peter
45c0ee7d58
What a job last did lived in the API process's memory and was gone at every restart; the audit log kept a run only when it was manual, failed or changed something. Now each run is a job_runs row written at its start and its end in its own session: who started it, its outcome as data, what it spent of the YouTube quota (quota_events.job_run_id) and the lines it logged (log_events.run_id, filterable in the log console). - a run a stopped process left open is marked interrupted at startup - "run now" is a one-shot job on the scheduler, which now always starts (interval jobs only with SCHEDULER_ENABLED); the manual actor is reset after the run, since pool threads are reused - the dashboard's last run comes from job_runs, so it survives a deploy - runs leave the audit log: migration 0109 moves the old job_run rows over (and back on downgrade); the audit page's run folding goes - job_run_retention_days (default 30) prunes old runs in audit_gc
549 lines
22 KiB
Python
549 lines
22 KiB
Python
"""Background scheduler: free RSS detection, enrichment of new videos, and quota-aware
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recent-first / deep backfill. Runs inside the API process (single worker)."""
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import contextvars
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import threading
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from datetime import datetime, timezone
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from typing import Callable
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from apscheduler.executors.pool import ThreadPoolExecutor
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from apscheduler.jobstores.base import ConflictingIdError
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from apscheduler.schedulers.background import BackgroundScheduler
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from sqlalchemy import select
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from app import audit, debugsession, jobruns, logs, logsink, progress, quota
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from app.config import settings
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from app.db import SessionLocal
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from app.downloads.gc import run_download_gc
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from app.models import SchedulerSetting
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from app.plex.sync import sync as run_plex_sync
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from app.plex.tmdb import run_enrich as run_tmdb_enrich
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from app.plex.tmdb import run_people_enrich
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from app.plex.watch_sync import run_plex_watch_reconcile, run_plex_watch_sync
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from app.notifications import create_notification
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from app.runstatus import RunStatus, RunTrigger
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from app.state import is_sync_paused
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from app.sync.autotag import run_autotag_all
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from app.sync.explore import purge_ephemeral
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from app.sync.maintenance import run_maintenance
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from app.sync.playlists import sync_all_playlists
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from app.sync.runner import (
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run_deep_backfill,
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run_enrich,
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run_recent_backfill,
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run_rss_poll,
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run_shorts,
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run_subscription_resync,
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)
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log = logs.get_logger(__name__)
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_scheduler: BackgroundScheduler | None = None
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# What is running RIGHT NOW, per job: the single-run claim and the live progress. In memory on
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# purpose — the scheduler lives in the API's one process, and progress changes every second. What a
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# run did is durable instead: its `job_runs` row (app/jobruns.py), which outlives a restart.
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_activity: dict[str, dict] = {}
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_activity_lock = threading.Lock()
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# Set (per-thread) by a manual "run now" trigger to the id of the admin who clicked, so that
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# run — and only that run, not the recurring scheduled ones — posts a completion notification
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# to their inbox. Unset (None) for scheduled runs.
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_manual_actor: contextvars.ContextVar[int | None] = contextvars.ContextVar(
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"manual_actor", default=None
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)
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# Each interval job's env/config default period (minutes). The admin can override any of
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# these at runtime (stored in scheduler_settings, applied live); see load_intervals.
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JOB_INTERVALS: dict[str, int] = {
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"rss_poll": settings.rss_poll_minutes,
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"enrich": settings.enrich_interval_minutes,
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"backfill": settings.backfill_interval_minutes,
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"autotag": settings.autotag_interval_minutes,
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"shorts": settings.shorts_probe_interval_minutes,
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"subscriptions": settings.subscriptions_resync_minutes,
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"playlist_sync": settings.playlist_sync_minutes,
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"maintenance": settings.maintenance_interval_minutes,
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"explore_cleanup": settings.explore_cleanup_minutes,
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"download_gc": settings.download_gc_minutes,
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"plex_sync": settings.plex_sync_interval_min,
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"plex_watch_sync": settings.plex_watch_sync_interval_min,
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"plex_watch_reconcile": settings.plex_watch_reconcile_interval_min,
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"tmdb_enrich": settings.tmdb_enrich_interval_min,
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"people_enrich": settings.people_enrich_interval_min,
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"audit_gc": settings.audit_gc_minutes,
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"log_gc": settings.log_gc_minutes,
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}
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# Sane bounds for an admin-set interval (minutes).
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MIN_INTERVAL = 1
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MAX_INTERVAL = 1440 # one day
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def load_intervals(db) -> dict[str, int]:
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"""Effective per-job intervals: the env/config defaults overlaid with any admin overrides
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saved in scheduler_settings."""
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iv = dict(JOB_INTERVALS)
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for row in db.execute(select(SchedulerSetting)).scalars():
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if row.job_id in iv and row.interval_minutes:
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iv[row.job_id] = row.interval_minutes
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return iv
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def apply_interval(db, job_id: str, minutes: int) -> int:
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"""Persist a job's interval override and reschedule the live job immediately (if the
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scheduler runs in this process). Returns the applied value."""
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if job_id not in JOB_INTERVALS:
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raise KeyError(job_id)
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minutes = max(MIN_INTERVAL, min(MAX_INTERVAL, int(minutes)))
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row = db.get(SchedulerSetting, job_id)
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if row is None:
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db.add(SchedulerSetting(job_id=job_id, interval_minutes=minutes))
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else:
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row.interval_minutes = minutes
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db.commit()
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if _scheduler is not None and _scheduler.get_job(job_id) is not None:
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_scheduler.reschedule_job(job_id, trigger="interval", minutes=minutes)
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log.info("rescheduled job %s to every %s min", job_id, minutes)
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return minutes
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def _record(name: str, **fields) -> None:
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with _activity_lock:
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_activity.setdefault(name, {}).update(fields)
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def _notify_done(
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db, actor_id: int, job_id: str, status: str, summary: str | None, result: dict | None = None
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) -> None:
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"""Post a completion notice to the triggering admin's inbox (manual runs only). Best
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effort — a notification failure must never mask the job's own outcome."""
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try:
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create_notification(
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db,
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user_id=actor_id,
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type="scheduler",
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title=f"{job_id}: {status}", # English fallback; UI renders translated from data
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body=summary,
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data={"job_id": job_id, "status": status, "summary": summary, "result": result},
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)
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except Exception:
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db.rollback()
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log.exception("failed to write completion notification for job %s", job_id)
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def _job(name: str, fn) -> None:
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# The one place both run paths converge (manual trigger AND scheduled fire), so claim the run
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# HERE, atomically: whichever reaches this first wins, the other skips. This is the real
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# single-run guard — trigger_job's pre-check is only a fast best-effort answer for the UI.
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if not _claim_running(name):
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log.info("job %s already running — skipping this trigger", name)
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return
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actor_id = _manual_actor.get() # set only for a manual "run now" trigger
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trigger = RunTrigger.SCHEDULED if actor_id is None else RunTrigger.MANUAL
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_record(name, progress=None) # running already claimed
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db = SessionLocal()
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try:
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try:
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run_id = jobruns.start(name, trigger, actor_id)
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except Exception:
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# No row to tell it in: the database is what failed. The log is the record.
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log.exception("job %s not started: its run could not be recorded", name)
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return
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with logs.bind(run_id=run_id):
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_run(db, name, fn, run_id, actor_id)
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finally:
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# ALWAYS release the claim, even if the body raised before its own status update — so a
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# failure can never wedge the job as permanently "running".
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_record(name, running=False, progress=None)
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db.close()
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def _run(db, name: str, fn, run_id: int, actor_id: int | None) -> None:
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"""The run itself, inside its `job_runs` row: the pause-skip, the work, and its outcome — on
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the row, and in the triggering admin's inbox for a manual run."""
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def sink(current, total, phase):
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_record(name, progress={"current": current, "total": total, "phase": phase})
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try:
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if is_sync_paused(db):
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log.info("job %s skipped (sync paused)", name)
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jobruns.finish(run_id, RunStatus.SKIPPED, summary="sync paused", result=_PAUSED)
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if actor_id is not None:
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_notify_done(db, actor_id, name, "skipped", "sync paused", _PAUSED)
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return
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with progress.bind(sink):
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result = fn(db)
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summary = _summarize(result)
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data = _result_data(result)
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log.info("job %s -> %s", name, result)
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jobruns.finish(run_id, RunStatus.OK, summary=summary, result=data)
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if actor_id is not None:
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_notify_done(db, actor_id, name, "ok", summary, data)
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except Exception as exc:
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db.rollback()
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log.exception("job %s failed", name, extra=logs.event("scheduler.job.failed", job=name))
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err = str(exc) or exc.__class__.__name__
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# A failure the job named (progress.JobFailed) rides along as data, as a result does, so the
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# client says it in the reader's language; `err` stays the English line.
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failure = (
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{"failure": exc.reason, **exc.params} if isinstance(exc, progress.JobFailed) else None
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)
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jobruns.finish(run_id, RunStatus.ERROR, error=err, failure=failure)
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if actor_id is not None:
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_notify_done(db, actor_id, name, "error", err, failure)
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# A skipped run's result, in the same shape as a job's own `{"skipped": …}`.
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_PAUSED = {"skipped": "sync paused"}
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def _result_data(result) -> dict | None:
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"""A job's result as the client names it in the reader's language: the dict itself (counts,
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a `skipped` / `reason` word, a nested dict per phase), `enrich`'s bare count as one. The one-line
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`_summarize` is the English fallback."""
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if isinstance(result, dict):
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return result or None
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if isinstance(result, int) and not isinstance(result, bool):
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return {"enriched": result}
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return None
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def _summarize(result) -> str | None:
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"""Compact one-line rendering of a job's return value for the dashboard."""
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if result is None:
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return None
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if isinstance(result, dict):
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return ", ".join(f"{k}={v}" for k, v in result.items()) or None
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return str(result)
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def _iso(ts: datetime | None) -> str | None:
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return ts.astimezone(timezone.utc).isoformat() if ts else None
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def scheduler_snapshot(db) -> dict:
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"""What the scheduler is doing, for the admin dashboard: each job's live state from this process
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and its last run from `job_runs`, so a restart no longer blanks "when did it last run, and how
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did it go". `running_here` is False where the scheduler runs no interval jobs
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(SCHEDULER_ENABLED=false), so the UI can say so while still showing the DB-derived history,
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queue and quota."""
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running_here = _scheduler is not None and settings.scheduler_enabled
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next_runs: dict[str, str | None] = {}
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live_intervals: dict[str, int] = {}
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if _scheduler is not None:
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for job in _scheduler.get_jobs():
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nr = getattr(job, "next_run_time", None)
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next_runs[job.id] = nr.astimezone(timezone.utc).isoformat() if nr else None
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iv = getattr(job.trigger, "interval", None)
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if iv is not None:
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live_intervals[job.id] = round(iv.total_seconds() / 60)
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with _activity_lock:
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acts = {k: dict(v) for k, v in _activity.items()}
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last = jobruns.last_runs(db)
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jobs = []
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for job_id, default_interval in JOB_INTERVALS.items():
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a = acts.get(job_id, {})
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run = last.get(job_id)
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# A run still going has no outcome yet: only an ended run says how it went.
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ended = run if run is not None and run.status != RunStatus.RUNNING else None
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jobs.append(
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{
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"id": job_id,
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"interval_minutes": live_intervals.get(job_id, default_interval),
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"next_run": next_runs.get(job_id),
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"running": a.get("running", False),
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"status": ended.status if ended else None,
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"last_started": _iso(run.started_at) if run else None,
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"last_finished": _iso(run.finished_at) if run else None,
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"last_result": ended.summary if ended else None,
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# The outcome as data: the result, or on a failed run the failure the job named.
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"result": _outcome_data(ended),
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"last_error": ended.error if ended else None,
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"last_trigger": run.trigger if run else None,
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"last_run_id": run.id if run else None,
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"quota_units": ended.quota_units if ended else None,
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"progress": a.get("progress"),
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}
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)
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return {"running_here": running_here, "enabled": settings.scheduler_enabled, "jobs": jobs}
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def _outcome_data(run) -> dict | None:
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if run is None:
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return None
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return run.failure if run.status == RunStatus.ERROR else run.result
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def _rss_job() -> None:
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_job("rss_poll", run_rss_poll)
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def _enrich_job() -> None:
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def work(db):
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with quota.attribute(None, quota.QuotaAction.VIDEOS_ENRICH):
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return run_enrich(db)
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_job("enrich", work)
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def _backfill_job() -> None:
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# Recent-first for not-yet-synced channels, then deep backfill for the rest. All
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# background spend is attributed to the system (no actor), split by action.
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def work(db):
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with quota.attribute(None, quota.QuotaAction.VIDEOS_BACKFILL_RECENT):
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recent = run_recent_backfill(db, max_channels=25)
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with quota.attribute(None, quota.QuotaAction.VIDEOS_BACKFILL_FULL):
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deep = run_deep_backfill(db, max_channels=10)
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return {"recent": recent, "deep": deep}
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_job("backfill", work)
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def _autotag_job() -> None:
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_job("autotag", lambda db: run_autotag_all(db, only_missing=True))
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def _shorts_job() -> None:
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_job("shorts", run_shorts)
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def _subscriptions_job() -> None:
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def work(db):
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with quota.attribute(None, quota.QuotaAction.SUBSCRIPTIONS_RESYNC):
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return run_subscription_resync(db)
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_job("subscriptions", work)
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def _playlist_sync_job() -> None:
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# Mirror each read-scope user's YouTube playlists; per-user quota attribution is
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# handled inside sync_all_playlists.
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_job("playlist_sync", sync_all_playlists)
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def _maintenance_job() -> None:
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def work(db):
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with quota.attribute(None, quota.QuotaAction.MAINTENANCE_REVALIDATE):
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return run_maintenance(db)
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_job("maintenance", work)
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def _explore_cleanup_job() -> None:
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# Reclaim un-kept ephemeral discovery content (explored channels + live-search results) after
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# their grace periods. 0 quota (pure DB), so no quota attribution.
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_job("explore_cleanup", purge_ephemeral)
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def _download_gc_job() -> None:
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# Retention GC for the download center: pre-expiry warnings, TTL deletion, LRU eviction — and
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# the week-ahead warning before an API token runs out (`apitokens.warn_expiring`).
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# Pure disk/DB work (no YouTube quota).
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_job("download_gc", run_download_gc)
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def _plex_sync_job() -> None:
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# Mirror the enabled Plex library sections into the local catalog. Pure metadata (no YouTube
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# quota); a no-op when the Plex module is disabled.
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_job("plex_sync", run_plex_sync)
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def _plex_watch_sync_job() -> None:
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# Incremental Plex→Siftlode watch-state pull (history + on-deck) + re-push of unsynced local
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# states. Cheap; a no-op when Plex is disabled or no owner link has sync enabled.
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_job("plex_watch_sync", run_plex_watch_sync)
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def _plex_watch_reconcile_job() -> None:
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# Full watch-state reconcile (whole-section rescan) — catches un-watches the incremental feed
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# can't. Heavier, so it runs far less often (daily default).
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_job("plex_watch_reconcile", run_plex_watch_reconcile)
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def _tmdb_enrich_job() -> None:
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# Enrich a batch of mirrored Plex titles from TMDB (keywords/crew/budget/revenue/TMDb score/
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# recommendations/backdrops), matched by guid. External metadata (no YouTube quota); a no-op when
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# the TMDB key is unset or the Plex catalog is empty.
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_job("tmdb_enrich", run_tmdb_enrich)
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def _people_enrich_job() -> None:
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# Make the people local: a batch of person bios from TMDB and a batch of portraits resized
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# through Plex, so a person page and a cast strip never wait on an outside service.
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_job("people_enrich", run_people_enrich)
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def _audit_gc_job() -> None:
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# The history cleanup: audit-log rows past audit_retention_days, YouTube quota history past
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# quota_retention_days and finished runs past job_run_retention_days (0 = keep forever, each).
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# Pure DB, no quota.
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def work(db):
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from app import sysconfig
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reaped = audit.gc(db, sysconfig.get_int(db, "audit_retention_days"))
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quota_reaped = quota.gc(db, sysconfig.get_int(db, "quota_retention_days"))
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runs_reaped = jobruns.gc(db, sysconfig.get_int(db, "job_run_retention_days"))
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db.commit()
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return {"reaped": reaped, "quota_reaped": quota_reaped, "runs_reaped": runs_reaped}
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_job("audit_gc", work)
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def _log_gc_job() -> None:
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# Prune log_events rows older than log_retention_days — the limit the privacy policy states, so
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# this runs hourly rather than daily. Pure DB, no quota.
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def work(db):
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from app import sysconfig
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days = sysconfig.get_int(db, "log_retention_days")
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reaped = logsink.gc(db, days)
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# Debug sessions go with the rows they bundle; an open one past its cap is closed.
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closed = debugsession.sweep(db)
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sessions = debugsession.gc(db, days)
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db.commit()
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return {"reaped": reaped, "sessions_closed": closed, "sessions_reaped": sessions}
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_job("log_gc", work)
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# job_id -> wrapper. The single source of truth for which jobs exist and how to run one,
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# shared by start_scheduler (recurring registration) and trigger_job (manual "run now").
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JOB_FUNCS: dict[str, Callable[[], None]] = {
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"rss_poll": _rss_job,
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"enrich": _enrich_job,
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"backfill": _backfill_job,
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"autotag": _autotag_job,
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"shorts": _shorts_job,
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"subscriptions": _subscriptions_job,
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"playlist_sync": _playlist_sync_job,
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"maintenance": _maintenance_job,
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"explore_cleanup": _explore_cleanup_job,
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"download_gc": _download_gc_job,
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"plex_sync": _plex_sync_job,
|
|
"plex_watch_sync": _plex_watch_sync_job,
|
|
"plex_watch_reconcile": _plex_watch_reconcile_job,
|
|
"tmdb_enrich": _tmdb_enrich_job,
|
|
"people_enrich": _people_enrich_job,
|
|
"audit_gc": _audit_gc_job,
|
|
"log_gc": _log_gc_job,
|
|
}
|
|
|
|
|
|
def _is_running(job_id: str) -> bool:
|
|
with _activity_lock:
|
|
return bool(_activity.get(job_id, {}).get("running"))
|
|
|
|
|
|
def _claim_running(job_id: str) -> bool:
|
|
"""Atomically claim a job as running: True if it was idle (and marks it running now), False if
|
|
it was already running. Holding the lock across BOTH the check and the set is what makes the
|
|
single-run guard race-free — called once at the top of _job so a manual trigger and a scheduled
|
|
fire (or two rapid triggers) converge here and exactly one wins. Released in _job's finally."""
|
|
with _activity_lock:
|
|
if _activity.get(job_id, {}).get("running"):
|
|
return False
|
|
_activity.setdefault(job_id, {})["running"] = True
|
|
return True
|
|
|
|
|
|
def running_job_ids() -> set[str]:
|
|
"""Ids of jobs executing right now (any trigger). Lets non-admin surfaces — e.g. the
|
|
header's sync indicator — reflect real activity instead of just pending-work counts."""
|
|
with _activity_lock:
|
|
return {jid for jid, a in _activity.items() if a.get("running")}
|
|
|
|
|
|
def _as_actor(fn: Callable[[], None], actor_id: int | None) -> Callable[[], None]:
|
|
"""`fn` run as `actor_id`'s manual run. The actor is RESET afterwards: the executor's pool
|
|
threads are reused and keep their context from task to task, so an actor left set would make
|
|
the next scheduled run that thread picks up look like this admin's manual run."""
|
|
|
|
def run() -> None:
|
|
token = _manual_actor.set(actor_id)
|
|
try:
|
|
fn()
|
|
finally:
|
|
_manual_actor.reset(token)
|
|
|
|
return run
|
|
|
|
|
|
def trigger_job(job_id: str, actor_id: int | None = None) -> str:
|
|
"""Run a job now, independent of its interval schedule: a one-shot job on the same scheduler,
|
|
so a manual run shares the scheduled runs' executor and shutdown — no thread of its own. The
|
|
wrapper handles its own DB session, run row and pause-skip. Returns "started",
|
|
"already_running", or raises KeyError for an unknown job.
|
|
|
|
`actor_id` (the admin who clicked "run now") is set in a contextvar inside the run's thread, so
|
|
the run is recorded as theirs and posts a completion notification to their inbox."""
|
|
fn = JOB_FUNCS.get(job_id)
|
|
if fn is None:
|
|
raise KeyError(job_id)
|
|
# Best-effort fast answer for the UI; the authoritative single-run guard is _job's atomic claim
|
|
# (so a manual trigger and a scheduled fire can't both run). A race here just means the loser's
|
|
# _job claims, sees it's taken, and skips — no double run.
|
|
if _is_running(job_id):
|
|
return "already_running"
|
|
sched = _scheduler
|
|
if sched is None:
|
|
# Only between shutdown and exit: start_scheduler runs at every API startup.
|
|
raise RuntimeError("the scheduler is not running")
|
|
|
|
try:
|
|
# One id per job: a second click before the first one-shot has even started is the same
|
|
# run, not a queued second one. misfire_grace_time=None: a busy pool delays it, never drops it.
|
|
sched.add_job(_as_actor(fn, actor_id), "date", id=f"{job_id}@now", misfire_grace_time=None)
|
|
# silent-ok: not a failure — the answer is the 409 the admin sees ("already running").
|
|
except ConflictingIdError:
|
|
return "already_running"
|
|
return "started"
|
|
|
|
|
|
def trigger_all(actor_id: int | None = None) -> list[str]:
|
|
"""Trigger every job that isn't already running; returns the ids actually started."""
|
|
started = []
|
|
for job_id in JOB_FUNCS:
|
|
if trigger_job(job_id, actor_id) == "started":
|
|
started.append(job_id)
|
|
return started
|
|
|
|
|
|
# Room for every job at once (a "run all") beside a few ad-hoc runs: APScheduler's default pool of
|
|
# 10 would queue a scheduled fire behind long manual runs.
|
|
_POOL_SIZE = len(JOB_FUNCS) + 8
|
|
|
|
|
|
def start_scheduler() -> None:
|
|
"""Start this process's scheduler. It always starts, so "run now" works on every install; only
|
|
with SCHEDULER_ENABLED does it also register the recurring interval jobs. Before anything can
|
|
run, every run a previous process left open is closed as interrupted."""
|
|
global _scheduler
|
|
if _scheduler is not None:
|
|
return
|
|
left = jobruns.interrupt_leftovers()
|
|
if left:
|
|
log.warning("%s run(s) left running by the previous process marked interrupted", left)
|
|
scheduler = BackgroundScheduler(
|
|
timezone="UTC", executors={"default": ThreadPoolExecutor(_POOL_SIZE)}
|
|
)
|
|
if settings.scheduler_enabled:
|
|
# Effective intervals = env defaults overlaid with any admin overrides from the DB.
|
|
db = SessionLocal()
|
|
try:
|
|
iv = load_intervals(db)
|
|
finally:
|
|
db.close()
|
|
for job_id, fn in JOB_FUNCS.items():
|
|
scheduler.add_job(fn, "interval", minutes=iv[job_id], id=job_id)
|
|
scheduler.start()
|
|
_scheduler = scheduler
|
|
log.info("scheduler started (interval jobs %s)", "on" if settings.scheduler_enabled else "off")
|
|
|
|
|
|
def shutdown_scheduler() -> None:
|
|
global _scheduler
|
|
if _scheduler is not None:
|
|
_scheduler.shutdown(wait=False)
|
|
_scheduler = None
|