qx-worker 1.1.0

Qx worker runtime: NATS consumer with retry/DLQ/tracing

qx-worker

NATS JetStream consumer runtime for Qx integration-event handlers. Pulls messages in configurable batches, deserialises them via EventRegistry, dispatches to handlers through the Mediator, and acks/naks based on the handler result.

What lives here

• qx.worker.WorkerRuntime — the main consumer loop. Fetches batches concurrently, opens a per-message DI scope, sets RequestContext from the event envelope (correlation_id, tenant_id, trace context), calls mediator.consume_integration(), and acks on success or naks on failure (letting JetStream handle retry and DLQ semantics via max_deliver).

Usage

from qx.events import EventRegistry, NatsConsumer, NatsSettings, create_nats_connection
from qx.worker import WorkerRuntime
import asyncio, signal

async def main() -> None:
    nc = await create_nats_connection(settings.nats.url)
    consumer = NatsConsumer(nc, settings.nats)

    registry = EventRegistry()
    registry.register(UserRegisteredIntegration)

    worker = WorkerRuntime(
        container=container,
        consumer=consumer,
        registry=registry,
        mediator=mediator,
        concurrency=8,
    )

    loop = asyncio.get_running_loop()
    loop.add_signal_handler(signal.SIGTERM, lambda: asyncio.create_task(worker.stop()))

    await worker.run()

Integration event handler (registered via Mediator)

from qx.cqrs import integration_event_handler

@integration_event_handler(UserRegisteredIntegration)
class SendWelcomeEmailHandler:
    async def handle(self, event: UserRegisteredIntegration) -> Result[None]:
        await self._mailer.send_welcome(event.email, event.name)
        return Result.success(None)

Design rules

• No in-process retry loop — the worker only decides ack vs nak. JetStream redelivers nak'd messages with exponential backoff up to max_deliver; after that, the message lands in the DLQ stream.
• Concurrent processing — concurrency controls how many messages are processed in parallel per fetch() batch using asyncio.gather. Each message gets its own DI scope so shared state (sessions, connections) does not leak between concurrent handlers.
• Observability — each message processing opens an OTel span linked to the incoming trace context extracted from NATS headers, so the full trace chain (HTTP → outbox relay → worker) is visible in your tracing backend.
• Graceful shutdown — worker.stop() sets an internal asyncio.Event; the loop drains the current batch before exiting.