# mq-bridge library
[](https://crates.io/crates/mq-bridge)
[](https://docs.rs/mq-bridge)
[](https://marcomq.github.io/mq-bridge/dev/bench/)
[](LICENSE)
```text
┌────── 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 `CanonicalMessage`s and handlers.
## Architecture
See [ARCHITECTURE.md](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](ARCHITECTURE.md).
## 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](https://crates.io/crates/mqi/) 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:
| 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 `CanonicalMessage`s 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:
| **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.
```rust
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`.
```rust
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.
```rust
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`):**
```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.
```rust
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).
```rust
// 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](CONFIGURATION.md).
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:
```sh
cargo test --test integration_test --release -- --ignored --nocapture --test-threads=1
```
To run the criterion benchmarks:
```sh
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](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.