP	N/A	Native (Implicit)	Yes (HTTP 500)
IBM MQ	Set topic	No	Yes (Tx Rollback)
Kafka	Omit group_id	Emulated (Header)	Eventual (Skip Offset)
Memory	Set subscribe_mode: true	Emulated (Metadata)	Yes (Re-queue), by default disabled
MongoDB	Set change_stream: true	Emulated (Metadata)	Yes (Unlock)
MQTT	Set clean_session: true	Emulated (Property)	Eventual (Skip Ack)
NATS	Set subscriber_mode: true	Native (Inbox)	Yes (JetStream Nak)
Sled	Set delete_after_read: false	No	Yes (Tx Rollback)
SQLx	Not supported	No	Eventual (Skip Delete)
WebSocket	N/A	No	No
ZeroMQ	Set socket_type: "sub"	Native (REQ/REP)	No

Feature Details

• Request-Reply:
  • Native: Uses protocol-level correlation (e.g., HTTP connection, NATS reply subject).
  • Emulated: Publishes a new message to a reply destination (specified by the reply_to metadata field) carrying a correlation_id metadata field.
• Nack Support: If "Yes", the backend supports explicit negative acknowledgement triggering redelivery. "Eventual" means redelivery depends on timeout or connection drop. "Simulated" is handled in-memory by the bridge.

Response Endpoint

The response output endpoint sends a reply back to the original requester. This is useful for synchronous request-reply flows, for example HTTP-to-NATS-to-HTTP. Use response: {} as the output endpoint configuration.

• Caveats:
  • If the input does not support responses (e.g., File, SQLx), 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 for running mq-bridge as a standalone app, for example as a Docker container configured via YAML or environment variables: https://github.com/marcomq/mq-bridge-app

Programmatic Handlers

For business logic, mq-bridge provides a handler layer separate from transport-level middleware. This is where message-specific code usually belongs.

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, so handlers do not need to repeat the same parsing code.

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);
        // Logic here...
        // Automatically maps () to Handled::Ack
    })
    .add("delete_user", |cmd: DeleteUser| async move {
        println!("Handling delete_user: {}", cmd.id);
        // Logic here...
        // Automatically maps () to 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 for interactive services such as web APIs. A client can send a request and wait for the matching response, while the bridge keeps the correlation details away from the handler.

The response output is the most direct option and the safest one under concurrency.

The response Output Endpoint (Recommended)

For request-response routes, use the dedicated response endpoint in the 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).

The response stays in the same execution context as the request, so concurrent requests do not need to share a reply queue and race on correlation IDs.

Example: MongoDB Request-Response

For example, a service can write a request document to MongoDB and wait for a reply. The bridge reads the document, runs the handler, and writes the result back to the 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 can be used for CQRS-style flows. With routes and typed handlers, it can act as a command bus and an event bus without becoming a domain framework.

• 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(()) is equivalent to Handled::Ack
        Ok(())
});

Configuration

All routes and endpoints can be defined via a configuration file (for example mq-bridge.yaml), JSON, or environment variables. For a complete reference of options, middleware, and examples, see the Configuration Guide.

Important route-level knobs:

• batch_size: maximum messages per route iteration. Defaults to 1; increase it when throughput matters.
• concurrency: number of route workers. Defaults to 1; useful for high-latency handlers or endpoints.
• commit_concurrency_limit: maximum queued in-flight commit operations used by ordered commit sequencing. Defaults to 4096.

Middleware can be attached to inputs or outputs. The most commonly used ones are retry, dlq, deduplication, limiter, and cookie_jar. Retry/DLQ are especially useful with batching because partial failures can be retried or sent to a DLQ without treating the entire batch as equally broken.

Running Tests

The project includes integration and performance tests. Most backend tests 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.

Contributing

Contributions are welcome. See CONTRIBUTING.md for setup notes, code style, and pull request guidelines.

AI Disclaimer

This library has been written with a lot of AI assistance.

The core started as my own code, and many endpoints and docs were expanded with help from Gemini, CodeRabbit, Claude, and Codex. The useful part was speed: once the endpoint traits were stable, adding more transports became much easier. The dangerous part is the usual one: generated code can look plausible while still missing important details. I am aware that in year 2026, AI is still not generating perfect code and sometimes breaks simple stuff or forgets important lines during refactorings that later result in severe bugs.

For that reason I reviewed each commit manually to prevent hard-to-fix architectural issuess and cleaned up, and refactored the generated output.

I do trust the current code as much as if it would be completely written by myself.

Due to the large feature set, there may still be unfixed issues. The current focus is testing and documentation.

Some parts of the code are more verbose than I would write by hand, but I kept the readable parts when they worked well. I am not a native English speaker, so AI assistance is also useful for documentation. The important part is that the code is reviewed and tested, not that every sentence or helper function looks hand-typed from the first draft.

License

mq-bridge is licensed under the MIT License.