mq-bridge-py 0.3.3

Python bindings for mq-bridge (full: all brokers incl. Kafka)

mq-bridge Python bindings

Thin Python bindings for the Rust mq-bridge core.

Install

pip install mq-bridge-py is all you need. Both packages install the same import path: mq_bridge.

Package	Install	Includes
Default	pip install mq-bridge-py	Full set on glibc-Linux/macOS/Windows-x64 (Kafka, AWS, gRPC, MongoDB, SQLx + basic); reduced set automatically on musl/Alpine and Windows arm64 (no Kafka/SQLx/gRPC)
Basic	pip install mq-bridge-py-basic	HTTP, NATS, MQTT, AMQP, WebSocket, ZeroMQ, MongoDB, AWS — the lean set on every platform

mq-bridge-py resolves by platform automatically: pip installs the full wheel on glibc-Linux/macOS/Windows-x64 and the reduced (basic-feature) wheel on musl/Alpine and Windows arm64 — no marker or manual choice needed. Kafka/SQLx/gRPC/static-IBM-MQ don't build on those targets, so calling them there raises a clear runtime error; everything else works identically. Install mq-bridge-py-basic only if you explicitly want the lean build on a full-support system too. Memory and file endpoints are always present.

The public API stays close to mq-bridge itself:

• Route.from_file(path, name=None) loads a route from a YAML/JSON file. The three constructors differ only by source: from_file (path), from_str (in-memory YAML/JSON string), from_config (Python dict)
• The name is optional: pass it to pick one entry out of a routes:/publishers: document, or omit it to treat the whole config as a single bare route/endpoint body
• Route.with_handler(...) attaches a raw Message handler, with lazy json()/text() readers and with_json()/with_payload() response helpers
• Route.add_handler(kind, ...) uses mq-bridge's kind dispatch and delivers decoded JSON
• RetryableError and NonRetryableError let Python handlers signal retry intent
• Publisher.from_file(path, name=None) (plus from_str / from_config) builds a publisher endpoint

from_yaml / from_yaml_str remain as deprecated aliases for from_file / from_str.

• Publisher.send_json(...) and Publisher.request_json(...) serialize Python JSON values in Rust

The Python surface is synchronous and blocking. Tokio, broker I/O, routing, and batching all stay in Rust.

Quick start: publish a message with no route/config file

For ad hoc testing (e.g. seeding a topic by hand) you don't need a route, a handler, or a config file — Publisher.from_config takes a plain dict and send_json blocks until the broker acks it:

from mq_bridge import Publisher

endpoint = {"kafka": {"brokers": "localhost:9092", "topic": "orders"}}
pub = Publisher.from_config(endpoint)
for i in range(5):
    pub.send_json({"order_id": i, "amount": i * 10})
print("published 5 messages")

For a truly file-free one-off, paste the same lines into python -c "...".

Swap the endpoint dict for any other transport (nats, amqp, mqtt, mongodb, memory, file, ...) — see Config types and schema below for the full shape of each. send_json accepts an optional metadata dict as a second positional argument (e.g. {'kind': 'order.created'}) and Publisher has no close()/context-manager form, so let the script exit once sends finish rather than reusing a long-lived instance across many short runs.

Config types and schema

mq-bridge-app can create and test route and endpoint JSON/YAML through its UI. It does not replace your Python code or handlers, but it is useful when you want a known-good connection and route shape before pasting the configuration into Python. Load the generated config with Route.from_config, Route.from_file, Publisher.from_config, or Publisher.from_file.

For the from_config / from_str mappings, mq_bridge.config ships TypedDict definitions so editors autocomplete the config keys (input, output, batch_size, every transport config, middleware, …):

from mq_bridge import Route
from mq_bridge.config import ConfigDocument

config: ConfigDocument = {
    "routes": {
        "orders": {
            "input": {"memory": {"topic": "orders.in", "capacity": 1600}},
            "output": {"response": {}},
            "batch_size": 128,
        }
    }
}
route = Route.from_config(config, "orders")

These types are generated from the JSON Schema, which the extension produces on demand from the Rust models — there is no checked-in schema copy to drift:

from mq_bridge import config_schema

schema = config_schema()        # the JSON Schema as a dict

config_schema() is handy for editor validation of YAML configs too — dump it to a file and point your # yaml-language-server: $schema= line at it. The types are regenerated with uv run python scripts/gen_config_types.py (a test fails if they drift from the schema).

Running a route

Route.run() blocks the calling thread until another thread calls stop() — it deploys the route and then parks. This is convenient for a process whose only job is the route, but it is a common trap: nothing after route.run() executes until the route stops.

To keep running Python code after the route is up, use start() (non-blocking) or the context-manager form:

route = Route.from_config(config, "orders_route").with_handler(handle)

# Non-blocking: deploys, returns, and runs on a background thread.
route.start()
publisher.send_json({"order_id": 42}, {"kind": "order.created"})
route.stop()
route.join()   # optional: wait for a clean shutdown

# Or scope it to a block — starts on enter, stops + joins on exit:
with Route.from_config(config, "orders_route").with_handler(handle):
    publisher.send_json({"order_id": 42}, {"kind": "order.created"})

Configuration/connection errors surface from start() itself, not from a background thread. run() remains available for the blocking single-route case.

Pull-based consumer

Route is push-based: you attach a handler and the route drives it. When you instead want to pull messages on your own schedule — e.g. to feed a generator-style sink such as a dlt resource — use Consumer. It wraps any input endpoint and hands batches back to Python:

from mq_bridge import Consumer

consumer = Consumer.from_config({"nats": {"subject": "orders", "url": "nats://localhost:4222"}})

while not consumer.exhausted:
    batch = consumer.poll(max=500, timeout_ms=1000)   # [] on timeout
    if not batch:
        continue
    for message in batch:
        handle(message.json())
    consumer.commit()                                 # ack only after handling

poll() receives up to max messages without acknowledging them; commit() acks every batch returned since the last commit, advancing the consumer offset (or removing them from the queue). Committing only after the downstream write succeeds gives at-least-once delivery: a crash before commit() re-delivers the batch. poll() returns [] once timeout_ms elapses with nothing received (omit it to block until a message arrives), and sets exhausted once a bounded source (e.g. a file) is fully drained — streaming brokers never set it.

You must call commit() — it is not optional. It is the only thing that tells the broker a batch is done. If you keep polling without committing:

• the consumer offset never advances, so every message is re-delivered on the next run (and you reprocess from the start);
• most brokers stop sending once their unacknowledged/prefetch window fills, so poll() eventually stalls and returns nothing;
• the uncommitted batches are held in memory pending their ack, so the process grows unbounded.

Commit after each batch you have durably handled (as in the loops above). If a batch fails downstream, simply don't commit it — it will be redelivered.

Per-batch tokens: poll_batch / ack / nack

When you need to ack or release specific batches (rather than everything since the last commit), use the token form. poll_batch(max, timeout_ms) returns (messages, token); ack(token) commits just that batch, and nack(token) releases it for redelivery (nack() with no argument nacks every outstanding batch). This is the shape a dlt resource wants — poll → yield records → load package commits → ack(token) — and is demonstrated in examples/dlt_source.py with the wiring brief in examples/OMNILOAD_INTEGRATION.md.

messages, token = consumer.poll_batch(max=500, timeout_ms=1000)  # ([], None) on timeout
if token is not None:                  # nothing returned on an idle timeout
    # ... persist the batch downstream ...
    consumer.ack(token)                # or consumer.nack(token) to redeliver

Tokens stay outstanding until acked/nacked; commit() still acks every outstanding batch at once, so don't mix the two styles on one consumer. On cumulative-ack transports (Kafka), acking a later batch would implicitly ack the earlier ones, so ack(token) must follow receive order — acking out of order raises; ack the oldest outstanding batch first, or use commit(). Transports that ack each batch individually (NATS JetStream, AMQP, MQTT) accept any order.

At-least-once + idempotent merge. Redelivery (after a nack, a missed commit(), or an expired broker ack deadline) means a record can arrive twice. A downstream loader must dedup on a stable key — message.id is globally unique per source position (Kafka partition:offset, NATS stream_sequence, AMQP delivery tag) and makes a natural primary key. Source cursor fields are also available in message.metadata (mqb.src.kafka_topic/mqb.src.kafka_offset, mqb.src.nats_subject/mqb.src.nats_stream_sequence, mqb.src.amqp_routing_key/mqb.src.amqp_delivery_tag) when you opt in by setting the MQB_SOURCE_METADATA=1 environment variable (off by default).

Ack deadlines vs slow loads. JetStream AckWait (default 30s), AMQP prefetch/consumer-timeout and MQTT inflight windows each bound how long a batch may stay un-acked. Keep batch_size × per-record handling cost under the smallest deadline (or raise it in the endpoint config); past it the broker redelivers — correctness is preserved by idempotent merge, but reload work is wasted. Kafka has no per-message nack: nack there leaves the offset unadvanced, so redelivery happens on the next run/rebalance, not immediately.

consumer.status() returns a snapshot dict (healthy, target, pending, capacity, error, details). pending is the broker backlog/lag where the transport reports it — Kafka offset lag, AMQP queue depth, NATS JetStream num_pending — so pending == 0 is a precise "caught up" check for a bounded drain; it is None where the broker exposes no backlog (core NATS, MQTT), where you fall back to a timeout_ms that returns []. It's a point-in-time snapshot, not a guarantee.

consumer.close() releases the broker connection; it's idempotent, and poll() /status() raise afterwards. Python is garbage-collected, so close explicitly (or use the context-manager form, which closes on exit) rather than relying on the object being collected:

with Consumer.from_config(cfg) as consumer:
    batch = consumer.poll(max=500, timeout_ms=1000)
    ...
    consumer.commit()
# connection released here

The endpoint config decides durability exactly as a route input does: a consumer-group config resumes from the last commit, a subscriber config receives only new messages. Consumer.from_file / from_str accept the same shapes, plus a named entry under a consumers: document section.

As a dlt resource this is a few lines:

import dlt
from mq_bridge import Consumer

@dlt.resource(name="orders")
def orders():
    consumer = Consumer.from_config({"nats": {"subject": "orders", "url": "nats://localhost:4222"}})
    while not consumer.exhausted:
        batch = consumer.poll(max=500, timeout_ms=1000)
        if not batch:
            break                 # nothing more pending this run
        yield [m.json() for m in batch]
        consumer.commit()

Logging

By default the Rust core's internal tracing events go nowhere. Call init_logging once at startup to route them into the standard logging module, then configure output as usual:

import logging
from mq_bridge import init_logging

logging.basicConfig(level=logging.INFO)
init_logging()  # or init_logging("debug")

Events land on a logger named after the emitting Rust module, :: mapped to . (e.g. mq_bridge.route). level seeds the Rust-side filter (default "warn"); the MQ_BRIDGE_LOG / RUST_LOG environment variables take precedence over it. Filtering happens in Rust, so suppressed events never cross into Python. Call it once per process — a second call raises.

Tuning (environment variables)

These knobs are read from the environment at startup:

Variable	Default	Effect
MQ_BRIDGE_PY_HANDLER_EXECUTOR	worker	worker runs handlers on a dedicated interpreter thread that coalesces queued batches under one GIL acquisition (best under load); direct calls the handler inline.
MQ_BRIDGE_PY_HANDLER_CONCURRENCY	CPU count	Max in-flight handler batches. 0 disables the limit.
MQ_BRIDGE_PY_GC_MODE	default	default leaves CPython's cyclic GC alone; count disables it and runs gc.collect() every N messages; off disables it entirely (pure refcounting).
MQ_BRIDGE_PY_GC_THRESHOLD	100000	Messages between collections when MQ_BRIDGE_PY_GC_MODE=count.

Local development

uv is a good fit here for the Python-side developer workflow, while maturin stays the build backend:

cd python/mq-bridge-py
uv sync --group dev --no-install-project
uv run maturin develop
uv run pytest -q

Performance smoke tests are skipped by default because they start routes and measure local throughput:

cd python/mq-bridge-py
MQ_BRIDGE_RUN_PERF_TESTS=1 uv run pytest -q -m performance

Examples

Raw message handler:

cd python/mq-bridge-py
uv run python examples/raw_route.py

Kind-based JSON handler:

cd python/mq-bridge-py
uv run python examples/json_route.py

Memory benchmark:

cd python/mq-bridge-py
uv run maturin develop --release
uv run python examples/bench_memory.py --messages 100000

Analysis

HTTP comparison benchmark, driven by a native load generator (wrk) so the client is never the bottleneck. It boots each server itself (mq-bridge in worker and direct executor modes, plus FastAPI, Starlette, Sanic, aiohttp, and FastStream when installed) and drives each with wrk:

cd python/mq-bridge-py
uv run maturin develop --release
uv sync --group bench   # optional Python HTTP peers
uv run python analysis/bench_http_native.py --connections 1,8,32 --duration 8

Requires wrk on PATH (brew install wrk). The FastStream target compares its ASGI custom-route path over Uvicorn; it is not a broker-backed subscriber/publisher benchmark. The examples use included sample configs or create temporary configs.