simple_module_background_tasks

Celery + Redis background-task module for simple_module apps. Provides a pre-configured Celery instance, a task registration hook, and an admin UI for monitoring + retrying failed/stuck tasks.

Install

pip install simple_module_background_tasks

Requires a Redis broker. The broker / result-backend URLs are module settings (broker_url, default redis://localhost:6379/0; result_backend, default redis://localhost:6379/1) configured from the DB settings store via the admin UI — they are not read from environment variables at runtime. These fields are requires_restart (changing them needs a worker/web restart).

What it provides

  • Zero-config task discovery — any installed module that ships a tasks.py has its tasks autodiscovered (celery.autodiscover_tasks imports <package>.tasks for every installed module). No per-module registration hook.
  • Admin UI at /admin/background-tasks — list recent runs, retry failed, inspect tracebacks (gated by the background_tasks.view permission).
  • build_celery(settings) factory in background_tasks.celery_app, plus bind_task_context / get_log_context / install_log_filter exported from the package root (import name background_tasks, distribution name simple_module_background_tasks).

Usage

Declare a task in a module's tasks.py with Celery's @shared_task — it's autodiscovered, no registration hook needed:

# modules/reports/reports/tasks.py
from celery import shared_task


@shared_task(name="reports.generate")
def generate_report(report_id: int) -> None: ...

Declaring background_tasks as a depends_on ensures the Celery app is built before your tasks run:

class ReportsModule(ModuleBase):
    meta = ModuleMeta(name="reports", depends_on=["background_tasks"])

Enqueue from an endpoint:

generate_report.delay(report_id=42)

Run a worker locally (the scaffolded host ships a scripts/run_worker.py that builds the app via build_celery):

uv run celery -A scripts.run_worker:celery worker -l info
uv run celery -A scripts.run_worker:celery beat   -l info

Worker log context

Every worker log line automatically carries the Celery task identifiers that fired it. A LogContextFilter is attached when the Celery app is built (build_celery) and the task_prerun / task_postrun signals bind task_id + task_name into contextvars for the task's duration:

{"level": "INFO", "logger": "reports.tasks", "message": "ingest done",
 "task_id": "9c2a…", "task_name": "reports.generate"}

Use bind_task_context(...) to attach app-level identifiers (the domain job_id that named a Celery task is the canonical example):

from background_tasks import bind_task_context
from celery import shared_task


@shared_task
def process_dataset(job_id: int) -> None:
    with bind_task_context(job_id=job_id):
        logger.info("starting ingest")  # now carries job_id too

Bindings nest cleanly and restore on exit. structlog users can mount the same contextvars directly via structlog.contextvars.merge_contextvars.

Depends on

  • simple_module_core, simple_module_db, simple_module_hosting, simple_module_settings
  • celery[redis]>=5.4, redis>=5

License

MIT — see LICENSE.

Project links

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  • Released:
Newer release available (0.0.33)

1 maintainer

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Credits

Author: Anto Subash

GitHub Statistics

Data verified by PyPI on Aug 10, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

MIT
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Requires

Python >=3.12

Tags

background-jobs celery redis simple-module task-queue

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