Aurora-Core/app/workers/worker.py

"""arq worker process entrypoint.

Run with: `arq app.workers.worker.WorkerSettings`

Workers scale independently of the API (section 1) — run as many worker
processes/containers as needed; arq's Redis-backed queue coordinates them so
each job is picked up by exactly one worker.
"""

from __future__ import annotations

from arq.connections import RedisSettings
from arq.cron import cron

from app.core.config import get_settings
from app.core.logging import configure_logging, get_logger
from app.workers.tasks import (
    download_artwork_task,
    ingest_collection_task,
    ingest_movie_task,
    ingest_person_task,
    ingest_season_task,
    ingest_series_task,
    refresh_trending_task,
)

logger = get_logger(__name__)


async def startup(ctx: dict) -> None:
    configure_logging()
    logger.info("worker_started")


async def shutdown(ctx: dict) -> None:
    logger.info("worker_stopped")


class WorkerSettings:
    functions = [
        ingest_movie_task,
        ingest_person_task,
        ingest_series_task,
        ingest_season_task,
        ingest_collection_task,
        download_artwork_task,
    ]
    # Twice a day (UTC) rather than run_at_startup=True -- a worker
    # container restart (common during deploys) must not hammer TMDb with
    # a full trending refresh every time it comes up.
    cron_jobs = [cron(refresh_trending_task, hour={3, 15}, minute=0)]
    on_startup = startup
    on_shutdown = shutdown
    redis_settings = RedisSettings.from_dsn(get_settings().REDIS_URL)
    max_jobs = get_settings().WORKER_CONCURRENCY
    job_timeout = 300
    max_tries = 5