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What is mq-bridge?

mq-bridge is an asynchronous Rust library that moves messages and data between systems: Kafka, NATS, RabbitMQ, MQTT, MongoDB, PostgreSQL/MySQL/SQLite, Postgres CDC, ClickHouse, Redis Streams, HTTP, WebSocket, gRPC, ZeroMQ, AWS SQS/SNS, IBM MQ, cloud object storage, files, and in-memory channels. Every endpoint sits behind the same batch-shaped receive_batch / send_batch traits, so any source can feed any sink.

A route connects one input to one output and shapes the data on the way: transform, filter, switch, fan out, enrich, retry, rate-limit, deduplicate, or answer a request, all by configuration. The engine is fast: a typing transform runs at ~1.6M rows/s, ~19.5x Redpanda Connect on the same job. Structural endpoints such as switch, fanout and sequence nest freely inside a route, and wrong delivery combinations fail when the route starts, not in production. Where configuration isn't enough, you add a handler in Rust, Python or Node.js. Batching, ack/nack, commit ordering and broker I/O stay in the Tokio-based core, so your code works with plain CanonicalMessages and doesn't handle Kafka offsets or AMQP nacks.

Try it in the CLI, ship it in your service: mq-bridge-app runs a route from the command line (mqb), as a server, desktop app or MCP server; the same config runs as a library inside your Rust, Python or Node.js service when you need lower latency or want the pipeline in your production code. No daemon or platform to operate.

Bridge between ecosystems: two plugins add over 100 more inputs and outputs: Redpanda Connect's components and faucet's connectors. They load only when a route uses them and run in the same process, so one route can read with one framework, process with a second and write with a third, under one ack chain and with mq-bridge's retry, DLQ and deduplication around all of it. See Combine ecosystems in one route.

Related MCP server: SEOSiri Data Pipeline MCP

Use cases

Problem

Guide with a runnable example

A RAG or semantic-search index drifts from its Postgres source

Keep a vector index in sync with Postgres: CDC → embeddings → Qdrant

Search results show what MongoDB no longer has

Keep a search index in sync with MongoDB: change stream → Meilisearch

The warehouse is a day behind and misses every change in between

Stream Postgres changes into a warehouse: CDC → Bloblang → Snowflake

Free-text messages need routing by what they say

Classify and route messages with an LLM

Events need a database record and per-key totals to act on

Enrich events and add per-key totals in Postgres

A service must update its database and publish an event atomically

Transactional outbox with Postgres and Kafka

Tests for Kafka, NATS or RabbitMQ consumers need a running broker

Test message handlers without a broker

Quick Start

Install the library for your language:

Language

Package

Install

Rust

mq-bridge

cargo add mq-bridge --features kafka,nats,yaml

Python

mq-bridge (PyPI)

pip install mq-bridge

Node.js

mq-bridge (npm)

npm install mq-bridge

C / C++

mq_bridge.h

cargo build --release -p mq-bridge-c (build from source)

Nim

mq_bridge

nimble install in bindings/nim, plus the C library

Describe a route — here Kafka → NATS with retries — in routes.yaml:

kafka_to_nats:
  input:
    kafka: { url: "localhost:9092", topic: "orders", group_id: "bridge" }
  output:
    middlewares:
      - retry: { max_attempts: 5 }
    nats: { url: "nats://localhost:4222", subject: "orders.processed" }

Run it:

from mq_bridge import Route
Route.from_file("routes.yaml", "kafka_to_nats").run()  # blocks; use start() to keep going
import { Route } from "mq-bridge";
Route.fromFile("routes.yaml", "kafka_to_nats").start();
mq_bridge::deploy_file("routes.yaml").await?; // every route in the file, in the background

Attach a handler (with_handler / withHandler) when you need business logic in between. No code at all? mq-bridge-app runs the same engine from the command line or a desktop UI:

mqb copy 'kafka://localhost:9092?topic=orders' 'nats://localhost:4222?subject=orders.processed'

Capabilities at a glance

Every row works from the Rust crate, the Python and Node.js packages, and the zero-code app; the Cargo feature is only needed when you build the Rust crate yourself. The linked book documents the library as well as the app.

Capability

How (endpoint / middleware · Cargo feature)

Docs

Message brokers: Kafka, NATS (JetStream), RabbitMQ (AMQP), MQTT, Redis Streams, AWS SQS/SNS, IBM MQ, ZeroMQ

kafka, nats, amqp, mqtt, redis_streams, aws, ibmmq, zeromq endpoints · features kafka, nats, amqp, mqtt, redis-streams, aws, ibm-mq, zeromq

Connectors

Change Data Capture (CDC): stream Postgres or MongoDB changes to Kafka, files, a warehouse, …

postgres_cdc (logical replication, pgoutput) · postgres-cdc; mongodb with consume: capture_all / capture_new (change streams) · mongodb

Postgres CDC tutorial · MongoDB

SQL databases: PostgreSQL, MySQL, MariaDB, SQLite as source (cursor polling) or sink (insert / upsert)

sqlx endpoint · sqlx (sqlite for SQLite only)

PostgreSQL / MySQL / SQLite · Upserts

MongoDB as source or sink

mongodb endpoint · mongodb

MongoDB

ClickHouse bulk insert and resumable reads

clickhouse endpoint · clickhouse

ClickHouse

Search engines and other bulk document APIs (Meilisearch, Typesense, Elasticsearch, Qdrant, PostgREST / Supabase)

http_bulk endpoint · http-bulk

HTTP bulk

LLMs and embeddings over an OpenAI-compatible API (OpenAI, Ollama, vLLM, …): classify, summarise or embed each message

openai_chat, openai_embeddings endpoints · http-bulk

OpenAI-compatible APIs · LLM cookbook

Object storage: S3 (and S3-compatible, e.g. R2), GCS, Azure Blob, or a local directory, as Parquet, JSONL or CSV

object_store endpoint with format: parquet | raw | json | csv · object-store, parquet

Object storage

Files: CSV and JSONL, read and write

file endpoint · built in

File

Analytics / warehouses: Snowflake, BigQuery, Databricks / Spark, Athena / Trino, DuckDB via Parquet on S3/GCS/Azure. These are not native connectors: mq-bridge writes the Parquet files, the warehouse loads or queries them

object_store with format: parquet · parquet

Snowflake · BigQuery · Databricks / Spark · Athena / Trino · DuckDB

HTTP, gRPC, WebSocket as server or client, including request/reply

http, grpc, websocket endpoints · http, grpc, websocket

HTTP · gRPC · Request / reply

Schema validation and type coercion (JSON Schema subset, field mapping)

transform middleware · built in

Transform & schema mapping

Reliability: retries, dead-letter queue, deduplication, rate limiting

retry, dlq, deduplication, limiter middleware · dedup for deduplication

Middleware reference · Delivery guarantees

Broker-free tests: run a route or handler against in-memory channels, no Docker

memory endpoint · built in

Embed the library

ETL without code

mq-bridge-app: mqb copy <from> <to>, YAML routes, desktop UI, MCP server

Quick start: mqb copy

More systems through plugins: Apache Pulsar, Meilisearch, Redpanda Connect's inputs, outputs and processors, and faucet's sources and sinks (MySQL / SQL Server / Oracle CDC, Snowflake, BigQuery, Iceberg, Delta, GraphQL APIs, …)

native plugins · plugin

Plugin endpoints · Connect plugin: inputs, outputs, processors · faucet plugin

The Connect plugin links only Redpanda Connect components free of Redpanda Community License code (bundled code is Apache-2.0 and MIT), so some upstream components, such as its Kafka, AWS and Snowflake ones, are not included; see what is not included. Native endpoints and the faucet plugin cover most of that gap.

Combine ecosystems in one route

No single framework has every connector and processor. mq-bridge combines three in one route:

Parts from

What they bring

Count

mq-bridge, native

Kafka, NATS, RabbitMQ, MQTT, IBM MQ, Postgres and MongoDB CDC, SQL, object storage, paged HTTP APIs (http_bulk), HTTP / gRPC / WebSocket servers, …; transform, switch, fanout, aggregate, lookup, retry, DLQ, dedup

20+ transports

Connect plugin

Redpanda Connect's components; Bloblang processors as middleware on any endpoint

56 inputs, 68 outputs, 86 processors

faucet plugin

faucet's connectors: MySQL / SQL Server / Oracle CDC, Snowflake, BigQuery, Iceberg, Delta, GraphQL, Singer taps; exactly-once into supporting sinks

41 sources, 34 sinks

Plugins load only when a route names them and run in-process. Acks, commits, retry, dlq and deduplication work across all parts, and plugin endpoints nest inside switch, fanout and sequence. Where a system has both, prefer the native endpoint.

Scenario

Native

Connect

faucet

Postgres into Snowflake, backfill then changes, exactly once

postgres_cdc, retry, dlq

connect_mapping

snowflake sink

MySQL, SQL Server or Oracle changes into Kafka

kafka output, transform

connect_mapping

mysql-cdc, mssql-cdc, oracle-cdc

Sensor data: live statistics, alerts to a time-series DB, all to the lake

mqtt, aggregate, switch, fanout

questdb output

iceberg or delta sink

Install the Connect plugin with brew, conda, pip or npm (install), the faucet plugin with pip install mq-bridge-faucet or npm install mq-bridge-faucet (README).

For AI agents: llms.txt is a curated index of the documentation, and npx skills add marcomq/mq-bridge installs a skill that teaches Claude Code, Codex and other agents to drive mqb.

Benchmarks

Like-for-like ETL comparisons, measured through the zero-code mq-bridge-app; every number, baseline and command is in the ETL benchmark harness:

Scenario

mq-bridge

Compared with

CSV → JSONL, 1M mixed-type rows (~116 MiB)

3,134,796 rows/s, ~29 MiB RAM

Meltano (tap-csv → target-jsonl): 9,771 rows/s, ~444 MiB RAM — mq-bridge ~321x fasterDuckDB, all cores: 2,109,704 rows/s — mq-bridge ~1.5x faster, ~18x less memory

Kafka → file, 1M rows, no transform

878,105 rows/s, ~160 MiB RAM

Sea Streamer, mimalloc build: 492,465 rows/s, ~894 MiB RAM — mq-bridge ~1.8x faster (also ~1.8x vs. its default-allocator build)

CSV → JSONL, 1M rows, typing transform

1,594,896 rows/s

Redpanda Connect 4.112.0, Bloblang mapping: 81,893 rows/s — mq-bridge ~19.5x faster, outputs asserted identical

Kafka → JSONL, 1M rows, projection

766,280 rows/s

Redpanda Connect 4.112.0: 65,741 rows/s — mq-bridge ~11.7x faster, under half the memory, identical sink bytes

The CSV row reuses an existing, independently published 1M-row CSV → JSONL workload, on a byte-identical fixture: 7 mixed-type columns, seed 42, 121,981,421 bytes, SHA-256 a84894e0…0c45b221 (full definition). Whole-process wall-clock, no transformation, Meltano run on the same machine and file.

All figures: mq-bridge 0.4.20, except the Redpanda Connect rows, which were measured together with their native mq-bridge figures in one later session (scenario 10). DuckDB is a throughput ceiling for the conversion itself, not an ETL tool.

External benchmark: both the Rust library (mq-bridge) and the Python binding (mq-bridge-py) are entries on the third-party http-arena.com leaderboard, which compares HTTP frameworks by requests per second (live, so rankings shift over time). It measures mq-bridge's HTTP (and WebSocket) serving path, which is one endpoint among many, not broker or ETL throughput; those are covered under Benchmarks. See the Python analysis notes for the local comparison harness.

Why mq-bridge

What you get from one engine, whichever language you call it from:

  • 20+ native transports, 100+ more via plugin, one API: every connector listed above, plus dir_spool (a crash-safe directory FIFO queue), behind the same receive_batch / send_batch shape.

  • Redpanda Connect reach: the Connect plugin adds Redpanda Connect's production-proven components — 56 inputs and 68 outputs as endpoints, plus 86 processors, most of which also run as middleware on any endpoint — while mq-bridge keeps routing, batching, retries, DLQ and deduplication.

  • faucet reach: the faucet plugin adds faucet's 41 sources and 34 sinks as one faucet endpoint: MySQL / SQL Server / Oracle CDC, Snowflake, BigQuery, Iceberg, Delta, GraphQL APIs and Singer taps, with commit-token exactly-once into sinks that support it.

  • The best of three frameworks in one route: native endpoints, Connect and faucet parts mix freely in one route and one process; see Combine ecosystems in one route.

  • Change Data Capture: stream row-level changes from Postgres (logical replication / pgoutput) and MongoDB (change streams) as flat rows with an operation marker.

  • Restart-safe delivery: batch-aware ack/nack with commit sequencing for cumulative-ack brokers; the integration suite shows no data loss during in-flight broker restarts, including a Postgres CDC restart-safety test.

  • Reliability middleware, not a framework: retries, dead-letter queues, deduplication, rate limiting, and cookie/session persistence wrap any endpoint.

  • Polyglot on one engine: the same Rust core ships as native Python and Node.js bindings; routing, batching, and broker I/O stay in Rust.

  • TLS everywhere, one config shape: a single TlsConfig block (CA bundle, client cert/key for mTLS, insecure-skip) is reused across transports. ca_file: "$.os" trusts the OS CA store and "$.bundled" the built-in Mozilla roots; what each endpoint supports.

  • Self-hosted, no daemon: generate config in the optional UI, paste it into your code, run it in-process. No hosted control plane, no separate scheduler.

Prefer not to write code? mq-bridge-app runs the exact same engine as a standalone, zero-code ETL service configured entirely by YAML or environment variables. It ships a Postman-style UI to build, send, and inspect messages against a route, and can import Postman collections and AsyncAPI documents to scaffold routes and endpoints. Everything about the app — install, CLI, UI, cookbook, tuning — is in the 📖 book.

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.

Documentation

The 📖 mq-bridge book covers the library and the app. Connectors, middleware and tuning use the same settings in both.

I want to…

Read

Embed the library, write handlers, publish from code

Quick start: library · Embed the library · Bindings API · docs.rs

Move data without code

Quick start: mqb copy · Desktop UI · CLI · MCP server

Configure a connector

Connectors, with YAML and URL examples for each

Know what each backend supports (subscriber mode, request-reply, nack, CDC vs. polling)

Overview & capabilities · Capability tables (acknowledgement, commit order, replies, TLS)

Add retries, DLQ, dedup, transform, routing

Cookbook · Middleware & structural endpoints

Understand delivery guarantees and idempotent sinks

Delivery guarantees

Build request/reply or CQRS-style flows

Request / reply · Embed the library: CQRS

Tune throughput

Performance tuning

Write my own endpoint, middleware or plugin

EXTENDING.md · PLUGINS.md

Understand the internals

ARCHITECTURE.md · CONFIGURATION.md

Status

mq-bridge was created in 2025. Its reliability behavior is exercised by an automated integration and performance suite across supported endpoints, in each of the queue and subscriber modes that endpoint supports:

  • All endpoints showed no data loss during in-flight broker restarts; MQTT publish confirmation was hardened until a chaos test drove in-flight loss to zero.

  • Postgres CDC has a restart-safety test: an un-acked, in-flight batch is redelivered after a database restart with no loss and no gap.

It is used as rust library in armature and as python lib in omniload.

Stability and Rust version

Since 1.0 the Rust API follows semantic versioning: the crate root and the modules models, traits, errors, outcomes, extensions, plugin (the SDK), type_handler, publisher, route and checkpoint. The implementation modules endpoints, middleware, support and test_utils may change in a minor release, and so may the major version of a re-exported driver crate (mq_bridge::sqlx, mq_bridge::rdkafka, ...).

A minor release may add a configuration field, a variant of a configuration or error enum, and raise the minimum Rust version. Code that writes config structs with ..Default::default() and gives a match on these enums a _ arm compiles unchanged across such releases.

The minimum Rust version is 1.89. The features aws, sqlx and postgres-cdc, and so full, need 1.94.1; zeromq-omq needs 1.93.

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"

Criterion numbers vary with the machine they run on; for comparable throughput figures use the integration performance test above, or see the ETL benchmarks.

Contributing

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

AI Disclaimer

This library was written with a lot of AI assistance. The core started as my own code; many endpoints and docs were expanded with Gemini, CodeRabbit, Claude, and Codex, which made adding transports much faster once the endpoint traits were stable.

Generated code can look plausible while missing important details, so I review every commit manually, clean up and refactor the output, and rely on the integration suite to catch regressions. I trust the current code as much as if I had written it myself. Given the large feature set there may still be open issues; the current focus is testing and documentation.

License

mq-bridge is dual-licensed under either the MIT license or the Apache License, Version 2.0, at your option.

Binary distributions carry their applicable third-party terms in THIRD_PARTY_LICENSES.txt. Their current maintenance process is documented in docs/THIRD_PARTY_LICENSES.md.

Contribution

Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in mq-bridge by you, as defined in the Apache-2.0 license, shall be dual licensed as above, without any additional terms or conditions.

Available Tools

11 tools
bridge_statusA
Read-only

Report every mq-bridge process this user is running on this machine — CLI jobs (mqb copy, headless configs), other MCP servers, the web UI and the desktop app — with their routes and consumers: connector types, running/healthy, outcome, current rate (throughput), average_throughput, total messages (message_sequence), pending and started_at_ms. Read-only; routes of other processes cannot be stopped from here. The entry whose pid equals current_pid is this server. Error text of other processes is redacted to error.

Inputs: no named parameters.

describeA
Read-only

Describe the endpoints and middlewares this build supports, the same data as mqb list --json / mqb describe <name> --json: each endpoint's endpoint type (the JSON key publish/start_route take), URI form, example and typed params (also its config fields), and each middleware's params. Pass name to narrow it to one entry. Call it before writing publisher or route JSON for a connector you have not used yet.

Inputs: name.

generate_cli_commandA
Read-only

Generate a copyable, headless mqb command for a route. Accepts the same route JSON and tuning overrides as start_route, but does not start anything. The command embeds canonical JSON with POSIX shell quoting so fields that have no compact copy-URI representation are preserved. Credential values are replaced by environment variable placeholders and the required variable names are returned separately.

Inputs: route (required), name, batch_size, concurrency.

list_routesA
Read-only

List the routes started by this server, with their live connection health, total messages moved, current message rate, and achieved average rate. For one route's full status use route_status; for routes of other mq-bridge processes use bridge_status.

Inputs: no named parameters.

publishA

Publish one message or a batch of messages to any endpoint. The endpoint is supplied inline in publisher as JSON keyed by type (e.g. {"kafka": {"url": "...", "topic": "..."}}); this is independent of routes. Provide either message or messages; each message has a payload and optional metadata headers (e.g. kind for the message type, correlation_id). Returns the send outcome (an acknowledgement, or the endpoint's reply for request/reply endpoints). Use it for ad-hoc sends; to move data from a source endpoint, use start_route. Messages are written to the external system and cannot be recalled.

Inputs: publisher (required), name, message, messages.

route_messagesA
Destructive

Return the most recent messages captured on a route, newest last. Only routes started with capture_last capture anything. Reads are destructive: each message is returned once, so repeated calls yield only what arrived since the last call.

Inputs: name (required).

route_statusA
Read-only

Report the live status of one route (by name) or all routes: connection health, whether it has finished, the total number of messages it has moved, its current rate (messages_per_second), and the rate it achieved overall (average_messages_per_second over elapsed_s). Quote the average for a finished job: the current rate decays to ~0 once a route stops moving data. To wait for a job to finish, call wait_route once instead of polling this.

Inputs: name.

server_infoA
Read-only

Report this MCP server's build identity: crate version, git_hash (with a -dirty suffix if the working tree had uncommitted changes at build time), build profile (debug/release), and build_time. Use it to confirm which binary is actually running before trusting throughput numbers.

Inputs: no named parameters.

start_routeA
Destructive

Create and run a route that moves messages from its input (source) endpoint to its output (sink) endpoint, each keyed by connector type. Supports route options like concurrency, batch_size, and exit_on_empty (drain the source then exit; otherwise the route runs continuously until stopped). Either endpoint may carry a middlewares array — retry, dlq, deduplication, limiter, transform, compression, encryption, pack/unpack, buffer, delay, timeout, weak_join, cookie_jar, metrics — e.g. {"sqlx": {...}, "middlewares": [{"retry": {"max_attempts": 3}}]}. concurrency/batch_size may be given inside route or as top-level arguments — the top-level argument wins, and an unset value becomes the app default (4 / 1024, or the batch size the output endpoint's schema declares). Returns the route name for use with wait_route / route_status / stop_route, plus the tuning that took effect.

Inputs: route (required), name, batch_size, concurrency, +1 more.

stop_routeA
Destructive

Stop a route started by this server, by name. Use it to end a continuous route or abandon a drain; a route started with exit_on_empty stops by itself, so wait for it with wait_route instead. Sends the shutdown signal and returns without waiting for the route to exit; the route is removed from list_routes, and messages captured for route_messages but not yet read are discarded (their count is returned). Returns the total messages it moved plus the rate it achieved (average_messages_per_second over elapsed_s).

Inputs: name (required).

wait_routeA
Read-only

Block until a route finishes, then report how it ended. Use this instead of polling route_status for a job started with exit_on_empty: it costs one tool call regardless of how long the job runs. Returns finished (false if timeout_ms elapsed first — the route is left running), the outcome, the total messages moved and the rate achieved. An outcome of failed carries the cause in error. Default timeout 60000 ms, maximum 18000000 ms.

Inputs: name (required), timeout_ms.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

    TDQS

    A4.3/5.0

    Scored across 11 tools

    Disambiguation4/5

    Most tools are clearly separated and the descriptions actively cross-reference each other (bridge_status vs list_routes vs route_status, wait_route vs polling, generate_cli_command vs start_route). The one real overlap is that route_status called without a `name` returns the same all-routes summary as list_routes, and the cluster of status/health tools (server_info, bridge_status, list_routes, route_status, wait_route) takes careful reading to sort out.

    Naming Consistency4/5

    A predictable scheme runs through almost everything: verb_noun for actions (start_route, stop_route, list_routes, wait_route, generate_cli_command) and noun_status/noun_messages for read-only reporters. The only deviations are the bare verbs `describe` and `publish`, plus `server_info`, which don't fit either pattern cleanly but are still readable.

    Tool Count5/5

    Eleven tools is squarely in the well-scoped range, and each maps to a distinct step in the job lifecycle: discover schemas, generate a command, start/wait/stop a route, read status and captured messages, plus machine-level and build identity. Nothing looks like filler.

    Completeness4/5

    The route lifecycle is fully covered — schema discovery via describe, optional command generation, start, wait, status, message capture, and stop — plus ad-hoc publish and cross-process visibility via bridge_status. Minor gaps remain: there is no ad-hoc single-message consume/poll (you must stand up a route with capture_last), no pause/resume, and stopping routes belonging to other processes is explicitly impossible by design.

    Maintenance

    ActivityActive
    ResponsivenessUsually handled

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